This file is indexed.

/usr/include/apop.h is in libapophenia2-dev 1.0+ds-7.

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
/** \file  */
/* Copyright (c) 2005--2014 by Ben Klemens.  Licensed under the GPLv2; see COPYING. */

/* Here are the headers for all of apophenia's functions, typedefs, static variables and
macros. All of these begin with the apop_ (or Apop_ or APOP_) prefix.

There used to be a series of sub-headers, but they never provided any serious
benefit. Please use your text editor's word-search feature to find any elements you
may be looking for. About a third of the file is comments and doxygen documentation,
so syntax highlighting that distinguishes code from comments will also help to make
this more navigable.*/

/** \defgroup all_public Public functions, structs, and types
\addtogroup all_public
@{
*/

#pragma once
#ifdef	__cplusplus
extern "C" {
#endif

/** \cond doxy_ignore */
#ifndef _GNU_SOURCE
#define  _GNU_SOURCE //for asprintf
#endif

#include <assert.h>
#include <signal.h> //raise(SIGTRAP)
#include <string.h>
#include <gsl/gsl_rng.h>
#include <gsl/gsl_matrix.h>


            //////Optional arguments

/* A means of providing more script-like means of sending arguments to a function.

These macros are intended as internal. If you are interested in using this mechanism
in out-of-Apophenia work, grep docs/documentation.h for optionaldetails to find notes
on how these are used (Doxygen doesn't use that page),
*/
#define apop_varad_head(type, name) type variadic_##name(variadic_type_##name varad_in)

#define apop_varad_declare(type, name, ...) \
    typedef struct {                        \
                __VA_ARGS__ ;               \
            } variadic_type_##name;         \
    apop_varad_head(type, name);

#define apop_varad_var(name, value) name = varad_in.name ? varad_in.name : (value);
#define apop_varad_link(name,...) variadic_##name((variadic_type_##name) {__VA_ARGS__})

/** \endcond */ //End of Doxygen ignore.


            //////The types and functions that act on them

/** This structure holds the names of the components of the \ref apop_data set. You may never have to worry about it directly, because most operations on \ref apop_data sets will take care of the names for you.
*/
typedef struct{
    char *title;
	char * vector;
	char ** col;
	char ** row;
	char ** text;
	int colct, rowct, textct;
} apop_name;

/** The \ref apop_data structure represents a data set. See \ref dataoverview.*/
typedef struct apop_data{
    gsl_vector  *vector;
    gsl_matrix  *matrix;
    apop_name   *names;
    char        ***text;
    size_t      textsize[2];
    gsl_vector  *weights;
    struct apop_data   *more;
    char        error;
} apop_data;

/* Settings groups. For internal use only; see apop_settings.c and 
   settings.h for related machinery. */
typedef struct {
    char name[101];
    unsigned long name_hash;
    void *setting_group;
    void *copy;
    void *free;
} apop_settings_type;

/** A statistical model. See \ref modelsec for details. */
typedef struct apop_model apop_model;

/** The elements of the \ref apop_model type, representing a statistical model. See \ref
 modelsec and \ref modeldetails for use and details.  */
struct apop_model{
    char name[101]; 
    int vsize, msize1, msize2, dsize;
    apop_data *data;
    apop_data *parameters;
    apop_data *info;
    void (*estimate)(apop_data * data, apop_model *params); 
    long double (*p)(apop_data *d, apop_model *params);
    long double (*log_likelihood)(apop_data *d, apop_model *params);
    long double (*cdf)(apop_data *d, apop_model *params);
    long double (*constraint)(apop_data *data, apop_model *params);
    int (*draw)(double *out, gsl_rng* r, apop_model *params);
    void (*prep)(apop_data *data, apop_model *params);
    apop_settings_type *settings;
    void *more;
    size_t more_size;
    char error;
};

/** The global options. */
typedef struct{
    int verbose; /**< Set this to zero for silent mode, one for errors and warnings. default = 0. */
    char stop_on_warning; /**< See \ref debugging . */
    char output_delimiter[100]; /**< The separator between elements of output tables. The default is "\t", but 
                                for LaTeX, use "&\t", or use "|" to get pipe-delimited output. */
    char input_delimiters[100]; /**< Deprecated. Please use per-function inputs to \ref apop_text_to_db and \ref apop_text_to_data. Default = "|,\t" */
    char *db_name_column; /**< If not NULL or <tt>""</tt>, the name of the column in your tables that holds row names.*/
    char *nan_string; /**< The string used to indicate NaN. Default: <tt>"NaN</tt>. Comparisons are case-insensitive.*/
    char db_engine; /**< If this is 'm', use mySQL, else use SQLite. */
    char db_user[101]; /**< Username for database login. Max 100 chars.  */
    char db_pass[101]; /**< Password for database login. Max 100 chars.  */
    FILE *log_file;  /**< The file handle for the log. Defaults to \c stderr, but change it with, e.g.,
                           <tt>apop_opts.log_file = fopen("outlog", "w");</tt> */

#define Autoconf_no_atomics 1

    #if __STDC_VERSION__ > 201100L && !defined(__STDC_NO_ATOMICS__) && Autoconf_no_atomics==0
        _Atomic(int) rng_seed;
    #else
        int rng_seed;
    #endif
    float version;
} apop_opts_type;

extern apop_opts_type apop_opts;

apop_name * apop_name_alloc(void);
int apop_name_add(apop_name * n, char const *add_me, char type);
void  apop_name_free(apop_name * free_me);
void  apop_name_print(apop_name * n);
#ifdef APOP_NO_VARIADIC
 void  apop_name_stack(apop_name * n1, apop_name *nadd, char type1, char typeadd) ;
#else
 void apop_name_stack_base(apop_name * n1, apop_name *nadd, char type1, char typeadd) ;
 apop_varad_declare(void, apop_name_stack, apop_name * n1; apop_name *nadd; char type1; char typeadd);
#define apop_name_stack(...) apop_varad_link(apop_name_stack, __VA_ARGS__)
#endif

apop_name * apop_name_copy(apop_name *in);
int  apop_name_find(const apop_name *n, const char *findme, const char type);

void apop_data_add_names_base(apop_data *d, const char type, char const ** names);

/** Add a list of names to a data set.

\li Use this with a list of names that you type in yourself, like
\code
apop_data_add_names(mydata, 'c', "age", "sex", "height");
\endcode
Notice the lack of curly braces around the list.

\li You may have an array of names, probably autogenerated, that you would like to
add. In this case, make certain that the last element of the array is \c NULL, and
call the base function:
\code
char **[] colnames = {"age", "sex", "height", NULL};
apop_data_add_names_base(mydata, 'c', colnames);
\endcode
But if you forget the \c NULL marker, this has good odds of segfaulting. You may prefer to use a \c for loop that inserts each name in turn using \ref apop_name_add.

\see \ref apop_name_add, although \ref apop_data_add_names will be more useful in most cases. 
*/
#define apop_data_add_names(dataset, type, ...) apop_data_add_names_base((dataset), (type), (char const*[]) {__VA_ARGS__, NULL}) 


/** Free an \ref apop_data structure.
 
\li As with \c free(), it is safe to send in a \c NULL pointer (in which case the function does nothing).
\li If the \c more pointer is not \c NULL, I will free the pointed-to data set first.
If you don't want to free data sets down the chain, set <tt>more=NULL</tt> before calling this.
\li This is actually a macro (that calls \ref apop_data_free_base). It
sets \c freeme to \c NULL when it's done, because there's nothing safe you can do with the
freed location, and you can later safely test conditions like <tt>if (data) ...</tt>.
*/
#define apop_data_free(freeme) (apop_data_free_base(freeme) ? 0 : ((freeme)= NULL))

char        apop_data_free_base(apop_data *freeme);
#ifdef APOP_NO_VARIADIC
 apop_data * apop_data_alloc(const size_t size1, const size_t size2, const int size3) ;
#else
 apop_data * apop_data_alloc_base(const size_t size1, const size_t size2, const int size3) ;
 apop_varad_declare(apop_data *, apop_data_alloc, const size_t size1; const size_t size2; const int size3);
#define apop_data_alloc(...) apop_varad_link(apop_data_alloc, __VA_ARGS__)
#endif

#ifdef APOP_NO_VARIADIC
 apop_data * apop_data_calloc(const size_t size1, const size_t size2, const int size3) ;
#else
 apop_data * apop_data_calloc_base(const size_t size1, const size_t size2, const int size3) ;
 apop_varad_declare(apop_data *, apop_data_calloc, const size_t size1; const size_t size2; const int size3);
#define apop_data_calloc(...) apop_varad_link(apop_data_calloc, __VA_ARGS__)
#endif

#ifdef APOP_NO_VARIADIC
 apop_data * apop_data_stack(apop_data *m1, apop_data * m2, char posn, char inplace) ;
#else
 apop_data * apop_data_stack_base(apop_data *m1, apop_data * m2, char posn, char inplace) ;
 apop_varad_declare(apop_data *, apop_data_stack, apop_data *m1; apop_data * m2; char posn; char inplace);
#define apop_data_stack(...) apop_varad_link(apop_data_stack, __VA_ARGS__)
#endif

apop_data ** apop_data_split(apop_data *in, int splitpoint, char r_or_c);
apop_data * apop_data_copy(const apop_data *in);
void        apop_data_rm_columns(apop_data *d, int *drop);
void apop_data_memcpy(apop_data *out, const apop_data *in);
#ifdef APOP_NO_VARIADIC
 double * apop_data_ptr(apop_data *data, int row, int col, const char *rowname, const char *colname, const char *page) ;
#else
 double * apop_data_ptr_base(apop_data *data, int row, int col, const char *rowname, const char *colname, const char *page) ;
 apop_varad_declare(double *, apop_data_ptr, apop_data *data; int row; int col; const char *rowname; const char *colname; const char *page);
#define apop_data_ptr(...) apop_varad_link(apop_data_ptr, __VA_ARGS__)
#endif

#ifdef APOP_NO_VARIADIC
 double apop_data_get(const apop_data *data, size_t row, int  col, const char *rowname, const char *colname, const char *page) ;
#else
 double apop_data_get_base(const apop_data *data, size_t row, int  col, const char *rowname, const char *colname, const char *page) ;
 apop_varad_declare(double, apop_data_get, const apop_data *data; size_t row; int  col; const char *rowname; const char *colname; const char *page);
#define apop_data_get(...) apop_varad_link(apop_data_get, __VA_ARGS__)
#endif

#ifdef APOP_NO_VARIADIC
 int apop_data_set(apop_data *data, size_t row, int col, const double val, const char *rowname, const char * colname, const char *page) ;
#else
 int apop_data_set_base(apop_data *data, size_t row, int col, const double val, const char *rowname, const char * colname, const char *page) ;
 apop_varad_declare(int, apop_data_set, apop_data *data; size_t row; int col; const double val; const char *rowname; const char * colname; const char *page);
#define apop_data_set(...) apop_varad_link(apop_data_set, __VA_ARGS__)
#endif

void apop_data_add_named_elmt(apop_data *d, char *name, double val);
int apop_text_set(apop_data *in, const size_t row, const size_t col, const char *fmt, ...);
apop_data * apop_text_alloc(apop_data *in, const size_t row, const size_t col);
void apop_text_free(char ***freeme, int rows, int cols);
#ifdef APOP_NO_VARIADIC
 apop_data * apop_data_transpose(apop_data *in, char transpose_text, char inplace) ;
#else
 apop_data * apop_data_transpose_base(apop_data *in, char transpose_text, char inplace) ;
 apop_varad_declare(apop_data *, apop_data_transpose, apop_data *in; char transpose_text; char inplace);
#define apop_data_transpose(...) apop_varad_link(apop_data_transpose, __VA_ARGS__)
#endif

gsl_matrix * apop_matrix_realloc(gsl_matrix *m, size_t newheight, size_t newwidth);
gsl_vector * apop_vector_realloc(gsl_vector *v, size_t newheight);

#define apop_data_prune_columns(in, ...) apop_data_prune_columns_base((in), (char *[]) {__VA_ARGS__, NULL})
apop_data* apop_data_prune_columns_base(apop_data *d, char **colnames);

#ifdef APOP_NO_VARIADIC
 apop_data * apop_data_get_page(const apop_data * data, const char * title, const char match) ;
#else
 apop_data * apop_data_get_page_base(const apop_data * data, const char * title, const char match) ;
 apop_varad_declare(apop_data *, apop_data_get_page, const apop_data * data; const char * title; const char match);
#define apop_data_get_page(...) apop_varad_link(apop_data_get_page, __VA_ARGS__)
#endif

apop_data * apop_data_add_page(apop_data * dataset, apop_data *newpage,const char *title);
#ifdef APOP_NO_VARIADIC
 apop_data* apop_data_rm_page(apop_data * data, const char *title, const char free_p) ;
#else
 apop_data* apop_data_rm_page_base(apop_data * data, const char *title, const char free_p) ;
 apop_varad_declare(apop_data*, apop_data_rm_page, apop_data * data; const char *title; const char free_p);
#define apop_data_rm_page(...) apop_varad_link(apop_data_rm_page, __VA_ARGS__)
#endif

#ifdef APOP_NO_VARIADIC
 apop_data * apop_data_rm_rows(apop_data *in, int *drop, int (*do_drop)(apop_data* , void*), void* drop_parameter) ;
#else
 apop_data * apop_data_rm_rows_base(apop_data *in, int *drop, int (*do_drop)(apop_data* , void*), void* drop_parameter) ;
 apop_varad_declare(apop_data *, apop_data_rm_rows, apop_data *in; int *drop; int (*do_drop)(apop_data* , void*); void* drop_parameter);
#define apop_data_rm_rows(...) apop_varad_link(apop_data_rm_rows, __VA_ARGS__)
#endif


//in apop_asst.c:
#ifdef APOP_NO_VARIADIC
 apop_data * apop_model_draws(apop_model *model, int count, apop_data *draws) ;
#else
 apop_data * apop_model_draws_base(apop_model *model, int count, apop_data *draws) ;
 apop_varad_declare(apop_data *, apop_model_draws, apop_model *model; int count; apop_data *draws);
#define apop_model_draws(...) apop_varad_link(apop_model_draws, __VA_ARGS__)
#endif



/* Convenience functions to convert among vectors (gsl_vector), matrices (gsl_matrix), 
  arrays (double **), and database tables */

