This file is indexed.

/usr/share/octave/packages/statistics-1.2.4/doc-cache is in octave-statistics 1.2.4-1.

This file is owned by root:root, with mode 0o644.

The actual contents of the file can be viewed below.

   1
   2
   3
   4
   5
   6
   7
   8
   9
  10
  11
  12
  13
  14
  15
  16
  17
  18
  19
  20
  21
  22
  23
  24
  25
  26
  27
  28
  29
  30
  31
  32
  33
  34
  35
  36
  37
  38
  39
  40
  41
  42
  43
  44
  45
  46
  47
  48
  49
  50
  51
  52
  53
  54
  55
  56
  57
  58
  59
  60
  61
  62
  63
  64
  65
  66
  67
  68
  69
  70
  71
  72
  73
  74
  75
  76
  77
  78
  79
  80
  81
  82
  83
  84
  85
  86
  87
  88
  89
  90
  91
  92
  93
  94
  95
  96
  97
  98
  99
 100
 101
 102
 103
 104
 105
 106
 107
 108
 109
 110
 111
 112
 113
 114
 115
 116
 117
 118
 119
 120
 121
 122
 123
 124
 125
 126
 127
 128
 129
 130
 131
 132
 133
 134
 135
 136
 137
 138
 139
 140
 141
 142
 143
 144
 145
 146
 147
 148
 149
 150
 151
 152
 153
 154
 155
 156
 157
 158
 159
 160
 161
 162
 163
 164
 165
 166
 167
 168
 169
 170
 171
 172
 173
 174
 175
 176
 177
 178
 179
 180
 181
 182
 183
 184
 185
 186
 187
 188
 189
 190
 191
 192
 193
 194
 195
 196
 197
 198
 199
 200
 201
 202
 203
 204
 205
 206
 207
 208
 209
 210
 211
 212
 213
 214
 215
 216
 217
 218
 219
 220
 221
 222
 223
 224
 225
 226
 227
 228
 229
 230
 231
 232
 233
 234
 235
 236
 237
 238
 239
 240
 241
 242
 243
 244
 245
 246
 247
 248
 249
 250
 251
 252
 253
 254
 255
 256
 257
 258
 259
 260
 261
 262
 263
 264
 265
 266
 267
 268
 269
 270
 271
 272
 273
 274
 275
 276
 277
 278
 279
 280
 281
 282
 283
 284
 285
 286
 287
 288
 289
 290
 291
 292
 293
 294
 295
 296
 297
 298
 299
 300
 301
 302
 303
 304
 305
 306
 307
 308
 309
 310
 311
 312
 313
 314
 315
 316
 317
 318
 319
 320
 321
 322
 323
 324
 325
 326
 327
 328
 329
 330
 331
 332
 333
 334
 335
 336
 337
 338
 339
 340
 341
 342
 343
 344
 345
 346
 347
 348
 349
 350
 351
 352
 353
 354
 355
 356
 357
 358
 359
 360
 361
 362
 363
 364
 365
 366
 367
 368
 369
 370
 371
 372
 373
 374
 375
 376
 377
 378
 379
 380
 381
 382
 383
 384
 385
 386
 387
 388
 389
 390
 391
 392
 393
 394
 395
 396
 397
 398
 399
 400
 401
 402
 403
 404
 405
 406
 407
 408
 409
 410
 411
 412
 413
 414
 415
 416
 417
 418
 419
 420
 421
 422
 423
 424
 425
 426
 427
 428
 429
 430
 431
 432
 433
 434
 435
 436
 437
 438
 439
 440
 441
 442
 443
 444
 445
 446
 447
 448
 449
 450
 451
 452
 453
 454
 455
 456
 457
 458
 459
 460
 461
 462
 463
 464
 465
 466
 467
 468
 469
 470
 471
 472
 473
 474
 475
 476
 477
 478
 479
 480
 481
 482
 483
 484
 485
 486
 487
 488
 489
 490
 491
 492
 493
 494
 495
 496
 497
 498
 499
 500
 501
 502
 503
 504
 505
 506
 507
 508
 509
 510
 511
 512
 513
 514
 515
 516
 517
 518
 519
 520
 521
 522
 523
 524
 525
 526
 527
 528
 529
 530
 531
 532
 533
 534
 535
 536
 537
 538
 539
 540
 541
 542
 543
 544
 545
 546
 547
 548
 549
 550
 551
 552
 553
 554
 555
 556
 557
 558
 559
 560
 561
 562
 563
 564
 565
 566
 567
 568
 569
 570
 571
 572
 573
 574
 575
 576
 577
 578
 579
 580
 581
 582
 583
 584
 585
 586
 587
 588
 589
 590
 591
 592
 593
 594
 595
 596
 597
 598
 599
 600
 601
 602
 603
 604
 605
 606
 607
 608
 609
 610
 611
 612
 613
 614
 615
 616
 617
 618
 619
 620
 621
 622
 623
 624
 625
 626
 627
 628
 629
 630
 631
 632
 633
 634
 635
 636
 637
 638
 639
 640
 641
 642
 643
 644
 645
 646
 647
 648
 649
 650
 651
 652
 653
 654
 655
 656
 657
 658
 659
 660
 661
 662
 663
 664
 665
 666
 667
 668
 669
 670
 671
 672
 673
 674
 675
 676
 677
 678
 679
 680
 681
 682
 683
 684
 685
 686
 687
 688
 689
 690
 691
 692
 693
 694
 695
 696
 697
 698
 699
 700
 701
 702
 703
 704
 705
 706
 707
 708
 709
 710
 711
 712
 713
 714
 715
 716
 717
 718
 719
 720
 721
 722
 723
 724
 725
 726
 727
 728
 729
 730
 731
 732
 733
 734
 735
 736
 737
 738
 739
 740
 741
 742
 743
 744
 745
 746
 747
 748
 749
 750
 751
 752
 753
 754
 755
 756
 757
 758
 759
 760
 761
 762
 763
 764
 765
 766
 767
 768
 769
 770
 771
 772
 773
 774
 775
 776
 777
 778
 779
 780
 781
 782
 783
 784
 785
 786
 787
 788
 789
 790
 791
 792
 793
 794
 795
 796
 797
 798
 799
 800
 801
 802
 803
 804
 805
 806
 807
 808
 809
 810
 811
 812
 813
 814
 815
 816
 817
 818
 819
 820
 821
 822
 823
 824
 825
 826
 827
 828
 829
 830
 831
 832
 833
 834
 835
 836
 837
 838
 839
 840
 841
 842
 843
 844
 845
 846
 847
 848
 849
 850
 851
 852
 853
 854
 855
 856
 857
 858
 859
 860
 861
 862
 863
 864
 865
 866
 867
 868
 869
 870
 871
 872
 873
 874
 875
 876
 877
 878
 879
 880
 881
 882
 883
 884
 885
 886
 887
 888
 889
 890
 891
 892
 893
 894
 895
 896
 897
 898
 899
 900
 901
 902
 903
 904
 905
 906
 907
 908
 909
 910
 911
 912
 913
 914
 915
 916
 917
 918
 919
 920
 921
 922
 923
 924
 925
 926
 927
 928
 929
 930
 931
 932
 933
 934
 935
 936
 937
 938
 939
 940
 941
 942
 943
 944
 945
 946
 947
 948
 949
 950
 951
 952
 953
 954
 955
 956
 957
 958
 959
 960
 961
 962
 963
 964
 965
 966
 967
 968
 969
 970
 971
 972
 973
 974
 975
 976
 977
 978
 979
 980
 981
 982
 983
 984
 985
 986
 987
 988
 989
 990
 991
 992
 993
 994
 995
 996
 997
 998
 999
1000
1001
1002
1003
1004
1005
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
1017
1018
1019
1020
1021
1022
1023
1024
1025
1026
1027
1028
1029
1030
1031
1032
1033
1034
1035
1036
1037
1038
1039
1040
1041
1042
1043
1044
1045
1046
1047
1048
1049
1050
1051
1052
1053
1054
1055
1056
1057
1058
1059
1060
1061
1062
1063
1064
1065
1066
1067
1068
1069
1070
1071
1072
1073
1074
1075
1076
1077
1078
1079
1080
1081
1082
1083
1084
1085
1086
1087
1088
1089
1090
1091
1092
1093
1094
1095
1096
1097
1098
1099
1100
1101
1102
1103
1104
1105
1106
1107
1108
1109
1110
1111
1112
1113
1114
1115
1116
1117
1118
1119
1120
1121
1122
1123
1124
1125
1126
1127
1128
1129
1130
1131
1132
1133
1134
1135
1136
1137
1138
1139
1140
1141
1142
1143
1144
1145
1146
1147
1148
1149
1150
1151
1152
1153
1154
1155
1156
1157
1158
1159
1160
1161
1162
1163
1164
1165
1166
1167
1168
1169
1170
1171
1172
1173
1174
1175
1176
1177
1178
1179
1180
1181
1182
1183
1184
1185
1186
1187
1188
1189
1190
1191
1192
1193
1194
1195
1196
1197
1198
1199
1200
1201
1202
1203
1204
1205
1206
1207
1208
1209
1210
1211
1212
1213
1214
1215
1216
1217
1218
1219
1220
1221
1222
1223
1224
1225
1226
1227
1228
1229
1230
1231
1232
1233
1234
1235
1236
1237
1238
1239
1240
1241
1242
1243
1244
1245
1246
1247
1248
1249
1250
1251
1252
1253
1254
1255
1256
1257
1258
1259
1260
1261
1262
1263
1264
1265
1266
1267
1268
1269
1270
1271
1272
1273
1274
1275
1276
1277
1278
1279
1280
1281
1282
1283
1284
1285
1286
1287
1288
1289
1290
1291
1292
1293
1294
1295
1296
1297
1298
1299
1300
1301
1302
1303
1304
1305
1306
1307
1308
1309
1310
1311
1312
1313
1314
1315
1316
1317
1318
1319
1320
1321
1322
1323
1324
1325
1326
1327
1328
1329
1330
1331
1332
1333
1334
1335
1336
1337
1338
1339
1340
1341
1342
1343
1344
1345
1346
1347
1348
1349
1350
1351
1352
1353
1354
1355
1356
1357
1358
1359
1360
1361
1362
1363
1364
1365
1366
1367
1368
1369
1370
1371
1372
1373
1374
1375
1376
1377
1378
1379
1380
1381
1382
1383
1384
1385
1386
1387
1388
1389
1390
1391
1392
1393
1394
1395
1396
1397
1398
1399
1400
1401
1402
1403
1404
1405
1406
1407
1408
1409
1410
1411
1412
1413
1414
1415
1416
1417
1418
1419
1420
1421
1422
1423
1424
1425
1426
1427
1428
1429
1430
1431
1432
1433
1434
1435
1436
1437
1438
1439
1440
1441
1442
1443
1444
1445
1446
1447
1448
1449
1450
1451
1452
1453
1454
1455
1456
1457
1458
1459
1460
1461
1462
1463
1464
1465
1466
1467
1468
1469
1470
1471
1472
1473
1474
1475
1476
1477
1478
1479
1480
1481
1482
1483
1484
1485
1486
1487
1488
1489
1490
1491
1492
1493
1494
1495
1496
1497
1498
1499
1500
1501
1502
1503
1504
1505
1506
1507
1508
1509
1510
1511
1512
1513
1514
1515
1516
1517
1518
1519
1520
1521
1522
1523
1524
1525
1526
1527
1528
1529
1530
1531
1532
1533
1534
1535
1536
1537
1538
1539
1540
1541
1542
1543
1544
1545
1546
1547
1548
1549
1550
1551
1552
1553
1554
1555
1556
1557
1558
1559
1560
1561
1562
1563
1564
1565
1566
1567
1568
1569
1570
1571
1572
1573
1574
1575
1576
1577
1578
1579
1580
1581
1582
1583
1584
1585
1586
1587
1588
1589
1590
1591
1592
1593
1594
1595
1596
1597
1598
1599
1600
1601
1602
1603
1604
1605
1606
1607
1608
1609
1610
1611
1612
1613
1614
1615
1616
1617
1618
1619
1620
1621
1622
1623
1624
1625
1626
1627
1628
1629
1630
1631
1632
1633
1634
1635
1636
1637
1638
1639
1640
1641
1642
1643
1644
1645
1646
1647
1648
1649
1650
1651
1652
1653
1654
1655
1656
1657
1658
1659
1660
1661
1662
1663
1664
1665
1666
1667
1668
1669
1670
1671
1672
1673
1674
1675
1676
1677
1678
1679
1680
1681
1682
1683
1684
1685
1686
1687
1688
1689
1690
1691
1692
1693
1694
1695
1696
1697
1698
1699
1700
1701
1702
1703
1704
1705
1706
1707
1708
1709
1710
1711
1712
1713
1714
1715
1716
1717
1718
1719
1720
1721
1722
1723
1724
1725
1726
1727
1728
1729
1730
1731
1732
1733
1734
1735
1736
1737
1738
1739
1740
1741
1742
1743
1744
1745
1746
1747
1748
1749
1750
1751
1752
1753
1754
1755
1756
1757
1758
1759
1760
1761
1762
1763
1764
1765
1766
1767
1768
1769
1770
1771
1772
1773
1774
1775
1776
1777
1778
1779
1780
1781
1782
1783
1784
1785
1786
1787
1788
1789
1790
1791
1792
1793
1794
1795
1796
1797
1798
1799
1800
1801
1802
1803
1804
1805
1806
1807
1808
1809
1810
1811
1812
1813
1814
1815
1816
1817
1818
1819
1820
1821
1822
1823
1824
1825
1826
1827
1828
1829
1830
1831
1832
1833
1834
1835
1836
1837
1838
1839
1840
1841
1842
1843
1844
1845
1846
1847
1848
1849
1850
1851
1852
1853
1854
1855
1856
1857
1858
1859
1860
1861
1862
1863
1864
1865
1866
1867
1868
1869
1870
1871
1872
1873
1874
1875
1876
1877
1878
1879
1880
1881
1882
1883
1884
1885
1886
1887
1888
1889
1890
1891
1892
1893
1894
1895
1896
1897
1898
1899
1900
1901
1902
1903
1904
1905
1906
1907
1908
1909
1910
1911
1912
1913
1914
1915
1916
1917
1918
1919
1920
1921
1922
1923
1924
1925
1926
1927
1928
1929
1930
1931
1932
1933
1934
1935
1936
1937
1938
1939
1940
1941
1942
1943
1944
1945
1946
1947
1948
1949
1950
1951
1952
1953
1954
1955
1956
1957
1958
1959
1960
1961
1962
1963
1964
1965
1966
1967
1968
1969
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
2036
2037
2038
2039
2040
2041
2042
2043
2044
2045
2046
2047
2048
2049
2050
2051
2052
2053
2054
2055
2056
2057
2058
2059
2060
2061
2062
2063
2064
2065
2066
2067
2068
2069
2070
2071
2072
2073
2074
2075
2076
2077
2078
2079
2080
2081
2082
2083
2084
2085
2086
2087
2088
2089
2090
2091
2092
2093
2094
2095
2096
2097
2098
2099
2100
2101
2102
2103
2104
2105
2106
2107
2108
2109
2110
2111
2112
2113
2114
2115
2116
2117
2118
2119
2120
2121
2122
2123
2124
2125
2126
2127
2128
2129
2130
2131
2132
2133
2134
2135
2136
2137
2138
2139
2140
2141
2142
2143
2144
2145
2146
2147
2148
2149
2150
2151
2152
2153
2154
2155
2156
2157
2158
2159
2160
2161
2162
2163
2164
2165
2166
2167
2168
2169
2170
2171
2172
2173
2174
2175
2176
2177
2178
2179
2180
2181
2182
2183
2184
2185
2186
2187
2188
2189
2190
2191
2192
2193
2194
2195
2196
2197
2198
2199
2200
2201
2202
2203
2204
2205
2206
2207
2208
2209
2210
2211
2212
2213
2214
2215
2216
2217
2218
2219
2220
2221
2222
2223
2224
2225
2226
2227
2228
2229
2230
2231
2232
2233
2234
2235
2236
2237
2238
2239
2240
2241
2242
2243
2244
2245
2246
2247
2248
2249
2250
2251
2252
2253
2254
2255
2256
2257
2258
2259
2260
2261
2262
2263
2264
2265
2266
2267
2268
2269
2270
2271
2272
2273
2274
2275
2276
2277
2278
2279
2280
2281
2282
2283
2284
2285
2286
2287
2288
2289
2290
2291
2292
2293
2294
2295
2296
2297
2298
2299
2300
2301
2302
2303
2304
2305
2306
2307
2308
2309
2310
2311
2312
2313
2314
2315
2316
2317
2318
2319
2320
2321
2322
2323
2324
2325
2326
2327
2328
2329
2330
2331
2332
2333
2334
2335
2336
2337
2338
2339
2340
2341
2342
2343
2344
2345
2346
2347
2348
2349
2350
2351
2352
2353
2354
2355
2356
2357
2358
2359
2360
2361
2362
2363
2364
2365
2366
2367
2368
2369
2370
2371
2372
2373
2374
2375
2376
2377
2378
2379
2380
2381
2382
2383
2384
2385
2386
2387
2388
2389
2390
2391
2392
2393
2394
2395
2396
2397
2398
2399
2400
2401
2402
2403
2404
2405
2406
2407
2408
2409
2410
2411
2412
2413
2414
2415
2416
2417
2418
2419
2420
2421
2422
2423
2424
2425
2426
2427
2428
2429
2430
2431
2432
2433
2434
2435
2436
2437
2438
2439
2440
2441
2442
2443
2444
2445
2446
2447
2448
2449
2450
2451
2452
2453
2454
2455
2456
2457
2458
2459
2460
2461
2462
2463
2464
2465
2466
2467
2468
2469
2470
2471
2472
2473
2474
2475
2476
2477
2478
2479
2480
2481
2482
2483
2484
2485
2486
2487
2488
2489
2490
2491
2492
2493
2494
2495
2496
2497
2498
2499
2500
2501
2502
2503
2504
2505
2506
2507
2508
2509
2510
2511
2512
2513
2514
2515
2516
2517
2518
2519
2520
2521
2522
2523
2524
2525
2526
2527
2528
2529
2530
2531
2532
2533
2534
2535
2536
2537
2538
2539
2540
2541
2542
2543
2544
2545
2546
2547
2548
2549
2550
2551
2552
2553
2554
2555
2556
2557
2558
2559
2560
2561
2562
2563
2564
2565
2566
2567
2568
2569
2570
2571
2572
2573
2574
2575
2576
2577
2578
2579
2580
2581
2582
2583
2584
2585
2586
2587
2588
2589
2590
2591
2592
2593
2594
2595
2596
2597
2598
2599
2600
2601
2602
2603
2604
2605
2606
2607
2608
2609
2610
2611
2612
2613
2614
2615
2616
2617
2618
2619
2620
2621
2622
2623
2624
2625
2626
2627
2628
2629
2630
2631
2632
2633
2634
2635
2636
2637
2638
2639
2640
2641
2642
2643
2644
2645
2646
2647
2648
2649
2650
2651
2652
2653
2654
2655
2656
2657
2658
2659
2660
2661
2662
2663
2664
2665
2666
2667
2668
2669
2670
2671
2672
2673
2674
2675
2676
2677
2678
2679
2680
2681
2682
2683
2684
2685
2686
2687
2688
2689
2690
2691
2692
2693
2694
2695
2696
2697
2698
2699
2700
2701
2702
2703
2704
2705
2706
2707
2708
2709
2710
2711
2712
2713
2714
2715
2716
2717
2718
2719
2720
2721
2722
2723
2724
2725
2726
2727
2728
2729
2730
2731
2732
2733
2734
2735
2736
2737
2738
2739
2740
2741
2742
2743
2744
2745
2746
2747
2748
2749
2750
2751
2752
2753
2754
2755
2756
2757
2758
2759
2760
2761
2762
2763
2764
2765
2766
2767
2768
2769
2770
2771
2772
2773
2774
2775
2776
2777
2778
2779
2780
2781
2782
2783
2784
2785
2786
2787
2788
2789
2790
2791
2792
2793
2794
2795
2796
2797
2798
2799
2800
2801
2802
2803
2804
2805
2806
2807
2808
2809
2810
2811
2812
2813
2814
2815
2816
2817
2818
2819
2820
2821
2822
2823
2824
2825
2826
2827
2828
2829
2830
2831
2832
2833
2834
2835
2836
2837
2838
2839
2840
2841
2842
2843
2844
2845
2846
2847
2848
2849
2850
2851
2852
2853
2854
2855
2856
2857
2858
2859
2860
2861
2862
2863
2864
2865
2866
2867
2868
2869
2870
2871
2872
2873
2874
2875
2876
2877
2878
2879
2880
2881
2882
2883
2884
2885
2886
2887
2888
2889
2890
2891
2892
2893
2894
2895
2896
2897
2898
2899
2900
2901
2902
2903
2904
2905
2906
2907
2908
2909
2910
2911
2912
