R (>= 2.10), nnet, quantreg, MASS, locfit
R (>= 2.10), abc, abind, parallel, pls, plyr
R(>= 2.15.2), Matrix, coda, mvtnorm, MCMCpack, spatstat,
parallel
R (>= 2.10.0), ade4, tkrplot, shapefiles, sp
R (>= 3.0.1), sp, methods, deldir, ade4, adehabitatMA,
adehabitatLT
R (>= 3.0.1), sp, methods, ade4, adehabitatMA, adehabitatHR
R (>= 2.10.0), sp, methods, ade4, adehabitatMA, CircStats
R (>= 2.13.0), car (>= 2.0-1), lmtest, sandwich, survival, zoo
R (>= 2.12.0), stats, grDevices, graphics, Matrix, igraph
R (>= 2.15.3), foreign, utils, Rcpp (>= 0.10.0.4),
RcppArmadillo
R (>= 3.0.0), methods, stats, lme4, boot
methods, stats, modeltools, nnls (>= 1.1)
R (>= 3.0.0), RSQLite, DBI, lubridate, jsonlite
R (>= 2.15.0), methods, graphics, tcltk, corpcor
R (>= 2.0.0), qtl, gtools, fields, lattice
R (>= 2.15.1), splancs, Hmisc, shapefiles
R (>= 2.10), proto, cairoDevice, gWidgetsRGtk2, partitions
R (>= 2.7.0), DiceDesign, lhs, metRology, msm, sensitivity
emulator (>= 1.2-13), calibrator (>= 1.2-5), approximator (>=
1.2-6)
R (>= 1.4.1), bayesm, coda, MASS, stats, mice, survival,
foreign,nnet
R (>= 2.14.0), rJava (>= 0.8-4), car, randomForest, missForest
R (>= 2.6), runjags (>= 0.9.5), coda, lattice, stats, utils
R(>= 3.0.1), MCMCpack, mvtnorm, Rlab, truncdist, truncnorm
R (>= 2.14.2), gWidgets, gWidgetsRGtk2, bayesTFR (>= 4.0-0),
bayesLife (>= 2.0-0), bayesPop (>= 5.0-0)
splines, truncdist, GIGrvg, normalp, gsl
R (>= 2.14.1), gplots, xtable, grDevices, mnormt, MASS,
bayesm, boa, e1071, gtools
R (>= 2.12.1), R2jags, QRM, polspline, MCMCpack
coda, lattice, MASS, MCMCpack, RColorBrewer
R (>= 3.0.0), survival, coda, smoothSurv
R(>= 2.15.2), lme4 (>= 0.999375-35), MASS, VGAM, mvtnorm,
matrixcalc, coda
R (>= 2.10.0), c3net, infotheo, igraph, Matrix, lattice
methods, stats, graphics, foreach, iterators, grid, Rcpp (>=
0.9.2)
R (>= 2.10.0), Biobase (>= 2.5.5), methods, geneplotter
R (>= 2.10), utils, proto, ggplot2, relations, psychotools
R (>= 2.0), mvtnorm, car, lattice, combinat
R (>= 2.10), MASS, grid, colorspace, lattice
methods, stats, utils, bigmemory (>= 4.0.0)
R (>= 3.0), grDevices, graphics, stats, utils, reader (>=
1.0.1), NCmisc (>= 1.1), bigmemory (>= 4.0.0), biganalytics
R (>= 2.10.0), methods, stats, graphics, utils
rexpokit, cladoRcpp, ape, phylobase, methods
stats, utils, sp, raster, parallel, reshape
R (>= 2.12), amap, e1071, scales, cluster, rgl
R (>= 2.15), utils, rpanel, tkrplot, MASS, boot
R (>= 2.7.0), bootstrap, monreg, R2WinBUGS, coda
R (>= 1.7), Amelia, MASS, lavaan, sem, snowfall
R (>= 3.0.0), nortest, xtable, moments, knitr
BoomSpikeSlab, zoo, xts, Boom, R(>= 3.0.2)
R (>= 2.15.0), lattice, igraph, MASS, Matrix
R (>= 2.10), car, heplots (>= 0.8.6), graphics, stats
R (>= 2.14), MASS, MatchIt, nnet, numDeriv
Rcpp (>= 0.9.10), parallel, pcaPP (>= 1.8-1), robustbase
R (>= 2.10), grDevices, graphics, stats, utils
R (>= 2.10), parallel, DNAcopy, DPpackage
R (>= 2.10), class, e1071, gclus, lars, MASS, mclust, nnet,
pcaPP, pls, rpart, robustbase, som
ChemometricsWithRData (>= 0.1.3), MASS, pls
R (>= 2.11.0), methods, stats, pls, plyr, reshape2, ggplot2
R (>= 2.10.0), DBI, RSQLite, RGtk2, hash, ADGofTest
R (>= 2.15.0), CNVassocData, mixdist, mclust, survival
SparseM, quantreg (>= 4.65),
splines,grDevices,graphics,stats,utils
R (>= 2.10), Hmisc, munsellinterpol, pracma
R (>= 2.13.1), Hmisc, xtable, gdata, SNPassoc
R (>= 2.2.0), tensorA, robustbase, energy, bayesm
R (>= 3.0.0), RCurl, bitops, httr, rjson
R (>= 2.14.0), survival, Matrix, prodlim
Matrix, parallel, survival, timereg, R (>= 2.13.0)
R (>= 2.10), coda, Matrix, splines, methods
pcaMethods (>= 1.24.0), Biobase, methods
R (>= 3.0.2), rJava (>= 0.8-3), CommonJavaJars (>= 1.0.5),
JavaGD, ggplot2
R (>= 3.0.0), Matrix, tseriesChaos, fields
R (>= 2.9.2), cudaBayesregData, oro.nifti
feature, flowCore, geometry, hdrcde, ks, misc3d, ptinpoly, rgl
car, effects, lattice, sm, MASS, nnet, splines, pscl, gdata,
xtable
R (>= 2.11.0), xtable, akima, R.oo, R.methodsS3, matlab,
lattice, locfit, graphics, grDevices, grid
R (>= 2.14), lattice, coda, igraph, graph
Deducer, psych, GPArotation, mvnormtest, irr, klaR
JavaGD (>= 0.6-0), Deducer (>= 0.7-4), sp, maptools,
OpenStreetMap, scales, rgdal
R (>= 2.15), methods, affy, affxparser, plyr, R.utils, utils,
oligo
R (>= 2.15.2), forecast (>= 3.09), rainbow, ftsa, cobs
R (>= 2.5.0), abind, grDevices, rgl, circular
R (>= 3.0.0), ggplot2, Rcpp (>= 0.11.2), rrcov, np, methods,
MASS
DiceKriging (>= 1.2), rgenoud, MASS, lhs
R (>= 2.4.0), stats, lattice, PolynomF, fda
R (>= 2.01), deSolve, rootSolve, bvpSolve, ReacTran, diagram,
deTestSet, scatterplot3d
R (>= 2.14.0), maptools, spatstat, spdep
R(>= 2.14.0), methods, graphics, startupmsg, sfsmisc,
SweaveListingUtils
R(>= 2.8.0), methods, graphics, mvtnorm, setRNG(>= 2006.2-1),
distr(>= 2.2), distrEx(>= 2.2), distrSim(>= 2.2)
R (>= 2.15), Matrix, gamlr, parallel, methods
R(>= 2.6.0), methods, graphics, setRNG(>= 2006.2-1),
distrSim(>= 2.2), startupmsg
R (>= 2.10.0), methods, snowfall, boot, MASS
graphics, grDevices, stats, utils, coneproj, Matrix, MASS
R (>= 2.10), MASS, nlme, survival, splines
R (>= 2.0.0), car, gtools, MASS, magic, methods, plotrix,
stats
R (>= 2.0.0), spatstat (>= 1.22-0), RandomFields, mgcv
shapefiles, splancs, mrds, mgcv, methods
survival, ggplot2, gridExtra, aod, proto, R (>= 3.1.0)
R (>= 3.0.2), ggplot2, moments, tgp, tseries
separationplot, ensembleBMA, plyr, methods, Hmisc, abind
R (>= 2.10), operators, topmodel, DEoptim, XML
R (>= 2.01), rootSolve, diagram, deSolve
R (>= 3.1.1), bigsplines, eegkitdata, ica, rgl
R (>= 3.0.0), survival (>= 2.37-6), stats, graphics
R (>= 2.10.0), locfit, lattice, latticeExtra, grid, xlsx
deSolve, numDeriv, parallel, phenology, Hmisc, R (>= 2.14.0)
R (>= 3.0.1), GPArotation, limSolve, shape
R (>= 2.10), sna, network, MASS, stringr, gdata
dismo, raster, rJava (>= 0.5-0), methods, R (>= 2.10)
R (>= 2.6.2), foreign, survival, MASS, nnet
R (>= 2.12.0), lassoshooting (>= 0.1.4), Matrix, methods,
graph
statnet.common (>= 3.1-0), network (>= 1.7-1)
statnet.common (>= 3.1-0), ergm (>= 3.1-0), network
CDVine, ggplot2, gridExtra, reshape2, ycinterextra
R (>= 2.15.0), methods, aod, VGAM (>= 0.8.4), GenomicRanges
(>= 1.8.10), Rsamtools
R (>= 3.0.0), methods, sp, spdep, rgeos, deldir
R (>= 2.0.0), graphics, stats, tseries, forecast
R (>= 2.10.0), Lmoments, distillery, car
R (>= 2.14.0), plotrix, FactoMineR, Matrix
R (>= 2.7.0), methods, rgenoud (>= 5.4-7), gWidgetsRGtk2 (>=
0.0-31), stats4, rrcov, Matrix
sensitivity, igraph, DiceKriging (>= 1.4)
R (>= 2.4.0), methods, timeDate, timeSeries, fBasics
R (>= 2.4.0), timeDate, timeSeries, fBasics, fOptions
R (>= 2.15.1), timeDate, timeSeries, fBasics
stats, MASS, methods, timeDate, timeSeries (>= 2100.84)
R (>= 2.4.0), timeDate, timeSeries, fBasics
R (>= 2.6.0), fBasics, fOptions, fExoticOptions
R (>= 2.15.1), timeDate, timeSeries, fBasics, fMultivar
R (>= 2.10.0), splines, Matrix, graphics
R (>= 2.11.0), foreach, mvtnorm, matrixcalc, numDeriv
R (>= 2.10), fda, splines, MASS, mgcv, rpart
R (>= 3.0.0), methods, maps, mapdata, proj4, geoR, sp
R (>= 2.4.0), methods, timeDate, timeSeries, fBasics, fOptions
R (>= 2.15.0), mvtnorm, tmvtnorm, fCertificates, Matrix
R (>= 2.4.0), timeDate, timeSeries, fBasics, fGarch, fTrading
R (>= 2.10), methods, graphics, tcltk, tkrplot, R2HTML
R (>= 2.15.0), stats, graphics, methods, timeDate, timeSeries,
fBasics (>= 2100.78)
R (>= 2.0.0), methods, rgl, RColorBrewer
R (>= 2.14.0), methods, filehash (>= 1.0), DBI, RSQLite
R (>= 2.13.0), methods, timeDate, timeSeries
gWidgets, tcltk, tkrplot, RGtk2, gWidgetsRGtk2, cairoDevice
R (>= 2.0.0), lattice, leaps, ltsa, bestglm
R (>= 2.14.0), graphics, grid, lattice, modeltools
R (>= 2.6), deSolve, rootSolve, minpack.lm, MASS, coda
R (>= 2.4.0), methods, timeDate, timeSeries, fBasics, fGarch
R (>= 3.0.2), stats, graphics, zoo, timeDate
R (>= 2.9.0), operator.tools(>= 1.2.1), utils, methods
R (>= 2.0.0), graphics, stats, forecast, fma, expsmooth,
lmtest, tseries
futile.any (>= 1.3.0), futile.logger (>= 1.3.0), timeDate,
quantmod
R (>= 2.10), survival, boot, MASS, survC1
R (>= 2.11), methods, quadprog, Rglpk, timeSeries
R (>= 3.1), methods, smacof, vegan, ellipse
R (>= 2.8.1), classInt, colorspace, methods, rgl, sp
R (>= 3.0.0), caret, gsubfn, utils, parallel
R (>= 2.4.0), urca, methods, timeDate, timeSeries, fBasics
R (>= 2.12.0), methods, grDevices, graphics, stats
R (>= 2.15.0), MultinomialCI, parallel, FuzzyNumbers, DEoptim
R (>= 2.7.0), stats, matrixStats, R.methodsS3
R (>= 2.15.0), graphics, stats, splines, utils, gamlss.data,
gamlss.dist (>= 4.3.1), nlme
R (>= 2.2.1), gamlss, gamlss.dist, survival
R (>= 2.4.0), rpanel (>= 1.1-1), gamlss, gamlss.tr,
gamlss.util
R (>= 2.15.0), gamlss (>= 2.4.0), colorspace, Matrix, MASS,
rgenoud, zoo, gamlss.add (>= 2.15.0), spam
R (>= 2.9.0), survival, lattice, splines, parallel
R (>= 2.11.1), RSQLite, plyr, reshape, lattice
R (>= 2.10), graphics, methods, flexclust (>= 1.0-0),
Rgraphviz
R (>= 2.10), nlme, sandwich, geoR, betareg, Formula
R (>= 2.15.0), methods, MASS, utils, GenABEL.data
R (>= 3.0.0), survival, cluster, class, parallel
R(>= 2.15-0), rgeos, classInt, sp, maptools, geosphere
R (>= 2.15.0), geoR, gstat, genalg, limSolve, MASS, plyr,
TeachingDemos, gsl
R (>= 3.0.2), ggplot2, grid, Matrix, lpSolve, reshape2
R (>= 2.11.0), R.utils, R.oo, R.methodsS3, abind, ncdf
R(>= 2.7), methods, numDeriv, graphics, stats, gplots
R (>= 2.15.0), maptools, sp, RColorBrewer, MASS, rgeos
R (>= 2.6.0), alabama, nleqslv, BB, SQUAREM
R (>= 2.10.0), methods, stats, graphics, fGarch, fastICA
R (>= 2.15.0), grid, gridGraphviz, gridSVG
R (>= 2.10.1), grDevices, graphics, stats, utils, MASS, rgl,
misc3d
R (>= 3.0.0), ggplot2, xtable, stringr, RUnit, plyr
R (>= 3.0.2), Hmisc, mvtnorm, MCMCpack, graphics
R (>= 2.10), CompQuadForm, e1071, gee, geepack, Matrix
R (>= 2.12.0), methods, digest, memoise, tcltk(>= 2.7.0),
gWidgets2
MASS, mgcv, parallel, GenABEL, DatABEL, tools, filehash
R (>= 3.0), glmnet, foreach, MASS, iterators
R (>= 2.10), utils, Matrix, MASS, hglm.data
R (>= 3.0.2), lattice, stats, grid, latticeExtra, multcomp
truncnorm, MASS, mvtnorm, Matrix, coda, spsurvey, MCMCpack,
xtable, mc2d
methods, grid, lattice, survival (>= 2.37-6), Formula
MASS, mvtnorm, corpcor, numDeriv, stats, boot
R (>= 2.15), methods, AnnotationDbi (>= 1.9.7)
R (>= 2.10), Rgraphviz, functional, plotrix, Hmisc
R (>= 3.0.1), lattice, coda, MASS, MCMCpack
R (>= 3.0.0), Matrix, lattice, igraph, MASS
R (>= 2.15), methods, utils, lattice, grid, mvtnorm
R (>= 3.1.1), lpSolve, MASS, car, lsmeans, multcompView
R (>= 2.11.0), fields, deldir, rgeos, sp, spdep
R (>= 2.0.1), utils, stats, MASS, cluster
R (>= 2.15.0), lattice, caTools, gdata, gtools, gplots,
parallel
R (>= 2.10), tmvtnorm, norm, pcaMethods, impute
prettyGraphs (>= 2.1.4), ExPosition (>= 2.0.0)
R (>= 2.13.0), deSolve, nlme, lattice, compiler
R (>= 2.2.1), stats, survival, perm (>= 1.0), Icens, MLEcens
R (>= 3.0.0), stats, lattice, moments, methods
graphics, grDevices, stats, utils, coneproj, Matrix
partitions, stats, VGAM, MASS, ape, apTreeshape, geiger,
gtools
quadprog, randtoolbox, parallel, R (>= 2.15)
R (>= 2.13.0), graphics, stats, grDevices
R (>= 3.0.0), Rcpp, plyr, lme4, doParallel
R (>= 3.0.0), MASS, nlme, splines, survival
R (>= 2.13.0), nlme, MASS, boot, survival, lattice, statmod,
gdata
R(>= 2.1.0), methods, lpSolve, quadprog, kernlab
R (>= 3.0.0), mvtnorm, snowfall, corpcor
R(>= 2.11.0), methods, stats, graphics, ltm
R (>= 2.13.1), compiler, rootSolve, emplik, Rcpp
DiceKriging, rgenoud, MASS, lhs, pbivnorm, randtoolbox,
DiceView
e1071, caret, nnet, MASS, caTools, mda, glmnet, randomForest,
mvpart
statnet.common, network, ergm (>= 3.1.2)
R (>= 2.14), lava (>= 1.1), mvtnorm, survival
R (>= 2.6.0), grDevices, stats, mvtnorm, VGAM
R (>= 2.10), lattice, lmom, lmomRFA, latticeExtra
R (>= 2.14.1), sn, moments, BB, Bolstad2, optimx, Rmpfr
R (>= 2.10), DEoptim, ggplot2, gtools, rtf
R (>= 3.0.0), Matrix, stats, methods, lme4 (>= 1.0)
R (>= 1.8.0), igraph, MASS, vegan, Matrix
R (>= 2.15.0), base, methods, utils, grDevices, graphics,
stats
R (>= 2.13.0), multcomp, data.table, pROC, car
R (>= 3.0.0), Matrix, lme4 (>= 1.0), nlme, methods
R (>= 2.7.0), MASS, msm, mvtnorm, polycor, sfsmisc, ltm,
Hmisc, rms
LS2W(>= 1.3-1), matrixStats, geoR, RandomFields, spdep
R (>= 2.12.1), LICORS, RColorBrewer, fields, gam, Matrix
mvtnorm, fBasics, stats, utils, graphics, grDevices
R (>= 2.4.1), party, plsgenomics, st, e1071
R (>= 2.8.0), MASS, sn, methods, miscTools
R (>= 3.0.0), methods, MALDIquant (>= 1.8)
R (>= 2.15.1), methods, genefilter, metaMA, xtable, multtest,
gtools, grid, GeneMeta
R (>= 3.0.0), grDevices, graphics, stats, utils
R (>= 2.6.0), MASS (>= 7.2-1), graphics, grDevices, stats
R (>= 2.14.0), stats, utils, methods, Matrix (>= 1.0-1)
R (>= 2.13.2), corpcor, foreach, multcomp, randomForest, MASS
R (>= 2.10), graphics, stats, tseries, forecast
lattice, locfit, maptools, quantreg, RANN, SparseM
MASS, boot, RColorBrewer, scatterplot3d, tcltk, tcltk2,
tkrplot, rpanel, graphics, rgl, smacof
R (>= 2.15.1), MASS, Matrix, lpSolve, sandwich, mvtnorm
R (>= 2.15.0), seqinr, pcaMethods, Matrix,
ROCR,Hmisc,KernSmooth, methods, seqLogo
R (>= 2.9.0), lattice, stats, methods, utils, MASS
mclust, mvtnorm, ellipse, gtools, gplots
stats, mvtnorm, ellipse, bbmle, rjags, coda
R (>= 2.10.0), proto, foreach, iterators
astroFns, plotrix, graphics, R (>= 2.10)
reshape, lattice, XML, MASS, methods, grid
prettyGraphs (>= 2.0.0), ExPosition (>= 2.0.0)
R (>= 2.10.1), evd, numDeriv, corpcor, fields
R (>= 2.10), Formula, truncreg, maxLik, pbivnorm
R (>= 2.10), ggplot2, maptools, RColorBrewer, rgdal, grid
mstate , prodlim, cmprsk, riskRegression
R(>= 2.10.0), VIM, mi, mice, Amelia, lattice, vcd
R (>= 2.14), graphics, grDevices, methods, stats, utils
Bessel, stats, MASS, mvtnorm, ghyp, numDeriv, R (>= 3.1.1)
R (>= 2.0.0), tcltk, tcltk2, MASS, RODBC
R (>= 3.0), graphics, stats, utils, base
R (>= 2.0.0), urca, stabledist, fGarch, lattice
R (>= 2.10.0), methods, magic, partitions, emulator, Oarray
(>= 1.4-5)
R (>= 2.9.2), ellipse, ICS, ICSNP, SpatialNP, mvtnorm, survey
R (>= 2.8.1), PolynomF (>= 0.93), SparseM (>= 0.79), stats,
utils, base
R (>= 2.13.0), randomForest, raster, gbm, rgdal
R (>= 2.3.0), stats, impute, grDevices, dynamicTreeCut
R (>= 2.14.0), methods, mvtnorm, ncvreg, actuar, mgcv
reshape2, ggplot2, lubridate, stats, lmtest, methods, plyr
IRanges, RWeka, rJava, R.utils, R (>= 3.1.0)
R (>= 2.10), Matrix, stats, class, glmnet, stats4, e1071, rgl,
RColorBrewer, colorspace
R (>= 2.0), methods, class, graph, lattice
R (>= 2.15.0), base, stringr, graphics, methods, XML,
sensitivity, lhs, rgl
XML, geosphere (>= 1.2-15), sp, RSQLite, NightDay
stats, graphics, mvtnorm (>= 0.8-0), survival (>= 2.35-7),
TH.data (>= 1.0-2)
ggplot2, plyr, psych, reshape, proto, grid, xtable, R (>= 3.0)
lars (>= 0.9-8), penalized, polspline, rpart
car, pdist, pls, gplots, mvtnorm, robustbase, gtools, bitops,
caTools, pcaPP, rrcov
R (>= 2.8.0), mutoss (>= 0.1-6), multcomp, plotrix
R (>= 3.1.0), Formula, nlme, MNM, quantreg, Rfit
R(>= 2.9.1), phytools, ape, corpcor, subplex
R (>= 2.14.0), graphics, stats, grDevices
R (>= 2.7.1), randomForest, pamr, kernlab, class
R(>= 2.9.1), ouch, ape, numDeriv, mvtnorm, corpcor
R (>= 3.0), network (>= 1.9),networkDynamic (>=
0.6),animation,sna
optimbase (>= 1.0-9), optimsimplex (>= 1.0-5), methods
R (>= 3.1.1), BiocGenerics, Biobase, igraph, limma
R (>= 3.0.0), stringr, hash, iterators, abind, methods
R (>= 2.9.2), MASS, psych, boot, lattice
R (>= 2.0.0), alr3, car, drc, HydroMe, lattice, lmtest, MASS,
NISTnls, nlme, nls2, nlstools, NRAIA, sandwich
R (>= 3.0.0), methods, utils, pkgmaker (>= 0.20), registry,
rngtools (>= 1.2.3), cluster
R (>= 1.8.0), lpSolve, gdata, Hmisc, rms, geometry, psych, pwt
R (>= 2.3.0), DistributionUtils, GeneralizedHyperbolic
R (>= 2.9.1), splines, graphics, Rglpk, Benchmarking
R (>= 2.10), combinat, MASS, partitions, stats, survival
R (>= 2.10), nutshell.bbdb, nutshell.audioscrobbler
R (>= 3.0.2), graphics, methods, ncdf4, proj4
graphics, grDevices, stats, utils, R (>= 2.10)
rJava, methods, raster, rgdal, R (>= 2.10.0)
mvtnorm, clinfun, stats, graphics, R (>= 2.14.1)
R (>= 2.15.1), stats, methods, graphics, digest
R (>= 2.15.0), methods, graphics, lattice, hash, grid
R (>= 2.10), methods, XML, RCurl, geosphere
R(>= 2.9.1), methods, stats, graphics, subplex
R (>= 3.0.0), methods, ggplot2, network, knitr
R (>= 3.0.0), methods, survival, quantreg, Formula
R (>= 3.0.0), quantreg, MatrixModels, outliers, pcaPP
rioja, vegan, TeachingDemos, gstat, fields, mapproj
R (>= 3.0.1), parallel, mboost, party, iterators
R (>= 2.14.0), methods, grid, zoo, sandwich (>= 1.1-1),
strucchange, stats, modeltools (>= 0.2-21)
R (>= 2.14), svmpath, kernlab, affy, Biobase, ROCR, igraph,
lpSolve
methods, mvtnorm, foreach, reshape, ggplot2
R (>= 3.0.0), methods, graphics, grid, ggplot2, gridExtra,
reshape2
R (>= 3.0.0), methods, rlecuyer, pbdMPI (>= 0.2-3)
R (>= 3.0.0), polycor, ltm, stats, ggplot2, mc2d
R (>= 2.4.0), MasterBayes, MCMCglmm, kinship2, grid
R (>= 3.0.1), stats, Matrix, CompQuadForm, survey, kinship2
(>= 1.5.4)
e1071, MASS, corpcor, statmod, tgp, mlegp, lhs
R (>= 2.15.1), lattice, fda, quadprog, latticeExtra
R (>= 2.15.1), lattice, quadprog, TSP, fda, Matrix
R (>= 2.2.0), methods, utils, graphics, stats
R(>= 3.0), methods, graphics, ggplot2 (>= 0.9.3)
R (>= 2.14.1), ggplot2 (>= 0.9.2), parallel, robustbase
methods, grid, xtable, RColorBrewer, gdata
R (>= 2.14), gstat, RColorBrewer, lattice, pheno
R (>= 2.14.1), graphics, stats, utils, qtl, VGAM, mixtools
R (>= 1.8.0), survival, graphics, splines
methods, phylobase (>= 0.6.2), ape (>= 2.5)
R (>= 2.10.0), RGtk2, gWidgetsRGtk2, cairoDevice, lattice,
rggobi, ggplot2, proto
gWidgets, tcltk, tkrplot, RGtk2,
gWidgetsRGtk2,cairoDevice,numDeriv
R (>= 2.7.0), lattice (>= 0.16-3), cairoDevice, gWidgetsRGtk2
(>= 0.0-45), grid
R (>= 2.14.2), MASS, splines, limma, R.oo
R (>= 3.0.1), amap, diagram, tester, turner
R (>= 3.0.0), methods, rlecuyer, pbdMPI (>= 0.2-2), MASS
R (>= 2.10), methods, graphics, grid, lattice, nlme
R (>= 2.10), methods, lattice, portfolio (>= 0.4.0)
R (>= 2.12.0), Hmisc, ROCR, epitools, PBSmodelling
R (>= 2.10), methods, sp, gstat, GenKern, rgdal (>= 0.7-4),
fields
R (>= 2.10.0), boot, methods, stats, stats4, lme4, Hmisc
ggplot2, MASS, RColorBrewer, reshape, methods, plyr, R (>=
3.0.0)
R (>= 2.13.0), MASS, tmvtnorm, Rcpp (>= 0.10.1), ff,
minpack.lm
bayesm, LearnBayes, SuppDists, mvtnorm, R (>= 2.12.2)
R (>= 2.14.1), stats, utils, graphics, grDevices, fftw (>=
1.0-3)
psych, plyr, rtf, R2HTML, mokken, lavaan, semTools
R (>= 2.15.0), methods, dcmle, dclone, stats4
R (>= 2.14.0), abind, tcltk, grDevices, utils, QCA (>= 0.4-6)
R (>= 3.0.0), ggplot2 (>= 0.9.3.1), qdapDictionaries (>=
1.0.2), qdapTools (>= 1.0.1), RColorBrewer
R (>= 2.13.0), MASS, minpack.lm, rgl, robustbase, Matrix
R (>= 3.0.1), gsl, Matrix, mvtnorm, numDeriv, timeSeries
R (>= 2.10), stats, qtl (>= 1.9.43), lattice, coda, tools,
MASS
R (>= 2.10), stats, qtl, lattice, corpcor, mnormt
R (>= 2.10), qtl, igraph, sem, graph, pcalg
Defaults, xts(>= 0.9-0), zoo, TTR(>= 0.2), methods
R (>= 3.0.0), stats, agricolae, stringr, pixmap, shiny, xtable
R (>= 2.13.0), BayesXsrc, colorspace, mgcv
R (>= 3.0.0), fields, MCMCpack, methods, mixOmics, mvtnorm,
snowfall
R (>= 3.0.0), graphics, grDevices, methods, rgl, geometry,
oro.dicom (>= 0.4.1), ptinpoly
plyr, RCurl, reshape2, png, ggplot2 (>= 0.9.2.1), RJSONIO
R (>= 2.14.0), MASS, foreach, doParallel
R (>= 3.0.0), methods, utils, RSQLite, rgdal
R(>= 2.10.0), sp, RANN, PBSmapping, RgoogleMaps
R (>= 2.15.0), lattice, plyr, pander, yaml
R (>= 2.14.0), methods, raster (>= 2.0-12), lattice,
latticeExtra, hexbin
R (>= 3.0.0), grDevices, utils, splines, RcmdrMisc (>= 1.0-2),
car (>= 2.0-21)
R (>= 3.0.0), grDevices, utils, car, sandwich
R (>= 2.10), Rcmdr (>= 1.7.0), coin, survival, multcomp
Rcmdr (>= 1.8-3), TeachingDemos, tseries,
abind,MASS,xts,forecast
R (>= 3.0.2), HH (>= 2.3-42), lattice, grid, grDevices,
latticeExtra, rgl
R (>= 2.13.0), Rcmdr (>= 1.4-6),lattice,grid,grDevices
R (>= 2.10), Rcmdr (>= 1.7.0), pROC, ROCR, ResourceSelection,
PredictABEL
Rcmdr (>= 1.3-0), tcltk, car, RColorBrewer, colorspace,vcd
methods, tm (>= 0.6), NLP, slam, zoo, lattice
R (>= 2.10.0), MASS, methods, Matrix, lattice
R (>= 2.15.0), sandwich, lmtest, AER, Formula
R (>= 2.10.0), Matrix, registry, arules (>= 1.0-1), proxy
R (>= 2.14.0), methods, mvtnorm, boot, Matrix
R(>= 2.2.1), MASS, boot, survey, mitools, graphics
R (>= 2.10.0), methods, stats, proxy (>= 0.4-7), MASS,
clusterGeneration, igraph
R (>= 2.10), Rcpp (>= 0.10.3), raster, foreach, methods
gWidgets, gWidgetstcltk(>= 0.0-44), Renext(>= 1.5-0)
R(>= 3.0.0), stats, utils, graphics, grDevices, RColorBrewer
methods, splines, mvtnorm, mgcv, Rniftilib
R (>= 2.13.0), datasets, stats, grDevices, graphics, methods,
grid, lattice, ggplot2
RMOAjars (>= 1.0), rJava (>= 0.6-3), methods
R (>= 3.0), copula, RGENERATE,RMAWGEN,blockmatrix,Matrix
Hmisc (>= 3.12-2), survival (>= 2.37-6), lattice, SparseM
R (>= 2.10.0), methods, graphics, tcltk, graph
R (>= 2.10), abind, maps, fields, tgp, fossil, tcltk,
RColorBrewer
R (>= 2.10.0), methods, XMLRPC, RCurl, XML, stats
R (>= 2.10), utils, robustbase, rrcov, GGally, MASS, pls
robustbase, quantreg, splines, BB, rgenoud
fit.models, MASS, lattice, robustbase, rrcov, stats
R (>= 2.9.2), robustbase, stats, graphics, survival
R (>= 2.10), splines, survival, penalized, survivalROC
R (>= 2.10), splines, date, survival, relsurv
R (>= 2.13.0), utils, tools, methods, brew
R (>= 2.15.0), graphics, stats, grDevices
R (>= 2.13.0), rjson, RCurl, seqinr, knitcitations
R(>= 3.1.0), dplyr (>= 0.2), RSQLite, RSQLite.extfuns
R(>= 2.11.0), mc2d, eha, msm, tcltk, tkrplot
R (>= 2.15.0), lavaan (>= 0.5.11), ggplot2, lattice, gridExtra
R (>= 2.10), sqldf, XML, scales, FactoMineR, ggplot2
R (>= 2.15.0), methods, sybil, RBGL, graph
R (>= 2.15.1), biwt, cairoDevice, fBasics, grDevices, gplots,
gWidgets, gWidgetsRGtk2, minerva, parmigene, stringr, snowfall
R (>= 2.10.0), stats, truncnorm, parallel
R(>= 2.15.1), stats, utils, grDevices, graphics, parallel
R (>= 2.14), methods, grid, foreign, memisc, psych, ggplot2
(>= 0.9.2)
R (>= 2.14.1), GEOmap, geomapdata, maps, mapproj
neldermead (>= 1.0-8), lattice, grid, deSolve, utils
R(>= 2.10), FKF(>= 0.1.0), mvtnorm, methods, RUnit
R (>= 2.6.0), stats, grDevices, graphics, MASS
R (>= 2.10), brew, knitr,data.table,xtable
sdcMicro (>= 4.1.0), gWidgetsRGtk2, tcltk, cairoDevice
stringr, methods, Rcpp (>= 0.11.0), Rglpk, lpSolveAPI,
data.table
R (>= 3.0.1), ggplot2 (>= 0.9.3), MASS, mgcv, survival, coda
R (>= 2.15.3), survival, NADA, lubridate
R (>= 2.14.0), sem, stats, MASS, matrixcalc
R (>= 3.0.0), copynumber, parallel, squash
graphics, grDevices, stats, utils, lattice
stats, grDevices, graphics, R (>= 2.0.0)
R (>= 3.0.2), stats, poibin, prot2D, discreteMTP
hdrcde, coda, MASS, bayesm, mnormt, spatstat
R (>= 2.10), MASS, corpcor, mmap, ade4, infotheo
R (>= 2.5.1), hdrcde, locfit, MCMCpack, tcltk
stats, graphics, grDevices, utils, rcdk, R (>= 2.13)
flsa, igraph, dse, Matrix, R (>= 2.10.1)
R (>= 2.10), Matrix, coxme, survival, CompQuadForm
R (>= 2.3.0), DistributionUtils, GeneralizedHyperbolic, RUnit
doParallel, foreach, parallel, methods, stats
R (>= 3.0.0), haplo.stats, survival, mvtnorm, parallel
R (>= 2.10), IRanges, GenomicRanges, Biostrings, Rsamtools
R (>= 2.14.1), aqp, lattice, munsell, splancs, methods
R (>= 2.10), methods, lattice, latticeExtra, zoo
R (>= 2.15.0), XML, RCurl (>= 1.4-1), sp, methods, graphics
Rcpp (>= 0.9.6), RcppEigen (>= 0.3.1), Matrix, methods
lars, elasticnet, MASS, mda, R (>= 2.10)
R (>= 1.9.0), Matrix, spdep, mvtnorm, tmvtnorm
R (>= 3.0.1), cubature, mvtnorm, SpatialExtremes
parallel, iterators, foreach, rgdal, raster, R (>= 3.0.0)
R (>= 2.10.0), spatstat (>= 1.37-0), fields (>= 6.8),
smoothie, smatr, turboEM
R (>= 3.0.2), stats, graphics, grDevices, utils
R (>= 1.8.0), coda, magic, abind, Formula
R (>= 2.14.0), moments, ezsim,tmvtnorm, mvtnorm
R (>= 3.0.0), lars, randomForest, parallel
R (>= 2.15.0), MASS, nlme, spdep, plm, Matrix, bdsmatrix,
spam, ibdreg, car, lmtest, Ecdat, maxLik
R (>= 3.0.0), base, methods, datasets, utils, grDevices,
graphics, stats
R (>= 2.9.2), rlecuyer, ff (>= 2.1-1), randomForest
R (>= 2.14.0), coda, forecast (>= 3.0.2), spacetime
R (>= 2.14.0), gsubfn (>= 0.6), proto, RSQLite (>= 0.8-0),
RSQLite.extfuns
R (>= 2.5.0), methods, grid, lattice, graphics, base, stats
R (>= 2.10), survival, mgcv, R2HTML, gss, codetools
R (>= 3.0.0), ggplot2, grid, scales, gridExtra, RColorBrewer
R (>= 2.10), splancs, KernSmooth, spatstat
R (>= 3.0.0), methods, pedigree, Rgraphviz(>= 2.6.0)
graphics, grDevices, utils, stats, R (>= 3.1)
R (>= 2.15.2), methods, grDevices, graphics, stats, utils,
Rcpp, sp (>= 1.0-15), xtable, polyCub (>= 0.4-2)
stats, utils, graphics, splines, R (>= 2.13.0)
R (>= 2.10), survival, stats, ggplot2, data.table, rpart
R (>= 2.15.3), mcmc, coda, survival, normalp, R.utils, BMS
grid, quadprog, maps, maptools, mapdata, dichromat
R (>= 2.10.0), e1071, hexbin, qualV, klaR, som
R (>= 2.11.0), graphics, utils, stats, methods
R (>= 2.10), graphics, grDevices, methods, stats, utils,
timeDate (>= 2150.95)
prettyGraphs (>= 2.1.4), TExPosition (>= 2.6.10), ExPosition
(>= 2.8.19), InPosition (>= 0.12.7)
R (>= 2.7.0), MASS, boot, tcltk, splines, lattice
R (>= 1.9.0), mvtnorm, utils, Matrix, stats4, gmm
R (>= 2.14.0), tourr (>= 0.5.1), colorspace, RGtk2, gWidgets,
Cairo
R (>= 2.10), XML, RCurl, plyr, methods, taxize
R (>= 2.12.1), graphics, grDevices, methods, stats, utils
R (>= 2.8.1), TraMineR (>= 1.8-4), RColorBrewer, combinat,
survival
R (>= 3.0.0), utils, grDevices, graphics, stats
R (>= 2.15.0), grDevices, graphics, stats
ape, Matrix, subplex, TreeSim (>= 2.1), deSolve
ggplot2, scales, reshape2, ez, R (>= 3.0)
R(>= 2.10), stats, methods, truncreg, boot
R (>= 2.5.1),leaps, locfit, mgcv, tseries
methods, grDevices, graphics, stats, tools, MASS
sva, limma, matrixStats, corpcor, stats, MASS
R (>= 2.12.0), doParallel, foreach, numDeriv, quantreg
R (>= 2.10), gbm (>= 1.5-3), survey, xtable, latticeExtra
R (>= 2.12.0), methods, reshape, lattice, Rcpp (>= 0.8.0)
R (>= 3.0.0), RItools, MASS, coin, tables, penalized
R (>= 2.10), maptools, sp, foreign, methods, grDevices, base,
stats, utils
R (>= 2.15.0), MASS, HistData, Hmisc, quantreg
R (>= 3.1.0), parallel, splines, ggplot2, quantreg
R (>= 2.10), methods, fields, boot, CircStats, MASS, dtw
R (>= 3.0.0), methods, stats, splines, stats4
R (>= 2.15.2), RJSONIO, RCurl, zoo, stats, pracma, Rcpp (>=
0.10.3)
R (>= 2.13), tcltk, tkrplot,VIM (>=
4.0.0),survey,gWidgetsRGtk2
R (>= 2.14.0), stats, utils, gridBase, flip (>= 2.1),
matrixStats, Hmisc
R (>= 2.12.2),utils, stats, grDevices, graphics
R(>= 3.0.1), stringdist, lattice, latticeExtra
R (>= 2.15.1), lattice, latticeExtra, XML
R (>= 2.10.0), graphics, grDevices, stats, utils
R (>= 2.14), dynamicTreeCut (>= 1.62), flashClust
R (>= 2.10), gtools, gWidgets, gWidgetsRGtk2, coda,
cairoDevice
R (>= 3.0.2), splus2R (>= 1.2-0), ifultools (>= 2.0-0), MASS,
graphics, stats
R (>= 2.14.0),stats,utils,grDevices,datasets,x12 (>= 1.3.0)
R (>= 2.2.0), methods, lattice, grid, gam
grid, lattice, vcd, MASS, colorspace, barcode, gpairs
R (>= 2.10), rgl, geometry, LeafAngle, devtools
methods, zoo, stats4, utils, expm, cubature, mvtnorm
R (>= 2.14), boot, MASS, methods, sandwich
R (>= 3.0.2), rjags, coda, matrixcalc, Formula
utils, stats, MASS, igraph, ape, shiny, ggplot2
data.table, plyr, caret, seqinr, lattice, randomForest, ROCR,
reshape2, protiq
ggplot2, RCurl, plyr, stringr, reshape, reshape2, httr, grid,
RJSONIO, lubridate
graphics, stats, car, scatterplot3d, colorspace, fda,
mgcv, MASS, stats, graphics, grid, brglm, princurve, lattice
shapes, rgl, archetypes, nnls, depth, FNN, ICGE, cluster
methods, MASS, evd, BB, numDeriv, ggplot2
rjson, plyr, assertthat, httr, leafletR (>= 0.1-1)
lubridate, classInt, reshape2, rgdal, plotKML
Rcpp (>= 0.11.1), methods, stats, graphics
nlme, lattice, graphics, stats, tools, utils
genefilter, Biobase, seqinr, multtest, gtools, edgeR, DESeq,
aroma.light
plyr, reshape, grid, lattice, cluster, sp, Hmisc, stringr,
scales, plotrix, RColorBrewer
scatterplot3d, pixmap, tkrplot, plotrix, mvtnorm, deSolve,
lattice, multcompView
checkmate (>= 1.4), BBmisc (>= 1.7), DBI, RSQLite, plyr
checkmate (>= 1.4), utils, stats, digest, DBI, RSQLite,
sendmailR, brew, stringr, fail (>= 1.2)
pbapply, mvtnorm, stringr, utils, graphics
sp, maptools, colorspace (>= 0.97), coda, splines
Rcpp (>= 0.10.3), MASS, mnormt, fitdistrplus, denstrip
DEoptim, aws, grid, ggplot2, reshape2, shiny, methods
RcmdrMisc (>= 1.0-1), Rcmdr (>= 2.1-0), car (>= 2.0-21),
rpart, flexclust, clv
hdrcde, utils, MASS, ellipse, coda, mclust
maps, plotrix, sqldf, plyr, taxize, treemap, ggplot2, grid
plm, Formula, MASS, Hmisc, deSolve, pracma, caTools,
matrixStats
ICSNP, sciplot, plotrix, ggplot2, png, grid
knitr, markdown, RJSONIO, xtable, plyr, reshape2
graphics, methods, stats, flexmix, Formula, lmtest,
modeltools, sandwich
graphics, stats, strucchange, zoo, forecast, sp, raster
parallel, methods, bigmemory.sri, BH, irlba
optimx, FD, parallel, xtable, plotrix, gdata
abind, rasterVis, pROC, nnet, gbm, mda, randomForest, rpart,
MASS, methods
geosphere, ks, lazyData, raster, rgdal, rgeos, rworldmap,
rworldxtra, sp
XML, httr, stringr, assertthat, jsonlite, reshape, plyr
stats, optimx (>= 2013.8.6), numDeriv, lattice, rgenoud,
parallel, mvtnorm, rlecuyer
igraph, ROCR, gbm, colorRamps, gplots, gtools, pamr,
randomForest, Boruta, caret, tgp, mlegp, penalizedSVM
coda, splines, actuar, rjags, R2OpenBUGS, ggplot2, plyr
mda, ncvreg, mboost, gbm, earth, elasticnet, rms
glmnet, survival, parallel, mlegp, tgp, peperr, penalizedSVM,
lattice
corpcor, evd, qpcR, Matrix, igraph, fdrtool, shape
deSolve, FME, survey, ggplot2, reshape2, shiny, grid, rgdal,
maptools, sp
car, reshape2, foreach, methods, plyr, nlme, BradleyTerry2
XML, R.utils, rjson, RMySQL, RCurl, stringr, yaml, boot,
foreach, data.table, glmnet, ggplot2
tcltk, rjags, psych, mvtnorm, polycor, MASS, methods
Rcpp, statmod, randtoolbox, reshape2, plyr
MASS, psych, polycor, sfsmisc, Rcpp (>= 0.11),
VGAM (>= 0.9-3), methods, grid, MASS, lattice, survival,
multcomp, nlme
Matrix, actuar, Hmisc, methods, stats, statmod, reshape2,
MASS, lattice, grid, tweedie, utils
pls, amap, chemometrics, robustbase, RColorBrewer, plyr,
pcaPP, mvtnorm, mvoutlier, grid, rgl, R.utils, mclust, MASS,
baseline, IDPmisc, gsubfn, lattice, seriation
lmtest, MASS, outliers, ptw, quantreg, Rfit, robustbase,
shiny, signal
ggplot2, scales, plyr, Hmisc, grid, acs, stringr
mvtnorm, msm, methods, Rcpp (>= 0.11.0), igraph, numDeriv,
pbivnorm
Rcpp (>= 0.9.10), plyr, ggplot2, lattice, methods, R2jags,
reshape2
utils, methods, grid, ggplot2, sp, raster, reshape2, plyr,
scales
tmvtnorm, mvtnorm, truncnorm, MASS, mclust, msm
igraph, Matrix, lattice, gdata, XML, cluster, clusterSim
Hmisc, MASS, GPArotation, lpSolve, nonparaeff
robustbase, ggplot2, boot, parallel, scales
stats, graphics, methods, stats4, Matrix, lattice, gsl,
ADGofTest, stabledist (>= 0.6-4), mvtnorm, pspline
copula, vines (>= 1.1.0), mvtnorm, truncnorm
expm, numDeriv, corpcor, phangorn, rgenoud, parallel
ggplot2, ggthemes, arm, COUNT, gridExtra, pcaPP, mvtnorm,
shiny
biglm, ggplot2, minqa, nlme, reshape2, statmod, stats, stats4,
tweedie
MASS, crossdes (>= 1.1-1), xtable, methods, Matrix, multcomp,
stats4, digest
RJSONIO, XML, RCurl, base64, methods, markdown
grDevices, graphics, methods, parallel, utils
ggplot2, KFAS, plyr, reshape2, tableplot, methods
stringr, XML, reshape2, RCurl, lubridate, plyr
rJava, e1071, scales, plyr, foreign, multcomp, effects
utils, stats, magrittr (>= 1.0.1), whisker
reshape2, ggplot2 (>= 0.9.1), plyr, scales, grid, GGally
httr (>= 0.4), RCurl, utils, tools, methods, memoise, whisker,
evaluate, digest, rstudioapi, jsonlite
distr(>= 2.2.0), distrEx(>= 2.2.0), distrSim(>= 2.2.0),
distrTEst(>= 2.2.0), distrTeach(>= 2.2.0), RandVar(>= 0.7),
distrMod(>= 2.2.0), MASS, methods, SweaveListingUtils,
startupmsg
stats, graphics, grDevices, utils, splines, nlme
stats, utils, graphics, vcd, combinat, MASS
binom, chipPCR, e1071, dgof, pracma, rateratio.test, signal,
shiny, spatstat
gmp (>= 0.5-2), graphics, grDevices, grid, stats, utils,
digest (>= 0.2.3), lattice (>= 0.13-6), stringr (>= 0.4), XML
(>= 2.1-0)
assertthat, utils, methods, Rcpp, magrittr, Lahman, hflights
car, clinfun, mgcv, multcomp, pgirmess, DTK, segmented,
mvtnorm
methods, parallel, oro.nifti (>= 0.3.9), oro.dicom, gsl
shape, fields, rgeos, maptools, rgdal, sp, spam, hydroGOF,
topmodel, intervals, tools
methods, reshape, plyr, nleqslv, ggplot2, grid
fields, nortest, quadprog, Kendall, KernSmooth, lmtest, som,
spam, stats
MASS, stringr, rjags, coda, foreach, doParallel,random,
parallel
httr (>= 0.3), plyr, assertthat, whisker, dplyr (>= 0.2),
jsonlite, lubridate, leafletR (>= 0.1-1)
adehabitatHR, adehabitatMA, biomod2, dismo, ecodist, maptools,
randomForest, spatstat, raster, rms
stringr, maptools, grid, arm, data.table, memisc
siggenes, affy, multtest, survival, xtable, gcrma,
heatmap.plus, biomaRt, GSA, MASS, FactoMineR, cluster,
AnnotationDbi
deSolve (>= 1.10-6), ergm (>= 3.1.2), RColorBrewer,
statnet.common
robustbase (>= 0.9-10), coda, trust, Matrix
graphics, stats, methods, MASS, splines, Matrix
grid, colorspace, hexbin, scales, ggplot2, reshape2, plyr, TSP
foreach, ggplot2, parallel, digest, plyr, reshape, Jmisc
grid, methods, modeltools (>= 0.2-16), nnet, stats, stats4
tseries, fracdiff, Rcpp (>= 0.11.0), nnet, colorspace,
parallel
MASS, cluster, mclust, flexmix, prabclus, class, diptest,
mvtnorm, robustbase, kernlab, trimcluster
sapa(>= 2.0-0), wmtsa (>= 2.0-0), scatterplot3d
plyr, dplyr (>= 0.2), data.table, biglm, fastcluster,
FactoMineR, stats
sfsmisc(>= 1.0-26), BsMD, utils, scatterplot3d, igraph(>= 0.7)
plyr, reshape2, ggplot2, grid, scales, foreign, tcltk, tcltk2,
gridExtra
lambda.r (>= 1.1.6), lambda.tools, futile.logger (>= 1.3.0),
RMTstat
car, mgcv, DAAG, MASS, rpart, randomForest, lattice,
latticeExtra, ape, KernSmooth
cubature, hypergeo, laeken, numDeriv, stats
oro.nifti, movMF, grid, gridExtra, colorspace, geometry, gsl,
abind
MASS, mvtnorm, subplex, deSolve (>= 1.7), digest, Rcpp (>=
0.9.0), coda
MASS, methods, msm, mstate, plyr, graphics, stats, utils,
data.table
kinship2, methods, bootstrap, Matrix, lattice, quadprog,
foreach, parallel, doParallel
constrainedKriging(>= 0.2-1), lmtest, nlme, nleqslv, quantreg,
RandomFields(>= 3.0.10), robustbase(>= 0.90-2), snowfall(>=
1.84-6)
KernSmooth, rgeos, robustbase, splancs, stats, tcltk
rgdal, geosphere, RCurl, rgeos, plyr, ggplot2, grid, sp,
stringr, animation, rasterVis
proto, scales, RgoogleMaps, png, plyr, reshape2, grid, rjson,
mapproj
plyr (>= 1.7.1), digest, grid, gtable (>= 0.1.1), reshape2,
scales (>= 0.2.3), proto, MASS
ggplot2, plyr, proto, grid, scales, stringr
plyr, gtable, scales, reshape2, proto, MASS, sp, grid
assertthat, RJSONIO, shiny (>= 0.10), magrittr, dplyr (>=
0.2), htmltools (>= 0.2.4)
jpeg, tiff, logging, parallel, PET, ggplot2, EBImage
MASS, PolynomF, multcomp (>= 1.1), mvtnorm, Matrix,
CommonJavaJars (>= 1.0.5), stats4
grid, stringr, lattice, sp, utils, methods
MASS, stats, graphics, Matrix, nnet, qvcalc (>= 0.8-3), relimp
plyr, grid, gProfileR, reshape2, limma, gtable
grid, barcode, lattice, vcd, MASS, colorspace
gtools, gdata, stats, caTools, KernSmooth
gWidgets, RGtk2, gWidgetsRGtk2, MASS, cluster, rgl, GUniFrac,
ape
RColorBrewer, plyr, gridExtra (>= 0.9.1), reshape2, grid
Matrix, RBGL, igraph, graph, Rcpp (>= 0.11.1)
rms (>= 4.1-3), lattice, latticeExtra, Formula, survival
methods, sp (>= 1.0-8), RSAGA, dismo, rgdal, raster, aqp,
plotKML, gstat, stats, boot, rpart, quantregForest,
randomForest, nlme
methods, lattice, sp (>= 0.9-72), zoo, spacetime (>= 1.0-0),
FNN
lattice, grid, graphics, grDevices, stats, utils
reshape2, leaps, vcd, colorspace, RColorBrewer, Rcmdr
grid, RColorBrewer, plyr, tkrgl, tcltk, jpeg, png
ggplot2 (>= 0.9.2), stats, stats4, methods, plyr, reshape2,
MASS, Matrix, Rcpp
latticeExtra, cluster, rpart, nnet, acepack, foreign
RCurl (>= 1.95-0), stringr (>= 0.6.1), digest, tools, methods,
jsonlite
Hmisc, sp, lattice, zoo(>= 1.7-2), grid, lhs
methods, zoo, lubridate, climdex.pcic (>= 1.1-1), PCICt
caret, reshape2, glmnet, pls, rda, Cubist, ridge, gbm, mboost,
rpart,
gstat (>= 0.9-36), automap, mvtnorm, MASS, evd
shiny, knitcitations, RCurl, mvtnorm, knitr
utils, graphics, network (>= 1.4-1-1), igraph (>= 0.6-0)
rpart (>= 3.1-8), MASS, survival, nnet, class, prodlim
foreach, parallel, gplots, colorspace, plotrix
Rcpp (>= 0.10.5), data.table, lattice, grid, knitr, markdown
RefManageR (>= 0.8.2), digest, httr (>= 0.3)
evaluate (>= 0.5.3), digest, formatR (>= 0.10), highr (>=
0.3), markdown (>= 0.6.5), stringr (>= 0.6), tools
plyr, reshape, ggplot2, MASS, emdbook, coda
stats4, stats, graphics, MASS, mnormt, pbivnorm, quadprog
ape, picante, geiger, fBasics, SDMTools, vegan
car, leaps, MBESS, foreign, gdata, triangle
maps, raster, XML, geosphere, maptools, sp
RandomFields, iterators, ncdf, methods, rpanel (>= 1.1-3),
fields, maptools, Matrix, rgeos
RColorBrewer, mvtnorm, zoo, FNN, fields, locfit, Matrix
plyr, harvestr, stringr, dostats, foreach
graphics, grid, splines, parallel, MASS, nlme, lattice, minqa
(>= 1.1.15), nloptr
stats, methods, nlme, lmeSplines, parallel, reshape, gdata,
gplots, ggplot2, graphics
psych, gvlma, AICcmodavg, lme4, pbkrtest
MASS, grid, png, RgoogleMaps, RColorBrewer
gRbase, rms, stats, statmod, graphics, speedglm
gdata, shapes, e1071, pcaMethods, MASS, miscTools
base64enc, digest, downloader, readBrukerFlexData (>= 1.7),
readMzXmlData (>= 2.7), XML
MergeMaid, GeneMeta, xtable, methods, metaArray
RColorBrewer, Hmisc, quantreg, classInt, sp, colorspace,
reshape2
foreign (>= 0.8), methods, grid, lattice, stats
R2admb, truncnorm, coda, optimx (>= 2013.8.6), ggplot2,
Matrix, numDeriv, Rcpp (>= 0.9.13), plyr
igraph, Matrix, matlab, expm, stats4, parallel
RSQLite, gdistance, geosphere, sp, raster, ncdf, plotrix,
shape
nlme, mvtnorm, KFAS (>= 1.0.1), stats, utils, graphics
dfoptim, mvtnorm, seriation, igraph, gplots, cba
assertive, matrixcalc, psych, lavaan, MASS
Matrix, survival, splines, lattice, nnls, quadprog, utils
Rcpp (>= 0.11.2), plyr, colorspace, MASS
MASS, lhs, plyr, BBmisc, ParamHelpers, BatchJobs,
BatchExperiments, ggplot2, metafor
methods, stats, parallel, ggplot2, plyr, reshape2
abind, car, foreign, lme4 (>= 0.999999-2), MASS, Matrix (>=
1.0), methods, nnet, stats, R2WinBUGS
sirt (>= 0.42), lme4, MBESS, pls, mitools, bayesm, Rcpp (>=
0.11-0), inline, car, foreign
stats, systemfit (>= 1.1-12), miscTools (>= 0.6-0)
miscTools (>= 0.6-1), systemfit (>= 1.0-0), MASS
parallel, data.table, gridExtra, Rcpp (>= 0.11.1), xtable
DiceKriging, e1071, kernlab, Matrix, mvtnorm, rgenoud
splines, fastGHQuad, mnormt, parallel, coda