//From vector
gsl_vector *apop_vector_copy(const gsl_vector *in);
#ifdef APOP_NO_VARIADIC
 gsl_matrix * apop_vector_to_matrix(const gsl_vector *in, char row_col) ;
#else
 gsl_matrix * apop_vector_to_matrix_base(const gsl_vector *in, char row_col) ;
 apop_varad_declare(gsl_matrix *, apop_vector_to_matrix, const gsl_vector *in; char row_col);
#define apop_vector_to_matrix(...) apop_varad_link(apop_vector_to_matrix, __VA_ARGS__)
#endif


//From matrix
gsl_matrix *apop_matrix_copy(const gsl_matrix *in);
#ifdef APOP_NO_VARIADIC
 apop_data *apop_db_to_crosstab(char const*tabname, char const*row, char const*col, char const*data, char is_aggregate) ;
#else
 apop_data * apop_db_to_crosstab_base(char const*tabname, char const*row, char const*col, char const*data, char is_aggregate) ;
 apop_varad_declare(apop_data *, apop_db_to_crosstab, char const*tabname; char const*row; char const*col; char const*data; char is_aggregate);
#define apop_db_to_crosstab(...) apop_varad_link(apop_db_to_crosstab, __VA_ARGS__)
#endif


//From array
#ifdef APOP_NO_VARIADIC
 gsl_vector * apop_array_to_vector(double *in, int size) ;
#else
 gsl_vector * apop_array_to_vector_base(double *in, int size) ;
 apop_varad_declare(gsl_vector *, apop_array_to_vector, double *in; int size);
#define apop_array_to_vector(...) apop_varad_link(apop_array_to_vector, __VA_ARGS__)
#endif

/** \cond doxy_ignore */   //Deprecated
#define apop_text_add apop_text_set
#define apop_line_to_vector apop_array_to_vector
/** \endcond */

//From text
#ifdef APOP_NO_VARIADIC
 apop_data * apop_text_to_data(char const *text_file, int has_row_names, int has_col_names, int const *field_ends, char const *delimiters) ;
#else
 apop_data * apop_text_to_data_base(char const *text_file, int has_row_names, int has_col_names, int const *field_ends, char const *delimiters) ;
 apop_varad_declare(apop_data *, apop_text_to_data, char const *text_file; int has_row_names; int has_col_names; int const *field_ends; char const *delimiters);
#define apop_text_to_data(...) apop_varad_link(apop_text_to_data, __VA_ARGS__)
#endif

#ifdef APOP_NO_VARIADIC
 int apop_text_to_db(char const *text_file, char *tabname, int has_row_names, int has_col_names, char **field_names, int const *field_ends, apop_data *field_params, char *table_params, char const *delimiters, char if_table_exists) ;
#else
 int apop_text_to_db_base(char const *text_file, char *tabname, int has_row_names, int has_col_names, char **field_names, int const *field_ends, apop_data *field_params, char *table_params, char const *delimiters, char if_table_exists) ;
 apop_varad_declare(int, apop_text_to_db, char const *text_file; char *tabname; int has_row_names; int has_col_names; char **field_names; int const *field_ends; apop_data *field_params; char *table_params; char const *delimiters; char if_table_exists);
#define apop_text_to_db(...) apop_varad_link(apop_text_to_db, __VA_ARGS__)
#endif


//rank data
apop_data *apop_data_rank_expand (apop_data *in);
#ifdef APOP_NO_VARIADIC
 apop_data *apop_data_rank_compress (apop_data *in, int min_bins) ;
#else
 apop_data * apop_data_rank_compress_base(apop_data *in, int min_bins) ;
 apop_varad_declare(apop_data *, apop_data_rank_compress, apop_data *in; int min_bins);
#define apop_data_rank_compress(...) apop_varad_link(apop_data_rank_compress, __VA_ARGS__)
#endif


//From crosstabs
void apop_crosstab_to_db(apop_data *in, char *tabname, char *row_col_name, 
						char *col_col_name, char *data_col_name);

//packing data into a vector
#ifdef APOP_NO_VARIADIC
 gsl_vector * apop_data_pack(const apop_data *in, gsl_vector *out, char more_pages, char use_info_pages) ;
#else
 gsl_vector * apop_data_pack_base(const apop_data *in, gsl_vector *out, char more_pages, char use_info_pages) ;
 apop_varad_declare(gsl_vector *, apop_data_pack, const apop_data *in; gsl_vector *out; char more_pages; char use_info_pages);
#define apop_data_pack(...) apop_varad_link(apop_data_pack, __VA_ARGS__)
#endif

#ifdef APOP_NO_VARIADIC
 void apop_data_unpack(const gsl_vector *in, apop_data *d, char use_info_pages) ;
#else
 void apop_data_unpack_base(const gsl_vector *in, apop_data *d, char use_info_pages) ;
 apop_varad_declare(void, apop_data_unpack, const gsl_vector *in; apop_data *d; char use_info_pages);
#define apop_data_unpack(...) apop_varad_link(apop_data_unpack, __VA_ARGS__)
#endif


#define apop_vector_fill(avfin, ...) apop_vector_fill_base((avfin), (double []) {__VA_ARGS__})
#define apop_data_fill(adfin, ...) apop_data_fill_base((adfin), (double []) {__VA_ARGS__})
#define apop_text_fill(dataset, ...)   apop_text_fill_base((dataset), (char* []) {__VA_ARGS__, NULL})
#define apop_data_falloc(sizes, ...) apop_data_fill(apop_data_alloc sizes, __VA_ARGS__)
    
apop_data *apop_data_fill_base(apop_data *in, double []);
gsl_vector *apop_vector_fill_base(gsl_vector *in, double []);
apop_data *apop_text_fill_base(apop_data *data, char* text[]);

            //// Models and model support functions

extern apop_model *apop_beta;
extern apop_model *apop_bernoulli;
extern apop_model *apop_binomial;
extern apop_model *apop_chi_squared;
extern apop_model *apop_dirichlet;
extern apop_model *apop_exponential;
extern apop_model *apop_f_distribution;
extern apop_model *apop_gamma;
extern apop_model *apop_improper_uniform;
extern apop_model *apop_iv;
extern apop_model *apop_kernel_density;
extern apop_model *apop_loess;
extern apop_model *apop_logit;
extern apop_model *apop_lognormal;
extern apop_model *apop_multinomial;
extern apop_model *apop_multivariate_normal;
extern apop_model *apop_normal;
extern apop_model *apop_ols;
extern apop_model *apop_pmf;
extern apop_model *apop_poisson;
extern apop_model *apop_probit;
extern apop_model *apop_t_distribution;
extern apop_model *apop_uniform;
//extern apop_model *apop_wishart;
extern apop_model *apop_wls;
extern apop_model *apop_yule;
extern apop_model *apop_zipf;

//model transformations
extern apop_model *apop_coordinate_transform;
extern apop_model *apop_composition;
extern apop_model *apop_dconstrain;
extern apop_model *apop_mixture;
extern apop_model *apop_cross;

/** Alias for the \ref apop_normal distribution, qv. */
#define apop_gaussian apop_normal
#define apop_OLS apop_ols
#define apop_PMF apop_pmf
#define apop_F_distribution apop_f_distribution
#define apop_IV apop_iv


void apop_model_free (apop_model * free_me);
#ifdef APOP_NO_VARIADIC
 void apop_model_print (apop_model * model, FILE *output_pipe) ;
#else
 void apop_model_print_base(apop_model * model, FILE *output_pipe) ;
 apop_varad_declare(void, apop_model_print, apop_model * model; FILE *output_pipe);
#define apop_model_print(...) apop_varad_link(apop_model_print, __VA_ARGS__)
#endif

void apop_model_show (apop_model * print_me); //deprecated
apop_model * apop_model_copy(apop_model *in); //in apop_model.c
apop_model * apop_model_clear(apop_data * data, apop_model *model);

apop_model * apop_estimate(apop_data *d, apop_model *m);
void apop_score(apop_data *d, gsl_vector *out, apop_model *m);
double apop_log_likelihood(apop_data *d, apop_model *m);
double apop_p(apop_data *d, apop_model *m);
double apop_cdf(apop_data *d, apop_model *m);
int apop_draw(double *out, gsl_rng *r, apop_model *m);
void apop_prep(apop_data *d, apop_model *m);
apop_model *apop_parameter_model(apop_data *d, apop_model *m);
apop_data * apop_predict(apop_data *d, apop_model *m);

apop_model *apop_beta_from_mean_var(double m, double v); //in apop_beta.c

#define apop_model_set_parameters(in, ...) apop_model_set_parameters_base((in), (double []) {__VA_ARGS__})
apop_model *apop_model_set_parameters_base(apop_model *in, double ap[]);

//apop_mixture.c
/** Produce a model as a linear combination of other models. See the documentation for the \ref apop_mixture model. 

\param ... A list of models, either all parameterized or all unparameterized. See
examples in the \ref apop_mixture documentation.
*/
#define apop_model_mixture(...) apop_model_mixture_base((apop_model *[]){__VA_ARGS__, NULL})
apop_model *apop_model_mixture_base(apop_model **inlist);

//transform/apop_cross.c.
apop_model *apop_model_cross_base(apop_model *mlist[]);
#define apop_model_cross(...) apop_model_cross_base((apop_model *[]){__VA_ARGS__, NULL})

        ////More functions

    //The variadic versions, with lots of options to input extra parameters to the
    //function being mapped/applied
#ifdef APOP_NO_VARIADIC
 apop_data * apop_map(apop_data *in, double (*fn_d)(double), double (*fn_v)(gsl_vector*),
                double (*fn_r)(apop_data *), double (*fn_dp)(double, void *), double (*fn_vp)(gsl_vector*, void *),
                double (*fn_rp)(apop_data *, void *), double (*fn_dpi)(double, void *, int),
                double (*fn_vpi)(gsl_vector*, void *, int), double (*fn_rpi)(apop_data*, void *, int),
                double (*fn_di)(double, int), double (*fn_vi)(gsl_vector*, int), double (*fn_ri)(apop_data*, int),
                void *param, int inplace, char part, int all_pages) ;
#else
 apop_data * apop_map_base(apop_data *in, double (*fn_d)(double), double (*fn_v)(gsl_vector*),
                double (*fn_r)(apop_data *), double (*fn_dp)(double, void *), double (*fn_vp)(gsl_vector*, void *),
                double (*fn_rp)(apop_data *, void *), double (*fn_dpi)(double, void *, int),
                double (*fn_vpi)(gsl_vector*, void *, int), double (*fn_rpi)(apop_data*, void *, int),
                double (*fn_di)(double, int), double (*fn_vi)(gsl_vector*, int), double (*fn_ri)(apop_data*, int),
                void *param, int inplace, char part, int all_pages) ;
 apop_varad_declare(apop_data *, apop_map, apop_data *in; double (*fn_d)(double); double (*fn_v)(gsl_vector*);
                double (*fn_r)(apop_data *); double (*fn_dp)(double, void *); double (*fn_vp)(gsl_vector*, void *);
                double (*fn_rp)(apop_data *, void *); double (*fn_dpi)(double, void *, int);
                double (*fn_vpi)(gsl_vector*, void *, int); double (*fn_rpi)(apop_data*, void *, int);
                double (*fn_di)(double, int); double (*fn_vi)(gsl_vector*, int); double (*fn_ri)(apop_data*, int);
                void *param; int inplace; char part; int all_pages);
#define apop_map(...) apop_varad_link(apop_map, __VA_ARGS__)
#endif

#ifdef APOP_NO_VARIADIC
 double apop_map_sum(apop_data *in, double (*fn_d)(double), double (*fn_v)(gsl_vector*),
                double (*fn_r)(apop_data *), double (*fn_dp)(double, void *), double (*fn_vp)(gsl_vector*, void *),
                double (*fn_rp)(apop_data *, void *), double (*fn_dpi)(double, void *, int),
                double (*fn_vpi)(gsl_vector*, void *, int), double (*fn_rpi)(apop_data*, void *, int),
                double (*fn_di)(double, int), double (*fn_vi)(gsl_vector*, int), double (*fn_ri)(apop_data*, int),
                void *param, char part, int all_pages) ;
#else
 double apop_map_sum_base(apop_data *in, double (*fn_d)(double), double (*fn_v)(gsl_vector*),
                double (*fn_r)(apop_data *), double (*fn_dp)(double, void *), double (*fn_vp)(gsl_vector*, void *),
                double (*fn_rp)(apop_data *, void *), double (*fn_dpi)(double, void *, int),
                double (*fn_vpi)(gsl_vector*, void *, int), double (*fn_rpi)(apop_data*, void *, int),
                double (*fn_di)(double, int), double (*fn_vi)(gsl_vector*, int), double (*fn_ri)(apop_data*, int),
                void *param, char part, int all_pages) ;
 apop_varad_declare(double, apop_map_sum, apop_data *in; double (*fn_d)(double); double (*fn_v)(gsl_vector*);
                double (*fn_r)(apop_data *); double (*fn_dp)(double, void *); double (*fn_vp)(gsl_vector*, void *);
                double (*fn_rp)(apop_data *, void *); double (*fn_dpi)(double, void *, int);
                double (*fn_vpi)(gsl_vector*, void *, int); double (*fn_rpi)(apop_data*, void *, int);
                double (*fn_di)(double, int); double (*fn_vi)(gsl_vector*, int); double (*fn_ri)(apop_data*, int);
                void *param; char part; int all_pages);
#define apop_map_sum(...) apop_varad_link(apop_map_sum, __VA_ARGS__)
#endif


    //the specific-to-a-type versions, quicker and easier when appropriate.
gsl_vector *apop_matrix_map(const gsl_matrix *m, double (*fn)(gsl_vector*));
gsl_vector *apop_vector_map(const gsl_vector *v, double (*fn)(double));
void apop_matrix_apply(gsl_matrix *m, void (*fn)(gsl_vector*));
void apop_vector_apply(gsl_vector *v, void (*fn)(double*));
gsl_matrix * apop_matrix_map_all(const gsl_matrix *in, double (*fn)(double));
void apop_matrix_apply_all(gsl_matrix *in, void (*fn)(double *));

double apop_vector_map_sum(const gsl_vector *in, double(*fn)(double));
double apop_matrix_map_sum(const gsl_matrix *in, double (*fn)(gsl_vector*));
double apop_matrix_map_all_sum(const gsl_matrix *in, double (*fn)(double));