2913
2914
2915
2916
2917
2918
2919
2920
2921
2922
2923
2924
2925
2926
2927
2928
2929
2930
2931
2932
2933
2934
2935
2936
2937
2938
2939
2940
2941
2942
2943
2944
2945
2946
2947
2948
2949
2950
2951
2952
2953
2954
2955
2956
2957
2958
2959
2960
2961
2962
2963
2964
2965
2966
2967
2968
2969
2970
2971
2972
2973
2974
2975
2976
2977
2978
2979
2980
2981
2982
2983
2984
2985
2986
2987
2988
2989
2990
2991
2992
2993
2994
2995
2996
2997
2998
2999
3000
3001
3002
3003
3004
3005
3006
3007
3008
3009
3010
3011
3012
3013
3014
3015
3016
3017
3018
3019
3020
3021
3022
3023
3024
3025
3026
3027
3028
3029
3030
3031
3032
3033
3034
3035
3036
3037
3038
3039
3040
3041
3042
3043
3044
3045
3046
3047
3048
3049
3050
3051
3052
3053
3054
3055
3056
3057
3058
3059
3060
3061
3062
3063
3064
3065
3066
3067
3068
3069
3070
3071
3072
3073
3074
3075
3076
3077
3078
3079
3080
3081
3082
3083
3084
3085
3086
3087
3088
3089
3090
3091
3092
3093
3094
3095
3096
3097
3098
3099
3100
3101
3102
3103
3104
3105
3106
3107
3108
3109
3110
3111
3112
3113
3114
3115
3116
3117
3118
3119
3120
3121
3122
3123
3124
3125
3126
3127
3128
3129
3130
3131
3132
3133
3134
3135
3136
3137
3138
3139
3140
3141
3142
3143
3144
3145
3146
3147
3148
3149
3150
3151
3152
3153
3154
3155
3156
3157
3158
3159
3160
3161
3162
3163
3164
3165
3166
3167
3168
3169
3170
3171
3172
3173
3174
3175
3176
3177
3178
3179
3180
3181
3182
3183
3184
3185
3186
3187
3188
3189
3190
3191
3192
3193
3194
3195
3196
3197
3198
3199
3200
3201
3202
3203
3204
3205
3206
3207
3208
3209
3210
3211
3212
3213
3214
3215
3216
3217
3218
3219
3220
3221
3222
3223
3224
3225
3226
3227
3228
3229
3230
3231
3232
3233
3234
3235
3236
3237
3238
3239
3240
3241
3242
3243
3244
3245
3246
3247
3248
3249
3250
3251
3252
3253
3254
3255
3256
3257
3258
3259
3260
3261
3262
3263
3264
3265
3266
3267
3268
3269
3270
3271
3272
3273
3274
3275
3276
3277
3278
3279
3280
3281
3282
3283
3284
3285
3286
3287
3288
3289
3290
3291
3292
3293
3294
3295
3296
3297
3298
3299
3300
3301
3302
3303
3304
3305
3306
3307
3308
3309
3310
3311
3312
3313
3314
3315
3316
3317
3318
3319
3320
3321
3322
3323
3324
3325
3326
3327
3328
3329
3330
3331
3332
3333
3334
3335
3336
3337
3338
3339
3340
3341
3342
3343
3344
3345
3346
3347
3348
3349
3350
3351
3352
3353
3354
3355
3356
3357
3358
3359
3360
3361
3362
3363
3364
3365
3366
3367
3368
3369
3370
3371
3372
3373
3374
3375
3376
3377
3378
3379
3380
3381
3382
3383
3384
3385
3386
3387
3388
3389
3390
3391
3392
3393
3394
3395
3396
3397
3398
3399
3400
3401
3402
3403
3404
3405
3406
3407
3408
3409
3410
3411
3412
3413
3414
3415
3416
3417
3418
3419
3420
3421
3422
3423
3424
3425
3426
3427
3428
3429
3430
3431
3432
3433
3434
3435
3436
3437
3438
3439
3440
3441
3442
3443
3444
3445
3446
3447
3448
3449
3450
3451
3452
3453
3454
3455
3456
3457
3458
3459
3460
3461
3462
3463
3464
3465
3466
3467
3468
3469
3470
3471
3472
3473
3474
3475
3476
3477
3478
3479
3480
3481
3482
3483
3484
3485
3486
3487
3488
3489
3490
3491
3492
3493
3494
3495
3496
3497
3498
3499
3500
3501
3502
3503
3504
3505
3506
3507
3508
3509
3510
3511
3512
3513
3514
3515
3516
3517
3518
3519
3520
3521
3522
3523
3524
3525
3526
3527
3528
3529
3530
3531
3532
3533
3534
3535
3536
3537
3538
3539
3540
3541
3542
3543
3544
3545
3546
3547
3548
3549
3550
3551
3552
3553
3554
3555
3556
3557
3558
3559
3560
3561
3562
3563
3564
3565
3566
3567
3568
3569
3570
3571
3572
3573
3574
3575
3576
3577
3578
3579
3580
3581
3582
3583
3584
3585
3586
3587
3588
3589
3590
3591
3592
3593
3594
3595
3596
3597
3598
3599
3600
3601
3602
3603
3604
3605
3606
3607
3608
3609
3610
3611
3612
3613
3614
3615
3616
3617
3618
3619
3620
3621
3622
3623
3624
3625
3626
3627
3628
3629
3630
3631
3632
3633
3634
3635
3636
3637
3638
3639
3640
3641
3642
3643
3644
3645
3646
3647
3648
3649
3650
3651
3652
3653
3654
3655
3656
3657
3658
3659
3660
3661
3662
3663
3664
3665
3666
3667
3668
3669
3670
3671
3672
3673
3674
3675
3676
3677
3678
3679
3680
3681
3682
3683
3684
3685
3686
3687
3688
3689
3690
3691
3692
3693
3694
3695
3696
3697
3698
3699
3700
3701
3702
3703
3704
3705
3706
3707
3708
3709
3710
3711
3712
3713
3714
3715
3716
3717
3718
3719
3720
3721
3722
3723
3724
3725
3726
3727
3728
3729
3730
3731
3732
3733
3734
3735
3736
3737
3738
3739
3740
3741
3742
3743
3744
3745
3746
3747
3748
3749
3750
3751
3752
3753
3754
3755
3756
3757
3758
3759
3760
3761
3762
3763
3764
3765
3766
3767
3768
3769
3770
3771
3772
3773
3774
3775
3776
3777
3778
3779
3780
3781
3782
3783
3784
3785
3786
3787
3788
3789
3790
3791
3792
3793
3794
3795
3796
3797
3798
3799
3800
3801
3802
3803
3804
3805
3806
3807
3808
3809
3810
3811
3812
3813
3814
3815
3816
3817
3818
3819
3820
3821
3822
3823
3824
3825
3826
3827
3828
3829
3830
3831
3832
3833
3834
3835
3836
3837
3838
3839
3840
3841
3842
3843
3844
3845
3846
3847
3848
3849
3850
3851
3852
3853
3854
3855
3856
3857
3858
3859
3860
3861
3862
3863
3864
3865
3866
3867
3868
3869
3870
3871
3872
3873
3874
3875
3876
3877
3878
3879
3880
3881
3882
3883
3884
3885
3886
3887
3888
3889
3890
3891
3892
3893
3894
3895
3896
3897
3898
3899
3900
3901
3902
3903
3904
3905
3906
3907
3908
3909
3910
3911
3912
3913
3914
3915
3916
3917
3918
3919
3920
3921
3922
3923
3924
3925
3926
3927
3928
3929
3930
3931
3932
3933
3934
3935
3936
3937
3938
3939
3940
3941
3942
3943
3944
3945
3946
3947
3948
3949
3950
3951
3952
3953
3954
3955
3956
3957
3958
3959
3960
3961
3962
3963
3964
3965
3966
3967
3968
3969
3970
3971
3972
3973
3974
3975
3976
3977
3978
3979
3980
3981
3982
3983
3984
3985
3986
3987
3988
3989
3990
3991
3992
3993
3994
3995
3996
3997
3998
3999
4000
4001
4002
4003
4004
4005
4006
4007
4008
4009
4010
4011
4012
4013
4014
4015
4016
4017
4018
4019
4020
4021
4022
4023
4024
4025
4026
4027
4028
4029
4030
4031
4032
4033
4034
4035
4036
4037
4038
4039
4040
4041
4042
4043
4044
4045
4046
4047
4048
4049
4050
4051
4052
4053
4054
4055
4056
4057
4058
4059
4060
4061
4062
4063
4064
4065
4066
4067
4068
4069
4070
4071
4072
4073
4074
4075
4076
4077
4078
4079
4080
4081
4082
4083
4084
4085
4086
4087
4088
4089
4090
4091
4092
4093
4094
4095
4096
4097
4098
4099
4100
4101
4102
4103
4104
4105
4106
4107
4108
4109
4110
4111
4112
4113
4114
4115
4116
4117
4118
4119
4120
4121
4122
4123
4124
4125
4126
4127
4128
4129
4130
4131
4132
4133
4134
4135
4136
4137
4138
4139
4140
4141
4142
4143
4144
4145
4146
4147
4148
4149
4150
4151
4152
4153
4154
4155
4156
4157
4158
4159
4160
4161
4162
4163
4164
4165
4166
4167
4168
4169
4170
4171
4172
4173
4174
4175
4176
4177
4178
4179
4180
4181
4182
4183
4184
4185
4186
4187
4188
4189
4190
4191
4192
4193
4194
4195
4196
4197
4198
4199
4200
4201
4202
4203
4204
4205
4206
4207
4208
4209
4210
4211
4212
4213
4214
4215
4216
4217
4218
4219
4220
4221
4222
4223
4224
4225
4226
4227
4228
4229
4230
4231
4232
4233
4234
4235
4236
4237
4238
4239
4240
4241
4242
4243
4244
4245
4246
4247
4248
4249
4250
4251
4252
4253
4254
4255
4256
4257
4258
4259
4260
4261
4262
4263
4264
4265
4266
4267
4268
4269
4270
4271
4272
4273
4274
4275
4276
4277
4278
4279
4280
4281
4282
4283
4284
4285
4286
4287
4288
4289
4290
4291
4292
4293
4294
4295
4296
4297
4298
4299
4300
4301
4302
4303
4304
4305
4306
4307
4308
4309
4310
4311
4312
4313
4314
4315
4316
4317
4318
4319
4320
4321
4322
4323
4324
4325
4326
4327
4328
4329
4330
4331
4332
4333
4334
4335
4336
4337
4338
4339
4340
4341
4342
4343
4344
4345
4346
4347
4348
4349
4350
4351
4352
4353
4354
4355
4356
4357
4358
4359
4360
4361
4362
4363
4364
4365
4366
4367
4368
4369
4370
4371
4372
4373
4374
4375
4376
4377
4378
4379
4380
4381
4382
4383
4384
4385
4386
4387
4388
4389
4390
4391
4392
4393
4394
4395
4396
4397
4398
4399
4400
4401
4402
4403
4404
4405
4406
4407
4408
4409
4410
4411
4412
4413
4414
4415
4416
4417
4418
4419
4420
4421
4422
4423
4424
4425
4426
4427
4428
4429
4430
4431
4432
4433
4434
4435
4436
4437
4438
4439
4440
4441
4442
4443
4444
4445
4446
4447
4448
4449
4450
4451
4452
4453
4454
4455
4456
4457
4458
4459
4460
4461
4462
4463
4464
4465
4466
4467
4468
4469
4470
4471
4472
4473
4474
4475
4476
4477
4478
4479
4480
4481
4482
4483
4484
4485
4486
4487
4488
4489
4490
4491
4492
4493
4494
4495
4496
4497
4498
4499
4500
4501
4502
4503
4504
4505
4506
4507
4508
4509
4510
4511
4512
4513
4514
4515
4516
4517
4518
4519
4520
4521
4522
4523
4524
4525
4526
4527
4528
4529
4530
4531
4532
4533
4534
4535
4536
4537
4538
4539
4540
4541
4542
4543
4544
4545
4546
4547
4548
4549
4550
4551
4552
4553
4554
4555
4556
4557
4558
4559
4560
4561
4562
4563
4564
4565
4566
4567
4568
4569
4570
4571
4572
4573
4574
4575
4576
4577
4578
4579
4580
4581
4582
4583
4584
4585
4586
4587
4588
4589
4590
4591
4592
4593
4594
4595
4596
4597
4598
4599
4600
4601
4602
4603
4604
4605
4606
4607
4608
4609
4610
4611
4612
4613
4614
4615
4616
4617
4618
4619
4620
4621
4622
4623
4624
4625
4626
4627
4628
4629
4630
4631
4632
4633
4634
4635
4636
4637
4638
4639
4640
4641
4642
4643
4644
4645
4646
4647
4648
4649
4650
4651
4652
4653
4654
4655
4656
4657
4658
4659
4660
4661
4662
4663
4664
4665
4666
4667
4668
4669
4670
4671
4672
4673
4674
4675
4676
4677
4678
4679
4680
4681
4682
4683
4684
4685
4686
4687
4688
4689
4690
4691
4692
4693
4694
4695
4696
4697
4698
4699
4700
4701
4702
4703
4704
4705
4706
4707
4708
4709
4710
4711
4712
4713
4714
4715
4716
4717
4718
4719
4720
4721
4722
4723
4724
4725
4726
4727
4728
4729
4730
4731
4732
4733
4734
4735
4736
4737
4738
4739
4740
4741
4742
4743
4744
4745
4746
4747
4748
4749
4750
4751
4752
4753
4754
4755
4756
4757
4758
4759
4760
4761
4762
4763
4764
4765
4766
4767
4768
4769
4770
4771
4772
4773
4774
4775
4776
4777
4778
4779
4780
4781
4782
4783
4784
4785
4786
4787
4788
4789
4790
4791
4792
4793
4794
4795
4796
4797
4798
4799
4800
4801
4802
4803
4804
4805
4806
4807
4808
4809
4810
4811
4812
4813
4814
4815
4816
4817
4818
4819
4820
4821
4822
4823
4824
4825
4826
4827
4828
4829
4830
4831
4832
4833
4834
4835
4836
4837
4838
4839
4840
4841
4842
4843
4844
4845
4846
4847
4848
4849
4850
4851
4852
4853
4854
4855
4856
4857
4858
4859
4860
4861
4862
4863
4864
4865
4866
4867
4868
4869
4870
4871
4872
4873
4874
4875
4876
4877
4878
4879
4880
4881
4882
4883
4884
4885
4886
4887
4888
4889
4890
4891
4892
4893
4894
4895
4896
4897
4898
4899
4900
4901
4902
4903
4904
4905
4906
4907
4908
4909
4910
4911
4912
4913
4914
4915
4916
4917
4918
4919
4920
4921
4922
4923
4924
4925
4926
4927
4928
4929
4930
4931
4932
4933
4934
4935
4936
4937
4938
4939
4940
4941
4942
4943
4944
4945
4946
4947
4948
4949
4950
4951
4952
4953
4954
4955
4956
4957
4958
4959
4960
4961
4962
4963
4964
4965
4966
4967
4968
4969
4970
4971
4972
4973
4974
4975
4976
4977
4978
4979
4980
4981
4982
4983
4984
4985
4986
4987
4988
4989
4990
4991
4992
4993
4994
4995
4996
4997
4998
4999
5000
5001
5002
5003
5004
5005
5006
5007
5008
5009
5010
5011
5012
5013
5014
5015
5016
5017
5018
5019
5020
5021
5022
5023
5024
5025
5026
5027
5028
5029
5030
5031
5032
5033
5034
5035
5036
5037
5038
5039
5040
5041
5042
5043
5044
5045
5046
5047
5048
5049
5050
5051
5052
5053
5054
5055
5056
5057
5058
5059
5060
5061
5062
5063
5064
5065
5066
5067
5068
5069
5070
5071
5072
5073
5074
5075
5076
5077
5078
5079
5080
5081
5082
5083
5084
5085
5086
5087
5088
5089
5090
5091
5092
5093
5094
5095
5096
5097
5098
5099
5100
5101
5102
5103
5104
5105
5106
5107
5108
5109
5110
5111
5112
5113
5114
5115
5116
5117
5118
5119
5120
5121
5122
5123
5124
5125
5126
5127
5128
5129
5130
5131
5132
5133
5134
5135
5136
5137
5138
5139
5140
5141
5142
5143
5144
5145
5146
5147
5148
5149
5150
5151
5152
5153
5154
5155
5156
5157
5158
5159
5160
5161
5162
5163
5164
5165
5166
5167
5168
5169
5170
5171
5172
5173
5174
5175
5176
5177
5178
5179
5180
5181
5182
5183
5184
5185
5186
5187
5188
5189
5190
5191
5192
5193
5194
5195
5196
5197
5198
5199
5200
5201
5202
5203
5204
5205
5206
5207
5208
5209
5210
5211
5212
5213
5214
5215
5216
5217
5218
5219
5220
5221
5222
5223
5224
5225
5226
5227
5228
5229
5230
5231
5232
5233
5234
5235
5236
5237
5238
5239
5240
5241
5242
5243
5244
5245
5246
5247
5248
5249
5250
5251
5252
5253
5254
5255
5256
5257
5258
5259
5260
5261
5262
5263
5264
5265
5266
5267
5268
5269
5270
5271
5272
5273
5274
5275
5276
5277
5278
5279
5280
5281
5282
5283
5284
5285
5286
5287
5288
5289
5290
5291
5292
5293
5294
5295
5296
5297
5298
5299
5300
5301
5302
5303
5304
5305
5306
5307
5308
5309
5310
5311
5312
5313
5314
5315
5316
5317
5318
5319
5320
5321
5322
5323
5324
5325
5326
5327
5328
5329
5330
5331
5332
5333
5334
5335
5336
5337
5338
5339
5340
5341
5342
5343
5344
5345
5346
5347
5348
5349
5350
5351
5352
5353
5354
5355
5356
5357
5358
5359
5360
5361
5362
5363
5364
5365
5366
5367
5368
5369
5370
5371
5372
5373
5374
5375
5376
5377
5378
5379
5380
5381
5382
5383
5384
5385
5386
5387
5388
5389
5390
5391
5392
5393
5394
5395
5396
5397
5398
5399
5400
5401
5402
5403
5404
5405
5406
5407
5408
5409
5410
5411
5412
5413
5414
5415
5416
5417
5418
5419
5420
5421
5422
5423
5424
5425
5426
5427
5428
5429
5430
5431
5432
5433
5434
5435
5436
5437
5438
5439
5440
5441
5442
5443
5444
5445
5446
5447
5448
5449
5450
5451
5452
5453
5454
5455
5456
5457
5458
5459
5460
5461
5462
5463
5464
5465
5466
5467
5468
5469
5470
5471
5472
5473
5474
5475
5476
5477
5478
5479
5480
5481
5482
5483
5484
5485
5486
5487
5488
5489
5490
5491
5492
5493
5494
5495
5496
5497
5498
5499
5500
5501
5502
5503
5504
5505
5506
5507
5508
5509
5510
5511
5512
5513
5514
5515
5516
5517
5518
5519
5520
5521
5522
5523
5524
5525
5526
5527
5528
5529
5530
5531
5532
5533
5534
5535
5536
5537
5538
5539
5540
5541
5542
5543
5544
5545
5546
5547
5548
5549
5550
5551
5552
5553
5554
5555
5556
5557
5558
5559
5560
5561
5562
5563
5564
5565
5566
5567
5568
5569
5570
5571
5572
5573
5574
5575
5576
5577
5578
5579
5580
5581
5582
5583
5584
5585
5586
5587
5588
5589
5590
5591
5592
5593
5594
5595
5596
5597
5598
5599
5600
5601
5602
5603
5604
5605
5606
5607
5608
5609
5610
5611
5612
5613
5614
5615
5616
5617
5618
5619
5620
5621
5622
5623
5624
5625
5626
5627
5628
5629
5630
5631
5632
5633
5634
5635
5636
5637
5638
5639
5640
5641
5642
5643
5644
5645
5646
5647
5648
5649
5650
5651
5652
5653
5654
5655
5656
5657
5658
5659
5660
5661
5662
5663
5664
5665
5666
5667
5668
5669
5670
5671
5672
5673
5674
5675
5676
5677
5678
5679
5680
5681
5682
5683
5684
5685
5686
5687
5688
5689
5690
5691
5692
5693
5694
5695
5696
5697
5698
5699
5700
5701
5702
5703
5704
5705
5706
5707
5708
5709
5710
5711
5712
5713
5714
5715
5716
5717
5718
5719
5720
5721
5722
5723
5724
5725
5726
5727
5728
5729
5730
5731
5732
5733
5734
5735
5736
5737
5738
5739
5740
5741
5742
5743
5744
5745
5746
5747
5748
5749
5750
5751
5752
5753
5754
5755
5756
5757
5758
5759
5760
5761
5762
5763
5764
5765
5766
5767
5768
5769
5770
5771
5772
5773
5774
5775
5776
# doc-cache created by Octave 4.0.0
# name: cache
# type: cell
# rows: 3
# columns: 110
# name: <cell-element>
# type: sq_string
# elements: 1
# length: 20
anderson_darling_cdf