stats, grDevices, class, nnet, MASS, e1071, randomForest,
ipred
parallelMap (>= 1.1), codetools, survival, checkmate (>= 1.1)
methods, ade4, sp, shapes, jpeg, spdep, ape
Rvcg (>= 0.7), rgl (>= 0.93.963), colorRamps, foreach (>=
1.4.0), Matrix (>= 1.0-1), parallel, yaImpute, doParallel (>=
1.0.6), Rcpp
mosaicData, MASS, grid, reshape2, plyr, methods, utils,
splines, latticeExtra, gridExtra, ggdendro
stringr, rSymPy, rJython, rJava, rjson, partitions, plyr,
stats
KernSmooth, xtable, coda, bit, mvtnorm, lattice
utils, emulator (>= 1.2-13), mvtnorm, methods
plotrix, multtest (>= 2.2.0), multcomp (>= 1.1-0), methods
Rcpp, MASS, methods, stats, tweedie, statmod
RANN, igraph, methods, filehash, digest, nat.utils (>= 0.4.2),
plyr
RNetCDF, chron, parallel, abind, plotrix, raster,
RColorBrewer, JBTools
AnnotationDbi, Matrix, ROCR, graph, igraph, samr
parallel, rootSolve, dtw, WGCNA, minet, Matrix, minerva,
combinat, igraph
functional, ggplot2, plyr, reshape2, stringr
httr, RCurl, plyr, rjson, stringr, reshape2, ggplot2, scales
graphics, stats, stringr, digest, grid, grDevices, gridBase,
colorspace, RColorBrewer, foreach, doParallel, ggplot2,
reshape2
agricolae, ash, binom, BSDA, coin, epitools, fANCOVA, gtools,
Hmisc, km.ci, metafor, nortest, np, quantreg, Rfit, SemiPar,
SuppDists, waveslim
methods, rjson, RCurl, reshape2, triangle, httpRequest,
digest, xtable, ggplot2, rgdal, sp
mvtnorm, orthopolynom, nlme, crossdes, polynom
utils, grid, plyr, reshape2, mgcv, lattice, latticeExtra,
cluster, RColorBrewer, Rcpp
parallel, tools, stats, evaluate (>= 0.5.1), httpuv (>= 1.3),
knitr (>= 1.4), jsonlite (>= 0.9.9), devtools (>= 1.5), httr
(>= 0.3), brew, pander
methods, grid, colorspace, plyr, stringr, abind, rgl,
GPArotation, psych, XML, tcltk
plyr, maptools, openair, raster, rgdal, reshape, doParallel,
parallel, foreach, sp
methods, lattice, parallel, boot, hwriter, plotrix,
Ckmeans.1d.dp, pkgutils (>= 0.6.0), yaml (>= 2.1.7), splines,
mgcv, grofit (>= 1.1), rjson (>= 0.2.12), nlme, DBI
MASS, randomForest, e1071,ipred, msm, Matrix
bitops, splines, graphics, grDevices, methods, utils
grDevices, methods, oro.dicom (>= 0.3.7), oro.nifti (>=
0.3.9), utils
utils, RColorBrewer, ape, reshape2, sna, plyr, ggmap, scales,
rjson, networkDynamic
numDeriv, corpcor, lattice, grDevices, phytools, phangorn
locfit, gtools, caTools, pscl, Imap, RCurl, devtools, raster,
sp, rgdal, ggplot2, lattice, RColorBrewer, agricolae, plyr
slam, Rglpk, quadprog, FRAPO, corpcor, parallel
graphics, utils, methods, abind, graph, RBGL, igraph, ggm,
corpcor, robustbase, vcd, Rcpp
httr, zoo, xts, XML, lubridate, jsonlite, reshape2
spsurvey, xtable, XML, stringr, sp, car, pbapply, MASS, plyr,
lattice, latticeExtra, grid, VecStatGraphs2D, mvtsplot
quadprog, igraph (>= 0.6), Matrix, fastmatch
animation, clusterGeneration, mnormt, msm, numDeriv, phangorn
(>= 1.6-3), plotrix, scatterplot3d
Matrix (>= 1.0-0), numDeriv, limSolve (>= 1.5.2), rgl,
scatterplot3d, qvalue, Iso(>= 0.0-5), quadprog (>= 1.5-3),
kernlab
XML2R (>= 0.0.6), plyr, MASS, hexbin, mgcv
methods, tools, codetools, digest, stringr, xtable, grDevices
RGtk2 (>= 2.20.1), gWidgets (>= 0.0-30), gridBase, grDevices,
graphics, stats, utils
methods, XML, sp, raster, rgdal, spacetime, colorspace,
plotrix, dismo, aqp, pixmap, plyr, stringr, colorRamps, scales,
gstat, zoo, RColorBrewer, RSAGA, classInt
mvtnorm, boot, Formula, plsdof, MASS, plsRglm, betareg
survival, plsRglm, lars, pls, kernlab, mixOmics, risksetROC,
survcomp, survAUC, rms
utils, methods, objectSignals (>= 0.10.2)
foreach, Formula, glmnet (>= 1.9-5), iterators, Matrix, ncvreg
(>= 2.4-0), Rcpp (>= 0.11.0), stringr
stats, graphics, methods, mvtnorm, deSolve, coda
lattice, RgoogleMaps, gap, calibrate, xtable, plyr, dismo,
reshape, ggplot2, R.utils, ade4, pegas, genetics, rgdal,
gdistance, vegan, sp, mmod
vegan, ggplot2, phangorn, ape, igraph, methods, ade4, pegas,
reshape2
ggplot2, grid, XML, ape, httr, lubridate, plyr, scales,
stringr, foreach
stringr, codetools, methods, Rcpp (>= 0.11.0)
graphics, methods, stats, lattice, Formula (>= 1.1-0),
modeltools
XML, stringr, RCurl, wordcloud, tm, GOsummaries, RColorBrewer
data.table, plyr, stringr, RJSONIO, httr
chron, dplyr, gdata, grid, gridExtra, igraph, NLP, openNLP (>=
0.2-1), parallel, plotrix, RCurl, reports, reshape2, scales,
stringdist, tm (>= 0.6), tools, venneuler, wordcloud, xlsx, XML
psych, ellipse, lavaan, sem, plyr, Hmisc, igraph, jpeg, png,
colorspace, Matrix, sna, corpcor, reshape2, ggplot2, glasso,
huge
reshape2, methods, scales, grid, parallel
methods, graphics, quantreg, testthat, abind, zoo, rje,
snowfall
abind, coda (>= 0.13), methods, R2WinBUGS, parallel
grDevices, lattice, lme4, cairoDevice, gWidgets,
gWidgetsRGtk2, RGtk2Extras, MASS, utils, latticeExtra, statmod
coda, corpcor, gplots, gtools, maps, pso, RCurl, tcltk
stats, expm, reshape, ggplot2, igraph, Hmisc
gridExtra, RColorBrewer, indicspecies, Heatplus,
splitstackshape, gplots, plyr, reshape2, scales, labdsv, grid,
stringr, ggmap, permute
colorspace, graphics, methods, spam, tcltk2, tcltk, tkrplot
methods, Rcpp (>= 0.11.1), parallel, doParallel, iterators,
foreach (>= 1.4.2), ggplot2, pROC, gtools
sp, raster, maptools, RSQLite.extfuns, lattice, RColorBrewer,
classInt
data.table, Biostrings, Rsamtools, GenomicRanges, PropCIs
methods, car, nnet, RSNNS, e1071, randomForest
grid, grDevices, sp (>= 1.0-6), zoo, RColorBrewer
stringr, XML, RCurl, scrapeR, gdata, scales, rgdal, maptools,
raster, sp, tools
XML, RCurl, rjson, wordcloud, rtematres, RColorBrewer
plyr, RCurl, gdata, KEGGREST, data.table, stringr
plyr, httr, mapproj, ggplot2, grid, gridExtra, maptools, sp,
data.table, rjson
rJava, rcdklibs (>= 1.5.8), methods, png, iterators
tcltk, tcltk2 (>= 1.2-6), markdown, knitr, abind
car (>= 2.0-21), RcmdrMisc (>= 1.0-1), nnet, foreign, rpart,
rpart.plot
Rcmdr (>= 2.1-1), tcltk, tcltk2, utils, ca, R2HTML (>= 2.3.0),
RColorBrewer, latticeExtra, stringi
methods, Rcpp (>= 0.11.0), RApiSerialize
XML, RCurl, OAIHarvester, ape, gdata, plyr, stringr, RJSONIO
XML, RCurl, RJSONIO, utils, plyr, tools, bibtex, lubridate,
stringr, methods
lattice, boot, mgcv (>= 1.7-28), MASS, glmnet, magic, nlme,
wavethresh, Matrix, gamm4, matrixStats, lme4, splines, RLRsim
cluster, stats, slam, sets (>= 1.0-4), graphics, grDevices
sfsmisc, combinat, FRACTION, mcmc, PhaseType (>= 0.1.3),
actuar, HI
survival, xtable, aod, cmprsk, geoR, stringr,
foreach, iterators, pls, Rcpp (>= 0.11.0)
RJSONIO, httr (>= 0.3), httpuv, plyr, yaml, ggplot2, XML
httr, data.table, assertthat, ggplot2, rjson
RCurl, jsonlite, httr (>= 0.3), httpuv, shiny (>= 0.10.0)
ggplot2, lubridate, plyr, httr, reshape2, gridExtra, scales,
data.table
XML, RCurl (>= 1.6), plyr, ggplot2, maps, httr, maptools,
rgdal, rgeos, sp, RJSONIO, data.table, stringr, assertthat,
whisker
tcltk, tcltk2, RCurl, tools, knitr, markdown
graphics, stats, lattice, grid, SparseM, xtable, svd, abind
MASS, quantreg, nlme, mgcv, stringr, magic, robustbase, Rcpp
(>= 0.11.1)
parallel, snowfall, matrixcalc, msm, coda
tools, utils, knitr (>= 1.6), yaml (>= 2.1.5), htmltools (>=
0.2.4), caTools
Rsolnp, MASS, parallel, Matrix, zoo, xts, Bessel, ff, shape,
pcaPP, spd, Rcpp
nnet, kknn, kernlab, rpart, plotrix, lattice, methods
quantreg, nlme, rpart, polspline, multcomp
httr, lubridate, plyr, ggplot2, scales, sp, rgdal, rgeos,
maptools, stringr, XML, data.table, assertthat, jsonlite
XML2R, RCurl, zoo, rjson, rgdal, sp, stringr
lattice, RColorBrewer, Biobase, RandVar(>= 0.9.2), distr(>=
2.5.2)
BiocGenerics, Biobase, beadarray, lattice, RColorBrewer,
distr(>= 2.5.2)
stats, graphics, utils, methods, DEoptimR
ggplot2, lattice, nlme, methods, robustbase (>= 0.9.5), xtable
methods, ROI (>= 0.1-0), Rsymphony, slam
stringr (>= 0.5), brew, digest, methods, Rcpp (>= 0.11.0)
Rcpp, DBI, lubridate, reshape2, assertthat
RCurl, RJSONIO, XML, plyr, stringr, googleVis, httr,
lubridate, reshape2, data.table, whisker, solr, assertthat
quantreg, limma, lattice, stats4, gam, gplots, grid, ggplot2,
Hmisc, Biobase
RPMG, RSEIS, GEOmap, MBA, minpack.lm, rgl
BBmisc, glmnet, superpc, survcomp, Matrix
jsonlite (>= 0.9.5), httr (>= 0.3), plyr, base64enc, digest,
stringr
httr, plyr, jsonlite, stringr, assertthat
methods, randomForest, tree, nnet, tm, e1071, ipred, caTools,
maxent, glmnet, tau
Rsolnp, nloptr, ks, numDeriv, spd, xts, zoo, chron,
SkewHyperbolic, expm, Rcpp
parallel, lattice (>= 0.20-10), coda (>= 0.16-1), stats, utils
ade4, boot, car, lme4 (>= 1.0-4), lsmeans (>= 2.0.1), MASS,
mixOmics (>= 5.0.2), multcomp, statmod, stats
RCurl (>= 1.6), plyr, RJSONIO, httr (>= 0.2), ggplot2, maps
ggplot2, httr, plyr, rgdal, jsonlite, reshape2, sp
RWekajars (>= 3.7.11), rJava (>= 0.6-3), graphics, stats,
utils, grid
RColorBrewer, dichromat, munsell (>= 0.2), plyr (>= 1.2),
labeling, methods
methods, stats, graphics, Matrix, splines
Matrix, utils, stats, iterators, RCurl, zoo, graphics
car, robustbase, cluster, MASS, e1071, tools, Rcpp,
methods,sets
abind, MASS, utils, parallel, nlme, sp, mgcv, raster
Rcpp (>= 0.11.1), glasso, parallel, Rmixmod
MASS, magic, polycor, VineCopula, VGAM, survey, trust,
matrixStats, Matrix, sn
qgraph (>= 1.2.4), lavaan (>= 0.5-11), sem (>= 3.1-0), plyr,
XML, igraph (>= 0.6-3), lisrelToR, rockchalk, colorspace,
corpcor, methods
TSP, grid, cluster, gclus, colorspace, MASS
Cairo, colorspace, data.table (>= 1.8.10), doParallel,
foreach, grid, gridBase, iterators, jsonlite, methods, plyr,
quantreg, reshape2, RSQLite, sn (>= 1.0-0), splines
igraph, cluster, lattice, latticeExtra, vegan, sp, reshape2,
Hmisc, scales, circular, RColorBrewer, plyr
tools, utils, httpuv (>= 1.2.2), mime (>= 0.1.3), RJSONIO,
xtable, digest, htmltools (>= 0.2.6), R6 (>= 2.0)
boot, coin, CompQuadForm, energy, graphics, grDevices,
KernSmooth, mvoutlier, robustbase, stats, utils
car, DataCombine, data.table (>= 1.9.2), dplyr, ggplot2,
gridExtra, MASS, mgcv, plyr, reshape2, stats, stringr,
survival, quadprog
parallel, utils, methods, grid, sfsmisc, gridBase (>= 0.4-6),
colorspace
sfsmisc, TAM (>= 1.0-3), CDM (>= 3.3), mirt (>= 1.5),
combinat, ic.infer, sm, psych, pbivnorm, gtools, Rcpp (>=
0.11.0), qgraph, lavaan, semPlot, igraph, coda, mvtnorm, MASS,
Matrix
qcc, lattice, grid, ggplot2, reshape2, nortest, testthat
ggplot2, MASS, car, foreign, plyr, psych, reshape2, scales
slam (>= 0.1-31), clue (>= 0.3-39), cluster, stats, utils
graphics, stats, polynom, scatterplot3d, Hmisc, colorspace,
nnls
Matrix, gplots, gtools,PMA, elasticnet, pcaPP, igraph
pracma, oce, ineq, vegan, tuneR, seewave
methods, sp (>= 1.0-14), zoo (>= 1.7-9), xts (>= 0.8-8),
intervals, lattice, rgeos
methods, spdep, foreach, iterators, lme4, nlme, Matrix
stats, graphics, Matrix, MASS, lpSolveAPI (>= 5.5.0.14),
proxy, geometry, Rcpp (>= 0.11.0), nlme
glasso, longitudinal, flare, MASS, mvtnorm, network, abind
distillery, maps, boot, CircStats, fastcluster, waveslim
mgcv, deldir (>= 0.0-21), abind, tensor, polyclip (>= 1.3-0),
goftest
iterators, RandomFields, fields, rgl, Matrix, stringr
LearnBayes, deldir, boot (>= 1.3-1), parallel, splines, coda,
nlme, MASS
Rssa (>= 0.11), raster, nnet, abind, RNetCDF, ncdf.tools,
foreach, JBTools, parallel, DistributionUtils, RColorBrewer
akima, coda, cluster, grid, gridExtra, MASS, MCMCpack,
mvtnorm, R2WinBUGS, reshape, scales, splines, parallel
rinat (>= 0.1.4), rbison (>= 0.3.2), rgbif (>= 0.7.0),
ecoengine (>= 1.4), rebird (>= 0.1.1), AntWeb (>= 0.6.1), plyr,
ggmap, maptools, rworldmap, sp, rgeos, lubridate, RColorBrewer,
httr, XML, rgdal, RJSONIO, leafletR (>= 0.2-0), data.table,
assertthat, whisker
MASS, igraph (>= 0.6), maptools, lattice, methods
cluster, e1071, MASS, MBA, mgcv, rgl, robustbase, xtable
bezier (>= 1.1), grid, rjson, shiny, Rcpp (>= 0.9.9), jpeg
matrixStats, splines, slam, lda, stringr
animation, clue, cluster, clusterGeneration, fpc, MASS,
mlbench
multtest, igraph, rjson, ggplot2, reshape2
gtable, ggplot2, gWidgets, plyr, scales, gridExtra, grid
tcltk, tcltk2, ape, pamr, e1071, class, lattice, tsne
graphics, gridExtra, utils, combinat, Hmisc, zoo, km.ci,
KMsurv, gam
stringr, testthat, httr, yaml, RCurl, digest, tools
lattice, igraph, MASS, LDheatmap, qtl, synbreedData (>= 1.3)
Rcpp (>= 0.10.4), methods, graphics, Matrix, compiler, stats,
parallel
mvtnorm, CDM (>= 3.4), tensor, sfsmisc, GPArotation, psych,
Rcpp
taRifx, sp, rgdal, RJSONIO, rgeos, RCurl, methods
lambda.r (>= 1.1.6), lambda.tools, futile.logger (>= 1.3.7),
futile.matrix (>= 1.2.1), tawny.types (>= 1.1.2), zoo, xts,
PerformanceAnalytics, quantmod
lambda.r (>= 1.1.0), lambda.tools, futile.logger (>= 1.3.0),
futile.options, zoo, xts, quantmod
XML, RCurl (>= 1.6), stringr, plyr, httr (>= 0.2), jsonlite,
foreach, ape, Taxonstand, reshape2, data.table, vegan,
assertthat, bold,
ggplot2, GSA, multtest, reshape2, gtools, gplots, cluster,
stringr, splines, grid
foreach, zoo, fields, animation, geoR, tcltk, utils,
RcppArmadillo
lmtest, Rcpp (>= 0.10.6), rgl, sandwich, urca, zoo
vcd, gplots, colorspace, splines, gclus, nnls (>= 1.1),
deSolve, minpack.lm (>= 1.1-1), MASS
parallel, slam (>= 0.1-31), stats, tools, utils
rgeos, grid, gridBase, sp, rgdal, classInt, RColorBrewer,
igraph, fields, vegan
NLP (>= 0.1-2), tm (>= 0.6), boilerpipeR, RCurl, XML, RJSONIO
stats4, methods, modeltools, slam, tm (>= 0.6)
graph, gRbase (>= 1.7), qpgraph, fields (>= 7.0)
methods, lattice, sp (>= 1.0-14), spacetime (>= 1.0-0), rgdal,
rgeos
XML, RCurl, methods, utils, reshape2, data.table
abind, plyr, ggplot2, lmtest, lmodel2, np, boot
colorspace, data.table (>= 1.8.8), ggplot2, grid, gridBase,
igraph, RColorBrewer, shiny (>= 0.8.0)
mnormt, mgcv, nnet, tseriesChaos, tseries, utils, vars, urca,
forecast, MASS, Matrix, foreach
methods, DBI, tframe, tframePlus(>= 2011.8-1), fame (>= 2.12),
tis
methods, DBI, zoo, tframe (>= 2011.11-1), tframePlus
methods, DBI, tframe, tframePlus, zoo, tseries
methods, DBI, tframe (>= 2011.11-1), tframePlus, rjson
methods, DBI, tframe, TSsql (>= 2013.9-1)
methods, DBI, tframe, tframePlus , TSsql (>= 2013.9-1)
forecast, polynom, stsm, stsm.class, KFKSDS
methods, DBI, tframe, tframePlus, TSsql (>= 2013.9-1)
Hmisc, pastecs, reshape, wq, e1071 , timeSeries, fBasics ,
relimp, multcomp, nlme, tcltk, lubridate, tcltk2
DoseFinding, MASS, mvtnorm, quadprog, SEL, splines
Brobdingnag (>= 1.1-8), partitions (>= 1.9-14), polynom
plyr, psych, pwr, fBasics, e1071, ltm, MBESS, foreign, knitr,
xtable, GGally, grid, lavaan
quadprog, Matrix, MASS, CompQuadForm, RLRsim, SPA3G, mvtnorm,
nlme, stats
reshape2, foreach, lpSolve, plyr, gdata, MASS
stats, grid, graphics, methods, nlme, rpart, numDeriv, ucminf,
zoo, sandwich, strucchange
ggplot2, plyr, maptools, gridSVG, grid, rjson, GGally, Rook,
dplyr
car, grDevices, robustbase, stats, sp,
vcd,MASS,nnet,e1071,methods,Rcpp,glmnet
colorspace, ggplot2, grid, lubridate, plyr, RColorBrewer,
testit, zoo
stats, grDevices, utils, matrixStats (>= 0.8.1), Hmisc,
impute, splines, foreach, doParallel, reshape
shiny (>= 0.10.0), plyr, reshape2, googleVis, wpp2012, Hmisc,
ggplot2
wrspathrowData, sp, raster, rgeos, rgdal
stats4, utils, methods, statnet, statnet.common, network, sna,
texreg (>= 1.33), Matrix, parallel, boot, coda, stats, ROCR,
igraph, vegan, lme4 (>= 1.0)
httr, RCurl, rjson, plyr, jsonlite, stringr
grid, gridExtra, lattice, foreign, reshape2
utils, graphics, grDevices, lattice (>= 0.20-27)
Rcpp (>= 0.11.1), RcppArmadillo, RcppEigen
sglOptim, Rcpp, RcppProgress, RcppArmadillo, BH
Rcpp, RcppProgress, RcppArmadillo, BH, sglOptim
boot, car, ggplot2, plyr, HH, ICC, vcd, Hmisc, manipulate
waveslim, splancs, MASS, maptools, spdep, pixmap, ape,
deldir, ade4TkGUI
genetics, spdep, tripack, pegas, seqinr, adehabitat,
parallel, akima, maps, splancs, hierfstat
boot, dynlm, effects, foreign, ineq, KernSmooth, lattice,
MASS, mlogit, nlme, nnet, np, plm, pscl, quantreg, ROCR,
sampleSelection, scatterplot3d, strucchange, systemfit, rgl,
truncreg, tseries, urca
agricolae (>= 1.2), car, coin, corrgram, equivalence, FrF2,
gam, gstat, HH, knitr, latticeExtra, lme4 (>= 1.1-5), mapproj,
maps, MASS, MCMCglmm, mgcv, nlme, pcaMethods, pls, sp,
survival, vcd
coxme, lme4, MASS, Matrix, maxlike, nlme, nnet, ordinal,
pscl, R2jags, R2OpenBUGS, R2WinBUGS, stats4, survival,
unmarked, VGAM
testthat, roxygen2, rplos, knitr, jsonlite
knitr, biclust, calibrate, mvtnorm, SportsAnalytics,
RColorBrewer, plotrix, abind
colorspace, maptools, foreign, MASS, ape, rgeos, soilDB,
latticeExtra, digest, maps
survival, Hmisc, xtable, R2HTML, cacheSweave, weaver
GLAD, DNAcopy, cghFLasso, flsa, cghseg, grid, gada, plyr,
ggplot2 (>= 0.9.0), RColorBrewer, reshape2, lattice, proto,
changepoint
doMC, foreach, testthat, knitr, markdown, arm, lme4, xtable,
languageR
sparseHessianFD(>= 0.1.1), sparseMVN(>= 0.1.0), mvtnorm,
trustOptim (>= 0.8.3), plyr (>= 1.8)
rworldmap, snowFT, googleVis, rgdal, rworldxtra, wpp2008,
wpp2010
emdbook, rms, ggplot2, RUnit, MuMIn, AICcmodavg, Hmisc,
optimx (>= 2013.8.6), knitr, testthat
coin, multcomp, lme4, e1071, entropy, archetypes, Rgraphviz,
psychotree
survey, intsvy, svyPVpack, lavaan.survey, Hmisc, car
vegan (>= 1.17-12), permute, lattice, MASS, mgcv, cluster,
car, RODBC, rpart, effects, multcomp, ellipse, maptree, sp,
splancs, spatial, akima, nnet, dismo, raster (>= 2.0-31),
rgdal, gbm, randomForest, gam, earth, mda, kernlab, e1071,
tools, methods
parallel, graph, Rgraphviz, lattice, gRain
knitr, xtable, mlbench, nnet, class, e1071, randomForest
alr4, boot, leaps, lme4, lmtest, nlme, quantreg, sandwich,
mgcv, pbkrtest (>= 0.3-2), rgl (>= 0.93.960), survival, survey
boot, deldir, foreign, grid, maptools, Matrix, nlme,
shapefiles, sp, spdep, splines
e1071, earth (>= 2.2-3), fastICA, gam, ipred, kernlab, klaR,
MASS, ellipse, mda, mgcv, mlbench, nnet, party (>= 0.9-99992),
pls, pROC, proxy, randomForest, RANN, spls
dse, e1071, expsmooth, fBasics, FitARMA, fGarch, polynom,
TSA, xtable
rms, qvcalc, glmmML, nnet, pscl, VGAM, gee, mlogit, Ecdat,
geepack, mgcv, rpart, party, ordinal, lme4, vcdExtra, glmnet,
mboost, class, e1071, flexmix, lqa, lpSolve, GAMBoost,
penalized
lattice, vegan, FD, spacodiR, entropart, fBasics, picante
rgl, grid, ggplot2, gridExtra, testthat, knitr
nnet, randomForest, ada, rrcov, sfsmisc, ipred, fastICA, rda,
TIMP, class, e1071, rpart, cluster, ALS, ptw, dtw, boot, leaps,
lars, elasticnet, subselect, kohonen, signal, mclust
drc, knitr, markdown, MBmca (>= 0.0.3-4), qpcR, RDML, xtable
e1071, lpSolve (>= 5.5.7), quadprog (>= 1.4-8), relations
Biobase, annotate, GO.db, moe430a.db, RankAggreg, kohonen,
mclust
CGHcall, CGHregions, CNVtools, xtable, MASS
snowfall, lars, ridge, tree, randomForest
KernSmooth, MASS, kernlab, mvtnorm, vcd, tcltk, dichromat
inSilicoDb, limma, hgu133a.db, GSVA, GSVAdata
RandomFields, spam, scatterplot3d, fields, mapproj
lattice, nlme, Hmisc, geepack, MASS, sandwich
MASS, KernSmooth, sfsmisc, bbmle, scatterplot3d, Rmpfr,
partitions, polynom, rugarch, colorspace, mvnormtest, tseries,
zoo
np, npRmpi, rgl, quadprog, quantreg, logspline
leaps, oz, lme4, quantreg, knitr, boot, rpart, randomForest,
MASS, survival
MASS, rpart, ipred, e1071, randomForest, mboost, TH.data
chron, ggplot2 (>= 0.9.0), plyr, reshape, testthat (>= 0.4),
hexbin, fastmatch, nlme, xts, bit64
bitops, minpack.lm, splines, XML, oro.dicom (>= 0.3.7)
MASS, lattice, rjags (>= 3-5), R2WinBUGS, R2OpenBUGS, BRugs,
snow, rlecuyer
dendextendRcpp (>= 0.5.0), knitr, testthat, colorspace, plyr,
profdpm, microbenchmark, gplots, DendSer, dynamicTreeCut,
pvclust
knitr, RColorBrewer, gplots, heatmap.plus, seriation
testthat (>= 0.7), roxygen2 (>= 4.0.2), BiocInstaller, Rcpp
(>= 0.10.0), MASS, rmarkdown, knitr
MASS, inlinedocs, ggplot2 (>= 0.9.1), lattice, alphahull,
reshape2, nlme, ElemStatLearn, proto, lars, latticeExtra
ggplot2, gridExtra, ggdendro, scales, seriation
rJava (>= 0.5-0), rgdal, rgeos, gbm, splines, XML, ROCR,
deldir, gstat, randomForest, kernlab, maptools
numDeriv, minqa, lubridate, expm, caper, geiger
xlsx, sendplot, plotrix, foreach, iterators, doParallel,
parallel
mgcv, survival, lme4, gee, geepack, mvmeta
pbkrtest (>= 0.3-8), ggplot2, multcomp, geepack, lme4
doParallel, doMPI, doRedis, rbenchmark, RUnit, devtools,
knitr, bibtex
numDeriv, Rsolnp, quadprog, parallel, multcomp
foreach, forecast, iterators, RUnit (>= 0.4.25), waveslim
RSQLite, RSQLite.extfuns, RMySQL, MonetDB.R, RPostgreSQL,
data.table, bigrquery, testthat, knitr, microbenchmark,
ggplot2, mgcv
its (>= 1.0.9), lattice, MASS, MCMCpack, quantreg (>= 3.82),
randomForest, sandwich, tseries
cluster, mlbench, nnet, randomForest, rpart, SparseM, xtable,
Matrix, MASS
car, systemfit, sem, lmtest, sandwich, gdata, RCurl, XML, tis
car, systemfit, sem, lmtest, sandwich, gdata, RCurl, XML,
tis, Ecdat, fda
gam, splines, MASS, class, leaps, mda, lasso2, lars, boot,
prim, earth
hgu133plus2.db, lumi, vsn, GOstats, GO.db
R2WinBUGS, rgl, ellipse, coda, bbmle, reshape, SuppDists,
numDeriv, testthat
caret, cmprsk, glmnet, Hmisc, MASS, party, pamr,
randomForest, RColorBrewer, survival, testthat (>= 0.8.1)
Rcmdr, gWidgets (>= 0.0-45), gWidgetstcltk (>= 0.0-44)
splines, nlme, survival, mstate, etm, MASS, cmprsk
lattice, latticeExtra, sna, Rglpk, snow, latentnet
Formula, kernlab, lattice, mlbench, multcomp, party, rpart,
xtable
corpcor, mvnmle, polycor, nFactors, Rgraphviz, mvnormtest,
energy, GPArotation, sem, MASS, psych
methods, mnormt, ecodist, mvnormtest, energy, RUnit
MASS, faraway, Hmisc, DAAG, multcomp, vcd, car, alr3,
corrgram, BradleyTerry2, cubature, Devore6, knitr
RJSONIO, RCurl, httr, XML, Rlab, xtable, tcltk, speedglm,
glmnet
deSolve, R.matlab, quadprog, DierckxSpline, nlme, lattice,
RCurl, zoo
moments, distrEx, tseries, urca, lmtest, sandwich, psych,
GPArotation, chron, polynom, fUnitRoots, e1071
codetools, diptest, ellipse, gclus, lme4 (>= 1.0), MASS, mgcv
(>= 1.8-0), mlbench, mlogit, multcomp, mvtnorm, survival
tcltk, tkrplot, fastICA, adimpro, oro.nifti
MASS, hflights, cluster, ggplot2, testthat, RSQLite
class, cvTools, glmnet, speedglm, gplots, RColorBrewer, nlme,
rpart
ada, arm, Boruta, bst, C50, car, caTools, class, Cubist,
e1071, earth (>= 2.2-3), elasticnet, ellipse, evtree,
extraTrees, fastICA, foba, gam, gbm (>= 2.1), glmnet (>= 1.8),
hda, HDclassif, Hmisc, ipred, kernlab, kknn, klaR, kohonen,
KRLS, lars, leaps, LogicReg, MASS, mboost, mda, mgcv, mlbench,
neuralnet, nnet, nodeHarvest, obliqueRF, pamr, partDSA, party
(>= 0.9-99992), penalized, penalizedLDA, pls, pROC, proxy,
qrnn, quantregForest, randomForest, RANN, relaxo, rFerns, rocc,
rpart, rrcov, RRF, rrlda, RSNNS, RWeka (>= 0.4-1), sda,
sparseLDA (>= 0.1-1), spls, stepPlr, superpc
leaps, quantreg, sp, diagram, oz, forecast, SMIR, kernlab,
Ecdat, mlbench, DAAGbio, knitr
BradleyTerry2, coxme, rms, MASS, NCBI2R, foreign, grid,
haplo.stats, kinship2, magic, mets, nlme, pedigree, pedigreemm,
regress, survival
qvalue, genetics, haplo.stats, DatABEL (>= 0.9-0), hglm,
MetABEL, PredictABEL, VariABEL, bigRR
scatterplot3d, geoRglm, lattice, graphics, tcltk
geoR, RandomFields, rgdal, parallel, mapmisc, ellipse
MASS, signal, seewave, zoo, mmap, bitops, matlab
quantreg, Hmisc, mapproj, maps, hexbin, maptools, multcomp,
nlme, testthat, knitr, mgcv
MASS, mgcv, testthat (>= 0.8.1), knitr (>= 1.6)
MASS, aod, lme4, pscl, leaps, survival, nnet
RUnit, Deducer, knitr, graph (>= 1.20), mutoss, boot
mvtnorm, car, stabledist, MASS, timeDate, timeSeries
gplots, gtools, Matrix, nlme, lme4 (>= 0.999999-0)
RGraphics, pixmap, EBImage, ggplot2, lattice
rjson, RCurl, tools, soiltexture, spatstat, stringr, plyr,
XML, splines, mda, psych, nortest, fossil, AICcmodavg,
maptools, nnet, SDMTools, rJava (>= 0.5-0), gdalUtils
rgdal (>= 0.5.2), rgeos, fields, mapdata, maptools, xts
boot, chron, doBy, grid, lattice, quantreg, reshape, svUnit,
tcltk, zoo
lme4, nlme, amap, nbpMatching, lattice, lmerTest
testthat, dostats, doParallel, foreach, MCMCpack, boot
rgl, candisc, effects, reshape, gplots, nlme, lattice,
reshape2, corrgram, FRB, animation
methods, abind, mvtnorm, car, RcmdrPlugin.HH
timeDate, robustbase, cubature, mvtnorm, chron, quantmod
gtools, KernSmooth, maps, ggplot2, proto, grid, reshape,
plyr, lattice, jpeg, car, gplots, sp, heplots
mvbutils, FuncMap, lattice, reshape, ggplot2, knitr, rgl,
bipartite, xtable, sna
lattice, MASS, scatterplot3d (>= 0.3-23), alr3, ape (>= 1.6),
coin (>= 0.3-3), flexmix (>= 1.1-0), gee (>= 4.13-10), lme4 (>=
0.98-1), mclust (>= 3.0-0), party (>= 0.2-8), randomForest (>=
4.5-12), rmeta (>= 2.12), vcd (>= 0.9-3), survival, rpart,
gamair, multcomp (>= 1.0-3), sandwich, mboost, KernSmooth,
Matrix, boot, mgcv, mvtnorm, partykit, tm, wordcloud, TH.data
XML, testthat (>= 0.8.0), png, jpeg, httpuv, knitr
knitr, knitcitations, adegenet, genetics, parallel
hydroGOF(>= 0.3-5), hydroTSM(>= 0.3-6), xts(>= 0.8-2),
parallel, scatterplot3d, vioplot, SWAT2R
R.matlab, latticeExtra, tripack, deldir, rgl, plotrix,
testthat, ggplot2 (>= 0.9.2), playwith, latticist, svUnit, sp,
baseline, compiler, inline, Rcpp, MASS, fastcluster, pls
testthat, roxygen2, devtools, knitr, microbenchmark, XML,
magrittr
igraphdata, stats4, rgl, tcltk, graph, ape
XML, devtools, rjson, data.table, plyr, ggplot2, sp, tools
MCMCglmm, truncnorm, mvtnorm, methods, MCMCpack, fields
actuar, aplpack, boot, coin, combinat, DAAG, diagram, distr,
distrEx, distrTeach, e1071, HH (>= 2.1-32), Hmisc, lattice,
lmtest, mvtnorm, odfWeave, prob, qcc, RcmdrPlugin.IPSUR (>=
0.1-6), Rcmdr, reshape, scatterplot3d, TeachingDemos (>= 2.5),
vcd
testthat, knitr, reshape2, httr (>= 0.4), rjson, plyr
scatterplot3d (>= 0.3-22), som, mlbench, rpart, e1071
ggplot2, plyr, reshape2, googleVis, testthat, microbenchmark
testthat, roxygen2, corrplot, ggplot2, igraph, maps, mapproj,
maptools, plyr, reshape2, xtable,
lattice, ggplot2, googleVis, data.table, vcd, plyr, reshape2,
zipcode
cluster, e1071, rms, Hmisc, MASS, rpart, lattice, zipfR,
lme4, multcomp, lmerTest, optimx
maps, mapproj, deldir, tripack, quantreg, zoo, MASS, mgcv
mets (>= 0.2.7), graph, Rgraphviz, igraph (>= 0.6), Matrix,
lme4, geepack, gof (>= 0.9), foreach, survival, testthat, ascii
Matrix, reshape2, ggplot2 (>= 0.9.1), penalized
rtracklayer, GenomicRanges, chopsticks (>= 1.18.0), lattice,
ggplot2
compiler, igraph, knitr, Rcgmin, stargazer
mvtnorm, elasticnet, MASS, randomForest, pls, gtools
knitr, boot, PKPDmodels, MEMSS, testthat (>= 0.8.1), ggplot2,
mlmRev, optimx (>= 2013.8.6), gamm4, pbkrtest
car, strucchange, sandwich, dynlm, stats4, survival, AER
nlme, lme4, pbkrtest (>= 0.4-1), lattice, multcompView, car,
mediation
coda, mcmcplots, ggmcmc, knitr, ggplot2, gridExtra, reshape2
SuperLearner, memoise, DEoptim, testthat, tmle
knitr, testthat (>= 0.8), RNetCDF (>= 1.6.1)
RColorBrewer, geonames, classInt, rgdal, knitr
rgeos (>= 0.1-8), spatstat (>= 1.18), PBSmapping,
RColorBrewer
cem, optmatch, Matching, nnet, rpart, mgcv, WhatIf
plspm, simsem, RUnit, parallel, semPLS, boot
MASS, sem, boot, nlme, gsl, lavaan, parallel, snow, OpenMx
party (>= 1.0-3), TH.data, MASS, fields, BayesX, gbm,
mlbench, RColorBrewer, rpart (>= 4.0-3)
car, classInt, mlogit, RColorBrewer, spatstat, spdep
boot, car, ConvergenceConcepts, geepack, lmtest, strucchange,
plm, zoo
mgcv, quantreg, VGAM, SuppDists, lme4 (>= 1.0), survival,
parallel
knitr, devtools, TeachingDemos, RColorBrewer, reshape2
lme4, numDeriv, polycor, BiasedUrn, survival, Epi, minqa,
multcomp, plyr, igraph, testthat, CompQuadForm, R.rsp, nloptr
MASS, GEOquery, pcaPP, affy, hgu133plus2.db, doMC, doSMP,
ellipse, impute
doMC, doSNOW, FactoMineR, matrixStats, gdata, gtools,
survival
R2WinBUGS, BRugs, rjags, coda, lattice, lme4
lava.tobit (>= 0.4-7), prodlim, testthat, ucminf
tseries, pastecs, locfit, tseriesChaos, RTisean, tsDyn,
forecast
AGD, gamlss, lme4, mitools, nlme, pan, survival, Zelig
testthat, plink, boot, latticeExtra, shiny, knitr, parallel
e1071, mvtnorm, AcceptanceSampling, boot, car, Dodge,
tseries, qcc, DoE.base, FrF2, rsm, LearnBayes, ggplot2, grid,
DiceEval, DiceKriging, DiceDesign, DiceView, lhs, survival
bigmemory, GeneSelector, gplots, nnet, cairoDevice, fBasics
testthat, ada, adabag, caret, class, cluster, clusterSim,
clValid, cmaes, CoxBoost, crs, DiceKriging, DiceOptim,
DiscriMiner, e1071, earth, emoa, FNN, FSelector, gbm, GenSA,
ggplot2, glmnet, Hmisc, irace, kernlab, kknn, klaR, LiblineaR,
mboost, mco, mda, mlbench, mRMRe, nnet, party, penalized, pls,
pROC, randomForest, randomForestSRC, reshape2, rrlda,
robustbase, rpart, rsm, RWeka, ROCR, stepPlr
party, rpart, glmnet, rms, randomForest, randomForestSRC
dclone, ggmcmc, ggplot2, gridExtra, lattice, rjags
NHANES, RCurl, maptools, vcd, testthat, knitr, R.rsp, tools,
parallel, mapproj
RCurl, RUnit, adehabitatHR, adehabitatLT, circular, ggmap,
mapproj, maptools, testthat
lattice, tm (>= 0.5-8.5), vcd, SnowballC, HSAUR2, colorspace,
corpus.useR.2008.abstracts
aod, asreml, happy.hbrem, Heatplus, MASS, VPdtw, lattice
animation, KernSmooth, rgl, plotrix, ggplot2 (>= 0.9), sna
lme4 (>= 0.999375-16), nlme, robustbase, mboost, coin, MASS,
car, foreign, xtable, lmtest, coxme (>= 2.2-1), SimComp, ISwR
MASS, lattice, testthat, arm, ggplot2, rbenchmark, scales,
vegan
stats4, nlme, mgcv (>= 1.7.5), lme4 (>= 0.999375-16), gamm4,
MASS, nnet, survival, geepack
mvtnorm, mclust, lattice, flexclust, nlme, RLRsim, multcomp,
ape, MASS, sem, KernSmooth, scatterplot3d
knitr, testthat, mvtnorm, microbenchmark, MASS, plyr
gRbase, hash, Biobase, VGAM, MASS, pcalg, survival, methods
reshape, WWGbook, lattice, ellipse, roxygen2, testthat
minpack.lm, optimx, Rvmmin, Rcgmin, numDeriv
devtools, ggplot2, knitr, plyr, scales, testit, testthat,
xtable
RcppOctave (>= 0.11), fastICA, doMPI, bigmemory (>= 4.2),
synchronicity, corpcor, xtable, devtools, knitr, bibtex, RUnit,
mail, Biobase
CCA, glmnet, MASS, PMA, testthat (>= 0.8), roxygen2
MASS, RSQLite, arules, lattice, survival
hexbin, RgoogleMaps, png, mapdata, KernSmooth
RAppArmor, sendmailR, RProtoBuf (>= 0.4)
ggplot2 (>= 0.9.0), sp, colorspace, maptools
optparse, gplots, opmdata (>= 0.4.0), testthat, multcomp,
KEGGREST, pathview, mboostDevel (>= 2.3.0), gridExtra
BB, ucminf, Rcgmin, Rvmmin, minqa, setRNG, dfoptim, svUnit
testthat, grid, ggplot2, ggthemes, knitr, AUC, scales,
stringr, plyr, XML
boot, RItools, biglm, testthat, survival, brglm, arm
BradleyTerry2, prefmod, psychotree, psychotools, psychomix
testthat, parallel, Rmpi, BatchJobs (>= 1.2)
TH.data (>= 1.0-3), mlbench, colorspace, MASS, mvtnorm, vcd,
ipred
XML, pmml, rJava, rpart, mvtnorm, Formula, sandwich,
strucchange, vcd, AER, mlbench, TH.data (>= 1.0-3), coin, RWeka
(>= 0.4-19), mvpart, datasets, psychotools, psychotree
PerformanceAnalytics, foreach, grid, latticeExtra, testthat,
doParallel, knitr
MASS, Matrix, Rgraphviz, mvtnorm, sfsmisc
timereg, randomForestSRC, party, cmprsk, rpart, doMC,
CoxBoost, crrstep, riskRegression, lava, Hmisc
Hmisc, MASS, quantmod, gamlss, gamlss.dist, robustbase,
quantreg, gplots
vegan (>= 2.0-0), testthat (>= 0.5), parallel
parallel, rgl, seqLogo, seqinr, xtable, flashClust, phytools
zoo, xts, highfrequency, TTR, timeDate, quantmod
DBI, dplyr, RSQLite, RSQLite.extfuns, parallel, knitr,
animation, shiny, testthat, ggsubplot, rgl
devtools (>= 0.8), bibtex, RUnit, testthat, knitr,
ReportingTools, hwriter, argparse
ggplot2, Hmisc, grid, gridExtra, lattice, knitr, testthat
utils, adehabitat, maptools, fossil, spcosa, rjson,
animation, spatstat, RCurl, rgbif, Hmisc, GSIF, stpp, uuid,
intervals, reshape
plsdepot, FactoMineR, ggplot2, reshape, testthat, knitr
abind, testthat, tcltk, foreach, doMC, itertools, iterators
ada, amap, arules, glmnet, nnet, rattle, rpart,
randomForestSRC, randomForest, kernlab, e1071, mlbench,
pmmlTransformations
lattice, testthat, mvtnorm, statmod, rgeos, gpclib
fGarch, FitAR, FGN, TSA, vars, tseries, forecast, akima
GO.db, org.Sc.sgd.db, org.Hs.eg.db, KEGG.db, org.Ag.eg.db,
org.At.tair.db, org.Bt.eg.db, org.Ce.eg.db, org.Cf.eg.db,
org.Dm.eg.db, org.Dr.eg.db, org.EcK12.eg.db, org.EcSakai.eg.db,
org.Gg.eg.db, org.Mm.eg.db, org.Mmu.eg.db, org.Pf.plasmo.db,
org.Pt.eg.db, org.Rn.eg.db, org.Sco.eg.db, org.Ss.eg.db,
org.Xl.eg.db
doBy, foreign, pps, reshape, sampling, samplingbook, survey
microbenchmark, tcltk, MASS, logcondens, doMC, doSNOW
qtbase, RGtk2, cairoDevice, gWidgetsRGtk2, gWidgets, tcltk
foreign, reshape, plyr, stringr, ggplot2, lubridate, log4r,
RMySQL, RSQLite, gdata, RODBC, RJDBC, xlsx, tuneR, pixmap,
data.table, RPostgreSQL, GetoptLong, testthat, Defaults
MCMCpack, car, lmtest, sandwich, zoo, coda, gam, vcd, mvtnorm
bspec, ggplot2 (>= 0.9), knitr, multitaper, plyr, RSEIS,
rbenchmark, reshape2
MASS, parallel, GPArotation, mvtnorm, sem, lavaan, Rcsdp,
graph, Rgraphviz
koRpus, knitr, lda, proxy, stringi, SnowballC, testthat
tripack, akima, MASS, survival, rgl, logspline, nor1mix,
MatrixModels, Matrix, Formula, zoo, dynlm
bbmle, lattice, coda, lme4, ggplot2, testthat, knitr
akima, coda, maptools, MBA, parallel, shapefiles, sp, spdep,
splines
boot, splines, survival, stats, cluster, nlme, rpart, nnet,
utils
rgdal (>= 0.8-12), rgeos (>= 0.3-1), ncdf, ncdf4, igraph,
snow, tcltk, rasterVis
parallel, rgl, ggplot2, colorspace, dichromat
RGtk2, pmml (>= 1.2.13), bitops, colorspace, ada, amap,
arules, arulesViz, biclust, cairoDevice, cba, corrplot, descr,
doBy, dplyr, e1071, ellipse, fBasics, foreign, fpc, gdata,
ggdendro, ggplot2, gplots, graph, grid, gtools, gWidgetsRGtk2,
hmeasure, Hmisc, kernlab, latticist, Matrix, methods, mice,
nnet, odfWeave, party, playwith, plyr, psych, randomForest,
RBGL, RColorBrewer, reshape, rggobi, RGtk2Extras, ROCR, RODBC,
rpart, rpart.plot, SnowballC, stringr, survival, timeDate, tm,
verification, weightedKmeans, XML, xlsx, pkgDepTools, Rgraphviz
aplpack, colorspace, effects (>= 3.0-1), e1071, foreign,
grid, Hmisc, lattice, leaps, lmtest, MASS, mgcv, multcomp (>=
0.991-2), nlme, nnet, relimp, rgl, rJava, RODBC, sem (>= 2.1-1)
abind, aod, aplpack, car, clinfun, cmprsk, foreign, meta,
metatest, multcomp, mvtnorm, optmatch, pROC, RODBC, tableone,
XLConnect
abind, car, distr, distrEx, e1071, effects (>= 1.0-7),
foreign, grid, lattice, lmtest, MASS, mgcv, multcomp (>=
0.991-2), nlme, nnet, qcc, relimp, RODBC
knitr, rmarkdown, compute.es, ggplot2, gridExtra, scales
lme4, leaps, mvtnorm, gmodels, abind, lattice, xtable,
pbkrtest, vcd, multcomp, e1071, nnet
YaleToolkit, MASS, lattice, qgraph, maps, ade4, effects,
abind, nnet, vcd, car, gpairs, Hmisc
SnowballC, ROpenOffice, RODBC, tm.plugin.factiva (>= 1.4),
tm.plugin.lexisnexis (>= 1.1), tm.plugin.europresse (>= 1.1),
tm.plugin.alceste (>= 1.1), twitteR
RUnit, inline, rbenchmark, highlight, pkgKitten (>= 0.1.2)
car, cluster, foreign, GPArotation, MASS, mclust, psych,
UsingR, vcd
devtools, knitr, methods, RODBC, testit, testthat
maps, lattice, latticeExtra, grid, gridExtra, sp
gtools (>= 3.4.1), BatchJobs (>= 1.2), BiocParallel (>=
0.4.1)
fortunes, stringr, sos, XLConnect, reshape2, ggplot2,
foreign, lattice
PBSmapping, maptools, RColorBrewer, sp, rgdal, loa
agricolae, animation, CircStats, circular, cluster,
colorspace, diagram, dichromat, gplots, graph, gridBase,
gridExtra, gridSVG, grImport, gWidgets, gWidgetsRGtk2, hexbin,
Hmisc, hyperdraw, hypergraph, igraph, iplots, ipred,
latticeExtra, mapdata, maps, maptools, MASS, misc3d, munsell,
network, openair, oz, party, pixmap, playwith, plotrix, pmg,
png, quantmod, raster, Rcmdr, RColorBrewer, rgdal, rggobi, rgl,
RGraphics, Rgraphviz, scatterplot3d, soiltexture, sp,
SVGAnnotation, symbols, TeachingDemos, tools, vcd, vcdExtra,
venneuler, vrmlgen, XML
tcltk, odfWeave, R2HTML, RColorBrewer, sp
e1071, rggobi, reshape, methods, jpeg, png, tkrplot, fftw,
foreach, doSNOW
Hmisc, Biobase, genefilter, golubEsets, chemometrics
MASS, polynom, sfsmisc (>= 1.0-20), Matrix
PairViz, scagnostics, rgl, MASS, hexbin, RDRToolbox, vegan,
RnavGraphImageData, png, Rgraphviz
RMySQL (>= 0.9-3), RSQLite, SSOAP, XMLSchema, testthat, XML,
maptools, sp, RColorBrewer, RPostgreSQL (>= 0.4), geonames
grid, MASS, lattice, boot, cluster, Matrix, robust,
fit.models, MPV, xtable, ggplot2, RColorBrewer, reshape2,
sfsmisc, catdata
grid, lattice, cluster, mclust, MASS, ellipse
RODBC, mlbench, verification, PASWR, scatterplot3d
gridExtra, knitr, onion, orientlib, rgl, sphereplot, testthat
tkrplot, rgl, sp, geoR, RandomFields, akima, MASS, denstrip,
lattice, sm, maps, mgcv
testthat, roxygen2, ggplot2, reshape2, gridExtra, numDeriv,
knitr, mirt
fields, SDMTools, tkrplot, rgl, qgraph, aplpack, AICcmodavg
tcltk, network, codetools, lattice, MASS, parallel, xtable,
tools
rgdal, tripack, colorspace, dichromat, rgl, XML
rgdal, rworldxtra, RColorBrewer, classInt, cshapes, ncdf,
raster
GO.db, GOstats, ROCR, ape, car, eigenmodel, ergm, fdrtool,
ggplot2, huge, kernlab, lattice, mixer, network,
networkDynamic, networkTomography, ngspatial, org.Sc.sgd.db,
sna, vioplot
car, lmtest, strucchange, AER, survival, MASS, scatterplot3d
MASS, RSQLite (>= 0.4-15), oligoClasses (>= 1.1.9)
parallel, Amelia, mice, foreign, OpenMx, GPArotation
rgl, evd, numbers, DiceKriging, ks, fanovaGraph
datasets, tcltk, cluster, MASS, Matrix, nlme, lokern
randomNames (>= 0.0-5), parallel, SGPdata (>= 8.0-0)
quantmod, MASS, TTR, testthat, sandwich, knitr
datasets, Cairo (>= 1.5-5), testthat, knitr (>= 1.6),
markdown
Rmpi, Hmisc, copula, foreach, doParallel, fGarch, robustbase
parallel, Amelia, quantreg, splines, foreign, KernSmooth,
semTools, OpenMx, copula
eRm, ltm, MCMCglmm, mokken, mRm, plink, GPArotation, fdrtool,
lme4, difR, semTools, MultiLCIRT, rcppbugs, IsingFit,
MplusAutomation (>= 0.6)
cluster, coin, lsmeans, lmtest, stringdist
MASS, lattice, multcomp, car, leaps, CCA, Hmisc
rpanel, tkrplot, rgl, misc3d, akima, gam
lattice, foreign, gdata, car, dglm, gnm, MASS, npmlreg,
survival
lattice, rgdal, RCurl, XML, SSOAP, XMLSchema, dismo
spacetime (>= 0.2-0), gstat, maps, maptools, mapdata,
cshapes, xts, xtable, rgdal
RColorBrewer, rgdal (>= 0.8-7), rgeos (>= 0.2-20), gstat
adehabitatLT, cshapes, diveMove, foreign, gstat (>= 1.0-16),
maps, mapdata, maptools, plm, raster, RColorBrewer, rgdal,
RPostgreSQL, knitr, googleVis
sm, maptools, gsl, locfit, spatial, rpanel, tkrplot,
scatterplot3d, RandomFields (>= 3.0.0), Matrix
spam(>= 0.13-1), RANN, RColorBrewer, lattice, xtable,
maptools (>= 0.5-4), foreign
lattice, RColorBrewer, rgdal, SDMTools, foreach
spdep, parallel, snow, maptools (>= 0.7-32)
base, RBGL, XML, graphics, png, graph, EBImage, igraph
AlgDesign, BB, cmaes, DEoptim, dfoptim, DiceKriging,
DoE.wrapper, earth, fields, FrF2, GenSA, ggplot2, gtools,
lattice, lhs, lme4, MASS, mco, minqa, mlegp, monmlp, nloptr,
kernlab, penalizedSVM, pso, qrnn, randomForest, rgenoud, rgl,
rgp (>= 0.3-4), rsm, tgp, twiddler
cluster, stringdist, RUnit, Matrix, SparseM, multtest,
Biostrings, ShortRead, golubEsets, RankProd
RH2, RMySQL, RPostgreSQL, svUnit, tcltk, MASS
tcltk, RPostgreSQL, RODBC, RMySQL, RSQLite, sqldf, xtable
Matrix, fBasics, FMStable, RUnit, Rmpfr, sfsmisc
ggplot2, gridExtra, grid, testthat, lattice
igraph, SnowballC, tm, huge, glmnet, Matrix, jsonlite, clue,
wordcloud
stats4, car, dynlm, e1071, foreach, lmtest, mvtnorm, tseries