        // Some output routines
#ifdef APOP_NO_VARIADIC
 void apop_matrix_print(const gsl_matrix *data, char const *output_name, FILE *output_pipe, char output_type, char output_append) ;
#else
 void apop_matrix_print_base(const gsl_matrix *data, char const *output_name, FILE *output_pipe, char output_type, char output_append) ;
 apop_varad_declare(void, apop_matrix_print, const gsl_matrix *data; char const *output_name; FILE *output_pipe; char output_type; char output_append);
#define apop_matrix_print(...) apop_varad_link(apop_matrix_print, __VA_ARGS__)
#endif

#ifdef APOP_NO_VARIADIC
 void apop_vector_print(gsl_vector *data, char const *output_name, FILE *output_pipe, char output_type, char output_append) ;
#else
 void apop_vector_print_base(gsl_vector *data, char const *output_name, FILE *output_pipe, char output_type, char output_append) ;
 apop_varad_declare(void, apop_vector_print, gsl_vector *data; char const *output_name; FILE *output_pipe; char output_type; char output_append);
#define apop_vector_print(...) apop_varad_link(apop_vector_print, __VA_ARGS__)
#endif

#ifdef APOP_NO_VARIADIC
 void apop_data_print(const apop_data *data, char const *output_name, FILE *output_pipe, char output_type, char output_append) ;
#else
 void apop_data_print_base(const apop_data *data, char const *output_name, FILE *output_pipe, char output_type, char output_append) ;
 apop_varad_declare(void, apop_data_print, const apop_data *data; char const *output_name; FILE *output_pipe; char output_type; char output_append);
#define apop_data_print(...) apop_varad_link(apop_data_print, __VA_ARGS__)
#endif


void apop_matrix_show(const gsl_matrix *data);
void apop_vector_show(const gsl_vector *data);
void apop_data_show(const apop_data *data);


        //statistics
#ifdef APOP_NO_VARIADIC
 double apop_vector_mean(gsl_vector const *v, gsl_vector const *weights);
#else
 double apop_vector_mean_base(gsl_vector const *v, gsl_vector const *weights);
 apop_varad_declare(double, apop_vector_mean, gsl_vector const *v; gsl_vector const *weights);
#define apop_vector_mean(...) apop_varad_link(apop_vector_mean, __VA_ARGS__)
#endif

#ifdef APOP_NO_VARIADIC
 double apop_vector_var(gsl_vector const *v, gsl_vector const *weights);
#else
 double apop_vector_var_base(gsl_vector const *v, gsl_vector const *weights);
 apop_varad_declare(double, apop_vector_var, gsl_vector const *v; gsl_vector const *weights);
#define apop_vector_var(...) apop_varad_link(apop_vector_var, __VA_ARGS__)
#endif

#ifdef APOP_NO_VARIADIC
 double apop_vector_skew_pop(gsl_vector const *v, gsl_vector const *weights);
#else
 double apop_vector_skew_pop_base(gsl_vector const *v, gsl_vector const *weights);
 apop_varad_declare(double, apop_vector_skew_pop, gsl_vector const *v; gsl_vector const *weights);
#define apop_vector_skew_pop(...) apop_varad_link(apop_vector_skew_pop, __VA_ARGS__)
#endif

#ifdef APOP_NO_VARIADIC
 double apop_vector_kurtosis_pop(gsl_vector const *v, gsl_vector const *weights);
#else
 double apop_vector_kurtosis_pop_base(gsl_vector const *v, gsl_vector const *weights);
 apop_varad_declare(double, apop_vector_kurtosis_pop, gsl_vector const *v; gsl_vector const *weights);
#define apop_vector_kurtosis_pop(...) apop_varad_link(apop_vector_kurtosis_pop, __VA_ARGS__)
#endif

#ifdef APOP_NO_VARIADIC
 double apop_vector_cov(gsl_vector const *v1, gsl_vector const *v2,
                                         gsl_vector const *weights);
#else
 double apop_vector_cov_base(gsl_vector const *v1, gsl_vector const *v2,
                                         gsl_vector const *weights);
 apop_varad_declare(double, apop_vector_cov, gsl_vector const *v1; gsl_vector const *v2;
                                         gsl_vector const *weights);
#define apop_vector_cov(...) apop_varad_link(apop_vector_cov, __VA_ARGS__)
#endif


#ifdef APOP_NO_VARIADIC
 double apop_vector_distance(const gsl_vector *ina, const gsl_vector *inb, const char metric, const double norm) ;
#else
 double apop_vector_distance_base(const gsl_vector *ina, const gsl_vector *inb, const char metric, const double norm) ;
 apop_varad_declare(double, apop_vector_distance, const gsl_vector *ina; const gsl_vector *inb; const char metric; const double norm);
#define apop_vector_distance(...) apop_varad_link(apop_vector_distance, __VA_ARGS__)
#endif


#ifdef APOP_NO_VARIADIC
 void apop_vector_normalize(gsl_vector *in, gsl_vector **out, const char normalization_type) ;
#else
 void apop_vector_normalize_base(gsl_vector *in, gsl_vector **out, const char normalization_type) ;
 apop_varad_declare(void, apop_vector_normalize, gsl_vector *in; gsl_vector **out; const char normalization_type);
#define apop_vector_normalize(...) apop_varad_link(apop_vector_normalize, __VA_ARGS__)
#endif


apop_data * apop_data_covariance(const apop_data *in);
apop_data * apop_data_correlation(const apop_data *in);
long double apop_vector_entropy(gsl_vector *in);
long double apop_matrix_sum(const gsl_matrix *m);
double apop_matrix_mean(const gsl_matrix *data);
void apop_matrix_mean_and_var(const gsl_matrix *data, double *mean, double *var);
apop_data * apop_data_summarize(apop_data *data);
#ifdef APOP_NO_VARIADIC
 double * apop_vector_percentiles(gsl_vector *data, char rounding)  ;
#else
 double * apop_vector_percentiles_base(gsl_vector *data, char rounding)  ;
 apop_varad_declare(double *, apop_vector_percentiles, gsl_vector *data; char rounding);
#define apop_vector_percentiles(...) apop_varad_link(apop_vector_percentiles, __VA_ARGS__)
#endif


apop_data *apop_test_fisher_exact(apop_data *intab); //in apop_fisher.c

//from apop_t_f_chi.c:
#ifdef APOP_NO_VARIADIC
 int apop_matrix_is_positive_semidefinite(gsl_matrix *m, char semi) ;
#else
 int apop_matrix_is_positive_semidefinite_base(gsl_matrix *m, char semi) ;
 apop_varad_declare(int, apop_matrix_is_positive_semidefinite, gsl_matrix *m; char semi);
#define apop_matrix_is_positive_semidefinite(...) apop_varad_link(apop_matrix_is_positive_semidefinite, __VA_ARGS__)
#endif

double apop_matrix_to_positive_semidefinite(gsl_matrix *m);
long double apop_multivariate_gamma(double a, int p);
long double apop_multivariate_lngamma(double a, int p);

//apop_tests.c
apop_data *	apop_t_test(gsl_vector *a, gsl_vector *b);
apop_data *	apop_paired_t_test(gsl_vector *a, gsl_vector *b);
#ifdef APOP_NO_VARIADIC
 apop_data* apop_anova(char *table, char *data, char *grouping1, char *grouping2) ;
#else
 apop_data* apop_anova_base(char *table, char *data, char *grouping1, char *grouping2) ;
 apop_varad_declare(apop_data*, apop_anova, char *table; char *data; char *grouping1; char *grouping2);
#define apop_anova(...) apop_varad_link(apop_anova, __VA_ARGS__)
#endif

#define apop_ANOVA apop_anova
#ifdef APOP_NO_VARIADIC
 apop_data * apop_f_test (apop_model *est, apop_data *contrast) ;
#else
 apop_data * apop_f_test_base(apop_model *est, apop_data *contrast) ;
 apop_varad_declare(apop_data *, apop_f_test, apop_model *est; apop_data *contrast);
#define apop_f_test(...) apop_varad_link(apop_f_test, __VA_ARGS__)
#endif

#define apop_F_test apop_f_test

//from the regression code:
#define apop_estimate_r_squared(in) apop_estimate_coefficient_of_determination(in)

apop_data * apop_text_unique_elements(const apop_data *d, size_t col);
gsl_vector * apop_vector_unique_elements(const gsl_vector *v);
#ifdef APOP_NO_VARIADIC
 apop_data * apop_data_to_factors(apop_data *data, char intype, int incol, int outcol) ;
#else
 apop_data * apop_data_to_factors_base(apop_data *data, char intype, int incol, int outcol) ;
 apop_varad_declare(apop_data *, apop_data_to_factors, apop_data *data; char intype; int incol; int outcol);
#define apop_data_to_factors(...) apop_varad_link(apop_data_to_factors, __VA_ARGS__)
#endif

#ifdef APOP_NO_VARIADIC
 apop_data * apop_data_get_factor_names(apop_data *data, int col, char type) ;
#else
 apop_data * apop_data_get_factor_names_base(apop_data *data, int col, char type) ;
 apop_varad_declare(apop_data *, apop_data_get_factor_names, apop_data *data; int col; char type);
#define apop_data_get_factor_names(...) apop_varad_link(apop_data_get_factor_names, __VA_ARGS__)
#endif


#ifdef APOP_NO_VARIADIC
 apop_data * apop_data_to_dummies(apop_data *d, int col, char type, int keep_first, char append, char remove) ;
#else
 apop_data * apop_data_to_dummies_base(apop_data *d, int col, char type, int keep_first, char append, char remove) ;
 apop_varad_declare(apop_data *, apop_data_to_dummies, apop_data *d; int col; char type; int keep_first; char append; char remove);
#define apop_data_to_dummies(...) apop_varad_link(apop_data_to_dummies, __VA_ARGS__)
#endif


#ifdef APOP_NO_VARIADIC
 long double apop_model_entropy(apop_model *in, int draws) ;
#else
 long double apop_model_entropy_base(apop_model *in, int draws) ;
 apop_varad_declare(long double, apop_model_entropy, apop_model *in; int draws);
#define apop_model_entropy(...) apop_varad_link(apop_model_entropy, __VA_ARGS__)
#endif

#ifdef APOP_NO_VARIADIC
 long double apop_kl_divergence(apop_model *from, apop_model *to, int draw_ct, gsl_rng *rng) ;
#else
 long double apop_kl_divergence_base(apop_model *from, apop_model *to, int draw_ct, gsl_rng *rng) ;
 apop_varad_declare(long double, apop_kl_divergence, apop_model *from; apop_model *to; int draw_ct; gsl_rng *rng);
#define apop_kl_divergence(...) apop_varad_link(apop_kl_divergence, __VA_ARGS__)
#endif


apop_data *apop_estimate_coefficient_of_determination (apop_model *);
void apop_estimate_parameter_tests (apop_model *est);

//Bootstrapping & RNG
apop_data * apop_jackknife_cov(apop_data *data, apop_model *model);
#ifdef APOP_NO_VARIADIC
 apop_data * apop_bootstrap_cov(apop_data *data, apop_model *model, gsl_rng* rng, int iterations, char keep_boots, char ignore_nans, apop_data **boot_store) ;
#else
 apop_data * apop_bootstrap_cov_base(apop_data *data, apop_model *model, gsl_rng* rng, int iterations, char keep_boots, char ignore_nans, apop_data **boot_store) ;
 apop_varad_declare(apop_data *, apop_bootstrap_cov, apop_data *data; apop_model *model; gsl_rng* rng; int iterations; char keep_boots; char ignore_nans; apop_data **boot_store);
#define apop_bootstrap_cov(...) apop_varad_link(apop_bootstrap_cov, __VA_ARGS__)
#endif

gsl_rng *apop_rng_alloc(int seed);
double apop_rng_GHgB3(gsl_rng * r, double* a); //in apop_asst.c

#define apop_rng_get_thread(thread_in) apop_rng_get_thread_base(#thread_in[0]=='\0' ? -1: (thread_in+0))
gsl_rng *apop_rng_get_thread_base(int thread);

int apop_arms_draw (double *out, gsl_rng *r, apop_model *m);


    // maximum likelihod estimation related functions

#ifdef APOP_NO_VARIADIC
 gsl_vector * apop_numerical_gradient(apop_data * data, apop_model* model, double delta) ;
#else
 gsl_vector * apop_numerical_gradient_base(apop_data * data, apop_model* model, double delta) ;
 apop_varad_declare(gsl_vector *, apop_numerical_gradient, apop_data * data; apop_model* model; double delta);
#define apop_numerical_gradient(...) apop_varad_link(apop_numerical_gradient, __VA_ARGS__)
#endif

#ifdef APOP_NO_VARIADIC
 apop_data * apop_model_hessian(apop_data * data, apop_model *model, double delta) ;
#else
 apop_data * apop_model_hessian_base(apop_data * data, apop_model *model, double delta) ;
 apop_varad_declare(apop_data *, apop_model_hessian, apop_data * data; apop_model *model; double delta);
#define apop_model_hessian(...) apop_varad_link(apop_model_hessian, __VA_ARGS__)
#endif

#ifdef APOP_NO_VARIADIC
 apop_data * apop_model_numerical_covariance(apop_data * data, apop_model *model, double delta) ;
#else
 apop_data * apop_model_numerical_covariance_base(apop_data * data, apop_model *model, double delta) ;
 apop_varad_declare(apop_data *, apop_model_numerical_covariance, apop_data * data; apop_model *model; double delta);
#define apop_model_numerical_covariance(...) apop_varad_link(apop_model_numerical_covariance, __VA_ARGS__)
#endif


void apop_maximum_likelihood(apop_data * data, apop_model *dist);

#ifdef APOP_NO_VARIADIC
 apop_model * apop_estimate_restart (apop_model *e, apop_model *copy, char * starting_pt, double boundary) ;
#else
 apop_model * apop_estimate_restart_base(apop_model *e, apop_model *copy, char * starting_pt, double boundary) ;
 apop_varad_declare(apop_model *, apop_estimate_restart, apop_model *e; apop_model *copy; char * starting_pt; double boundary);
#define apop_estimate_restart(...) apop_varad_link(apop_estimate_restart, __VA_ARGS__)
#endif


//in apop_linear_constraint.c
#ifdef APOP_NO_VARIADIC
 long double  apop_linear_constraint(gsl_vector *beta, apop_data * constraint, double margin) ;
#else
 long double apop_linear_constraint_base(gsl_vector *beta, apop_data * constraint, double margin) ;
 apop_varad_declare(long double, apop_linear_constraint, gsl_vector *beta; apop_data * constraint; double margin);
#define apop_linear_constraint(...) apop_varad_link(apop_linear_constraint, __VA_ARGS__)
#endif


//in apop_model_fix_params.c
apop_model * apop_model_fix_params(apop_model *model_in);
apop_model * apop_model_fix_params_get_base(apop_model *model_in);



            //////vtables
/** \cond doxy_ignore */

/* This declares the vtable macros for each procedure that uses the mechanism.