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 2183
 -- Function File: P = anderson_darling_cdf (A, N)

     Return the CDF for the given Anderson-Darling coefficient A
     computed from N values sampled from a distribution.  For a vector
     of random variables X of length N, compute the CDF of the values
     from the distribution from which they are drawn.  You can uses
     these values to compute A as follows:

          A = -N - sum( (2*i-1) .* (log(X) + log(1 - X(N:-1:1,:))) )/N;

     From the value A, 'anderson_darling_cdf' returns the probability
     that A could be returned from a set of samples.

     The algorithm given in [1] claims to be an approximation for the
     Anderson-Darling CDF accurate to 6 decimal points.

     Demonstrate using:

          n = 300; reps = 10000;
          z = randn(n, reps);
          x = sort ((1 + erf (z/sqrt (2)))/2);
          i = [1:n]' * ones (1, size (x, 2));
          A = -n - sum ((2*i-1) .* (log (x) + log (1 - x (n:-1:1, :))))/n;
          p = anderson_darling_cdf (A, n);
          hist (100 * p, [1:100] - 0.5);

     You will see that the histogram is basically flat, which is to say
     that the probabilities returned by the Anderson-Darling CDF are
     distributed uniformly.

     You can easily determine the extreme values of P:

          [junk, idx] = sort (p);

     The histograms of various P aren't very informative:

          histfit (z (:, idx (1)), linspace (-3, 3, 15));
          histfit (z (:, idx (end/2)), linspace (-3, 3, 15));
          histfit (z (:, idx (end)), linspace (-3, 3, 15));

     More telling is the qqplot:

          qqplot (z (:, idx (1))); hold on; plot ([-3, 3], [-3, 3], ';;'); hold off;
          qqplot (z (:, idx (end/2))); hold on; plot ([-3, 3], [-3, 3], ';;'); hold off;
          qqplot (z (:, idx (end))); hold on; plot ([-3, 3], [-3, 3], ';;'); hold off;

     Try a similarly analysis for Z uniform:

          z = rand (n, reps); x = sort(z);

     and for Z exponential:

          z = rande (n, reps); x = sort (1 - exp (-z));

     [1] Marsaglia, G; Marsaglia JCW; (2004) "Evaluating the Anderson
     Darling distribution", Journal of Statistical Software, 9(2).

     See also: anderson_darling_test.


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 80
Return the CDF for the given Anderson-Darling coefficient A computed
from N valu



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 21
anderson_darling_test


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 1833
 -- Function File: [Q, ASQ, INFO] = = anderson_darling_test (X,
          DISTRIBUTION)

     Test the hypothesis that X is selected from the given distribution
     using the Anderson-Darling test.  If the returned Q is small,
     reject the hypothesis at the Q*100% level.

     The Anderson-Darling A^2 statistic is calculated as follows:

                        n
          A^2_n = -n - SUM (2i-1)/n log(z_i (1 - z_{n-i+1}))
                       i=1

     where z_i is the ordered position of the X's in the CDF of the
     distribution.  Unlike the Kolmogorov-Smirnov statistic, the
     Anderson-Darling statistic is sensitive to the tails of the
     distribution.

     The DISTRIBUTION argument must be a either "uniform", "normal", or
     "exponential".

     For "normal"' and "exponential" distributions, estimate the
     distribution parameters from the data, convert the values to CDF
     values, and compare the result to tabluated critical values.  This
     includes an correction for small N which works well enough for N >=
     8, but less so from smaller N.  The returned 'info.Asq_corrected'
     contains the adjusted statistic.

     For "uniform", assume the values are uniformly distributed in
     (0,1), compute A^2 and return the corresponding p-value from
     '1-anderson_darling_cdf(A^2,n)'.

     If you are selecting from a known distribution, convert your values
     into CDF values for the distribution and use "uniform".  Do not use
     "uniform" if the distribution parameters are estimated from the
     data itself, as this sharply biases the A^2 statistic toward
     smaller values.

     [1] Stephens, MA; (1986), "Tests based on EDF statistics", in
     D'Agostino, RB; Stephens, MA; (eds.)  Goodness-of-fit Techinques.
     New York: Dekker.

     See also: anderson_darling_cdf.


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 80
Test the hypothesis that X is selected from the given distribution using
the And



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 6
anovan


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 1469
 -- Function File: [PVAL, F, DF_B, DF_E] = anovan (DATA, GRPS)
 -- Function File: [PVAL, F, DF_B, DF_E] = anovan (DATA, GRPS, 'param1',
          VALUE1)
     Perform a multi-way analysis of variance (ANOVA). The goal is to
     test whether the population means of data taken from K different
     groups are all equal.

     Data is a single vector DATA with groups specified by a
     corresponding matrix of group labels GRPS, where GRPS has the same
     number of rows as DATA.  For example, if DATA = [1.1;1.2]; GRPS=
     [1,2,1; 1,5,2]; then data point 1.1 was measured under conditions
     1,2,1 and data point 1.2 was measured under conditions 1,5,2.  Note
     that groups do not need to be sequentially numbered.

     By default, a 'linear' model is used, computing the N main effects
     with no interactions.  this may be modified by param 'model'

     p= anovan(data,groups, 'model', modeltype) - modeltype = 'linear':
     compute N main effects - modeltype = 'interaction': compute N
     effects and N*(N-1) two-factor interactions - modeltype = 'full':
     compute interactions at all levels

     Under the null of constant means, the statistic F follows an F
     distribution with DF_B and DF_E degrees of freedom.

     The p-value (1 minus the CDF of this distribution at F) is returned
     in PVAL.

     If no output argument is given, the standard one-way ANOVA table is
     printed.

     BUG: DFE is incorrect for modeltypes != full


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 49
Perform a multi-way analysis of variance (ANOVA).



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 8
betastat


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 1036
 -- Function File: [M, V] = betastat (A, B)
     Compute mean and variance of the beta distribution.

     Arguments
     ---------

        * A is the first parameter of the beta distribution.  A must be
          positive

        * B is the second parameter of the beta distribution.  B must be
          positive
     A and B must be of common size or one of them must be scalar

     Return values
     -------------

        * M is the mean of the beta distribution

        * V is the variance of the beta distribution

     Examples
     --------

          a = 1:6;
          b = 1:0.2:2;
          [m, v] = betastat (a, b)

          [m, v] = betastat (a, 1.5)

     References
     ----------

       1. Wendy L. Martinez and Angel R. Martinez.  'Computational
          Statistics Handbook with MATLAB'. Appendix E, pages 547-557,
          Chapman & Hall/CRC, 2001.

       2. Athanasios Papoulis.  'Probability, Random Variables, and
          Stochastic Processes'.  McGraw-Hill, New York, second edition,
          1984.


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 51
Compute mean and variance of the beta distribution.



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 8
binostat


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 1100
 -- Function File: [M, V] = binostat (N, P)
     Compute mean and variance of the binomial distribution.

     Arguments
     ---------

        * N is the first parameter of the binomial distribution.  The
          elements of N must be natural numbers

        * P is the second parameter of the binomial distribution.  The
          elements of P must be probabilities
     N and P must be of common size or one of them must be scalar

     Return values
     -------------

        * M is the mean of the binomial distribution

        * V is the variance of the binomial distribution

     Examples
     --------

          n = 1:6;
          p = 0:0.2:1;
          [m, v] = binostat (n, p)

          [m, v] = binostat (n, 0.5)

     References
     ----------

       1. Wendy L. Martinez and Angel R. Martinez.  'Computational
          Statistics Handbook with MATLAB'. Appendix E, pages 547-557,
          Chapman & Hall/CRC, 2001.

       2. Athanasios Papoulis.  'Probability, Random Variables, and
          Stochastic Processes'.  McGraw-Hill, New York, second edition,
          1984.


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 55
Compute mean and variance of the binomial distribution.



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 7
boxplot


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 2365
 -- Function File: S = boxplot (DATA, NOTCHED, SYMBOL, VERTICAL,
          MAXWHISKER, ...)
 -- Function File: [... H]= boxplot (...)

     Produce a box plot.

     The box plot is a graphical display that simultaneously describes
     several important features of a data set, such as center, spread,
     departure from symmetry, and identification of observations that
     lie unusually far from the bulk of the data.

     DATA is a matrix with one column for each data set, or data is a
     cell vector with one cell for each data set.

     NOTCHED = 1 produces a notched-box plot.  Notches represent a
     robust estimate of the uncertainty about the median.

     NOTCHED = 0 (default) produces a rectangular box plot.

     NOTCHED in (0,1) produces a notch of the specified depth.  notched
     values outside (0,1) are amusing if not exactly practical.

     SYMBOL sets the symbol for the outlier values, default symbol for
     points that lie outside 3 times the interquartile range is 'o',
     default symbol for points between 1.5 and 3 times the interquartile
     range is '+'.

     SYMBOL = '.'  points between 1.5 and 3 times the IQR is marked with
     '.'  and points outside 3 times IQR with 'o'.

     SYMBOL = ['x','*'] points between 1.5 and 3 times the IQR is marked
     with 'x' and points outside 3 times IQR with '*'.

     VERTICAL = 0 makes the boxes horizontal, by default VERTICAL = 1.

     MAXWHISKER defines the length of the whiskers as a function of the
     IQR (default = 1.5).  If MAXWHISKER = 0 then 'boxplot' displays all
     data values outside the box using the plotting symbol for points
     that lie outside 3 times the IQR.

     Supplemental arguments are concatenated and passed to plot.

     The returned matrix S has one column for each data set as follows:

     1       Minimum
     2       1st quartile
     3       2nd quartile (median)
     4       3rd quartile
     5       Maximum
     6       Lower confidence limit for median
     7       Upper confidence limit for median

     The returned structure H has hanldes to the plot elements, allowing
     customization of the visualization using set/get functions.

     Example

          title ("Grade 3 heights");
          axis ([0,3]);
          tics ("x", 1:2, {"girls"; "boys"});
          boxplot ({randn(10,1)*5+140, randn(13,1)*8+135});


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 19
Produce a box plot.



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 8
caseread


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 262
 -- Function File: NAMES = caseread (FILENAME)
     Read case names from an ascii file.

     Essentially, this reads all lines from a file as text and returns
     them in a string matrix.

     See also: casewrite, tblread, tblwrite, csv2cell, cell2csv, fopen.


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 35
Read case names from an ascii file.



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 9
casewrite


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 254
 -- Function File: casewrite (STRMAT, FILENAME)
     Write case names to an ascii file.

     Essentially, this writes all lines from STRMAT to FILENAME (after
     deblanking them).

     See also: caseread, tblread, tblwrite, csv2cell, cell2csv, fopen.


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 34
Write case names to an ascii file.



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 3
cdf


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 2118
 -- Function File: RETVAL = cdf (NAME, X, ...)
     Return cumulative density function of NAME function for value X.
     This is a wrapper around various NAMEcdf and NAME_cdf functions.
     See the individual functions help to learn the signification of the
     arguments after X.  Supported functions and corresponding number of
     additional arguments are:

       function               alternative                      args
     -------------------------------------------------------------------------
       "beta"                 "beta"                           2
       "bino"                 "binomial"                       2
       "cauchy"                                                2
       "chi2"                 "chisquare"                      1
       "discrete"                                              2
       "exp"                  "exponential"                    1
       "f"                                                     2
       "gam"                  "gamma"                          2
       "geo"                  "geometric"                      1
       "gev"                  "generalized extreme value"      3
       "hyge"                 "hypergeometric"                 3
       "kolmogorov_smirnov"                                    1
       "laplace"                                               2
       "logistic"                                              0
       "logn"                 "lognormal"                      2
       "norm"                 "normal"                         2
       "poiss"                "poisson"                        2
       "rayl"                 "rayleigh"                       1
       "t"                                                     1
       "unif"                 "uniform"                        2
       "wbl"                  "weibull"                        2

     See also: betacdf, binocdf, cauchy_cdf, chi2cdf, discrete_cdf,
     expcdf, fcdf, gamcdf, geocdf, gevcdf, hygecdf,
     kolmogorov_smirnov_cdf, laplace_cdf, logistic_cdf, logncdf,
     normcdf, poisscdf, raylcdf, tcdf, unifcdf, wblcdf.


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 64
Return cumulative density function of NAME function for value X.



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 8
chi2stat


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 842
 -- Function File: [M, V] = chi2stat (N)
     Compute mean and variance of the chi-square distribution.

     Arguments
     ---------

        * N is the parameter of the chi-square distribution.  The
          elements of N must be positive

     Return values
     -------------

        * M is the mean of the chi-square distribution

        * V is the variance of the chi-square distribution

     Example
     -------

          n = 1:6;
          [m, v] = chi2stat (n)

     References
     ----------

       1. Wendy L. Martinez and Angel R. Martinez.  'Computational
          Statistics Handbook with MATLAB'. Appendix E, pages 547-557,
          Chapman & Hall/CRC, 2001.

       2. Athanasios Papoulis.  'Probability, Random Variables, and
          Stochastic Processes'.  McGraw-Hill, New York, second edition,
          1984.


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 57
Compute mean and variance of the chi-square distribution.



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 11
cl_multinom


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 2968
 -- Function File: CL = cl_multinom (X, N, B, CALCULATION_TYPE ) -
          Confidence level of multinomial portions
     Returns confidence level of multinomial parameters estimated p = x
     / sum(x) with predefined confidence interval B.  Finite population
     is also considered.

     This function calculates the level of confidence at which the
     samples represent the true distribution given that there is a
     predefined tolerance (confidence interval).  This is the upside
     down case of the typical excercises at which we want to get the
     confidence interval given the confidence level (and the estimated
     parameters of the underlying distribution).  But once we accept
     (lets say at elections) that we have a standard predefined maximal
     acceptable error rate (e.g.  B=0.02 ) in the estimation and we just
     want to know that how sure we can be that the measured proportions
     are the same as in the entire population (ie.  the expected value
     and mean of the samples are roghly the same) we need to use this
     function.

     Arguments
     ---------

        * X : int vector : sample frequencies bins
        * N : int : Population size that was sampled by x.  If N<sum(x),
          infinite number assumed
        * B : real, vector : confidence interval if vector, it should be
          the size of x containing confence interval for each cells if
          scalar, each cell will have the same value of b unless it is
          zero or -1 if value is 0, b=.02 is assumed which is standard
          choice at elections otherwise it is calculated in a way that
          one sample in a cell alteration defines the confidence
          interval
        * CALCULATION_TYPE : string : (Optional), described below
          "bromaghin" (default) - do not change it unless you have a
          good reason to do so "cochran" "agresti_cull" this is not
          exactly the solution at reference given below but an
          adjustment of the solutions above

     Returns
     -------

     Confidence level.

     Example
     -------

     CL = cl_multinom( [27;43;19;11], 10000, 0.05 ) returns 0.69
     confidence level.

     References
     ----------

     "bromaghin" calculation type (default) is based on is based on the
     article Jeffrey F. Bromaghin, "Sample Size Determination for
     Interval Estimation of Multinomial Probabilities", The American
     Statistician vol 47, 1993, pp 203-206.

     "cochran" calculation type is based on article Robert T. Tortora,
     "A Note on Sample Size Estimation for Multinomial Populations", The
     American Statistician, , Vol 32.  1978, pp 100-102.

     "agresti_cull" calculation type is based on article in which
     Quesenberry Hurst and Goodman result is combined A. Agresti and
     B.A. Coull, "Approximate is better than \"exact\" for interval
     estimation of binomial portions", The American Statistician, Vol.
     52, 1998, pp 119-126


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 80
Returns confidence level of multinomial parameters estimated p = x /
sum(x) with



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 8
cmdscale


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 2704
 -- Function File: Y = cmdscale (D)
 -- Function File: [Y, E ] = cmdscale (D)
     Classical multidimensional scaling of a matrix.

     Takes an N by N distance (or difference, similarity, or
     dissimilarity) matrix D.  Returns Y, a matrix of N points with
     coordinates in P dimensional space which approximate those
     distances (or differences, similarities, or dissimilarities).  Also
     returns the eigenvalues E of 'B = -1/2 * J * (D.^2) * J', where 'J
     = eye(N) - ones(N,N)/N'.  P, the number of columns of Y, is equal
     to the number of positive real eigenvalues of B.

     D can be a full or sparse matrix or a vector of length 'N*(N-1)/2'
     containing the upper triangular elements (like the output of the
     'pdist' function).  It must be symmetric with non-negative entries
     whose values are further restricted by the type of matrix being
     represented:

     * If D is either a distance, dissimilarity, or difference matrix,
     then it must have zero entries along the main diagonal.  In this
     case the points Y equal or approximate the distances given by D.

     * If D is a similarity matrix, the elements must all be less than
     or equal to one, with ones along the the main diagonal.  In this
     case the points Y equal or approximate the distances given by 'D =
     sqrt(ones(N,N)-D)'.

     D is a Euclidean matrix if and only if B is positive semi-definite.
     When this is the case, then Y is an exact representation of the
     distances given in D.  If D is non-Euclidean, Y only approximates
     the distance given in D.  The approximation used by 'cmdscale'
     minimizes the statistical loss function known as STRAIN.

     The returned Y is an N by P matrix showing possible coordinates of
     the points in P dimensional space ('P < N').  The columns are
     correspond to the positive eigenvalues of B in descending order.  A
     translation, rotation, or reflection of the coordinates given by Y
     will satisfy the same distance matrix up to the limits of machine
     precision.

     For any 'K <= P', if the largest K positive eigenvalues of B are
     significantly greater in absolute magnitude than its other
     eigenvalues, the first K columns of Y provide a K-dimensional
     reduction of Y which approximates the distances given by D.  The
     optional return E can be used to consider various values of K, or
     to evaluate the accuracy of specific dimension reductions (e.g., 'K
     = 2').

     Reference: Ingwer Borg and Patrick J.F. Groenen (2005), Modern
     Multidimensional Scaling, Second Edition, Springer, ISBN:
     978-0-387-25150-9 (Print) 978-0-387-28981-6 (Online)

     See also: pdist.


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 47
Classical multidimensional scaling of a matrix.



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 6
combnk


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 91
 -- Function File: C = combnk (DATA, K)
     Return all combinations of K elements in DATA.


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 46
Return all combinations of K elements in DATA.



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 9
copulacdf


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 2183
 -- Function File: P = copulacdf (FAMILY, X, THETA)
 -- Function File: copulacdf ('t', X, THETA, NU)
     Compute the cumulative distribution function of a copula family.

     Arguments
     ---------

        * FAMILY is the copula family name.  Currently, FAMILY can be
          ''Gaussian'' for the Gaussian family, ''t'' for the Student's
          t family, ''Clayton'' for the Clayton family, ''Gumbel'' for
          the Gumbel-Hougaard family, ''Frank'' for the Frank family,
          ''AMH'' for the Ali-Mikhail-Haq family, or ''FGM'' for the
          Farlie-Gumbel-Morgenstern family.

        * X is the support where each row corresponds to an observation.

        * THETA is the parameter of the copula.  For the Gaussian and
          Student's t copula, THETA must be a correlation matrix.  For
          bivariate copulas THETA can also be a correlation coefficient.
          For the Clayton family, the Gumbel-Hougaard family, the Frank
          family, and the Ali-Mikhail-Haq family, THETA must be a vector
          with the same number of elements as observations in X or be
          scalar.  For the Farlie-Gumbel-Morgenstern family, THETA must
          be a matrix of coefficients for the Farlie-Gumbel-Morgenstern
          polynomial where each row corresponds to one set of
          coefficients for an observation in X.  A single row is
          expanded.  The coefficients are in binary order.

        * NU is the degrees of freedom for the Student's t family.  NU
          must be a vector with the same number of elements as
          observations in X or be scalar.

     Return values
     -------------

        * P is the cumulative distribution of the copula at each row of
          X and corresponding parameter THETA.

     Examples
     --------

          x = [0.2:0.2:0.6; 0.2:0.2:0.6];
          theta = [1; 2];
          p = copulacdf ("Clayton", x, theta)

          x = [0.2:0.2:0.6; 0.2:0.1:0.4];
          theta = [0.2, 0.1, 0.1, 0.05];
          p = copulacdf ("FGM", x, theta)

     References
     ----------

       1. Roger B. Nelsen.  'An Introduction to Copulas'.  Springer, New
          York, second edition, 2006.


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 64
Compute the cumulative distribution function of a copula family.



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 9
copulapdf


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 1494
 -- Function File: P = copulapdf (FAMILY, X, THETA)
     Compute the probability density function of a copula family.

     Arguments
     ---------

        * FAMILY is the copula family name.  Currently, FAMILY can be
          ''Clayton'' for the Clayton family, ''Gumbel'' for the
          Gumbel-Hougaard family, ''Frank'' for the Frank family, or
          ''AMH'' for the Ali-Mikhail-Haq family.

        * X is the support where each row corresponds to an observation.

        * THETA is the parameter of the copula.  The elements of THETA
          must be greater than or equal to '-1' for the Clayton family,
          greater than or equal to '1' for the Gumbel-Hougaard family,
          arbitrary for the Frank family, and greater than or equal to
          '-1' and lower than '1' for the Ali-Mikhail-Haq family.
          Moreover, THETA must be non-negative for dimensions greater
          than '2'.  THETA must be a column vector with the same number
          of rows as X or be scalar.

     Return values
     -------------

        * P is the probability density of the copula at each row of X
          and corresponding parameter THETA.

     Examples
     --------

          x = [0.2:0.2:0.6; 0.2:0.2:0.6];
          theta = [1; 2];
          p = copulapdf ("Clayton", x, theta)

          p = copulapdf ("Gumbel", x, 2)

     References
     ----------

       1. Roger B. Nelsen.  'An Introduction to Copulas'.  Springer, New
          York, second edition, 2006.


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 60
Compute the probability density function of a copula family.