arm, caret, class, e1071, earth, gam, gbm, glmnet, Hmisc,
ipred, lattice, LogicReg, MASS, mda, mlbench, nnet, parallel,
party, polspline, quadprog, randomForest, rpart, SIS, spls,
stepPlr
arm, caret, class, cvAUC, e1071, earth, gam, gbm, genefilter,
ggplot2, glmnet, Hmisc, ipred, lattice, LogicReg, MASS, mda,
mlbench, nloptr, nnet, parallel, party, polspline, quadprog,
randomForest, ROCR, rpart, SIS, spls, stepPlr, sva
parallel, lattice, grid, gridExtra, colorspace, scales,
animation, msm, spc, quadprog, memoise, polyclip, rgeos,
gpclib, maptools, intervals, spdep, numDeriv, maxLik, testthat,
coda, splancs, gamlss, INLA, runjags
foreign, survival, MASS, splines, KernSmooth, hexbin,
mitools, lattice, RSQLite, RODBC, quantreg, Matrix, parallel,
CompQuadForm
distr, MASS, survival, distrEx, Matrix, splines
glpkAPI (>= 1.2.8), cplexAPI (>= 1.2.4), clpAPI (>= 1.2.4),
lpSolveAPI (>= 5.5.2.0), parallel, grid
lattice, corrgram, perturb, car, vcd, abind
shiny (>= 0.6), knitr, classInt, ggplot2
boot, reshape, reshape2, ggplot2, grid, ggdendro, sets, mgcv
xtable, grid, lattice, caTools, pspline, ggplot2, gdata,
RSQLite, data.table, stringr
gdata, fields, maps, geoR, maptools, shapefiles, spatial,
mapproj, akima, mgcv, caTools, pspline
adabag, cmaes, parallel, e1071, powell, ROCR, randomForest,
rCMA, rSFA, twiddler
tkrplot, lattice, MASS, rgl, tcltk, tcltk2, R2wd, EBImage,
png, ggplot2, logspline, maptools
nlme, survival, network, ergm, lme4 (>= 1.0), gamlss
tfplot, zoo, xts, its, tis, WriteXLS(>= 1.7.0)
testthat (>= 0.6), evaluate, stringr, ggplot2, maps
filehash, Rcampdf, Rgraphviz, Rpoppler, SnowballC,
tm.lexicon.GeneralInquirer, XML
lasso2, lattice, lda, OAIHarvester, SnowballC, XML,
corpus.JSS.papers
TeachingDemos, ash, testthat, colorspace, ggplot2, reshape2
lattice, onion, plotrix, rgl, scatterplot3d
rgl, OpenStreetMap, RCurl, rjson, adehabitatLT
tframePlus, TSsql, TSMySQL, TSSQLite, TShistQuote
tframe, tframePlus (>= 2012.8-1), tfplot, TSdbi (>=
2013.9-1), TSsql (>= 2013.9-1), TSPostgreSQL, TSMySQL,
TSSQLite, TSodbc, TSgetSymbol (>= 2012.8-1), TShistQuote,
TSfame, TSxls, TSzip, TScompare, zoo, tseries, its, TSjson,
WriteXLS
zoo, tseries, tis, tfplot, tframePlus, RMySQL, RSQLite
snowfall, desire, igraph, gsl, class, tree, e1071,
randomForest, klaR
zoo, coin, ggplot2, vcd, lubridate, aplpack
KernSmooth, mvtnorm, kernlab, HSAUR, coin
MASS, mgcv, cluster, scatterplot3d, rgl, tcltk
vegan, labdsv, akima, gWidgets, googleVis, indicspecies,
knitr
BayesSingleSub, boot, devtools, knitr, scales, testthat
GO.db, org.Hs.eg.db, org.Mm.eg.db, AnnotationDbi, infotheo,
entropy, minet, survival
RUnit, lattice, ggplot2 (>= 0.9.3), fImport, forecast, zoo,
scales
vegan, ccaPP, randomForest, gam, fastICA
Amelia, mvtnorm, Formula, gee, survey, survival, systemfit,
MatchIt, MCMCpack, coda
coda, chron, DAAG, fts, its, ggplot2, mondate, scales,
strucchange, timeDate, timeSeries, tis, tseries, xts
RWeka, ape, cba, cclust, flexclust, flexmix, kernlab, mclust,
movMF, modeltools
R2WinBUGS, R2OpenBUGS, BRugs, R2jags, sp, nlme
survival, MASS, coxme, gee, geepack, glmmADMB, afex
chron, timeDate, zoo, xts, its, tis, timeSeries, fts, tseries
aod, coxme, glmmML, MCMCglmm, pscl, spdep, unmarked, ordinal,
gee, splm, logistf, caper, betareg, aods3, cplm
maps, RColorBrewer, pbdNCDF4 (>= 0.1-1), pmclust, MixSim,
EMCluster, sqldf, phyclust, MASS
DBI, RPostgreSQL, shiny, testthat, tools, rpart
AER, betareg, brglm, dynlm, eha, erer, ergm, gee, glmx, gmm,
lfe, lme4, lmtest, MASS, mclogit, mgcv, mlogit, nlme, nnet,
ordinal, plm, pscl, quantreg, rms, relevent, robustbase,
sampleSelection, spdep, stats, survey, survival, Zelig
BSD_2_clause + file LICENSE | GPL (>= 2)
GPL (>= 2) | BSD_2_clause + file LICENSE
GPL (>= 2) | BSD_3_clause + file LICENSE
LGPL (>= 2.0, < 3) | Mozilla Public License
GPL-2 | GPL-3 | BSD_3_clause + file LICENSE
c(
person("Maria", "Ortiz", role=c("aut")),
person("Henrik", "Bengtsson", role=c("aut", "cre", "cph"),
email="henrikb@braju.com"),
person("Angel", "Rubio", role=c("aut")))
c( person("Nicolas", "Gauvrit", role = "aut"), person("Henrik",
"Singmann", email = "singmann@gmail.com", role = c("aut", "cre")),
person("Fernando", "Soler Toscano", role = "ctb"), person("Hector",
"Zenil", role = "ctb") )
c(
person("Fernando", "Soler Toscano", role = "aut"),
person("Nicolas", "Gauvrit", role = "aut"),
person("Hector", "Zenil", role = "aut"),
person("Henrik", "Singmann",
email = "singmann@gmail.com", role =
c("aut", "cre")))
c(person(given = "Pedro Rafael", family = "Diniz Marinho", role =
c("aut", "cre"), email = "pedro.rafael.marinho@gmail.com"),
person(given = "Marcelo", family = "Bourguignon", role =
c("aut","ctb"), email = "m.p.bourguignon@gmail.com"),
person(given = "Cicero Rafael", family = "Barros Dias", role =
c("aut","ctb"), email = "cicerorafael@gmail.com"))
c(
person('Imanuel', 'Costigan', email = 'i.costigan@me.com',
role = c('aut', 'cre')),
person('Australian Energy Market Operator', role = 'cph'))
c(person(given = "Christian", family = "Kleiber", role = "aut", email = "Christian.Kleiber@unibas.ch"),
person(given = "Achim", family = "Zeileis", role = c("aut", "cre"), email = "Achim.Zeileis@R-project.org"))
c(person(given="Henrik", family="Singmann",
email="henrik.singmann@psychologie.uni-freiburg.de", role=c("aut", "cre")),
person(given="Ben", family="Bolker", role=c("ctb")), person(given="Søren",
family="Højsgaard", role=c("ctb")), person(given="John", family="Fox",
role=c("ctb")), person(given="Michael A.", family="Lawrence",
role=c("ctb")), person(given="Ulf", family="Mertens", role=c("ctb")) )
c(person("Francisco", "Balao", role = c("aut", "cre"),email = "fbalao@us.es"),person("Juan Luis", "García-Castaño", role = "aut",email = "jlgc@us.es"))
c( person("Marek", "Gagolewski", role = c("aut", "cre"),
email = "gagolews@rexamine.com"), person("Anna", "Cena", role =
"ctb", email = "cena@rexamine.com") )
c(person("Lutz", "Prechelt", role = c("aut","cre"),
email = "prechelt@inf.fu-berlin.de"))
c(person("Scott", "Chamberlain", role = c("aut", "cre"),
email = "myrmecocystus@gmail.com"),
person("Carl", "Boettiger", role = "aut",
email = "cboettig@gmail.com"),
person("Karthik", "Ram", role = "aut",
email = "karthik.ram@gmail.com"),
person("Fenner", "Martin", role = "aut",
email = "mfenner.ram@plos.org"))
c(person("Dong", "Wang", role = c("aut", "cre"), email = "dwangstat@gmail.com"))
c(person(given = "Anja", family = "Lange", role =
c("aut", "cre"), email = "anja.lange@uni-due.de"),
person(given = "Daniel", family = "Hoffmann", role = "aut", email
= "daniel.hoffmann@uni-due.de"))
c(person("Patrick", "Mair", role = c("aut", "cre"), email =
"mair@fas.harvard.edu"), person("Jan", "De Leeuw", role = "aut"))
c(person(given = c("Gavin", "L."), family = "Simpson",
role = c("aut", "cre"),
email = "ucfagls@gmail.com"),
person(given = "Jari", family = "Oksanen",
role = "aut"))
c(person("Lijia", "Yu", role = "ctb"),
person("Weicheng", "Zhu", role = "ctb"),
person("Yihui", "Xie", role = c("cre", "aut"), email = "xie@yihui.name"))
'Karthik Ram <karthik.ram@gmail.com> [aut, cre]'
c(person("Emmanuel", "Paradis", role = c("aut", "cre", "cph"), email = "Emmanuel.Paradis@ird.fr"),
person("Ben", "Bolker", role = c("aut", "cph")),
person("Julien", "Claude", role = c("aut", "cph")),
person("Hoa Sien", "Cuong", role = c("aut", "cph")),
person("Richard", "Desper", role = c("aut", "cph")),
person("Benoit", "Durand", role = c("aut", "cph")),
person("Julien", "Dutheil", role = c("aut", "cph")),
person("Olivier", "Gascuel", role = c("aut", "cph")),
person("Christoph", "Heibl", role = c("aut", "cph")),
person("Daniel", "Lawson", role = c("aut", "cph")),
person("Vincent", "Lefort", role = c("aut", "cph")),
person("Pierre", "Legendre", role = c("aut", "cph")),
person("Jim", "Lemon", role = c("aut", "cph")),
person("Rosemary", "McCloskey", role = c("aut", "cph")),
person("Johan", "Nylander", role = c("aut", "cph")),
person("Rainer", "Opgen-Rhein", role = c("aut", "cph")),
person("Andrei-Alin", "Popescu", role = c("aut", "cph")),
person("Klaus", "Schliep", role = c("aut", "cph")),
person("Korbinian", "Strimmer", role = c("aut", "cph")),
person("Damien", "de Vienne", role = c("aut", "cph")))
c(person("Manuel", "J. A. Eugster", role = c("aut", "cre"), email =
"manuel@mjae.net"), person("Friedrich", "Leisch", role = "aut"),
person("Sohan", "Seth", role = "ctb"))
c(
person("Przemyslaw", "Biecek", role = c("aut", "cre"),
email = "przemyslaw.biecek@gmail.com"),
person("Marcin", "Kosinski", role = c("aut"),
email = "m.p.kosinski@gmail.com")
)
c(person("Andrew", "Gelman", role = "aut", email = "gelman@stat.columbia.edu"),
person("Yu-Sung", "Su", role =  c("aut", "cre"), email = "suyusung@tsinghua.edu.cn"),
person("Masanao", "Yajima", role =  "ctb", email = "yajima@stat.ucla.edu"),
person("Jennifer", "Hill", role =  "ctb", email = "jennifer.hill@nyu.edu"),
person("Maria Grazia", "Pittau", role =  "ctb", email = "grazia@stat.columbia.edu"),
person("Jouni", "Kerman", role =  "ctb", email = "jouni@kerman.com"),
person("Tian", "Zheng", role =  "ctb", email = "tzheng@stat.columbia.edu"),
person("Vicent", "Dorie", role =  "ctb", email = "vjd4@nyu.edu")
)
c(
person("Henrik", "Bengtsson", role=c("aut", "cre", "cph"),
email="henrikb@braju.com"),
person("James", "Bullard", role="ctb"),
person("Kasper", "Hansen", role="ctb"),
person("Pierre", "Neuvial", role="ctb"),
person("Elizabeth", "Purdom", role="ctb"),
person("Mark", "Robinson", role="ctb"),
person("Ken", "Simpson", role="ctb"))
c(person("Henrik", "Bengtsson", role=c("aut", "cre", "cph"),
email = "henrikb@braju.com"))
c(
person("Henrik", "Bengtsson", role=c("aut", "cre", "cph"),
email="henrikb@braju.com"),
person("Pierre", "Neuvial", role="aut"))
c(
person("Henrik", "Bengtsson", role=c("aut", "cre", "cph"),
email="henrikb@braju.com"),
person("Mark", "Robinson", role="ctb"),
person("Ken", "Simpson", role="ctb"))
c(person("Derrick", "Turk", role = c("aut", "cre", "cph"),
email = "dwt@terminusdatascience.com"))
person("Ed", "Curry", role = c("aut", "cre"),email =
"e.curry@imperial.ac.uk")
c(person("Michael", "Hahsler", role = c("aut", "cre", "cph"),
email = "mhahsler@lyle.smu.edu"),
person("Christian", "Buchta", role = c("aut", "cph")),
person("Bettina", "Gruen", role = c("aut", "cph")),
person("Kurt", "Hornik", role = c("aut", "cph")),
person("Christian", "Borgelt", role = c("ctb", "cph")))
c(person("Karthik", "Ram", role="aut",
email="karthik.ram@gmail.com"),
person("Karl", "Broman", rol=c("aut","cre"),
email="kbroman@biostat.wisc.edu"))
person("Richard", "Cotton", role = c("aut", "cre"), email =
"richierocks@gmail.com")
c(person("Fatih Sunor", role = c("aut", "cre"),
email = "sunor@stanford.edu"))
c(person("Bettina", "Gruen", role = c("aut", "cre"),
email = "Bettina.Gruen@jku.at"),
person("Martyn", "Plummer", role = "ctb"))
c(person(given = "Daniel", family = "Adler", role = c("aut", "cre"), email = "dadler@uni-goettingen.de"),
person(given = "Thomas", family = "Kneib", role = "aut", email = "tkneib@uni-goettingen.de"),
person(given = "Stefan", family = "Lang", role = "aut", email = "Stefan.Lang@uibk.ac.at"),
person(given = "Nikolaus", family = "Umlauf", role = "aut", email = "Nikolaus.Umlauf@uibk.ac.at"),
person(given = "Achim", family = "Zeileis", role = "aut", email = "Achim.Zeileis@R-project.org"))
c(
person("Ravi", "Varadhan", role=c("aut","cph","trl"), email="rvaradhan@jhmi.edu"),
person("Paul", "Gilbert",  role=c("aut","cre"), email="pgilbert.ttv9z@ncf.ca"),
person("Marcos", "Raydan", role="ctb",
comment="with co-authors, wrote original algorithms in fortran. These provided some guidance for implementing R code in the BB package."),
person("JM", "Martinez", role="ctb",
comment="with co-authors, wrote original algorithms in fortran. These provided some guidance for implementing R code in the BB package."),
person("EG", "Birgin", role="ctb",
comment="with co-authors, wrote original algorithms in fortran. These provided some guidance for implementing R code in the BB package."),
person("W", "LaCruz", role="ctb",
comment="with co-authors, wrote original algorithms in fortran. These provided some guidance for implementing R code in the BB package.")
)
c(person("Ricardo", "de Matos Simoes", role=c("aut","cre"),
email="r.dematossimoes@qub.ac.uk"), person("Frank",
"Emmert-Streib", role=c("aut","cre"),
email="f.emmert-streib@qub.ac.uk"))
c(person("Majid", "Einian", role = c("aut", "cre"),
email = "einian85@gmail.com"))
c(person("Frank", "Schaarschmidt", role = c("aut", "cre"),
email = "schaarschmidt@biostat.uni-hannover.de"))
c(person("Manuel", "J. A. Eugster", role = c("aut", "cre"),
email = "manuel@mjae.net"))
c(person("Dieter William", "Joenssen", role = c("aut", "cre", "cph"),
email = "Dieter.Joenssen@TU-Ilmenau.de"),
person("Thomas", "Muellerleile", role = "ctb",
email = "Thomas.Muellerleile@TU-Ilmenau.de"))
c(person(given = "Willem", family = "van Loon", email = "willem.van.loon@rws.nl", role = c("aut", "cph")),
person(given = "Dennis", family = "Walvoort", email = "dennis.Walvoort@wur.nl", role = c("aut", "cre")))
c(person(given = "Achim", family = "Zeileis", role = c("aut", "cre"), email = "Achim.Zeileis@R-project.org"),
person(given = "Francisco", family = "Cribari-Neto", role = "aut", email = "cribari@ufpe.br"),
person(given = "Bettina", family = "Gruen", role = "aut", email = "Bettina.Gruen@jku.at"),
person(given = "Ioannis", family = "Kosmidis", role = "aut", email = "ioannis@stats.ucl.ac.uk"),
person(given = c("Alexandre", "B."), family = "Simas", role = "ctb", comment = "earlier version by"),
person(given = c("Andrea", "V."), family = "Rocha", role = "ctb", comment = "earlier version by"))
c(person(given = "Jan", family = "Verbesselt", role = c("aut", "cre"), email = "Jan.Verbesselt@wur.nl"),
person(given = "Achim", family = "Zeileis", role = "aut", email = "Achim.Zeileis@R-project.org"),
person(given = "Rob", family = "Hyndman", role = "ctb", email = "Rob.Hyndman@buseco.monash.edu.au"))
c(person("Nilotpal", "Sanyal", role = c("aut", "cre"),
email = "nsanyal_v@isical.ac.in"),
person("Marco A.R.", "Ferreira", role = "aut"))
c(person("Romain", "Francois", role = "aut",
email = "romain@r-enthusiasts.com"),
person("Kurt", "Hornik", role = c("ctb", "cre"),
email = "Kurt.Hornik@R-project.org"))
person("Claus-Christian", "Carbon", role = c("aut", "cre"), email ="ccc@experimental-psychology.com")
c(person("Christopher", "Paciorek", email = "paciorek@stat.berkeley.edu", role = c("aut", "cre")), person("Benjamin","Lipshitz", role = c("aut")), person(family = "Prabhat", role = c("ctb")), person("Cari", "Kaufman", role = c("ctb")), person("Tina", "Zhuo", role = c("ctb")), person("Rollin", "Thomas", role = c("ctb")))
c(person("Justin", "Donaldson", role=c("aut","cre"), email =
"donaldson@bigml.com"))
c(person("Aloysius", "Lim", role = c("aut", "cre"),
email="aloysius.lim@gmail.com"),
person("Leo", "Breiman", role = "aut"),
person("Adele", "Cutler", role = "aut"))
c(person("Nicholas J.", "Matzke", email = "matzke@berkeley.edu", role =
c("aut", "cre", "cph")))
person (c("Daniel", "T."), "Braithwaite", role=c("aut", "cre"),
email="contact.dtb@gmail.com")
c(
person("Xiaolin","Yang",role=c("aut","cre"), email="xyang@stat.cmu.edu"),
person("Stephen E.","Fienberg", role=c("aut"),email="fienberg@stat.cmu.edu"),
person("Alessandro","Rinaldo",role=c("aut"),email="rinaldo@stat.cmu.edu"),
person("Han","Liu",role=c("aut"),email="hanliu@princeton.edu"),
person("Michael","Rosenblum",role=c("aut"),email="mrosen@jhu.edu"))
c(person("Scott", "Chamberlain", role = c("aut", "cre"),
email = "myrmecocystus@gmail.com"))
c(person("Angelo", "Canty", role = "aut",
email = "cantya@mcmaster.ca"),
person("Brian", "Ripley", role = c("aut", "trl", "cre"),
email = "ripley@stats.ox.ac.uk",
comment = "author of parallel support"))
c(person("A. Jonathan R.", "Godfrey", role=c("aut", "cre"), email="a.j.godfrey@massey.ac.nz"),
person("Greg", "Snow", email = "greg.snow@imail.org", role = "ctb"),
person("Paul", "Murrell", email = "paul@stat.auckland.ac.nz", role = "ctb"),
person("Yihui", "Xie", email = "xie@yihui.name", role = "ctb"))
c(person("Lukasz", "Dziurzynski", role="aut"),
person("Edward", "Wadsworth", role="aut"),
person("Peter", "Fader", role="ctb"),
person("Elea", "McDonnell Feit", role="ctb"),
person("Daniel", "McCarthy", role=c("aut","cre","ctb"),
email="danielmc@wharton.upenn.edu"),
person("Bruce", "Hardie", role="ctb"),
person("Arun", "Gopalakrishnan", role="ctb"),
person("Eric", "Schwartz", role="ctb"),
person("Yao", "Zhang", role="ctb"))
person("Jeff", "Binder", role = c("aut", "cre"), email =
"extruded@gmail.com")
c(person(given = "Michael", family = "Greenacre", role = "aut", email = "michael.greenacre@upf.edu"),
person(given = "Oleg", family = "Nenadic", role = c("aut", "cre"), email = "onenadi@uni-goettingen.de"),
person(given = "Michael", family = "Friendly", role = "ctb"))
c(person("Anoop", "Shah", role=c("aut", "cre"), email="anoop@doctors.org.uk"),
person("Jonathan", "Bartlett", role="ctb"),
person("Harry", "Hemingway", role="ths"),
person("Owen", "Nicholas", role="ths"),
person("Aroon", "Hingorani", role="ths"))
c(
person("Maria", "Ortiz", role=c("aut", "ctb")),
person("Ander", "Aramburu", role=c("ctb")),
person("Henrik", "Bengtsson", role=c("aut", "cre", "cph"),
email="henrikb@braju.com"),
person("Pierre", "Neuvial", role=c("aut", "ctb")),
person("Angel", "Rubio", role=c("aut", "ctb")))
c(person(given = "Michael", family = "Friendly",
role=c("aut", "cre"), email="friendly@yorku.ca"), person(given
= "John", family = "Fox", role="aut"))
c(person(given='Oswaldo', family='Santos',
email='oswaldosant@gmail.com', role=c("aut", "cre")),
person(given='Marcos', family='Amaku',
email='amaku@vps.fmvz.usp.br', role="ctb"),
person(given='Fernando', family='Ferreira',
email='fernando@vps.fmvz.usp.br', role="ctb"))
c(person("John", "Fox", role = c("aut", "cre"), email = "jfox@mcmaster.ca"),
person("Sanford", "Weisberg", role = "aut", email = "sandy@umn.edu"),
person("Daniel", "Adler", role="ctb"),
person("Douglas", "Bates", role = "ctb"),
person("Gabriel", "Baud-Bovy", role = "ctb"),
person("Steve", "Ellison", role="ctb"),
person("David", "Firth", role = "ctb"),
person("Michael", "Friendly", role = "ctb"),
person("Gregor", "Gorjanc", role = "ctb"),
person("Spencer", "Graves", role = "ctb"),
person("Richard", "Heiberger", role = "ctb"),
person("Rafael", "Laboissiere", role = "ctb"),
person("Georges", "Monette", role = "ctb"),
person("Duncan", "Murdoch", role="ctb"),
person("Henric", "Nilsson", role = "ctb"),
person("Derek", "Ogle", role = "ctb"),
person("Brian", "Ripley", role = "ctb"),
person("William", "Venables", role = "ctb"),
person("Achim", "Zeileis", role = "ctb"),
person("R-Core", role="ctb"))
c(person("Susanne", "Rolinski", role = "aut"),
person("René", "Sachse", role = "aut"),
person("Thomas", "Petzoldt", role = c("aut", "cre"),
email = "thomas.petzoldt@tu-dresden.de"))
c(person("Andreas", "Alfons", email = "alfons@ese.eur.nl",
role = c("aut", "cre")), person("David", "Simcha", role = "ctb"))
c(person("Evgenia", "Dimitriadou", role = "aut"),
person("Kurt", "Hornik", role = c("ctb", "cre"),
email = "Kurt.Hornik@R-project.org"))
c(person("Baptiste", "Auguie",
email = "baptiste.auguie@gmail.com",
role = c("aut", "cre")),
person("LAPACK authors",
role = "cph",
comment = "copyrights apply to src/zgels.f"))
c(person("Emilio", "Torres Manzanera", role = c("aut",
"cre"), email = "torres@uniovi.es"))
c( person("Alexander", "Robitzsch", role = c("aut", "cre"),
email = "a.robitzsch@bifie.at"),
person("Thomas", "Kiefer", role = "aut"),
person("Ann Cathrice", "George", role = "aut"),
person("Ali", "Uenlue", role = "aut") )
c(person("Mauricio", "Zambrano-Bigiarini",
email="mzb.devel@gmail.com", role=c("aut","cre")),
person("Yasser", "Gonzalez Fernandez",
email="ygonzalezfernandez@gmail.com", role=c("aut")) )
c(person(c("Christopher","G."),"Green",role=c("aut","cre"),
email = "christopher.g.green@gmail.com"),
person(c("R.","Doug"),"Martin",role="ths"))
c(person("Markus", "Gesmann", role = c("aut", "cre"),
email = "markus.gesmann@googlemail.com"),
person("Daniel", "Murphy", role = "aut",
email="danielmarkmurphy@gmail.com"),
person("Wayne", "Zhang", role = "aut",
email="actuary_zhang@hotmail.com"))
c(person("Rebecca", "Killick",
role=c("aut","cre"),email="r.killick@lancs.ac.uk"),
person("Idris", "Eckley", role="aut"),
person("Kaylea", "Haynes", role="aut"),
person("Paul","Fearnhead",role="ctb"))
c(
person("Stefan", "Roediger",
email = "stefan.roediger@hs-lausitz.de",
role = c("cre", "aut")),
person("Michal", "Burdukiewicz",
email = "michalburdukiewicz@gmail.com", role = "aut"))
c(person("Ryan", "Sermas", role = "aut", email =
"DecisionAnalystR@decisionanalyst.com"), person(c("John","V"),
"Colias", role = c("ctb","cre"), email =
"jcolias@decisionanalyst.com"), person("Decision Analyst,
Inc.", role = "cph"))
c(person("David", "James", role = "aut",
comment = "S original"),
person("Kurt", "Hornik", role = c("aut", "trl", "cre"),
email = "Kurt.Hornik@R-project.org"),
person("Gabor", "Grothendieck", role = "ctb",
email = "ggrothendieck@gmail.com"))
c(person("Brian", "Ripley", role = c("aut", "cre", "cph"),
email = "ripley@stats.ox.ac.uk"),
person("William", "Venables", role = "cph"))
c(person("Roger", "Bivand", role=c("aut", "cre"), email="Roger.Bivand@nhh.no"), person("Hisaji", "Ono", role="ctb"), person("Richard", "Dunlap", role="ctb"), person("Matthieu", "Stigler", role="ctb"))
c(person("Volodymyr", "Melnykov", role = c("aut", "cre"), email =
"vmelnykov@cba.ua.edu"))
c(person("Gabriel", "Gelius-Dietrich", role = c("aut", "cre"), email = "geliudie@uni-duesseldorf.de"))
c(person("Kurt", "Hornik", role = c("aut", "cre"),
email = "Kurt.Hornik@R-project.org"),
person("Walter", "Böhm", role = "ctb"))
c(person("Martin","Maechler", role=c("aut","cre"), email="maechler@stat.math.ethz.ch")
,person("Peter", "Rousseeuw", role="aut", email="rousse@uia.ua.ac.be",
comment = "Fortran original")
,person("Anja", "Struyf", role="aut", comment= "S original")
,person("Mia", "Hubert", role="aut", email= "Mia.Hubert@uia.ua.ac.be", comment= "S original")
,person("Kurt", "Hornik", role=c("trl", "ctb"),
email="Kurt.Hornik@R-project.org", comment="port to R; maintenance(1999-2000)")
,person("Matthias", "Studer", role="ctb")
,person("Pierre", "Roudier", role="ctb")
)
c(person("Damien", "Leroux", role = c("aut", "cre"), email =
"damien.leroux@toulouse.inra.fr"), person("Brigitte", "Mangin",
role = "aut", email="brigitte.mangin@toulouse.inra.fr"),
person("Sylvain", "Jasson", role = "aut",
email="sylvain.jasson@toulouse.inra.fr"), person("Abdelaziz",
"Rahmani", role = "aut", email =
"rahmani.abdelaziz@gmail.com"))
c(person("Angelos", "Markos", email = "amarkos@gmail.com", role =
c("aut", "cre")), person("Alfonso", "Iodice D' Enza", role = "aut"),
person("Michel", "Van de Velden", role = "aut"))
c(person("Emmanuel", "Paradis", role = c("aut", "cre", "cph"), email = "Emmanuel.Paradis@ird.fr"))
c(person(given="Birk", family="Diedenhofen",
email="mail@birkdiedenhofen.de", role=c("aut", "cre")))
c(person("Martyn", "Plummer", role=c("aut","cre","trl"),
email="plummerm@iarc.fr"), person("Nicky", "Best", role="aut"),
person("Kate", "Cowles", role="aut"), person("Karen", "Vines",
role="aut"), person("Deepayan", "Sarkar", role="aut"),
person("Russell", "Almond", role="ctb"))
c(person(given = c("Gavin", "L."),
family = "Simpson",
role = c("aut","cre"),
email = "ucfagls@gmail.com"),
person(given = "Francisco",
family = "Rodriguez-Sanchez",
role = "ctb"))
c(person("Torsten", "Hothorn", role = c("aut", "cre"),
email = "Torsten.Hothorn@R-project.org"),
person("Kurt", "Hornik", role = "aut"),
person("Mark A.", "van de Wiel", role = "aut"),
person("Achim", "Zeileis", role = "aut"))
c(person("Chris", "Wallace", role=c("aut","cre"),
email = "chris.wallace@cimr.cam.ac.uk"),
person("Claudia","Giambartolomei", role="aut",
email="claudia.giambartolomei.10@ucl.ac.uk"),
person("Vincent","Plagnol", role="ctb",
email="v.plagnol@ucl.ac.uk"))
c(person(given = "Jose", family = "Gama", role = c("aut","cre"),email = "jgama@abo.fi"))
c(person(given = "Ross", family = "Ihaka", role = "aut", email = "ihaka@stat.auckland.ac.nz"),
person(given = "Paul", family = "Murrell", role = "aut", email = "paul@stat.auckland.ac.nz"),
person(given = "Kurt", family = "Hornik", role = "aut", email = "Kurt.Hornik@R-project.org"),
person(given = c("Jason", "C."), family = "Fisher", role = "aut", email = "jfisher@usgs.gov"),
person(given = "Achim", family = "Zeileis", role = c("aut", "cre"), email = "Achim.Zeileis@R-project.org"))
c(person("Gen", "Nowak", role = c("aut","cre"), email = "gen.nowak@gmail.com"), person("Robert", "Tibshirani", role = "aut"))
c(person(given = c("Willem", "M.", "G.", "M."), family = "van Loon",
email = "willem.van.loon@rws.nl", role = c("aut", "cph")),
person(given = c("Dennis", "J.", "J."), family = "Walvoort",
email = "dennis.Walvoort@wur.nl", role = c("aut", "cre")))
c(person(given = "Jose'", family = "Gama", role = c("aut","cre"),email = "jgama@abo.fi"),
person(given = "Nikolai", family = "Chernov", role = c("aut","cph")),
person(given = "Göran", family = "Högnäs", role = "ths"))
c(person("Yasser", "Gonzalez-Fernandez", role = c("aut", "cre"), email = "ygonzalezfernandez@gmail.com"),
person("Marta", "Soto", role = "aut", email = "mrosa@icimaf.cu"))
c(person("Clement", "THERY", role = c("aut", "cre"),
email = "clement.thery@arcelormittal.com"),
person("Christophe", "BIERNACKI", role = "ctb"),
person("Gaetan", "LORIDANT", role = "ctb"),
person("Florian", "WATRIN", role = "ctb"),
person("Quentin", "GRIMONPREZ", role = "ctb"),
person("Vincent", "KUBICKI", role = "ctb"),
person("Samuel", "BLANCK", role = "ctb"),
person("Jeremie","KELLNER",role="ctb")
)
c(person("Andrey", "Ziyatdinov",
email = "andrey.ziyatdinov@upc.edu", role = c("aut", "cre")),
person("Samir", "Kanaan-Izquierdo", role = "aut"),
person("Nickolay T.", "Trendafilov", role = "aut"),
person("Alexandre", "Perera-Lluna", role = "aut"))
c(person(given =c("Oscar", "A."), family = "Linares", role =
c("aut", "cre"), email = "OALinaresMD@gmail.com"),
person(given =c("David", "T."), family = "Daly", role = "aut",
email = "daviddalysr@att.net"))
"Gabor Csardi <csardi.gabor@gmail.com> [aut, cre]"
c(person(given = "Jakob", family = "Messner", role =
c("aut", "cre"), email = "Jakob.Messner@uibk.ac.at"),
person(given = "Achim", family = "Zeileis", role = "aut", email
= "Achim.Zeileis@R-project.org"))
c(person("Ling-Yun", "Wu", role = c("aut", "cre"), email =
"wulingyun@gmail.com"))
c(person(given = "Jeffrey S.", family = "Racine", role = c("aut","cre"), email = "racinej@mcmaster.ca"),
person(given = "Zhenghua", family = "Nie", role = c("aut"), email = "niez@mcmaster.ca"),
person(given = "Brian D.", family = "Ripley", role = c("ctb"), comment = "stepCV.R"))
c(person("Alejandro", "Jara", role = c("aut", "cre"), email
= "atjara@uc.cl"), person("Maria Jose", "Garcia-Zattera", role
= "ctb", email = "mjgarcia@uc.cl"))
c( person("Shai", "Shen-Orr", role=c("aut", "cre"),
email="shenorr@technion.ac.il") , person("Rob", "Tibshirani",
role="aut") , person("Narasimhan", "Balasubramanian",
role="aut") , person("David", "Wang", role="aut") ,
person("Renaud Gaujoux", role="ctb",
comment="roxygen/debug/CRAN-compliance") )
c(person(given = "Achim", family = "Zeileis", role = c("aut", "cre"), email = "Achim.Zeileis@R-project.org"),
person(given = "Kurt", family = "Hornik", role = "aut", email = "Kurt.Hornik@R-project.org"))
c(person("Wei-Chen", "Chen", role = c("aut", "cre"), email =
"wccsnow@gmail.com"),
person("Russell", "Zaretzki", role = "aut"),
person("William", "Howell", role = "aut"),
person("Drew", "Schmidt", role = "aut"),
person("Michael", "Gilchrist", role = "aut"),
person("Students", "REU13", role = "ctb"))
c(person(given = "José", family = "Gama", role = c("aut","cre","trl"),email = "jgama@abo.fi"),
person(given = "Maria", family = "Nyberg", role = "aut"),
person(given = "Göran", family = "Högnäs", role = "ths"),
person(given = "Brian", family = "Foutch", role = "ctb"),
person(given = "Mark", family = "Grundland", role = "ctb"),
person(given = "Neil", family = "Dodgson", role = "ctb"))
c(person("Ali", "Uenlue", email = "ali.uenlue@tum.de", role
= c("aut", "cre")), person("Anatol", "Sargin", email =
"anatol.sargin@generali.de", role = c("aut")))
c(person("Brecht", "Devleesschauwer",
role = c("aut", "cre"),
email = "Brecht.Devleesschauwer@UGent.be"),
person("Scott", "McDonald", role = "aut"),
person("Juanita", "Haagsma", role = "aut"),
person("Nicolas", "Praet", role = "aut"),
person("Arie", "Havelaar", role = "aut"),
person("Niko", "Speybroeck", role = "aut"))
c(person("Terry", "Therneau", role = "aut",
email = "therneau@mayo.edu"),
person("Thomas", "Lumley", role = "trl", comment = "R port"),
person("Kjetil", "Halvorsen", role = "trl", comment = "R port"),
person("Kurt", "Hornik", role = c("trl", "ctb", "cre"),
email = "Kurt.Hornik@R-project.org",
comment = "R port"))
c(person(given = "Angelo", family = "Mazza", role = c("aut", "cre"), email = "a.mazza@unict.it"),
person(given = "Antonio", family = "Punzo", role = "aut"))
c(person("Oleksii", "Pokotylo", role=c("aut", "cre"),
email = "alexey.pokotylo@gmail.com"),
person("Pavlo", "Mozharovskyi", role=c("aut"),
email = "mozharovskyi@statistik.uni-koeln.de"))
c(person("Efthimios", "Motakis", role=c("aut")),
person("Guy", "Nason", role=c("aut", "cre"),
email="g.p.nason@bristol.ac.uk"),
person("Piotr", "Fryzlewicz", role=c("aut")))
c(person("Adrian", "Dusa", role = c("aut", "cre"),
email = "dusa.adrian@unibuc.ro"))
c(person(given="Avraham", family="Adler", role=c("aut", "cph", "cre"), email="Avraham.Adler@gmail.com"))
c( person( "Sten", "Ilmjarv", role = c( "aut", "cre" ),
email = "sten.ilmjarv@gmail.com"), person( "Hendrik", "Luuk",
role = "aut", email = "hendrik.luuk@gmail.com" ) )
c(person("Tal", "Galili", role = c("aut", "cre", "cph"), email =
"tal.galili@gmail.com", comment = "http://www.r-statistics.com"),
person("Gavin", "Simpson", role = "ctb"), person("Gregory","Jefferis", role
= "ctb", email = "jefferis@gmail.com",
comment ="imported code from his dendroextras package"),
person("Marco", "Gallotta", role = "ctb", comment =
"a.k.a: marcog") , person("Johan", "Renaudie", role = "ctb", comment =
"https://github.com/plannapus"), person("The R Core Team", role = "ctb",
comment = "Thanks for the Infastructure, and code in the examples"),
person("Kurt", "Hornik", role = "ctb"), person("Uwe", "Ligges",
role = "ctb"), person("Andrej-Nikolai", "Spiess", role = "ctb"),
person("Steve", "Horvath",email = "SHorvath@mednet.ucla.edu", role =
"ctb"), person("Peter", "Langfelder",email = "Peter.Langfelder@gmail.com",
role = "ctb"), person("skullkey", role = "ctb"), person("Mark",
"Van Der Loo", email = "mark.vanderloo@gmail.com", comment =
"https://github.com/markvanderloo d3dendrogram", role = "ctb"),
person("Yoav", "Benjamini", role = "ths"))
c(person("Tal", "Galili", role = c("aut", "cre", "cph"), email =
"tal.galili@gmail.com", comment = "http://www.r-statistics.com"),
person("Romain", "Francois", role = "ctb"), person("Dirk", "Eddelbuettel",
role = "ctb"), person("Kevin", "Ushey", role = "ctb"),
person("Yoav", "Benjamini", role = "ths"))
c(person("David", "Ardia", role =
c("aut")),person("Katharine", "Mullen", role = c("aut", "cre"),
email="mullenkate@gmail.com"),person("Brian", "Peterson", role
= "aut"),person("Joshua", "Ulrich", role = "aut"))
c(person("Karline","Soetaert", role = c("aut"),
email = "karline.soetaert@nioz.nl"),
person("Thomas","Petzoldt", role = c("aut", "cre"),
email = "thomas.petzoldt@tu-dresden.de"),
person("R. Woodrow","Setzer", role = c("aut"),
email = "setzer.woodrow@epa.gov"),
person("odepack authors", role = "cph"))
c(person("Karline","Soetaert", role = c("aut","cre"), email
= "karline.soetaert@nioz.nl"), person("Jeff","Cash", role =
"aut", email = "j.cash@imperial.ac.uk"),
person("Francesca","Mazzia", role = "aut", email =
"mazzia@dm.uniba.it"), person("LAPACK authors", role = "cph"))
as.person(c(
"Hadley Wickham <hadley@rstudio.com> [aut, cre]",
"Winston Chang [aut]",
"RStudio [cph]",
"R Core team [ctb] (Some namespace and vignette code extracted from base R)"
))
c(person(given = "Thomas", family = "Lumley", role =
c("aut", "cre"), email = "tlumley@u.washington.edu"),
person(given = "Ken", family = "Knoblauch", role = "ctb", email
= "ken.knoblauch@inserm.fr"), person(given = "Scott", family =
"Waichler", role = "ctb", email = "scott.waichler@pnl.gov"),
person(given = "Achim", family = "Zeileis", role = "ctb", email
= "Achim.Zeileis@R-project.org"))
person("Baptiste", "Auguie", email = "baptiste.auguie@gmail.com",
role = c("aut", "cre"), comment = "the original data have their original
source listed in the help file.")
person(given  = c("Marco", "Johannes"),
family = "Maier",
email  = "marco.maier@wu.ac.at",
role   = c("cre", "aut"))
c(person("Ruth", "Heller", role = "aut"), person("Hadas",
"Gur", role = "aut"), person("Shay", "Yaacoby", role =
c("aut","cre"),email = "shay66@gmail.com"))
c(
person("Gaston", "Sanchez",
email = "gaston.stat@gmail.com", role = c("aut", "cre")),
person("Charles", "Determan", role = "ctb"))
c(person("Florian", "Camphausen", role=c("aut")),
person("Matthias", "Kohl", role=c("aut", "cph")),
person("Peter", "Ruckdeschel", role=c("cre", "cph"), email="Peter.Ruckdeschel@itwm.fraunhofer.de"),
person("Thomas", "Stabla", role=c("aut", "cph")),
person("R Core Team", role = c("ctb", "cph"),
comment="for source file ks.c/ routines 'pKS2' and 'pKolmogorov2x'"))
c( person(c("A.", "Jonathan", "R."), "Godfrey", role=c("aut", "cre"), email="a.j.godfrey@massey.ac.nz"), person("K. (Raj)", "Govindaraju", role="aut", email="k.govindaraju@massey.ac.nz"))
c(person("Ulrike", "Groemping", role = c("aut", "cre"), email =
"groemping@beuth-hochschule.de"), person("Boyko", "Amarov", role = "ctb"),
person("Hongquan", "Xu", role = "ctb"))
c(person("Ulrike", "Groemping", role = c("aut", "cre"), email =
"groemping@beuth-hochschule.de"), person("Lenth", "Russ", role = "ctb"))
c( person("Konstanze", "Krueger", role = "aut", email = "konstanze.Krueger@hfwu"),
person("Knut", "Krueger", role = c("cre","aut"),email = "Knut.Krueger@equine-science.de")
)
c(person("Andy", "Bunn", role = c("aut", "cph",
"cre", "trl"), email = "andy.bunn@wwu.edu"), person("Mikko",
"Korpela", role = c("aut", "trl")), person("Franco", "Biondi",
role = c("aut", "cph")), person("Filipe", "Campelo", role =
c("aut", "cph")), person("Pierre", "Mérian", role = c("aut",
"cph")), person("Manfred", "Mudelsee", role = "aut"),
person("Fares", "Qeadan", role = c("aut", "cph")),
person("Michael", "Schulz", role = "aut"), person("Christian",
"Zang", role = c("aut", "cph")), person("Jacob", "Cecile",
role = "ctb"))
c(person("Alejandro", "Jara", role = c("aut", "cre"), email
= "atjara@uc.cl"), person("Timothy", "Hanson", role = "ctb",
email = "hansont@stat.sc.edu"), person("Fernando", "Quintana",
role = "ctb", email = "quintana@mat.puc.cl"), person("Peter",
"Mueller", role = "ctb", email = "pmueller@math.utexas.edu"),
person("Gary", "Rosner", role = "ctb", email =
"grosner@jhmi.edu"))
c(person("Johannes", "Ranke", role = c("aut", "cre"),
email = "jranke@uni-bremen.de"))
c(person("Greg", "Hixon", role = c("aut", "cph"),
email = "ghixon@toxstrategies.com"),
person("Anne", "Bichteler", role = c("aut","cre"),
email = "abichteler@toxstrategies.com"),
person("Chad", "Thompson", role = "ctb",
email = "cthompson@toxstrategies.com"),
person("Liz", "Abraham", role = "ctb",
email = "labraham@toxstrategies.com"))
c(person("Ingo", "Feinerer", role = "aut"), person("Stefan", "Theussl", role = c("aut", "cre"), email = "Stefan.Theussl@R-project.org"), person("Christian", "Buchta", role = "ctb"))
c(person("Karsten", "Tabelow", role = c("aut", "cre"),
email = "karsten.tabelow@wias-berlin.de"),
person("Joerg", "Polzehl", role = c("aut"),
email = "joerg.polzehl@wias-berlin.de"),
person("Felix", "Anker", role = c("ctb")))
c(person("Xinyu", "Tang", role = c("aut", "cre"),
email = "xtang@uams.edu"),
person("Maria", "Melguizo", role = "aut",
email = "msmelguizo@uams.edu"))
person(given = "Achim", family = "Zeileis", role = c("aut", "cre"), email = "Achim.Zeileis@R-project.org")
c(person(given = "David", family = "Meyer", role = c("aut", "cre"), email = "David.Meyer@R-project.org"),
person(given = "Evgenia", family = "Dimitriadou", role = c("aut","cph")),
person(given = "Kurt", family = "Hornik", role = "aut"),
person(given = "Andreas", family = "Weingessel", role = "aut"),
person(given = "Friedrich", family = "Leisch", role = "aut"),
person(given = "Chih-Chung", family = "Chang", role = c("ctb","cph"), comment = "libsvm C++-code"),
person(given = "Chih-Chen", family = "Lin", role = c("ctb","cph"), comment = "libsvm C++-code"))
c(person("Karthik", "Ram", role = c("aut", "cre"), email =
"karthik.ram@gmail.com"))
c(person("Daniel", "McGlinn", role = c("aut", "cre"),
email = "danmcglinn@gmail.com"),
person("Ethan", "White", role = "aut",
email = "ethan@weecology.org"))
c(person(c("Jason", "P."), "Becker", role = c("ctb"),
email = "jason+sitemail@jbecker.co"),
person(c("Jared", "E."), "Knowles", role=c("aut", "cre"),
email="jknowles@gmail.com"))
c(person("John", "Fox", role = c("aut", "cre"), email = "jfox@mcmaster.ca"),
person("Sanford", "Weisberg", role = "aut", email = "sandy@umn.edu"),
person("Michael", "Friendly", role = "aut", email = "friendly@yorku.ca"),
person("Jangman", "Hong", role = "aut"),
person("Robert", "Andersen", role = "ctb"),
person("David", "Firth", role = "ctb"),
person("Steve", "Taylor", role = "ctb"))
c(person("Michaela", "Paul", role = c("aut"),
email = "michaela.paul@uzh.ch"),
person("Reinhard", "Furrer", role = c("cre", "ctb"),
email = "reinhard.furrer@math.uzh.ch"))
c(person("Wei-Chen", "Chen", role = c("aut", "cre"), email =
"wccsnow@gmail.com"),
person("Ranjan", "Maitra", role = "aut"),
person("Volodymyr", "Melnykov", role = "aut"))
c(person(given = "Erich", family = "Neuwirth", role =
c("aut", "cre"), email = "erich.neuwirth@univie.ac.at"))
c(person("Bendix", "Carstensen", role = c("aut", "cre"),
email = "bxc@steno.dk"),
person("Martyn", "Plummer", role = "aut",
email = "plummerm@iarc.fr"),
person("Esa", "Laara", role = "ctb"),
person("Michael", "Hills", role = "ctb"))
c(
person("Maria",
"Noremark",
role = c("aut")),
person("Stefan",
"Widgren",
role = c("aut", "cre"),
email = "stefan.widgren@sva.se"))
c(person(given='Oswaldo', family='Santos Baquero',
email='oswaldosant@gmail.com', role=c("aut", "cre")),
person(given='Fernando', family='Silveira Marques',
email='fernandosix@gmail.com', role="aut"))
c(person("Samuel", "Jenness",
role=c("cre","aut"), email="sjenness@uw.edu"),
person("Steven M.","Goodreau",
role=c("aut"), email="goodreau@uw.edu"),
person("Martina", "Morris",
role=c("aut"), email="morrism@uw.edu"),
person("Li", "Wang",
role=c("ctb"), email="lxwang@uw.edu"))
c(person("Tomas J. Aragon", "Developer", role = c("aut",
"cre"), email = "aragon@berkeley.edu"), person("Michael P.