--We want to have type-checking on the functions put into the vtables. Type checking
happens only with functions, not macros, so we need a type_check function for every
vtable.

--Only once in your codebase, you'll need to #define Declare_type_checking_fns to
actually define the type checking function. Everywhere else, the function is merely
declared.

--All other uses point to having a macro, such as using __VA_ARGS__ to allow any sort
of inputs to the hash.

--We want to have such a macro for every vtable. That means that we need a macro
to write macros. We can't do that with C macros, so this file uses m4 macros to
generate C macros.

--After the m4 definition of make_vtab_fns, each new vtable requires a typedef, a hash
definition, and a call to make_vtab_fns to do the rest.
*/


int apop_vtable_add(char const *tabname, void *fn_in, unsigned long hash);
void *apop_vtable_get(char const *tabname, unsigned long hash);
int apop_vtable_drop(char const *tabname, unsigned long hash);

typedef apop_model *(*apop_update_type)(apop_data *, apop_model* , apop_model*);
#define apop_update_hash(m1, m2) (          \
           ((m1)->log_likelihood ? (size_t)(m1)->log_likelihood : \
            (m1)->p              ? (size_t)(m1)->p*33 : \
            (m1)->draw           ? (size_t)(m1)->draw*33*27 \
                                 : 33*27*19) \
          +((m2)->log_likelihood ? (size_t)(m2)->log_likelihood : \
            (m2)->p              ? (size_t)(m2)->p*33 : \
            (m2)->draw           ? (size_t)(m2)->draw*33*27 \
                                 : 33*27*19 \
           ) * 37)
#ifdef Declare_type_checking_fns
void apop_update_type_check(apop_update_type in){ };
#else
void apop_update_type_check(apop_update_type in);
#endif
#define apop_update_vtable_add(fn, ...) apop_update_type_check(fn), apop_vtable_add("apop_update", fn, apop_update_hash(__VA_ARGS__))
#define apop_update_vtable_get(...) apop_vtable_get("apop_update", apop_update_hash(__VA_ARGS__))
#define apop_update_vtable_drop(...) apop_vtable_drop("apop_update", apop_update_hash(__VA_ARGS__))

typedef long double (*apop_entropy_type)(apop_model *model);
#define apop_entropy_hash(m1) ((size_t)(m1)->log_likelihood + 33 * (size_t)((m1)->p) + 27*(size_t)((m1)->draw))
#ifdef Declare_type_checking_fns
void apop_entropy_type_check(apop_entropy_type in){ };
#else
void apop_entropy_type_check(apop_entropy_type in);
#endif
#define apop_entropy_vtable_add(fn, ...) apop_entropy_type_check(fn), apop_vtable_add("apop_entropy", fn, apop_entropy_hash(__VA_ARGS__))
#define apop_entropy_vtable_get(...) apop_vtable_get("apop_entropy", apop_entropy_hash(__VA_ARGS__))
#define apop_entropy_vtable_drop(...) apop_vtable_drop("apop_entropy", apop_entropy_hash(__VA_ARGS__))

typedef void (*apop_score_type)(apop_data *d, gsl_vector *gradient, apop_model *params);
#define apop_score_hash(m1) ((size_t)((m1)->log_likelihood ? (m1)->log_likelihood : (m1)->p))
#ifdef Declare_type_checking_fns
void apop_score_type_check(apop_score_type in){ };
#else
void apop_score_type_check(apop_score_type in);
#endif
#define apop_score_vtable_add(fn, ...) apop_score_type_check(fn), apop_vtable_add("apop_score", fn, apop_score_hash(__VA_ARGS__))
#define apop_score_vtable_get(...) apop_vtable_get("apop_score", apop_score_hash(__VA_ARGS__))
#define apop_score_vtable_drop(...) apop_vtable_drop("apop_score", apop_score_hash(__VA_ARGS__))

typedef apop_model* (*apop_parameter_model_type)(apop_data *, apop_model *);
#define apop_parameter_model_hash(m1) ((size_t)((m1)->log_likelihood ? (m1)->log_likelihood : (m1)->p)*33 + (m1)->estimate ? (size_t)(m1)->estimate: 27)
#ifdef Declare_type_checking_fns
void apop_parameter_model_type_check(apop_parameter_model_type in){ };
#else
void apop_parameter_model_type_check(apop_parameter_model_type in);
#endif
#define apop_parameter_model_vtable_add(fn, ...) apop_parameter_model_type_check(fn), apop_vtable_add("apop_parameter_model", fn, apop_parameter_model_hash(__VA_ARGS__))
#define apop_parameter_model_vtable_get(...) apop_vtable_get("apop_parameter_model", apop_parameter_model_hash(__VA_ARGS__))
#define apop_parameter_model_vtable_drop(...) apop_vtable_drop("apop_parameter_model", apop_parameter_model_hash(__VA_ARGS__))

typedef apop_data * (*apop_predict_type)(apop_data *d, apop_model *params);
#define apop_predict_hash(m1) ((size_t)((m1)->log_likelihood ? (m1)->log_likelihood : (m1)->p)*33 + (m1)->estimate ? (size_t)(m1)->estimate: 27)
#ifdef Declare_type_checking_fns
void apop_predict_type_check(apop_predict_type in){ };
#else
void apop_predict_type_check(apop_predict_type in);
#endif
#define apop_predict_vtable_add(fn, ...) apop_predict_type_check(fn), apop_vtable_add("apop_predict", fn, apop_predict_hash(__VA_ARGS__))
#define apop_predict_vtable_get(...) apop_vtable_get("apop_predict", apop_predict_hash(__VA_ARGS__))
#define apop_predict_vtable_drop(...) apop_vtable_drop("apop_predict", apop_predict_hash(__VA_ARGS__))

typedef void (*apop_model_print_type)(apop_model *params, FILE *out);
#define apop_model_print_hash(m1) ((m1)->log_likelihood ? (size_t)(m1)->log_likelihood : \
            (m1)->p ? (size_t)(m1)->p*33 : \
            (m1)->estimate ? (size_t)(m1)->estimate*33*33 : \
            (m1)->draw ? (size_t)(m1)->draw*33*27  : \
            (m1)->cdf ? (size_t)(m1)->cdf*27*27  \
            : 27)
#ifdef Declare_type_checking_fns
void apop_model_print_type_check(apop_model_print_type in){ };
#else
void apop_model_print_type_check(apop_model_print_type in);
#endif
#define apop_model_print_vtable_add(fn, ...) apop_model_print_type_check(fn), apop_vtable_add("apop_model_print", fn, apop_model_print_hash(__VA_ARGS__))
#define apop_model_print_vtable_get(...) apop_vtable_get("apop_model_print", apop_model_print_hash(__VA_ARGS__))
#define apop_model_print_vtable_drop(...) apop_vtable_drop("apop_model_print", apop_model_print_hash(__VA_ARGS__))

/** \endcond */ //End of Doxygen ignore.


        //////Asst

long double apop_generalized_harmonic(int N, double s) __attribute__ ((__pure__));

apop_data * apop_test_anova_independence(apop_data *d);
#define apop_test_ANOVA_independence(d) apop_test_anova_independence(d)

#ifdef APOP_NO_VARIADIC
 int apop_regex(const char *string, const char* regex, apop_data **substrings, const char use_case) ;
#else
 int apop_regex_base(const char *string, const char* regex, apop_data **substrings, const char use_case) ;
 apop_varad_declare(int, apop_regex, const char *string; const char* regex; apop_data **substrings; const char use_case);
#define apop_regex(...) apop_varad_link(apop_regex, __VA_ARGS__)
#endif


int apop_system(const char *fmt, ...) __attribute__ ((format (printf,1,2)));

//Histograms and PMFs
gsl_vector * apop_vector_moving_average(gsl_vector *, size_t);
apop_data * apop_histograms_test_goodness_of_fit(apop_model *h0, apop_model *h1);
apop_data * apop_test_kolmogorov(apop_model *m1, apop_model *m2);
apop_data *apop_data_pmf_compress(apop_data *in);
#ifdef APOP_NO_VARIADIC
 apop_data * apop_data_to_bins(apop_data const *indata, apop_data const *binspec, int bin_count, char close_top_bin) ;
#else
 apop_data * apop_data_to_bins_base(apop_data const *indata, apop_data const *binspec, int bin_count, char close_top_bin) ;
 apop_varad_declare(apop_data *, apop_data_to_bins, apop_data const *indata; apop_data const *binspec; int bin_count; char close_top_bin);
#define apop_data_to_bins(...) apop_varad_link(apop_data_to_bins, __VA_ARGS__)
#endif

#ifdef APOP_NO_VARIADIC
 apop_model * apop_model_to_pmf(apop_model *model, apop_data *binspec, long int draws, int bin_count) ;
#else
 apop_model * apop_model_to_pmf_base(apop_model *model, apop_data *binspec, long int draws, int bin_count) ;
 apop_varad_declare(apop_model *, apop_model_to_pmf, apop_model *model; apop_data *binspec; long int draws; int bin_count);
#define apop_model_to_pmf(...) apop_varad_link(apop_model_to_pmf, __VA_ARGS__)
#endif


//text conveniences
#ifdef APOP_NO_VARIADIC
 char* apop_text_paste(apop_data const*strings, char *between, char *before, char *after, char *between_cols, int (*prune)(apop_data* , int , int , void*), void* prune_parameter) ;
#else
 char* apop_text_paste_base(apop_data const*strings, char *between, char *before, char *after, char *between_cols, int (*prune)(apop_data* , int , int , void*), void* prune_parameter) ;
 apop_varad_declare(char*, apop_text_paste, apop_data const*strings; char *between; char *before; char *after; char *between_cols; int (*prune)(apop_data* , int , int , void*); void* prune_parameter);
#define apop_text_paste(...) apop_varad_link(apop_text_paste, __VA_ARGS__)
#endif

/** Notify the user of errors, warning, or debug info. 

writes to \ref apop_opts.log_file, which is a \c FILE handle. The default is \c stderr,
but use \c fopen to attach to a file.

 \param verbosity   At what verbosity level should the user be warned? E.g., if level==2, then print iff apop_opts.verbosity >= 2.
 \param ... The message to write to the log (presuming the verbosity level is high
enough). This can be a printf-style format with following arguments, 
e.g., <tt>apop_notify(0, "Beta is currently %g", beta)</tt>.
*/
#define Apop_notify(verbosity, ...) {\
    if (apop_opts.verbose != -1 && apop_opts.verbose >= verbosity) {  \
        if (!apop_opts.log_file) apop_opts.log_file = stderr; \
        fprintf(apop_opts.log_file, "%s: ", __func__); fprintf(apop_opts.log_file, __VA_ARGS__); fprintf(apop_opts.log_file, "\n");   \
        fflush(apop_opts.log_file); \
} }

/** \cond doxy_ignore */
#define Apop_maybe_abort(level) \
            {if ((apop_opts.verbose >= level && apop_opts.stop_on_warning == 'v') \
                 || (apop_opts.stop_on_warning=='w') ) \
                raise(SIGTRAP);}
/** \endcond */

/** Execute an action and print a message to the current \c FILE handle held by <tt>apop_opts.log_file</tt> (default: \c stderr).
 
\param test The expression that, if true, triggers the action.
\param onfail If the assertion fails, do this. E.g., <tt>out->error='x'; return GSL_NAN</tt>. Notice that it is OK to include several lines of semicolon-separated code here, but if you have a lot to do, the most readable option may be <tt>goto outro</tt>, plus an appropriately-labeled section at the end of your function.
\param level Print the warning message only if \ref apop_opts_type "apop_opts.verbose" is greater than or equal to this. Zero usually works, but for minor infractions use one, or for more verbose debugging output use 2.
\param ... The error message in printf form, plus any arguments to be inserted into the printf string. I'll provide the function name and a carriage return.

Some examples:

\code
//the typical case, stopping function execution:
Apop_stopif(isnan(x), return NAN, 0, "x is NAN; failing");

//Mark a flag, go to a cleanup step
Apop_stopif(x < 0, needs_cleanup=1; goto cleanup, 0, "x is %g; cleaning up and exiting.", x);

//Print a diagnostic iff <tt>apop_opts.verbose>=1</tt> and continue
Apop_stopif(x < 0,  , 1, "warning: x is %g.", x);
\endcode

\li If \c apop_opts.stop_on_warning is nonzero and not <tt>'v'</tt>, then a failed test halts via \c abort(), even if the <tt>apop_opts.verbose</tt> level is set so that the warning message doesn't print to screen. Use this when running via debugger.
\li If \c apop_opts.stop_on_warning is <tt>'v'</tt>, then a failed test halts via \c abort() iff the verbosity level is high enough to print the error.
*/
#define Apop_stopif(test, onfail, level, ...) do {\
     if (test) {  \
        Apop_notify(level,  __VA_ARGS__);   \
        Apop_maybe_abort(level)  \
        onfail;  \
    } } while(0)

#define apop_errorlevel -5

/** \cond doxy_ignore */
//For use in stopif, to return a blank apop_data set with an error attached.
#define apop_return_data_error(E) {apop_data *out=apop_data_alloc(); out->error='E'; return out;}

/* The Apop_stopif macro is currently favored, but there's a long history of prior
   error-handling setups. Consider all of the Assert... macros below to be deprecated.
*/
#define Apop_assert_c(test, returnval, level, ...) \
    Apop_stopif(!(test), return returnval, level, __VA_ARGS__)

#define Apop_assert(test, ...) Apop_assert_c((test), 0, apop_errorlevel, __VA_ARGS__)

//For things that return void. Transitional and deprecated at birth.
#define Apop_assert_n(test, ...) Apop_assert_c((test),  , apop_errorlevel, __VA_ARGS__)
#define Apop_assert_negone(test, ...) Apop_assert_c((test), -1, apop_errorlevel, __VA_ARGS__)
/** \endcond */ //End of Doxygen ignore.