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 9
copularnd


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 1886
 -- Function File: X = copularnd (FAMILY, THETA, N)
 -- Function File: copularnd (FAMILY, THETA, N, D)
 -- Function File: copularnd ('t', THETA, NU, N)
     Generate random samples from a copula family.

     Arguments
     ---------

        * FAMILY is the copula family name.  Currently, FAMILY can be
          ''Gaussian'' for the Gaussian family, ''t'' for the Student's
          t family, or ''Clayton'' for the Clayton family.

        * THETA is the parameter of the copula.  For the Gaussian and
          Student's t copula, THETA must be a correlation matrix.  For
          bivariate copulas THETA can also be a correlation coefficient.
          For the Clayton family, THETA must be a vector with the same
          number of elements as samples to be generated or be scalar.

        * NU is the degrees of freedom for the Student's t family.  NU
          must be a vector with the same number of elements as samples
          to be generated or be scalar.

        * N is the number of rows of the matrix to be generated.  N must
          be a non-negative integer and corresponds to the number of
          samples to be generated.

        * D is the number of columns of the matrix to be generated.  D
          must be a positive integer and corresponds to the dimension of
          the copula.

     Return values
     -------------

        * X is a matrix of random samples from the copula with N samples
          of distribution dimension D.

     Examples
     --------

          theta = 0.5;
          x = copularnd ("Gaussian", theta);

          theta = 0.5;
          nu = 2;
          x = copularnd ("t", theta, nu);

          theta = 0.5;
          n = 2;
          x = copularnd ("Clayton", theta, n);

     References
     ----------

       1. Roger B. Nelsen.  'An Introduction to Copulas'.  Springer, New
          York, second edition, 2006.


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 45
Generate random samples from a copula family.



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 8
crossval


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 2102
 -- Function File: RESULTS = crossval (F, X, Y[, PARAMS])
     Perform cross validation on given data.

     F should be a function that takes 4 inputs XTRAIN, YTRAIN, XTEST,
     YTEST, fits a model based on XTRAIN, YTRAIN, applies the fitted
     model to XTEST, and returns a goodness of fit measure based on
     comparing the predicted and actual YTEST.  'crossval' returns an
     array containing the values returned by F for every
     cross-validation fold or resampling applied to the given data.

     X should be an N by M matrix of predictor values

     Y should be an N by 1 vector of predicand values

     PARAMS may include parameter-value pairs as follows:

     "KFold"
          Divide set into K equal-size subsets, using each one
          successively for validation.

     "HoldOut"
          Divide set into two subsets, training and validation.  If the
          value K is a fraction, that is the fraction of values put in
          the validation subset (by default K=0.1); if it is a positive
          integer, that is the number of values in the validation
          subset.

     "LeaveOut"
          Leave-one-out partition (each element is placed in its own
          subset).  The value is ignored.

     "Partition"
          The value should be a CVPARTITION object.

     "Given"
          The value should be an N by 1 vector specifying in which
          partition to put each element.

     "stratify"
          The value should be an N by 1 vector containing class
          designations for the elements, in which case the "KFold" and
          "HoldOut" partitionings attempt to ensure each partition
          represents the classes proportionately.

     "mcreps"
          The value should be a positive integer specifying the number
          of times to resample based on different partitionings.
          Currently only works with the partition type "HoldOut".

     Only one of "KFold", "HoldOut", "LeaveOut", "Given", "Partition"
     should be specified.  If none is specified, the default is "KFold"
     with K = 10.

     See also: cvpartition.


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 39
Perform cross validation on given data.



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 4
dcov


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 354
 -- Function File: [DCOR, DCOV, DVARX, DVARY] = dcov (X, Y, INDEX=1)
     Distance covariance and correlation statistics.

     It returns distace correlation (DCOR), distance covariance (DCOV),
     diatance variace on x (DVARX) and distance variance on y (DVARY).

     Reference: https://en.wikipedia.org/wiki/Distance_correlation

     See also: cov.


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 47
Distance covariance and correlation statistics.



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 10
dendrogram


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 581
 -- Function File: P = dendrogram (TREE)
 -- Function File: P, T = dendrogram (TREE)
 -- Function File: P, T, PERM = dendrogram (TREE)
     Plots a dendrogram using the output of function 'linkage'.

     t is a vector containing the leaf node number for each object in
     the original dataset.  For now, all objects are leaf nodes.

     perm is the permutation of the input objects used to display the
     dendrogram, in left-to-right order.

     TODO: Return handle to lines to set properties TODO: Rescale the
     plot automatically based on data.

     See also: linkage.


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 58
Plots a dendrogram using the output of function 'linkage'.



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 7
expstat


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 844
 -- Function File: [M, V] = expstat (L)
     Compute mean and variance of the exponential distribution.

     Arguments
     ---------

        * L is the parameter of the exponential distribution.  The
          elements of L must be positive

     Return values
     -------------

        * M is the mean of the exponential distribution

        * V is the variance of the exponential distribution

     Example
     -------

          l = 1:6;
          [m, v] = expstat (l)

     References
     ----------

       1. Wendy L. Martinez and Angel R. Martinez.  'Computational
          Statistics Handbook with MATLAB'. Appendix E, pages 547-557,
          Chapman & Hall/CRC, 2001.

       2. Athanasios Papoulis.  'Probability, Random Variables, and
          Stochastic Processes'.  McGraw-Hill, New York, second edition,
          1984.


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 58
Compute mean and variance of the exponential distribution.



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 4
ff2n


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 98
 -- Function File: ff2n (N)
     Full-factor design with n binary terms.

     See also: fullfact.


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 39
Full-factor design with n binary terms.



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 5
fstat


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 1165
 -- Function File: [MN, V] = fstat (M, N)
     Compute mean and variance of the F distribution.

     Arguments
     ---------

        * M is the first parameter of the F distribution.  The elements
          of M must be positive

        * N is the second parameter of the F distribution.  The elements
          of N must be positive
     M and N must be of common size or one of them must be scalar

     Return values
     -------------

        * MN is the mean of the F distribution.  The mean is undefined
          for N not greater than 2

        * V is the variance of the F distribution.  The variance is
          undefined for N not greater than 4

     Examples
     --------

          m = 1:6;
          n = 5:10;
          [mn, v] = fstat (m, n)

          [mn, v] = fstat (m, 5)

     References
     ----------

       1. Wendy L. Martinez and Angel R. Martinez.  'Computational
          Statistics Handbook with MATLAB'. Appendix E, pages 547-557,
          Chapman & Hall/CRC, 2001.

       2. Athanasios Papoulis.  'Probability, Random Variables, and
          Stochastic Processes'.  McGraw-Hill, New York, second edition,
          1984.


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 48
Compute mean and variance of the F distribution.



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 8
fullfact


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 261
 -- Function File: fullfact (N)
     Full factorial design.

     If N is a scalar, return the full factorial design with N binary
     choices, 0 and 1.

     If N is a vector, return the full factorial design with choices 1
     through N_I for each factor I.


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 22
Full factorial design.



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 6
gamfit


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 295
 -- Function File: MLE = gamfit (DATA)
     Calculate gamma distribution parameters.

     Find the maximum likelihood estimators (MLEs) of the Gamma
     distribution of DATA.  MLE is a two element vector with shape
     parameter A and scale B.

     See also: gampdf, gaminv, gamrnd, gamlike.


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 40
Calculate gamma distribution parameters.



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 7
gamlike


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 224
 -- Function File: X = gamlike ([A B], R)
     Calculates the negative log-likelihood function for the Gamma
     distribution over vector R, with the given parameters A and B.

     See also: gampdf, gaminv, gamrnd, gamfit.


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 80
Calculates the negative log-likelihood function for the Gamma
distribution over 



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 7
gamstat


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 1038
 -- Function File: [M, V] = gamstat (A, B)
     Compute mean and variance of the gamma distribution.

     Arguments
     ---------

        * A is the first parameter of the gamma distribution.  A must be
          positive

        * B is the second parameter of the gamma distribution.  B must
          be positive
     A and B must be of common size or one of them must be scalar

     Return values
     -------------

        * M is the mean of the gamma distribution

        * V is the variance of the gamma distribution

     Examples
     --------

          a = 1:6;
          b = 1:0.2:2;
          [m, v] = gamstat (a, b)

          [m, v] = gamstat (a, 1.5)

     References
     ----------

       1. Wendy L. Martinez and Angel R. Martinez.  'Computational
          Statistics Handbook with MATLAB'. Appendix E, pages 547-557,
          Chapman & Hall/CRC, 2001.

       2. Athanasios Papoulis.  'Probability, Random Variables, and
          Stochastic Processes'.  McGraw-Hill, New York, second edition,
          1984.


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 52
Compute mean and variance of the gamma distribution.



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 7
geomean


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 175
 -- Function File: geomean (X)
 -- Function File: geomean (X, DIM)
     Compute the geometric mean.

     This function does the same as 'mean (x, "g")'.

     See also: mean.


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 27
Compute the geometric mean.



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 7
geostat


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 853
 -- Function File: [M, V] = geostat (P)
     Compute mean and variance of the geometric distribution.

     Arguments
     ---------

        * P is the rate parameter of the geometric distribution.  The
          elements of P must be probabilities

     Return values
     -------------

        * M is the mean of the geometric distribution

        * V is the variance of the geometric distribution

     Example
     -------

          p = 1 ./ (1:6);
          [m, v] = geostat (p)

     References
     ----------

       1. Wendy L. Martinez and Angel R. Martinez.  'Computational
          Statistics Handbook with MATLAB'. Appendix E, pages 547-557,
          Chapman & Hall/CRC, 2001.

       2. Athanasios Papoulis.  'Probability, Random Variables, and
          Stochastic Processes'.  McGraw-Hill, New York, second edition,
          1984.


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 56
Compute mean and variance of the geometric distribution.



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 6
gevcdf


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 1260
 -- Function File: P = gevcdf (X, K, SIGMA, MU)
     Compute the cumulative distribution function of the generalized
     extreme value (GEV) distribution.

     Arguments
     ---------

        * X is the support.

        * K is the shape parameter of the GEV distribution.  (Also
          denoted gamma or xi.)
        * SIGMA is the scale parameter of the GEV distribution.  The
          elements of SIGMA must be positive.
        * MU is the location parameter of the GEV distribution.
     The inputs must be of common size, or some of them must be scalar.

     Return values
     -------------

        * P is the cumulative distribution of the GEV distribution at
          each element of X and corresponding parameter values.

     Examples
     --------

          x = 0:0.5:2.5;
          sigma = 1:6;
          k = 1;
          mu = 0;
          y = gevcdf (x, k, sigma, mu)

          y = gevcdf (x, k, 0.5, mu)

     References
     ----------

       1. Rolf-Dieter Reiss and Michael Thomas.  'Statistical Analysis
          of Extreme Values with Applications to Insurance, Finance,
          Hydrology and Other Fields'.  Chapter 1, pages 16-17,
          Springer, 2007.

     See also: gevfit, gevinv, gevlike, gevpdf, gevrnd, gevstat.


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 80
Compute the cumulative distribution function of the generalized extreme
value (G



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 6
gevfit


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 1158
 -- Function File: PARAMHAT, PARAMCI = gevfit (DATA, PARMGUESS)
     Find the maximum likelihood estimator (PARAMHAT) of the generalized
     extreme value (GEV) distribution to fit DATA.

     Arguments
     ---------

        * DATA is the vector of given values.
        * PARMGUESS is an initial guess for the maximum likelihood
          parameter vector.  If not given, this defaults to [0; 1; 0].

     Return values
     -------------

        * PARMHAT is the 3-parameter maximum-likelihood parameter vector
          [K; SIGMA; MU], where K is the shape parameter of the GEV
          distribution, SIGMA is the scale parameter of the GEV
          distribution, and MU is the location parameter of the GEV
          distribution.
        * PARAMCI has the approximate 95% confidence intervals of the
          parameter values based on the Fisher information matrix at the
          maximum-likelihood position.

     Examples
     --------

          data = 1:50;
          [pfit, pci] = gevfit (data);
          p1 = gevcdf(data,pfit(1),pfit(2),pfit(3));
          plot(data, p1)

     See also: gevcdf, gevinv, gevlike, gevpdf, gevrnd, gevstat.


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 80
Find the maximum likelihood estimator (PARAMHAT) of the generalized
extreme valu



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 11
gevfit_lmom


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 1193
 -- Function File: PARAMHAT, PARAMCI = gevfit_lmom (DATA)
     Find an estimator (PARAMHAT) of the generalized extreme value (GEV)
     distribution fitting DATA using the method of L-moments.

     Arguments
     ---------

        * DATA is the vector of given values.

     Return values
     -------------

        * PARMHAT is the 3-parameter maximum-likelihood parameter vector
          [K; SIGMA; MU], where K is the shape parameter of the GEV
          distribution, SIGMA is the scale parameter of the GEV
          distribution, and MU is the location parameter of the GEV
          distribution.
        * PARAMCI has the approximate 95% confidence intervals of the
          parameter values (currently not implemented).

     Examples
     --------

          data = gevrnd (0.1, 1, 0, 100, 1);
          [pfit, pci] = gevfit_lmom (data);
          p1 = gevcdf (data,pfit(1),pfit(2),pfit(3));
          [f, x] = ecdf (data);
          plot(data, p1, 's', x, f)

     See also: gevfit.

     References
     ----------

       1. Ailliot, P.; Thompson, C. & Thomson, P. Mixed methods for
          fitting the GEV distribution, Water Resources Research, 2011,
          47, W05551


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 80
Find an estimator (PARAMHAT) of the generalized extreme value (GEV)
distribution



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 6
gevinv


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 1214
 -- Function File: X = gevinv (P, K, SIGMA, MU)
     Compute a desired quantile (inverse CDF) of the generalized extreme
     value (GEV) distribution.

     Arguments
     ---------

        * P is the desired quantile of the GEV distribution.  (Between 0
          and 1.)
        * K is the shape parameter of the GEV distribution.  (Also
          denoted gamma or xi.)
        * SIGMA is the scale parameter of the GEV distribution.  The
          elements of SIGMA must be positive.
        * MU is the location parameter of the GEV distribution.
     The inputs must be of common size, or some of them must be scalar.

     Return values
     -------------

        * X is the value corresponding to each quantile of the GEV
          distribution

     References
     ----------

       1. Rolf-Dieter Reiss and Michael Thomas.  'Statistical Analysis
          of Extreme Values with Applications to Insurance, Finance,
          Hydrology and Other Fields'.  Chapter 1, pages 16-17,
          Springer, 2007.
       2. J. R. M. Hosking (2012).  'L-moments'.  R package, version
          1.6.  URL: http://CRAN.R-project.org/package=lmom.

     See also: gevcdf, gevfit, gevlike, gevpdf, gevrnd, gevstat.


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 80
Compute a desired quantile (inverse CDF) of the generalized extreme
value (GEV) 



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 7
gevlike


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 1419
 -- Function File: NLOGL, GRAD, ACOV = gevlike (PARAMS, DATA)
     Compute the negative log-likelihood of data under the generalized
     extreme value (GEV) distribution with given parameter values.

     Arguments
     ---------

        * PARAMS is the 3-parameter vector [K, SIGMA, MU], where K is
          the shape parameter of the GEV distribution, SIGMA is the
          scale parameter of the GEV distribution, and MU is the
          location parameter of the GEV distribution.
        * DATA is the vector of given values.

     Return values
     -------------

        * NLOGL is the negative log-likelihood.
        * GRAD is the 3 by 1 gradient vector (first derivative of the
          negative log likelihood with respect to the parameter values)
        * ACOV is the 3 by 3 Fisher information matrix (second
          derivative of the negative log likelihood with respect to the
          parameter values)

     Examples
     --------

          x = -5:-1;
          k = -0.2;
          sigma = 0.3;
          mu = 0.5;
          [L, ~, C] = gevlike ([k sigma mu], x);

     References
     ----------

       1. Rolf-Dieter Reiss and Michael Thomas.  'Statistical Analysis
          of Extreme Values with Applications to Insurance, Finance,
          Hydrology and Other Fields'.  Chapter 1, pages 16-17,
          Springer, 2007.

     See also: gevcdf, gevfit, gevinv, gevpdf, gevrnd, gevstat.


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 80
Compute the negative log-likelihood of data under the generalized
extreme value 



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 6
gevpdf


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 1252
 -- Function File: Y = gevpdf (X, K, SIGMA, MU)
     Compute the probability density function of the generalized extreme
     value (GEV) distribution.

     Arguments
     ---------

        * X is the support.

        * K is the shape parameter of the GEV distribution.  (Also
          denoted gamma or xi.)
        * SIGMA is the scale parameter of the GEV distribution.  The
          elements of SIGMA must be positive.
        * MU is the location parameter of the GEV distribution.
     The inputs must be of common size, or some of them must be scalar.

     Return values
     -------------

        * Y is the probability density of the GEV distribution at each
          element of X and corresponding parameter values.

     Examples
     --------

          x = 0:0.5:2.5;
          sigma = 1:6;
          k = 1;
          mu = 0;
          y = gevpdf (x, k, sigma, mu)

          y = gevpdf (x, k, 0.5, mu)

     References
     ----------

       1. Rolf-Dieter Reiss and Michael Thomas.  'Statistical Analysis
          of Extreme Values with Applications to Insurance, Finance,
          Hydrology and Other Fields'.  Chapter 1, pages 16-17,
          Springer, 2007.

     See also: gevcdf, gevfit, gevinv, gevlike, gevrnd, gevstat.


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 80
Compute the probability density function of the generalized extreme
value (GEV) 



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 6
gevrnd


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 857
 -- Function File: gevrnd (K, SIGMA, MU)
 -- Function File: gevrnd (K, SIGMA, MU, R)
 -- Function File: gevrnd (K, SIGMA, MU, R, C, ...)
 -- Function File: gevrnd (K, SIGMA, MU, [SZ])
     Return a matrix of random samples from the generalized extreme
     value (GEV) distribution with parameters K, SIGMA, MU.

     When called with a single size argument, returns a square matrix
     with the dimension specified.  When called with more than one
     scalar argument the first two arguments are taken as the number of
     rows and columns and any further arguments specify additional
     matrix dimensions.  The size may also be specified with a vector SZ
     of dimensions.

     If no size arguments are given, then the result matrix is the
     common size of the input parameters.

     See also: gevcdf, gevfit, gevinv, gevlike, gevpdf, gevstat.


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 80
Return a matrix of random samples from the generalized extreme value
(GEV) distr



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 7
gevstat


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 731
 -- Function File: [M, V] = gevstat (K, SIGMA, MU)
     Compute the mean and variance of the generalized extreme value
     (GEV) distribution.

     Arguments
     ---------

        * K is the shape parameter of the GEV distribution.  (Also
          denoted gamma or xi.)
        * SIGMA is the scale parameter of the GEV distribution.  The
          elements of SIGMA must be positive.
        * MU is the location parameter of the GEV distribution.
     The inputs must be of common size, or some of them must be scalar.

     Return values
     -------------

        * M is the mean of the GEV distribution

        * V is the variance of the GEV distribution

     See also: gevcdf, gevfit, gevinv, gevlike, gevpdf, gevrnd.


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 80
Compute the mean and variance of the generalized extreme value (GEV)
distributio



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 8
harmmean


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 176
 -- Function File: harmmean (X)
 -- Function File: harmmean (X, DIM)
     Compute the harmonic mean.

     This function does the same as 'mean (x, "h")'.

     See also: mean.


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 26
Compute the harmonic mean.



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 5
hist3


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 1325
 -- Function File: hist3(X)
 -- Function File: hist3(X, NBINS)
 -- Function File: hist3(X, 'Nbins', NBINS)
 -- Function File: hist3(X, CENTERS)
 -- Function File: hist3(X, 'Centers', CENTERS)
 -- Function File: hist3(X, 'Edges', EDGES)
 -- Function File: N = hist3(X, ...)
 -- Function File: [N, C] = hist3(X, ...)
     Plots a 2D histogram of the N x 2 matrix X with 10 equally spaced
     bins in both the x and y direction using the 'mesh' function

     The number of equally spaced bins to compute histogram can be
     specified with NBINS.  If NBINS is a 2 element vector, use the two
     values as the number of bins in the x and y axis, respectively,
     otherwise, use the same value for each.

     The centers of the histogram bins can be specified with CENTERS.
     CENTERS should be a cell array containing two arrays of the bin
     centers on the x and y axis, respectively.

     The edges of the histogram bins can be specified with EDGES.  EDGES
     should be a cell array containing two arrays of the bin edges on
     the x and y axis, respectively.

     N returns the 2D array of bin counts, and does not plot the
     histogram

     N and C returns the 2D array of bin counts in N and the bin centers
     in the 2 element cell array C, and does not plot the histogram

     See also: hist, mesh.


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 80
Plots a 2D histogram of the N x 2 matrix X with 10 equally spaced bins
in both t



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 7
histfit


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 410
 -- Function File: histfit (DATA, NBINS)

     Plot histogram with superimposed fitted normal density.

     'histfit (DATA, NBINS)' plots a histogram of the values in the
     vector DATA using NBINS bars in the histogram.  With one input
     argument, NBINS is set to the square root of the number of elements
     in data.