Fay", "User", role = "ctb", email = "mfay@niaid.nih.gov"),
person("Daniel Wollschlaeger", "User", role = "ctb", email =
"dwoll@psychologie.uni-kiel.de"))
c(
person("Mark S.", "Handcock", role=c("aut"), email="handcock@stat.ucla.edu"),
person("David R.", "Hunter", role=c("aut"), email="dhunter@stat.psu.edu"),
person("Carter T.", "Butts", role=c("aut"), email="buttsc@uci.edu"),
person("Steven M.", "Goodreau", role=c("aut"), email="goodreau@u.washington.edu"),
person("Pavel N.", "Krivitsky", role=c("aut","cre"), email="pavel@uow.edu.au"),
person("Martina", "Morris", role=c("aut"), email="morrism@u.washington.edu"))
c(
person("Pavel N.", "Krivitsky", role=c("aut","cre"), email="pavel@uow.edu.au"),
person("Mark S.", "Handcock", role=c("ctb"), email="handcock@stat.ucla.edu"),
person("David R.", "Hunter", role=c("ctb"), email="dhunter@stat.psu.edu"))
c( person("Omer Nebil", "Yaveroglu", role=c("aut", "cre"), email="o.yaveroglu10@imperial.ac.uk"),
person("Sean M.", "Fitzhugh", role=c("aut"), email="sean.fitzhugh@uci.edu"),
person("Maciej", "Kurant", role=c("aut"), email="maciej.kurant@gmail.com"),
person("Athina", "Markopoulou", role=c("aut"), email="athina@uci.edu"),
person("Natasa", "Przulj", role=c("aut"), email="natasha@imperial.ac.uk"),
person("Carter T.", "Butts", role=c("aut"), email="buttsc@uci.edu"))
c(person(given="Patrick", family="Mair", email="mair@fas.harvard.edu", role=c("cre","aut")),
person(given="Reinhold", family="Hatzinger", role="aut"),
person(given=c("Marco", "J."), family="Maier", email="marco.maier@wu.ac.at", role="aut"))
c(person("Marcello", "Chiodi", role = c("aut", "cre"),
email = "marcello.chiodi@unipa.it"),
person("Giada", "Adelfio", role = c("aut"),
email = "giada.adelfio@unipa.it"))
c(person("Hadley", "Wickham", role = "aut"), person("Yihui", "Xie",
role = c("cre", "ctb"), email = "xie@yihui.name"), person("Barret",
"Schloerke", role = "ctb"))
c(person("Bernhard", "Pfaff", email =
"bernhard@pfaffikus.de", role = c("aut", "cre")),
person("Alexander", "McNeil", email = "mcneil@math.ethz.ch",
role = "aut", comment = "S original (EVIS)"), person("Alec",
"Stephenson", email = "alec_stephenson@hotmail.com", role =
"trl", comment = "R port of EVIS"))
c(person(given = "Thomas", family = "Grubinger", role = c("aut", "cre"), email = "Thomas.Grubinger@scch.at"),
person(given = "Achim", family = "Zeileis", role = "aut", email = "Achim.Zeileis@R-project.org"),
person(given = "Karl-Peter", family = "Pfeiffer", role = "aut", email = "Karl-Peter.Pfeiffer@i-med.ac.at"))
c(person("Torsten", "Hothorn", role = c("aut", "cre"),
email = "Torsten.Hothorn@R-project.org"),
person("Kurt", "Hornik", role = "aut"))
c(person(given = "Achim", family = "Zeileis", role = c("aut", "cre"), email = "Achim.Zeileis@R-project.org"),
person(given = "Bettina", family = "Gruen", role = "aut", email = "Bettina.Gruen@jku.at"),
person(given = "Friedrich", family = "Leisch", role = "aut", email = "Friedrich.Leisch@R-project.org"),
person(given = "Nikolaus", family = "Umlauf", role = "aut", email = "Nikolaus.Umlauf@uibk.ac.at"))
c(person("Fabian", "Sobotka", role = "cre", email = "fabian.sobotka@wiwi.uni-goettingen.de"),
person("Sabine", "Schnabel", role = "aut", email = "sabine.schnabel@wur.nl"),
person("Linda", "Schulze Waltrup", role = "aut", email = "lschulze_waltrup@stat.uni-muenchen.de"),
person("Paul", "Eilers", role = "ctb", email = "p.eilers@erasmusmc.nl"),
person("Thomas", "Kneib", role = "ths", email = "tkneib@uni-goettingen.de"),
person("Goeran", "Kauermann", role = "ctb", email = "goeran.kauermann@stat.uni-muenchen.de"))
c(person("Roger B.", "Sidje", email =
"roger.b.sidje@ua.edu", role = c("aut", "cph")), person("Niels
Richard", "Hansen", email = "Niels.R.Hansen@math.ku.dk", role =
c("aut", "cre", "cph")) )
c(
person("Phil", "Chalmers",
email = "rphilip.chalmers@gmail.com", role = c("aut", "cre")))
person("Daniel", "Müllner", email =
"daniel@danifold.net", role = c("aut", "cph", "cre"))
person("Kaveh", "Vakili", email =
"kaveh.vakili@wis.kuleuven.be", role = c("aut", "cre"))
person("Kaveh", "Vakili", email =
"vakili.kaveh.email@gmail.com", role = c("aut", "cre"))
c(person("Manuel Febrero Bande",role="aut", email = "manuel.febrero@usc.es"), person("Manuel Oviedo de la Fuente",role=c("aut","cre"), email="manuel.oviedo@usc.es"),
person("Pedro Galeano",role="ctb"),
person("Alicia Nieto",role="ctb"),
person("Eduardo Garcia-Portugues",role="ctb"))
c(person("Ali", "Uenlue", email = "ali.uenlue@tum.de", role
= c("aut", "cre")), person("Thomas", "Kiefer", email =
"t.kiefer@bifie.at", role = c("aut", "trl"), comment = "Based
on original MATLAB source by Ehtibar N. Dzhafarov."))
c( person(c('David', 'V.'), 'Dias', role = c('aut', 'cre'),
email = 'dvdscripter@gmail.com') )
c( person("Douglas", "Nychka", role = c("aut", "cre"),
email = "nychka@ucar.edu"),
person("Reinhard", "Furrer", role = c("aut"),
email = "reinhard.furrer@math.uzh.ch"),
person("Stephan", "Sain", role = "aut",
email = "ssain@ucar.edu"))
c(person(given  = 'Benjamin',
family = 'Baumgartner',
email  = 'benjamin@baumgrt.com',
role   = c('aut', 'cre')),
person(given  = 'Riccardo',
family = 'Gatto',
email  = 'gatto@stat.unibe.ch',
role   = c('ctb', 'ths')))
c(person("Marie Laure", "Delignette-Muller", role = c("aut", "cre"), email= "marielaure.delignettemuller@vetagro-sup.fr"),person("Christophe", "Dutang", role = "aut", email = "christophe.dutang@univ-lemans.fr"),person("Regis", "Pouillot", role = "ctb", email = "rpouillot@yahoo.fr"),person("Jean-Baptiste", "Denis", role = "ctb", email = "jbdenis@jouy.inra.fr"))
c(person(given="Friedrich", family="Leisch",
email="Friedrich.Leisch@R-project.org", role=c("aut", "cre")),
person(given="Evgenia", family="Dimitriadou", role="ctb"))
c(person("Bettina", "Gruen", role = c("aut", "cre"),
email = "Bettina.Gruen@jku.at"),
person("Friedrich", "Leisch", role = "aut"),
person("Deepayan", "Sarkar", role = "ctb"))
c(person("Gen", "Nowak", role = c("aut","cre"), email = "gen.nowak@gmail.com"), person("Trevor", "Hastie", role = "aut"), person(c("Jonathan", "R."), "Pollack", role = "aut"), person("Robert", "Tibshirani", role = "aut"), person("Nicholas", "Johnson", role = "aut"))
c(person("Karsten", "Tabelow", role = c("aut", "cre"),
email = "tabelow@wias-berlin.de"),
person("Joerg", "Polzehl", role = c("aut"),
email = "polzehl@wias-berlin.de"),
person("Brandon", "Whitcher", role = c("ctb")))
structure(
c(person("R Core Team",
email = "R-core@R-project.org", role = c("aut", "cph", "cre")),
person("Roger", "Bivand", role = c("ctb", "cph")),
person(c("Vincent", "J."), "Carey", role = c("ctb", "cph")),
person("Saikat", "DebRoy", role = c("ctb", "cph")),
person("Stephen", "Eglen", role = c("ctb", "cph")),
person("Rajarshi", "Guha", role = c("ctb", "cph")),
person("Nicholas", "Lewin-Koh", role = c("ctb", "cph")),
person("Mark", "Myatt", role = c("ctb", "cph")),
person("Ben", "Pfaff", role = "ctb"),
person("Brian", "Quistorff", role = "ctb"),
person("Frank", "Warmerdam", role = c("ctb", "cph")),
person("Stephen", "Weigand", role = c("ctb", "cph")),
person("Free Software Foundation, Inc.", role = "cph")))
c(person(given = "Achim", family = "Zeileis", role = c("aut", "cre"), email = "Achim.Zeileis@R-project.org"),
person(given = "Yves", family = "Croissant", role = "aut", email = "Yves.Croissant@univ-reunion.fr"))
c(person("Bernhard", "Pfaff", email =
"bernhard@pfaffikus.de", role = c("aut", "cre")),
person("Miguel Sousa", "Lobo", email =
"mlobo@isl.stanford.edu", role = "ctb", comment = "SOCP"),
person("Lieven", "Vandenberghe", email =
"vandenbe@isl.stanford.edu", role = "ctb", comment = "SOCP"),
person("Stephen", "Boyd", email = "boyd@isl.stanford.edu", role
= "ctb", comment = "SOCP"), person("Herve", "Lebret", role =
"ctb", comment = "SOCP"))
c(person("John", "Dziak",
role = c("aut", "cre"),
email = "jjd264@psu.edu"),
person("Mariya", "Shiyko",
role = "aut",
email = "M.Shiyko@neu.edu"))
c(
person("Marek", "Gagolewski", role = c("aut", "cre"),
email = "gagolews@rexamine.com"),
person("Jan", "Caha", role = "ctb",
email = "jan.caha@upol.cz"))
c(person(given = "Achim", family = "Zeileis", role = c("aut", "cre"), email = "Achim.Zeileis@R-project.org"),
person(given = "Ajay", family = "Shah", role = "ctb"),
person(given = "Ila", family = "Patnaik", role = "ctb"),
person(given = "Anmol", family = "Sethy", role = "ctb"))
c(person(c("Curtis", "S."), "Signorino", role = "aut"),
person("Brenton", "Kenkel", role = c("aut", "cre"),
email = "brenton.kenkel@gmail.com"))
c(person("Lincoln", "Mullen", role = c("aut", "cre"),
email = "lincoln@lincolnmullen.com"),
person("Cameron", "Blevins", role = "ctb"),
person("Ben", "Schmidt", role = "ctb"))
c( person("Louis",			"Houde", 		role="aut"),
person("Jean-Francois",	"Lefebvre",	role="aut", email="jf_lef@yahoo.ca"),
person("Valery",		"Roy-Lagace",	role="aut"),
person("Sebastien",		"Lemieux",	role="aut"),
person("Michael J.",		"Fromberger",	role="ctb"),
person("Marie-Helene",	"Roy-Gagnon",	role="cre", email="mroygagn@uottawa.ca"))
c(person(given = "Michael", family = "Friendly",
role=c("aut", "cre"), email="friendly@yorku.ca"))
c(
person( "Andreas", "Papritz", role = c( "cre", "aut" ),
email =  "andreas.papritz@env.ethz.ch" ),
person( "Cornelia", "Schwierz", role = "ctb" ))
c(person("Davide", "Morselli", role =c("aut","cre"), email =
"davide.morselli@unil.ch"), person("Mathieu", "Cossuta", role = "aut",
email = "mcossutta@gmail.com"), person("Till", "Junge", role = "aut", email
= "till.junge@gmail.com"), person("Sandra", "Penic", role = "aut", email =
"sandra.penic@unil.ch"), person("Guy", "Elcheroth", role = "ctb", email =
"guy.elcheroth@unil.ch"), person("Stephanie", "Glaeser", role = "ctb"))
c(
person("Robert J.", "Hijmans", role = c("cre", "aut"),  email = "r.hijmans@gmail.com"),
person("Ed", "Williams", role = "ctb"),
person("Chris", "Vennes", role = "ctb"))
c(person("Alex", "Zvoleff", email="azvoleff@conservation.org",
role=c("aut", "cre")))
c(person("Andrie", "de Vries", role=c("aut", "cre"),
email="apdevries@gmail.com"), person(c("Brian", "D."), "Ripley", role =
c("aut"), comment = "author of package tree"))
c(person("Jeffrey B.", "Arnold", role = c("aut", "cre"),
email = "jeffrey.arnold@gmail.com"))
person("Alex", "Zvoleff", email="azvoleff@conservation.org",
role=c("aut", "cre"))
c(person("Sanjay
Chaudhuri","Developer",role=c("aut","cre"),email="sanjay@stat.nus.edu.sg"),
person("Mark S.
Handcock","Developer",email="handcock@stat.ucla.edu"),
person("Michael S.
Rendall","Developer",email="mrendall@rand.org"))
c(person(given = "Achim", family = "Zeileis", role = c("aut", "cre"), email = "Achim.Zeileis@R-project.org"),
person(given = "Roger", family = "Koenker", role = "aut"),
person(given = "Philipp", family = "Doebler", role = "aut"))
c(person(given = "Achim", family = "Zeileis", role = c("aut", "cre"), email = "Achim.Zeileis@R-project.org"),
person(given = "Thomas", family = "Windberger", role = "aut"))
c(person("Kornelius", "Rohmeyer", role = c("aut", "cre"),  email = "rohmeyer@small-projects.de"),
person("Florian", "Klinglmueller", role = "aut", email = "float@lefant.net"))
c(
person("Xiaobei", "Zhao", email="xiaobei@binf.ku.dk",
role=c("aut", "cre", "cph")),
person("Albin", "Sandelin", email="albin@binf.ku.dk",
role=c("aut"))
)
c(person("Julian", "Faraway", role = "aut"),
person("George", "Marsaglia", role = "aut"),
person("John",   "Marsaglia", role = "aut"),
person("Adrian", "Baddeley", role = c("aut", "cre"),
email = "Adrian.Baddeley@uwa.edu.au"))
person("Bernhard", "Pfaff", email = "bernhard@pfaffikus.de",
role = c("aut", "cre"))
c(person("Markus", "Gesmann", role = c("aut", "cre"),
email = "markus.gesmann@googlemail.com"),
person("Diego", "de Castillo", role = "aut",
email = "decastillo@gmail.com"),
person("Joe", "Cheng", role = "ctb",
email = "joe@rstudio.com"))
c(person("Paul", "Murrell", role = c("cre", "aut"),
email = "paul@stat.auckland.ac.nz"),
person("Simon", "Potter", role = "aut",
email = "simon.potter@auckland.ac.nz"))
c(person("Paul", "Murrell", role = c("aut", "cre"),
email = "p.murrell@auckland.ac.nz"),
person("Richard", "Walton", role = "aut"),
person("Simon", "Potter", role = "ctb"))
c(person("Fang", "Liu", role = c("aut", "cre"), email = "fang.liu.131@nd.edu"))
c(person("Tomislav", "Hengl", role = c("cre", "aut"), email = "tom.hengl@wur.nl"),
person("Bas", "Kempen", role = "ctb", email = "bas.kempen@wur.nl"),
person("Gerard", "Heuvelink", role = "ctb", email = "gerard.heuvelink@wur.nl"),
person("Brendan", "Malone", role = "ctb", email = "brendan.malone@sydney.edu.au"),
person("Hannes", "Reuter", role = "ctb", email = "hannes.reuter@wur.nl"))
c(person("Edzer", "Pebesma", role = c("aut", "cre"), email = "edzer.pebesma@uni-muenster.de"),
person("Benedikt", "Graeler", role = "aut"))
c(person(given = "Michael", family = "Friendly", role=c("aut", "cre"), email="friendly@yorku.ca"),
person(given = "Stephane", family = "Dray", role="aut"))
c(person(given = c("Andreas", "Dominik"), family = "Cullmann",
email = "r-package_handtill2001@arcor.de", role = c("aut","cre"), comment = NULL,
first = NULL, last = NULL)
, person(given = c("Edgar"), family=c("Kublin"), role = c("ctb"))
)
c(person("Albart", "Coster", role = c("aut", "cre"),
email = "albart@dairyconsult.nl"),
person("John", "Bastiaansen", role = "aut",
email = "john.bastiaansen@wur.nl"))
c(person(given = "Pedro Rafael", family = "Diniz Marinho", role =
c("aut", "cre"), email = "pedro.rafael.marinho@gmail.com"),
person(given = "Francisco", family = "Cribari Neto", role =
c("aut","ctb"), email = "cribari@de.ufpe.br"))
c(person("Quentin", "Grimonprez", role = c("aut","cre"), email =
"quentin.grimonprez@inria.fr"), person("Serge", "Iovleff", role = "ctb"))
c(person("Justin", "Esarey", role = c("aut", "cre"), email =
"justin@justinesarey.com"), person("Andrew", "Pierce", role = "aut", email
= "awpierc@emory.edu"), person("Jericho", "Du", role= "aut", email =
"jericho.du@gmail.com"))
c(person(given = "John", family = "Fox", role="aut"),
person(given = "Michael", family = "Friendly", role=c("aut", "cre"), email="friendly@yorku.ca"),
person(given = "Georges", family = "Monette", role="ctb"))
c(person(given="Daniel C.",family="Bowman", role=c("aut", "cre"),
email="daniel.bowman@unc.edu"),
person(given="Jonathan M.", family="Lees", role=c("ctb")))
c(person("Chris", "Walsh", role = c("aut","cre"), email =
"cwalsh@unimelb.edu.au"), person("Ralph", "Mac Nally", role =
"aut", email = "Ralph.MacNally@sci.monash.edu.au"))
c(
person("Romain", "Francois", role = c("aut", "cre"), email = "romain@r-enthusiasts.com" ),
person("Andre", "Simon", role = "ctb" )
)
c(person(given = "Michael", family = "Friendly", role=c("aut", "cre"), email="friendly@yorku.ca"),
person(given = "Stephane", family = "Dray", role="ctb"),
person(given = "Hadley", family = "Wickham", role="ctb"),
person(given = "James", family = "Hanley", role="ctb"),
person(given = "Dennis", family = "Murphy", role="ctb")
)
c(person("Murray", "Stokely", role = c("aut", "cre"),
email = "mstokely@google.com"),
person("Tim", "Hesterberg", role = "ctb", email = "rocket@google.com"))
c(person("Ingo", "Feinerer", role = "aut"), person("Stefan", "Theussl", role = c("aut", "cre"), email = "Stefan.Theussl@R-project.org"))
c(person("Bryan", "Hanson", role = c("aut", "cre"), email = "hanson@depauw.edu"),
person("Vesna", "Memisevic", role = "ctb"),
person("Jonathan", "Chung", role = "ctb"))
c(person("Patrick", "Mair", role = c("aut", "cre"), email = "mair@fas.harvard.edu"), person("Jan", "De Leeuw", role = "aut"))
c(person("Dieter William", "Joenssen", role = c("aut", "cre", "cph"),
email = "Dieter.Joenssen@googlemail.com"))
c(person("Alexander", "Rumyantsev", role = c("aut", "cre"),
email = "ar0@sampo.ru"))
c(person("Hugo", "Quené", role = c("aut", "cre"),
email = "h.quene@uu.nl") )
c(person("Guanhua", "Chen", role = c("aut","cre"), email =
"guanhuac@live.unc.edu"), person("Michael", "Kosorok", role =
c("aut"), email = "kosorok@unc.edu"),
person("Shannon","Holloway", role="ctb", email="sthollow@ncsu.edu"))
c(
person("Hadley", "Wickham", email = "hadley@rstudio.com",
role = c("cre", "aut", "cph")),
person("RStudio", role = "cph"))
c(person("Jon", "Wakefield", role = c("aut", "cre"),
email = "jonno@u.washington.edu"),
person("Martyn", "Plummer", role="ctb"))
c(person("Mauricio", "Zambrano-Bigiarini", email = "mzb.devel@gmail.com", role=c("aut", "cre") ) )
c(person("Mauricio", "Zambrano-Bigiarini", email="mzb.devel@gmail.com", role=c("aut","cre")), person("Rodrigo", "Rojas", email="Rodrigo.RojasMujica@gmail.com", role=c("ctb")) )
c(person("Mauricio", "Zambrano-Bigiarini", email = "mzb.devel@gmail.com", role=c("aut", "cre", "cph") ) )
person("Nathaniel", "Hallinan", role = c("aut", "cre"),
email = "nmhallinan@gmail.com")
person(given="Jack O.", family="Wasey", role=c("aut", "cre"),
email=c("jack@jackwasey.com"))
c(person("Itziar", "Irigoien", role = c("aut", "cre"),
email = "itziar.irigoien@ehu.es"),
person("Concepcion", "Arenas", role = "aut",
email="carenas@ub.edu"))
c(person("Thomas J.", "Leeper", role = c("aut", "cre"),
email = "thosjleeper@gmail.com"),
person("Aaron", "Statham", role = c("ctb"),
email = "aaron.l.statham@gmail.com"),
person("Yihui", "Xie", role = c("ctb"))
)
c(person(given = "Achim", family = "Zeileis", role = c("aut", "cre"), email = "Achim.Zeileis@R-project.org"),
person(given = "Christian", family = "Kleiber", role = "ctb", email = "Christian.Kleiber@unibas.ch"))
person(given="Kristian", family="Markon", role = c("aut",
"cre"), email = "kristian-markon@uiowa.edu")
c(person("Toby Dylan","Hocking",role=c("aut","cre"),
email="toby@sg.cs.titech.ac.jp"),
person("Keith","Ponting",role="aut"),
person("Thomas","Wutzler",role="aut"),
person("Philippe","Grosjean",role="aut"),
person("Markus","Müller",role="aut"),
person("R Core Team",role=c("ctb","cph")))
c(person("Tal", "Galili", role = c("aut", "cre", "cph"), email =
"tal.galili@gmail.com", comment = "http://www.r-statistics.com"),
person("Barry", "Rowlingson", role = "ctb", email =
"b.rowlingson@lancaster.ac.uk"), person("Boris", "Hejblum", email=
"boris.hejblum@isped.u-bordeaux2.fr>", role = "ctb") , person("Dason", role
= "ctb"), person("Felix", "Schonbrodt", role = "ctb"), person("G.",
"Grothendieck", role = "ctb") , person("GERGELY", "DAROCZI", role = "ctb")
, person("Heuristic", "Andrew", role = "ctb") , person("James", role =
"ctb", comment = "http://stackoverflow.com/users/269476/james"),
person("Thomas", "Leeper", role = "ctb", comment =
"http://thomasleeper.com/"), person("VitoshKa", role = "ctb"),
person("Yihui", "Xie", role = "ctb", comment = "http://yihui.name"),
person("Michael", "Friendly", role = "ctb", comment = "http://datavis.ca/")
, person("Kornelius", "Rohmeyer", email = "rohmeyer@small-projects.de",
role = "ctb", comment = "http://algorithm-forge.com/techblog/"),
person("Dieter", "Menne", email = "dieter.menne@menne-biomed.de", role =
"ctb"), person("Tyler", "Hunt", comment = "https://github.com/JackStat",
role = "ctb"), person("Takekatsu", "Hiramura", email = "thira@plavox.info",
comment = "https://github.com/hiratake55", role = "ctb"))
c(person("Andrea", "Peters", role = "aut"),
person("Torsten", "Hothorn", role = c("aut", "cre"),
email = "Torsten.Hothorn@R-project.org"),
person("Brian D.", "Ripley", role = "ctb"),
person("Terry", "Therneau", role = "ctb"),
person("Beth", "Atkinson", role = "ctb"))
c(person("Christian", "Buchta", role = "aut"),
person("Kurt", "Hornik", role = c("aut", "cre"),
email = "Kurt.Hornik@R-project.org"))
c(person("Patrick", "Mair", role = c("aut", "cre"), email =
"mair@fas.harvard.edu"), person("Jan", "De Leeuw", role = "aut"),
person("Kurt", "Hornik", role = "aut"))
c(person(given="David B.",family="Dahl",role=c("aut","cre"),email="dahl@stat.byu.edu"),
person(family="Scala developers",role="ctb",comment="see http://scala-lang.org/"),
person(family="beanshell2 developers",role="ctb",comment="see https://code.google.com/p/beanshell2/"),
person(family="jline2 developers",role="ctb",comment="see https://github.com/jline/jline2"))
c(person("Soo-Heang", "Eo", role = c("aut", "cre"),
email = "eo.sooheang@gmail.com"),
person("Seung-Mo", "Hong", role = "ctb"),
person("HyungJun", "Cho", role = c("aut","ths")))
c(person("Alexandros", "Karatzoglou", role = c("aut", "cre"),
email = "alexis@ci.tuwien.ac.at"),
person("Alex", "Smola", role = "aut"),
person("Kurt", "Hornik", role = "aut"))
c(person("Matt", "Wand", role = "aut",
email = "Matt.Wand@uts.edu.au"),
person("Brian", "Ripley", role = c("trl", "cre", "ctb"),
email = "ripley@stats.ox.ac.uk",
comment = "R port and updates"))
c(person(given="Meik", family="Michalke",
email="meik.michalke@hhu.de", role=c("aut", "cre")))
c(person("Mamoun O.", "Benghezal", role = c("aut", "cre"), email =
"mobenghezal@gmail.com"), person("Christophe", "Genolini", role = c("ctb"),
email = "christophe.genolini@u-paris10.fr"))
person("Carl", "Boettiger", role=c("aut", "cre"),
email="cboettig@gmail.com")
c(person("Adam", "Vogt", role = "ctb"),
person("Alastair", "Andrew", role = "ctb"),
person("Alex", "Zvoleff", role = "ctb"),
person("Ashley", "Manton", role = "ctb"),
person("Brian", "Diggs", role = "ctb"),
person("Cassio", "Pereira", role = "ctb"),
person("Fitch", "Simeon", role = "ctb"),
person("Hadley", "Wickham", role = "ctb"),
person("Heewon", "Jeon", role = "ctb"),
person("Jeff", "Arnold", role = "ctb"),
person("Jeremy", "Stephens", role = "ctb"),
person("Jim", "Hester", role = "ctb"),
person("Joe", "Cheng", role = "ctb"),
person("Jonathan", "Keane", role = "ctb"),
person("J.J.", "Allaire", role = "ctb"),
person("Johan", "Toloe", role = "ctb"),
person("Joseph", "Larmarange", role = "ctb"),
person("Kirill", "Mueller", role = "ctb"),
person("Kohske", "Takahashi", role = "ctb"),
person("Michael", "Friendly", role = "ctb"),
person("Michel", "Kuhlmann", role = "ctb"),
person("Nacho", "Caballero", role = "ctb"),
person("Nick", "Salkowski", role = "ctb"),
person("Noam", "Ross", role = "ctb"),
person("Ramnath", "Vaidyanathan", role = "ctb"),
person("Richard", "Cotton", role = "ctb"),
person("Romain", "Francois", role = "ctb"),
person("Sietse", "Brouwer", role = "ctb"),
person("Simon", "de Bernard", role = "ctb"),
person("Taiyun", "Wei", role = "ctb"),
person("Thibaut", "Lamadon", role = "ctb"),
person("Tom", "Torsney-Weir", role = "ctb"),
person("Trevor", "Davis", role = "ctb"),
person("Weicheng", "Zhu", role = "ctb"),
person("Wush", "Wu", role = "ctb"),
person("Yihui", "Xie", email = "xie@yihui.name", role = c("aut", "cre")))
c(person(given="m.eik", family="michalke",
email="meik.michalke@hhu.de", role=c("aut", "cre")), person(given="Earl",
family="Brown", email="eabrown@csumb.edu", role=c("ctb")),
person(given="Alberto", family="Mirisola", role=c("ctb")),
person(given="Alexandre", family="Brulet", role=c("ctb")),
person(given="Laura", family="Hauser", role=c("ctb")))
c(person("Fritz", "Scholz",role=c("aut","cre"),email =
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email= "angiezh@u.washington.edu"))
c(person(given = "Christina", family = "Stahl", role = c("aut", "cre"), email = "christina.stahl@chello.at"),
person(given = "David", family = "Meyer", role = c("aut")))
c(person("John", "Ehrlinger", role=c("aut", "cre"),
email="john.ehrlinger@gmail.com"), person("Hemant", "Ishwaran",
role="aut"))
c(person("Andreas", "Alfons", email = "alfons@ese.eur.nl",
role = c("aut", "cre")), person("Josef", "Holzer", role = "aut"),
person("Matthias", "Templ", role = "aut"))
c(person(given = "Michael", family = "Friendly",
role=c("aut", "cre"), email="friendly@yorku.ca"),
person(given= "Dennis", family = "Murphy", role="ctb"),
person(given ="Martin", family = "Monkman", role="ctb"),
person(given ="Chris", family = "Dalzell", role="ctb"))
person(given = "Jeffrey W.", family = "Hollister",
role = c("aut","cre"),
email = "hollister.jeff@epa.gov")
c(person("Richard", "Zijdeman", role = c("aut", "cre"),
email = "richard.zijdeman@iisg.nl"),
person("Josemiguel", "Lana-Berasain", role = "ctb",
email = "josem.lana@unavarra.es"))
c(
person("Pavel N.", "Krivitsky", role=c("aut","cre"), email="pavel@uow.edu.au"),
person("Mark S.", "Handcock", role=c("aut"), email="handcock@stat.ucla.edu"),
person("Susan M.", "Shortreed", role=c("ctb")),
person("Jeremy", "Tantrum", role=c("ctb")),
person("Peter D.", "Hoff", role=c("ctb")))
c(
person("Douglas", "Nychka", role = c("aut", "cre"),
email = "nychka@ucar.edu"),
person("Dorit", "Hammerling", role = c("aut"),
email = "hammerling@samsi.info"),
person("Stephan", "Sain", role = "aut",
email = "ssain@ucar.edu"),
person("Nathan", "Lenssen", role = "aut",
email = "lenssen@ucar.edu"))
c(person(given = "Yves", family = "Rosseel",
role  = c("aut", "cre"),
email = "Yves.Rosseel@UGent.be"),
person(given = "Daniel", family = "Oberski",
role = "ctb",
email = "daniel.oberski@gmail.com"),
person(given = "Jarrett", family = "Byrnes",
role = "ctb",
email = "byrnes@nceas.ucsb.edu"),
person(given = "Leonard", family = "Vanbrabant",
role = "ctb",
email = "L.G.F.Vanbrabant@uu.nl"),
person(given = "Victoria", family = "Savalei",
role = "ctb",
email = "vsavalei@ubc.ca"),
person(given = "Ed", family = "Merkle",
role = "ctb",
email = "merklee@missouri.edu"),
person(given = "Michael", family = "Hallquist",
role = "ctb",
email = "michael.hallquist at gmail.com"),
person(given = "Mijke", family = "Rhemtulla",
role = "ctb",
email = "mijke@ku.edu"),
person(given = "Myrsini", family = "Katsikatsou",
role = "ctb",
email = "mirtok2@gmail.com"),
person(given = "Mariska", family = "Barendse",
role = "ctb",
email = "m.t.barendse@rug.nl")
)
c(person("Antonio", "Coppola", role = c("aut", "cre", "cph"), email = "acoppola@college.harvard.edu"),
person("Brandon", "Stewart", role = c("aut", "cph")),
person("Naoaki", "Okazaki", role = c("aut", "cph")),
person("David", "Ardia", role = c("ctb", "cph")),
person("Dirk", "Eddelbuettel", role = c("ctb", "cph")),
person("Katharine", "Mullen", role = c("ctb", "cph")),
person("Jorge", "Nocedal", role = c("ctb", "cph")))
c(person("Eero", "Liski", role = c("aut", "cre"), email =
"liskieero@gmail.com"), person("Klaus",
"Nordhausen", role = "aut"), person("Hannu", "Oja", role =
"aut"), person("Anne", "Ruiz-Gazen", role = "aut"))
c(person("David", "Leonard", role = c("aut", "cre"),
email = "davidsleonard@outlook.com"))
c(person("Karline","Soetaert", role = c("aut","cre"), email
= "karline.soetaert@nioz.nl"), person("Karel","Van den
Meersche", role = "aut"), person("Dick","van Oevelen", role =
"aut", email = "dick.vanoevelen@nioz.nl"), person("LAPACK
authors", role = "cph"))
c(person("Bryan", "Hanson", role = c("aut", "cre"), email =
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c( person("Marc", "Adams", role= c("aut", "cre"), email= "lm.br.pkg@gmail.com"),
person("authors of R function 'lm'", role = "ctb", comment = "general interface"),
person("authors of 'lm.gls'", role = "ctb", comment = "interface and R code for matrix weights"),
person("U.S. NIST", role = "ctb", comment = "C++ code for TNT::Vector template") )
c(person("Douglas","Bates", role="aut"),
person("Martin","Maechler", role="aut"),
person("Ben","Bolker",email="bbolker+lme4@gmail.com", role=c("aut","cre")),
person("Steven","Walker",role="aut"),
person("Rune Haubo Bojesen","Christensen", role="ctb"),
person("Henrik","Singmann", role="ctb"),
person("Bin", "Dai", role="ctb"))
c(person("Alexandra", "Kuznetsova", role = c("aut", "cre"),
email = "alku@dtu.dk"),
person("Per", "Bruun Brockhoff", role = c("aut", "ths")),
person("Rune", "Haubo Bojesen Christensen", role = "aut"))
c(person(given = "Torsten", family = "Hothorn", role = "aut", email = "Torsten.Hothorn@R-project.org"),
person(given = "Achim", family = "Zeileis", role = c("aut", "cre"), email = "Achim.Zeileis@R-project.org"),
person(given = c("Richard", "W."), family = "Farebrother", role = "aut", comment = "pan.f"),
person(given = "Clint", family = "Cummins", role = "aut", comment = "pan.f"),
person(given = "Giovanni", family = "Millo", role = "ctb"),
person(given = "David", family = "Mitchell", role = "ctb"))
c(
person("Walerian", "Sokolowski",
role = c("aut", "cre", "cph"),
email = "walerian.sokolowski@wlogsolutions.com"),
person("Wit", "Jakuczun",
role = c("aut", "cph"),
email = "Wit.Jakuczun@wlogsolutions.com"),
person("Natalia", "Okinczyc",
role = "aut",
email = "natalia.okinczyc@wlogsolutions.com"),
person("Bogumil", "Kaminski",
role = "aut",
email = "bkamins@sgh.waw.pl"))
c(person("Guy", "Nason", role=c("aut", "cre"),
email="g.p.nason@bristol.ac.uk"))
c( person("John Myles", "White", role = c("aut", "cph")),
person("Kenton", "White", role = "ctb"), person("Kirill", "Müller", email
= "krlmlr+r@mailbox.org", role = c("ctb", "cre")))
person("Milan", "Bouchet-Valat", email="nalimilan@club.fr",
role=c("aut", "cre"))
c(person("Ajith", "Dissanayake", role = c("aut", "cre"),
email = "sudeera333@gmail.com"), person("P.", "Wijekoon", role
= ("aut"),email="pushpaw@pdn.ac.lk"), person("R-core",role =
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c(person("Idris","Eckley",role=c("aut","cre"), email="i.eckley@lancaster.ac.uk"),
person("Guy","Nason",role="aut", email="g.p.nason@bristol.ac.uk"),
person("Sarah", "Taylor", role="ctb", email="s.taylor14@lancaster.ac.uk"),
person("Matthew", "Nunes", role="ctb",email="m.nunes@lancaster.ac.uk"))
c(person("Sarah", "Taylor", role="aut"), person("Matt",
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c(person("Fritz Guenther", role = c("aut","cre"),
email ="fritz.guenther@uni-tuebingen.de"))
c(person("Joshua", "Schwab", role=c("aut", "cre"),
email="joshuaschwab@yahoo.com"), person("Samuel", "Lendle", role="aut"),
person("Maya", "Petersen", role="aut"), person("Mark", "van der Laan",
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c(person("Garrett", "Grolemund", email = "garrett@rstudio.com",
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person("Vitalie", "Spinu", role = "ctb"), person("Imanuel", "Constigan",
role = "ctb"), person("Chel Hee", "Lee", role = "ctb"), person("Richard",
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person("Winston", "Chang", role = "ctb"))
c(person("Sebastian", "Kreutzer", role = c("aut","trl","cre"),
email = "sebastian.kreutzer@u-bordeaux-montaigne.fr"),
person("Christoph", "Schmidt", role = c("aut")),
person("Margret C.", "Fuchs", role = c("aut")),
person("Michael", "Dietze", role = c("aut")),
person("Manfred", "Fischer", role = c("aut","trl")),
person("Christoph", "Burow", role = c("aut")),
person("Markus", "Fuchs", role = "ths"))
c(person(given = "Emmanuel", family = "Lazaridis",
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c(person("Yi", "Liu", role = c("aut", "cre"), email = "liuyipei@stanford.edu"),
person("Richard A.", "Olshen", , role = "aut", email = "olshen@stanford.edu"),
person("Andrew Z.", "Fire",  role = "aut", email = "afire@stanford.edu"),
person("Scott D.", "Boyd", role = "aut", email = "sboyd1@stanford.edu"))
c(person("Sebastian", "Gibb", role=c("aut", "cre"),
email="mail@sebastiangibb.de"), person("Korbinian", "Strimmer",
role="ths"))
c(person("Sebastian", "Gibb", role=c("aut", "cre"),
email="mail@sebastiangibb.de"), person("Pietro", "Franceschi",
role=c("ctb"), email="pietro.franceschi@fmach.it"))
c(
person("Roger", "Bivand", role = c("cre", "aut"), email = "Roger.Bivand@nhh.no"),
person("Nicholas", "Lewin-Koh", role = "aut"),
person("Edzer", "Pebesma", role = "ctb"),
person("Eric", "Archer", role = "ctb"),
person("Adrian", "Baddeley", role = "ctb"),
person("Hans-Jörg", "Bibiko", role = "ctb"),
person("Steven", "Brey", role = "ctb"),
person("Jonathan", "Callahan", role = "ctb"),
person("German", "Carrillo", role = "ctb"),
person("Stéphane", "Dray", role = "ctb"),
person("David", "Forrest", role = "ctb"),
person("Michael", "Friendly", role = "ctb"),
person("Patrick", "Giraudoux", role = "ctb"),
person("Duncan", "Golicher", role = "ctb"),
person("Virgilio", "Gómez Rubio", role = "ctb"),
person("Patrick", "Hausmann", role = "ctb"),
person("Karl Ove", "Hufthammer", role = "ctb"),
person("Thomas", "Jagger", role = "ctb"),
person("Sebastian", "Luque", role = "ctb"),
person("Don", "MacQueen", role = "ctb"),
person("Andrew", "Niccolai", role = "ctb"),
person("Oscar", "Perpiñán Lamigueiro", role = "ctb"),
person("Tom", "Short", role = "ctb"),
person("Greg", "Snow", role = "ctb"),
person("Ben", "Stabler", role = "ctb"),
person("Murray", "Stokely", role = "ctb"),
person("Rolf", "Turner", role = "ctb"))
c(person("Elisa",
"Loza",role=c("aut","cre"),email="elisa.loza@rothamsted.ac.uk"),
person("David", "Phillips",
role=c("aut","com"),email="david.phillips@rothamsted.ac.uk"))
c(person(c("Steven", "E."), "Pav", role=c("aut","cre"),
email="shabbychef@gmail.com"))
c(person(given = c("Andreas", "Dominik"), family = "Cullmann",
email = "r-package_masae@arcor.de", role = c("aut","cre"), comment = NULL,
first = NULL, last = NULL)
, person(given = c("Daniel"), family=c("Mandallaz"), role = c("ctb"))
, person(given = c("Alexander", "Francis"), family=c("Massey"), role = c("ctb"))
)
c(person("Brian", "Ripley", role = c("aut", "cre", "cph"),
email = "ripley@stats.ox.ac.uk"),
person("Bill", "Venables", role = "ctb"),
person(c("Douglas", "M."), "Bates", role = "ctb"),
person("Kurt", "Hornik", role = "trl",
comment = "partial port ca 1998"),
person("Albrecht", "Gebhardt", role = "trl",
comment = "partial port ca 1998"),
person("David", "Firth", role = "ctb"))
c(person("Mikko", "Rönkkö", role=c("aut", "cre"),
email = "mikko.ronkko@aalto.fi"))
c(
person("Henrik", "Bengtsson", role=c("aut", "cre", "cph"),
email="henrikb@braju.com"),
person("Hector", "Corrada Bravo", role="ctb"),
person("Robert", "Gentleman", role="ctb"),
person("Ola", "Hossjer", role="ctb"),
person("Harris", "Jaffee", role="ctb"),
person("Peter", "Langfelder", role="ctb"))
c(person("Torsten", "Hothorn", role = c("aut", "cre"),
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person("Peter", "Buehlmann", role = "aut"),
person("Thomas", "Kneib", role = "aut"),
person("Matthias", "Schmid", role = "aut"),
person("Benjamin", "Hofner", role = "aut"),
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person("Fabian", "Scheipl", role = "ctb"))
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person("Jean-Baptiste", "Denis", role = "ctb", email = "jbdenis@jouy.inra.fr"))
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person(c("Jonathan", "W."), "Moore", role = "ctb"),
person(c("Michelle", "M."), "McClure", role = "ctb"),
person(c("Nicholas", "K."), "Dulvy", role = "ctb"),
person(c("Andrew", "B."), "Cooper", role = "ctb"))
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c(person("Guido","Schwarzer", email="sc@imbi.uni-freiburg.de", role=c("aut","cre")),
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person(c("Daniel", "T."), "Braithwaite",
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person("Stephane", "Hoareau", role = "aut", email = "sjlhoaro@univ-reunion.fr"))
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person("Karin", "Groothuis-Oudshoorn", role = "aut",
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person("Alexander", "Robitzsch", role = "ctb",
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person("Gerko","Vink", role = "ctb",
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person("Lisa","Doove", role = "ctb",
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person("Shahab","Jolani", role = "ctb",
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person("Ari", "Friedman", role=c("ctb")))
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person("José Luis", "Cañadas Reche", email = "canadasreche@gmail.com", role="ctb"))
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person("Tony", "Olsen",  role="aut",
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person("Marc", "Weber",  role="ctb",
email = "Weber.Marc@epa.gov"),
person("Michael", "McManus",  role="ctb",
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person("Tom", "Kincaid", role="cre",
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c(person(given="Rick", family="Pechter",
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c(person("Jeffrey", "Horner", role = "ctb"),
person("Yihui", "Xie", role = c("aut", "cre"), email = "xie@yihui.name"))
c(person("Michele", "Filosi", role = c("aut","cre" ), email
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person("Samantha", "Riccadonna", role = "ctb", email =
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c(person("Revolution Analytics", role="aut"), person("Andrie", "de
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person("Joshua", family="Pritikin", role=c('ctb')),
person("Mateusz", family="Zoltak", role=c('ctb')))
c( person("Phil", family="Chalmers", email =
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c(person("Georgios", "Papageorgiou", role = c("aut", "cre"),
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c(person("Derek", "Young", role = c("aut", "cre"),
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person("Tatiana", "Benaglia", role = "aut"),
person("Didier", "Chauveau", role = "aut"),
person("David", "Hunter", role = "aut"),
person("Ryan", "Elmore", role = "ctb"),
person("Thomas", "Hettmansperger", role = "ctb"),
person("Hoben", "Thomas", role = "ctb"),
person("Fengjuan", "Xuan", role = "ctb"))
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person("Katrin", "Lindenberger", role = "ctb"),
person("René", "Lehmann", role = "ctb"),
person("Eurofins Regulatory AG", role = "cph"))
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person(c("Laurence", "T."), "Maloney", role = "aut"))
person("Yves", "Croissant", role = c("aut", "cre"), email = "yves.croissant@univ-reunion.fr")
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person("Jakob", "Richter", email = "code@jakob-r.de", role = "aut"),
person("Tobias", "Kuehn", email = "tobi.kuehn@gmx.de", role = "aut"),
person("Erich", "Studerus", email = "erich.studerus@upkbs.ch", role =
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person("Thomas Lumley", role = "ctb"),
person("Anthony Damico", role = "ctb"))
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email = "Berwin.Turlach@gmail.com"),
person("Kevin", "Murray",
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email = "Kevin.Murray@uwa.edu.au")
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person("Bettina", "Grün", role = "aut"))
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person("David", "Kellen", role =  "aut"),
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person("Christian", "Mueller", role =  "ctb"),
person("Akhil S", "Bhel", role =  "ctb"))
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c(person("Patrick", "Brandt", role=c("aut", "cre"),
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person("W. Ryan", "Davis", role=c("cre")))
c(person("John", "Marden", role = c("aut", "cph"),
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person("James", "Balamuta", role = c("cre", "ctb",
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person("Hervé", "RICHARD", role = "ctb",
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person("Solomon", "Messing", role = "ctb"),
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person("Richard M.", "Heiberger", role = "ctb"),
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person("Frank", "Bretz", role = "aut"),
person("Tetsuhisa", "Miwa", role = "aut"),
person("Xuefei", "Mi", role = "aut"),
person("Friedrich", "Leisch", role = "ctb"),
person("Fabian", "Scheipl", role = "ctb"),
person("Bjoern", "Bornkamp", role = "ctb"),
person("Martin", "Maechler", role = "ctb"),
person("Torsten", "Hothorn", role = c("aut", "cre"),
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person("Martina", "Morris", role=c("ctb"), email="morrism@u.washington.edu"))
c(person("Gerta","Rücker", role="aut"),
person("Guido","Schwarzer", email="sc@imbi.uni-freiburg.de", role=c("aut","cre")),
person("Ulrike","Krahn", role="aut"), person("Jochem","König", role="aut"))
