r-cran-robustbase 0.90-2-1 / usr / lib / R / site-library / robustbase / NAMESPACE

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useDynLib(robustbase, .registration=TRUE)

if(FALSE) {##MM
    stopifnot(require(codetoolsBioC), require(robustbase))
    findExternalDeps("robustbase") }
importFrom("graphics", plot)# < more ?
importFrom("stats", alias, anova, case.names, coef, confint, cov,
           dummy.coef, family, fitted, fivenum,
           lm.fit, logLik, nobs,
           model.frame, predict, residuals, terms, vcov,
           variable.names, weights,
           ## would like to import even more *.lm methods, as we wrap them,
           ## but we cannot import non-exported methods
           residuals.lm, model.matrix.lm
           )
## ^^^^ MASS has a bit more; take it as example
if(getRversion() >= "3.1.0")
importFrom("stats", .lm.fit, confint.lm, dummy.coef.lm)

importFrom("utils", globalVariables)

## MASS has many rlm S3 methods; should we use some of them - for rnls() ?!
## we also currently use MASS::cov.rob(); but probably only temporarily
importFrom("methods", new, setClass, setMethod)

importFrom("DEoptimR", JDEoptim)

## Functions defined in this package
export(Sn, Qn, Qn.old,
       s_Sn, s_Qn, s_mad, s_IQR,

       summarizeRobWeights,
       wgt.himedian,

       h.alpha.n,
       covMcd, robMD,
       ltsReg,
       tolEllipsePlot,
       ## RENAME ?!? :
       covPlot, ltsPlot,
       ## NO! ddplot, distplot, chi2qqplot
       rrcov.control,##  << RENAME --- FIXME

       huberM,
       covOGK, covGK, hard.rejection, scaleTau2,
       psiFunc, huberPsi, hampelPsi,
## Not yet:
##     tukeyPsi, # = biweight / bisquare
       tukeyChi, tukeyPsi1, # TODO deprecate: see ./TODO
       Mpsi, Mchi, Mwgt, MrhoInf, .Mpsi, .Mchi, .Mwgt, .MrhoInf,
       .Mwgt.psi1,
       .Mchi.tuning.default, .Mpsi.tuning.default, .regularize.Mpsi,
       .Mchi.tuning.defaults,.Mpsi.tuning.defaults,

       lmrob, lmrob.fit,
       lmrob.fit.MM, lmrob..M..fit, lmrob..D..fit,
       lmrob.S, lmrob.lar, lmrob.M.S,
       lmrob.control,
       splitFrame,

       mc, # Mia Hubers's medcouple
       adjbox,
       adjboxStats,
       adjOutlyingness,

       glmrob,
       nlrob, nlrob.control
       , glmrobMqle.control, glmrobBY.control, glmrobMT.control
       , BYlogreg ## R/BYlogreg.R FIXME: add to glmrob() and "deprecate"
       ## , glmrobMT ## R/MTestimador2.R
       , sigma, estimethod
       )


## S3 methods for ``our own'' S3 generics:
S3method(ltsReg, default)
S3method(ltsReg, formula)

S3method(mc, default)
S3method(adjbox, default)
S3method(adjbox, formula)


## Register all the methods for S3 generics elsewhere
## in case namespace is loaded but not currently attached.

S3method(anova, lmrob)
S3method(anova, glmrob)

S3method(alias, lmrob)
S3method(case.names, lmrob)
S3method(confint, lmrob)
S3method(dummy.coef, lmrob)
S3method(estimethod, nlrob)
S3method(family, lmrob)
S3method(kappa, lmrob)
S3method(labels, lmrob)
S3method(model.matrix, lmrob)
S3method(nobs, lmrob)
S3method(residuals, lmrob)
S3method(variable.names, lmrob)
S3method(weights, glmrob)
S3method(weights, lmrob)
S3method(weights, nlrob, weights.lmrob)# use the same as "lmrob"

S3method(formula, nlrob)
S3method(fitted, nlrob)
S3method(plot, lts)
S3method(plot, mcd)
S3method(plot, lmrob)
S3method(predict, lmrob)
S3method(predict, glmrob)
S3method(predict, nlrob)

S3method(print, glmrob)
S3method(print, lts)
S3method(print, mcd)
S3method(print, nlrob)
S3method(print, lmrob)
S3method(print, lmrob.S)
S3method(print, summary.glmrob)
S3method(print, summary.lmrob)
S3method(print, summary.nlrob)
S3method(print, summary.lts)

S3method(qr, lmrob)
## lmrob uses residuals.default
S3method(residuals, nlrob)
S3method(residuals, glmrob)

S3method(summary, glmrob)
S3method(summary, lmrob)
S3method(summary, lts)
S3method(summary, mcd)
S3method(summary, nlrob)

S3method(sigma, lmrob)
S3method(sigma, nlrob)
S3method(sigma, nls)
S3method(vcov, glmrob)
S3method(vcov, lmrob)
S3method(vcov, nlrob)
##not there: S3method(print, summary.mcd)

## S4 Generics {only those defined in this package}:
export(
       chgDefaults
       )

exportClasses(
              "functionX",   # function(x, <tune_par>): VECTORIZED in 'x'
              "functionXal", # a functional: function of tuning par.s only
              "psi_func"     # containing  rho(), psi(), psi'(), ...
              )

exportMethods(
              chgDefaults,
              plot
              )

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