     Example

          histfit (randn (100, 1))

     See also: bar,hist, pareto.


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 55
Plot histogram with superimposed fitted normal density.



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 11
hmmestimate


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 4212
 -- Function File: [TRANSPROBEST, OUTPROBEST] = hmmestimate (SEQUENCE,
          STATES)
 -- Function File: hmmestimate (..., 'statenames', STATENAMES)
 -- Function File: hmmestimate (..., 'symbols', SYMBOLS)
 -- Function File: hmmestimate (..., 'pseudotransitions',
          PSEUDOTRANSITIONS)
 -- Function File: hmmestimate (..., 'pseudoemissions', PSEUDOEMISSIONS)
     Estimate the matrix of transition probabilities and the matrix of
     output probabilities of a given sequence of outputs and states
     generated by a hidden Markov model.  The model assumes that the
     generation starts in state '1' at step '0' but does not include
     step '0' in the generated states and sequence.

     Arguments
     ---------

        * SEQUENCE is a vector of a sequence of given outputs.  The
          outputs must be integers ranging from '1' to the number of
          outputs of the hidden Markov model.

        * STATES is a vector of the same length as SEQUENCE of given
          states.  The states must be integers ranging from '1' to the
          number of states of the hidden Markov model.

     Return values
     -------------

        * TRANSPROBEST is the matrix of the estimated transition
          probabilities of the states.  'transprobest(i, j)' is the
          estimated probability of a transition to state 'j' given state
          'i'.

        * OUTPROBEST is the matrix of the estimated output
          probabilities.  'outprobest(i, j)' is the estimated
          probability of generating output 'j' given state 'i'.

     If ''symbols'' is specified, then SEQUENCE is expected to be a
     sequence of the elements of SYMBOLS instead of integers.  SYMBOLS
     can be a cell array.

     If ''statenames'' is specified, then STATES is expected to be a
     sequence of the elements of STATENAMES instead of integers.
     STATENAMES can be a cell array.

     If ''pseudotransitions'' is specified then the integer matrix
     PSEUDOTRANSITIONS is used as an initial number of counted
     transitions.  'pseudotransitions(i, j)' is the initial number of
     counted transitions from state 'i' to state 'j'.  TRANSPROBEST will
     have the same size as PSEUDOTRANSITIONS.  Use this if you have
     transitions that are very unlikely to occur.

     If ''pseudoemissions'' is specified then the integer matrix
     PSEUDOEMISSIONS is used as an initial number of counted outputs.
     'pseudoemissions(i, j)' is the initial number of counted outputs
     'j' given state 'i'.  If ''pseudoemissions'' is also specified then
     the number of rows of PSEUDOEMISSIONS must be the same as the
     number of rows of PSEUDOTRANSITIONS.  OUTPROBEST will have the same
     size as PSEUDOEMISSIONS.  Use this if you have outputs or states
     that are very unlikely to occur.

     Examples
     --------

          transprob = [0.8, 0.2; 0.4, 0.6];
          outprob = [0.2, 0.4, 0.4; 0.7, 0.2, 0.1];
          [sequence, states] = hmmgenerate (25, transprob, outprob);
          [transprobest, outprobest] = hmmestimate (sequence, states)

          symbols = {'A', 'B', 'C'};
          statenames = {'One', 'Two'};
          [sequence, states] = hmmgenerate (25, transprob, outprob,
                               'symbols', symbols, 'statenames', statenames);
          [transprobest, outprobest] = hmmestimate (sequence, states,
                                       'symbols', symbols,
                                       'statenames', statenames)

          pseudotransitions = [8, 2; 4, 6];
          pseudoemissions = [2, 4, 4; 7, 2, 1];
          [sequence, states] = hmmgenerate (25, transprob, outprob);
          [transprobest, outprobest] = hmmestimate (sequence, states, 'pseudotransitions', pseudotransitions, 'pseudoemissions', pseudoemissions)

     References
     ----------

       1. Wendy L. Martinez and Angel R. Martinez.  'Computational
          Statistics Handbook with MATLAB'. Appendix E, pages 547-557,
          Chapman & Hall/CRC, 2001.

       2. Lawrence R. Rabiner.  A Tutorial on Hidden Markov Models and
          Selected Applications in Speech Recognition.  'Proceedings of
          the IEEE', 77(2), pages 257-286, February 1989.


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 80
Estimate the matrix of transition probabilities and the matrix of output
probabi



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 11
hmmgenerate


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 2469
 -- Function File: [SEQUENCE, STATES] = hmmgenerate (LEN, TRANSPROB,
          OUTPROB)
 -- Function File: hmmgenerate (..., 'symbols', SYMBOLS)
 -- Function File: hmmgenerate (..., 'statenames', STATENAMES)
     Generate an output sequence and hidden states of a hidden Markov
     model.  The model starts in state '1' at step '0' but will not
     include step '0' in the generated states and sequence.

     Arguments
     ---------

        * LEN is the number of steps to generate.  SEQUENCE and STATES
          will have LEN entries each.

        * TRANSPROB is the matrix of transition probabilities of the
          states.  'transprob(i, j)' is the probability of a transition
          to state 'j' given state 'i'.

        * OUTPROB is the matrix of output probabilities.  'outprob(i,
          j)' is the probability of generating output 'j' given state
          'i'.

     Return values
     -------------

        * SEQUENCE is a vector of length LEN of the generated outputs.
          The outputs are integers ranging from '1' to 'columns
          (outprob)'.

        * STATES is a vector of length LEN of the generated hidden
          states.  The states are integers ranging from '1' to 'columns
          (transprob)'.

     If ''symbols'' is specified, then the elements of SYMBOLS are used
     for the output sequence instead of integers ranging from '1' to
     'columns (outprob)'.  SYMBOLS can be a cell array.

     If ''statenames'' is specified, then the elements of STATENAMES are
     used for the states instead of integers ranging from '1' to
     'columns (transprob)'.  STATENAMES can be a cell array.

     Examples
     --------

          transprob = [0.8, 0.2; 0.4, 0.6];
          outprob = [0.2, 0.4, 0.4; 0.7, 0.2, 0.1];
          [sequence, states] = hmmgenerate (25, transprob, outprob)

          symbols = {'A', 'B', 'C'};
          statenames = {'One', 'Two'};
          [sequence, states] = hmmgenerate (25, transprob, outprob,
                               'symbols', symbols, 'statenames', statenames)

     References
     ----------

       1. Wendy L. Martinez and Angel R. Martinez.  'Computational
          Statistics Handbook with MATLAB'. Appendix E, pages 547-557,
          Chapman & Hall/CRC, 2001.

       2. Lawrence R. Rabiner.  A Tutorial on Hidden Markov Models and
          Selected Applications in Speech Recognition.  'Proceedings of
          the IEEE', 77(2), pages 257-286, February 1989.


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 71
Generate an output sequence and hidden states of a hidden Markov model.



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 10
hmmviterbi


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 2622
 -- Function File: VPATH = hmmviterbi (SEQUENCE, TRANSPROB, OUTPROB)
 -- Function File: hmmviterbi (..., 'symbols', SYMBOLS)
 -- Function File: hmmviterbi (..., 'statenames', STATENAMES)
     Use the Viterbi algorithm to find the Viterbi path of a hidden
     Markov model given a sequence of outputs.  The model assumes that
     the generation starts in state '1' at step '0' but does not include
     step '0' in the generated states and sequence.

     Arguments
     ---------

        * SEQUENCE is the vector of length LEN of given outputs.  The
          outputs must be integers ranging from '1' to 'columns
          (outprob)'.

        * TRANSPROB is the matrix of transition probabilities of the
          states.  'transprob(i, j)' is the probability of a transition
          to state 'j' given state 'i'.

        * OUTPROB is the matrix of output probabilities.  'outprob(i,
          j)' is the probability of generating output 'j' given state
          'i'.

     Return values
     -------------

        * VPATH is the vector of the same length as SEQUENCE of the
          estimated hidden states.  The states are integers ranging from
          '1' to 'columns (transprob)'.

     If ''symbols'' is specified, then SEQUENCE is expected to be a
     sequence of the elements of SYMBOLS instead of integers ranging
     from '1' to 'columns (outprob)'.  SYMBOLS can be a cell array.

     If ''statenames'' is specified, then the elements of STATENAMES are
     used for the states in VPATH instead of integers ranging from '1'
     to 'columns (transprob)'.  STATENAMES can be a cell array.

     Examples
     --------

          transprob = [0.8, 0.2; 0.4, 0.6];
          outprob = [0.2, 0.4, 0.4; 0.7, 0.2, 0.1];
          [sequence, states] = hmmgenerate (25, transprob, outprob)
          vpath = hmmviterbi (sequence, transprob, outprob)

          symbols = {'A', 'B', 'C'};
          statenames = {'One', 'Two'};
          [sequence, states] = hmmgenerate (25, transprob, outprob,
                               'symbols', symbols, 'statenames', statenames)
          vpath = hmmviterbi (sequence, transprob, outprob,
                  'symbols', symbols, 'statenames', statenames)

     References
     ----------

       1. Wendy L. Martinez and Angel R. Martinez.  'Computational
          Statistics Handbook with MATLAB'. Appendix E, pages 547-557,
          Chapman & Hall/CRC, 2001.

       2. Lawrence R. Rabiner.  A Tutorial on Hidden Markov Models and
          Selected Applications in Speech Recognition.  'Proceedings of
          the IEEE', 77(2), pages 257-286, February 1989.


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 80
Use the Viterbi algorithm to find the Viterbi path of a hidden Markov
model give



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 8
hygestat


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 1333
 -- Function File: [MN, V] = hygestat (T, M, N)
     Compute mean and variance of the hypergeometric distribution.

     Arguments
     ---------

        * T is the total size of the population of the hypergeometric
          distribution.  The elements of T must be positive natural
          numbers

        * M is the number of marked items of the hypergeometric
          distribution.  The elements of M must be natural numbers

        * N is the size of the drawn sample of the hypergeometric
          distribution.  The elements of N must be positive natural
          numbers
     T, M, and N must be of common size or scalar

     Return values
     -------------

        * MN is the mean of the hypergeometric distribution

        * V is the variance of the hypergeometric distribution

     Examples
     --------

          t = 4:9;
          m = 0:5;
          n = 1:6;
          [mn, v] = hygestat (t, m, n)

          [mn, v] = hygestat (t, m, 2)

     References
     ----------

       1. Wendy L. Martinez and Angel R. Martinez.  'Computational
          Statistics Handbook with MATLAB'. Appendix E, pages 547-557,
          Chapman & Hall/CRC, 2001.

       2. Athanasios Papoulis.  'Probability, Random Variables, and
          Stochastic Processes'.  McGraw-Hill, New York, second edition,
          1984.


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 61
Compute mean and variance of the hypergeometric distribution.



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 8
iwishpdf


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 658
 -- Function File: Y = iwishpdf (W, TAU, DF, LOG_Y=false)
     Compute the probability density function of the Wishart
     distribution

     Inputs: A P x P matrix W where to find the PDF and the P x P
     positive definite scale matrix TAU and scalar degrees of freedom
     parameter DF characterizing the inverse Wishart distribution.  (For
     the density to be finite, need DF > (P - 1).)  If the flag LOG_Y is
     set, return the log probability density - this helps avoid
     underflow when the numerical value of the density is very small

     Output: Y is the probability density of Wishart(SIGMA, DF) at W.

     See also: iwishrnd, wishpdf.


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 68
Compute the probability density function of the Wishart distribution



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 8
iwishrnd


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 1041
 -- Function File: [W[, DI]] = iwishrnd (PSI, DF[, DI][, N=1])
     Return a random matrix sampled from the inverse Wishart
     distribution with given parameters

     Inputs: the P x P positive definite matrix TAU and scalar degrees
     of freedom parameter DF (and optionally the transposed Cholesky
     factor DI of SIGMA = 'inv(Tau)').  DF can be non-integer as long as
     DF > D

     Output: a random P x P matrix W from the inverse Wishart(TAU, DF)
     distribution.  ('inv(W)' is from the Wishart('inv(Tau)', DF)
     distribution.)  If N > 1, then W is P x P x N and holds N such
     random matrices.  (Optionally, the transposed Cholesky factor DI of
     SIGMA is also returned.)

     Averaged across many samples, the mean of W should approach TAU /
     (DF - P - 1).

     Reference: Yu-Cheng Ku and Peter Bloomfield (2010), Generating
     Random Wishart Matrices with Fractional Degrees of Freedom in OX,
     http://www.gwu.edu/~forcpgm/YuChengKu-030510final-WishartYu-ChengKu.pdf

     See also: wishrnd, iwishpdf.


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 80
Return a random matrix sampled from the inverse Wishart distribution
with given 



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 9
jackknife


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 2008
 -- Function File: JACKSTAT = jackknife (E, X, ...)
     Compute jackknife estimates of a parameter taking one or more given
     samples as parameters.  In particular, E is the estimator to be
     jackknifed as a function name, handle, or inline function, and X is
     the sample for which the estimate is to be taken.  The I-th entry
     of JACKSTAT will contain the value of the estimator on the sample X
     with its I-th row omitted.

          jackstat(I) = E(X(1 : I - 1, I + 1 : length(X)))

     Depending on the number of samples to be used, the estimator must
     have the appropriate form: If only one sample is used, then the
     estimator need not be concerned with cell arrays, for example
     jackknifing the standard deviation of a sample can be performed
     with 'JACKSTAT = jackknife (@std, rand (100, 1))'.  If, however,
     more than one sample is to be used, the samples must all be of
     equal size, and the estimator must address them as elements of a
     cell-array, in which they are aggregated in their order of
     appearance:

          JACKSTAT = jackknife(@(x) std(x{1})/var(x{2}), rand (100, 1), randn (100, 1)

     If all goes well, a theoretical value P for the parameter is
     already known, N is the sample size, 'T = N * E(X) - (N - 1) *
     mean(JACKSTAT)', and 'V = sumsq(N * E(X) - (N - 1) * JACKSTAT - T)
     / (N * (N - 1))', then '(T-P)/sqrt(V)' should follow a
     t-distribution with N-1 degrees of freedom.

     Jackknifing is a well known method to reduce bias; further details
     can be found in:
        * Rupert G. Miller: The jackknife-a review; Biometrika (1974)
          61(1): 1-15; doi:10.1093/biomet/61.1.1
        * Rupert G. Miller: Jackknifing Variances; Ann.  Math.  Statist.
          Volume 39, Number 2 (1968), 567-582;
          doi:10.1214/aoms/1177698418
        * M. H. Quenouille: Notes on Bias in Estimation; Biometrika Vol.
          43, No.  3/4 (Dec., 1956), pp.  353-360;
          doi:10.1093/biomet/43.3-4.353


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 80
Compute jackknife estimates of a parameter taking one or more given
samples as p



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 6
jsucdf


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 260
 -- Function File: jsucdf (X, ALPHA1, ALPHA2)
     For each element of X, compute the cumulative distribution function
     (CDF) at X of the Johnson SU distribution with shape parameters
     ALPHA1 and ALPHA2.

     Default values are ALPHA1 = 1, ALPHA2 = 1.


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 80
For each element of X, compute the cumulative distribution function
(CDF) at X o



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 6
jsupdf


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 256
 -- Function File: jsupdf (X, ALPHA1, ALPHA2)
     For each element of X, compute the probability density function
     (PDF) at X of the Johnson SU distribution with shape parameters
     ALPHA1 and ALPHA2.

     Default values are ALPHA1 = 1, ALPHA2 = 1.


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 80
For each element of X, compute the probability density function (PDF) at
X of th



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 6
kmeans


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 133
 -- Function File: [IDX, CENTERS] = kmeans (DATA, K, PARAM1, VALUE1,
          ...)
     K-means clustering.

     See also: linkage.


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 19
K-means clustering.



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 7
linkage


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 2930
 -- Function File: Y = linkage (D)
 -- Function File: Y = linkage (D, METHOD)
 -- Function File: Y = linkage (X, METHOD, METRIC)
 -- Function File: Y = linkage (X, METHOD, ARGLIST)

     Produce a hierarchical clustering dendrogram

     D is the dissimilarity matrix relative to n observations, formatted
     as a (n-1)*n/2x1 vector as produced by 'pdist'.  Alternatively, X
     contains data formatted for input to 'pdist', METRIC is a metric
     for 'pdist' and ARGLIST is a cell array containing arguments that
     are passed to 'pdist'.

     'linkage' starts by putting each observation into a singleton
     cluster and numbering those from 1 to n.  Then it merges two
     clusters, chosen according to METHOD, to create a new cluster
     numbered n+1, and so on until all observations are grouped into a
     single cluster numbered 2(n-1).  Row k of the (m-1)x3 output matrix
     relates to cluster n+k: the first two columns are the numbers of
     the two component clusters and column 3 contains their distance.

     METHOD defines the way the distance between two clusters is
     computed and how they are recomputed when two clusters are merged:

     '"single" (default)'
          Distance between two clusters is the minimum distance between
          two elements belonging each to one cluster.  Produces a
          cluster tree known as minimum spanning tree.

     '"complete"'
          Furthest distance between two elements belonging each to one
          cluster.

     '"average"'
          Unweighted pair group method with averaging (UPGMA). The mean
          distance between all pair of elements each belonging to one
          cluster.

     '"weighted"'
          Weighted pair group method with averaging (WPGMA). When two
          clusters A and B are joined together, the new distance to a
          cluster C is the mean between distances A-C and B-C.

     '"centroid"'
          Unweighted Pair-Group Method using Centroids (UPGMC). Assumes
          Euclidean metric.  The distance between cluster centroids,
          each centroid being the center of mass of a cluster.

     '"median"'
          Weighted pair-group method using centroids (WPGMC). Assumes
          Euclidean metric.  Distance between cluster centroids.  When
          two clusters are joined together, the new centroid is the
          midpoint between the joined centroids.

     '"ward"'
          Ward's sum of squared deviations about the group mean (ESS).
          Also known as minimum variance or inner squared distance.
          Assumes Euclidean metric.  How much the moment of inertia of
          the merged cluster exceeds the sum of those of the individual
          clusters.

     *Reference* Ward, J. H. Hierarchical Grouping to Optimize an
     Objective Function J. Am.  Statist.  Assoc.  1963, 58, 236-244,
     <http://iv.slis.indiana.edu/sw/data/ward.pdf>.

See also: pdist,squareform.


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 44
Produce a hierarchical clustering dendrogram



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 8
lognstat


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 1076
 -- Function File: [M, V] = lognstat (MU, SIGMA)
     Compute mean and variance of the lognormal distribution.

     Arguments
     ---------

        * MU is the first parameter of the lognormal distribution

        * SIGMA is the second parameter of the lognormal distribution.
          SIGMA must be positive or zero
     MU and SIGMA must be of common size or one of them must be scalar

     Return values
     -------------

        * M is the mean of the lognormal distribution

        * V is the variance of the lognormal distribution

     Examples
     --------

          mu = 0:0.2:1;
          sigma = 0.2:0.2:1.2;
          [m, v] = lognstat (mu, sigma)

          [m, v] = lognstat (0, sigma)

     References
     ----------

       1. Wendy L. Martinez and Angel R. Martinez.  'Computational
          Statistics Handbook with MATLAB'. Appendix E, pages 547-557,
          Chapman & Hall/CRC, 2001.

       2. Athanasios Papoulis.  'Probability, Random Variables, and
          Stochastic Processes'.  McGraw-Hill, New York, second edition,
          1984.


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 56
Compute mean and variance of the lognormal distribution.



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 3
mad


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 767
 -- Function File: mad (X)
 -- Function File: mad (X, FLAG)
 -- Function File: mad (X, FLAG, DIM)
     Compute the mean/median absolute deviation of X.

     The mean absolute deviation is computed as

          mean (abs (X - mean (X)))

     and the median absolute deviation is computed as

          median (abs (X - median (X)))

     Elements of X containing NaN or NA values are ignored during
     computations.

     If FLAG is 0, the absolute mean deviation is computed, and if FLAG
     is 1, the absolute median deviation is computed.  By default FLAG
     is 0.

     This is done along the dimension DIM of X.  If this variable is not
     given, the mean/median absolute deviation s computed along the
     smallest dimension of X.

     See also: std.


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 48
Compute the mean/median absolute deviation of X.



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 5
mnpdf


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 1686
 -- Function File: Y = mnpdf (X, P)
     Compute the probability density function of the multinomial
     distribution.

     Arguments
     ---------

        * X is vector with a single sample of a multinomial distribution
          with parameter P or a matrix of random samples from
          multinomial distributions.  In the latter case, each row of X
          is a sample from a multinomial distribution with the
          corresponding row of P being its parameter.

        * P is a vector with the probabilities of the categories or a
          matrix with each row containing the probabilities of a
          multinomial sample.

     Return values
     -------------

        * Y is a vector of probabilites of the random samples X from the
          multinomial distribution with corresponding parameter P.  The
          parameter N of the multinomial distribution is the sum of the
          elements of each row of X.  The length of Y is the number of
          columns of X.  If a row of P does not sum to '1', then the
          corresponding element of Y will be 'NaN'.