//Missing data
#ifdef APOP_NO_VARIADIC
 apop_data * apop_data_listwise_delete(apop_data *d, char inplace) ;
#else
 apop_data * apop_data_listwise_delete_base(apop_data *d, char inplace) ;
 apop_varad_declare(apop_data *, apop_data_listwise_delete, apop_data *d; char inplace);
#define apop_data_listwise_delete(...) apop_varad_link(apop_data_listwise_delete, __VA_ARGS__)
#endif

apop_model * apop_ml_impute(apop_data *d, apop_model* meanvar);

#ifdef APOP_NO_VARIADIC
 apop_model *apop_model_metropolis(apop_data *d, gsl_rng* rng, apop_model *m);
#else
 apop_model * apop_model_metropolis_base(apop_data *d, gsl_rng* rng, apop_model *m);
 apop_varad_declare(apop_model *, apop_model_metropolis, apop_data *d; gsl_rng* rng; apop_model *m);
#define apop_model_metropolis(...) apop_varad_link(apop_model_metropolis, __VA_ARGS__)
#endif

#ifdef APOP_NO_VARIADIC
 apop_model * apop_update(apop_data *data, apop_model *prior, apop_model *likelihood, gsl_rng *rng) ;
#else
 apop_model * apop_update_base(apop_data *data, apop_model *prior, apop_model *likelihood, gsl_rng *rng) ;
 apop_varad_declare(apop_model *, apop_update, apop_data *data; apop_model *prior; apop_model *likelihood; gsl_rng *rng);
#define apop_update(...) apop_varad_link(apop_update, __VA_ARGS__)
#endif


#ifdef APOP_NO_VARIADIC
 double apop_test(double statistic, char *distribution, double p1, double p2, char tail) ;
#else
 double apop_test_base(double statistic, char *distribution, double p1, double p2, char tail) ;
 apop_varad_declare(double, apop_test, double statistic; char *distribution; double p1; double p2; char tail);
#define apop_test(...) apop_varad_link(apop_test, __VA_ARGS__)
#endif


//apop_sort.c
#ifdef APOP_NO_VARIADIC
 apop_data *apop_data_sort(apop_data *data, apop_data *sort_order, char asc, char inplace, double *col_order);
#else
 apop_data * apop_data_sort_base(apop_data *data, apop_data *sort_order, char asc, char inplace, double *col_order);
 apop_varad_declare(apop_data *, apop_data_sort, apop_data *data; apop_data *sort_order; char asc; char inplace; double *col_order);
#define apop_data_sort(...) apop_varad_link(apop_data_sort, __VA_ARGS__)
#endif


//raking
#ifdef APOP_NO_VARIADIC
 apop_data * apop_rake(char const *margin_table, char * const*var_list, 
                    int var_ct, char * const *contrasts, int contrast_ct, 
                    char const *structural_zeros, int max_iterations, double tolerance, 
                    char const *count_col, char const *init_table, 
                    char const *init_count_col, double nudge) ;
#else
 apop_data * apop_rake_base(char const *margin_table, char * const*var_list, 
                    int var_ct, char * const *contrasts, int contrast_ct, 
                    char const *structural_zeros, int max_iterations, double tolerance, 
                    char const *count_col, char const *init_table, 
                    char const *init_count_col, double nudge) ;
 apop_varad_declare(apop_data *, apop_rake, char const *margin_table; char * const*var_list; 
                    int var_ct; char * const *contrasts; int contrast_ct; 
                    char const *structural_zeros; int max_iterations; double tolerance; 
                    char const *count_col; char const *init_table; 
                    char const *init_count_col; double nudge);
#define apop_rake(...) apop_varad_link(apop_rake, __VA_ARGS__)
#endif



#include <gsl/gsl_cdf.h>
#include <gsl/gsl_blas.h>
#include <gsl/gsl_sf_log.h>
#include <gsl/gsl_sf_exp.h>
#include <gsl/gsl_linalg.h>
#include <gsl/gsl_sf_gamma.h>
#include <gsl/gsl_sf_psi.h>
#include <gsl/gsl_randist.h>
#include <gsl/gsl_histogram.h>
#include <gsl/gsl_statistics_double.h>


    //Some linear algebra utilities

double apop_det_and_inv(const gsl_matrix *in, gsl_matrix **out, int calc_det, int calc_inv);
#ifdef APOP_NO_VARIADIC
 apop_data * apop_dot(const apop_data *d1, const apop_data *d2, char form1, char form2) ;
#else
 apop_data * apop_dot_base(const apop_data *d1, const apop_data *d2, char form1, char form2) ;
 apop_varad_declare(apop_data *, apop_dot, const apop_data *d1; const apop_data *d2; char form1; char form2);
#define apop_dot(...) apop_varad_link(apop_dot, __VA_ARGS__)
#endif

#ifdef APOP_NO_VARIADIC
 int         apop_vector_bounded(const gsl_vector *in, long double max) ;
#else
 int apop_vector_bounded_base(const gsl_vector *in, long double max) ;
 apop_varad_declare(int, apop_vector_bounded, const gsl_vector *in; long double max);
#define apop_vector_bounded(...) apop_varad_link(apop_vector_bounded, __VA_ARGS__)
#endif

gsl_matrix * apop_matrix_inverse(const gsl_matrix *in) ;
double      apop_matrix_determinant(const gsl_matrix *in) ;
//apop_data*  apop_sv_decomposition(gsl_matrix *data, int dimensions_we_want);
#ifdef APOP_NO_VARIADIC
 apop_data *  apop_matrix_pca(gsl_matrix *data, int const dimensions_we_want) ;
#else
 apop_data * apop_matrix_pca_base(gsl_matrix *data, int const dimensions_we_want) ;
 apop_varad_declare(apop_data *, apop_matrix_pca, gsl_matrix *data; int const dimensions_we_want);
#define apop_matrix_pca(...) apop_varad_link(apop_matrix_pca, __VA_ARGS__)
#endif

#ifdef APOP_NO_VARIADIC
 gsl_vector * apop_vector_stack(gsl_vector *v1, gsl_vector const * v2, char inplace) ;
#else
 gsl_vector * apop_vector_stack_base(gsl_vector *v1, gsl_vector const * v2, char inplace) ;
 apop_varad_declare(gsl_vector *, apop_vector_stack, gsl_vector *v1; gsl_vector const * v2; char inplace);
#define apop_vector_stack(...) apop_varad_link(apop_vector_stack, __VA_ARGS__)
#endif

#ifdef APOP_NO_VARIADIC
 gsl_matrix * apop_matrix_stack(gsl_matrix *m1, gsl_matrix const * m2, char posn, char inplace) ;
#else
 gsl_matrix * apop_matrix_stack_base(gsl_matrix *m1, gsl_matrix const * m2, char posn, char inplace) ;
 apop_varad_declare(gsl_matrix *, apop_matrix_stack, gsl_matrix *m1; gsl_matrix const * m2; char posn; char inplace);
#define apop_matrix_stack(...) apop_varad_link(apop_matrix_stack, __VA_ARGS__)
#endif


void apop_vector_log(gsl_vector *v);
void apop_vector_log10(gsl_vector *v);
void apop_vector_exp(gsl_vector *v);

                ////Subsetting macros

/** \cond doxy_ignore */
/** These are all deprecated.*/
#define APOP_SUBMATRIX(m, srow, scol, nrows, ncols, o) gsl_matrix apop_mm_##o = gsl_matrix_submatrix((m), (srow), (scol), (nrows),(ncols)).matrix;\
gsl_matrix * o = &( apop_mm_##o );                                                  // Use \ref Apop_subm. 
#define Apop_submatrix APOP_SUBMATRIX

#define Apop_col_v(m, col, v) gsl_vector apop_vv_##v = ((col) == -1) ? (gsl_vector){} : gsl_matrix_column((m)->matrix, (col)).vector;\
gsl_vector * v = ((col)==-1) ? (m)->vector : &( apop_vv_##v );                      // Use \ref Apop_cv.

#define Apop_row_v(m, row, v) Apop_matrix_row((m)->matrix, row, v)                  // Use \ref Apop_rv.
#define Apop_rows(d, rownum, len, outd) apop_data *outd = Apop_rs(d, rownum, len)   // Use \ref Apop_rs.
#define Apop_row(d, row, outd) Apop_rows(d, row, 1, outd)                           // Use \ref Apop_r.
#define Apop_cols(d, colnum, len, outd) apop_data *outd =  Apop_cs(d, colnum, len); // Use \ref Apop_cs.
/** \endcond */ //End of Doxygen ignore.

/** \def Apop_row_tv(m, row_name, v)
 After this call, \c v will hold a \c gsl_vector view of an \ref apop_data set \c m. The view will consist only of the row with name \c row_name.
 Unlike \ref Apop_rv, the second argument is a row name, that I'll look up using \ref apop_name_find, and the third is the name of the view to be generated.
\see Apop_rs, Apop_r, Apop_rv, Apop_row_t, Apop_mrv
*/
#define Apop_row_tv(m, row, v) gsl_vector apop_vv_##v = gsl_matrix_row((m)->matrix, apop_name_find((m)->names, row, 'r')).vector;\
gsl_vector * v = &( apop_vv_##v );

/** \def Apop_col_tv(m, col_name, v)
After this call, \c v will hold a \c gsl_vector view of the \ref apop_data set \c m.
The view will consist only of the column with name \c col_name.
Unlike \ref Apop_cv, the second argument is a column name, that I'll look up using \ref apop_name_find, and the third is the name of the view to be generated.
\see Apop_cs, Apop_c, Apop_cv, Apop_col_t, Apop_mcv
*/
#define Apop_col_tv(m, col, v) gsl_vector apop_vv_##v = gsl_matrix_column((m)->matrix, apop_name_find((m)->names, col, 'c')).vector;\
gsl_vector * v = &( apop_vv_##v );

/** \def Apop_row_t(m, row_name, v)
 After this call, \c v will hold an \ref apop_data view of an \ref apop_data set \c m. The view will consist only of the row with name \c row_name.
 Unlike \ref Apop_r, the second argument is a row name, that I'll look up using \ref apop_name_find, and the third is the name of the view to be generated.
\see Apop_rs, Apop_r, Apop_rv, Apop_row_tv, Apop_mrv
*/
#define Apop_row_t(d, rowname, outd) int apop_row_##outd = apop_name_find((d)->names, rowname, 'r'); Apop_rows(d, apop_row_##outd, 1, outd)

/** \def Apop_col_t(m, col_name, v)
 After this call, \c v will hold a view of the \ref apop_data set \c m. The view will consist only of a \c gsl_vector view of the column of the \ref apop_data set \c m with name \c col_name.
 Unlike \ref Apop_c, the second argument is a column name, that I'll look up using \ref apop_name_find, and the third is the name of the view to be generated.
\see Apop_cs, Apop_c, Apop_cv, Apop_col_tv, Apop_mcv
*/
#define Apop_col_t(d, colname, outd) int apop_col_##outd = apop_name_find((d)->names, colname, 'c'); Apop_cols(d, apop_col_##outd, 1, outd)

// The above versions relied on gsl_views, which stick to C as of 1989 CE.
// Better to just create the views via designated initializers.


/** \def Apop_subm(data_to_view, srow, scol, nrows, ncols)
Generate a view of a submatrix within a \c gsl_matrix. Like \ref Apop_r, et al., the view is an automatically-allocated variable that is lost once the program flow leaves the scope in which it is declared.

\param data_to_view The root matrix
\param srow the first row (in the root matrix) of the top of the submatrix
\param scol the first column (in the root matrix) of the left edge of the submatrix
\param nrows number of rows in the submatrix
\param ncols number of columns in the submatrix
\return An automatically-allocated view of type \c gsl_matrix.
*/
#define Apop_subm(matrix_to_view, srow, scol, nrows, ncols)(                  \
        (!(matrix_to_view)                                                   \
            || (matrix_to_view)->size1 < (srow)+(nrows) || (srow) < 0        \
            || (matrix_to_view)->size2 < (scol)+(ncols) || (scol) < 0) ? NULL \
        : &(gsl_matrix){.size1=(nrows), .size2=(ncols),                         \
             .tda=(matrix_to_view)->tda,                                  \
             .data=gsl_matrix_ptr((matrix_to_view), (srow), (scol))}      \
        )

/** Get a vector view of a single row of a \ref gsl_matrix.

\param matrix_to_vew A \ref gsl_matrix.
\param row An integer giving the row to be viewed.
\return A \c gsl_vector view of the given row. The view is automatically allocated,
  and disappears as soon as the program leaves the scope in which it is declared.

See \ref apop_vector_correlation for an example of use.
\see Apop_r, Apop_rv
*/
#define Apop_mrv(matrix_to_view, row) Apop_rv(&(apop_data){.matrix=matrix_to_view}, row)

/** Get a vector view of a single column of a \ref gsl_matrix.

\param matrix_to_vew A \ref gsl_matrix.
\param row An integer giving the column to be viewed.
\return A \c gsl_vector view of the given column. The view is automatically allocated,
  and disappears as soon as the program leaves the scope in which it is declared.

\code 
gsl_matrix *m = apop_query_to_data("select col1, col2, col3 from data")->matrix;
printf("The correlation coefficient between columns two "
       "and three is %g.\n", apop_vector_correlation(Apop_mcv(m, 2), Apop_mcv(m, 3)));
\endcode 

\see Apop_r, Apop_cv
*/
#define Apop_mcv(matrix_to_view, col) Apop_cv(&(apop_data){.matrix=matrix_to_view}, col)

/** \def Apop_rv(d, row)
A macro to generate a temporary one-row view of the matrix in an \ref apop_data set \c d, pulling out only
row \c row. The view is a \c gsl_vector set.