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person("Roberto", "Visintainer", role = "aut", email = "visintainer@fbk.eu"),
person("Samantha", "Riccadonna", role = "aut", email = "samantha.riccadonna@fmach.it"),
person("Giuseppe", "Jurman", role = "ctb", email = "jurman@fbk.eu"),
person("Cesare", "Furlanello", role = "ctb", email = "furlan@fbk.eu"))
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person("Carter T.", "Butts", role=c("aut","cre"), email="buttsc@uci.edu"),
person("David", "Hunter", role=c("ctb"), email="dhunter@stat.psu.edu"),
person("Mark", "Handcock", role=c("ctb"), email="handcock@stat.ucla.edu"),
person("Skye", "Bender-deMoll", role=c("ctb"), email="skyebend@uw.edu"),
person("Jeffrey", "Horner", role=c("ctb"), email="jeffrey.horner@gmail.com"))
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person("Carter T.", "Butts", role=c("aut"), email="buttsc@uci.edu"),
person("Ayn", "Leslie-Cook", role=c("aut"), email="aynlc3@uw.edu"),
person("Pavel N.", "Krivitsky", role=c("aut"), email="pavel@uow.edu.au"),
person("Skye", "Bender-deMoll", role=c("aut","cre"), email="skyebend@uw.edu"),
person("Zack", "Almquist", role=c("ctb"), email="almquist@uci.edu"),
person("David R.", "Hunter", role=c("ctb"), email="dhunter@stat.psu.edu"),
person("Li","Wang",role=c("ctb"),email="lxwang@gmail.com"),
person("Kirk","Li",role=c("ctb"),email="kirkli@uw.edu"),
person("Steven M.","Goodreau",role=c("ctb"),email="goodreau@uw.edu"),
person("Martina", "Morris", role=c("ctb"), email="morrism@u.washington.edu"))
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person("Skye", "Bender-deMoll", role=c("cre","aut"), email="skyebend@uw.edu"))
c(person("Martin", "Lysy", role = c("aut", "cre"),
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person("Ashley D.", "Stasko", role = c("aut", "ctb")),
person("Heidi K.", "Swanson", role = c("aut", "ctb")))
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person("Douglas", "Bates", role = "aut", comment = "up to 2007"),
person("Saikat", "DebRoy", role = "ctb", comment = "up to 2002"),
person("Deepayan", "Sarkar", role = "ctb", comment = "up to 2005"),
person("EISPACK authors", role = "ctb", comment = "src/rs.f"),
person("R-core", email = "R-core@R-project.org",
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person("Kurt", "Hornik", role = c("aut", "cre"),
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person("Enrico", "Schumann",
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email = "es@enricoschumann.net")
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c(person(given="Frederic", family="Commo", role=c("aut", "cre"), email="fredcommo@gmail.com"),
person(given="Brian M.", family="Bot", role="aut"))
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c(person("Alberto", "Viglione", role=c("aut", "cre"), email="viglione@hydro.tuwien.ac.at"),
person("Jonathan R. M.", "Hosking", role="ctb"),
person("Francesco", "Laio", role="ctb"),
person("Alan", "Miller", role="ctb"),
person("Eric", "Gaume", role="ctb"),
person("Olivier", "Payrastre", role="ctb"),
person("Jose Luis", "Salinas", role="ctb"),
person("Chi Cong", "N'guyen", role="ctb"),
person("Karine", "Halbert", role="ctb"))
c(person("Benjamin", "Jones", role = c("aut", "cre"),
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c(person('Phil', 'Chalmers',
email='rphilip.chalmers@gmail.com',
role=c('cre', 'aut')),
person('Carrie', 'Smith',
email='carrie.elizabeth@gmail.com',
role=c('ctb')),
person('Matthew', 'Sigal',
email='matthewsigal@gmail.com',
role=c('ctb')))
c(person("Sebastian", "Dümcke", role=c("aut","cre")
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person("Achim","Tresch",role="ths"))
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person("Augusto", "Garcia", role = "ctb", email =
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c(person("Kurt", "Hornik", role = c("aut", "cre"),
email = "Kurt.Hornik@R-project.org"),
person("The Apache Software Foundation",
role = c("ctb", "cph"),
comment = "Apache OpenNLP Java libraries"),
person("JWNL development team",
role = c("ctb", "cph"),
comment = "JWNL Java WordNet Library"))
person("Mark", "Heckmann", email = "heckmann@uni-bremen.de",
role = c("aut", "cre"))
c(
person("Alexander", "Walker",
email = "Alexander.Walker1989@gmail.com", role = c("aut", "cre")),
person("Luca", "Braglia", role = "ctb"))
c( person(given = c("John", "C."), family = "Nash", role =
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c( person(given = c("John", "C."), family = "Nash", role =
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c(
person("Stephanie", "Locke",
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c(person("Manuel", "Fontenla", role = c("aut", "cre"),
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person(given="Rune Haubo Bojesen", family="Christensen",
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person(given = "Huixia Judy", family = "Wang", role = "ctb"),
person(given = "Lan", family = "Wang", role = "ctb"),
person(given = "HyungJun", family = "Cho", role = "aut",
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c(person(given = "Soo-Heang", family = "Eo", role = c("aut","cre"),
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person(given = "HyungJun", family = "Cho", role = "aut",
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c(person("Bill", "Venables", role = "aut",
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person("Kurt", "Hornik", role = c("aut", "cre"),
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c(person("Yang", "Lu", role = c("aut", "cre"),
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person("David", "Kane", role = c("aut"),
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c(person("Frank", "Schaarschmidt", role=c("aut", "cre"),
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person( "Daniel", "Gerhard", role="aut"))
c(person("Sara", "Varela", role = c("aut", "cre"),
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person("Javier", "González Hernández", role = "aut",
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person("Luciano", "Fabris Sgarbi", role = "aut",
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c(person("Bernd", "Bischl", email = "bernd_bischl@gmx.net", role =
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person("Faustino", "Prieto", role = "aut", email = "faustino.prieto@unican.es"),
person("Jose Maria", "Sarabia", role = "aut", email = "jose.sarabia@unican.es"))
c(person("Torsten", "Hothorn", role = c("aut", "cre"),
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person("Achim", "Zeileis", role = "aut"))
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person("Frederic", "Ibanez", role = "aut",
email = "ibanez@obs-vlfr.fr"),
person("Michele", "Etienne", role = "ctb",
email = "etienne@obs-vlfr.fr"))
c(person(given="Gavin", family="Thomas",
email="gavin.thomas@sheffield.ac.uk", role=c("aut", "cre")))
c(
person("Gaston", "Sanchez",
email = "gaston.stat@gmail.com", role=c("aut", "cre")),
person("Tomas", "Aluja", role=c("aut", "ths")))
c(person("Rafael", "Maia", role = c("aut", "cre"),
email = "rm72@zips.uakron.edu"),
person("Chad", "Eliason", role = "aut",
email = "cme16@zips.uakron.edu"),
person("Pierre-Paul", "Bitton", role = "aut",
email = "bittonp@uwindsor.ca"))
c(person("Drew", "Schmidt", role = c("aut", "cre"), email =
"schmidt@math.utk.edu"), person("Wei-Chen", "Chen", role =
"aut"), person("George", "Ostrouchov", role = "aut"),
person("Pragneshkumar", "Patel", role = "aut"))
c(person("Drew", "Schmidt", role = c("aut", "cre"), email =
"schmidt@math.utk.edu"), person("Wei-Chen", "Chen", role =
"aut"), person("George", "Ostrouchov", role = "aut"),
person("Pragneshkumar", "Patel", role = "aut"),
person("R Core team", role = "ctb", comment = "some wrappers
taken from the base and stats packages"))
c(person("Wei-Chen", "Chen", role = c("aut", "cre"), email =
"wccsnow@gmail.com"),
person("George", "Ostrouchov", role = "aut"),
person("Drew", "Schmidt", role = "aut"),
person("Pragneshkumar", "Patel", role = "aut"),
person("Hao", "Yu", role = "aut"),
person("Christian", "Heckendorf", role = "ctb", comment = "FreeBSD"),
person("Brian", "Ripley", role = "ctb", comment = "Windows HPC Pack
2012"),
person("R Core team", role = "ctb", comment = "some functions are
modified from the base packages"))
c(person("Pragneshkumar", "Patel", role = c("aut", "cre"), email =
"pragnesh@utk.edu"),
person("George", "Ostrouchov", role = "aut"),
person("Wei-Chen", "Chen", role = "aut"),
person("Drew", "Schmidt", role = "aut"),
person("David", "Pierce", role = "aut"))
c(person("Wei-Chen", "Chen", role = c("aut", "cre"),
email = "wccsnow@gmail.com"),
person("Drew", "Schmidt", role = "aut"),
person("Gaurav", "Sehrawat", role="aut"),
person("Pragneshkumar", "Patel", role = "aut"),
person("George", "Ostrouchov", role ="aut"))
c(person("Wei-Chen", "Chen", role = c("aut", "cre"), email =
"wccsnow@gmail.com"), person("Drew", "Schmidt", role = "aut"),
person("George", "Ostrouchov", role = "aut"),
person("Pragneshkumar", "Patel", role = "aut"),
person("Brian", "Ripley", role = "ctb", comment = "Solaris"))
c(person("Przemyslaw", "Biecek", role = c("aut", "cre"),
email = "przemyslaw.biecek@gmail.com"))
c(person("Albart", "Coster", role = c("aut", "cre"), email =
"albart@dairyconsult.nl"))
c(person("Alessandro Samuel-Rosa", role = c("aut", "cre"), email = "alessandrosamuelrosa@gmail.com"),
person("Lucia H. C. Anjos", role = "ths", email = "lanjos@ufrrj.br"),
person("Gustavo M. Vasques", role = "ths", email = "gustavo.vasques@embrapa.br"),
person("Gerard B. M. Heuvelink", role = "ths", email = "gerard.heuvelink@wur.nl"))
c(person("Emmanuel", "Paradis", role = c("aut", "cre", "cph"), email = "Emmanuel.Paradis@ird.fr"),
person("Klaus", "Schliep", role = c("aut", "cph")),
person("Alastair", "Potts", role = c("aut", "cph")),
person("David", "Winter", role = c("aut", "cph")))
c(
person(given=c("Brian","G."),family="Peterson",role=c("cre","aut","cph"), email="brian@braverock.com")
, person(given="Peter",family="Carl",role=c("aut","cph"), email="peter@braverock.com")
, person(given="Kris",family="Boudt",role=c("ctb","cph"))
, person(given="Ross",family="Bennett",role="ctb")
, person(given="Joshua",family="Ulrich",role="ctb")
, person(given="Eric",family="Zivot",role="ctb")
, person(given="Matthieu",family="Lestel",role="ctb")
, person(given="Kyle",family="Balkissoon",role="ctb")
, person(given="Diethelm",family="Wuertz",role="ctb")
)
c(person(given = "Gavin L.", family = "Simpson",
email = "ucfagls@gmail.com",
role = c("aut", "cph", "cre")),
person(given = "R Core Team", role = "cph"),
person(given = "Douglas M.", family = "Bates", role = "ctb"),
person(given = "Jari", family = "Oksanen", role = "ctb"))
person("Andreas", "Alfons", email = "alfons@ese.eur.nl",
role = c("aut", "cre"))
c(person("Helios", "De Rosario-Martinez", role=c("aut", "cre"),
email="helios.derosario@gmail.com"),
person("John", "Fox", role="ctb"),
person("R Core Team", role="ctb"))
c(person("Michael", "Donohue", role = c("aut", "cre"),
email = "mdonohue@ucsd.edu"),
person("Ronghui", "Xu", role = "aut"))
c(person("Wei-Chen", "Chen", role = c("aut", "cre"), email =
"wccsnow@gmail.com"), person("Karin", "Dorman", role =
"aut"))
c(person("Baptiste", "Auguie", email = "baptiste.auguie@gmail.com",
role = c("aut", "cre"), comment = "Some functions ported from the original
Matlab SPLAC code by E.C. Le Ru and P. G. Etchegoin"), person("Steven",
"Johnson", role = c("aut", "cph"), comment = "C code for the cubature
library"))
c(person(given = "Yves", family = "Croissant", role = c("aut", "cre"), email = "yves.croissant@univ-reunion.fr"),
person(given = "Giovanni", family = "Millo", role = "aut", email = "Giovanni_Millo@Generali.com"),
person(given = "Arne", family = "Henningsen", role = "ctb", email = "arne.henningsen@googlemail.com"),
person(given = "Ott", family = "Toomet", role = "ctb", email = "otoomet@gmail.com"),
person(given = "Christian", family = "Kleiber", role = "ctb", email = "Christian.Kleiber@unibas.ch"),
person(given = "Achim", family = "Zeileis", role = "ctb", email = "Achim.Zeileis@R-project.org"))
c(person(given = "Fuquan", family = "Zhang", email =
"zfq777@gmail.com", role = c("aut", "cre")))
c(person("Tomislav", "Hengl", role = c("cre", "aut"),
email = "tom.hengl@wur.nl"),
person("Pierre", "Roudier", role = "ctb"),
person("Dylan", "Beaudette", role = "ctb"),
person("Edzer", "Pebesma", role = "ctb"),
person("Michael", "Blaschek", role = "ctb"))
c(person("Arni", "Magnusson", role=c("aut","cre"),
email="arnima@hafro.is"), person("Ian", "Stewart", role="aut"))
c(person("Bethany", "Kok", role = c('ctb')), person("Jolynn",
"Pek", role = c('ctb')), person("Sonya", "Sterba", role = c('ctb')),
person("Dan", "Bauer", role = c('ctb')), person("Phil", "Chalmers", email =
"rphilip.chalmers@gmail.com", role = c('cre', 'ctb')))
person("Gaston", "Sanchez", role=c("aut", "cre"), email =
"gaston.stat@gmail.com")
c(
person("Gaston", "Sanchez",
email = "gaston.stat@gmail.com", role = c("aut", "cre")),
person("Laura", "Trinchera", role = "aut"),
person("Giorgio", "Russolillo", role = "aut"))
person("Christoph", "Waldhauser", email="chw@kdss.at", role=c("aut",
"cre"))
c(person("Wei-Chen", "Chen", role = c("aut", "cre"), email =
"wccsnow@gmail.com"), person("George", "Ostrouchov", role = "aut"))
c(person("Eunjeong", "Park", role = c("aut", "cre"),
email = "ejpark04@snu.ac.kr"),
person("Jong Hee", "Park", role = "aut",
email = "jongheepark@snu.ac.kr"))
c(person("Jeffrey B.", "Arnold", email = "jeffrey.arnold@gmail.com",
role = c("aut", "cre")),
person("Thomas J.", "Leeper", role = c("aut"),
email = "thosjleeper@gmail.com"))
c(person("Sebastian", "Meyer",
email = "Sebastian.Meyer@ifspm.uzh.ch",
role = c("aut","cre","trl")),
person("Michael", "Hoehle",
email = "Michael.Hoehle@stat.uni-muenchen.de",
role = c("ths")))
c(person("Bill", "Venables", role = "aut",
email = "Bill.Venables@adelaide.edu.au",
comment = "S original"),
person("Kurt", "Hornik", role = c("aut", "cre"),
email = "Kurt.Hornik@R-project.org",
comment = "R port"),
person("Martin", "Maechler", role = "aut",
email = "maechler@stat.math.ethz.ch",
comment = "R port"))
c(person(given=c("Aaron","A."),family="King",
role=c("aut","cre"),email="kingaa@umich.edu"),
person(given=c("Edward","L."),family="Ionides",role=c("aut")),
person(given=c("Carles"),family="Breto",role=c("aut")),
person(given=c("Stephen","P."),family="Ellner",role=c("ctb")),
person(given=c("Matthew","J."),family="Ferrari",role=c("ctb")),
person(given=c("Bruce","E."),family="Kendall",role=c("ctb")),
person(given=c("Michael"),family="Lavine",role=c("ctb")),
person(given="Dao",family="Nguyen",role=c("ctb")),
person(given=c("Daniel","C."),family="Reuman",role=c("ctb")),
person(given=c("Helen"),family="Wearing",role=c("ctb")),
person(given=c("Simon","N."),family="Wood",role=c("ctb"))
)
c(person("Andrew C.","Hooker",
email="andrew.hooker@farmbio.uu.se",
role=c("aut","cre","trl","cph")),
person("Sebastian","Ueckert",
role="aut",
comment="MATLAB version"),
person("Marco","Foracchia",
role="aut",
comment="O-Matrix version"),
person("Joakim", "Nyberg",
role="aut",
comment="MATLAB version"),
person("Eric","Stroemberg",
role="ctb",
comment="MATLAB version")
)
c(person("Lutz", "Hamel", role = c("aut", "cre"), email = "hamel@cs.uri.edu"),
person("Benjamin", "Ott", role = "aut"), person("Gregory", "Breard", role = "aut"))
c(person("Detlew", "Labes", role = c("aut","cre"), email =
"DetlewLabes@gmx.de"),
person("Helmut", "Schuetz", role = c("ctb"), email =
"helmut.schuetz@bebac.at"))
person(given="Colin", family="Gillespie",
email="csgillespie@gmail.com", role = c("aut", "cre"))
c(person("Jingfan", "Sun", role=c("aut", "cre"),
email="sunjfsjfan@gmail.com"))
c(person("Detlew", "Labes", role = c("aut","cre"), email =
"DetlewLabes@gmx.de"),
person("Helmut", "Schuetz", role = c("aut"), email =
"helmut.schuetz@bebac.at"))
c(person(given = "Reinhold", family = "Hatzinger", role = "aut"),
person(given = c("Marco", "Johannes"), family = "Maier",
email = "marco.maier@wu.ac.at", role = "cre"))
c(person("Brecht", "Devleesschauwer",
role = c("aut", "cre"), email = "Brecht.Devleesschauwer@UGent.be"),
person("Paul", "Torgerson", role = "aut"),
person("Johannes", "Charlier", role = "aut"),
person("Bruno", "Levecke", role = "aut"),
person("Nicolas", "Praet", role = "aut"),
person("Pierre", "Dorny", role = "aut"),
person("Dirk", "Berkvens", role = "aut"),
person("Niko", "Speybroeck", role = "aut"))
person(given = "G. Jay", family = "Kerns", role = c("aut", "cre", "cph"), email = "gkerns@ysu.edu")
c( person("Aleksandar", "Blagotic", role = "ctb"), person("Diego",
"Valle-Jones", role = "ctb"), person("Jeffrey", "Breen", role = "ctb"),
person("Joakim", "Lundborg", role = "ctb"), person("John Myles", "White",
role = c("aut", "cph")), person("Josh", "Bode", role = "ctb"),
person("Kenton", "White", role = "ctb"), person("Kirill", "Mueller", email
= "mail@kirill-mueller.de", role = c("ctb", "cre")), person("Matteo",
"Redaelli", role = "ctb"), person("Noah", "Lorang", role = "ctb"),
person("Patrick", "Schalk", role = "ctb"))
c(person(given = "David", family = "Meyer", role = c("aut", "cre"), email = "David.Meyer@R-project.org"),
person(given = "Christian", family = "Buchta", role = "aut"))
c(
person("Hadley", "Wickham",
email = "h.wickham@gmail.com", role = c("aut", "cre")),
person("R Core team", role = "ctb",
comment = "Some code extracted from base R"))
c(
person("Henrik", "Bengtsson", role=c("aut", "cre", "cph"),
email="henrikb@braju.com"),
person("Pierre", "Neuvial", role="aut"),
person("Venkatraman E.", "Seshan", role="aut"),
person("Adam B.", "Olshen", role="aut"),
person("Paul T.", "Spellman", role="aut"),
person("Richard A.", "Olshen", role="aut"))
c(person(c("Maja","Pohar","Perme"),role="aut",email="maja.pohar@mf.uni-lj.si"),person("Mette",
"Gerster", role =
c("aut","cre"),email="mgerster@health.sdu.dk"))
c(person(given = "Baolin", family = "Wu", role = c("aut","cph"),email = "baolin@umn.edu"),
person(given = "Nianjun", family = "Liu", role = c("aut","cph"),email = "nliu@ms.soph.uab.edu"),
person(given = "Hongyu", family = "Zhao", role = c("aut","cph"),email = "hongyu.zhao@yale.edu"),
person(given = "Jose", family = "Gama", role = "cre",email = "jgama@abo.fi"))
c(person(given = "Hannah", family = "Frick", role = c("aut", "cre"), email = "Hannah.Frick@uibk.ac.at"),
person(given = "Carolin", family = "Strobl", role = "aut", email = "Carolin.Strobl@psychologie.uzh.ch"),
person(given = "Friedrich", family = "Leisch", role = "aut", email = "Friedrich.Leisch@R-project.org"),
person(given = "Achim", family = "Zeileis", role = "aut", email = "Achim.Zeileis@R-project.org"))
c(person(given = "Achim", family = "Zeileis", role = c("aut", "cre"), email = "Achim.Zeileis@R-project.org"),
person(given = "Carolin", family = "Strobl", role = "aut", email = "Carolin.Strobl@psychologie.uzh.ch"),
person(given = "Florian", family = "Wickelmaier", role = "aut", email = "Florian.Wickelmaier@uni-tuebingen.de"),
person(given = "Basil", family = "Abou El-Komboz", role = "aut"),
person(given = "Julia", family = "Kopf", role = "aut"))
c(person(family="Connelly", given="Brian", email="bdc@bconnelly.net",
role=c("aut", "cre")))
c(person(given = "Achim", family = "Zeileis", role = c("aut", "cre"), email = "Achim.Zeileis@R-project.org"),
person(given = "Guan", family = "Yang", role = "aut"))
c(person("Francisco J.",  "Viciana", email= "franciscoj.viciana@juntadeandalucia.es", role='aut'), person("Carlos J.", "Gil Bellosta", email="cgb@datanalytics.com", role=c('cre', 'aut')), person("Oscar", "Perpinan Lamigueiro", email="oscar.perpinan@upm.es", role='aut'), person("Emilio", "Torres Manzanera", email="torres@uniovi.es", role='ctb'), person("Vicente David", "Canto Casasola", email= "vicented.canto.sspa@juntadeandalucia.es", role='ctb'), person("Jorge Ivan", "Velez", email="jorgeivanvelez@gmail.com", role='ctb'))
c(person("Adrian", "Dusa", role = c("aut", "cre"),
email = "dusa.adrian@unibuc.ro"),
person("Alrik", "Thiem", role = "aut",
email = "alrik.thiem@unige.ch"))
c(person("John", "Fox", role = c("aut", "cre"), email =
"jfox@mcmaster.ca"), person("Adrian", "Dusa", role = "aut",
email = "dusadrian@unibuc.ro") )
c(person("Bryan", "Goodrich", role = "ctb"), person("Dason",
"Kurkiewicz", role = "ctb"), person("Tyler", "Rinker", email =
"tyler.rinker@gmail.com", role = c("aut", "cre")))
c(person("Jason", "Gray", "", role = "ctb"),
person("Tyler", "Rinker", email = "tyler.rinker@gmail.com",
role = c("aut", "cre")))
c(person("Bryan", "Goodrich", role = "ctb"), person("Dason",
"Kurkiewicz", role = "ctb"), person("Kirill", "Muller", role = "ctb"),
person("Tyler", "Rinker", email = "tyler.rinker@gmail.com",
role = c("aut", "cre")))
c(person(given="Aiora", family="Zabala", email="aiora.zabala@gmail.com", role=c("aut", "cre")))
c(person("Bernhard", "Pfaff", email = "bernhard@pfaffikus.de", role = c("aut", "cre")), person("Marius", "Hofert", email = "marius.hofert@math.ethz.ch", role = "ctb"), person("Alexander", "McNeil", email = "mcneil@math.ethz.ch", role = "aut", comment = "S-Plus original (QRMlib)"), person("Scott", "Ulmann", role = "trl", comment = "First R port as package QRMlib"))
c(person("Roger", "Koenker",  role = c("cre","aut"), email =  "rkoenker@illinois.edu"),
person("Stephen", "Portnoy",  role = c("ctb"),
comment = "Contributions to Censored QR code", email =  "sportnoy@illinois.edu"),
person(c("Pin", "Tian"), "Ng",  role = c("ctb"),
comment = "Contributions to Sparse QR code", email =  "pin.ng@nau.edu"),
person("Achim", "Zeileis",  role = c("ctb"),
comment = "Contributions to dynrq code essentially identical to his dynlm code",
email =  "Achim.Zeileis@uibk.ac.at"),
person("Philip", "Grosjean",  role = c("ctb"),
comment = "Contributions to nlrq code", email =  "phgrosjean@sciviews.org"),
person(c("Brian", "D"), "Ripley",  role = c("trl","ctb"),
comment = "Initial (2001) R port from S (to my everlasting shame -- how could I have been so
slow to adopt R!) and for numerous other suggestions and useful advice", email =
"ripley@stats.ox.ac.uk"))
c(person(given = c("Vito","M.","R."), family = "Muggeo", role =
c("aut", "cre"), email = "vito.muggeo@unipa.it"))
person("Tobias", "Kley", role=c("aut","cre"),
email = "tobias.kley@ruhr-uni-bochum.de")
c(person("Julien", "Barnier", email="julien.barnier@ens-lyon.fr",
role=c("aut","cre")), person("François", "Briatte",
email="f.briatte@ed.ac.uk", role="aut"), person("Joseph", "Larmarange",
email="joseph@larmarange.net", role="aut"))
c(person(given = "Cho-Jui", family = "Hsieh", role = "aut"),
person(given = "Matyas A.", family = "Sustik", role = c("aut",
"cre"), email = "sustik@cs.utexas.edu"), person(given =
"Inderjit S.", family = "Dhillon", role = "aut"), person(given
= "Pradeep", family = "Ravikumar", role = "aut"))
c(person(given = "Elise", family = "Dusseldorp", role =
c("aut", "cre","cph"), email = "elise.dusseldorp@tno.nl"),person("Lisa","Doove",role=c("aut"),email="lisa.doove@ppw.kuleuven.be"),
person("Cor","Ninaber",comment="supported with the plot function",role="ctb"), person(given = "Iven", family = "Van Mechelen", role = c("aut","cph"), email
= "iven.vanmechelen@ppw.kuleuven.be"))
c(person(given = "Nikolaus", family = "Umlauf", role = c("aut", "cre"), email = "Nikolaus.Umlauf@uibk.ac.at"),
person(given = "Thomas", family = "Kneib", role = "aut", email = "tkneib@uni-goettingen.de"),
person(given = "Stefan", family = "Lang", role = "aut", email = "Stefan.Lang@uibk.ac.at"),
person(given = "Achim", family = "Zeileis", role = "aut", email = "Achim.Zeileis@R-project.org"))
c(person("Arni", "Magnusson", role="aut"),
person("Julian", "Burgos", role=c("aut","cre"), email="julian@hafro.is"),
person(c("Gregory","R."), "Warnes", role="ctb"))
c(person("Gianluca", "Campanella", role=c("cre"),
email="gianluca@campanella.org"), person("Benoit", "Liquet",
role="aut", email="b.liquet@uq.edu.au"), person("Marc",
"Chadeau-Hyam", role="aut", email="m.chadeau@imperial.ac.uk"),
person("Leonardo", "Bottolo", role="ctb",
email="l.bottolo@imperial.ac.uk"), person("Sylvia",
"Richardson", role="ctb",
email="sylvia.richardson@mrc-bsu.cam.ac.uk"))
c(person("Eric", "Lecoutre", email="eric.lecoutre@gmail.com",
role=c("aut")),
person("Milan", "Bouchet-Valat", email="nalimilan@club.fr",
role=c("cre", "ctb")),
person("Thomas", "Friedrichsmeier", email="tfry@users.sf.net",
role=c("ctb")))
c(person("Zhengzheng", "Zhang", role = c("aut", "cre"),email =
"zhengzheng236@gmail.com"), person("Chris", "Charlton",  role = "aut"),
person("Richard", "Parker", role = "aut"), person("George", "Leckie", role
= "aut"), person("William", "Browne", role = "aut"))
"Winston Chang <winston@stdout.org> [aut, cre]"
c(person(given = "Jose'", family = "Gama", role = c("aut","cre","trl"),email = "jgama@abo.fi"),
person(given = "Vlad", family = "Maximov", role = "aut"),
person(given = "Sebastian", family = "O.H. Madgwick", role = "aut"),
person(given = "Alain", family = "Barraud", role = "ctb"),
person(given = "Göran", family = "Högnäs", role = "ths"))
c(person("Brian J", "Smith", role = c("aut", "cre"),
email = "brian-j-smith@uiowa.edu"),
person("Jun", "Yan", role = "aut",
email = "jun.yan@uconn.edu"),
person("Mary Kathryn", "Cowles", role = "aut",
email = "kate-cowles@uiowa.edu"))
c(
person("Hadley", "Wickham", email = "h.wickham@gmail.com",
role = c("trl", "cre", "cph")),
person("RStudio", role = "cph"),
person("Sridhar", "Ratnakumar", role = "aut"),
person("Trent", "Mick", role = "aut"),
person("ActiveState", role = "cph", comment =
"R/appdir.r, R/cache.r, R/data.r, R/log.r translated from appdirs"),
person("Eddy", "Petrisor", role = "ctb"),
person("Trevor", "Davis", role = c("trl", "aut")),
person("Gabor", "Csardi", role = "ctb"),
person("Gregory", "Jefferis", role = "ctb")
)
c(person(given="Reinhold", family="Hatzinger",
email="reinhold.hatzinger@wu.ac.at", role=c("cre","aut")),
person(given="Patrick", family="Mair",
email="patrick.mair@wu.ac.at", role=c("cre","aut")),
person(given=c("Norman", "D."), family="Verhelst",
email="norman.verhelst@cito.nl", role=c("cre","aut")))
c(
person("Robert J.", "Hijmans", role = c("cre", "aut"),  email = "r.hijmans@gmail.com"),
person("Jacob", "van Etten", role = "ctb"),
person("Matteo", "Mattiuzzi", role = "ctb"),
person("Michael", "Sumner", role = "ctb"),
person("Jonathan A.", "Greenberg", role = "ctb"),
person("Oscar", "Perpinan Lamigueiro", role = "ctb"),
person("Andrew", "Bevan", role = "ctb"),
person("Etienne B.", "Racine", role = "ctb"),
person("Ashton", "Shortridge", role = "ctb"))
c(person("Oscar", "Perpinan Lamigueiro", email="oscar.perpinan@gmail.com", role=c('cre', 'aut')), person("Robert", "Hijmans", email= "r.hijmans@gmail.com", role='aut'))
c(person("Graham", "Williams",
role=c("aut", "cph", "cre"),
email="Graham.Williams@togaware.com"),
person("Mark", "Vere Culp", role="cph"),
person("Ed", "Cox", role="ctb"),
person("Anthony", "Nolan", role="ctb"),
person("Denis", "White", role="cph"),
person("Daniele", "Medri", role="ctb"),
person("Akbar", "Waljee", role="ctb",
comment="OOB AUC for Random Forest"),
person("Brian", "Ripley", role="cph",
comment="Author of original print.summary.nnet"))
c(person("Claas-Thido", "Pfaff",
email="claas-thido.pfaff@idiv-biodiversity.de", role=c("cre","aut")),
person("Karin", "Nadrowski", email="nadrowski@uni-leipzig.de", role="aut"),
person("Xingxing", "Man", email="manxingxing@gmail.com", role="aut"))
c( person("Wacek", "Kusnierczyk", role=c("aut", "cre"),
email="waku@idi.ntnu.no"), person("Dirk", "Eddelbuettel",
role="ctb", email="edd@debian.org"), person("Berend",
"Hasselman", role="ctb", email="bhh@xs4all.nl"))
c(person("Scott", "Chamberlain", role = c("aut", "cre"),
email = "myrmecocystus@gmail.com"),
person("Karthik", "Ram", role = "aut",
email = "karthik.ram@gmail.com"))
c(person("Ivan", "Yevshin", role = c("aut", "cre"), email = "ivan@dote.ru"),
person("Tagir", "Valeev", role = "aut"))
c(person('Rajarshi', 'Guha', role=c('aut','cre', 'cph'), email='rajarshi.guha@gmail.com'))
c(person('Rajarshi', 'Guha', role=c('aut','cre'), email='rajarshi.guha@gmail.com'),
person('CDK Project', '', role=c('ctb', 'cph'), comment='CDK Java library contained in multiple jar files'))
c(person("Alex","Lisovich",role=c("aut","cre"),email="alex.lisovich@gmail.com"),
person("Roger","Day",role="aut",email="day01@pitt.edu"),
person("Sun Microsystems, Inc",role="cph",
comment="swing-layout-1.0.4.jar, download from http://www.java2s.com/Code/Jar/s/Downloadswinglayout104jar.htm"))
c(person("John", "Fox", role = c("aut", "cre"), email = "jfox@mcmaster.ca"),
person("Milan", "Bouchet-Valat", role = "aut"),
person("Liviu", "Andronic", role = "ctb"),
person("Michael", "Ash", role = "ctb"),
person("Theophilius", "Boye", role = "ctb"),
person("Stefano", "Calza", role = "ctb"),
person("Andy", "Chang", role = "ctb"),
person("Philippe", "Grosjean", role = "ctb"),
person("Richard", "Heiberger", role = "ctb"),
person("Kosar", "Karimi Pour", role = "ctb"),
person("G. Jay", "Kerns", role = "ctb"),
person("Renaud", "Lancelot", role = "ctb"),
person("Matthieu", "Lesnoff", role = "ctb"),
person("Uwe", "Ligges", role = "ctb"),
person("Samir", "Messad", role = "ctb"),
person("Martin", "Maechler", role = "ctb"),
person("Robert", "Muenchen", role = "ctb"),
person("Duncan", "Murdoch", role = "ctb"),
person("Erich", "Neuwirth", role = "ctb"),
person("Dan", "Putler", role = "ctb"),
person("Brian", "Ripley", role = "ctb"),
person("Miroslav", "Ristic", role = "ctb"),
person("Peter", "Wolf", role = "ctb"),
person("Kevin", "Wright", role="ctb")
)
c(person("John", "Fox", role = c("aut", "cre"), email = "jfox@mcmaster.ca"),
person("Robert", "Muenchen", role = "ctb"),
person("Dan", "Putler", role = "ctb")
)
c(person("Ulrike", "Groemping", role = c("aut", "cre"), email =
"groemping@beuth-hochschule.de"), person("Fox", "John", role = "ctb"))
c(person("G. Jay", "Kerns", role = c("aut", "cre"), email = "gkerns@ysu.edu"),
person("Theophilus", "Boye", role = "ctb"), person("Tyler", "Drombosky", role = "ctb")
)
c(person("Milan", "Bouchet-Valat", email="nalimilan@club.fr", role=c("aut", "cre")),
person("Gilles", "Bastin", email="gilles.bastin@sciencespo-grenoble.fr", role=c("aut")))
c(
person("Romain", "Francois", role = c("aut", "cre"), email = "romain@r-enthusiasts.com"),
person("Kevin", "Ushey", role = "aut", email = "kevinushey@gmail.com"),
person("John", "Chambers", role = "ctb", email = "jmc@r-project.org")
)
c(person("Hadley", "Wickham", role = "aut"),
person("Yihui", "Xie", email = "xie@yihui.name", role = c("aut", "cre")))
c(
person("Konstantin A.", "Blagodatskikh",
email = "k.blag@yandex.ru", role = c("cre", "aut")),
person("Stefan", "Roediger",
email = "stefan.roedigier@hs-lausitz.de",
role = "aut"),
person("Michal", "Burdukiewicz",
email = "michalburdukiewicz@gmail.com", role = "aut"))
c(person("Scott", "Chamberlain", role = c("aut", "cre"),
email = "myrmecocystus@gmail.com"), person("Carl", "Boettiger",
role = "aut", email = "cboettig@gmail.com"), person("Karthik",
"Ram", role = "aut", email = "karthik.ram@gmail.com"))
c( person("Mark S.", "Handcock", role=c("aut","cre"),
email="handcock@stat.ucla.edu"), person("Krista J.", "Gile", role=c("aut"),
email="gile@math.umass.edu"), person("Ian E.", "Fellows", role=c("aut"),
email="ian@fellstat.com"), person("W. Whipple", "Neely", role=c("aut"),
email="wwneely@stat.washington.edu"))
"John Harrison <johndharrison0@gmail.com> [aut, cre]"
person("Sebastian", "Gibb", role=c("aut", "cre"),
email="mail@sebastiangibb.de")
c(person("Rafael", "Maia", role = c("aut", "cre"),
email = "rm72@zips.uakron.edu"),
person("Scott", "Chamberlain", role = "aut",
email = "myrmecocystus@gmail.com"),
person("Andy", "Teucher", role = "aut",
email = "andy.teucher@gmail.com"))
c(person("Marko", "Nagode", role = c("aut", "cre"),
email = "marko.nagode@fs.uni-lj.si"))
c(person("Michael", "Hahsler", role = c("aut", "cre", "cph"),
email = "mhahsler@lyle.smu.edu"))
person(given=c("Marco", "Johannes"), family="Maier", email="marco.maier@wu.ac.at", role=c("cre","aut"))
c(person("Will", "Beasley", role = c("aut", "cre"), email =
"wibeasley@hotmail.com"), person("David", "Bard", role = "ctb"),
person("Thomas", "Wilson", role = "ctb"), person(given="John J",
family="Aponte", role = "ctb"), person("Rollie", "Parrish",
role = "ctb"))
person(c("Mathew", "W."), "McLean", role = c("aut", "cre"),
email = "mathew.w.mclean@gmail.com")
c(person("Ciprian Crainiceanu", role = "aut", email =
"ccrainic@jhsph.edu"), person("Philip Reiss", role = "aut", email =
"phil.reiss@nyumc.org"), person("Jeff Goldsmith", role = "aut", email =
"jeff.goldsmith@columbia.edu"), person("Lei Huang", role = c("aut","cre"),
email = "huangracer@gmail.com"), person("Lan Huo", role = "aut", email =
"lan.huo@nyumc.org"), person("Fabian Scheipl", role = "aut", email =
"fabian.scheipl@stat.uni-muenchen.de"), person("Bruce Swihart", role =
"ctb", email = "bswihart@jhsph.edu"), person("Sonja Greven", role = "ctb"),
person("Jaroslaw Harezlak", role="ctb"), person("Madan Gopal Kundu",
role="ctb"), person("Yihong Zhao", role="ctb"), person("Matt McLean",
role="ctb"), person("Luo Xiao", role="ctb", email = "lxiao@jhsph.edu"))
c(person("Ulrike", "Groemping", role = c("aut", "cre"), email =
"groemping@beuth-hochschule.de"), person("Lehrkamp", "Matthias", role = "ctb"))
c(person(given = "David", family = "Meyer", role = c("aut")),
person(given = "Kurt", family = "Hornik", role = c("aut", "cre"),
email = "Kurt.Hornik@R-project.org"),
person(given = "Christian", family = "Buchta", role = "ctb"))
person("Ting-Kam Leonard", "Wong", role = c("aut", "cre"),
email = "wongting@uw.edu")
c(person("Michael", "Hahsler", role = c("aut", "cre", "cph"),
email = "mhahsler@lyle.smu.edu"),
person("Margaret H.", "Dunham", role = c("aut", "cph")))
c(person("David", "Winter", role=c("aut", "cre"),
email = "david.winter@gmail.com"),
person("Scott", "Chamberlain", role="ctb",
email = "myrmecocystus@gmail.com"))
c(
person("David", "Gohel", role = c("aut", "cre"),
email = "david.gohel@lysis-consultants.fr"),
person("Bootstrap", role = c("ctb", "cph"),
comment = "Bootstrap development team"),
person("jQuery", role = c("ctb", "cph"),
comment = "The jQuery Foundation"),
person("Dmitry Baranovskiy", role = c("ctb", "cph"),
comment = "raphael and g.raphael javascript libraries" ) )
c(person("David", "Gohel", role = c("aut", "cre"),
email = "david.gohel@lysis-consultants.fr"),
person("Jason", "Harrop", role = c("ctb", "cph"),
comment = "docx4j java library"),
person("Apache Software Foundation", role = c("ctb", "cph"),
comment = "Apache Commons java libraries and log4j" ),
person("SLF4J", role = c("ctb", "cph"),
comment = "Simple Logging Facade for Java" ) )
'Gerhard Schöfl <gschofl@yahoo.de> [aut, cre]'
c(person("Nicholas J.", "Matzke", email = "matzke@berkeley.edu", role =
c("aut", "cre", "cph")), person("Roger B.", "Sidje", email =
"roger.b.sidje@ua.edu", role = c("aut", "cph")))
c(person(given="Daniel C.",family="Bowman", role=c("aut", "cre"),
email="daniel.bowman@unc.edu"))
c(person("Carl", "Boettiger", role = c("aut", "cre"), email =
"cboettig@gmail.com"), person("Scott", "Chamberlain", role = "aut"),
person("Karthik", "Ram", role = "aut"), person("Edmund", "Hart",
role="aut"))
c(person("Carl", "Boettiger", role = c("aut", "cre"), email =
"cboettig@gmail.com"), person("Duncan", "Temple Lang", role = "aut"),
person(c("Peter", "C."), "Wainwright", role = "aut"))
c(person("Karthik", "Ram", role = c("aut", "cre"), email =
"karthik.ram@gmail.com"), person("Carl", "Boettiger", role = "aut"),
person("Andrew", "Dyck", role = "aut"))
c(person("Andrie", "de Vries", role=c("aut", "cre"),
email="apdevries@gmail.com"), person("Joris", "Meys", role =
c("aut")))
c(
person(given = "Artem", family = "Klevtsov",
email = "a.a.klevtsov@gmail.com", role = c("aut", "cre")),
person(given = "Philipp", family = "Upravitelev",
email = "upravitelev@gmail.com", role = "ctb"),
person(given = "Olga", family = "Shramko",
email = "ol1709@yahoo.com", role = "ctb"))
c(person("Scott", "Chamberlain", role = c("aut", "cre"), email =
"myrmecocystus@gmail.com"),
person("Karthik", "Ram", role = "aut", email =
"karthik.ram@gmail.com"))
c(person("Scott", "Chamberlain", role = c("aut", "cre"),
email = "myrmecocystus@gmail.com"),
person("Karthik", "Ram", role = "aut",
email = "karthik.ram@gmail.com"),
person("Vijay", "Barve", role = "aut",
email = "vijay.barve@gmail.com"),
person("Dan", "Mcglinn", role = "aut",
email = "danmcglinn@gmail.com"))
c(
person("Roger", "Bivand", role = c("cre", "aut"), email = "Roger.Bivand@nhh.no"),
person("Tim", "Keitt", role = "aut"),
person("Barry", "Rowlingson", role = "aut"),
person("Edzer", "Pebesma", role = "ctb"),
person("Michael", "Sumner", role = "ctb"),
person("Robert", "Hijmans", role = "ctb"),
person("Even", "Rouault", role = "ctb"))
c(
person("Roger", "Bivand", role = c("cre", "aut"), email = "Roger.Bivand@nhh.no"),
person("Colin", "Rundel", role = "aut"),
person("Edzer", "Pebesma", role = "ctb"),
person("Karl Ove", "Hufthammer", role = "ctb"))
c(person("Stefan", "Theussl", role = c("aut", "cre"), email = "Stefan.Theussl@R-project.org"), person("Kurt", "Hornik", role = "aut"), person("Christian", "Buchta", role = "ctb"), person("Andrew", "Makhorin", role = "cph"), person("Timothy A.", "Davis", role = "cph"), person("Niklas", "Sorensson", role = "cph"), person("Mark", "Adler", role = "cph"), person("Jean-loup", "Gailly", role = "cph"))
c(person(given = "Göran", family = "Högnäs", role = "ths"),
person(given = "Nicolas", family = "Devillard", role = "aut"),
person(given = "Mauricio", family = "Galo", role = "ctb"),
person(given = "Patrick", family = "J. Bartlein", role = "ctb"),
person(given = "Oscar", family = "Perpiñán Lamigueiro", role = "ctb"),
person(given = "José", family = "Gama", role = c("aut","cre"),email = "jgama@abo.fi"))
c(person("David C", "Sterratt", role=c("aut", "cph", "cre"),
email="david.c.sterratt@ed.ac.uk"), person("Mikko", "Vihtakari",
role=c("aut", "cph"), email="mikko.vihtakari@uit.no"))
c(person("Talita", "Perciano", role = c("aut", "cre"),
email = "talitaperciano@gmail.com"),
person("Alejandro", "C Frery", role = "ctb",
email = "acfrery@pq.cnpq.br"))
c(person("Aleksander", "Mendyk", role = c("aut",
"cre"),email = "mfmendyk@cyf-kr.edu.pl"),person("Sebastian",
"Polak",role="aut",email="mfpolak@@cyf-kr.edu.pl"))
c(person("Martyn", "Plummer", role=c("aut","cre"),
email="plummerm@iarc.fr"),
person("Alexey", "Stukalov", role="ctb"))
c(person("Ingo", "Feinerer", role = "aut"),
person("Kurt", "Hornik", role = c("aut", "cre"),
email = "Kurt.Hornik@R-project.org"),
person("Eibe", "Frank", role = c("ctb", "cph"),
comment = "KEA Java library"),
person("Olena", "Medelyan", role = c("ctb", "cph"),
comment = "KEA Java library"),
person("The Apache Software Foundation",
role = c("ctb", "cph"),
comment = "Apache Commons Logging and Apache Jena Java libraries"),
person("International Business Machines Corporation and others",
role = c("ctb", "cph"),
comment = "International Components for Unicode for Java (ICU4J)"),
person("Martin", "Porter", role = c("ctb", "cph"),
comment = "Snowball base"),
person("Richard", "Boulton", role = c("ctb", "cph"),
comment = "Snowball Java library"),
person("University of Waikato", role = c("ctb", "cph"),
comment = "Weka Java library"))
c(
person("Vehbi Sinan", "Tunalioglu",
email="vst@vsthost.com",
role=c("aut", "cre")))
c( person("Stefan", "Widgren", role = c("aut", "cre"),
email = "stefan.widgren@sva.se", comment = "Author of the R
interface to the C-library matio"), person("Christopher",
"Hulbert", role = c("aut"), comment = "Author of the C-library
matio, http://sourceforge.net/projects/matio/"))
c(
person("Henrik", "Bengtsson", role=c("aut", "cre", "cph"),
email="henrikb@braju.com"),
person("Andy", "Jacobson", role="ctb", comment="Internal MAT v4 reader"),
person("Jason", "Riedy", role="ctb", email="ejr@cs.berkeley.edu",
comment="Support for reading compressed files (via 'Rcompression'), sparse matrices and UTF-encoded strings."))