     Examples
     --------

          x = [1, 4, 2];
          p = [0.2, 0.5, 0.3];
          y = mnpdf (x, p);

          x = [1, 4, 2; 1, 0, 9];
          p = [0.2, 0.5, 0.3; 0.1, 0.1, 0.8];
          y = mnpdf (x, p);

     References
     ----------

       1. Wendy L. Martinez and Angel R. Martinez.  'Computational
          Statistics Handbook with MATLAB'. Appendix E, pages 547-557,
          Chapman & Hall/CRC, 2001.

       2. Merran Evans, Nicholas Hastings and Brian Peacock.
          'Statistical Distributions'.  pages 134-136, Wiley, New York,
          third edition, 2000.


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 73
Compute the probability density function of the multinomial
distribution.



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 5
mnrnd


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 2222
 -- Function File: X = mnrnd (N, P)
 -- Function File: X = mnrnd (N, P, S)
     Generate random samples from the multinomial distribution.

     Arguments
     ---------

        * N is the first parameter of the multinomial distribution.  N
          can be scalar or a vector containing the number of trials of
          each multinomial sample.  The elements of N must be
          non-negative integers.

        * P is the second parameter of the multinomial distribution.  P
          can be a vector with the probabilities of the categories or a
          matrix with each row containing the probabilities of a
          multinomial sample.  If P has more than one row and N is
          non-scalar, then the number of rows of P must match the number
          of elements of N.

        * S is the number of multinomial samples to be generated.  S
          must be a non-negative integer.  If S is specified, then N
          must be scalar and P must be a vector.

     Return values
     -------------

        * X is a matrix of random samples from the multinomial
          distribution with corresponding parameters N and P.  Each row
          corresponds to one multinomial sample.  The number of columns,
          therefore, corresponds to the number of columns of P.  If S is
          not specified, then the number of rows of X is the maximum of
          the number of elements of N and the number of rows of P.  If a
          row of P does not sum to '1', then the corresponding row of X
          will contain only 'NaN' values.

     Examples
     --------

          n = 10;
          p = [0.2, 0.5, 0.3];
          x = mnrnd (n, p);

          n = 10 * ones (3, 1);
          p = [0.2, 0.5, 0.3];
          x = mnrnd (n, p);

          n = (1:2)';
          p = [0.2, 0.5, 0.3; 0.1, 0.1, 0.8];
          x = mnrnd (n, p);

     References
     ----------

       1. Wendy L. Martinez and Angel R. Martinez.  'Computational
          Statistics Handbook with MATLAB'. Appendix E, pages 547-557,
          Chapman & Hall/CRC, 2001.

       2. Merran Evans, Nicholas Hastings and Brian Peacock.
          'Statistical Distributions'.  pages 134-136, Wiley, New York,
          third edition, 2000.


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 58
Generate random samples from the multinomial distribution.



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 15
monotone_smooth


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 1783
 -- Function File: YY = monotone_smooth (X, Y, H)
     Produce a smooth monotone increasing approximation to a sampled
     functional dependence y(x) using a kernel method (an Epanechnikov
     smoothing kernel is applied to y(x); this is integrated to yield
     the monotone increasing form.  See Reference 1 for details.)

     Arguments
     ---------

        * X is a vector of values of the independent variable.

        * Y is a vector of values of the dependent variable, of the same
          size as X.  For best performance, it is recommended that the Y
          already be fairly smooth, e.g.  by applying a kernel smoothing
          to the original values if they are noisy.

        * H is the kernel bandwidth to use.  If H is not given, a
          "reasonable" value is computed.

     Return values
     -------------

        * YY is the vector of smooth monotone increasing function values
          at X.

     Examples
     --------

          x = 0:0.1:10;
          y = (x .^ 2) + 3 * randn(size(x)); %typically non-monotonic from the added noise
          ys = ([y(1) y(1:(end-1))] + y + [y(2:end) y(end)])/3; %crudely smoothed via
          moving average, but still typically non-monotonic
          yy = monotone_smooth(x, ys); %yy is monotone increasing in x
          plot(x, y, '+', x, ys, x, yy)

     References
     ----------

       1. Holger Dette, Natalie Neumeyer and Kay F. Pilz (2006), A
          simple nonparametric estimator of a strictly monotone
          regression function, 'Bernoulli', 12:469-490
       2. Regine Scheder (2007), R Package 'monoProc', Version 1.0-6,
          <http://cran.r-project.org/web/packages/monoProc/monoProc.pdf>
          (The implementation here is based on the monoProc function
          mono.1d)


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 80
Produce a smooth monotone increasing approximation to a sampled
functional depen



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 6
mvncdf


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 1238
 -- Function File: P = mvncdf (X, MU, SIGMA)
 -- Function File: mvncdf (A, X, MU, SIGMA)
 -- Function File: [P, ERR] = mvncdf (...)
     Compute the cumulative distribution function of the multivariate
     normal distribution.

     Arguments
     ---------

        * X is the upper limit for integration where each row
          corresponds to an observation.

        * MU is the mean.

        * SIGMA is the correlation matrix.

        * A is the lower limit for integration where each row
          corresponds to an observation.  A must have the same size as
          X.

     Return values
     -------------

        * P is the cumulative distribution at each row of X and A.

        * ERR is the estimated error.

     Examples
     --------

          x = [1 2];
          mu = [0.5 1.5];
          sigma = [1.0 0.5; 0.5 1.0];
          p = mvncdf (x, mu, sigma)

          a = [-inf 0];
          p = mvncdf (a, x, mu, sigma)

     References
     ----------

       1. Alan Genz and Frank Bretz.  Numerical Computation of
          Multivariate t-Probabilities with Application to Power
          Calculation of Multiple Constrasts.  'Journal of Statistical
          Computation and Simulation', 63, pages 361-378, 1999.


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 80
Compute the cumulative distribution function of the multivariate normal
distribu



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 6
mvnpdf


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 1599
 -- Function File: Y = mvnpdf (X)
 -- Function File: Y = mvnpdf (X, MU)
 -- Function File: Y = mvnpdf (X, MU, SIGMA)
     Compute multivariate normal pdf for X given mean MU and covariance
     matrix SIGMA.  The dimension of X is D x P, MU is 1 x P and SIGMA
     is P x P.  The normal pdf is defined as

          1/Y^2 = (2 pi)^P |SIGMA| exp { (X-MU)' inv(SIGMA) (X-MU) }

     *References*

     NIST Engineering Statistics Handbook 6.5.4.2
     http://www.itl.nist.gov/div898/handbook/pmc/section5/pmc542.htm

     *Algorithm*

     Using Cholesky factorization on the positive definite covariance
     matrix:

          R = chol (SIGMA);

     where R'*R = SIGMA.  Being upper triangular, the determinant of R
     is trivially the product of the diagonal, and the determinant of
     SIGMA is the square of this:

          DET = prod (diag (R))^2;

     The formula asks for the square root of the determinant, so no need
     to square it.

     The exponential argument A = X' * inv (SIGMA) * X

          A = X' * inv (SIGMA) * X
            = X' * inv (R' * R) * X
            = X' * inv (R) * inv(R') * X

     Given that inv (R') == inv(R)', at least in theory if not
     numerically,

          A  = (X' / R) * (X'/R)' = sumsq (X'/R)

     The interface takes the parameters to the multivariate normal in
     columns rather than rows, so we are actually dealing with the
     transpose:

          A = sumsq (X/r)

     and the final result is:

          R = chol (SIGMA)
          Y = (2*pi)^(-P/2) * exp (-sumsq ((X-MU)/R, 2)/2) / prod (diag (R))

     See also: mvncdf, mvnrnd.


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 80
Compute multivariate normal pdf for X given mean MU and covariance
matrix SIGMA.



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 6
mvnrnd


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 501
 -- Function File: S = mvnrnd (MU, SIGMA)
 -- Function File: S = mvnrnd (MU, SIGMA, N)
 -- Function File: S = mvnrnd (..., TOL)
     Draw N random D-dimensional vectors from a multivariate Gaussian
     distribution with mean MU(NxD) and covariance matrix SIGMA(DxD).

     MU must be N-by-D (or 1-by-D if N is given) or a scalar.

     If the argument TOL is given the eigenvalues of SIGMA are checked
     for positivity against -100*tol.  The default value of tol is
     'eps*norm (Sigma, "fro")'.


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 80
Draw N random D-dimensional vectors from a multivariate Gaussian
distribution wi



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 6
mvtcdf


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 1249
 -- Function File: P = mvtcdf (X, SIGMA, NU)
 -- Function File: mvtcdf (A, X, SIGMA, NU)
 -- Function File: [P, ERR] = mvtcdf (...)
     Compute the cumulative distribution function of the multivariate
     Student's t distribution.

     Arguments
     ---------

        * X is the upper limit for integration where each row
          corresponds to an observation.

        * SIGMA is the correlation matrix.

        * NU is the degrees of freedom.

        * A is the lower limit for integration where each row
          corresponds to an observation.  A must have the same size as
          X.

     Return values
     -------------

        * P is the cumulative distribution at each row of X and A.

        * ERR is the estimated error.

     Examples
     --------

          x = [1 2];
          sigma = [1.0 0.5; 0.5 1.0];
          nu = 4;
          p = mvtcdf (x, sigma, nu)

          a = [-inf 0];
          p = mvtcdf (a, x, sigma, nu)

     References
     ----------

       1. Alan Genz and Frank Bretz.  Numerical Computation of
          Multivariate t-Probabilities with Application to Power
          Calculation of Multiple Constrasts.  'Journal of Statistical
          Computation and Simulation', 63, pages 361-378, 1999.


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 80
Compute the cumulative distribution function of the multivariate
Student's t dis



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 6
mvtrnd


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 2021
 -- Function File: X = mvtrnd (SIGMA, NU)
 -- Function File: X = mvtrnd (SIGMA, NU, N)
     Generate random samples from the multivariate t-distribution.

     Arguments
     ---------

        * SIGMA is the matrix of correlation coefficients.  If there are
          any non-unit diagonal elements then SIGMA will be normalized,
          so that the resulting covariance of the obtained samples X
          follows: 'cov (x) = nu/(nu-2) * sigma ./ (sqrt (diag (sigma) *
          diag (sigma)))'.  In order to obtain samples distributed
          according to a standard multivariate t-distribution, SIGMA
          must be equal to the identity matrix.  To generate
          multivariate t-distribution samples X with arbitrary
          covariance matrix SIGMA, the following scaling might be used:
          'x = mvtrnd (sigma, nu, n) * diag (sqrt (diag (sigma)))'.

        * NU is the degrees of freedom for the multivariate
          t-distribution.  NU must be a vector with the same number of
          elements as samples to be generated or be scalar.

        * N is the number of rows of the matrix to be generated.  N must
          be a non-negative integer and corresponds to the number of
          samples to be generated.

     Return values
     -------------

        * X is a matrix of random samples from the multivariate
          t-distribution with N row samples.

     Examples
     --------

          sigma = [1, 0.5; 0.5, 1];
          nu = 3;
          n = 10;
          x = mvtrnd (sigma, nu, n);

          sigma = [1, 0.5; 0.5, 1];
          nu = [2; 3];
          n = 2;
          x = mvtrnd (sigma, nu, 2);

     References
     ----------

       1. Wendy L. Martinez and Angel R. Martinez.  'Computational
          Statistics Handbook with MATLAB'. Appendix E, pages 547-557,
          Chapman & Hall/CRC, 2001.

       2. Samuel Kotz and Saralees Nadarajah.  'Multivariate t
          Distributions and Their Applications'.  Cambridge University
          Press, Cambridge, 2004.


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 61
Generate random samples from the multivariate t-distribution.



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 6
nanmax


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 377
 -- Function File: [V, IDX] = nanmax (X)
 -- Function File: [V, IDX] = nanmax (X, Y)
     Find the maximal element while ignoring NaN values.

     'nanmax' is identical to the 'max' function except that NaN values
     are ignored.  If all values in a column are NaN, the maximum is
     returned as NaN rather than [].

     See also: max, nansum, nanmin, nanmean, nanmedian.


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 51
Find the maximal element while ignoring NaN values.



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 7
nanmean


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 337
 -- Function File: V = nanmean (X)
 -- Function File: V = nanmean (X, DIM)
     Compute the mean value while ignoring NaN values.

     'nanmean' is identical to the 'mean' function except that NaN
     values are ignored.  If all values are NaN, the mean is returned as
     NaN.

     See also: mean, nanmin, nanmax, nansum, nanmedian.


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 49
Compute the mean value while ignoring NaN values.



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 9
nanmedian


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 353
 -- Function File: V = nanmedian (X)
 -- Function File: V = nanmedian (X, DIM)
     Compute the median of data while ignoring NaN values.

     This function is identical to the 'median' function except that NaN
     values are ignored.  If all values are NaN, the median is returned
     as NaN.

     See also: median, nanmin, nanmax, nansum, nanmean.


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 53
Compute the median of data while ignoring NaN values.



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 6
nanmin


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 377
 -- Function File: [V, IDX] = nanmin (X)
 -- Function File: [V, IDX] = nanmin (X, Y)
     Find the minimal element while ignoring NaN values.

     'nanmin' is identical to the 'min' function except that NaN values
     are ignored.  If all values in a column are NaN, the minimum is
     returned as NaN rather than [].

     See also: min, nansum, nanmax, nanmean, nanmedian.


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 51
Find the minimal element while ignoring NaN values.



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 6
nanstd


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 926
 -- Function File: V = nanstd (X)
 -- Function File: V = nanstd (X, OPT)
 -- Function File: V = nanstd (X, OPT, DIM)
     Compute the standard deviation while ignoring NaN values.

     'nanstd' is identical to the 'std' function except that NaN values
     are ignored.  If all values are NaN, the standard deviation is
     returned as NaN. If there is only a single non-NaN value, the
     deviation is returned as 0.

     The argument OPT determines the type of normalization to use.
     Valid values are

     0:
          normalizes with N-1, provides the square root of best unbiased
          estimator of the variance [default]
     1:
          normalizes with N, this provides the square root of the second
          moment around the mean

     The third argument DIM determines the dimension along which the
     standard deviation is calculated.

     See also: std, nanmin, nanmax, nansum, nanmedian, nanmean.


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 57
Compute the standard deviation while ignoring NaN values.



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 6
nansum


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 343
 -- Function File: V = nansum (X)
 -- Function File: V = nansum (X, DIM)
     Compute the sum while ignoring NaN values.

     'nansum' is identical to the 'sum' function except that NaN values
     are treated as 0 and so ignored.  If all values are NaN, the sum is
     returned as 0.

     See also: sum, nanmin, nanmax, nanmean, nanmedian.


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 42
Compute the sum while ignoring NaN values.



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 6
nanvar


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 781
 -- Function File: nanvar (X)
 -- Function File: V = nanvar (X, OPT)
 -- Function File: V = nanvar (X, OPT, DIM)
     Compute the variance while ignoring NaN values.

     For vector arguments, return the (real) variance of the values.
     For matrix arguments, return a row vector containing the variance
     for each column.

     The argument OPT determines the type of normalization to use.
     Valid values are

     0:
          Normalizes with N-1, provides the best unbiased estimator of
          the variance [default].
     1:
          Normalizes with N, this provides the second moment around the
          mean.

     The third argument DIM determines the dimension along which the
     variance is calculated.

     See also: var, nanmean, nanstd, nanmax, nanmin.


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 47
Compute the variance while ignoring NaN values.



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 8
nbinstat


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 1149
 -- Function File: [M, V] = nbinstat (N, P)
     Compute mean and variance of the negative binomial distribution.

     Arguments
     ---------

        * N is the first parameter of the negative binomial
          distribution.  The elements of N must be natural numbers

        * P is the second parameter of the negative binomial
          distribution.  The elements of P must be probabilities
     N and P must be of common size or one of them must be scalar

     Return values
     -------------

        * M is the mean of the negative binomial distribution

        * V is the variance of the negative binomial distribution

     Examples
     --------

          n = 1:4;
          p = 0.2:0.2:0.8;
          [m, v] = nbinstat (n, p)

          [m, v] = nbinstat (n, 0.5)

     References
     ----------

       1. Wendy L. Martinez and Angel R. Martinez.  'Computational
          Statistics Handbook with MATLAB'. Appendix E, pages 547-557,
          Chapman & Hall/CRC, 2001.

       2. Athanasios Papoulis.  'Probability, Random Variables, and
          Stochastic Processes'.  McGraw-Hill, New York, second edition,
          1984.


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 64
Compute mean and variance of the negative binomial distribution.



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 22
normalise_distribution


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 2092
 -- Function File: NORMALISED = normalise_distribution (DATA)
 -- Function File: NORMALISED = normalise_distribution (DATA,
          DISTRIBUTION)
 -- Function File: NORMALISED = normalise_distribution (DATA,
          DISTRIBUTION, DIMENSION)

     Transform a set of data so as to be N(0,1) distributed according to
     an idea by van Albada and Robinson.  This is achieved by first
     passing it through its own cumulative distribution function (CDF)
     in order to get a uniform distribution, and then mapping the
     uniform to a normal distribution.  The data must be passed as a
     vector or matrix in DATA.  If the CDF is unknown, then [] can be
     passed in DISTRIBUTION, and in this case the empirical CDF will be
     used.  Otherwise, if the CDFs for all data are known, they can be
     passed in DISTRIBUTION, either in the form of a single function
     name as a string, or a single function handle, or a cell array
     consisting of either all function names as strings, or all function
     handles.  In the latter case, the number of CDFs passed must match
     the number of rows, or columns respectively, to normalise.  If the
     data are passed as a matrix, then the transformation will operate
     either along the first non-singleton dimension, or along DIMENSION
     if present.

     Notes: The empirical CDF will map any two sets of data having the
     same size and their ties in the same places after sorting to some
     permutation of the same normalised data:
          normalise_distribution([1 2 2 3 4])
          => -1.28  0.00  0.00  0.52  1.28

          normalise_distribution([1 10 100 10 1000])
          => -1.28  0.00  0.52  0.00  1.28

     Original source: S.J. van Albada, P.A. Robinson "Transformation of
     arbitrary distributions to the normal distribution with application
     to EEG test-retest reliability" Journal of Neuroscience Methods,
     Volume 161, Issue 2, 15 April 2007, Pages 205-211 ISSN 0165-0270,
     10.1016/j.jneumeth.2006.11.004.
     (http://www.sciencedirect.com/science/article/pii/S0165027006005668)


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 80
Transform a set of data so as to be N(0,1) distributed according to an
idea by v



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 8
normplot


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 441
 -- Function File: normplot (X)
     Produce normal probability plot for each column of X.

     The line joing the 1st and 3rd quantile is drawn on the graph.  If
     the underlying distribution is normal, the points will cluster
     around this line.

     Note that this function sets the title, xlabel, ylabel, axis, grid,
     tics and hold properties of the graph.  These need to be cleared
     before subsequent graphs using 'clf'.


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 53
Produce normal probability plot for each column of X.



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 8
normstat


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 1008
 -- Function File: [MN, V] = normstat (M, S)
     Compute mean and variance of the normal distribution.

     Arguments
     ---------

        * M is the mean of the normal distribution

        * S is the standard deviation of the normal distribution.  S
          must be positive
     M and S must be of common size or one of them must be scalar

     Return values
     -------------

        * MN is the mean of the normal distribution

        * V is the variance of the normal distribution

     Examples
     --------

          m = 1:6;
          s = 0:0.2:1;
          [mn, v] = normstat (m, s)

          [mn, v] = normstat (0, s)

     References
     ----------

       1. Wendy L. Martinez and Angel R. Martinez.  'Computational
          Statistics Handbook with MATLAB'. Appendix E, pages 547-557,
          Chapman & Hall/CRC, 2001.

       2. Athanasios Papoulis.  'Probability, Random Variables, and
          Stochastic Processes'.  McGraw-Hill, New York, second edition,
          1984.


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 53
Compute mean and variance of the normal distribution.



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 6
pcacov


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 667
 -- Function File: [COEFF] = pcacov(X)
 -- Function File: [COEFF,LATENT] = pcacov(X)
 -- Function File: [COEFF,LATENT,EXPLAINED] = pcacov(X)
        * pcacov performs principal component analysis on the nxn
          covariance matrix X
        * COEFF : a nxn matrix with columns containing the principal
          component coefficients
        * LATENT : a vector containing the principal component variances
        * EXPLAINED : a vector containing the percentage of the total
          variance explained by each principal component

     References
     ----------

       1. Jolliffe, I. T., Principal Component Analysis, 2nd Edition,
          Springer, 2002


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 80
   * pcacov performs principal component analysis on the nxn covariance
     mat



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 6
pcares


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 411
 -- Function File: [RESIDUALS,RECONSTRUCTED] =pcares(X, NDIM)
        * X : N x P Matrix with N observations and P variables, the
          variables will be mean centered
        * NDIM : Is a scalar indicating the number of principal
          components to use and should be <= P

     References
     ----------

       1. Jolliffe, I. T., Principal Component Analysis, 2nd Edition,
          Springer, 2002


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 80
   * X : N x P Matrix with N observations and P variables, the variables
     wi



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 5
pdist


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 2498
 -- Function File: Y = pdist (X)
 -- Function File: Y = pdist (X, METRIC)
 -- Function File: Y = pdist (X, METRIC, METRICARG, ...)

     Return the distance between any two rows in X.

     X is the NxD matrix representing Q row vectors of size D.