\code
gsl_vector *v = Apop_rv(your_data, i);

for (int i=0; i< your_data->matrix->size1; i++)
    printf("Σ_%i = %g\n", i, apop_vector_sum(Apop_r(your_data, i)));
\endcode

The view is automatically allocated, and disappears as soon as the program leaves the scope in which it is declared.
\see Apop_r, Apop_rv, Apop_row_tv, Apop_row_t, Apop_mrv
*/
#define Apop_rv(data_to_view, row) (                                            \
        ((data_to_view) == NULL || (data_to_view)->matrix == NULL               \
            || (data_to_view)->matrix->size1 <= (row) || (row) < 0) ? NULL        \
        : &(gsl_vector){.size=(data_to_view)->matrix->size2,                    \
             .stride=1, .data=gsl_matrix_ptr((data_to_view)->matrix, (row), 0)} \
        )

/** \def Apop_cv(d, col)
A macro to generate a temporary one-column view of the matrix in an \ref apop_data
set \c d, pulling out only column \c col. The view is a \c gsl_vector set.

As usual, column -1 is the vector element of the \ref apop_data set.

\code
gsl_vector *v = Apop_cv(your_data, i);

for (int i=0; i< your_data->matrix->size2; i++)
    printf("Σ_%i = %g\n", i, apop_vector_sum(Apop_c(your_data, i)));
\endcode

The view is automatically allocated, and disappears as soon as the program leaves the
scope in which it is declared.

\see Apop_cs, Apop_c, Apop_col_tv, Apop_col_t, Apop_mcv
*/
#define Apop_cv(data_to_view, col) (                                           \
          !(data_to_view) ? NULL                                               \
        : (col)==-1       ? (data_to_view)->vector                             \
        : (!(data_to_view)->matrix                                             \
            || (data_to_view)->matrix->size2 <= (col) || ((int)(col)) < -1) ? NULL    \
        : &(gsl_vector){.size=(data_to_view)->matrix->size1,                   \
             .stride=(data_to_view)->matrix->tda, .data=gsl_matrix_ptr((data_to_view)->matrix, 0, (col))} \
        )

/** \cond doxy_ignore */
/* Not (yet) for public use. */
#define Apop_subvector(v, start, len) (                                          \
        ((v) == NULL || (v)->size < ((start)+(len)) || (start) < 0) ? NULL      \
        : &(gsl_vector){.size=(len), .stride=(v)->stride, .data=(v)->data+(start*(v)->stride)})
/** \endcond */

/** \def Apop_rs(d, row, len)
A macro to generate a temporary view of \ref apop_data set \c d pulling only certain rows, beginning at row \c row
and having height \c len. 

The view is automatically allocated, and disappears as soon as the program leaves the scope in which it is declared.
\see Apop_r, Apop_rv, Apop_row_tv, Apop_row_t, Apop_mrv
*/
#define Apop_rs(d, rownum, len)(                                                 \
        (!(d) || (rownum) < 0) ? NULL                                            \
        : &(apop_data){                                                          \
         .names= ( !((d)->names) ? NULL :                                        \
            &(apop_name){                                                        \
                .title = (d)->names->title,                                      \
                .vector = (d)->names->vector,                                    \
                .col = (d)->names->col,                                          \
                .row = ((d)->names->row && (d)->names->rowct > (rownum)) ? &((d)->names->row[rownum]) : NULL,  \
                .text = (d)->names->text,                                        \
                .colct = (d)->names->colct,                                      \
                .rowct = (d)->names->row ? (GSL_MIN(1, GSL_MAX((d)->names->rowct - (int)(rownum), 0)))      \
                                          : 0,                                   \
                .textct = (d)->names->textct }),                                 \
        .vector= Apop_subvector((d->vector), (rownum), (len)),                   \
        .matrix = Apop_subm(((d)->matrix), (rownum), 0,  (len), (d)->matrix?(d)->matrix->size2:0),    \
        .weights =  Apop_subvector(((d)->weights), (rownum), (len)),             \
        .textsize[0]=(d)->textsize[0]> (rownum)+(len)-1 ? (len) : 0,                                   \
        .textsize[1]=(d)->textsize[1],                                           \
        .text = (d)->text ? &((d)->text[rownum]) : NULL,                         \
        })


/** \def Apop_cs(d, col, len)
A macro to generate a temporary view of \ref apop_data set \c d including only certain columns, beginning at column \c col and having length \c len. 

The view is automatically allocated, and disappears as soon as the program leaves the scope in which it is declared.
\see Apop_c, Apop_cv, Apop_col_tv, Apop_col_t, Apop_mcv
*/
#define Apop_cs(d, colnum, len) ( \
            (!(d)||!(d)->matrix || (d)->matrix->size2 <= (colnum)+(len)-1)       \
             ? NULL                                                              \
             : &(apop_data){                                                     \
                .vector= NULL,                                                   \
                .weights= (d)->weights,                                          \
                .matrix = Apop_subm((d)->matrix, 0, colnum, (d)->matrix->size1, (len)),\
                .textsize[0] = 0,                                                \
                .textsize[1] = 0,                                                \
                .text = NULL,                                                    \
                .names= (d)->names ? &(apop_name){                                                         \
                    .title = (d)->names->title,                                      \
                    .vector = NULL,                                                  \
                    .row = (d)->names->row,                                          \
                    .col = ((d)->names->col && (d)->names->colct > colnum) ? &((d)->names->col[colnum]) : NULL,  \
                    .text = NULL,                                                    \
                    .rowct = (d)->names->rowct,                                      \
                    .colct = (d)->names->col ? (GSL_MIN(len, GSL_MAX((d)->names->colct - colnum, 0)))      \
                                              : 0,                                   \
                    .textct = (d)->names->textct } : NULL \
            })

/** \def Apop_r(d, row)
A macro to generate a temporary one-row view of \ref apop_data set \c d, pulling out only
row \c row. The view is also an \ref apop_data set, with names and other decorations.
\code
//pull a single row
apop_data *v = Apop_r(your_data, 7);

//or loop through a sequence of one-row data sets.
apop_model *std = apop_model_set_parameters(apop_normal, 0, 1);
for (int i=0; i< your_data->matrix->size1; i++)
    printf("Std Normal CDF up to observation %i is %g\n",
                       i, apop_cdf(Apop_r(your_data, i), std));
\endcode

The view is automatically allocated, and disappears as soon as the program leaves the
scope in which it is declared.
\see Apop_rs, Apop_row_v, Apop_row_tv, Apop_row_t, Apop_mrv
*/
#define Apop_r(d, rownum) Apop_rs(d, rownum, 1)

/** \def Apop_c(d, col)
A macro to generate a temporary one-column view of \ref apop_data set \c d, pulling out only
column \c col. 
After this call, \c outd will be a pointer to this temporary
view, that you can use as you would any \ref apop_data set.
\see Apop_cs, Apop_cv, Apop_col_tv, Apop_col_t, Apop_mcv
*/
#define Apop_c(d, col) Apop_cs(d, col, 1)

/** \cond doxy_ignore */
#define APOP_COL Apop_col
#define apop_col Apop_col
#define APOP_COL_T Apop_col_t
#define apop_col_t Apop_col_t
#define APOP_COL_TV Apop_col_tv
#define apop_col_tv Apop_col_tv

#define APOP_ROW Apop_row
#define apop_row Apop_row
#define APOP_COLS Apop_cols
#define apop_cols Apop_cols
#define APOP_COL_V Apop_col_v
#define apop_col_v Apop_col_v
#define APOP_ROW_V Apop_row_v
#define apop_row_v Apop_row_v
#define APOP_ROWS Apop_rows
#define apop_rows Apop_rows
#define Apop_data_row Apop_row   #deprecated
#define APOP_ROW_T Apop_row_t
#define apop_row_t Apop_row_t
#define APOP_ROW_TV Apop_row_tv
#define apop_row_tv Apop_row_tv

/** Deprecated. Use Apop_mrv */
#define Apop_matrix_row(m, row, v) gsl_vector apop_vv_##v = gsl_matrix_row((m), (row)).vector;\
gsl_vector * v = &( apop_vv_##v );

/* Deprecated. Use Apop_mcv */
#define Apop_matrix_col(m, col, v) gsl_vector apop_vv_##v = gsl_matrix_column((m), (col)).vector;\
gsl_vector * v = &( apop_vv_##v );

#define APOP_MATRIX_ROW Apop_matrix_row 
#define apop_matrix_row Apop_matrix_row 
#define APOP_MATRIX_COL Apop_matrix_col 
#define apop_matrix_col Apop_matrix_col 
/** \endcond */


long double apop_vector_sum(const gsl_vector *in);
double apop_vector_var_m(const gsl_vector *in, const double mean);
#ifdef APOP_NO_VARIADIC
 double apop_vector_correlation(const gsl_vector *ina, const gsl_vector *inb, const gsl_vector *weights) ;
#else
 double apop_vector_correlation_base(const gsl_vector *ina, const gsl_vector *inb, const gsl_vector *weights) ;
 apop_varad_declare(double, apop_vector_correlation, const gsl_vector *ina; const gsl_vector *inb; const gsl_vector *weights);
#define apop_vector_correlation(...) apop_varad_link(apop_vector_correlation, __VA_ARGS__)
#endif

double apop_vector_kurtosis(const gsl_vector *in);
double apop_vector_skew(const gsl_vector *in);

#define apop_sum apop_vector_sum
#define apop_var apop_vector_var
#define apop_mean apop_vector_mean

        //////database utilities

#ifdef APOP_NO_VARIADIC
 int apop_table_exists(char const *name, char remove) ;
#else
 int apop_table_exists_base(char const *name, char remove) ;
 apop_varad_declare(int, apop_table_exists, char const *name; char remove);
#define apop_table_exists(...) apop_varad_link(apop_table_exists, __VA_ARGS__)
#endif


int apop_db_open(char const *filename);
#ifdef APOP_NO_VARIADIC
 int apop_db_close(char vacuum) ;
#else
 int apop_db_close_base(char vacuum) ;
 apop_varad_declare(int, apop_db_close, char vacuum);
#define apop_db_close(...) apop_varad_link(apop_db_close, __VA_ARGS__)
#endif


int apop_query(const char *q, ...) __attribute__ ((format (printf,1,2)));
apop_data * apop_query_to_text(const char * fmt, ...) __attribute__ ((format (printf,1,2)));
apop_data * apop_query_to_data(const char * fmt, ...) __attribute__ ((format (printf,1,2)));
apop_data * apop_query_to_mixed_data(const char *typelist, const char * fmt, ...) __attribute__ ((format (printf,2,3)));
gsl_vector * apop_query_to_vector(const char * fmt, ...) __attribute__ ((format (printf,1,2)));
double apop_query_to_float(const char * fmt, ...) __attribute__ ((format (printf,1,2)));

int apop_data_to_db(const apop_data *set, const char *tabname, char);


        //////Settings groups

    //Part I: macros and fns for getting/setting settings groups and elements

/** \cond doxy_ignore */
void * apop_settings_get_grp(apop_model *m, char *type, char fail);
void apop_settings_remove_group(apop_model *m, char *delme);
void apop_settings_copy_group(apop_model *outm, apop_model *inm, char *copyme);
void *apop_settings_group_alloc(apop_model *model, char *type, void *free_fn, void *copy_fn, void *the_group);
apop_model *apop_settings_group_alloc_wm(apop_model *model, char *type, void *free_fn, void *copy_fn, void *the_group);
/** \endcond */ //End of Doxygen ignore.

/** Retrieves a settings group from a model.  See \ref Apop_settings_get
to just pull a single item from within the settings group.

This macro returns NULL if a group of type \c type_settings isn't found attached
to model \c m, so you can easily put it in a conditional like
  \code 
  if (!apop_settings_get_group(m, "apop_ols")) ...
  \endcode

\param m An \ref apop_model
\param type A string giving the type of the settings group you are retrieving. E.g., for an \ref apop_mle_settings group, use only \c apop_mle.
\return A void pointer to the desired struct (or \c NULL if not found).
*/
#define Apop_settings_get_group(m, type) apop_settings_get_grp(m, #type, 'c')

/** Removes a settings group from a model's list. 
 
\li  If the so-named group is not found, do nothing.
*/
#define Apop_settings_rm_group(m, type) apop_settings_remove_group(m, #type)

/** Add a settings group. The first two arguments (the model you are
attaching to and the settings group name) are mandatory, and then you
can use the \ref designated syntax to specify default values (if any).
\return A pointer to the newly-prepped group.

See \ref modelsettings or \ref maxipage for examples.

\li If a settings group of the given type is already attached to the model, 
the previous version is removed. Use \ref Apop_settings_get to check whether a group
of the given type is already attached to a model, and \ref Apop_settings_set to modify
an existing group.
*/
#define Apop_settings_add_group(model, type, ...)  \
    apop_settings_group_alloc(model, #type, type ## _settings_free, type ## _settings_copy, type ##_settings_init ((type ## _settings) {__VA_ARGS__}))

/** Copy a model and add a settings group. Useful for models that require a settings group to function. See \ref Apop_settings_add_group.

\return A pointer to the newly-prepped model.
*/
#define apop_model_copy_set(model, type, ...)  \
    apop_settings_group_alloc_wm(apop_model_copy(model), #type, type ## _settings_free, type ## _settings_copy, type ##_settings_init ((type ## _settings) {__VA_ARGS__}))


/** This is the complement to \ref apop_model_set_parameters, for those models that are
 set up by adding settings group, rather than filling in a list of parameters.

For example, the \ref apop_kernel_density model is built by adding a \ref apop_kernel_density_settings group. From the example on the \ref apop_kernel_density page:

\code
apop_model *k2 = apop_model_set_settings(apop_kernel_density,
                    .base_data=d,
                    .set_fn = set_uniform_edges,
                    .kernel = apop_uniform);
\endcode

The name of the model and the settings group to be built must match, which is the case
for many model transformations, including \ref apop_dconstrain and \ref apop_cross. If the names do not match, use \ref apop_model_copy_set.
*/
#define Apop_model_set_settings(model, ...)  \
    apop_settings_group_alloc_wm(apop_model_copy(model), #model, model ## _settings_free, model ## _settings_copy, model ##_settings_init ((model ## _settings) {__VA_ARGS__}))

#define apop_model_set_settings Apop_model_set_settings

/** Retrieves a setting from a model.  See \ref Apop_settings_get_group to pull the entire group.

\param model An \ref apop_model.
\param type A string giving the type of the settings group you are retrieving, without the \c _settings ending. E.g., for an \ref apop_mle_settings group, use \c apop_mle.
\param setting The struct element you want to retrieve.
*/
#define Apop_settings_get(model, type, setting)  \
    (((type ## _settings *) apop_settings_get_grp(model, #type, 'f'))->setting)

/** Modifies a single element of a settings group to the given value. 