c(
person("Benjamin", "Auder", role=c("aut","cre"), email="benjamin.auder@gmail.com"),
person("Remi", "Lebret", role="aut", email="remi@lebret.ch"),
person("Serge", "Iovleff", role="aut", email="serge.iovleff@univ-lille1.fr"),
person("Florent", "Langrognet", role="aut", email="florent.langrognet@univ-fcomte.fr"),
person("C.", "Biernacki", role="ctb", email="christophe.biernacki@math.univ-lille1.fr"),
person("G.", "Celeux", role="ctb", email="gilles.celeux@math.u-psud.fr"),
person("G.", "Govaert", role="ctb", email="gerard.govaert@utc.fr"))
c(person("Jan", "Wijffels", role = c("aut", "cre"), email =
"jwijffels@bnosac.be"), person("BNOSAC", role = "cph"))
c(person("Carl", "Boettiger", role = c("cre", "aut"),
email="cboettig@gmail.com"), person("Scott", "Chamberlain", role =
"aut"), person("Hilmar", "Lapp", role = "aut"), person("Kseniia",
"Shumelchyk", role = "aut"), person("Rutger", "Vos", role = "aut"))
c(person("Hart", "Edmund", role = c("aut","cre"),
email = "Edmund.m.hart@gmail.com"),
person("Scott", "Chamberlain", role = "aut",
email = "myrmecocystus@gmail.com"),
person("Karthik", "Ram", role = "aut",
email = "karthik.ram@gmail.com"))
person("Andreas", "Alfons",
email = "alfons@ese.eur.nl",
role = c("aut", "cre"))
c(person("Brian", "Ripley", role = c("aut", "cre"),
email = "ripley@stats.ox.ac.uk"),
person("Michael", "Lapsley", role = "aut",
comment = "1999 to Oct 2002"))
c(person("Mateusz", "Zoltak", role = c("aut", "cre"),
email = "zozlak@zozlak.org"),
person("Brian", "Ripley", role = c("aut")),
person("Michael", "Lapsley", role = "aut"))
c(person("Kurt", "Hornik", role = "aut"), person("David", "Meyer", role = "aut"), person("Stefan", "Theussl", role = c("aut", "cre"), email = "Stefan.Theussl@R-Project.org"))
c(person("Stefan", "Theussl", role = c("aut", "cre"), email = "Stefan.Theussl@R-Project.org"))
c(
person("Hadley", "Wickham",, "h.wickham@gmail.com", c("aut", "cre", "cph")),
person("Peter", "Danenberg",, "pcd@roxygen.org", c("aut", "cph")),
person("Manuel", "Eugster", role = c("aut", "cph")),
person("RStudio", role = "cph")
)
c(person("Terry", "Therneau", role = "aut",
email = "therneau@mayo.edu"),
person("Beth", "Atkinson", role = "aut",
email = "atkinson@mayo.edu"),
person("Brian", "Ripley", role = c("aut", "trl", "cre"),
email = "ripley@stats.ox.ac.uk",
comment = "author of initial R port"))
c(person("Joshua",family="Pritikin", email="jpritikin@pobox.com",
role=c('cre','aut')), person("Jonathan",family="Weeks",
email="weeksjp@gmail.com", role=c('ctb')),
person("Li", family="Cai", role=c('ctb')), person("Carrie",
family="Houts", role=c('ctb')), person("Phil", "Chalmers", email =
"rphilip.chalmers@gmail.com", role = c("ctb")))
c(person("Scott", "Chamberlain", role = c("aut", "cre"),
email = "myrmecocystus@gmail.com"),
person("Carl", "Boettiger", role = "aut",
email = "cboettig@gmail.com"),
person("Karthik", "Ram", role = "aut",
email = "karthik.ram@gmail.com"))
c(person(given="Pedro-Pablo", family="Garrido Abenza", role=c("aut","cre"), email="pgarrido@umh.es"))
c(person(given = "Torben", family = "Schulz-Streeck", email
= "torben.schulz-streeck@uni-hohenheim.de", role = "aut"),
person(given = "Boubacar", family = "Estaghvirou", email =
"S.Ould@uni-hohenheim.de", role = "aut"), person(given =
"Frank",family = "Technow", email =
"Frank.Technow@uni-hohenheim.de", comment = "University of
Hohenheim, Institute of Plant Breeding, Seed Science and
Population Genetics, Stuttgart, Germany", role =
c("aut","cre")))
c(person(given="Natalia", family="Belgorodski", email="belgorodski@stat-up.de",
role=c("aut"), comment="STAT-UP Statistical Consulting"),
person(given="Matthias", family="Greiner", email="matthias.greiner@bfr.bund.de",
role=c("aut", "cre"), comment="Federal Institute for Risk Assessment, Germany"),
person(given="Kristin", family="Tolksdorf", email="kristin.tolksdorf@bfr.bund.de",
role=c("aut"), comment="Federal Institute for Risk Assessment, Germany"),
person(given="Katharina", family="Schueller", email="schueller@stat-up.de",
role=c("aut"), comment="STAT-UP Statistical Consulting"))
c(person( given="Daniel W.",family="Heck",email="dheck@mail.uni-mannheim.de", role=c("aut","cre")),
person(given="Morten",family="Moshagen",email="moshagen@uni-mannheim.de", rol=c("aut")))
c(
person("Emmanuel", "Blondel", role = c("aut", "cre"), email = "emmanuel.blondel1@gmail.com"),
person("Matthieu", "Stigler", role = c("ctb")))
c(
person("Scott", "Chamberlain", role = c("aut", "cre"),
email = "myrmecocystus@gmail.com"),
person("Kevin", "Ushey", role = "aut",
email = "kevinushey@gmail.com"))
c(person(given = "Peter", family = "Wittek", role = c("aut"), email = ""),
person(given = "Shichao", family = "Gao", role = c("cre"), email = "xgdgsc@gmail.com"))
c(person(given = "Jose'", family = "Gama", role = c("aut","cre"),email = "jgama@abo.fi"),
person(given = "John", family = "Fuller", role = c("aut","cph")),
person(given = "Paolo", family = "Leva", role = c("aut","cph")),
person(given = "Göran", family = "Högnäs", role = "ths"))
c(person("Scott", "Chamberlain", role = c("aut", "cre"),
email = "myrmecocystus@gmail.com"),
person("Thomas J.", "Leeper", role=c("ctb"),
email="thosjleeper@gmail.com"))
c(person("Reinhard", "Harter", role = "aut"),
person("Kurt", "Hornik", role = c("aut", "cre"),
email = "Kurt.Hornik@R-project.org"),
person("Stefan", "Theussl", role = "aut"),
person("Cyrille", "Szymanski", role = "ctb",
email = "cnszym@gmail.com"))
c(person(c("Michael","E."), "Schaffer", role=c("aut", "cre", "cph"), email = "mschaff@gmail.com"))
c(person("Scott", "Chamberlain", role = c("aut", "cre"),
email = "myrmecocystus@gmail.com"), person("Vijay", "Barve",
role = "aut", email = "vijay.barve@gmail.com"))
c(person("Edmund", "Hart", role = c("aut", "cre"),
email = "edmund.m.hart@gmail.com"))
c(person("Kurt", "Hornik", role = c("aut", "cre"),
email = "Kurt.Hornik@R-project.org"),
person("Christian", "Buchta", role = "ctb"),
person("Torsten", "Hothorn", role = "ctb"),
person("Alexandros", "Karatzoglou", role = "ctb"),
person("David", "Meyer", role = "ctb"),
person("Achim", "Zeileis", role = "ctb"))
c(person("Kurt", "Hornik", role = c("aut", "cre"),
email = "Kurt.Hornik@R-project.org"),
person("University of Waikato", role = c("ctb", "cph"),
comment = "Weka Java library"))
c(person("Dominik", "Wabersich",
role=c("aut", "cre"),
email="dominik.wabersich@gmail.com"))
c(person('Jeffrey H.', 'Gove', email='jgove@fs.fed.us', role=c('aut','cre')))
c(person(given = "Thomas", family = "Lumley", role = "aut", email = "tlumley@u.washington.edu"),
person(given = "Achim", family = "Zeileis", role = c("aut", "cre"), email = "Achim.Zeileis@R-project.org"))
person( "Anthony Joseph" , "Damico" , role = c( "aut" ,
"cre" ) , email = "ajdamico@gmail.com" )
c(person(given="Jesus",family="Palomo", role=c("aut",
"cre"),email="jesus.palomo@urjc.es"),
person(given="Gonzalo",family="Garcia-Donato",
role="aut",email="gonzalo.garciadonato@uclm.es"),
person(given="Rui",family="Paulo", role="aut",email="rui@iseg.utl.pt"),
person(given="James",family="Berger",
role="ctb",email="berger@stat.duke.edu"),
person(given="Maria Jesus",family="Bayarri",
role="ctb",email="susie.bayarri@uv.es"),
person(given="Jerome",family="Sacks",
role="ctb",email="sacks@niss.org"))
c(person("Paul", "Fischer", role = c("aut", "cre","cph"),
email = "pafi@dtu.dk"),person("Astrid", "Hilbert", role = c("ctb","cph"),
email = "astrid.hilbert@lnu.se"))
c(person("Matteo", "Dell'Omodarme", role = c("aut", "cre"),
email = "mattdell@fastmail.fm"), person("Giada", "Valle", role
= "aut"))
c(person("Seong-Yun", "Hong", role = c("aut", "cre"), email
= "hong.seongyun@gmail.com"), person("David", "O'Sullivan",
role = "ctb"))
c(person("Simon", "Potter",
role = c("aut", "trl", "cre"),
email = "simon.potter@auckland.ac.nz"),
person("Simon", "Sapin", role = "aut"),
person("Ian", "Bicking", role = "aut"))
c(person("John", "Fox", role = c("aut", "cre"), email = "jfox@mcmaster.ca"),
person("Zhenghua", "Nie", role = "aut", email="zhenghua.nie@gmail.com"),
person("Jarrett", "Byrnes", role = "aut", email="byrnes@nceas.ucsb.edu"),
person("Michael", "Culbertson", role = "ctb"),
person("Saikat", "DebRoy", role="ctb"),
person("Michael", "Friendly", role = "ctb"),
person(given="Richard H.", family="Jones", role="ctb"),
person("Adam", "Kramer", role = "ctb"),
person("Georges", "Monette", role = "ctb"),
person("R-Core", role="ctb")
)
c(person("Armin", "Monecke", role=c("aut", "cph","cre"),
email="armin.monecke@stat.uni-muenchen.de"),
person("Friedrich", "Leisch", role=c("aut", "ths")))
c(person("Sunthud", "Pornprasertmanit", role = c("aut",
"cre"), email = "psunthud@ku.edu"), person("Patrick", "Miller",
role = "aut", email="patr1ckm@ku.edu"), person("Alexander",
"Schoemann", role = "aut", email="schoemann@ku.edu"),
person("Yves", "Rosseel", role = "aut",
email="Yves.Rosseel@UGent.be"), person("Corbin", "Quick", role
= "ctb", email="corbinq@ku.edu"), person("Mauricio",
"Villarreal", role = "ctb", email="mgv@ku.edu"),
person("James", "Selig", role = "ctb", email="selig@unm.edu"),
person("Aaron", "Boulton", role = "ctb",
email="aboulton@ku.edu"), person("Kristopher", "Preacher", role
= "ctb", email="kris.preacher@vanderbilt.edu"), person("Donna",
"Coffman", role = "ctb", email="dlc30@psu.edu"),
person("Mijke", "Rhemtulla", role = "ctb",
email="mijke@ku.edu"), person("Alexander", "Robitzsch", role =
"ctb", email="a.robitzsch@bifie.at"), person("Craig", "Enders",
role = "ctb", email="Craig.Enders@asu.edu"), person("Ruber",
"Arslan", role = "ctb", email="rubenarslan@gmail.com"),
person("Bell", "Clinton", role = "ctb",
email="clintonbell@ku.edu"), person("Pavel", "Panko", role =
"ctb", email="pavel@ku.edu") )
c(person("Olaf", "Mersmann", role=c("aut", "cre"),
email="olafm@p-value.net"), person("Quinn", "Weber",
role="ctb"))
c(person(given="Rune Haubo Bojesen", family="Christensen",
email="rhbc@dtu.dk", role=c("aut", "cre")),
person(given="Per Bruun", family="Brockhoff",
email="perbb@dtu.dk", role=c("aut")))
c(person(given = "Laurent", family = "Gatto",
email = "lg390@cam.ac.uk",
role = c("aut", "cre")),
person(given = "Robert", family ="Stojnic",
email = "rs550@cam.ac.uk",
role = "aut"))
c(
person("Michael", "Hahsler", role = c("aut", "cre", "cph"),
email = "mhahsler@lyle.smu.edu"),
person("Christian", "Buchta", role = c("aut", "cph")),
person("Kurt", "Hornik", role = c("aut", "cph")),
person("Fionn", "Murtagh", role = c("ctb", "cph")),
person("Michael", "Brusco", role = c("ctb", "cph")),
person("Stephanie", "Stahl", role = c("ctb", "cph")),
person("Hans-Friedrich", "Koehn", role = c("ctb", "cph")))
c(person("Eric", "Lamb", role = c("aut", "cre"),email =
"eric.lamb@usask.ca"),person("Kerrie", "Mengersen", role =
"aut"),person("Katherine", "Stewart", role = "aut"),person("Udayanga",
"Attanayake", role = "aut"),person("Steven", "Siciliano", role = "aut"))
c(person("Benoit", "Lalloue", role = c("aut", "cre"), email
= "benoit.lalloue@ehesp.fr"), person("Severine", "Deguen", role
= c("ths", "aut"), email = "severine.deguen@ehesp.fr"),
person("Jean-Marie", "Monnez", role =c("ths", "aut"), email =
"jean-marie.monnez@univ-lorraine.fr"), person("Nolwenn", "Le
Meur", role = c("aut")))
c(person(given = "David", family = "Meyer", role = c("aut",
"cre"), email = "David.Meyer@R-project.org"), person(given =
"Kurt", family = "Hornik", role = c("aut")), person(given =
"Christian", family = "Buchta", role = "ctb"))
c(
person("Balasubramanian", "Narasimhan",
email = "naras@stat.stanford.edu", role = c("aut", "cre")),
person("Mei-Chiung", "Shih",
email = "meichiun@stanford.edu", role = "aut"))
c(person(given=c("Damian", "W."), family="Betebenner", email="dbetebenner@nciea.org", role=c("aut", "cre")),
person(given="Adam", family="Van Iwaarden", email="Vaniwaarden@colorado.edu", role="aut"),
person(given="Ben", family="Domingue", email="ben.domingue@gmail.com", role="aut"))
c(person("Liran","Lerman",role =
c("cre","aut"),email="llerman@ulb.ac.be"),person("Gianluca","Bontempi",role
=
c("aut"),email="gbonte@ulb.ac.be"),person("Olivier","Markowitch",role
= c("aut"),email="olivier.markowitch@ulb.ac.be"))
c(person("Uwe", "Ligges", role = c("aut", "cre"), email="ligges@statistik.tu-dortmund.de", comment = "new maintainer"),
person("Tom", "Short", role = "aut", email = "tshort@eprisolutions.com", comment="port to R"),
person("Paul", "Kienzle", role = "aut", comment="majority of the original sources"),
person("Sarah", "Schnackenberg", role = "ctb", comment="various test cases and bug fixes"),
person("David", "Billinghurst", role = "ctb"),
person("Hans-Werner", "Borchers", role = "ctb"),
person("Andre", "Carezia", role = "ctb"),
person("Pascal", "Dupuis", role = "ctb"),
person("John W.", "Eaton", role = "ctb"),
person("E.", "Farhi", role = "ctb"),
person("Kai", "Habel", role = "ctb"),
person("Kurt", "Hornik", role = "ctb"),
person("Sebastian", "Krey", role = "ctb"),
person("Bill", "Lash", role = "ctb"),
person("Friedrich", "Leisch", role = "ctb"),
person("Olaf", "Mersmann", role = "ctb"),
person("Paulo", "Neis", role = "ctb"),
person("Jaakko", "Ruohio", role = "ctb"),
person("Julius O.", "Smith III", role = "ctb"),
person("Doug", "Stewart", role = "ctb"),
person("Andreas", "Weingessel", role = "ctb"))
c(
person("Michal", "Burdukiewicz",
email = "michalburdukiewicz@gmail.com", role = c("cre", "aut")),
person("Piotr", "Sobczyk",
role = "aut"))
c(person("Olivier", "Lepais", role=c("aut", "cre"), email = "olepais@st-pee.inra.fr"), person("Jason", "Weir", role=c("aut")))
c(person("Sunthud", "Pornprasertmanit", role = c("aut",
"cre"), email = "psunthud@ku.edu"), person("Patrick", "Miller",
role = "aut", email="patr1ckm@ku.edu"), person("Alexander",
"Schoemann", role = "aut", email="schoemann@ku.edu"),
person("Corbin", "Quick", role = "ctb",
email="corbinq@gmail.com"), person("Terry", "Jorgensen", role =
"ctb", email="tdj@ku.edu"))
c(person("Kurt", "Hornik", role = c("aut", "cre"),
email = "Kurt.Hornik@R-project.org"),
person("Ingo", "Feinerer", role = "aut"),
person("Martin", "Kober", role = "aut"))
c(person("Kurt", "Hornik", role = c("aut", "cre"),
email = "Kurt.Hornik@R-project.org"),
person("David", "Meyer", role = "aut"),
person("Christian", "Buchta", role = "aut"))
c(person("Patrick", "Mair", role = c("aut", "cre"), email =
"mair@fas.harvard.edu"), person("Jan", "De Leeuw", role = "aut"),
person("Ingwer", "Borg", role = "ctb"), person("Patrick J. F.", "Groenen",
role = "ctb"))
c(person("Jeffrey B.", "Arnold", role = c("aut", "cre"),
email = "jeffrey.arnold@gmail.com"),
person("Alan", "Agresti", role = c("cph")),
person("Barbara", "Finlay", role = c("cph")))
person(given=c("Carter", "T."), family="Butts", role=c("aut","cre","cph"), email = "buttsc@uci.edu")
c(person("Mickael", "Canouil", role = c("aut", "cre"), email = "mickael.canouil@good.ibl.fr"), person("Loic", "Yengo", role = "ctb", email = "loic.yengo@good.ibl.fr"))
person("Scott", "Chamberlain", role = c("aut", "cre"),
email = "myrmecocystus@gmail.com")
person(given = "Daniel", family = "Nuest", role = c("cre",
"aut"), email = "daniel.nuest@uni-muenster.de")
c(person("Edzer", "Pebesma", role = c("aut", "cre"),
email = "edzer.pebesma@uni-muenster.de"),
person("Roger", "Bivand", role = "aut",
email = "Roger.Bivand@nhh.no"),
person("Barry", "Rowlingson", role = "ctb"),
person("Virgilio", "Gomez-Rubio", role = "ctb"))
c(
person("Balasubramanian", "Narasimhan",
email = "naras@stat.stanford.edu", role = c("aut", "cre")),
person("Mei-Chiung", "Shih",
email = "meichiun@stanford.edu", role = "aut"),
person("Pei", "He",
email = "he.pei.hepei@gmail.com", role = "aut"))
c(person("Edzer", "Pebesma", role = c("cre", "aut"),
email = "edzer.pebesma@uni-muenster.de"),
person("Benedikt", "Graeler", role = "ctb"),
person("Tom", "Gottfried", role = "ctb"),
person("Robert J.", "Hijmans", role = "ctb"))
c(person("Till", "Junge", role = c("aut", "cre"),
email = "till.junge@altermail.ch"),
person("Sandra", "Penic", role = "aut",
email = "sandra.penic@unil.ch"),
person("Mathieu", "Cossuta", role = "aut",
email = "mcossutta@gmail.com"),
person("Guy", "Elcheroth", role = "aut",
email = "guy.elcheroth@unil.ch"),
person("Stephanie", "Glaeser", role = "ctb",
email = "stephanie.glaeser@unil.ch"),
person("Davide", "Morselli", role = "ctb",
email = "davide.morselli@unil.ch"))
c(person("Reinhard", "Furrer", role = c("aut", "cre"),
email = "reinhard.furrer@math.uzh.ch"),
person("Florian", "Gerber", role = "ctb",
email = "florian.gerber@math.uzh.ch"))
c(person("François", "Rousset", role = c("aut", "cre", "cph"),
email = "francois.rousset@univ-montp2.fr"),
person("Jean-Baptiste", "Ferdy", role = c("aut","cph")))
c(person("Thomas", "Leeper", role = c("aut", "cre"), email =
"thosjleeper@gmail.com"), person("Andy", "Barbour", role =
"ctb"))
c(person("Brian", "Ripley", role = c("aut", "cre", "cph"),
email = "ripley@stats.ox.ac.uk"),
person("Roger", "Bivand", role = "ctb"),
person("William", "Venables", role = "cph"))
c( person(given = "Dennis", family = "Walvoort", email =
"dennis.Walvoort@wur.nl", role = c("aut", "cre", "cph")),
person(given = "Dick", family = "Brus", email =
"dick.brus@wur.nl", role = c("aut", "cph")), person(given =
"Jaap", family = "de Gruijter", email =
"Jaap.degruijter@wur.nl", role = c("aut", "cph")))
c(
person("Roger", "Bivand", role = c("cre", "aut"), email = "Roger.Bivand@nhh.no"),
person("Micah", "Altman", role = "ctb"),
person("Luc", "Anselin", role = "ctb"),
person("Renato", "Assunção", role = "ctb"),
person("Olaf", "Berke", role = "ctb"),
person("Andrew", "Bernat", role = "ctb"),
person("Guillaume", "Blanchet", role = "ctb"),
person("Eric", "Blankmeyer", role = "ctb"),
person("Marilia", "Carvalho", role = "ctb"),
person("Bjarke", "Christensen", role = "ctb"),
person("Yongwan", "Chun", role = "ctb"),
person("Carsten", "Dormann", role = "ctb"),
person("Stéphane", "Dray", role = "ctb"),
person("Virgilio", "Gómez-Rubio", role = "ctb"),
person("Rein", "Halbersma", role = "ctb"),
person("Elias", "Krainski", role = "ctb"),
person("Pierre", "Legendre", role = "ctb"),
person("Nicholas", "Lewin-Koh", role = "ctb"),
person("Hongfei", "Li", role = "ctb"),
person("Jielai", "Ma", role = "ctb"),
person("Giovanni", "Millo", role = "ctb"),
person("Werner", "Mueller", role = "ctb"),
person("Hisaji", "Ono", role = "ctb"),
person("Pedro", "Peres-Neto", role = "ctb"),
person("Gianfranco", "Piras", role = "ctb"),
person("Markus", "Reder", role = "ctb"),
person("Michael", "Tiefelsdorf", role = "ctb"),
person("Danlin", "Yu", role = "ctb"))
c(person("Stefan", "Siegert", role = c("aut", "cre"),
email = "s.siegert@exeter.ac.uk"))
c(
person('Santiago', 'Beguería', role=c('aut','cre'),
email='santiago.begueria@csic.es'),
person(c('Sergio','M.'), 'Vicente-Serrano', role='aut',
email='svicen@ipe.csic.es'))
c(
person("Roger", "Bivand", role = c("cre", "aut"), email = "Roger.Bivand@nhh.no"),
person("Rainer", "Krug", role = "ctb"),
person("Markus", "Neteler", role = "ctb"))
c(
person("Roger", "Bivand", role = c("cre", "aut"), email = "Roger.Bivand@nhh.no"),
person("Danlin", "Yu", role = "aut"),
person("Tomoki", "Nakaya", role = "ctb"),
person("Miquel-Angel", "Garcia-Lopez", role = "ctb"))
c(
person("Roger", "Bivand", role = c("cre"), email = "Roger.Bivand@nhh.no"),
person("Barry", "Rowlingson", role = "aut"),
person("Peter", "Diggle", role = "aut"),
person("Giovanni", "Petris", role = "ctb"),
person("Stephen", "Eglen", role = "ctb"))
c(person("Scott", "Chamberlain", role = c("aut", "cre"),
email = "myrmecocystus@gmail.com"),
person("Karthik", "Ram", role = "aut",
email = "karthik.ram@gmail.com"),
person("Ted", "Hart", role = "aut",
email = "edmund.m.hart@gmail.com"))
c(person("Tom", "Kincaid", role=c("aut", "cre"),
email = "Kincaid.Tom@epa.gov"),
person("Tony", "Olsen",  role="aut",
email = "Olsen.Tony@epa.gov"),
person("Don", "Stevens",  role="ctb"),
person("Christian", "Platt",  role="ctb"),
person("Denis", "White",  role="ctb"),
person("Richard", "Remington",  role="ctb"))
c(person("Sean", "Anderson", email =
"sean@seananderson.ca", role = c("aut", "cre")),
person("Cole", "Monnahan", role = "aut"),
person("Kelli", "Johnson", role = "aut"),
person("Kotaro", "Ono", role = "aut"),
person("Juan", "Valero", role = "aut"),
person("Curry", "Cunningham", role = "aut"),
person("Felipe", "Hurtado-Ferro", role = "aut"),
person("Roberto", "Licandeo", role = "aut"),
person("Carey", "McGilliard", role = "aut"),
person("Melissa", "Muradian", role = "ctb"),
person("Cody", "Szuwalski", role = "aut"),
person("Katyana", "Vert-pre", role = "aut"),
person("Athol", "Whitten", role = "aut"))
person("Andrie", "de Vries", role=c("aut", "cre"),
email="apdevries@gmail.com")
c(person(given = "David", family = "Meyer", role = c("aut", "cre"), email = "David.Meyer@R-project.org"),
person(given = "Torsten", family = "Hothorn", role = c("aut")),
person(given = "Friedrich", family = "Leisch", role = "aut"))
c(
person("Mark S.", "Handcock", role=c("aut"), email="handcock@stat.ucla.edu"),
person("David R.", "Hunter", role=c("aut"), email="dhunter@stat.psu.edu"),
person("Carter T.", "Butts", role=c("aut"), email="buttsc@uci.edu"),
person("Steven M.", "Goodreau", role=c("aut"), email="goodreau@u.washington.edu"),
person("Pavel N.", "Krivitsky", role=c("aut","cre"), email="pavel@uow.edu.au"),
person("Skye", "Bender-deMoll", role=c("aut"), email="skyebend@skyeome.net"),
person("Martina", "Morris", role=c("aut"), email="morrism@u.washington.edu")
)
c(
person("Pavel N.", "Krivitsky", role=c("aut","cre"), email="pavel@uow.edu.au"))
c(person(given = "Elise", family = "Dusseldorp", role =
c("aut", "cre","cph"), email = "elise.dusseldorp@tno.nl"),
person(given = "Claudio", family = "Conversano", role = c("aut", "cph"), email
= "conversa@unica.it"),person("Cor","Ninaber",role="ctb"),person("Kristof","Meers",role="ctb"),person("Peter","Neufeglise",role="trl"))
person("Gregor", "Kastner", role = c("aut", "cre"), email = "gregor.kastner@wu.ac.at")
c(person("Michael", "Hahsler", role = c("aut", "cre", "cph"),
email = "mhahsler@lyle.smu.edu"),
person("Matthew", "Bolanos", role = c("aut", "cph")),
person("John", "Forrest", role = c("aut", "cph")))
c( person("Mark", "van der Loo",
role=c("aut","cre"),email="mark.vanderloo@gmail.com") , person("Jan", "van
der Laan", role="ctb"))
c(person(given = "Achim", family = "Zeileis", role = c("aut", "cre"), email = "Achim.Zeileis@R-project.org"),
person(given = "Friedrich", family = "Leisch", role = "aut", email = "Friedrich.Leisch@R-project.org"),
person(given = "Kurt", family = "Hornik", role = "aut", email = "Kurt.Hornik@R-project.org"),
person(given = "Christian", family = "Kleiber", role = "aut", email = "Christian.Kleiber@unibas.ch"),
person(given = "Bruce", family = "Hansen", role = "ctb"))
c(person("Nathan", "Morris", role = c("aut", "cre"), email = "nathan.morris@cwru.edu"),
person("Yeunjoo", "Song", role = c("aut"), email = "yeunjoo.song@cwru.edu"),
person("Stephen", "Cahn", role = "ctb"))
c(person("Jorge" ,"Orestes Cerdeira", role="aut",
email="orestes@isa.utl.pt"), person("Pedro", "Duarte Silva,",
role="aut", email="psilva@porto.ucp.pt"), person("Jorge",
"Cadima,", role=c("aut","cre"), email="jcadima@isa.utl.pt"),
person("Manuel", "Minhoto,", role="aut",
email="minhoto@uevora.pt"))
c(MH = person("Michael", "Höhle",
email = "hoehle@math.su.se",
role = c("aut", "cre", "ths")),
SM = person("Sebastian", "Meyer",
email = "Sebastian.Meyer@ifspm.uzh.ch",
role = "aut"),
MP = person("Michaela", "Paul",
email = "Michaela.Paul@ifspm.uzh.ch",
role = "aut"),
LH = person("Leonhard", "Held",
email = "Leonhard.Held@ifspm.uzh.ch",
role = c("ctb", "ths")),
person("Thais", "Correa", role = "ctb"),
person("Mathias", "Hofmann", role = "ctb"),
person("Christian", "Lang", role = "ctb"),
person("Juliane", "Manitz", role = "ctb"),
person("Andrea", "Riebler", role = "ctb"),
person("Daniel", "Sabanés Bové", role = "ctb"),
person("Maëlle", "Salmon", role = "ctb"),
person("Dirk", "Schumacher", role = "ctb"),
person("Stefan", "Steiner", role = "ctb"),
person("Mikko", "Virtanen", role = "ctb"),
person("Valentin", "Wimmer", role = "ctb"),
person("R Core Team", role = "ctb",
comment = "A few code segments are modified versions of
code from base R"))
c(person(c("Terry", "M"), "Therneau",
email="therneau.terry@mayo.edu",
role=c("aut", "cre")),
person("Thomas", "Lumley", role=c("ctb", "trl"),
comment="original S->R port and maintainer until 2009"))
c(person("Philippe", "Grosjean", role = c("aut", "cre"),
email = "phgrosjean@sciviews.org"))
c(person("Philippe", "Grosjean", role = c("aut", "cre"),
email = "phgrosjean@sciviews.org"),
person("Romain", "Francois", role = "ctb",
email = "romain@r-enthusiasts.com"),
person("Kamil", "Barton", role = "ctb",
email = "kamil.barton@uni-wuerzburg.de"))
c(person("Philippe", "Grosjean", role = c("aut", "cre"),
email = "phgrosjean@sciviews.org"),
person("Matthew", "Dowle", role = "ctb",
email = "mdowle@mdowle.plus.com"))
c(person("Philippe", "Grosjean", role = c("aut", "cre"),
email = "phgrosjean@sciviews.org"),
person("Romain", "Francois", role = "aut",
email = "romain@r-enthusiasts.com"))
c(
person("Nick", "Carchedi", email = "nick.carchedi@gmail.com",
role = c("aut", "cre")),
person("Bill", "Bauer", role = "aut"),
person("Gina", "Grdina", role = "aut"),
person("Sean", "Kross", role = "aut")
)
c(person("Gabriel", "Gelius-Dietrich", role = c("aut", "cre"), email = "geliudie@uni-duesseldorf.de"),
person(c("C.", "Jonathan"), "Fritzemeier", role = "ctb"),
person("Rajen", "Piernikarczyk", role = "ctb"),
person(c("Marc", "Andre"), "Daxer", role = "ctb"),
person("Benjamin", "Braasch", role = "ctb"),
person("Abdelmoneim", "Desouki", role = "ctb"),
person(c("Martin", "J."), "Lercher", role = "ctb"))
c(person("Abdelmoneim", "Amer Desouki", role = c("aut","cre"), email = "abdelmoneim.amer@uni-duesseldorf.de"))
c(person("Abdelmoneim", c("Amer", "Desouki"), role =
c("aut", "cre"), email =
"Abdelmoneim.Amer@uni-duesseldorf.de"))
c(person("Gabriel", "Gelius-Dietrich", role = c("aut", "cre"), email = "geliudie@uni-duesseldorf.de"),
person("Deya", "Alzoubi", role = "ctb"))
c(person(given = "Ernest", family = "Kwan", role="aut"),
person(given = "Michael", family = "Friendly", role=c("aut",
"cre"), email="friendly@yorku.ca") )
person("Jimmy", "Oh", role = c("aut", "cre"), email = "joh024@aucklanduni.ac.nz")
c(person("Thomas", "Kiefer", role = c("aut"), email =
"t.kiefer@bifie.at"), person("Alexander", "Robitzsch", role =
c("aut","cre"), email = "a.robitzsch@bifie.at"),
person("Margaret", "Wu", role = "aut"))
c(person(given = "Edgar", family = "Kublin", role = "aut"),
person(given = "Johannes", family = "Breidenbach", role = c("ctb","cre"),
email = "Johannes.Breidenbach@SkogogLandskap.no"))
c(person("Christian", "Buchta", role = "aut"),
person("Kurt", "Hornik", role = c("aut", "cre"),
email = "Kurt.Hornik@R-project.org"),
person("Ingo", "Feinerer", role = "aut"),
person("David", "Meyer", role = "aut"))
c(person("Scott", "Chamberlain", role = c("aut", "cre"),
email = "myrmecocystus@gmail.com"),
person("Eduard", "Szoecs", role = "aut",
email = "eduardszoecs@gmail.com"),
person("Carl", "Boettiger", role = "aut",
email = "cboettig@gmail.com"),
person("Karthik", "Ram", role = "aut",
email = "karthik@ropensci.org"),
person("Ignasi", "Bartomeus", role = "aut",
email = "nacho.bartomeus@gmail.com"),
person("John", "Baumgartner", role = "aut",
email = "johnbb@student.unimelb.edu.au"))
c(person("Boris P.", "Hejblum", role = c("aut", "cre"),
email = "boris.hejblum@isped.u-bordeaux2.fr"))
c(
person("Pavel N.", "Krivitsky", role=c("aut","cre"), email="pavel@uow.edu.au"),
person("Mark S.", "Handcock", role=c("aut","ths"), email="handcock@stat.ucla.edu"),
person("David R.", "Hunter", role=c("ctb"), email="dhunter@stat.psu.edu"),
person("Steven M.", "Goodreau", role=c("ctb","ths"), email="goodreau@u.washington.edu"),
person("Martina", "Morris", role=c("ctb","ths"), email="morrism@u.washington.edu"),
person("Nicole Bohme", "Carnegie", role=c("ctb"), email="nicole.carnegie@nyu.edu"),
person("Carter T.", "Butts", role=c("ctb"), email="buttsc@uci.edu"),
person("Ayn", "Leslie-Cook", role=c("ctb"), email="aynlc3@uw.edu"),
person("Skye", "Bender-deMoll", role=c("ctb"), email="skyebend@skyeome.net"))
as.person(c(
"Hadley Wickham <hadley@rstudio.com> [aut, cre]",
"RStudio [cph]"))
c(person("Robert", "Krzyzanowski",
email = "technoguyrob@gmail.com", role = c("aut", "cre")))
c(person("Kurt", "Hornik", role = c("aut", "cre"),
email = "Kurt.Hornik@R-project.org"),
person("Johannes", "Rauch", role = "aut"),
person("Christian", "Buchta", role = "aut",
email = "christian.buchta@wu.ac.at"),
person("Ingo", "Feinerer", role = "aut",
email = "feinerer@logic.at"))
c(person("Torsten", "Hothorn", role = c("aut", "cre"),
email = "Torsten.Hothorn@R-project.org"))
c(person("Charlie", "Sharpsteen", role = "aut"), person("Cameron",
"Bracken", role = "aut"), person("Kirill", "Mueller", role = c("ctb",
"cre"), email = "mail@kirill-mueller.de"), person("Yihui", "Xie", role =
"ctb"))
c(person("Gianluca", "Campanella", role=c("aut", "cre"),
email="gianluca@campanella.org"),
person("Marc", "Chadeau-Hyam", role="aut",
email="m.chadeau@imperial.ac.uk"),
person("Maria", "De Iorio", role="ctb",
email="m.deiorio@ucl.ac.uk"),
person("Ajay", "Jasra", role="ctb",
email="staja@nus.edu.sg"))
c(person("Stefano", "Costa", role = c("aut", "cre"), email = "steko@iosa.it"),
person("Luca", "Bianconi", role = "aut", email = "luca@iosa.it"),
person("Elisabetta", "Starnini", role = "ctb"))
c(person("Ingo", "Feinerer", role = c("aut", "cre"),
email = "feinerer@logic.at"),
person("Kurt", "Hornik", role = "aut"),
person("Artifex Software, Inc.", role = c("ctb", "cph"),
comment = "pdf_info.ps taken from GPL Ghostscript"))
c(person("Milan", "Bouchet-Valat", email="nalimilan@club.fr",
role=c("aut", "cre")),
person("Grigorij", "Ljubownikow",
email="g.ljubownikow@auckland.ac.nz",
role="ctb"))
c(person("Ingo", "Feinerer", role = c("aut", "cre"),
email = "feinerer@logic.at"),
person("Wolfgang", "Mauerer", role = c("aut")))
c(person("Mario", "Annau", role = c("aut", "cre"),
email = "mario.annau@gmail.com"))
c(person("Derek S.", "Young", role = c("aut", "cre"),
email = "derek.young@uky.edu"))
c(person("Bettina", "Grün", role = c("aut", "cre"),
email = "Bettina.Gruen@jku.at"),
person("Kurt", "Hornik", role = "aut"))
c(person("Edzer", "Pebesma", role = c("cre", "aut"),
email = "edzer.pebesma@uni-muenster.de"),
person("Benedikt", "Klus", role = "aut"))
c(
person("Alexis", "Gabadinho", role = c("aut", "cph")),
person("Matthias", "Studer", role = c("aut", "cph")),
person("Nicolas", "Muller", role = "aut"),
person("Reto", "Buergin", role = "aut"),
person("Gilbert", "Ritschard", role = c("aut", "cre", "cph"),
email = "gilbert.ritschard@unige.ch"))
c(
person("Gilbert", "Ritschard", role = c("aut", "cre", "ths", "cph"),
email = "gilbert.ritschard@unige.ch"),
person("Matthias", "Studer", role = "aut"),
person("Reto", "Buergin", role = "aut"),
person("Alexis", "Gabadinho", role = "ctb"),
person("Nicolas", "Muller", role = "ctb"),
person("Patrick", "Rousset", role = "ctb"))
person(given = "Jeremy Thoms", family = "Hetzel", email =
"jthetzel@gmail.com", role = c("aut", "cre"))
person("Brian", "Ripley", role = c("aut", "cre"),
email = "ripley@stats.ox.ac.uk")
c(person("Carl", "Boettiger", role = c("aut", "cre"),
email = "cboettig@gmail.com"),
person("Duncan", "Temple Lang", role = "aut"))
c(person("Christian", "Zang", role = c("aut", "cre", "cph", "trl"),
email = "christian.zang@wzw.tum.de"), person("Franco", "Biondi", role =
c("ctb", "cph")))
c(person("Michael D.","Sumner", role = c("aut", "cre"),
email = "mdsumner@gmail.com"),
person("Sebastian", "Luque", role = "ctb",
email = "spluque@gmail.com"))
c(person("Michael D.","Sumner", role = c("aut", "cre"),
email = "mdsumner@gmail.com"),
person("Simon", "Wotherspoon", role = "ctb",
email = "Simon.Wotherspoon@utas.edu.au"))
c(person("Yves", "Croissant", role = c("aut", "cre"), email = "yves.croissant@univ-reunion.fr"),
person("Achim", "Zeileis", role = "aut", email = "Achim.Zeileis@R-project.org"))
c(person("Antonio Fabio", "Di Narzo",role = c("aut", "cre")),
person("Jose Luis", "Aznarte",role = "ctb"),
person("Matthieu", "Stigler", role = c("aut","cre"), email="Matthieu.Stigler@gmail.com"))
c(person("Adrian", "Trapletti", role = "aut", email =
"adrian@trapletti.org"), person("Kurt", "Hornik", role =
c("aut", "cre"), email = "Kurt.Hornik@R-project.org"),
person("Blake", "LeBaron", role = "ctb", comment = "BDS test
code"))
c(
person("Michael", "Hahsler", role = c("aut", "cre", "cph"),
email = "mhahsler@lyle.smu.edu"),
person("Kurt", "Hornik", role = c("aut", "cph")))
c(person(given="David", family="DEVREKER", role = c("aut"), email =
"David.Devreker@ifremer.fr" ), person(given="Alain", family="LEFEBVRE",
role = c("aut", "cre"), email = "Alain.Lefebvre@ifremer.fr" ) )
c(person(c("Ian", "H."), "Hutchinson", role = "aut",
comment = "author of tth/ttm C sources"),
person("Friedrich", "Leisch", role = c("aut", "cre"),
email = "Friedrich.Leisch@R-project.org",
comment = "author of R wrappers to tth/ttm"),
person("Achim", "Zeileis", role = "aut",
email = "Achim.Zeileis@R-project.org",
comment = "author of R wrappers to tth/ttm"))
c( person(given = c("Jennifer", "F."), family = "Bobb", role =
"aut", email = "jbobb@hsph.harvard.edu"), person(given =
"Ravi", family = "Varadhan", role = c("aut", "cre"), email =
"ravi.varadhan@jhu.edu"), person(given = "Hui", family =
"Zhao", role = "ctb"))
person("Jack", "Horne", role = c("aut", "cre"), email = "jack@jackhorne.net")
c(
person("Anna", "Cena", role = c("aut")),
person("Marek", "Gagolewski", role = c("aut")),
person("Marcin", "Kosinski", role = c("aut")),
person("Natalia", "Potocka", role = c("aut")),
person("Barbara", "Zogala-Siudem", role = c("aut", "cre"),
email = "zogala@rexamine.com")
)
c(person(given = "Mark", family = "Pinese", role = c("aut", "cre",
"cph"), email = "m.pinese@garvan.org.au"))
c(person("Bernhard", "Pfaff", email =
"bernhard@pfaffikus.de", role = c("aut", "cre")),
person("Matthieu", "Stigler", role = "ctb"))
c(person(given="Neil J.", family="Gunther", role
= "aut"), person(given="Stefan", family="Moeding", role
= c("aut", "cre"), email = "stm@moeding.net"))
c(person("Juris", "Breidaks", email = "Juris.Breidaks@csb.gov.lv",
role = c("aut", "cre")), person("Martins", "Liberts", role = "aut"))
c(person("Bernhard", "Pfaff", email = "bernhard@pfaffikus.de", role = c("aut", "cre")), person("Matthieu", "Stigler", role = "ctb"))
c(person("Joris", "Muller", email = "joris.muller@etu.unistra.fr",
role = c("aut", "cre")),
person("Erik-André","Sauleau", role = c("ctb")))
c(person(given = "David", family = "Meyer", role = c("aut", "cre"), email = "David.Meyer@R-project.org"),
person(given = "Achim", family = "Zeileis", role = c("aut")),
person(given = "Kurt", family = "Hornik", role = c("aut")),
person(given = "Florian", family = "Gerber", role = c("ctb")),
person(given = "Michael", family = "Friendly", role = "ctb"))
c(person(given = "Michael", family = "Friendly", role=c("aut", "cre"), email="friendly@yorku.ca"),
person(given = "Heather", family = "Turner", role="ctb"),
person(given = "Achim", family = "Zeileis", role="ctb"),
person(given = "Duncan", family = "Murdoch", role="ctb"),
person(given = "David", family = "Firth", role="ctb")
)
c(
person("Reto", "Buergin", role = c("aut", "cre", "cph"), email = "rbuergin@gmx.ch"),
person("Gilbert", "Ritschard", role = c("ctb", "ths"),
email = "gilbert.ritschard@unige.ch"))
c(person("Pieter", "Schoonees", email = "schoonees@gmail.com",
role = c("aut", "cre", "cph")))
c(person("Michael", "Sieger", role=c("cre","aut"),
email="michael.sieger@student.hswt.de"), person("Georg",
"Ohmayer", role=c("aut", "ths"),
email="georg.ohmayer@hswt.de"), person("Hochschule
Weihenstephan-Triesdorf", role=("cph")))
c(person(given=c("Drew", "Griffin"), family="Levy",
role = "aut",
email = "levy.drew@gene.com"),
person(given=c("David", "C."), family="Norris",
role = c("aut","cre"),
email = "david@UnusualSolutionsThatWork.com"))
c(person("Will", "Beasley", role = c("aut", "cre"), email =
"wibeasley@hotmail.com"), person("Joe", "Rodgers", role = "aut", email =
"joseph.l.rodgers@vanderbilt.edu"), person("Matthew", "Schuelke", role =
"ctb", email = "matthew.schuelke.ctr@us.af.mil"), person("Ronnie",
"Coleman", role = "ctb"), person("Mark Joseph", "Lachowicz", role =
"ctb", email = "mark.j.lachowicz@vanderbilt.edu"))
c(person("Guy", "Nason", role=c("aut", "cre"),
email="G.P.Nason@bristol.ac.uk"))
person("Matthias", "Studer", email = "matthias.studer@unige.ch", role = c("aut", "cre"))
c(person("Karthik", "Ram", role = c("aut", "cre"),
email = "karthik.ram@gmail.com"),
person("Richards", "Clark", role = "ctb",
email = "crichards@whoi.edu"))
c(person("Chris", "Wallace", role=c("aut","cre"),
email = "chris.wallace@cimr.cam.ac.uk"),
person("Olly","Burren", role="ctb",
email="oliver.burren@cimr.cam.ac.uk"))
c(person("Ingo", "Feinerer", role = "aut"),
person("Kurt", "Hornik", role = c("aut", "cre"),
email = "Kurt.Hornik@R-project.org"),
person("Mike", "Wallace", role = c("ctb", "cph"),
comment = "Jawbone Java WordNet API library"))
person(given = "Michael", family = "Friendly", role=c("aut",
"cre"), email="friendly@yorku.ca")
c(person("Graham", "Williams", email="graham.williams@togaware.com", role="aut"),
person("Joshua Z", "Huang", email="zx.huang@szu.edu.cn", role="aut"),
person("Xiaojun", "Chen", email="xjchen.hitsz@gmail.com", role="aut"),
person("Qiang", "Wang", role="aut"),
person("Longfei", "Xiao", role="aut"),
person("He", "Zhao", email="Simon.Yansen.Zhao@gmail.com", role="cre"))
c(person("Qinghan", "Meng", role="aut"),
person("He", "Zhao", email="Simon.Yansen.Zhao@gmail.com", role=c("aut", "cre")),
person("Graham", "Williams", email="graham.williams@togaware.com", role="aut"),
person("Junchao", "Lv", role="ctb"))
c(person(given = "Philip", family = "Leifeld", email = "philip.leifeld@uni-konstanz.de", role = c("aut", "cre")), person(given = c("Skyler", "J."), family = "Cranmer", email = "skyler@unc.edu", role = "aut"), person(given = c("Bruce", "A."), family = "Desmarais", email = "desmarais@polsci.umass.edu", role = "aut"))
c(person("Mirai Solutions GmbH", role = "aut",
email = "xlconnect@mirai-solutions.com"),
person("Martin", "Studer", role = "cre",
email = "martin.studer@mirai-solutions.com"),
person("The Apache Software Foundation", role = c("ctb", "cph"),
comment = "Apache POI, Apache Commons Codec, XML Commons External Components XML APIs"),
person("Stephen", "Colebourne", role = c("ctb", "cph"),
comment = "Joda-Time Java library"),
person("Metastuff, Ltd.", role = c("ctb", "cph"),
comment = "dom4j"))
c(person("Mirai Solutions GmbH", role = "aut",
email = "xlconnect@mirai-solutions.com"),
person("Martin", "Studer", role = "cre",
email = "martin.studer@mirai-solutions.com"),
person("The Apache Software Foundation", role = c("ctb", "cph"),
comment = "Apache POI, Apache XMLBeans"))
c(person(c("Adrian", "A."), "Dragulescu",
role = c("aut", "cre"),
email = "adrian.dragulescu@gmail.com"),
person("The Apache Software Foundation",
role = c("ctb", "cph"),
comment = "Apache POI, Apache XMLBeans", "Apache
Commons Codec"),
person("MetaStuff, Ltd.",
role = c("ctb", "cph"),
comment = "Dom4j"))
c(
person("Xiaobei", "Zhao", email="xiaobei@binf.ku.dk",
role=c("aut", "cre", "cph"))
)
c(person(given = "Achim", family = "Zeileis", role = c("aut", "cre"), email = "Achim.Zeileis@R-project.org"),
person(given = "Gabor", family = "Grothendieck", role = "aut", email = "ggrothendieck@gmail.com"),
person(given = c("Jeffrey", "A."), family = "Ryan", role = "aut", email = "jeff.a.ryan@gmail.com"),
person(given = "Felix", family = "Andrews", role = "ctb", email = "felix@nfrac.org"))
c(person("Philippe", "Grosjean", role = c("aut", "cre"),
email = "phgrosjean@sciviews.org"),
person("Kevin", "Denis", role = "aut",
email = "kevin.denis@umons.ac.be"))
c(person(given="Corentin M", family="Barbu",
email="corentin.barbu@gmail.com", role=c("aut", "cre")),
person(given="Sebastian", family="Gibb", role="ctb",
email="mail@sebastiangibb.de"))
Katalin Csillery, Louisiane Lemaire, Michael Blum and Olivier Francois
George Vega Yon <g.vegayon@gmail.com> [aut], Enyelbert Muñoz <enyeldoc2011@gmail.com> [ctb]
M. Catherine Duryea, Andrew D. Kern, Robert M. Cox, and Ryan Calsbeek
Matt Nunes [aut, cre],
Dennis Prangle [aut]
Kevin M. Middleton <middletonk@missouri.edu>, Randall Pruim
<rpruim@calvin.edu>
Tony Plate <tplate@acm.org> and Richard Heiberger
Yu Jiang, Steve Simon, Matthew S. Mayo , Rama Raghavan, Byron J. Gajewski
Frederico Zanqueta Poleto, Julio da Mota Singer, Carlos Daniel Paulino, Fabio Mathias Correa and Enio Galinkin Jelihovschi
Phil Spector, Jerome Friedman, Robert Tibshirani, Thomas Lumley
Andrea Benedetti, Sahir Rai Bhatnagar, Xiaofei Zhao
Maria Ortiz [aut],
Henrik Bengtsson [aut, cre, cph],
Angel Rubio [aut]
Dirk Van den Poel, Michel Ballings, Andrey Volkov, Jeroen D'haen, Michiel Van Herwegen
Nicolas Gauvrit [aut],
Henrik Singmann [aut, cre],
Fernando Soler Toscano [ctb],
Hector Zenil [ctb]
Fernando Soler Toscano [aut],
Nicolas Gauvrit [aut],
Hector Zenil [aut],
Henrik Singmann [aut, cre]
Chia-Yi Chiu (Rutgers, the State University of New Jersey) and Wenchao Ma (Rutgers, the State University of New Jersey)
William Shannon, Tao Li, Hong Xian, Jia Wang, Elena Deych,
Carlos Gonzalez
Vincent Goulet, Sébastien Auclair, Christophe Dutang, Xavier
Milhaud, Tommy Ouellet, Louis-Philippe Pouliot, Mathieu Pigeon
Mark Culp, Kjell Johnson, and George Michailidis
Alfaro, Esteban; Gamez, Matias and Garcia, Noelia
Manuel Wiesenfarth and Tatyana Krivobokova
Kristen Zygmunt, Eleanor Wong, Tom Fletcher
Marie Simeckova [aut], Thomas Rusch [aut], Petr Simecek [cre]
Stéphane Dray <stephane.dray@univ-lyon1.fr>, Anne-Béatrice Dufour <anne-beatrice.dufour@univ-lyon1.fr>, and Jean Thioulouse <jean.thioulouse@univ-lyon1.fr>, with contributions from Thibaut Jombart, Sandrine Pavoine, Jean R. Lobry, Sébastien Ollier, and Aurélie Siberchicot. Based on earlier work by Daniel Chessel.
Jean Thioulouse <jthioulouse@biomserv.univ-lyon1.fr>, Stephane
Dray <dray@biomserv.univ-lyon1.fr>
Thibaut Jombart, Caitlin Collins, Peter Solymos, Ismail Ahmed, Federico Calboli, Anne Cori
Clement Calenge, contributions from Mathieu Basille, Stephane Dray and Scott Fortmann-Roe
Clement Calenge, contributions from Scott Fortmann-Roe
Clement Calenge, contributions from Mathieu Basille
Clement Calenge, contributions from Stephane Dray and Manuela Royer
Thibaut Jombart <t.jombart@imperial.ac.uk>, Stephane Dray <stephane.dray@univ-lyon1.fr>
Pedro Rafael Diniz Marinho [aut, cre], Marcelo Bourguignon [aut, ctb], Cicero Rafael Barros Dias [aut, ctb]
Karsten Tabelow <tabelow@wias-berlin.de>, Joerg Polzehl
<polzehl@wias-berlin.de>
Matt Nunes <m.nunes@lancs.ac.uk> Marina Knight
David Ardia, Lennart F. Hoogerheide and Herman K. van Dijk
Imanuel Costigan [aut, cre],
Australian Energy Market Operator [cph]
Christian Kleiber [aut],
Achim Zeileis [aut, cre]
Henrik Singmann [aut, cre],
Ben Bolker [ctb],
Søren Højsgaard [ctb],
John Fox [ctb],
Michael A. Lawrence [ctb],
Ulf Mertens [ctb]
Francisco Balao [aut, cre],
Juan Luis García-Castaño [aut]
Sy Han (Steven) Chiou, Sangwook Kang, Jun Yan
Marek Gagolewski [aut, cre],
Anna Cena [ctb]
Niklas Hack, Werner Brannath, Matthias Brueckner
Fortran code by H. Akima
R port by Albrecht Gebhardt <albrecht.gebhardt@uni-klu.ac.at>
aspline function by Thomas Petzoldt <thomas.petzoldt@tu-dresden.de>
interp2xyz, enhancements and corrections by Martin Maechler <maechler@stat.math.ethz.ch>
Ravi Varadhan (with contributions from Gabor Grothendieck)
Luis Benites Sanchez <lsanchez@ime.usp.br>,
Victor Lachos <hlachos@ime.unicamp.br>
aLFQ: George Rosenberger, Hannes Roest, Christina Ludwig. AAindex: Barry Grant.