     The output is a dissimilarity matrix formatted as a row vector Y,
     (n-1)*n/2 long, where the distances are in the order [(1, 2) (1, 3)
     ... (2, 3) ... (n-1, n)].  You can use the 'squareform' function to
     display the distances between the vectors arranged into an NxN
     matrix.

     'metric' is an optional argument specifying how the distance is
     computed.  It can be any of the following ones, defaulting to
     "euclidean", or a user defined function that takes two arguments X
     and Y plus any number of optional arguments, where X is a row
     vector and and Y is a matrix having the same number of columns as
     X.  'metric' returns a column vector where row I is the distance
     between X and row I of Y.  Any additional arguments after the
     'metric' are passed as metric (X, Y, METRICARG1, METRICARG2 ...).

     Predefined distance functions are:

     '"euclidean"'
          Euclidean distance (default).

     '"seuclidean"'
          Standardized Euclidean distance.  Each coordinate in the sum
          of squares is inverse weighted by the sample variance of that
          coordinate.

     '"mahalanobis"'
          Mahalanobis distance: see the function mahalanobis.

     '"cityblock"'
          City Block metric, aka Manhattan distance.

     '"minkowski"'
          Minkowski metric.  Accepts a numeric parameter P: for P=1 this
          is the same as the cityblock metric, with P=2 (default) it is
          equal to the euclidean metric.

     '"cosine"'
          One minus the cosine of the included angle between rows, seen
          as vectors.

     '"correlation"'
          One minus the sample correlation between points (treated as
          sequences of values).

     '"spearman"'
          One minus the sample Spearman's rank correlation between
          observations, treated as sequences of values.

     '"hamming"'
          Hamming distance: the quote of the number of coordinates that
          differ.

     '"jaccard"'
          One minus the Jaccard coefficient, the quote of nonzero
          coordinates that differ.

     '"chebychev"'
          Chebychev distance: the maximum coordinate difference.

     See also: linkage, mahalanobis, squareform, pdist2.


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 46
Return the distance between any two rows in X.



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 6
pdist2


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 1734
 -- Function File: pdist2 (X, Y)
 -- Function File: pdist2 (X, Y, METRIC)
     Compute pairwise distance between two sets of vectors.

     Let X be an MxP matrix representing m points in P-dimensional space
     and Y be an NxP matrix representing another set of points in the
     same space.  This function computes the M-by-N distance matrix D
     where 'D(i,j)' is the distance between 'X(i,:)' and 'Y(j,:)'.

     The optional argument METRIC can be used to select different
     distances:

     "euclidean" (default)

     "sqeuclidean"
          Compute the squared euclidean distance, i.e., the euclidean
          distance before computing square root.  This is ideal when the
          interest is on the order of the euclidean distances rather
          than the actual distance value because it performs
          significantly faster while preserving the order.

     "chisq'"
          The chi-squared distance between two vectors is defined as:
          'd(x, y) = sum ((xi-yi)^2 / (xi+yi)) / 2'.  The chi-squared
          distance is useful when comparing histograms.

     "cosine"
          Distance is defined as the cosine of the angle between two
          vectors.

     "emd"
          Earth Mover's Distance (EMD) between positive vectors
          (histograms).  Note for 1D, with all histograms having equal
          weight, there is a simple closed form for the calculation of
          the EMD. The EMD between histograms X and Y is given by 'sum
          (abs (cdf (x) - cdf (y)))', where 'cdf' is the cumulative
          distribution function (computed simply by 'cumsum').

     "L1"
          The L1 distance between two vectors is defined as: 'sum (abs
          (x-y))'

     See also: pdist.


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 54
Compute pairwise distance between two sets of vectors.



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 10
plsregress


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 521
 -- Function File:
          [XLOADINGS,YLOADINGS,XSCORES,YSCORES,COEFFICIENTS,FITTED] =
          ...
     plsregress(X, Y, NCOMP)
        * X: Matrix of observations
        * Y: Is a vector or matrix of responses
        * NCOMP: number of components used for modelling
        * X and Y will be mean centered to improve accuracy

     References
     ----------

       1. SIMPLS: An alternative approach to partial least squares
          regression.  Chemometrics and Intelligent Laboratory Systems
          (1993)


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 80
plsregress(X, Y, NCOMP)
   * X: Matrix of observations
   * Y: Is a vector or ma



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 8
poisstat


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 862
 -- Function File: [M, V] = poisstat (LAMBDA)
     Compute mean and variance of the Poisson distribution.

     Arguments
     ---------

        * LAMBDA is the parameter of the Poisson distribution.  The
          elements of LAMBDA must be positive

     Return values
     -------------

        * M is the mean of the Poisson distribution

        * V is the variance of the Poisson distribution

     Example
     -------

          lambda = 1 ./ (1:6);
          [m, v] = poisstat (lambda)

     References
     ----------

       1. Wendy L. Martinez and Angel R. Martinez.  'Computational
          Statistics Handbook with MATLAB'. Appendix E, pages 547-557,
          Chapman & Hall/CRC, 2001.

       2. Athanasios Papoulis.  'Probability, Random Variables, and
          Stochastic Processes'.  McGraw-Hill, New York, second edition,
          1984.


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 54
Compute mean and variance of the Poisson distribution.



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 8
princomp


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 1261
 -- Function File: [COEFF] = princomp(X)
 -- Function File: [COEFF,SCORE] = princomp(X)
 -- Function File: [COEFF,SCORE,LATENT] = princomp(X)
 -- Function File: [COEFF,SCORE,LATENT,TSQUARE] = princomp(X)
 -- Function File: [...] = princomp(X,'econ')
        * princomp performs principal component analysis on a NxP data
          matrix X
        * COEFF : returns the principal component coefficients
        * SCORE : returns the principal component scores, the
          representation of X in the principal component space
        * LATENT : returns the principal component variances, i.e., the
          eigenvalues of the covariance matrix X.
        * TSQUARE : returns Hotelling's T-squared Statistic for each
          observation in X
        * [...]  = princomp(X,'econ') returns only the elements of
          latent that are not necessarily zero, and the corresponding
          columns of COEFF and SCORE, that is, when n <= p, only the
          first n-1.  This can be significantly faster when p is much
          larger than n.  In this case the svd will be applied on the
          transpose of the data matrix X

     References
     ----------

       1. Jolliffe, I. T., Principal Component Analysis, 2nd Edition,
          Springer, 2002


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 80
   * princomp performs principal component analysis on a NxP data matrix
     X




# name: <cell-element>
# type: sq_string
# elements: 1
# length: 6
qrandn


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 505
 -- Function File: Z = qrandn (Q, R,C)
 -- Function File: Z = qrandn (Q, [R,C])
     Returns random deviates drawn from a q-Gaussian distribution.

     Parameter Q charcterizes the q-Gaussian distribution.  The result
     has the size indicated by S.

     Reference: W. Thistleton, J. A. Marsh, K. Nelson, C. Tsallis (2006)
     "Generalized Box-Muller method for generating q-Gaussian random
     deviates" arXiv:cond-mat/0605570
     http://arxiv.org/abs/cond-mat/0605570

     See also: rand, randn.


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 61
Returns random deviates drawn from a q-Gaussian distribution.



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 6
random


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 3137
 -- Function File: R = random(NAME, ARG1)
 -- Function File: R = random(NAME, ARG1, ARG2)
 -- Function File: R = random(NAME, ARG1, ARG2, ARG3)
 -- Function File: R = random(NAME, ..., S1, ...)
     Generates pseudo-random numbers from a given one-, two-, or
     three-parameter distribution.

     The variable NAME must be a string that names the distribution from
     which to sample.  If this distribution is a one-parameter
     distribution ARG1 should be supplied, if it is a two-paramter
     distribution ARG2 must also be supplied, and if it is a
     three-parameter distribution ARG3 must also be present.  Any
     arguments following the distribution paramters will determine the
     size of the result.

     As an example, the following code generates a 10 by 20 matrix
     containing random numbers from a normal distribution with mean 5
     and standard deviation 2.
          R = random("normal", 5, 2, [10, 20]);

     The variable NAME can be one of the following strings

     "beta"
     "beta distribution"
          Samples are drawn from the Beta distribution.
     "bino"
     "binomial"
     "binomial distribution"
          Samples are drawn from the Binomial distribution.
     "chi2"
     "chi-square"
     "chi-square distribution"
          Samples are drawn from the Chi-Square distribution.
     "exp"
     "exponential"
     "exponential distribution"
          Samples are drawn from the Exponential distribution.
     "f"
     "f distribution"
          Samples are drawn from the F distribution.
     "gam"
     "gamma"
     "gamma distribution"
          Samples are drawn from the Gamma distribution.
     "geo"
     "geometric"
     "geometric distribution"
          Samples are drawn from the Geometric distribution.
     "hyge"
     "hypergeometric"
     "hypergeometric distribution"
          Samples are drawn from the Hypergeometric distribution.
     "logn"
     "lognormal"
     "lognormal distribution"
          Samples are drawn from the Log-Normal distribution.
     "nbin"
     "negative binomial"
     "negative binomial distribution"
          Samples are drawn from the Negative Binomial distribution.
     "norm"
     "normal"
     "normal distribution"
          Samples are drawn from the Normal distribution.
     "poiss"
     "poisson"
     "poisson distribution"
          Samples are drawn from the Poisson distribution.
     "rayl"
     "rayleigh"
     "rayleigh distribution"
          Samples are drawn from the Rayleigh distribution.
     "t"
     "t distribution"
          Samples are drawn from the T distribution.
     "unif"
     "uniform"
     "uniform distribution"
          Samples are drawn from the Uniform distribution.
     "unid"
     "discrete uniform"
     "discrete uniform distribution"
          Samples are drawn from the Uniform Discrete distribution.
     "wbl"
     "weibull"
     "weibull distribution"
          Samples are drawn from the Weibull distribution.

     See also: rand, betarnd, binornd, chi2rnd, exprnd, frnd, gamrnd,
     geornd, hygernd, lognrnd, nbinrnd, normrnd, poissrnd, raylrnd,
     trnd, unifrnd, unidrnd, wblrnd.


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 80
Generates pseudo-random numbers from a given one-, two-, or
three-parameter dist



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 10
randsample


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 501
 -- Function File: Y = randsample (V, K, REPLACEMENT=false [, W])
     Elements sampled from a vector.

     Returns K random elements from a vector V with N elements, sampled
     without or with REPLACEMENT.

     If V is a scalar, samples from 1:V.

     If sampling with replacement, can specify a weight vector W of the
     same size as V such that the probablility of each element being
     sampled is proportional to W.

     Randomization is performed using rand().

     See also: randperm.


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 31
Elements sampled from a vector.



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 7
raylcdf


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 1119
 -- Function File: P = raylcdf (X, SIGMA)
     Compute the cumulative distribution function of the Rayleigh
     distribution.

     Arguments
     ---------

        * X is the support.  The elements of X must be non-negative.

        * SIGMA is the parameter of the Rayleigh distribution.  The
          elements of SIGMA must be positive.
     X and SIGMA must be of common size or one of them must be scalar.

     Return values
     -------------

        * P is the cumulative distribution of the Rayleigh distribution
          at each element of X and corresponding parameter SIGMA.

     Examples
     --------

          x = 0:0.5:2.5;
          sigma = 1:6;
          p = raylcdf (x, sigma)

          p = raylcdf (x, 0.5)

     References
     ----------

       1. Wendy L. Martinez and Angel R. Martinez.  'Computational
          Statistics Handbook with MATLAB'. Appendix E, pages 547-557,
          Chapman & Hall/CRC, 2001.

       2. Athanasios Papoulis.  'Probability, Random Variables, and
          Stochastic Processes'.  pages 104 and 148, McGraw-Hill, New
          York, second edition, 1984.


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 74
Compute the cumulative distribution function of the Rayleigh
distribution.



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 7
raylinv


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 1177
 -- Function File: X = raylinv (P, SIGMA)
     Compute the quantile of the Rayleigh distribution.  The quantile is
     the inverse of the cumulative distribution function.

     Arguments
     ---------

        * P is the cumulative distribution.  The elements of P must be
          probabilities.

        * SIGMA is the parameter of the Rayleigh distribution.  The
          elements of SIGMA must be positive.
     P and SIGMA must be of common size or one of them must be scalar.

     Return values
     -------------

        * X is the quantile of the Rayleigh distribution at each element
          of P and corresponding parameter SIGMA.

     Examples
     --------

          p = 0:0.1:0.5;
          sigma = 1:6;
          x = raylinv (p, sigma)

          x = raylinv (p, 0.5)

     References
     ----------

       1. Wendy L. Martinez and Angel R. Martinez.  'Computational
          Statistics Handbook with MATLAB'. Appendix E, pages 547-557,
          Chapman & Hall/CRC, 2001.

       2. Athanasios Papoulis.  'Probability, Random Variables, and
          Stochastic Processes'.  pages 104 and 148, McGraw-Hill, New
          York, second edition, 1984.


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 50
Compute the quantile of the Rayleigh distribution.



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 7
raylpdf


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 1111
 -- Function File: Y = raylpdf (X, SIGMA)
     Compute the probability density function of the Rayleigh
     distribution.

     Arguments
     ---------

        * X is the support.  The elements of X must be non-negative.

        * SIGMA is the parameter of the Rayleigh distribution.  The
          elements of SIGMA must be positive.
     X and SIGMA must be of common size or one of them must be scalar.

     Return values
     -------------

        * Y is the probability density of the Rayleigh distribution at
          each element of X and corresponding parameter SIGMA.

     Examples
     --------

          x = 0:0.5:2.5;
          sigma = 1:6;
          y = raylpdf (x, sigma)

          y = raylpdf (x, 0.5)

     References
     ----------

       1. Wendy L. Martinez and Angel R. Martinez.  'Computational
          Statistics Handbook with MATLAB'. Appendix E, pages 547-557,
          Chapman & Hall/CRC, 2001.

       2. Athanasios Papoulis.  'Probability, Random Variables, and
          Stochastic Processes'.  pages 104 and 148, McGraw-Hill, New
          York, second edition, 1984.


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 70
Compute the probability density function of the Rayleigh distribution.



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 7
raylrnd


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 1525
 -- Function File: X = raylrnd (SIGMA)
 -- Function File: X = raylrnd (SIGMA, SZ)
 -- Function File: X = raylrnd (SIGMA, R, C)
     Generate a matrix of random samples from the Rayleigh distribution.

     Arguments
     ---------

        * SIGMA is the parameter of the Rayleigh distribution.  The
          elements of SIGMA must be positive.

        * SZ is the size of the matrix to be generated.  SZ must be a
          vector of non-negative integers.

        * R is the number of rows of the matrix to be generated.  R must
          be a non-negative integer.

        * C is the number of columns of the matrix to be generated.  C
          must be a non-negative integer.

     Return values
     -------------

        * X is a matrix of random samples from the Rayleigh distribution
          with corresponding parameter SIGMA.  If neither SZ nor R and C
          are specified, then X is of the same size as SIGMA.

     Examples
     --------

          sigma = 1:6;
          x = raylrnd (sigma)

          sz = [2, 3];
          x = raylrnd (0.5, sz)

          r = 2;
          c = 3;
          x = raylrnd (0.5, r, c)

     References
     ----------

       1. Wendy L. Martinez and Angel R. Martinez.  'Computational
          Statistics Handbook with MATLAB'. Appendix E, pages 547-557,
          Chapman & Hall/CRC, 2001.

       2. Athanasios Papoulis.  'Probability, Random Variables, and
          Stochastic Processes'.  pages 104 and 148, McGraw-Hill, New
          York, second edition, 1984.


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 67
Generate a matrix of random samples from the Rayleigh distribution.



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 8
raylstat


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 857
 -- Function File: [M, V] = raylstat (SIGMA)
     Compute mean and variance of the Rayleigh distribution.

     Arguments
     ---------

        * SIGMA is the parameter of the Rayleigh distribution.  The
          elements of SIGMA must be positive.

     Return values
     -------------

        * M is the mean of the Rayleigh distribution.

        * V is the variance of the Rayleigh distribution.

     Example
     -------

          sigma = 1:6;
          [m, v] = raylstat (sigma)

     References
     ----------

       1. Wendy L. Martinez and Angel R. Martinez.  'Computational
          Statistics Handbook with MATLAB'. Appendix E, pages 547-557,
          Chapman & Hall/CRC, 2001.

       2. Athanasios Papoulis.  'Probability, Random Variables, and
          Stochastic Processes'.  McGraw-Hill, New York, second edition,
          1984.


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 55
Compute mean and variance of the Rayleigh distribution.



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 7
regress


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 1319
 -- Function File: [B, BINT, R, RINT, STATS] = regress (Y, X, [ALPHA])
     Multiple Linear Regression using Least Squares Fit of Y on X with
     the model 'y = X * beta + e'.

     Here,

        * 'y' is a column vector of observed values
        * 'X' is a matrix of regressors, with the first column filled
          with the constant value 1
        * 'beta' is a column vector of regression parameters
        * 'e' is a column vector of random errors

     Arguments are

        * Y is the 'y' in the model
        * X is the 'X' in the model
        * ALPHA is the significance level used to calculate the
          confidence intervals BINT and RINT (see 'Return values'
          below).  If not specified, ALPHA defaults to 0.05

     Return values are

        * B is the 'beta' in the model
        * BINT is the confidence interval for B
        * R is a column vector of residuals
        * RINT is the confidence interval for R
        * STATS is a row vector containing:

             * The R^2 statistic
             * The F statistic
             * The p value for the full model
             * The estimated error variance

     R and RINT can be passed to 'rcoplot' to visualize the residual
     intervals and identify outliers.

     NaN values in Y and X are removed before calculation begins.


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 80
Multiple Linear Regression using Least Squares Fit of Y on X with the
model 'y =



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 10
regress_gp


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 1125
 -- Function File: [M, K] = regress_gp (X, Y, SP)
 -- Function File: [... YI DY] = regress_gp (..., XI)
     Linear scalar regression using gaussian processes.

     It estimates the model Y = X'*m for X R^D and Y in R. The
     information about errors of the predictions
     (interpolation/extrapolation) is given by the covarianve matrix K.
     If D==1 the inputs must be column vectors, if D>1 then X is n-by-D,
     with n the number of data points.  SP defines the prior covariance
     of M, it should be a (D+1)-by-(D+1) positive definite matrix, if it
     is empty, the default is 'Sp = 100*eye(size(x,2)+1)'.

     If XI inputs are provided, the model is evaluated and returned in
     YI.  The estimation of the variation of YI are given in DY.

     Run 'demo regress_gp' to see an examples.

     The function is a direc implementation of the formulae in pages
     11-12 of Gaussian Processes for Machine Learning.  Carl Edward
     Rasmussen and  Christopher K. I. Williams.  The MIT Press, 2006.
     ISBN 0-262-18253-X. available online at
     <http://gaussianprocess.org/gpml/>.

     See also: regress.


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 50
Linear scalar regression using gaussian processes.



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 8
repanova


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 694
 -- Function File: [PVAL, TABLE, ST] = repanova (X, COND)
 -- Function File: [PVAL, TABLE, ST] = repanova (X, COND, ['string' |
          'cell'])
     Perform a repeated measures analysis of variance (Repeated ANOVA).
     X is formated such that each row is a subject and each column is a
     condition.

     condition is typically a point in time, say t=1 then t=2, etc
     condition can also be thought of as groups.

     The optional flag can be either 'cell' or 'string' and reflects the
     format of the table returned.  Cell is the default.

     NaNs are ignored using nanmean and nanstd.

     This fuction does not currently support multiple columns of the
     same condition!


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 66
Perform a repeated measures analysis of variance (Repeated ANOVA).



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 8
runstest


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 1123
 -- Function File: H, P, STATS = runstest (X, V)
     Runs test for detecting serial correlation in the vector X.

     Arguments
     ---------

        * X is the vector of given values.
        * V is the value to subtract from X to get runs (defaults to
          'median(x)')

     Return values
     -------------

        * H is true if serial correlation is detected at the 95%
          confidence level (two-tailed), false otherwise.
        * P is the probablity of obtaining a test statistic of the
          magnitude found under the null hypothesis of no serial
          correlation.
        * STATS is the structure containing as fields the number of runs
          NRUNS; the numbers of positive and negative values of 'x - v',
          N1 and N0; and the test statistic Z.

     Note: the large-sample normal approximation is used to find H and
     P.  This is accurate if N1, N0 are both greater than 10.

     Reference: NIST Engineering Statistics Handbook, 1.3.5.13.  Runs
     Test for Detecting Non-randomness,
     http://www.itl.nist.gov/div898/handbook/eda/section3/eda35d.htm

     See also: .


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 59
Runs test for detecting serial correlation in the vector X.



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 8
signtest


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 1584
 -- Function File: [PVAL, H, STATS] = signtest (X)
 -- Function File: [PVAL, H, STATS] = signtest (X, M)
 -- Function File: [PVAL, H, STATS] = signtest (X, Y)
 -- Function File: [PVAL, H, STATS] = signtest (X, Y, NAME, VALUE)
     Test for median.

     Perform a signtest of the null hypothesis that X is from a
     distribution that has a zero median.

     If the second argument M is a scalar, the null hypothesis is that X
     has median m.

     If the second argument Y is a vector, the null hypothesis is that
     the distribution of 'X - Y' has zero median.