\li If <tt>model==NULL</tt>, fails silently. 
\li If <tt>model!=NULL</tt> but the given settings group is not found attached to the model, set <tt>model->error='s'</tt>.
*/
#define Apop_settings_set(model, type, setting, data)   \
    do {                                                \
        if (!(model)) continue; /* silent fail. */      \
        type ## _settings *apop_tmp_settings = apop_settings_get_grp(model, #type, 'c');  \
        Apop_stopif(!apop_tmp_settings, (model)->error='s', 0, "You're trying to modify a setting in " \
                        #model "'s setting group of type " #type " but that model doesn't have such a group."); \
    apop_tmp_settings->setting = (data);                \
    } while (0);

/** \cond doxy_ignore */
#define Apop_settings_add Apop_settings_set
#define APOP_SETTINGS_ADD Apop_settings_set
#define apop_settings_set Apop_settings_set
#define APOP_SETTINGS_GET Apop_settings_get
#define apop_settings_get Apop_settings_get
#define APOP_SETTINGS_ADD_GROUP Apop_settings_add_group
#define apop_settings_add_group Apop_settings_add_group
#define APOP_SETTINGS_GET_GROUP Apop_settings_get_group
#define apop_settings_get_group Apop_settings_get_group
#define APOP_SETTINGS_RM_GROUP Apop_settings_rm_group
#define apop_settings_rm_group Apop_settings_rm_group
#define Apop_model_copy_set apop_model_copy_set

//deprecated:
#define Apop_model_add_group Apop_settings_add_group

/** \endcond */ //End of Doxygen ignore.

/** Put this in your header file to declare the init, copy, and
free functions for ysg_settings. Of course, these functions will also have to be defined
in a .c file using \ref Apop_settings_init, \ref Apop_settings_copy, and \ref Apop_settings_free. */
#define Apop_settings_declarations(ysg) \
   ysg##_settings * ysg##_settings_init(ysg##_settings); \
   void * ysg##_settings_copy(ysg##_settings *); \
   void ysg##_settings_free(ysg##_settings *);

/** A convenience macro for declaring the initialization function for a new settings group.
See \ref settingswriting for details and an example.
*/
#define Apop_settings_init(name, ...)   \
    name##_settings *name##_settings_init(name##_settings in) {       \
        name##_settings *out = malloc(sizeof(name##_settings));     \
        *out = in; \
        __VA_ARGS__;            \
        return out; \
    }

/** \cond doxy_ignore */
#define Apop_varad_set(var, value) (out)->var = (in).var ? (in).var : (value);
/** \endcond */

/** A convenience macro for declaring the copy function for a new settings group.
See \ref settingswriting for details and an example.
*/
#define Apop_settings_copy(name, ...) \
    void * name##_settings_copy(name##_settings *in) {\
        name##_settings *out = malloc(sizeof(name##_settings)); \
        *out = *in; \
        __VA_ARGS__;    \
        return out;     \
    }

/** A convenience macro for declaring the delete function for a new settings group.
See \ref settingswriting for details and an example.
*/
#define Apop_settings_free(name, ...) \
    void name##_settings_free(name##_settings *in) {\
        __VA_ARGS__;    \
        free(in);  \
    }

        //Part II: the details of extant settings groups.


/** The settings for maximum likelihood estimation (including simulated annealing). */
typedef struct{
    double      *starting_pt;   /**< An array of doubles (e.g., <tt>(double*){2,4,6,8}</tt>) suggesting a starting point. 
                                  If NULL, use an all-ones vector.  If \c startv is a \c gsl_vector
                                  and is not a view of a matrix, use <tt>.starting_pt=startv->data</tt>.*/
    char *method; /**< The method to be used for the optimization. All strings are case-insensitive.

        <table>
<tr>
<td> String <td></td> Name  <td></td>  Notes
</td> </tr>
                                     
<tr><td> "NM simplex" </td><td> Nelder-Mead simplex </td><td> Does not use gradients at all. Can sometimes get stuck.</td></tr>

<tr><td> "FR cg"  </td><td> Conjugate gradient (Fletcher-Reeves) (default) </td><td> CG methods use derivatives. The converge to the optimum of a quadratic function in one step; performance degrades as the objective digresses from quadratic.</td></tr>

<tr><td> "BFGS cg" </td><td> Broyden-Fletcher-Goldfarb-Shanno conjugate gradient        </td><td>  </td></tr>

<tr><td> "PR cg"  </td><td> Polak-Ribiere conjugate gradient  </td><td>  </td></tr>

<tr><td> "Annealing"  </td><td> \ref simanneal "simulated annealing"         </td><td> Slow but works for objectives of arbitrary complexity, including stochastic objectives.</td></tr>

<tr><td> "Newton"</td><td> Newton's method  </td><td> Search by finding a root of the derivative. Expects that gradient is reasonably well-behaved. </td></tr>

<tr><td> "Newton hybrid"</td><td> Newton's method/gradient descent hybrid        </td><td>  Find a root of the derivative via the Hybrid method </td> If Newton proposes stepping outside of a certain interval, use an alternate method. See <a href="https://www.gnu.org/software/gsl/manual/gsl-ref_35.html#SEC494">the GSL manual</a> for discussion.</tr>

<tr><td> "Newton hybrid no scale"</td><td>  Newton's method/gradient descent hybrid with spherical scale</td><td>  As above, but use a simplified trust region. </td></tr>
</table> */
    double      step_size, /**< The initial step size. */
                tolerance, /**< The precision the minimizer uses in its stopping rule. Only vaguely related to the precision of the actual MLE.*/
delta;
    int         max_iterations; /**< Ignored by simulated annealing. Other methods halt (and set the \c "status" element of the output estimate's info page) if
                                 they do this many iterations without finding an optimum. */
    int         verbose; /**<	Give status updates as we go.  This is orthogonal to the 
                                <tt>apop_opts.verbose</tt> setting. */
    double      dim_cycle_tolerance; /**< If zero (the default), the usual procedure.
                             If \f$>0\f$, cycle across dimensions: fix all but the first dimension at the starting
                             point, optimize only the first dim. Then fix the all but the second dim, and optimize the
                             second dim. Continue through all dims, until the log likelihood at the outset of one cycle
                             through the dimensions is within this amount of the previous cycle's log likelihood. There
                             will be at least two cycles.
                             */
//simulated annealing (also uses step_size);
    int         n_tries, iters_fixed_T;
    double      k, t_initial, mu_t, t_min ;
    gsl_rng     *rng;
    apop_data   **path;    /**< If not \c NULL, record each vector tried by the optimizer as one row of this \ref apop_data set.
                              Each row of the \c matrix element holds the vector tried; the corresponding element in the \c vector is the evaluated value at that vector (after out-of-constraints penalties have been subtracted).
                              A new \ref apop_data set is allocated at the pointer you send in. This data set has no names; add them as desired. For a sample use, see \ref maxipage.
*/
} apop_mle_settings;

/** Settings for least-squares type models such as \ref apop_ols or \ref apop_iv */
typedef struct {
    int destroy_data; /**< If \c 'y', then the input data set may be normalized or otherwise mangled. */
    apop_data *instruments; /**< Use for the \ref apop_iv regression, qv. */
    char want_cov; /**< Deprecated. Please use \ref apop_parts_wanted_settings. */
    char want_expected_value; /**< Deprecated. Please use \ref apop_parts_wanted_settings. */
    apop_model *input_distribution; /**< The distribution of \f$P(Y|X)\f$ is specified by the model holding this struct, but the distribution of \f$X\f$ needs to be specified as well for any calculation of \f$P(Y)\f$. See the notes in the RNG section of the \ref apop_ols documentation. */
} apop_lm_settings;

/** The default is for the estimation routine to give some auxiliary information,
  such as a covariance matrix, predicted values, and common hypothesis tests.
  Some uses of a model depend on these items, but if they are a waste
  of time for your purposes, this settings group gives a quick way to bypass them all.

  Adding this settings group to your model without changing any default values---
  \code
  Apop_model_add_group(your_model, apop_parts_wanted);
  \endcode
  ---will turn off all of the auxiliary calculations covered, because the default value
  for all the switches is <tt>'n'</tt>, indicating that all elements are not wanted.

  From there, you can change some of the default <tt>'n'</tt>s to <tt>'y'</tt>s to retain some but not all auxiliary elements.  If you just want the parameters themselves and the covariance matrix:
  \code
  Apop_model_add_group(your_model, apop_parts_wanted, .covariance='y');
  \endcode

  \li Not all models support this, although the models with especially compute-intensive
  auxiliary info do (e.g., the maximum likelihood estimation system). Check the model's documentation. 

  \li Tests may depend on covariance, so <tt>.covariance='n', .tests='y'</tt> may be 
  treated as <tt>.covariance='y', .tests='y'</tt>.
*/
typedef struct {
    //init/copy/free are in apop_mle.c
    char covariance;    /*< If 'y', calculate the covariance matrix. Default 'n'. */
    char predicted;/*< If 'y', calculate the predicted values. This is typically as many
                     items as rows in your data set. Default 'n'. */
    char tests;/*< If 'y', run any hypothesis tests offered by the model's estimation routine. Default 'n'. */
    char info;/*< If 'y', add an info table with elements such as log likelihood or AIC. Default 'n'. */
} apop_parts_wanted_settings;

/** For use by \ref apop_cdf when the CDF is generated via Monte Carlo methods. */
typedef struct {
    int draws;  /**< For random draw methods, how many draws? Default: 10,000.*/
    gsl_rng *rng; /**< For random draw methods. See \ref apop_rng_get_thread on the default. */
    gsl_matrix *draws_made; /**< A store of random draws used to calcuate the CDF. Need only be generated once, and so stored here. */
    int *draws_refcount; /**< For internal use.*/
} apop_cdf_settings;


/** Settings for getting parameter models (i.e. the distribution of parameter estimates) */
typedef struct {
    apop_model *base;
    int index;
    gsl_rng *rng;
    int draws;
} apop_pm_settings;


/** Settings to accompany the \ref apop_pmf. */
typedef struct {
    gsl_vector *cmf;  /**< A cumulative mass function, for the purposes of making random draws.*/
    char draw_index;  /**< If \c 'y', then draws from the PMF return the integer index of the row drawn. 
                           If \c 'n' (the default), then return the data in the vector/matrix elements of the data set. */
    long double total_weight; /**< Keep the total weight, in case the input weights aren't normalized to sum to one. */
    int *cmf_refct;    /**< For internal use, so I can garbage-collect the CMF when needed. */
} apop_pmf_settings;


/** Settings for the \ref apop_kernel_density model. */
typedef struct{
    apop_data *base_data; /**< The data that will be smoothed by the KDE. */
    apop_model *base_pmf; /**< I actually need the data in a \ref apop_pmf. You can give
                            that to me explicitly, or I can wrap the <tt>.base_data</tt> in a PMF.  */
    apop_model *kernel; /**< The distribution to be centered over each data point. Default, 
                                    \ref apop_normal with std dev 1. */
    void (*set_fn)(apop_data*, apop_model*); /**< The function I will use for each data
                                                  point to center the kernel over each point.
            Default: set the upper-left element of the parameter set to the upper-left scalar in the data:
            <tt>apop_data_set(m->parameters, .val= apop_data_get(in));</tt>.
                                                  */
    int own_pmf, own_kernel; /**< For internal use only. */
}apop_kernel_density_settings;

struct apop_mcmc_settings;

/** A proposal distribution for \ref apop_mcmc_settings and its accompanying functions and
information.  By default, these will be \ref apop_multivariate_normal models. The \c
step_fn and \c adapt_fn have to be written around the model and your preferences.
For the defaults, the step function recenters the mean of the distribution around the
last accepted proposal, and the adapt function widens \f$\Sigma\f$ for the Normal if the
accept rate is too low; narrows it if the accept rate is too large.

You may provide an array of proposals. The length of the list of proposals
must match the number of chunks, as per the \c gibbs_chunks setting in the \ref
apop_mcmc_settings group that the array of proposals is a part of. Each proposal must
be initialized to include all elements, and the step and adapt functions probably have
to be written anew for each type of model.
*/
typedef struct apop_mcmc_proposal_s {
    apop_model *proposal; /**< The distribution from which test parameters will be
        drawn. After getting the draw using the \c draw method of the proposal, the base
        model's \c parameters element is filled using \ref apop_data_fill.
        If \c NULL, \ref apop_model_metropolis will use a Multivariate Normal with the
        appropriate dimension, mean zero, and covariance matrix I. If not \c NULL, be sure to
        parameterize your model with an initial position. */

    void (*step_fn)(double const *, struct apop_mcmc_proposal_s*, struct apop_mcmc_settings *); /**< Modifies the parameters of the
        proposal distribution given a successful draw. Typically, this function writes the
        drawn data point to the parameter set. If the draw is a scalar, the default
        function sets the 0th element of the model's \c parameter set with the draw
        (works for the \ref apop_normal and other models). If the draw has multiple
        dimensions, they are all copied to the parameter set, which must have the same
        size. */

    int (*adapt_fn)(struct apop_mcmc_proposal_s *ps, struct apop_mcmc_settings *ms); /**< Called
        every step, to adapt the proposal distribution using information to this point in
        the chain. */

    int accept_count, reject_count;  /**< If there are multiple \ref apop_mcmc_proposal_s structs for 
                                       multiple chunks, These count accepts/rejects for
                                       this chunk. The \ref apop_mcmc_settings group has
                                       a total for the aggregate across all chunks. */
} apop_mcmc_proposal_s;

/** Method settings for a model to be put through Bayesian updating. */
typedef struct apop_mcmc_settings {
    apop_data *data;
    long int periods; /**< For how many steps should the MCMC chain run? */
    double burnin; /**< What <em>percentage</em> of the periods should be ignored
                         as initialization. That is, this is a number between zero and one. */
    int histosegments; /**< If outputting a binned PMF, how many segments should it have? */
    double last_ll; /**< If you have already run MCMC, the last log likelihood in the chain.*/
    apop_model *pmf; /**< If you have already run MCMC, I keep a pointer to the model
            so far here. Use \ref apop_model_metropolis_draw to get one more draw.*/
    apop_model *base_model; /**< The model you provided with a \c log_likelihood or
            \c p element (which need not sum to one). You do not have to set this: if it is
            \c NULL on input to \ref apop_model_metropolis, I will fill it in.*/
    apop_mcmc_proposal_s *proposals; /**< The list of proposals. You can probably use
            the default of adaptive multivariate normals. See the \ref apop_mcmc_proposal_s
            struct for details. */
    int proposal_count; /**< The number of proposal sets; see \c gibbs_chunks below. */
    double target_accept_rate; /**< The desired acceptance rate, for use by adaptive proposals. Default: .35 */
    int accept_count;   /**< After calling \ref apop_model_metropolis, this will have the number of accepted proposals.*/
    int reject_count;   /**< After calling \ref apop_model_metropolis, this will have the number of rejected proposals.*/
    char gibbs_chunks;  /**< See the \ref apop_model_metropolis documentation for discussion.
                          