Jose Francisco Loff, Alberto Murta, Laurence Kell
Nathan Medina-Rodriguez and Angelo Santana
Scott Chamberlain [aut, cre],
Carl Boettiger [aut],
Karthik Ram [aut],
Fenner Martin [aut]
Daniel Merl <danmerl@gmail.com>, Leah R. Johnson <lrjohnson@uchicago.edu>, Robert B. Gramacy <rbgramacy@chicagobooth.edu>, and Marc S. Mangel <msmangel@ams.ucsc.edu>
James Honaker <jhonaker@iq.harvard.edu>, Gary King
<king@harvard.edu>, Matthew Blackwell
<m.blackwell@rochester.edu>
Peter Hoff, Bailey Fosdick, Alex Volfovsky, Kate Stovel
Manuel Castejon Limas, Joaquin B. Ordieres Mere, Ana Gonzalez
Marcos, Francisco Javier Martinez de Pison Ascacibar, Alpha V.
Pernia Espinoza, Fernando Alba Elias, Jose Maria Perez Ramos
Anja Lange [aut, cre],
Daniel Hoffmann [aut]
Patrick Mair [aut, cre], Jan De Leeuw [aut]
Gavin L. Simpson [aut, cre],
Jari Oksanen [aut]
P Lafaye de Micheaux <lafaye@dms.umontreal.ca>, J L Marchini
<marchini@stats.ox.ac.uk>
Wand, Jonathan and Gary King, and Olivia Lau
Lijia Yu [ctb],
Weicheng Zhu [ctb],
Yihui Xie [cre, aut]
Guillermo Vinue, Irene Epifanio, Amelia Simo, M. Victoria Ibanez, Juan Domingo, Guillermo Ayala
Matthieu Lesnoff <matthieu.lesnoff@cirad.fr> and Renaud
Lancelot <renaud.lancelot@cirad.fr>
Hakim-Moulay Dehbi (with contributions from Mario Cortina-Borja and Marco Geraci)
Ulrich Bodenhofer, Andreas Kothmeier, Johannes Palme
Emmanuel Paradis [aut, cre, cph],
Ben Bolker [aut, cph],
Julien Claude [aut, cph],
Hoa Sien Cuong [aut, cph],
Richard Desper [aut, cph],
Benoit Durand [aut, cph],
Julien Dutheil [aut, cph],
Olivier Gascuel [aut, cph],
Christoph Heibl [aut, cph],
Daniel Lawson [aut, cph],
Vincent Lefort [aut, cph],
Pierre Legendre [aut, cph],
Jim Lemon [aut, cph],
Rosemary McCloskey [aut, cph],
Johan Nylander [aut, cph],
Rainer Opgen-Rhein [aut, cph],
Andrei-Alin Popescu [aut, cph],
Klaus Schliep [aut, cph],
Korbinian Strimmer [aut, cph],
Damien de Vienne [aut, cph]
Daniel Sabanes Bove <daniel.sabanesbove@ifspm.uzh.ch> with
contributions by F. D. Colavecchia, R. C. Forrey, G. Gasaneo,
N. L. J. Michel, L. F.  Shampine, M. V. Stoitsov and H. A.
Watts.
Nicolas Bortolussi, Eric Durand, Michael Blum
<michael.blum@imag.fr>, Olivier Francois
<olivier.francois@imag.fr>
Jenise Swall, with contributions from Kristen Foley
Dylan Beaudette <debeaudette@ucdavis.edu>, Pierre Roudier <roudierp@landcareresearch.co.nz>
Andreas F. Hofmann, Karline Soetaert, Filip J.R. Meysman
S. Chakar, E. Lebarbier, C. Levy-Leduc, S. Robin
Manuel J. A. Eugster [aut, cre],
Friedrich Leisch [aut],
Sohan Seth [ctb]
Przemyslaw Biecek [aut, cre],
Marcin Kosinski [aut]
Trevor L Davis. Ports examples from the argparse Python module by the
Python Software Foundation. Ports examples from the getopt package by Allen
Day.
Andrew Gelman [aut],
Yu-Sung Su [aut, cre],
Masanao Yajima [ctb],
Jennifer Hill [ctb],
Maria Grazia Pittau [ctb],
Jouni Kerman [ctb],
Tian Zheng [ctb],
Vicent Dorie [ctb]
Henrik Bengtsson [aut, cre, cph],
James Bullard [ctb],
Kasper Hansen [ctb],
Pierre Neuvial [ctb],
Elizabeth Purdom [ctb],
Mark Robinson [ctb],
Ken Simpson [ctb]
Henrik Bengtsson [aut, cre, cph],
Pierre Neuvial [aut]
Henrik Bengtsson [aut, cre, cph],
Mark Robinson [ctb],
Ken Simpson [ctb]
Paulino Perez Rodriguez <perpdgo@colpos.mx> original C++ code
from Arnost Komarek based on ars.f written by P. Wild and W. R.
Gilks
Michael Hahsler [aut, cre, cph],
Christian Buchta [aut, cph],
Bettina Gruen [aut, cph],
Kurt Hornik [aut, cph],
Christian Borgelt [ctb, cph]
Christian Buchta and Michael Hahsler, with contributions from
Daniel Diaz
Karthik Ram [aut],
Karl Broman [aut, cre]
S original by David W. Scott R port by Albrecht Gebhardt
<albrecht.gebhardt@uni-klu.ac.at> adopted to recent S-PLUS by
Stephen Kaluzny <spk@insightful.com>
Julian Taylor <julian.taylor@adelaide.edu.au>, David Butler
<david.butler@daff.qld.gov.au>.
Randy Bui, Ron N. Buliung, Tarmo K. Remmel
S original by Barry W. Brown, James Lovato and Kathy Russel.
R port by Kjetil B Halvorsen <kjetil1001@gmail.com>
Eugene Dubossarsky (project leader, chief designer), Yuriy Tyshetskiy (design, implementation, testing)
Sebastian Schmidtlein, with contributions from Carsten Oldenburg and Hannes Feilhauer
Joerg Polzehl [aut, cre],
Felix Anker [ctb]
Robin K. S. Hankin (R) and Luke J. West (C++)
Joao Vitor Dias Monteiro <monte092@umn.edu>, Sudipto Banerjee
<baner009@umn.edu>, Gurumurthy Ramachandran <ramac002@umn.edu>
Adam B. Olshen, Richard A. Olshen, Barry S. Taylor
Jeff Enos <jeff@kanecap.com> and David Kane <dave@kanecap.com>,
with contributions from Kyle Campbell
<kyle.w.campbell@williams.edu>, Daniel Gerlanc
<daniel@gerlanc.com>, Aaron Schwartz
<Aaron.J.Schwartz@williams.edu>, Daniel Suo
<danielsuo@gmail.com>, Alexei Colin <acolin@fas.harvard.edu>,
and Luyi Zhao <luyizhao@gmail.com>
Denis jf Talbot, Geneviève Lefebvre, Juli Atherton
Dr. Pablo Emilio Verde <pabloemilio.verde@uni-duesseldorf.de> &
Arnold Sykosch <arnold.sykosch@uni-duesseldorf.de>
Dan Rabosky, Mike Grundler, Pascal Title, Carlos Anderson, Jeff Shi, Joseph Brown, Huateng Huang
John W. Emerson and Walton A. Green and John A. Hartigan
Zhi Ouyang <zo2@stat.duke.edu>, Merlise Clyde
<clyde@stat.duke.edu>, Robert Wolpert <wolpert@stat.duke.edu>
Adam Kapelner and Justin Bleich (R package), see COPYRIGHTS file for the authors of the java libraries
Merlise Clyde <clyde@stat.duke.edu> with contributions from
Michael Littman amd Joyee Ghosh
Romain Francois, based on code by Bob Trower available at
http://base64.sourceforge.net/
Kristian Hovde Liland and Bjørn-Helge Mevik
Fernando Colchero, Owen Jones, Maren Rebke
Pedro Cardoso, Francois Rigal, Jose Carlos Carvalho
Bernd Bischl <bernd_bischl@gmx.net>,
Michel Lang <michellang@gmail.com>,
Olaf Mersmann <olafm@p-value.net>
Bernd Bischl <bernd_bischl@gmx.net>,
Michel Lang <michellang@gmail.com>
Murali Haran <mharan@stat.psu.edu> and John Hughes
<hughesj@umn.edu>
Nicholas G. Polson, James G. Scott, and Jesse Windle
Gopal, V. and Fuentes, C. and Casella, G.
Aldo M. Garay <aldowmg@ime.usp.br>,
Victor H. Lachos <hlachos@ime.unicamp.br>
Richard D. Morey, Jeffrey N. Rouder, Tahira Jamil
Luz Marina Rondon <lumarp@gmail.com> and Heleno Bolfarine
The code in R and guide document were prepared by Margarita Marin,
Javier Rojas, Daniel Jaimes and Hugo Andres Gutierrez Rojas, under the
direction of professor Edilberto Cepeda-Cuervo and with collaboration of
Martha Corrales, Maria Fernanda Zarate, Ricardo Duplat and Luis F
Villarraga.
Hana Sevcikova, Adrian Raftery; original WinBugs code written by Jennifer Chunn
Michele B. Nuijten, Ruud Wetzels, Dora Matzke, Conor V. Dolan, and Eric-Jan Wagenmakers
Bettina Gruen [aut, cre],
Martyn Plummer [ctb]
Ander Wilson, Howard D. Bondell, and Brian J. Reich
Dries F. Benoit, Rahim Al-Hamzawi, Keming Yu, Dirk Van den Poel
Christian P. Robert, Universite Paris Dauphine, and Jean-Michel
Marin, Universite Montpellier 2
Chengchun Shi, with contributions from Peng Zhang
Hana Sevcikova (hanas@uw.edu), Leontine Alkema (alkema@nus.edu.sg), Adrian Raftery (raftery@uw.edu) with code contribution from Bailey Fosdick (bfosdick@uw.edu) and Patrick Gerland (gerland@un.org)
Fabio Mathias Correa <fmcorrea@uesc.br> and Julio Silvio de
Sousa Bueno Filho <juliobuenof@gmail.com>
Thomas Kneib, Felix Heinzl, Andreas Brezger, Daniel Sabanes Bove, Nadja Klein
Daniel Adler [aut, cre],
Thomas Kneib [aut],
Stefan Lang [aut],
Nikolaus Umlauf [aut],
Achim Zeileis [aut]
Josef C. Uyeda, Jon Eastman and Luke Harmon
Cathy W. S. Chen, Edward M.H. Lin, F.C. Liu, and Richard
Gerlach
Ravi Varadhan [aut, cph, trl],
Paul Gilbert [aut, cre],
Marcos Raydan [ctb] (with co-authors, wrote original algorithms in
fortran. These provided some guidance for implementing R code in
the BB package.),
JM Martinez [ctb] (with co-authors, wrote original algorithms in
fortran. These provided some guidance for implementing R code in
the BB package.),
EG Birgin [ctb] (with co-authors, wrote original algorithms in fortran.
These provided some guidance for implementing R code in the BB
package.),
W LaCruz [ctb] (with co-authors, wrote original algorithms in fortran.
These provided some guidance for implementing R code in the BB
package.)
Anton Gagin and Igor Levin with contributions from Charles R. Hogg III
Bernd Bischl <bernd_bischl@gmx.net>, Michel Lang
<lang@statistik.tu-dortmund.de>, Olaf Mersmann
<olafm@p-value.net>, Jakob Bossek <j.bossek@gmail.com>,
Jakob Richter <code@jakob-r.de>
Ben Bolker <bolker@mcmaster.ca>, R Development Core Team
Nielson, R. M., H. Sawyer, and T. L. McDonald (WEST, Inc.,
www.west-inc.com)
Sarvesh Nikumbh (BBO originally invented by Prof. Dan Simon,
Cleveland State University, Ohio)
Luca Tardella and Danilo Alunni Fegatelli
Ricardo de Matos Simoes and Frank Emmert-Streib
Aregay Mengsteab, Martin Otava, Tatsiana Khamiakova, Ewoud De Troyer
Karel Van den Meersche <karel.van_den_meersche@cirad.fr>, Karline Soetaert <karline.soetaert@nioz.nl>
Andrew Parnell, Thinh Doan and James Sweeney
Vahid PARTOVI NIA and Anthony C. DAVISON
Vijay Barve <vijay.barve@gmail.com>, Javier Otegui
<javier.otegui@gmail.com>
Shyam Ranganathan, Viktoria Spaiser, Richard P. Mann, David J.T. Sumpter
Anyiawung Chiara Forcheh, Geert Verbeke, Adetayo Kasim, Dan
Lin, Ziv Shkedy, Willem Talloen, Hinrich WH Gohlmann, Lieven
Clement.
Thoralf Mildenberger, Henrike Weinert, Sebastian Tiemeyer
Dieter William Joenssen [aut, cre, cph],
Thomas Muellerleile [ctb]
Grace Chiu <bentcable@gmail.com>, CSIRO Mathematics,
Informatics and Statistics, GPO Box 664, Canberra, ACT 2601,
Australia
Willem van Loon [aut, cph],
Dennis Walvoort [aut, cre]
Andres Baselga, David Orme, Sebastien Villeger, Julien De Bortoli and Fabien Leprieur
Achim Zeileis [aut, cre],
Francisco Cribari-Neto [aut],
Bettina Gruen [aut],
Ioannis Kosmidis [aut],
Alexandre B. Simas [ctb] (earlier version by),
Andrea V. Rocha [ctb] (earlier version by)
Jan Verbesselt [aut, cre], Achim Zeileis [aut], Rob Hyndman [ctb]
Giampiero Marra, Raffaella Calabrese and Silvia Angela Osmetti
Gustavo de los Campos, Paulino Perez Rodriguez,
Garcia-Bueno, J. A. & Nieto-Barajas, L. E.
Kwang Woo Ahn <kwooahn@mcw.edu>, Kung-Sik Chan
<kung-sik-chan@uiowa.edu>
Dirk Eddelbuettel, John W. Emerson and Michael J. Kane
Georg Luebeck <gluebeck@fhcrc.org> and Rafael Meza
<rmeza@umich.edu>
Nilotpal Sanyal [aut, cre],
Marco A.R. Ferreira [aut]
Romain Francois [aut],
Kurt Hornik [ctb, cre]
Sebastian Kaiser, Rodrigo Santamaria, Tatsiana, Khamiakova,
Martin Sill, Roberto Theron, Luis Quintales and Friedrich
Leisch.
Michael J. Kane, Bryan Lewis, and John W. Emerson
John W. Emerson <jayemerson@gmail.com> and Michael J. Kane
<kaneplusplus@gmail.com>
Christopher Paciorek [aut, cre],
Benjamin Lipshitz [aut],
Prabhat [ctb],
Cari Kaufman [ctb],
Tina Zhuo [ctb],
Rollin Thomas [ctb]
Mark Seligman, Chris Fraley, Tim Hesterberg
Michael J. Kane <kaneplusplus@gmail.com>, John W. Emerson <jayemerson@gmail.com>, and Peter Haverty <haverty.peter@gene.com>
Aloysius Lim [aut, cre],
Leo Breiman [aut],
Adele Cutler [aut]
Xia Shen, Moudud Alam and Lars Ronnegard
Michael J. Kane <kaneplusplus@gmail.com> and John W. Emerson <jayemerson@gmail.com>
M. Helena Gonçalves, M. Salomé Cabral and Adelchi Azzalini,
apart from a set of Fortran-77 subroutines written by R.
Piessens and E. de Doncker, belonging to the suite "Quadpack"
Friedrich Leisch and Andreas Weingessel and Kurt Hornik
Samuel V. Scarpino, Paul von Hippel, and Igor Holas
Terrence Brooks, Berkley Shands, Skye Buckner-Petty, Patricio S. La Rosa, Elena Deych, William D. Shannon
Boan Zhang, Christopher Bilder, Brad Biggerstaff, Frank
Schaarschmidt
Michael Hoehle with contributions by Wei Liu
Rune Haubo B Christensen and Merete K Hansen
Alessio Boattini and Federico C. F. Calboli; Vincente Canto
Cassola together with Martin Maechler authored the function
mtx.exp.
Paul J. McMurdie <mcmurdie@stanford.edu> and the
biom-format team http://biom-format.org/
Wilfried Thuiller, Damien Georges and Robin Engler
Matteo Re, Giorgio Valentini -- Universita' degli Studi di Milano
Franziska Hoffgaard <hoffgaard@bio.tu-darmstadt.de>, with
contributions from Philipp Weil <weil@bio.tu-darmstadt.de> and
Kay Hamacher <hamacher@bio.tu-darmstadt.de>
Julien Pele with Jean-Michel Becu, Herve Abdi, and Marie
Chabbert
Frederic Bertrand <frederic.bertrand@math.unistra.fr>, Myriam Maumy-Bertrand <myriam.maumy-bertrand@math.unistra.fr>.
Carsten F. Dormann, Jochen Fruend and Bernd Gruber, with additional code from Mariano Devoto, Jose Iriondo, Rouven Strauss and Diego Vazquez, also based on C-code developed by Nils Bluethgen, Aaron Clauset/Rouven Strauss and Miguel Rodriguez-Girones.
Ana Belen Nieto Librero<ananieto@usal.es>, Purificacion Galindo Villardon<pgalindo@usal.es>
Anthony la Grange, with some Fortran code adapted by NJ le Roux
from the original by PJ Rousseeuw, I Ruts and JW Tukey
S original by Steve Dutky <sdutky@terpalum.umd.edu> initial R
port and extensions by Martin Maechler; revised and modified
by Steve Dutky
Michael Genkin [aut], George Berry [aut], Liyuan Chen [aut], Matthew Brashears [aut]
Xiaolin Yang [aut, cre],
Stephen E. Fienberg [aut],
Alessandro Rinaldo [aut],
Han Liu [aut],
Michael Rosenblum [aut]
Parmeet Singh Bhatia <bhatia.parmeet@gmail.com>, Serge Iovleff, Gerard
Goavert, Vincent Brault, with contributions from Christophe Biernacki and
Gilles Celeux.
Adrian Raftery <raftery@stat.washington.edu>, Jennifer Hoeting,
Chris Volinsky, Ian Painter, Ka Yee Yeung
I. Prunster and L. E. Nieto. R implementation by Ernesto Barrios
Christoph Müssel, Martin Hopfensitz, Dao Zhou, Hans Kestler
Steven L. Scott is the sole author and creator of the BOOM
project.  Some code in the BOOM libraries has been modified from
other open source projects.  These include Cephes (obtained from
Netlib, written by Stephen L. Moshier), NEWUOA (M.J.D Powell,
obtained from Powell's web site), and a modified version of the R
math libraries (R core development team).  Original copyright
notices have been maintained in all source files.  In these cases,
copyright claimed by Steven L. Scott is limited to modifications
made to the original code.
c(person("Milan Hiersche", "Developer", email =
"mihi@uni-muenster.de")
Angelo Canty [aut],
Brian Ripley [aut, trl, cre] (author of parallel support)
Daniel Gerlanc <dgerlanc@enplusadvisors.com> and Kris Kirby
<kkirby@williams.edu>
Benjamin Baumgartner <benjamin@baumgrt.com>, Riccardo Gatto <gatto@stat.unibe.ch>
S original, from StatLib, by Rob Tibshirani.  R port by
Friedrich Leisch.
Miron Bartosz Kursa [aut, cre],
Witold Remigiusz Rudnicki [aut]
José Cláudio Faria, Clarice Garcia Borges Demétrio and Ivan Bezerra Allaman
Kjell Konis <kjell.konis@me.com>,
Victor M. Panaretos <victor.panaretos@epfl.ch>.
A. Jonathan R. Godfrey [aut, cre], Greg Snow [ctb], Paul Murrell [ctb], Yihui Xie [ctb]
Priyadarshana W.J.R.M. and Georgy Sofronov
Karl W Broman <kbroman@biostat.wisc.edu>, with contributions from Aimee
Teo Broman
OpenBUGS was developed by Andrew Thomas, Dave Lunn, David Spiegelhalter and Nicky Best.  R interface developed by Uwe Ligges, Sibylle Sturtz, Andrew Gelman, Gregor Gorjanc and Chris Jackson.  Linux port and most recent developments by Chris Jackson.
Ernesto Barrios based on Daniel Meyer's code.
Jari Miettinen, Klaus Nordhausen, Hannu Oja, Sara Taskinen
Carl J Schwarz <cschwarz@stat.sfu.ca> and
Simon J Bonner <simon.bonner@uky.edu>
Lukasz Dziurzynski [aut],
Edward Wadsworth [aut],
Peter Fader [ctb],
Elea McDonnell Feit [ctb],
Daniel McCarthy [aut, cre, ctb],
Bruce Hardie [ctb],
Arun Gopalakrishnan [ctb],
Eric Schwartz [ctb],
Yao Zhang [ctb]
Karline Soetaert <karline.soetaert@nioz.nl>,
Jeff Cash <j.cash@imperial.ac.uk>,
Francesca Mazzia <mazzia@dm.uniba.it>
Martin Sill, Thomas Hielscher, Manuela Zucknick, Natalia Becker.
Max Kuhn, Steve Weston, Nathan Coulter. C code for C5.0 by R. Quinlan
Michael Greenacre [aut],
Oleg Nenadic [aut, cre],
Michael Friendly [ctb]
Benjamin Saefken and David Ruegamer, with contributions from Sonja Greven and Thomas Kneib
Simon Urbanek <Simon.Urbanek@r-project.org>, Jeffrey Horner <jeff.horner@vanderbilt.edu>
Anoop Shah [aut, cre],
Jonathan Bartlett [ctb],
Harry Hemingway [ths],
Owen Nicholas [ths],
Aroon Hingorani [ths]
Maria Ortiz [aut, ctb],
Ander Aramburu [ctb],
Henrik Bengtsson [aut, cre, cph],
Pierre Neuvial [aut, ctb],
Angel Rubio [aut, ctb]
Peter Schlattmann <peter.schlattmann@mti.uni-jena.de>, Johannes
Hoehne
Anna L. Tyler, Wei Lu, Justin J. Hendrick, Vivek M. Philip, and Greg W. Carter
David Orme, Rob Freckleton, Gavin Thomas, Thomas Petzoldt, Susanne Fritz, Nick Isaac, Will Pearse
Oswaldo Santos [aut, cre],
Marcos Amaku [ctb],
Fernando Ferreira [ctb]
T. Tony Cai, Hongzhe Li, Weidong Liu and Jichun Xie
Vincent Brault, Jean-Patrick Baudry, Cathy Maugis and Bertrand
Michel
John Fox [aut, cre],
Sanford Weisberg [aut],
Daniel Adler [ctb],
Douglas Bates [ctb],
Gabriel Baud-Bovy [ctb],
Steve Ellison [ctb],
David Firth [ctb],
Michael Friendly [ctb],
Gregor Gorjanc [ctb],
Spencer Graves [ctb],
Richard Heiberger [ctb],
Rafael Laboissiere [ctb],
Georges Monette [ctb],
Duncan Murdoch [ctb],
Henric Nilsson [ctb],
Derek Ogle [ctb],
Brian Ripley [ctb],
William Venables [ctb],
Achim Zeileis [ctb],
R-Core [ctb]
Fraenzi Korner-Nievergelt, Ivo Niermann, Oliver Behr, Matthew A. Etterson, Robert Brinkmann, Pius Korner, Barbara Hellriegel, Tobias Roth
Susanne Rolinski [aut], René Sachse [aut], Thomas Petzoldt [aut, cre]
Max Kuhn. Contributions from Jed Wing, Steve Weston, Andre
Williams, Chris Keefer, Allan Engelhardt, Tony Cooper, Zachary Mayer
and the R Core Team
Helene Crepeau <Helene.Crepeau@mat.ulaval.ca> and Louis-Paul
Rivest <Louis-Paul.Rivest@mat.ulaval.ca> and Serge Couturier
<tuttu@videotron.ca> and Sophie Baillargeon
<Sophie.Baillargeon@mat.ulaval.ca>
Kate Cowles and Stephen Bonett; with thanks to Juan Cervantes,
Dong Liang, Alex Sawyer, and Michael Seedorff
Kate Cowles and Michael Seedorff and Alex Sawyer
Ported to R by Ted Harding and Fernando Tusell. Original by
Joseph L. Schafer <jls@stat.psu.edu>.
Adrien Taudiere, Cyrille Violle with contribution of Francois Munoz
David Magis (U Liege, Belgium), Gilles Raiche (UQAM, Canada), Juan Ramon Barrada (U Zaragoza, Spain)
Adam Glynn <aglynn@iq.harvard.edu>, Kevin Quinn
<kquinn@law.berkeley.edu>
Justin Hemann, David Barker, Suzanne Weller, Jason McFall
Christian Fong <christianfong@stanford.edu>, Marc Ratkovic <ratkovic@princeton.edu>, Kosuke Imai <kimai@princeton.edu>
Andreas Alfons [aut, cre],
David Simcha [ctb]
Josep Lluis Carrasco <jlcarrasco@ub.edu>, Josep Puig Martinez
<j.puig04@gmail.com>, Lluis Jover <lluis_jover@ub.edu >
Evgenia Dimitriadou [aut],
Kurt Hornik [ctb, cre]
Ravi Goyal, with contributions from Mark S. Handcock (ergm R package), David R. Hunter (ergm R package), Carter T. Butts (ergm R package), Steven M. Goodreau (ergm R package), Pavel N. Krivitsky (ergm R package), Martina Morris (ergm R package), Nicole Bohme Carnegie (ergm-user term degmix)
Baptiste Auguie [aut, cre],
LAPACK authors [cph] (copyrights apply to src/zgels.f)
Mathieu Vrac <mathieu.vrac@lsce.ipsl.fr> and Paul-Antoine
Michelangeli <pam@climpact.com>
Edward Grant, Kenneth Lange, Tong Tong Wu
Alexander Robitzsch [aut, cre], Thomas Kiefer [aut],
Ann Cathrice George [aut], Ali Uenlue [aut]
Ulf Schepsmeier, Eike Christian Brechmann
Yasser González Fernández <ygonzalezfernandez@gmail.com> and
Marta Rosa Soto Ortiz <mrosa@icimaf.cu>
Mauricio Zambrano-Bigiarini [aut, cre], Yasser Gonzalez
Fernandez [aut]
Katharine M. Mullen, Michael Halter, John Lu and Nathan Dodder
Stefano M. Iacus <stefano.iacus@unimi.it>,
Gary King <king@harvard.edu>,
Giuseppe Porro <Porro Giuseppe <giuseppe.porro@uninsubria.it>
Monique Bettio Massuia <moniquemassuia@gmail.com>, Larissa
Avila Matos <larissaam@ime.unicamp.br> and Victor Lachos
<hlachos@ime.unicamp.br>
A.I. McLeod, Nagham M. Mohammad, Justin Veenstra and Abdel El-Shaarawi
Christopher G. Green [aut, cre],
R. Doug Martin [ths]
Stefan Funke <s.funke@t-online.de> with contributions from
Patrick Mair <patrick.mair@wu-wien.ac.at>, Alexander von Eye
<voneye@msu.edu> (fCFA, kvCFA) and Joachim Harloff
<joachimharloff@joachimharloff.de>
Bill Pikounis [aut, cre, cph], John Oleynick [aut], Eva Ye [ctb]
Yuan-chin I. Chang, Yu-chia Chang, and Ling-wan Chen
Nicholas Austin Johnson <nickaj@stanford.edu>, Robert
Tibshirani <tibs@stat.stanford.edu>, Pei Wang
<pwang@fhcrc.org>.
Franck Picard, Mark Hoebeke, Emilie Lebarbier, Vincent Miele,
Guillem Rigaill, Stephane Robin
Markus Gesmann [aut, cre],
Daniel Murphy [aut],
Wayne Zhang [aut]
Matthias Wangler <mw@imbi.uni-freiburg.de>, Jan Beyersmann
<jan@fdm.uni-freiburg.de>
Rebecca Killick [aut, cre],
Idris Eckley [aut],
Kaylea Haynes [aut],
Paul Fearnhead [ctb]
Simen Gaure, Ragnar Frisch Centre for Economic Research, Oslo, Norway
Michel Lang <michellang@gmail.com>,
Bernd Bischl <bernd_bischl@gmx.net>
Lawrence Hudson with contributions from Dan Reuman and Rob Emerson
Alexandre Perera-Lluna and Andrey Ziyatdinov
Bryan A. Hanson DePauw University, Greencastle Indiana USA
Stefan Roediger [cre, aut],
Michal Burdukiewicz [aut]
Ryan Sermas, assisted by John V. Colias, Ph.D.
<DecisionAnalystR@decisionanalyst.com>
Ari Lamstein <arilamstein@gmail.com>[cre, aut],
Brian P Johnson <brian@pjohnson.info> [ctb, frontend animation code]
David James [aut] (S original),
Kurt Hornik [aut, trl, cre],
Gabor Grothendieck [ctb]
Beau Dabbs, Brian Junker, Mauricio Sadinle, Tracy Sweet, A.C. Thomas
Xi (Rossi) LUO with contributions from Dylan Small, Chiang-shan Li, and Paul Rosenbaum
Aurelie Siberchicot, Adrien Merville, Marie-Claude Bel-Venner and Samuel Venner
Juan Jose Fernandez-Duran and Maria Mercedes Gregorio-Dominguez
S-plus original by Ulric Lund <ulund@calpoly.edu>, R port by
Claudio Agostinelli <claudio@unive.it>
Ulric Lund <ulund@calpoly.edu>, Claudio Agostinelli <claudio@unive.it>
Laetitia Marisa, Aurelien de Reynies, Mickael Guedj
Linda DIB, Raphael CHAMPEIMONT, Alessandra CARBONE
Brian Ripley [aut, cre, cph],
William Venables [cph]
Martin Maechler partly based on code from Robert Gentleman
Eleni Chatzimichali <ea.chatzimichali@gmail.com> and Conrad Bessant <c.bessant@qmul.ac.uk>
David Bronaugh <bronaugh@uvic.ca> for the Pacific Climate Impacts
Consortium
T. Tony Cai, Weidong Liu and Xi (Rossi) Luo
Christian Panse <cp@fgcz.ethz.ch>, Ermir Qeli <ermir.qeli@gmail.com>
Kurt Hornik [aut, cre],
Walter Böhm [ctb]
Fang Chang <changf@mathstat.yorku.ca>, Vincent Carey
<stvjc@channing.harvard.edu>, Weiliang Qiu
<stwxq@channing.harvard.edu>, Ruben H. Zamar
<ruben@stat.ubc.ca>, Ross Lazarus
<ross.lazarus@channing.harvard.edu>, Xiaogang Wang
<stevenw@mathstat.yorku.ca>
Martin Maechler [aut, cre],
Peter Rousseeuw [aut] (Fortran original),
Anja Struyf [aut] (S original),
Mia Hubert [aut] (S original),
Kurt Hornik [trl, ctb] (port to R; maintenance(1999-2000)),
Matthias Studer [ctb],
Pierre Roudier [ctb]
Jose A. Castellanos-Garzon <jantonio_cu@ieee.org>, Fernando
Diaz <fdiaz@infor.uva.es>
Weiliang Qiu <stwxq@channing.harvard.edu>, Harry Joe
<harry@stat.ubc.ca>.
Nicholas G. Reich <nick@schoolph.umass.edu>, Daniel Obeng <dobeng@gmail.com>
Amy Kapp <Amy_Kapp@hotmail.com> and Rob Tibshirani
<robert.tibshirani@stanford.edu>
Marek Walesiak <marek.walesiak@ue.wroc.pl> Andrzej Dudek <andrzej.dudek@ue.wroc.pl>
Damien Leroux [aut, cre], Brigitte Mangin [aut], Sylvain Jasson
[aut], Abdelaziz Rahmani [aut]
Marie Chavent and Vanessa Kuentz and Benoit Liquet and Jerome Saracco
Angelos Markos [aut, cre],
Alfonso Iodice D' Enza [aut],
Michel Van de Velden [aut]
Douglas Whitaker <whitaker@ufl.edu> and Mary Christman
<mcxman@ufl.edu>
Nema Dean, Adrian E. Raftery, and Luca Scrucca
Guy Brock <guy.brock@louisville.edu>, Vasyl Pihur <vasyl.pihur@louisville.edu>,
Susmita Datta <susmita.datta@louisville.edu>, and
Somnath Datta <somnath.datta@louisville.edu>
Heike Trautmann <trautmann@statistik.tu-dortmund.de> and Olaf
Mersmann <olafm@statistik.tu-dortmund.de> and David Arnu
<david.arnu@statistik.tu-dortmund.de>
Kimberly Sellers <kfs7@georgetown.edu> ; Luis Costa <lc436@georgetown.edu>
Ana Belen Nieto Librero <ananieto@usal.es>, Priscila Willems
<pwillems@bariloche.inta.gov.ar>, Purificacion Galindo
Villardon <pgalindo@usal.es>
Nicholas G. Reich <nick@schoolph.umass.edu>, Justin Lessler
<jlessler@jhsph.edu>, Andrew Azman <andrewazman@gmail.com>
Pin T. Ng <Pin.Ng@nau.edu> and Martin Maechler
Pin T. Ng <Pin.Ng@nau.edu> and Xuming He, U. Illinois; R port
by Martin Maechler
Francesca Marta Lilja Di Lascio, Simone Giannerini
Original Matlab routines by C.J.F. ter Braak and A.P. Schaffers. R port by Gavin L. Simpson.
Function simpls based on simpls.fit (package pls) by Ron Wehrens and Bjorn-Helge Mevik.
Martyn Plummer [aut, cre, trl], Nicky Best [aut], Kate Cowles
[aut], Karen Vines [aut], Deepayan Sarkar [aut], Russell Almond
[ctb]
Yann Richet, Xavier Bay, Olivier Jacquet, Alexis Jinaphanh
Guillaume Guenard and Pierre Legendre, with contributions from Bertrand Pages
Gavin L. Simpson [aut, cre],
Francisco Rodriguez-Sanchez [ctb]
Torsten Hothorn [aut, cre],
Kurt Hornik [aut],
Mark A. van de Wiel [aut],
Achim Zeileis [aut]
M. Helena Gonçalves and M. Salomé Cabral,
apart from a set of Fortran-77 subroutines written by R. Piessens
and E. de Doncker, belonging to the suite "Quadpack"
Giles Hooker <gjh27@cornell.edu>,
Luo Xiao <lx42@cornell.edu>,
James Ramsay <ramsay@psych.mcgill.ca>
Chris Wallace [aut, cre],
Claudia Giambartolomei [aut],
Vincent Plagnol [ctb]
Ross Ihaka [aut],
Paul Murrell [aut],
Kurt Hornik [aut],
Jason C. Fisher [aut],
Achim Zeileis [aut, cre]
Mohamed LAIB, Imane REZGUI, Zoubida Gheribi-Aoulmi and Herve Monod
Kornelius Rohmeyer (R package), see COPYRIGHTS file for the authors of the java libraries
J. Malia Andrus, Timothy Kuehlhorn, Luis F. Rodriguez, Angela D. Kent, and Julie L. Zilles
Ana C. Braga, with contributions from Hugo Frade
Isaac Subirana, Joan Vila, Héctor Sanz, Gavin Lucas, Judith Peñafiel
and David Giménez
Umesh Nandal <u.k.nandal@amc.uva.nl>, Perry D. Moerland
<p.d.moerland@amc.uva.nl>
David Moriña, Centre Tecnològic de Nutrició i Salut and Albert Navarro, Universitat Autònoma de Barcelona
Kimberly Sellers <kfs7@georgetown.edu>, Thomas Lotze
<thomas.lotze@thomaslotze.com>
K. Gerald van den Boogaart <boogaart@hzdr.de>,
Raimon Tolosana, Matevz Bren
Bozidar V. Popovic, Saralees Nadarajah, Miroslav M. Ristic
Simone Padoan <simone.padoan@unibocconi.it>, Moreno Bevilacqua <moreno.bevilacqua@uv.cl>
Georg Heinze, Meinhard Ploner, Daniela Dunkler
S original by Alessandra R. Brazzale <alessandra.brazzale@unipd.it>.
R port by Alessandra R. Brazzale <alessandra.brazzale@unipd.it>, following
earlier work by Douglas Bates.
Sang-Yun Oh <syoh@lbl.gov>, Joong-Ho Won <wonj@stats.snu.ac.kr>
Willem M. G. M. van Loon [aut, cph],
Dennis J. J. Walvoort [aut, cre]
joerg-henrik heine, R.W. Alexandrowicz and some package testing by Mark
Stemmler
Jose' Gama [aut, cre],
Nikolai Chernov [aut, cph],
Göran Högnäs [ths]
Bernard Desgraupes (University of Paris Ouest - Lab Modal'X)
Andrzej Bak <andrzej.bak@ue.wroc.pl>, Tomasz Bartlomowicz
<tomasz.bartlomowicz@ue.wroc.pl>
Max Kuhn, contributions from Steve Weston, Jed Wing,
James Forester and Thorn Thaler
Bill J. Gray (www.projectpluto.com/calendar) and Thomas Lumley
P. Lafaye de Micheaux <lafaye@dms.umontreal.ca>, B. Liquet
<Benoit.Liquet@isped.u-bordeaux2.fr>.
Hanna Jankowski, Ivy Wang, Hugh McCague, Jon A. Wellner
Daniel M. Griffith, Joseph A. Veech, and Charles J. Marsh
Tyler Shimko <TShimko126@gmail.com>, Erik Andersen
<erik.andersen@northwestern.edu>
Emily Goren <emily.goren@gmail.com> and John Hughes <hughesj@umn.edu>
Marius Hofert <marius.hofert@uwaterloo.ca>,
Ivan Kojadinovic <ivan.kojadinovic@univ-pau.fr>,
Martin Maechler <maechler@stat.math.ethz.ch>, and
Jun Yan <jun.yan@uconn.edu>
Yasser Gonzalez-Fernandez [aut, cre],
Marta Soto [aut]
Marko Robnik-Sikonja <marko.robnik@fri.uni-lj.si>,
Petr Savicky <savicky@cs.cas.cz>
Jeremy M. Beaulieu <jbeaulieu@nimbios.org>, Jeffrey C. Oliver <jeffreycoliver@gmail.com>, Brian O'Meara <bomeara@utk.edu>
Juliane Sch\"afer, Rainer Opgen-Rhein, Verena Zuber, Miika Ahdesm\"aki,
A. Pedro Duarte Silva, and Korbinian Strimmer.
Clement THERY [aut, cre],
Christophe BIERNACKI [ctb],
Gaetan LORIDANT [ctb],
Florian WATRIN [ctb],
Quentin GRIMONPREZ [ctb],
Vincent KUBICKI [ctb],
Samuel BLANCK [ctb],
Jeremie KELLNER [ctb]
Pascal van Kooten <kootenpv@gmail.com>,
Gerko Vink <g.vink@uu.nl>
Rafael S. de Souza, Alberto Krone-Martins, Jonathan Elliott, Joseph
Hilbe
Hao Helen Zhang <hzhang@math.arizona.edu> and Chen-Yen Lin
<clin5@ncsu.edu>
Aurelie Bertrand <aurelie.bertrand@uclouvain.be> and Christian
M.  Hafner <christian.hafner@uclouvain.be>
Naisyin Wang <nwangaa@umich.edu> and Adrian Raftery
<raftery@uw.edu> with contributions from Chris Fraley
<fraley@uw.edu>
Richard Lockhart, Jon Taylor, Ryan Tibshirani, Rob Tibshirani
R by Meinhard Ploner, Fortran by Georg Heinze
R by Meinhard Ploner, Georg Heinze, Daniela Dunkler, Fortran by Georg Heinze
Tadeusz Bednarski <t.bednarski@prawo.uni.wroc.pl>, Filip
Borowicz <f.borowicz@prawo.uni.wroc.pl>
Alessandro Bellino and Daniela Baldantoni
Andrey Ziyatdinov [aut, cre],
Samir Kanaan-Izquierdo [aut],
Nickolay T. Trendafilov [aut],
Alexandre Perera-Lluna [aut]
Qianxing Mo, Mithat Gonen and Glenn Heller
Rihong Hui and Hanna Jankowski <hkj@mathstat.yorku.ca>
Oscar A. Linares [aut, cre],
David T. Daly [aut]
Jeremie Riou, Amadou Diakite, and Benoit Liquet
Emil A. Cornea, Bahjat F. Qaqish, and Joseph G. Ibrahim
Jakob Messner [aut, cre],
Achim Zeileis [aut]
Ruth Heller <ruheller@post.tau.ac.il>, Dylan Small
<dsmall@wharton.upenn.edu>, Paul Rosenbaum
<rosenbap@wharton.upenn.edu>
Kevin Jakob, Dr. Matthias Fischer & Stefan Kolb
Jeffrey S. Racine [aut, cre],
Zhenghua Nie [aut],
Brian D. Ripley [ctb] (stepCV.R)
Annick Joelle Nembot-Simo, Jinko Graham and Brad McNeney
S original by Alessandra R. Brazzale <alessandra.brazzale@unipd.it>.
R port by Alessandra R. Brazzale <alessandra.brazzale@unipd.it>.
Meng Li, Ana-Maria Staicu, and Howard D. Bondell
Nils B. Weidmann <nils.weidmann@uni-konstanz.de>, Kristian
Skrede Gleditsch <ksg@essex.ac.uk>
Alejandro Jara [aut, cre], Maria Jose Garcia-Zattera [ctb]
Shai Shen-Orr, Rob Tibshirani, Narasimhan Balasubramanian,
David Wang
Fortran original by G. Tunnicliffe-Wilson and Zhu Wang, R port by Zhu Wang with contribution from John Nash, Netlib and NAG authors
Achim Zeileis [aut, cre],
Kurt Hornik [aut]
C code by Steven G. Johnson, R by Balasubramanian Narasimhan
Wei-Chen Chen [aut, cre],
Russell Zaretzki [aut],
William Howell [aut],
Drew Schmidt [aut],
Michael Gilchrist [aut],
Students REU13 [ctb]
Max Kuhn, Steve Weston, Chris Keefer, Nathan Coulter. C code for Cubist by Ross Quinlan
Adelino Ferreira da Silva <afs@fct.unl.pt>
Xuan Liu <liuxuan@bu.edu> and Qiong Yang <qyang@bu.edu>
Eva Janousova, Tomas Pavlik, Jiri Mayer, Ladislav Dusek
George Luta <gl77@georgetown.edu>, Ulrike Naumann <u-naumann@web.de>
and Matt Wand <matt.wand@uts.edu.au>
Lingbing Feng, Gen Nowak, Alan. H. Welsh, Terry. J. O'Neill
Erin LeDell, Maya Petersen, Mark van der Laan
José Gama [aut, cre, trl],
Maria Nyberg [aut],
Göran Högnäs [ths],
Brian Foutch [ctb],
Mark Grundland [ctb],
Neil Dodgson [ctb]
Donghoh Kim <donghoh.kim@gmail.com>, Hee-Seok Oh
<heeseok@stats.snu.ac.kr>
Sergej Potapov, Werner Adler, Benjamin Hofner and Berthold Lausen.
Ali Uenlue [aut, cre], Anatol Sargin [aut]
Brecht Devleesschauwer [aut, cre], Scott McDonald [aut],
Juanita Haagsma [aut], Nicolas Praet [aut], Arie Havelaar [aut],
Niko Speybroeck [aut]
Dave Armstrong (UW-Milwaukee, Department of Political Science)
Artur Manukyan, Ahmet Sedef, Erhan Cene, Ibrahim Demir (Advisor)
Yurii Aulchenko, Stepan Yakovenko, Erik Roos, Marcel Kempenaar, Maksim
Struchalin
Madhav Kumar <madhavkumar2005@gmail.com> and Shreyes Upadhyay
<shreyes.upadhyay@gmail.com>
M Dowle, T Short, S Lianoglou, A Srinivasan with contributions from R Saporta, E Antonyan
Terry Therneau [aut],
Thomas Lumley [trl] (R port),
Kjetil Halvorsen [trl] (R port),
Kurt Hornik [trl, ctb, cre] (R port)
David Conde, Miguel A. Fernandez, Bonifacio Salvador
Hugh Chipman <hugh.chipman@gmail.com>, Robert McCulloch <robert.mcculloch1@gmail.com>, Vincent Dorie <vjd4@nyu.edu>
Dason Kurkiewicz, Heike Hofmann, Ulrike Genschel
Lori A. Shepherd <las65@buffalo.edu>Albert Vexler <avexler@buffalo.edu>Jeffrey C. Miecznikowski <jcm38@buffalo.edu>
Lori A. Shepherd <las65@buffalo.edu>, Wan-Min Tsai
<wan-min.tsai@yale.edu>, Albert Vexler <avexler@buffalo.edu>,
Jeffrey C. Miecznikowski <jcm38@buffalo.edu>
R Special Interest Group on Databases (R-SIG-DB)
Angelo Mazza [aut, cre],
Antonio Punzo [aut]
Eric Marcon, Gabriel Lang, Stephane Traissac, Florence Puech
Boj, Eva <evaboj@ub.edu>, Caballe, Adria <adria.caballe@upc.edu>, Delicado, Pedro <pedro.delicado@upc.edu> and Fortiana, Josep <fortiana@ub.edu>.
Brandon Whitcher and Volker Schmid, with contributions from
Andrew Thornton
Jing Yang, Hui Yu, Bao-Hong Liu, Zhongming Zhao, Lei Liu, Liang-Xiao Ma, Yi-Xue Li, Yuan-Yuan Li
Maria Dolores Martinez-Miranda, Jens Perch Nielsen and Richard Verrall
Virgilio Gómez-Rubio, Juan Ferrándiz-Ferragud and Antonio
López-Quílez, with contributions by Roger Bivand.
c( person("Diane", family="Losardo", email='dlosardo@amplify.com",
role = c("aut", "cre", "cph")), person("Margi", family="Dubal",
email='margidubal@gmail.com", role = c("aut", "cre", "cph")))
Zongshan Li with contributions from Jinlong Zhang
Oleksii Pokotylo [aut, cre],
Pavlo Mozharovskyi [aut]
Efthimios Motakis [aut],
Guy Nason [aut, cre],
Piotr Fryzlewicz [aut]
Przemyslaw Biecek (R code), Teresa Ledwina (support,
descriptions)
Susanne Gammelgaard Bottcher, Claus Dethlefsen.
Julien Stirnemann, Adeline Samson, Fabienne Comte. Contribution
from Claire Lacour.
Ian Fellows with contributions from others (see documentation).
Alberto Mirisola, Ian Fellows and Helios De Rosario-Martinez
Ian Fellows and Alex Rickett with contributions from Neal
Fultz.
Alex Rickett and Ian Fellows, with contributions from Neal Fultz
Mark van der Loo, Edwin de Jonge, and Sander Scholtus
David Magis (U Liege), Bruno Facon (Univ Lille-Nord de France)
Philipp Kraemer [aut, cre],
Gabriele Gerlach [aut]
Alexander Jueterbock, Philipp Kraemer, Gabriele Gerlach and Jana Deppermann
Sten Ilmjarv [aut, cre],
Hendrik Luuk [aut]
Rob J Hyndman with contributions from Heather Booth, Leonie Tickle and John Maindonald.
Tal Galili [aut, cre, cph] (http://www.r-statistics.com),
Gavin Simpson [ctb],
Gregory Jefferis [ctb] (imported code from his dendroextras package),
Marco Gallotta [ctb] (a.k.a: marcog),
Johan Renaudie [ctb] (https://github.com/plannapus),
The R Core Team [ctb] (Thanks for the Infastructure, and code in the
examples),
Kurt Hornik [ctb],
Uwe Ligges [ctb],
Andrej-Nikolai Spiess [ctb],
Steve Horvath [ctb],
Peter Langfelder [ctb],
skullkey [ctb],
Mark Van Der Loo [ctb] (https://github.com/markvanderloo d3dendrogram),
Yoav Benjamini [ths]
Tal Galili [aut, cre, cph] (http://www.r-statistics.com),
Romain Francois [ctb],
Dirk Eddelbuettel [ctb],
Kevin Ushey [ctb],
Yoav Benjamini [ths]
Nelo Magalhães (Univ. Paris Sud 11 - INRIA team Select) and Yves Rozenholc (Univ. Paris Descartes - INRIA team Select)
David Ardia, Katharine Mullen, Brian Peterson, and Joshua
Ulrich, with contributions from Kris Boudt
Eduardo L. T. Conceicao and Martin Maechler [ctb]
Ingmar Visser <i.visser@uva.nl>, Maarten Speekenbrink <m.speekenbrink@ucl.ac.uk>
Maxime Genest <maxime.genest@clevislauzon.qc.ca>, Jean-Claude
Masse <jcmasse@mat.ulaval.ca>, Jean-Francois Plante
<jfplante@hec.ca>.
Daniel Kosiorowski, Mateusz Bocian, Anna Wegrzynkiewicz and Zygmunt
Zawadzki from Cracow University of Economics.
Sara Lopez-Pintado <sl2929@columbia.edu> and Aurora Torrente
<etorrent@est-econ.uc3m.es>.
Jakson Aquino. Includes R source code and/or documentation
written by Dirk Enzmann, Marc Schwartz, Nitin Jain, and Stefan
Kraft
Hadley Wickham <hadley@rice.edu>,
Di Cook <dicook@iastate.edu>,
Andreas Buja <buja@wharton.upenn.edu>,
Barret Schloerke <bigbear@iastate.edu>
Andri Signorell. Includes R source code and/or documentation previously published by (in alphabetical order): Nanina Anderegg, Tomas Aragon, Antti Arppe, Adrian Baddeley, Ben Bolker, Frederico Caeiro, Stephane Champely, Daniel Chessel, Leanne Chhay, Michael Dewey, Harold C. Doran, Stephane Dray, Charles Dupont, Jeff Enos, Claus Ekstrom, Martin Elff, John Fox, Tal Galili, Matthias Gamer, Joseph L. Gastwirth, Yulia R. Gel, Juergen Gross, Gabor Grothendieck, Frank E. Harrell Jr, Michael Hoehle, Christian W. Hoffmann, Markus Huerzeler, Wallace W. Hui, Rob J. Hyndman, Pablo J. Villacorta Iglesias, Matthias Kohl, Mikko Korpela, Detlew Labes, Friederich Leisch, Jim Lemon, Martin Maechler, Arni Magnusson, Daniel Malter, George Marsaglia, John Marsaglia, Alina Matei, David Meyer, Weiwen Miao, Yongyi Min, Markus Naepflin, Daniel Navarro, Klaus Nordhausen, Derek Ogle, Nick Parsons, Sandrine Pavoine, Roland Rapold, William Revelle, Tyler Rinker, Caroline Rodriguez, Venkatraman E. Seshan, Greg Snow, Michael Smithson, Werner A. Stahel, Yves Tille, Adrian Trapletti, Kevin Ushey, Jeremy VanDerWal, Bill Venables, John Verzani, Gregory R. Warnes, Stefan Wellek, Peter Wolf, Daniel Wollschlaeger, Achim Zeileis
Yang Li <yang.li@rug.nl>, Morris Swertz <m.a.swertz@rug.nl>,
Gonzalo Vera <gonzalo.vera.rodriguez@gmail.com>, Rainer
Breitling <r.breitling@rug.nl>, Ritsert Jansen
<r.c.jansen@rug.nl>
Heike Trautmann, Detlef Steuer and Olaf Mersmann
Karline Soetaert [aut],
Thomas Petzoldt [aut, cre],
R. Woodrow Setzer [aut],
odepack authors [cph]
Peter Solymos, Monica Moreno, Subhash R Lele
Karline Soetaert and Jeff Cash and Franscesca Mazzia
Original by Jay L. Devore, modifications by Douglas Bates
<bates@stat.wisc.edu>, modifications  for 7th edition by John Verzani.