     The argument "alpha" can be used to specify the significance level
     of the test (the default value is 0.05).  The string argument
     "tail", can be used to select the desired alternative hypotheses.
     If "alt" is "both" (default) the null is tested against the
     two-sided alternative 'median (X) != M'.  If "alt" is "right" the
     one-sided alternative 'median (X) > M' is considered.  Similarly
     for "left", the one-sided alternative 'median (X) < M' is
     considered.  When "method" is "exact" the p-value is computed using
     an exact method (this is the default).  When "method" is
     "approximate" a normal approximation is used for the test
     statistic.

     The p-value of the test is returned in PVAL.  If H is 0 the null
     hypothesis is accepted, if it is 1 the null hypothesis is rejected.
     STATS is a structure containing the value of the test statistic
     (SIGN) and the value of the z statistic (ZVAL) (only computed when
     the 'method' is 'approximate'.


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 16
Test for median.



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 10
squareform


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 681
 -- Function File: Y = squareform (X)
 -- Function File: Y = squareform (X, "tovector")
 -- Function File: Y = squareform (X, "tomatrix")
     Converts a vector from the 'pdist' function into a distance matrix
     or a distance matrix back to vector form.

     If X is a vector, it must have 'length(X) = N * (N - 1) / 2' for
     some integer N.  The resulting matrix will be N by N.

     If X is a distance matrix, it must be square and the diagonal
     entries of X must all be zeros.  'squareform' will generate a
     warning if X is not symmetric.

     The second argument is used to specify the output type in case
     there is a single element.

     See also: pdist.


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 80
Converts a vector from the 'pdist' function into a distance matrix or a
distance



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 11
stepwisefit


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 1254
 -- Function File: X_USE, B, BINT, R, RINT, STATS = stepwisefit (Y, X,
          PENTER = 0.05, PREMOVE = 0.1)
     Linear regression with stepwise variable selection.

     Arguments
     ---------

        * Y is an N by 1 vector of data to fit.
        * X is an N by K matrix containing the values of K potential
          predictors.  No constant term should be included (one will
          always be added to the regression automatically).
        * PENTER is the maximum p-value to enter a new variable into the
          regression (default: 0.05).
        * PREMOVE is the minimum p-value to remove a variable from the
          regression (default: 0.1).

     Return values
     -------------

        * X_USE contains the indices of the predictors included in the
          final regression model.  The predictors are listed in the
          order they were added, so typically the first ones listed are
          the most significant.
        * B, BINT, R, RINT, STATS are the results of '[b, bint, r, rint,
          stats] = regress(y, [ones(size(y)) X(:, X_use)], penter);'

     References
     ----------

       1. N. R. Draper and H. Smith (1966).  'Applied Regression
          Analysis'.  Wiley.  Chapter 6.

     See also: regress.


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 51
Linear regression with stepwise variable selection.



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 8
tabulate


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 1885
 -- Function File: TABLE = tabulate (DATA, EDGES)

     Compute a frequency table.

     For vector data, the function counts the number of values in data
     that fall between the elements in the edges vector (which must
     contain monotonically non-decreasing values).  TABLE is a matrix.
     The first column of TABLE is the number of bin, the second is the
     number of instances in each class (absolute frequency).  The third
     column contains the percentage of each value (relative frequency)
     and the fourth column contains the cumulative frequency.

     If EDGES is missed the width of each class is unitary, if EDGES is
     a scalar then represent the number of classes, or you can define
     the width of each bin.  TABLE(K, 2) will count the value DATA (I)
     if EDGES (K) <= DATA (I) < EDGES (K+1).  The last bin will count
     the value of DATA (I) if EDGES(K) <= DATA (I) <= EDGES (K+1).
     Values outside the values in EDGES are not counted.  Use -inf and
     inf in EDGES to include all values.  Tabulate with no output
     arguments returns a formatted table in the command window.

     Example

          sphere_radius = [1:0.05:2.5];
          tabulate (sphere_radius)

     Tabulate returns 2 bins, the first contains the sphere with radius
     between 1 and 2 mm excluded, and the second one contains the sphere
     with radius between 2 and 3 mm.

          tabulate (sphere_radius, 10)

     Tabulate returns ten bins.

          tabulate (sphere_radius, [1, 1.5, 2, 2.5])

     Tabulate returns three bins, the first contains the sphere with
     radius between 1 and 1.5 mm excluded, the second one contains the
     sphere with radius between 1.5 and 2 mm excluded, and the third
     contains the sphere with radius between 2 and 2.5 mm.

          bar (table (:, 1), table (:, 2))

     draw histogram.

     See also: bar, pareto.


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 26
Compute a frequency table.



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 7
tblread


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 770
 -- Function File: [DATA, VARNAMES, CASENAMES] = tblread (FILENAME)
 -- Function File: [DATA, VARNAMES, CASENAMES] = tblread (FILENAME,
          DELIMETER)
     Read tabular data from an ascii file.

     DATA is read from an ascii data file named FILENAME with an
     optional DELIMETER.  The delimeter may be any single character or
        * "space" " " (default)
        * "tab" "\t"
        * "comma" ","
        * "semi" ";"
        * "bar" "|"

     The DATA is read starting at cell (2,2) where the VARNAMES form a
     char matrix from the first row (starting at (1,2)) vertically
     concatenated, and the CASENAMES form a char matrix read from the
     first column (starting at (2,1)) vertically concatenated.

     See also: tblwrite, csv2cell, cell2csv.


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 37
Read tabular data from an ascii file.



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 8
tblwrite


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 757
 -- Function File: tblwrite (DATA, VARNAMES, CASENAMES, FILENAME)
 -- Function File: tblwrite (DATA, VARNAMES, CASENAMES, FILENAME,
          DELIMETER)
     Write tabular data to an ascii file.

     DATA is written to an ascii data file named FILENAME with an
     optional DELIMETER.  The delimeter may be any single character or
        * "space" " " (default)
        * "tab" "\t"
        * "comma" ","
        * "semi" ";"
        * "bar" "|"

     The DATA is written starting at cell (2,2) where the VARNAMES are a
     char matrix or cell vector written to the first row (starting at
     (1,2)), and the CASENAMES are a char matrix (or cell vector)
     written to the first column (starting at (2,1)).

     See also: tblread, csv2cell, cell2csv.


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 36
Write tabular data to an ascii file.



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 8
trimmean


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 386
 -- Function File: A = trimmean (X, P)

     Compute the trimmed mean.

     The trimmed mean of X is defined as the mean of X excluding the
     highest and lowest P percent of the data.

     For example

          mean ([-inf, 1:9, inf])

     is NaN, while

          trimmean ([-inf, 1:9, inf], 10)

     excludes the infinite values, which make the result 5.

     See also: mean.


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 25
Compute the trimmed mean.



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 5
tstat


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 840
 -- Function File: [M, V] = tstat (N)
     Compute mean and variance of the t (Student) distribution.

     Arguments
     ---------

        * N is the parameter of the t (Student) distribution.  The
          elements of N must be positive

     Return values
     -------------

        * M is the mean of the t (Student) distribution

        * V is the variance of the t (Student) distribution

     Example
     -------

          n = 3:8;
          [m, v] = tstat (n)

     References
     ----------

       1. Wendy L. Martinez and Angel R. Martinez.  'Computational
          Statistics Handbook with MATLAB'. Appendix E, pages 547-557,
          Chapman & Hall/CRC, 2001.

       2. Athanasios Papoulis.  'Probability, Random Variables, and
          Stochastic Processes'.  McGraw-Hill, New York, second edition,
          1984.


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 58
Compute mean and variance of the t (Student) distribution.



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 5
ttest


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 1791
 -- Function File: [H, PVAL, CI, STATS] = ttest (X)
 -- Function File: [H, PVAL, CI, STATS] = ttest (X, M)
 -- Function File: [H, PVAL, CI, STATS] = ttest (X, Y)
 -- Function File: [H, PVAL, CI, STATS] = ttest (X, M, NAME, VALUE)
 -- Function File: [H, PVAL, CI, STATS] = ttest (X, Y, NAME, VALUE)
     Test for mean of a normal sample with known variance.

     Perform a T-test of the null hypothesis 'mean (X) == M' for a
     sample X from a normal distribution with unknown mean and unknown
     std deviation.  Under the null, the test statistic T has a
     Student's t distribution.  The default value of M is 0.

     If the second argument Y is a vector, a paired-t test of the
     hypothesis 'mean (X) = mean (Y)' is performed.

     Name-Value pair arguments can be used to set various options.
     "alpha" can be used to specify the significance level of the test
     (the default value is 0.05).  "tail", can be used to select the
     desired alternative hypotheses.  If the value is "both" (default)
     the null is tested against the two-sided alternative 'mean (X) !=
     M'.  If it is "right" the one-sided alternative 'mean (X) > M' is
     considered.  Similarly for "left", the one-sided alternative 'mean
     (X) < M' is considered.  When argument X is a matrix, "dim" can be
     used to selection the dimension over which to perform the test.
     (The default is the first non-singleton dimension).

     If H is 0 the null hypothesis is accepted, if it is 1 the null
     hypothesis is rejected.  The p-value of the test is returned in
     PVAL.  A 100(1-alpha)% confidence interval is returned in CI.
     STATS is a structure containing the value of the test statistic
     (TSTAT), the degrees of freedom (DF) and the sample standard
     deviation (SD).


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 53
Test for mean of a normal sample with known variance.



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 6
ttest2


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 1738
 -- Function File: [H, PVAL, CI, STATS] = ttest2 (X, Y)
 -- Function File: [H, PVAL, CI, STATS] = ttest2 (X, Y, NAME, VALUE)
     Test for mean of a normal sample with known variance.

     Perform a T-test of the null hypothesis 'mean (X) == M' for a
     sample X from a normal distribution with unknown mean and unknown
     std deviation.  Under the null, the test statistic T has a
     Student's t distribution.

     If the second argument Y is a vector, a paired-t test of the
     hypothesis 'mean (X) = mean (Y)' is performed.

     The argument "alpha" can be used to specify the significance level
     of the test (the default value is 0.05).  The string argument
     "tail", can be used to select the desired alternative hypotheses.
     If "alt" is "both" (default) the null is tested against the
     two-sided alternative 'mean (X) != M'.  If "alt" is "right" the
     one-sided alternative 'mean (X) > M' is considered.  Similarly for
     "left", the one-sided alternative 'mean (X) < M' is considered.
     When "vartype" is "equal" the variances are assumed to be equal
     (this is the default).  When "vartype" is "unequal" the variances
     are not assumed equal.  When argument X is a matrix the "dim"
     argument can be used to selection the dimension over which to
     perform the test.  (The default is the first non-singleton
     dimension.)

     If H is 0 the null hypothesis is accepted, if it is 1 the null
     hypothesis is rejected.  The p-value of the test is returned in
     PVAL.  A 100(1-alpha)% confidence interval is returned in CI.
     STATS is a structure containing the value of the test statistic
     (TSTAT), the degrees of freedom (DF) and the sample standard
     deviation (SD).


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 53
Test for mean of a normal sample with known variance.



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 8
unidstat


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 882
 -- Function File: [M, V] = unidstat (N)
     Compute mean and variance of the discrete uniform distribution.

     Arguments
     ---------

        * N is the parameter of the discrete uniform distribution.  The
          elements of N must be positive natural numbers

     Return values
     -------------

        * M is the mean of the discrete uniform distribution

        * V is the variance of the discrete uniform distribution

     Example
     -------

          n = 1:6;
          [m, v] = unidstat (n)

     References
     ----------

       1. Wendy L. Martinez and Angel R. Martinez.  'Computational
          Statistics Handbook with MATLAB'. Appendix E, pages 547-557,
          Chapman & Hall/CRC, 2001.

       2. Athanasios Papoulis.  'Probability, Random Variables, and
          Stochastic Processes'.  McGraw-Hill, New York, second edition,
          1984.


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 63
Compute mean and variance of the discrete uniform distribution.



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 8
unifstat


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 1093
 -- Function File: [M, V] = unifstat (A, B)
     Compute mean and variance of the continuous uniform distribution.

     Arguments
     ---------

        * A is the first parameter of the continuous uniform
          distribution

        * B is the second parameter of the continuous uniform
          distribution
     A and B must be of common size or one of them must be scalar and A
     must be less than B

     Return values
     -------------

        * M is the mean of the continuous uniform distribution

        * V is the variance of the continuous uniform distribution

     Examples
     --------

          a = 1:6;
          b = 2:2:12;
          [m, v] = unifstat (a, b)

          [m, v] = unifstat (a, 10)

     References
     ----------

       1. Wendy L. Martinez and Angel R. Martinez.  'Computational
          Statistics Handbook with MATLAB'. Appendix E, pages 547-557,
          Chapman & Hall/CRC, 2001.

       2. Athanasios Papoulis.  'Probability, Random Variables, and
          Stochastic Processes'.  McGraw-Hill, New York, second edition,
          1984.


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 65
Compute mean and variance of the continuous uniform distribution.



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 7
vartest


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 1490
 -- Function File: [H, PVAL, CI, STATS] = vartest (X, Y)
 -- Function File: [H, PVAL, CI, STATS] = vartest (X, Y, NAME, VALUE)
     Perform a F-test for equal variances.

     If the second argument Y is a vector, a paired-t test of the
     hypothesis 'mean (X) = mean (Y)' is performed.

     The argument "alpha" can be used to specify the significance level
     of the test (the default value is 0.05).  The string argument
     "tail", can be used to select the desired alternative hypotheses.
     If "alt" is "both" (default) the null is tested against the
     two-sided alternative 'mean (X) != M'.  If "alt" is "right" the
     one-sided alternative 'mean (X) > M' is considered.  Similarly for
     "left", the one-sided alternative 'mean (X) < M' is considered.
     When "vartype" is "equal" the variances are assumed to be equal
     (this is the default).  When "vartype" is "unequal" the variances
     are not assumed equal.  When argument X is a matrix the "dim"
     argument can be used to selection the dimension over which to
     perform the test.  (The default is the first non-singleton
     dimension.)

     If H is 0 the null hypothesis is accepted, if it is 1 the null
     hypothesis is rejected.  The p-value of the test is returned in
     PVAL.  A 100(1-alpha)% confidence interval is returned in CI.
     STATS is a structure containing the value of the test statistic
     (TSTAT), the degrees of freedom (DF) and the sample standard
     deviation (SD).


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 37
Perform a F-test for equal variances.



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 8
vartest2


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 1492
 -- Function File: [H, PVAL, CI, STATS] = vartest2 (X, Y)
 -- Function File: [H, PVAL, CI, STATS] = vartest2 (X, Y, NAME, VALUE)
     Perform a F-test for equal variances.

     If the second argument Y is a vector, a paired-t test of the
     hypothesis 'mean (X) = mean (Y)' is performed.

     The argument "alpha" can be used to specify the significance level
     of the test (the default value is 0.05).  The string argument
     "tail", can be used to select the desired alternative hypotheses.
     If "alt" is "both" (default) the null is tested against the
     two-sided alternative 'mean (X) != M'.  If "alt" is "right" the
     one-sided alternative 'mean (X) > M' is considered.  Similarly for
     "left", the one-sided alternative 'mean (X) < M' is considered.
     When "vartype" is "equal" the variances are assumed to be equal
     (this is the default).  When "vartype" is "unequal" the variances
     are not assumed equal.  When argument X is a matrix the "dim"
     argument can be used to selection the dimension over which to
     perform the test.  (The default is the first non-singleton
     dimension.)

     If H is 0 the null hypothesis is accepted, if it is 1 the null
     hypothesis is rejected.  The p-value of the test is returned in
     PVAL.  A 100(1-alpha)% confidence interval is returned in CI.
     STATS is a structure containing the value of the test statistic
     (TSTAT), the degrees of freedom (DF) and the sample standard
     deviation (SD).


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 37
Perform a F-test for equal variances.



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 5
vmpdf


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 318
 -- Function File: THETA = vmpdf (X, MU, K)
     Evaluates the Von Mises probability density function.

     The Von Mises distribution has probability density function
          f (X) = exp (K * cos (X - MU)) / Z ,
     where Z is a normalisation constant.  By default, MU is 0 and K is
     1.

     See also: vmrnd.


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 53
Evaluates the Von Mises probability density function.



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 5
vmrnd


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 517
 -- Function File: THETA = vmrnd (MU, K)
 -- Function File: THETA = vmrnd (MU, K, SZ)
     Draw random angles from a Von Mises distribution with mean MU and
     concentration K.

     The Von Mises distribution has probability density function
          f (X) = exp (K * cos (X - MU)) / Z ,
     where Z is a normalisation constant.

     The output, THETA, is a matrix of size SZ containing random angles
     drawn from the given Von Mises distribution.  By default, MU is 0
     and K is 1.

     See also: vmpdf.


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 80
Draw random angles from a Von Mises distribution with mean MU and
concentration 



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 7
wblstat


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 1096
 -- Function File: [M, V] = wblstat (SCALE, SHAPE)
     Compute mean and variance of the Weibull distribution.

     Arguments
     ---------

        * SCALE is the scale parameter of the Weibull distribution.
          SCALE must be positive

        * SHAPE is the shape parameter of the Weibull distribution.
          SHAPE must be positive
     SCALE and SHAPE must be of common size or one of them must be
     scalar

     Return values
     -------------

        * M is the mean of the Weibull distribution

        * V is the variance of the Weibull distribution

     Examples
     --------

          scale = 3:8;
          shape = 1:6;
          [m, v] = wblstat (scale, shape)

          [m, v] = wblstat (6, shape)

     References
     ----------

       1. Wendy L. Martinez and Angel R. Martinez.  'Computational
          Statistics Handbook with MATLAB'. Appendix E, pages 547-557,
          Chapman & Hall/CRC, 2001.

       2. Athanasios Papoulis.  'Probability, Random Variables, and
          Stochastic Processes'.  McGraw-Hill, New York, second edition,
          1984.


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 54
Compute mean and variance of the Weibull distribution.



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 7
wishpdf


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 644
 -- Function File: Y = wishpdf (W, SIGMA, DF, LOG_Y=false)
     Compute the probability density function of the Wishart
     distribution

     Inputs: A P x P matrix W where to find the PDF. The P x P positive
     definite matrix SIGMA and scalar degrees of freedom parameter DF
     characterizing the Wishart distribution.  (For the density to be
     finite, need DF > (P - 1).)  If the flag LOG_Y is set, return the
     log probability density - this helps avoid underflow when the
     numerical value of the density is very small

     Output: Y is the probability density of Wishart(SIGMA, DF) at W.

     See also: wishrnd, iwishpdf.


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 68
Compute the probability density function of the Wishart distribution



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 7
wishrnd


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 1004
 -- Function File: [W[, D]] = wishrnd (SIGMA, DF[, D][, N=1])
     Return a random matrix sampled from the Wishart distribution with
     given parameters

     Inputs: the P x P positive definite matrix SIGMA and scalar degrees
     of freedom parameter DF (and optionally the Cholesky factor D of
     SIGMA).  DF can be non-integer as long as DF > P

     Output: a random P x P matrix W from the Wishart(SIGMA, DF)
     distribution.  If N > 1, then W is P x P x N and holds N such
     random matrices.  (Optionally, the Cholesky factor D of SIGMA is
     also returned.)

     Averaged across many samples, the mean of W should approach
     DF*SIGMA, and the variance of each element W_ij should approach
     DF*(SIGMA_ij^2 + SIGMA_ii*SIGMA_jj)

     Reference: Yu-Cheng Ku and Peter Bloomfield (2010), Generating
     Random Wishart Matrices with Fractional Degrees of Freedom in OX,
     http://www.gwu.edu/~forcpgm/YuChengKu-030510final-WishartYu-ChengKu.pdf

     See also: iwishrnd, wishpdf.


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 80
Return a random matrix sampled from the Wishart distribution with given
paramete



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 5
ztest


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 1429
 -- Function File: [H, PVAL, CI, Z, ZCRIT] = ztest (X, M, S)
 -- Function File: [H, PVAL, CI, Z, ZCRIT] = ztest (X, M, S, NAME,
          VALUE)
     Test for mean of a normal sample with known variance.

     Perform a Z-test of the null hypothesis 'mean (X) == M' for a
     sample X from a normal distribution with unknown mean and known std
     deviation S.  Under the null, the test statistic Z follows a
     standard normal distribution.

     Name-Value pair arguments can be used to set various options.
     "alpha" can be used to specify the significance level of the test
     (the default value is 0.05).  "tail", can be used to select the
     desired alternative hypotheses.  If the value is "both" (default)
     the null is tested against the two-sided alternative 'mean (X) !=
     M'.  If it is "right" the one-sided alternative 'mean (X) > M' is
     considered.  Similarly for "left", the one-sided alternative 'mean
     (X) < M' is considered.  When argument X is a matrix, "dim" can be
     used to selection the dimension over which to perform the test.
     (The default is the first non-singleton dimension.)

     If H is 0 the null hypothesis is accepted, if it is 1 the null
     hypothesis is rejected.  The p-value of the test is returned in
     PVAL.  A 100(1-alpha)% confidence interval is returned in CI.  The
     test statistic value is returned in Z and the z critical value in
     ZCRIT.


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 53
Test for mean of a normal sample with known variance.