                          \c 'a': One step draws and accepts/rejects all parameters as a unit<br>

                             \c 'b': draw in blocks: the vector is a block, the matrix
                                is a separate block, the weights are a separate
                                block, and so on through every page of the model
                                parameters. Each block of parameters is drawn and
                                accepted/rejected as a unit. <br>

                             \c '1': draw each parameter and accept/reject separately. One
                                MCMC step consists of a set of draws for every
                                parameter.<br> */
    size_t *block_starts; /**< For internal use */
    int block_count, proposal_is_cp; /**< For internal use. */

    char start_at; /**< If \c '1' (the default), start with a first proposal of all
        1s. Even when this is a far-from-useful starting point, MCMC typically does a good
        job of crawling to better spots early in the chain.<br>
    The default when this is unset is to start at the \c parameters of the \ref apop_model sent in to \ref
    apop_model_metropolis.*/
    void (*base_step_fn)(double const *, struct apop_mcmc_proposal_s*, struct apop_mcmc_settings *); /**< If an \ref apop_mcmc_proposal_s struct has \c NULL \c step_fn, use this. If you don't want a step function, set this to a do-nothing function. */
    int (*base_adapt_fn)(struct apop_mcmc_proposal_s *ps, struct apop_mcmc_settings *ms); /**< If a \ref apop_mcmc_proposal_s has \c NULL \c adapt_fn, use this.  If you don't want an adapt function, set this to a do-nothing function.*/

} apop_mcmc_settings;

/** \cond doxy_ignore */
//Loess, including the old FORTRAN-to-C.
struct loess_struct {
	struct {
		long    n, p;
        double  *y, *x;
		double	*weights;
	} in;
	struct {
	        double  span;
	        long    degree;
	        long    normalize;
	        long    parametric[8];
	        long    drop_square[8];
	        char    *family;
	} model;
	struct {
	        char    *surface;
	        char    *statistics;
	        double  cell;
	        char    *trace_hat;
	        long    iterations;
	} control;
	struct {
		long	*parameter, *a;
		double	*xi, *vert, *vval;
	} kd_tree;
	struct {
		double	*fitted_values;
        double  *fitted_residuals;
		double  enp, s;
		double  one_delta, two_delta;
		double	*pseudovalues;
		double	trace_hat;
		double	*diagonal;
		double	*robust;
		double  *divisor;
	} out;
};
/** \endcond */ //End of Doxygen ignore.

/** The code for the loess system is based on FORTRAN code from 1988,
overhauled in 1992, linked in to Apophenia in 2009. The structure that
does all the work, then, is a \c loess_struct that you should
basically take as opaque. 

The useful settings from that struct re-appear in the \ref
apop_loess_settings struct so you can set them directly, and then the
settings init function will copy your preferences into the working struct.

The documentation for the elements is cut/pasted/modified from Cleveland,
Grosse, and Shyu.
*/
typedef struct {
    apop_data *data;
    struct  loess_struct lo_s; /**< 

<tt>.data</tt>: Mandatory. Your input data set.

<tt>.lo_s.model.span</tt>:	smoothing parameter. Default is 0.75.

<tt>.lo_s.model.degree</tt>: overall degree of locally-fitted polynomial. 1 is
		locally-linear fitting and 2 is locally-quadratic fitting. Default is 2.

<tt>.lo_s.normalize</tt>:	Should numeric predictors
		be normalized?	If \c 'y' - the default - the standard normalization
		is used. If \c 'n', no normalization is carried out.

\c .lo_s.model.parametric:	for two or more numeric predictors, this argument
		specifies those variables that should be
		conditionally-parametric. The argument should be a logical
		vector of length \c p, specified in the order of the predictor
		group ordered in \c x.  Default is a vector of 0's of length \c p.

\c .lo_s.model.drop_square:	for cases with degree = 2, and with two or more
		numeric predictors, this argument specifies those numeric
		predictors whose squares should be dropped from the set of
		fitting variables. The method of specification is the same as
		for parametric.  Default is a vector of 0's of length p.

\c .lo_s.model.family: the assumed distribution of the errors. The values may be 
        <tt>"gaussian"</tt> or <tt>"symmetric"</tt>. The first value is the default.
        If the second value is specified, a robust fitting procedure is used.

\c lo_s.control.surface:	determines whether the fitted surface is computed
        <tt>"directly"</tt> at all points  or whether an <tt>"interpolation"</tt>
        method is used. The default, interpolation, is what most users should use
		unless special circumstances warrant.

\c lo_s.control.statistics:	determines whether the statistical quantities are 
    computed <tt>"exactly"</tt> or approximately, where <tt>"approximate"</tt>
    is the default. The former should only be used for testing the approximation in
    statistical development and is not meant for routine usage because computation
    time can be horrendous.

    \c lo_s.control.cell: if interpolation is used to compute the surface,
    this argument specifies the maximum cell size of the k-d tree. Suppose k =
    floor(n*cell*span) where n is the number of observations.  Then a cell is
    further divided if the number of observations within it is greater than or
    equal to k. default=0.2

\c lo_s.control.trace_hat: Options are <tt>"approximate"</tt>, <tt>"exact"</tt>, and <tt>"wait.to.decide"</tt>.	
    When lo_s.control.surface is <tt>"approximate"</tt>, determines
    the computational method used to compute the trace of the hat
    matrix, which is used in the computation of the statistical
    quantities.  If "exact", an exact computation is done; normally
    this goes quite fast on the fastest machines until n, the number
    of observations is 1000 or more, but for very slow machines,
    things can slow down at n = 300.  If "wait.to.decide" is selected,
    then a default is chosen in loess();  the default is "exact" for
    n < 500 and "approximate" otherwise.  If surface is "exact", an
    exact computation is always done for the trace. Set trace_hat to
    "approximate" for large dataset will substantially reduce the
    computation time.

\c lo_s.model.iterations:	if family is <tt>"symmetric"</tt>, the number of iterations 
    of the robust fitting method.  Default is 0 for
    lo_s.model.family = gaussian; 4 for family=symmetric.

    That's all you can set. Here are some output parameters:

\c fitted_values:	fitted values of the local regression model

\c fitted_residuals:	residuals of the local regression fit

   \c  enp:		equivalent number of parameters.

   \c  s:		estimate of the scale of the residuals.

   \c  one_delta:	a statistical parameter used in the computation of standard errors.

   \c  two_delta:	a statistical parameter used in the computation of standard errors.

   \c  pseudovalues:	adjusted values of the response when robust estimation is used.

\c trace_hat:	trace of the operator hat matrix.

   \c  diagonal:	diagonal of the operator hat matrix.

   \c  robust:		robustness weights for robust fitting.

   \c  divisor:	normalization divisor for numeric predictors.
*/

    int     want_predict_ci; /**< If \c 'y' (the default), calculate the
                                confidence bands for predicted values */
    double  ci_level; /**< If running a prediction, the level at which
                        to calculate the confidence interval. default: 0.95 */
} apop_loess_settings;


    /** \cond doxy_ignore */
typedef struct point {    /* a point in the x,y plane */
  double x,y;             /* x and y coordinates */
  double ey;              /* exp(y-ymax+YCEIL) */
  double cum;             /* integral up to x of rejection envelope */
  int f;                  /* is y an evaluated point of log-density */
  struct point *pl,*pr;   /* envelope points to left and right of x */
} POINT;

/* This includes the envelope info and the metropolis steps. */
typedef struct {  /* attributes of the entire rejection envelope */
  int cpoint;              /* number of POINTs in current envelope */
  int npoint;              /* max number of POINTs allowed in envelope */
  double ymax;             /* the maximum y-value in the current envelope */
  POINT *p;                /* start of storage of envelope POINTs */
  double *convex;          /* adjustment for convexity */
  double metro_xprev;      /* previous Markov chain iterate */
  double metro_yprev;      /* current log density at xprev */
} arms_state;
    /** \endcond */

/** For use with \ref apop_arms_draw, to perform derivative-free adaptive rejection sampling with metropolis step. 

That function generates default values for this struct if you do not attach one to the
model beforehand, via a form like <tt>apop_model_add_group(your_model, apop_arms,
.model=your_model, .xl=8, .xr =14);</tt>. If you initialize it manually via \ref
apop_settings_add_group, the \c model element is mandatory; you'll get a run-time
complaint if you forget it.
*/
typedef struct {
    double *xinit;  /**< A <tt>double*</tt> giving starting values for x in ascending
                      order, e.g., <tt>(double *){1, 10, 100}</tt>.  . Default: -1,
                      0, 1. If this isn't \c NULL, I need at least three items, and
                      the length in \c ninit. */
    double  xl;     /**< Left bound. If you don't give me one, I'll use min[min(xinit)/10, min(xinit)*10].*/
    double  xr;     /**< Right bound. If you don't give me one, I'll use max[max(xinit)/10, max(xinit)*10]. */
    double convex;  /**< Adjustment for convexity */
    int ninit;      /**< The length of \c xinit.*/
    int npoint;     /**< Maximum number of envelope points. I \c malloc space for this many <tt>double</tt>s at the outset. Default = 1e5. */
   char do_metro;   /**< Set to \c 'y' if the metropolis step is required (i.e.,
                           if you're not sure if the function is log-concave).*/
   double xprev;    /**< For internal use; please ignore. Previous value from Markov chain. */
   int neval;       /**< On exit, the number of function evaluations performed */
   arms_state *state;
   apop_model *model; /**< The model from which to draw. Mandatory. Must have either a \c log_likelihood or \c p method.*/
} apop_arms_settings;


/** The settings to accompany the \ref apop_cross model, representing the cross product of two models (or, via recursion, a list of models of arbitrary length).*/
typedef struct {
    char *splitpage;    /**< The name of the page at which to split the data. If \c NULL, I send the entire data set to both models as needed. */
    apop_model *model1; /**< The first model in the stack.*/
    apop_model *model2; /**< The second model.*/
} apop_cross_settings;

typedef struct {
    apop_data *(*base_to_transformed)(apop_data*); /**< The function to transform the model from pre-transform space to post-transform space. */
    apop_data *(*transformed_to_base)(apop_data*); /**< The function to transform from post-transform space back to pre-transform space. If this function does not exist, using a Jacobian-based transformation is probably not mathematically correct. */
    double (*jacobian_to_base)(apop_data*); /**< The derivative of the \c transformed_to_base function. */
    apop_model *base_model;  /**< The pre-transformation model. */
} apop_coordinate_transform_settings;/**< Settings for an \ref apop_coordinate_transform model; see its documentation for notes and an example.
*/

/** For use with the \ref apop_dconstrain model. See its documentation for an example. 
*/
typedef struct {
    apop_model *base_model; /**< The model, before constraint. */
    double (*constraint)(apop_data *, apop_model *); /**< The constraint. Return 1 if the data is in the constraint; zero if out. */
    double (*scaling)(apop_model *); /**< Optional. Return the percent of the model density inside the constraint. */
    gsl_rng *rng; /**< If you don't provide a \c scaling function, I calculate the in-constraint model density via random draws.
                       If no \c rng is provided, I use a default RNG; see \ref apop_rng_get_thread. */
    int draw_ct; /**< How many draws to make for calculating the in-constraint model density via random draws. Current default: 1e4. */
} apop_dconstrain_settings;

typedef struct {
    apop_model *generator_m;
    apop_model *ll_m;
    int draw_ct;
} apop_composition_settings;/**< All of the elements of this struct should be considered private.*/

/** For mixture distributions, typically set up using \ref apop_model_mixture. See
\ref apop_mixture for discussion. Please consider all elements but \c model_list and \c
weights as private and subject to change. See the examples for use of these elements.  
*/
typedef struct {
    gsl_vector *weights;     /**< The likelihood of a draw from each component. */
    apop_model **model_list; /**< A \c NULL-terminated list of component models. */
    int model_count;
    int *param_sizes;  /**< The number of parameters for each model. Useful for unpacking the params. */
    apop_model *cmf;   /**< For internal use by the draw method. */
    int *cmf_refct;    /**< For internal use, so I can garbage-collect the CMF when needed. */
} apop_mixture_settings;

    //Models built via call to apop_model_copy_set.

#define apop_model_dcompose(...) Apop_model_set_settings(apop_composition, __VA_ARGS__)
#define apop_model_dconstrain(...) Apop_model_set_settings(apop_dconstrain, __VA_ARGS__)
#define apop_model_coordinate_transform(...) Apop_model_set_settings(apop_coordinate_transform, __VA_ARGS__)

//Doxygen drops whatever is after these declarations, so I put them last.
Apop_settings_declarations(apop_lm)
Apop_settings_declarations(apop_pm)
Apop_settings_declarations(apop_pmf)
Apop_settings_declarations(apop_mle)
Apop_settings_declarations(apop_cdf)
Apop_settings_declarations(apop_arms)
Apop_settings_declarations(apop_mcmc)
Apop_settings_declarations(apop_loess)
Apop_settings_declarations(apop_cross)
Apop_settings_declarations(apop_mixture)
Apop_settings_declarations(apop_dconstrain)
Apop_settings_declarations(apop_composition)
Apop_settings_declarations(apop_parts_wanted)
Apop_settings_declarations(apop_kernel_density)
Apop_settings_declarations(apop_coordinate_transform)

#ifdef	__cplusplus
}
#endif

/** @} */ //End doxygen's all_public grouping

//Part of the intent of a convenience header like this is that you
//don't have to remember what else you're including. So here are 
//some other common GSL headers:
#include <math.h>
#include <gsl/gsl_sort.h>
#include <gsl/gsl_eigen.h>
#include <gsl/gsl_sort_vector.h>
#include <gsl/gsl_permutation.h>
#include <gsl/gsl_integration.h>