Hadley Wickham [aut, cre],
Winston Chang [aut],
RStudio [cph],
R Core team [ctb] (Some namespace and vignette code extracted from base
R)
Victor H. Cervantes <vcervantes@icfes.gov.co, vhcervantesb@unal.edu.co>
Ravi Varadhan, Johns Hopkins University, and Hans W. Borchers,
ABB Corporate Research.
Peter K Dunn <pdunn2@usc.edu.au> and Gordon K Smyth
Alba Martinez-Ruiz <amartine@ucsc.cl> and Claudia Martinez-Araneda <cmartinez@ucsc.cl>
Taylor B. Arnold, John W. Emerson, R Core Team and contributors
worldwide
Jessica Franco, Delphine Dupuy, Olivier Roustant, Guillaume
Damblin and Bertrand Iooss.
Olivier Roustant, David Ginsbourger, Yves Deville. Contributors: Clement Chevalier, Yann Richet.
D. Ginsbourger, V. Picheny, O. Roustant, with contributions by
C. Chevalier and T. Wagner
Yann Richet, Yves Deville, Clement Chevalier
Thomas Lumley [aut, cre], Ken Knoblauch [ctb], Scott Waichler
[ctb], Achim Zeileis [ctb]
Baptiste Auguie [aut, cre] (the original data have their original
source listed in the help file.)
P.L. Davies, U. Gather, M. Meise, D.Mergel, T. Mildenberger. Additional Code by T. Bernholt and T. Hofmeister
David Magis (U Liege), Sebastien Beland (UQAM) and Gilles
Raiche (UQAM)
Dirk Eddelbuettel <edd@debian.org> with contributions
by Antoine Lucas, Jarek Tuszynski, Henrik Bengtsson, Simon Urbanek,
Mario Frasca, Bryan Lewis, Murray Stokely, Hannes Muehleisen and
Duncan Murdoch.
Ross Dwyer, Emily Best and Anne Goldizen
Cenny Taslim <taslim.2@osu.edu>, with contributions from  Dustin Potter, Abbasali Khalili and Shili Lin <shili@stat.osu.edu>.
Vonn Walter, Andrew B. Nobel, and Fred A. Wright
Martin Maechler (originally from Fortran and S-plus by Dario Ringach, NYU.edu)
Audrey Qiuyan Fu, Steven Russell, Sarah J. Bray and Simon
Tavare
W. E. Wansouwe, C. C. Kokonendji and D.T. Kolyang.
Mikkel Meyer Andersen and Poul Svante Eriksen
Alessandro Barbiero <alessandro.barbiero@unimi.it>, Riccardo
Inchingolo <riccardo.arner@libero.it>
Ruth Heller [aut], Hadas Gur [aut], Shay Yaacoby [aut, cre]
J. Mark Rowland, Clifford Qualls, and Christian Gunning
Gaston Sanchez [aut, cre],
Charles Determan [ctb]
Robert J. Hijmans, Steven Phillips, John Leathwick and Jane Elith
Derek Beaton [aut, com, ctb], Cherise Chin Fatt [ctb], Herve Abdi [aut, cre]
Florian Camphausen [aut],
Matthias Kohl [aut, cph],
Peter Ruckdeschel [cre, cph],
Thomas Stabla [aut, cph],
R Core Team [ctb, cph] (for source file ks.c/ routines 'pKS2' and
'pKolmogorov2x')
Florian Camphausen, Matthias Kohl, Peter Ruckdeschel, Thomas Stabla
Matthias Kohl, Peter Ruckdeschel, Nataliya Horbenko
Nataliya Horbenko, Matthias Kohl, Daria Pupashenko, Myhailo Pupashenko, Peter
Ruckdeschel
Peter Ruckdeschel, Matthias Kohl, Anja Hueller, Eleonara Feist
Daniel Laydon, Aaron Sim, Charles Bangham, Becca Asquith
Richard G. FitzJohn <fitzjohn@zoology.ubc.ca>, with GeoSSE and
ClaSSE by Emma E. Goldberg <eeg@uic.edu>, and BiSSE-ness by Karen
Magnuson-Ford.
David A. Swan <dswan@fhcrc.org> with contributions from Craig A
Magaret <cmagaret@fhcrc.org> and Matthew M Cousins
<matthew.m.cousins@gmail.com>
Marcelino de la Cruz Rot and Philip M. Dixon
Giovanni Petris <GPetris@uark.edu>.
Original C code for ARMS by Wally Gilks.
Tyler H. McCormick, Adrian Raftery, David Madigan
Aleksandra Maj, Agnieszka Prochenka, Piotr Pokarowski
Torben Tvedebrink <tvede@math.aau.dk> and James Curran <j.curran@auckland.ac.nz>. Mikkel Meyer Andersen <mikl@math.aau.dk> assisted on the multicore/CPU implementation.
Junyao Chen, Cuiyi He, Yuanrui Wu, Mengqing Sun
Charles Doss, Vladimir Minin, Marc Suchard
Søren Højsgaard <sorenh@math.aau.dk> and Ulrich Halekoh <uhalekoh@health.sdu.dk> with contributions from Jim Robison-Cox, Kevin Wright, Alessandro A. Leidi and others.
Jean-Baptiste Denis <Jean-Baptiste.Denis@jouy.inra.fr>, Regis Pouillot <RPouillot@yahoo.fr>, Kien Kieu <Kien.Kieu@jouy.inra.fr>
Ulrike Groemping [aut, cre],
Boyko Amarov [ctb],
Hongquan Xu [ctb]
Ulrike Groemping [aut, cre],
Lenth Russ [ctb]
Konstanze Krueger [aut],
Knut Krueger [cre, aut]
Bjoern Bornkamp, Jose Pinheiro, Frank Bretz
Andy Bunn [aut, cph, cre, trl], Mikko Korpela [aut, trl], Franco Biondi [aut, cph], Filipe Campelo [aut, cph], Pierre Mérian [aut, cph], Manfred Mudelsee [aut], Fares Qeadan [aut, cph], Michael Schulz [aut], Christian Zang [aut, cph], Jacob Cecile [ctb]
Hadley Wickham <h.wickham@gmail.com>,
Romain Francois <romain@r-enthusiasts.com>
Alejandro Jara [aut, cre], Timothy Hanson [ctb], Fernando
Quintana [ctb], Peter Mueller [ctb], Gary Rosner [ctb]
Christian Ritz <ritz@bioassay.dk>, Jens Strebig <jcs@life.ku.dk>
Johan Zetterqvist <johan.zetterqvist@ki.se> , Arvid Sjölander <arvid.sjolander@ki.se> with contributions from Alexander Ploner.
Keyan Salari, Robert Tibshirani, Jonathan R. Pollack
Greg Hixon [aut, cph],
Anne Bichteler [aut, cre],
Chad Thompson [ctb],
Liz Abraham [ctb]
Ravi Varadhan <rvaradhan@jhmi.edu> and Wenliang Yao <yaow080@gmail.com>
Ingo Feinerer [aut],
Stefan Theussl [aut, cre],
Christian Buchta [ctb]
David L. Miller, Eric Rexstad, Louise Burt, Mark V. Bravington,
Sharon Hedley.
Peter Mayer, Denis Larocque, Matthias Schmid
Carla Moreira, Jacobo de Uña-Álvarez and Rosa Crujeiras
Karsten Tabelow [aut, cre],
Joerg Polzehl [aut],
Felix Anker [ctb]
Xinyu Tang [aut, cre],
Maria Melguizo [aut]
Jose G. Clavel, Shizuiko Nishisato and Antonio Pita
Lei ZHANG, Hao JIANG and Chen XUE(Equally contributed, the order is
decided by the time the author joined the project. )
Peter Langfelder <Peter.Langfelder@gmail.com> and Bin Zhang <binzhang.ucla@gmail.com>,
with contributions from Steve Horvath <SHorvath@mednet.ucla.edu>
Peter Metcalfe, based on Fortran code by Keith Beven and Jim Freer
Robert B. Gramacy <rbgramacy@chicagobooth.edu> and Matt A. Taddy <taddy@chicagobooth.edu>
Yves Rozenholc (MAP5, Univ. Paris Descartes), Christophe Pouzat (MAP5, Univ. Paris Descartes) and Tiffany Lieury (Cerebral Physiology lab, Univ. Paris Descartes)
Joel Dubin, Dandi Qiao, Hans-Georg Mueller.
Christopher Gandrud, Guy D Whitten, and Laron K Williams
Xiaojing Wang <wangxj03@gmail.com>,
Jun Yan <jun.yan@uconn.edu>, and
Ming-Hui Chen <ming-hui.chen@uconn.edu>
David Meyer [aut, cre],
Evgenia Dimitriadou [aut, cph],
Kurt Hornik [aut],
Andreas Weingessel [aut],
Friedrich Leisch [aut],
Chih-Chung Chang [ctb, cph] (libsvm C++-code),
Chih-Chen Lin [ctb, cph] (libsvm C++-code)
Carlos Fonseca, Luis Paquete, Thomas Stützle, Manuel
López-Ibáñez and Marco Chiarandini.
Vasilis Dakos <vasilis.dakos@gmail.com>, with contributions from S.R.
Carpenter, T. Cline, L. Lahti
Stephen Milborrow.  Derived from mda:mars by Trevor Hastie and
Rob Tibshirani.  Uses Alan Miller's Fortran utilities
with Thomas Lumley's leaps wrapper.
Franck Jabot, Thierry Faure, Nicolas Dumoullin
Jacob M. Montgomery, Florian Hollenbach, and Michael D. Ward
Marcelino de la Cruz Rot, with contributions of Philip M. Dixon
and Jose M. Blanco-Moreno
Fuka DR, Walter MT, Archibald JA, Steenhuis TS, and Easton ZM
Karline Soetaert <karline.soetaert@nioz.nl>, Peter MJ Herman <peter.herman@nioz.nl>
Daniel McGlinn [aut, cre],
Ethan White [aut]
Olivier Broenniman <olivier.broennimann@unil.ch> and
Blaise Petitpierre <bpetitpierre@gmail.com> and
Christophe Randin <christophe.randin@unibas.ch> and
Robin Engler <robin.engler@gmail.com> and
Frank Breiner <frank.breiner@unil.ch> and
Manuela D`Amen<manuela.damen@unil.ch> and
Loic Pellissier <loic.pellissier@unifr.ch> and
Julien Pottier <julien.pottier@clermont.inra.fr> and
Dorothea Pio <Dorothea.Pio@fauna-flora.org> and
Ruben Garcia Mateo <rubeng.mateo@gmail.com> and
Valeria Di Cola <valeria.dicola@unil.ch> and
Wim Hordijk <wim@WorldWideWanderings.net> and
Anne Dubuis <anne.dubuis@gmail.com> and
Daniel Scherrer <daniel.scherrer@unil.ch> and
Nicolas Salamin <nicolas.salamin@unil.ch> and
Antoine Guisan <antoine.guisan@unil.ch>
J. Guitton, M. Colleter, P. Gatti, and D. Gascuel
Alexandre Adalardo de Oliveira and Paulo Inacio Prado <ecovirtualpackage@gmail.com>
Raphaël Coudret, Benoît Liquet and Jérôme Saracco
Murilo Coutinho Silva (University of Brasilia, Brazil), George Freitas von Borries (University of Brasilia, Brazil)
Jason P. Becker [ctb],
Jared E. Knowles [aut, cre]
John Fox [aut, cre],
Sanford Weisberg [aut],
Michael Friendly [aut],
Jangman Hong [aut],
Robert Andersen [ctb],
David Firth [ctb],
Steve Taylor [ctb]
Michaela Paul [aut], Reinhard Furrer [cre, ctb]
James J. Yang, Jia Li, Anne Buu, and L. Keoki Williams
Olivia Lau <olivia.lau@post.harvard.edu>, Ryan T. Moore
<rtm@wustl.edu>, Michael Kellermann <kellerm@post.harvard.edu>
Mehmet Hakan Satman (PhD), Erkin Diyarbakirlioglu (PhD)
Edmunds Cers <edmunds.cers@gmail.com>, Janis Valeinis <valeinis@lu.lv>
Hui Zou <hzou@stat.umn.edu> and Trevor Hastie
<hastie@stanford.edu>
Material from the book's webpage, R port and packaging by
Kjetil Halvorsen
Duncan Murdoch <murdoch@stats.uwo.ca> and E. D. Chow (porting
to R by Jesus M. Frias Celayeta <iosu@bureau.ucc.ie>)
Julien Tomas, Frederic Planchet, Wassim Youssef
Nicolas Servant, Eleonore Gravier, Pierre Gestraud, Cecile Laurent, Caroline Paccard, Anne Biton, Jonas Mandel, Bernard Asselain, Emmanuel Barillot, Philippe Hupe
Wei-Chen Chen [aut, cre],
Ranjan Maitra [aut],
Volodymyr Melnykov [aut]
Simon Urbanek <urbanek@research.att.com>, Yossi Rubner
Laura Villanova <laura.villanova@monash.edu>, Kate Smith-Miles
<kate.smith-miles@monash.edu> and Rob J Hyndman
<rob.hyndman@monash.edu>.
Mai Zhou.(Art Owen for el.test(), Yifan Yang for C part)
William H. Barton <bbarton@lexmark.com> under the supervision
of Dr. Mai Zhou <mai@ms.uky.edu>
Yuki Shiraito <shiraito@princeton.edu>, Kosuke Imai <kimai@princeton.edu>
R port by Douglas Bates <bates@stat.wisc.edu> and Karen A.F.
Copeland <karen@boulderstats.com>
Robert Muscarella, Peter J. Galante, Mariano Soley-Guardia, Robert A. Boria, Jamie M. Kass, Maria Uriarte and Robert P. Anderson
Fournier Gwenaelle, Cadoret Marine, Fournier Olivier, Le Poder
Francois, Bouche Jerome, Le Sebastien
Yanchun Bao <yanchun.bao@brunel.ac.uk>, Veronica Vinciotti<veronica.vinciotti@brunel.ac.uk>
Chris Fraley, Adrian E. Raftery, J. McLean Sloughter, Tilmann
Gneiting, University of Washington.
RA Yuen, Tilmann Gneiting, Thordis Thorarinsdottir, Chris
Fraley
Lijuan Cao <lcao@jcsu.edu> and Michael Grabchak <mgrabcha@uncc.edu>
Nhu Le, Jim Zidek, Rick White, and Davor Cubranic, with
Fortran code for Sampson-Guttorp estimation authored by Paul D.
Sampson, Peter Guttorp, Wendy Meiring, and Catherine Hurley, and
Runge-Kutta-Fehlberg method implementation by H.A. Watts and L.F. Shampine.
Bendix Carstensen [aut, cre],
Martyn Plummer [aut],
Esa Laara [ctb],
Michael Hills [ctb]
Raymond Walters, Charles Laurin, Gitta Lubke
Maria Noremark [aut],
Stefan Widgren [aut, cre]
Oswaldo Santos Baquero [aut, cre],
Fernando Silveira Marques [aut]
Samuel Jenness [cre, aut],
Steven M. Goodreau [aut],
Martina Morris [aut],
Li Wang [ctb]
Chris Groendyke <cgroendyke@gmail.com>,
David Welch <David Welch <david.welch@auckland.ac.nz>,
David R. Hunter <dhunter@stat.psu.edu>,
Mark Stevenson <mark.stevenson1@unimelb.edu.au> with contributions from Telmo Nunes, Cord Heuer, Jonathon Marshall, Javier Sanchez, Ron Thornton, Jeno Reiczigel, Jim Robison-Cox, Paola Sebastiani, Peter Solymos, Kazuki Yoshida and Simon Firestone
Tomas J. Aragon Developer [aut, cre], Michael P. Fay User
[ctb], Daniel Wollschlaeger User [ctb]
Rebecka Jornsten, Tobias Abenius, Sven Nelander
Yi Yang <yiyang@umn.edu>, Hui Zou <hzou@stat.umn.edu>
Mark S. Handcock [aut],
David R. Hunter [aut],
Carter T. Butts [aut],
Steven M. Goodreau [aut],
Pavel N. Krivitsky [aut, cre],
Martina Morris [aut]
Pavel N. Krivitsky [aut, cre],
Mark S. Handcock [ctb],
David R. Hunter [ctb]
Omer Nebil Yaveroglu [aut, cre],
Sean M. Fitzhugh [aut],
Maciej Kurant [aut],
Athina Markopoulou [aut],
Natasa Przulj [aut],
Carter T. Butts [aut]
Patrick Mair [cre, aut],
Reinhold Hatzinger [aut],
Marco J. Maier [aut]
David Causeur (Agrocampus, Rennes, France) and Ching-Fan Sheu (National Cheng-Kung University, Tainan, Taiwan)
Jean-Charles Croix, Thierry Moudiki, Frédéric Planchet, Wassim
Youssef
Jean-Charles Croix, Thierry Moudiki, Frederic Planchet, Wassim Youssef
Felix Kaminsky <fkamins@uni-goettingen.de>, inspired by the
estout package for Stata.
Abdollah Jalilian <jalilian@razi.ac.ir>, based on Fortran code
by Jiancang Zhuang
Marcello Chiodi [aut, cre],
Giada Adelfio [aut]
Ashis Saha, with contribution from Jaewoo Kang
Hadley Wickham [aut],
Yihui Xie [cre, ctb],
Barret Schloerke [ctb]
Danlu Guo <danlu.guo@adelaide.edu.au>, Seth Westra <swestra@civeng.adelaide.edu.au>
Alec Stephenson. Function fbvpot by Chris Ferro.
Ajay Shah, Vimal Balasubramaniam, Vikram Bahure
Bernhard Pfaff [aut, cre], Alexander McNeil [aut] (S original
(EVIS)), Alec Stephenson [trl] (R port of EVIS)
Carl Scarrott and Yang Hu, University of Canterbury
B G Manjunath and Frederico Caeiro; guidance from Prof. M. Ivette Gomes and Prof. M. Isabel Fraga Alves
Thomas Grubinger [aut, cre],
Achim Zeileis [aut],
Karl-Peter Pfeiffer [aut]
Guogen Shan <guogen.shan@unlv.edu>, Weizhen Wang
<weizhen.wang@wright.edu>
Torsten Hothorn [aut, cre],
Kurt Hornik [aut]
Achim Zeileis [aut, cre],
Bettina Gruen [aut],
Friedrich Leisch [aut],
Nikolaus Umlauf [aut]
David Bolin <davidbolin@gmail.com> and Finn Lindgren <finn.lindgren@gmail.com>
Eric Batista Ferreira, Portya Piscitelli Cavalcanti, Denismar
Alves Nogueira
Eric Batista Ferreira, Pórtya Piscitelli Cavalcanti, Denismar
Alves Nogueira
Fabian Sobotka <fabian.sobotka@wiwi.uni-goettingen.de>,
Sabine Schnabel <sabine.schnabel@wur.nl>
and Linda Schulze Waltrup <lschulze_waltrup@stat.uni-muenchen.de>
with contributions from
Paul Eilers <p.eilers@erasmusmc.nl>,
Thomas Kneib <tkneib@uni-goettingen.de>
and Goeran Kauermann <goeran.kauermann@stat.uni-muenchen.de>.
Mathieu Pigeon, Michel Jacques, Vincent Goulet
Vincent Goulet, Christophe Dutang, Martin Maechler,
David Firth, Marina Shapira, Michael Stadelmann,
expm-developers@lists.R-forge.R-project.org
Roger B. Sidje [aut, cph], Niels Richard Hansen [aut, cre, cph]
Derek Beaton, Cherise R. Chin Fatt, Herve Abdi
Gabriel E Leventhal, partly adapted from MATLAB code by Awad H. Al-Mohy and using the routines DLNAC1 and DLARPC by Sheung Hun Cheng, and DLAPST from ScaLAPACK.
Alexey Novikov, Ruslan Pusev, Maxim Yakovlev
Reinhard Simon (CIP), David M. Spooner (USDA)
Haizhen Wu <h.wu2@massey.ac.nz>,
A. Jonathan R. Godfrey <A.J.Godfrey@massey.ac.nz>,
Kondaswamy Govindaraju <k.govindaraju@massey.ac.nz>
Dean Langan, Alexander Sutton, Julian PT Higgins, Walter
Gregory
B N Mandal <mandal.stat@gmail.com> and Jun Ma <jun.ma@mq.edu.au>
Mark van der Loo <mark.vanderloo@gmail.com>
Campo Elias Pardo <cepardot@unal.edu.co>, Pedro Cesar del Campo
<pcdelcampon@unal.edu.co> and Camilo Jose Torres
<cjtorresj@unal.edu.co>, with the contributions from Ivan Diaz
<ildiazm@unal.edu.co>, Mauricio Sadinle
<msadinleg@unal.edu.co>.
Francois Husson, Julie Josse, Sebastien Le, Jeremy Mazet
Dave Armstrong (Department of Political Science, UW-Milwaukee)
Emeline Perthame (Agrocampus Ouest, Rennes, France), Chloe Friguet
(Universite de Bretagne Sud, Vannes, France) and David Causeur (Agrocampus
Ouest, Rennes, France)
David Causeur, Chloe Friguet, Magalie Houee-Bigot, Maela Kloareg
Jana Fruth, Thomas Muehlenstaedt, Olivier Roustant, Malte Jastrow
Michael C. J. Kao <michael.kao@fao.org>, Markus Gesmann, Filippo Gheri
Damon Julien <julien.damon@free.fr> Guillas Serge
<guillas@uchicago.edu>
Diethelm Wuertz and many others, see the SOURCE file
Rmetrics Core Team,
Diethelm Wuertz [aut],
Tobias Setz [cre]
Yohan Chalabi [ctb]
Haotian Pang, Han Liu and Robert Vanderbei
Piercesare Secchi, Simone Vantini, and Paolo Zanini
J L Marchini <marchini@stats.ox.ac.uk>, C Heaton
<chrisheaton99@yahoo.com> and B D Ripley
<ripley@stats.ox.ac.uk>
Lutz Duembgen, Klaus Nordhausen, Heike Schuhmacher
Simon Urbanek <Simon.Urbanek@r-project.org>, Jeffrey Horner
<jeffrey.horner@gmail.com>
Stefan Kloessner <S.Kloessner@mx.uni-saarland.de>, Sven Wagner
<sven.wagner@mx.uni-saarland.de>
Alexandre BUREAU <alexandre.bureau@msp.ulaval.ca> and Jordie Croteau
<jordie.croteau@crulrg.ulaval.ca>
Diethelm Wuertz and Rmetrics core team members, uses code
builtin from the following R contributed packages: gmm from
Pierre Chauss, gld from Robert King, gss from Chong Gu, nortest
from Juergen Gross, HyperbolicDist from David Scott, sandwich
from Thomas Lumley and Achim Zeileis, fortran/C code from
Kersti Aas and akima (0.5-1: R code under GPL) from Albrecht
Gebhardt
Davide Altomare, Guido Consonni and Luca La Rocca
Adrian Andronache <adi.andronache@gmail.com>, Luca Agnelli
<luca.agnelli@gmail.com>
Yang Feng, Richard J. Samworth and Yi Yu
Etienne Laliberté, Pierre Legendre, Bill Shipley
J. O. Ramsay <ramsay@psych.mcgill.ca>, Hadley Wickham
<h.wickham@gmail.com>, Spencer Graves
<spencer.graves@prodsyse.com>, Giles Hooker <gjh27@cornell.edu>
Alice Parodi, Mirco Patriarca, Laura Sangalli, Piercesare Secchi, Simone Vantini, Valeria Vitelli
Manuel Febrero Bande [aut],
Manuel Oviedo de la Fuente [aut, cre],
Pedro Galeano [ctb],
Alicia Nieto [ctb],
Eduardo Garcia-Portugues [ctb]
James G. Scott, with contributions from Rob Kass and Jesse Windle
Tarn Duong <tarn.duong@gmail.com> & Matt Wand <Matt.Wand@uts.edu.au>
Ravi Varadhan, Johns Hopkins University, and MKG Subramaniam,
AT&T Reserach Labs.
Ali Uenlue [aut, cre], Thomas Kiefer [aut, trl] (Based on
original MATLAB source by Ehtibar N.  Dzhafarov.)
Samuel Soubeyrand, Cindy E. Morris, E. Keith Bigg
Daniel Adler <dadler@uni-goettingen.de>,
Christian Gläser <christian_glaeser@gmx.de>,
Oleg Nenadic <onenadi@uni-goettingen.de>,
Jens Oehlschlägel <Jens.Oehlschlaegel@truecluster.com>,
Walter Zucchini <wzucchi@uni-goettingen.de>
Edwin de Jonge, Jan Wijffels, Jan van der Laan
Sebastian Krey <skrey@statistik.tu-dortmund.de> Uwe Ligges
<ligges@statistik.tu-dortmund.de> Olaf Mersmann
<olafm@statistik.tu-dortmund.de>
Diethelm Wuertz and Yohan Chalabi with contribution from Michal
Miklovic, Chris Boudt, Pierre Chausse and others
Douglas Nychka [aut, cre], Reinhard Furrer [aut], Stephan Sain [aut]
Alexandre Brouste <Alexandre.Brouste@univ-lemans.fr>, Sophie
Lambert-Lacroix <Sophie.Lambert@imag.fr>.
Peter Carl, Dirk Eddelbuettel, Jeffrey Ryan, Joshua Ulrich,
Brian G. Peterson, Garrett See
Bingpei Wu & Jiajun He & Sijie Chen & Yangyang Liu
Marc Ratkovic <ratkovic@princeton.edu>, Kosuke Imai
<kimai@princeton.edu>,
Nicholas Lange, Tom Fletcher, Kristen Zygmunt
Benjamin Baumgartner [aut, cre],
Riccardo Gatto [ctb, ths]
Yoli Shavit, Fiona Kathryn Hamey and Pietro Lio'
A.I. McLeod, Ying Zhang and Changjiang Xu
Marie Laure Delignette-Muller [aut, cre],
Christophe Dutang [aut],
Regis Pouillot [ctb],
Jean-Baptiste Denis [ctb]
Simon L. Rinderknecht and Peter Reichert <peter.reichert@eawag.ch>
David Luethi, Philipp Erb, Simon Otziger
Xingguo Li, Tuo Zhao, Lie Wang, Xiaoming Yuan and Han Liu
code by Fionn Murtagh and R development team, modifications and
packaging by Peter Langfelder
Friedrich Leisch [aut, cre], Evgenia Dimitriadou [ctb]
Bettina Gruen [aut, cre],
Friedrich Leisch [aut],
Deepayan Sarkar [ctb]
Rune Hoff with contributions from Jon Michael Gran and Daniel Farewell
Livio Finos, with contributions by Dario Basso, Aldo Solari, Jelle Goeman and Marco Rinaldo. Special thanks are due to Ivan Marin-Franch and Fredrik Nilsson for the debugging and the good questions.
Gen Nowak [aut, cre],
Trevor Hastie [aut],
Jonathan R. Pollack [aut],
Robert Tibshirani [aut],
Nicholas Johnson [aut]
Constantinos Mavridis and Ioannis N. Athanasiadis
Gerald Jurasinski, Franziska Koebsch, Anke Guenther, Sascha Beetz
Karline Soetaert <karline.soetaert@nioz.nl>,
Thomas Petzoldt <thomas.petzoldt@tu-dresden.de>
Karsten Tabelow [aut, cre],
Joerg Polzehl [aut],
Brandon Whitcher [ctb]
Alina Beygelzimer, Sham Kakadet and John Langford (cover tree library),
Sunil Arya and David Mount (ANN library for the kd-tree approach), Shengqiao Li
Winston Chang,
Alexej Kryukov,
Paul Murrell
Rob J Hyndman <Rob.Hyndman@monash.edu> with contributions from
George Athanasopoulos, Slava Razbash, Drew Schmidt, Zhenyu Zhou, Yousaf Khan,
Christoph Bergmeir, Earo Wang
R Core Team [aut, cph, cre],
Roger Bivand [ctb, cph],
Vincent J. Carey [ctb, cph],
Saikat DebRoy [ctb, cph],
Stephen Eglen [ctb, cph],
Rajarshi Guha [ctb, cph],
Nicholas Lewin-Koh [ctb, cph],
Mark Myatt [ctb, cph],
Ben Pfaff [ctb],
Brian Quistorff [ctb],
Frank Warmerdam [ctb, cph],
Stephen Weigand [ctb, cph],
Free Software Foundation, Inc. [cph]
Miriam Marusiakova (Centre of Biomedical Informatics, Institute
of Computer Science, Academy of Sciences of the Czech Republic)
Alessandro Barbiero, Pier Alda Ferrari, Giancarlo Manzi
Nicola Puletti, Marco Mura, Cristiano Castaldi, Maurizio Marchi, Ugo
Chiavetta, Roberto Scotti
Gregory R. Warnes <greg@random-technologies-llc.com>. Financial
support for some aspects of this package provided by Metrum
Research Group, LLC <http://www.metrumrg.com>.
Achim Zeileis [aut, cre],
Yves Croissant [aut]
Achim Zeileis (R code) and the R community (fortunes).
Contributions (fortunes and/or code) by Torsten Hothorn,
Peter Dalgaard, Uwe Ligges, Kevin Wright, Martin Maechler,
Kjetil Brinchmann Halvorsen, Kurt Hornik, Duncan Murdoch,
Andy Bunn, Ray Brownrigg, Roger Bivand, Spencer Graves,
Jim Lemon, Christian Kleiber, David L. Reiner,
Berton Gunter, Roger Koenker, Charles Berry, Marc Schwartz,
Michael Dewey, Ben Bolker, Peter Dunn, Sarah Goslee, Simon
Blomberg, Bill Venables, Roland Rau, Thomas Petzoldt, Rolf
Turner, Mark Leeds, Emmanuel Charpentier, Chris Evans, Paolo
Sonego, Peter Ehlers, Detlef Steuer, Tal Galili, Greg Snow,
Brian D. Ripley, Michael Sumner, David Winsemius, Liviu
Andronic, Brian Diggs, Matthieu Stigler, Michael Friendly,
Dirk Eddelbuettel, Richard M. Heiberger, Patrick Burns,
Dieter Menne, Andrie de Vries, Barry Rowlingson, Renaud
Lancelot, R. Michael Weylandt, Jon Olav Skoien, Francois
Morneau, Antony Unwin, Joshua Wiley, Terry Therneau, Bryan
Hanson, Henrik Singmann, Eduard Szoecs.
Originally written for S-Plus by: Kjell Konis
<kkonis@insightful.com> and Marco Riani <mriani@unipr.it>
Ported to R by Luca Scrucca <luca@stat.unipg.it>
Jie Peng <jie@wald.ucdavis.edu> , Debashis Paul
<debashis@wald.ucdavis.edu>
Patricia Román-Román, Juan J. Serrano-Pérez and Francisco
Torres-Ruiz.
S original by Chris Fraley, U.Washington, Seattle.  R port by
Fritz Leisch at TU Wien; since 2003-12: Martin Maechler;
fdGPH(), fdSperio(), etc by Valderio Reisen and Artur Lemonte.
William Constantine and Donald Percival (Applied Physics
Laboratory, University of Washington)
Hana Sevcikova <hanas@uw.edu>,
Don Percival <dbp@apl.washington.edu>,
Tilmann Gneiting <tilmann@stat.washington.edu>
Virginie Rondeau, Juan R. Gonzalez, Yassin Mazroui, Audrey
Mauguen, Agnieszka Krol-Listwon, Amadou Diakite and Alexandre Laurent
Antonio Arcos <arcos@ugr.es>, Maria del Mar Rueda <mrueda@ugr.es>,
Maria Giovanna Ranalli <giovanna.ranalli@stat.unipg.it> and David Molina
<dmolinam@ugr.es>
Bernhard Pfaff [aut, cre], Miguel Sousa Lobo [ctb] (SOCP),
Lieven Vandenberghe [ctb] (SOCP), Stephen Boyd [ctb] (SOCP),
Herve Lebret [ctb] (SOCP)
Ella Roelant, Stefan Van Aelst, Gert Willems
Lala Septem Riza, Christoph Bergmeir, Francisco Herrera, and
Jose Manuel Benitez
Steven Spiriti <puzzlesteven@gmail.com>, Philip Smith
<philip.smith@ttu.edu>, Pierre Lecuyer
<lecuyer@iro.UMontreal.ca>,
Norm Matloff <normmatloff@gmail.com> and Yingkang Xie
<yingkang.xie@gmail.com>
Giangiacomo Bravo <giangiacomo.bravo@unito.it>, Lucia Tamburino
<lucia.tamburino@alice.it>.
Jakub Szlek <j.szlek@uj.edu.pl>, acknowledgments to Aleksander
Mendyk, contributions from stackoverflow and
r-help@r-project.org mailing list community.
Acknowledgments: This work was funded by Poland-Singapore bilateral cooperation
project no 2/3/POL-SIN/2012.
Laurie Davies <Laurie.Davies@uni-essen.de> Arne Kovac
<A.Kovac@bristol.ac.uk>
John Dziak [aut, cre],
Mariya Shiyko [aut]
Marek Gagolewski [aut, cre],
Jan Caha [ctb]
Craig Knott, Luke Hovell, Nathan Karimian with supervision from
Dr. Jon Garibaldi
Marta Sestelo, Nora M. Villanueva, Javier Roca-Pardinas
Xianli Wang, Alan Cantin, Marc-Andre Parisien, Mike Wotton, Kerry Anderson, and Mike Flannigan
Achim Zeileis [aut, cre],
Ajay Shah [ctb],
Ila Patnaik [ctb],
Anmol Sethy [ctb]
Sophie Lebre <sophie.lebre@icube.unistra.fr>, original
version 1.0 by Sophie Lebre
<sophie.lebre@icube.unistra.fr>, contribution of Julien
Chiquet <julien.chiquet@genopole.cnrs.fr> to version 2.0
Regis K. Gallon (MNHN), Jerome Fournier (CNRS|MNHN)
Sebnem Er, Timur Keskinturk, Charlie Daly
Leonardo Sandrini-Neto & Mauricio G. Camargo
Michael Krabbe Borregaard, Thomas Matthews & Karl Ugland
Benjamin Hofner, Andreas Mayr, Nora Fenske, Matthias Schmid
Curtis S. Signorino [aut],
Brenton Kenkel [aut, cre]
Mikis Stasinopoulos <d.stasinopoulos@londonmet.ac.uk>, Bob
Rigby <r.rigby@londonmet.ac.uk>
Vlasios Voudouris <vlasios.voudouris@abm-analytics.com>
with contributions from Calliope Akantziliotou,
and Marco Enea
Mikis Stasinopoulos <d.stasinopoulos@londonmet.ac.uk>, Bob Rigby <r.rigby@londonmet.ac.uk>
Mikis Stasinopoulos <d.stasinopoulos@londonmet.ac.uk>, Bob
Rigby <r.rigby@londonmet.ac.uk>, Nicoleta Mortan
Mikis Stasinopoulos <d.stasinopoulos@londonmet.ac.uk>, Bob
Rigby <r.rigby@londonmet.ac.uk>
Mikis Stasinopoulos <d.stasinopoulos@londonmet.ac.uk>, Bob Rigby <r.rigby@londonmet.ac.uk>, Paul Eilers <p.eilers@erasmusmc.nl>, Brian Marx \email{bmarx@LSU.EDU}, Konstantinos Pateras <kostas.pateras@gmail.com>  with contributions from Larisa Kosidou.
Mikis Stasinopoulos <d.stasinopoulos@londonmet.ac.uk>, Bob
Rigby <r.rigby@londonmet.ac.uk> with contributions from
Calliope Akantziliotou, Gillian Heller, Raydonal Ospina
<rospina@ime.usp.br>, Nicoletta Motpan, Fiona McElduff, Vlasios
Voudouris, Majid Djennad, Marco Enea and Alexios Ghalanos.
Mikis Stasinopoulos <d.stasinopoulos@londonmet.ac.uk>, Bob
Rigby <r.rigby@londonmet.ac.uk> with contributions from
Philippe Lambert.
Fernanda De Bastiani, Mikis Stasinopoulos,
Mikis Stasinopoulos <d.stasinopoulos@londonmet.ac.uk>, Bob
Rigby <r.rigby@londonmet.ac.uk>, Paul Eilers
<p.eilers@erasmusmc.nl>
Martha Corrales and Edilberto Cepeda-Cuervo, with the colaboration of Maria Fernanda Zarate,Ricardo Duplat and Campo Elias Pardo.
Zhaoyuan Fang, Weidong Tian and Hongbin Ji
Jing Hua Zhao and colleagues with inputs from Kurt Hornik and
Brian Ripley
Monique Graf <monique.p.n.graf@bluewin.ch>, Desislava Nedyalkova <desislava.nedyalkova@gmail.com>.
Greg Ridgeway <gregridgeway@gmail.com> with contributions from
others
Michelli Barros <michelli.karinne@gmail.com>, Victor Leiva
<victor.leiva@uv.cl, victor.leiva@yahoo.com> and Gilberto A.
Paula <giapaula@ime.usp.br>
Global Paleofire Working Group <paleofire@gmail.com>
Theresa Scharl, Ingo Voglhuber, Friedrich Leisch
Sang-Yun Oh <sang.oh@stanford.edu>
Kshitij Khare <kdkhare@stat.ufl.edu>
Bala Rajaratnam <brajarat@stanford.edu>
Jonathan Asher Greenberg and Matteo Mattiuzzi
Gregory R. Warnes, Ben Bolker, Gregor Gorjanc, Gabor
Grothendieck, Ales Korosec, Thomas Lumley, Don MacQueen, Arni
Magnusson, Jim Rogers, and others
Stephen R. Haptonstahl, Thomas Scherer, Timo Thoms, and Patrick Wheatley
Vincent J Carey. Ported to R by Thomas Lumley (versions 3.13
and 4.4) and Brian Ripley <ripley@stats.ox.ac.uk> (version
4.13).  Note that maintainers are not available to give advice
on using a package they did not author.
Søren Højsgaard, <sorenh@math.aau.dk>, Ulrich Halekoh <Ulrich.Halekoh@agrsci.dk>, Jun Yan <jun.yan@uconn.edu>,
Luke Harmon <lukeh@uidaho.edu>, Jason Weir, Chad Brock, Rich
Glor, Wendell Challenger, Gene Hunt, Rich FitzJohn, Matt Pennell, Graham Slater, Joseph Brown, Josef Uyeda, and Jon Eastman <jonathan.eastman@gmail.com>
Luisa Salazar Vizcaya, Nello Blaser, Thomas Gsponer
Maksim Struchalin <m.v.struchalin@mail.ru> and GenABEL project developers <genabel.project@gmail.com>
Jiri Kalina, Jana Klanova, Ladislav Dusek, Tom Harner, Jana Boruvkova, Jiri Jarkovsky
Lincoln Mullen [aut, cre],
Cameron Blevins [ctb],
Ben Schmidt [ctb]
Miika Ahdesmaki, Konstantinos Fokianos, and Korbinian Strimmer.
Gilles Guillot, Filipe Santos, Arnaud Estoup
Juliane Schaefer, Rainer Opgen-Rhein, and
Korbinian Strimmer.
Gregory Warnes, with contributions from Gregor Gorjanc,
Friedrich Leisch, and Michael Man.
David Lucy <d.lucy@lancaster.ac.uk> and Robert Aykroyd <r.g.aykroyd@leeds.ac.uk>
Louis Houde [aut],
Jean-Francois Lefebvre [aut],
Valery Roy-Lagace [aut],
Sebastien Lemieux [aut],
Michael J. Fromberger [ctb],
Marie-Helene Roy-Gagnon [cre]
Matthew Friedlander, Adrian Dobra, Helene Massam, and Laurent Briollais
Olga Vesselova, Matthew Friedlander, Laurent Briollais, Adrian Dobra, Helene Massam.
Sylvain Gubian, Yang Xiang, Brian Suomela, Julia Hoeng, PMP SA.
Artur Agostinho Araujo, Luis Meira-Machado
<lmachado@math.uminho.pt> and Susana Faria
<sfaria@math.uminho.pt>
Evangelos Evangelou <e.evangelou@maths.bath.ac.uk>, Vivekananda Roy
<vroy@iastate.edu>
Simeon Lisovski, Silke Bauer, Tamara Emmenegger
C. B. Barber, Kai Habel, Raoul Grasman, Robert B. Gramacy, Andreas
Stahel and David C. Sterratt
Dean Adams, Michael Collyer, Erik Otarola-Castillo, Emma Sherratt
Paulo J. Ribeiro Jr <paulojus@ufpr.br> and Peter J. Diggle
<p.diggle@lancaster.ac.uk>
Ole F. Christensen <OleF.Christensen@agrsci.dk> and  Paulo J. Ribeiro Jr <Paulo.Ribeiro@est.ufpr.br>
Andreas Papritz [cre, aut],
Cornelia Schwierz [ctb]
Davide Morselli [aut, cre],
Mathieu Cossuta [aut],
Till Junge [aut],
Sandra Penic [aut],
Guy Elcheroth [ctb],
Stephanie Glaeser [ctb]
Robert J. Hijmans [cre, aut],
Ed Williams [ctb],
Chris Vennes [ctb]
Carlos Melo <cmelo@udistrital.edu.co>, Alí Santacruz, Oscar
Melo <oomelom@unal.edu.co> and others
Patrick Brown <patrick.brown@utoronto.ca>[aut, cre], Robert Hijmans [ctb]
Emanuele Cordano, Daniele Andreis, Fabio Zottele
Yves Aragon, Thibault Laurent, Lauriane Robidou, Anne
Ruiz-Gazen, Christine Thomas-Agnan
Barret Scloerke <schloerke@gmail.com>, with contributions from Dianne
Cook <dicook@iastate.edu> and Hadley Wickham <h.wickham@gmail.com>.
Allen Day.
Contributions from Trevor L Davis.
Thiago do Rego Sousa, Cira Etheowalda Guevara Otiniano and Silvia Regina Costa Lopes
Barret Schloerke <schloerke@gmail.com>, Jason Crowley
<crowley.jason.s@gmail.com>, Di Cook <dicook@iastate.edu>, Heike Hofmann
<hofmann@iastate.edu>, Hadley Wickham <h.wickham@iastate.edu>, and Francois
Briatte <f.briatte@ed.ac.uk>, Moritz Marbach
<mmarbach@mail.uni-mannheim.de>, and Edwin Thoen <edwinthoen@@gmail.com>
Andrie de Vries [aut, cre],
Brian D. Ripley [aut] (author of package tree)
Elisa Frutos Bernal <efb@usal.es>, Purificacion Galindo Villardon <pgalindo@usal.es>
Vincent T van Hees [aut, cre], Zhou Fang [ctb], Jing Hua Zhao [ctb]
Giovanni M. Marchetti, Mathias Drton, Kayvan Sadeghi
David Kahle <david.kahle@gmail.com>, Hadley Wickham
<h.wickham@gmail.com>
Xavier Fernández i Marín <xavier.fim@gmail.com>
Annie Bouvier, Christophe Giraud, Sylvie Huet, Verzelen N.
Hadley Wickham <h.wickham@gmail.com>, Winston Chang <winston@stdout.org>
Garrett Grolemund <garrett@rstudio.com>, Hadley
Wickham <h.wickham@gmail.com>
Jens Henrik Badsberg, Claus Dethlefsen, Luca La Rocca
William T.M. Dunsmuir <w.dunsmuir@unsw.edu.au>, Cenanning Li
<cli113@aucklanduni.ac.nz>, and David J. Scott
<d.scott@auckland.ac.nz>
Jerome Friedman, Trevor Hastie and Rob Tibshirani
Robert King <Robert.King@newcastle.edu.au>, Ben Dean <benjamin.dean@uon.edu.au>, Sigbert Klinke
Steve Su, with contributions from: Diethelm Wuertz, Martin Maechler and Rmetrics core team members for low discrepancy algorithm, Juha Karvanen for L moments codes,  Robert King for gld C codes and starship codes, Benjamin Dean for corrections and input in ks.gof code and R core team for histsu function.
David Duffy <David.Duffy@qimr.edu.au>.
C code in emgllmfitter by Andreas Borg <borg@imbei.uni-mainz.de>
Ian Marschner (using code from glm and glm.fit in the stats package)
Sanjay Chaudhuri <sanjay@stat.nus.edu.sg>, Mark S. Handcock
<handcock@stat.ucla.edu>, Michael S. Rendall
<mrendall@rand.org>.
Jeff Gill, George Casella, Minjung Kyung, and Jonathan Rapkin
Michele Morara, Louise Ryan, Subharup Guha, Christopher
Paciorek
Jerome Friedman, Trevor Hastie, Noah Simon, Rob Tibshirani
Wiebke Werft, Axel Benner and Douglas M. Potter
Achim Zeileis [aut, cre],
Roger Koenker [aut],
Philipp Doebler [aut]
Anne-Laure Boulesteix <boulesteix@ibe.med.uni-muenchen.de>,
Torsten Hothorn <torsten.hothorn@stat.uni-muenchen.de>.
Achim Zeileis [aut, cre],
Thomas Windberger [aut]
Anders Ellern Bilgrau, Martin Boegsted, Poul Svante Eriksen
Kornelius Rohmeyer [aut, cre],
Florian Klinglmueller [aut]
Xiaobei Zhao [aut, cre, cph],
Albin Sandelin [aut]
Gregory R. Warnes. Includes R source code and/or documentation
contributed by Ben Bolker, Thomas Lumley, and Randall C
Johnson. Contributions from Randall C. Johnson are Copyright
(2005) SAIC-Frederick, Inc. Funded by the Intramural Research
Program, of the NIH, National Cancer Institute, Center for
Cancer Research under NCI Contract NO1-CO-12400.
Antoine Lucas, Immanuel Scholz, Rainer Boehme <rb-gmp@reflex-studio.de>,
Sylvain Jasson <jasson@toulouse.inra.fr>,
Martin Maechler <maechler@stat.math.ethz.ch>
Jose M. Maisog, Guoli Wang, Karthik Devarajan
Elizabeth Gonzalez-Estrada, Jose A. Villasenor-Alva
Julian Faraway [aut],
George Marsaglia [aut],
John Marsaglia [aut],
Adrian Baddeley [aut, cre]
Markus Gesmann [aut, cre],
Diego de Castillo [aut],
Joe Cheng [ctb]
Roger D. Peng <rpeng@jhsph.edu> with contributions from Duncan
Murdoch and Barry Rowlingson; GPC library by Alan Murta
Brahim Brahim and Sun Makosso-Kallyth <sun.makosso-kallyth@crchuq.ulaval.ca>
Elizabeth Gonzalez Estrada, Jose A. Villasenor Alva
Blake MacDoanld, Hugh Chipman, Pritam Ranjan
Gregory R. Warnes, Ben Bolker, Lodewijk Bonebakker, Robert
Gentleman, Wolfgang Huber Andy Liaw, Thomas Lumley, Martin
Maechler, Arni Magnusson, Steffen Moeller, Marc Schwartz, Bill
Venables
Cyprien Mbogning <cyprien.mbogning@inserm.fr> and Wilson Toussile
Arne B. Gjuvsland <arne.gjuvsland@nmbu.no> and Yunpeng Wang <yunpeng.wng@gmail.com>
Juri Reimand <juri.reimand@ut.ee>, Raivo Kolde
<rkolde@gmail.com>, Tambet Arak <tambet.arak@gmail.com>
Alfredo Kalaitzis <alkalait@gmail.com>,
Antti Honkela <antti.honkela@tkk.fi>,
Pei Gao <pg349@medschl.cam.ac.uk>,
Neil D. Lawrence <N.Lawrence@dcs.shef.ac.uk>
Josh Buckner <nullsatz@gmail.com>, Mark Seligman
<mseligman@rapidbiologics.com>, Justin Wilson
Farzad Noorian, Anthony Mihirana de Silva
Deepak N. Ayyala, Shili Lin, Department of Statistics, The Ohio State University, Columbus, Ohio, USA.
Robert M. Pruzek <RMPruzek@yahoo.com> and James E. Helmreich <James.Helmreich@Marist.edu>
Brian A. Danielak <itsme@briandk.com>, Robert M. Pruzek
<RMPruzek@yahoo.com>, with contributions from: William E. J.
Doane <wil@DrDoane.com>, James E. Helmreich
<James.Helmreich@Marist.edu>, Jason Bryer <jason@bryer.org>
Smitha Ankinakatte <Smitha.AA@agrsci.dk> and David Edwards <David.Edwards@agrsci.dk>
Gabriel Coelho Goncalves de Abreu <abreu_ga@yahoo.com.br>,
Rodrigo Labouriau <Rodrigo.Labouriau@agrsci.dk>,
David Edwards <David.Edwards@agrsci.dk>.
Brahim Brahim <brahim.brahim@bigdatavisualizations.com> and Sun Makosso-Kallyth <sun.makosso-kallyth@crchuq.ulaval.ca>
Søren Højsgaard <sorenh@math.aau.dk> with contributions from Claus Dethlefsen <cld@rn.dk> and Clive Bowsher <C.Bowsher@statslab.cam.ac.uk>
Søren Højsgaard <sorenh@math.aau.dk>, Steffen L. Lauritzen
<steffen@stats.ox.ac.uk>
Frank E Harrell Jr <f.harrell@vanderbilt.edu>
Paul Murrell [cre, aut],
Simon Potter [aut]
Paul Murrell [aut, cre],
Richard Walton [aut],
Simon Potter [ctb]
Dimitris Rizopoulos <d.rizopoulos@erasmusmc.nl> and Spyridoula
Tsonaka <s.tsonaka@lumc.nl>
Dingfeng Jiang <dingfengjiang at gmail.com>
Daniel Amorese, Paul A. Rydelek and Jean-Robert Grasso
Andy Leung, Mike Danilov, Victor Yohai, Ruben Zamar
Tomislav Hengl [cre, aut],
Bas Kempen [ctb],
Gerard Heuvelink [ctb],
Brendan Malone [ctb],
Hannes Reuter [ctb]
Robin K. S. Hankin; qrng functions by Duncan Murdoch and
multimin by Andrew Clausen
Edzer Pebesma [aut, cre],
Benedikt Graeler [aut]
Mohammad Hossein Dehghan, Thierry Duchesne and Sophie Baillargeon
Gregory R. Warnes, Ben Bolker, and Thomas Lumley
Marco Toledo Bastos & Cornelius Puschmann
Michael Friendly [aut, cre], Stephane Dray [aut]
Anne-Lise Caillat, Christophe Dutang, V'eronique Larrieu and
Triet Nguyen
Carlo Albert <carlo.albert@eawag.ch> & Sören Vogel <soeren.vogel@posteo.ch>
Edsel A. Pena <pena@stat.sc.edu> and Elizabeth H. Slate <slateeh@musc.edu>
Ming-Huei Chen <mhchen@bu.edu> and Qiong Yang <qyang@bu.edu>
Daniel Taliun <Daniel.Taliun@eurac.edu>, Christian Fuchsberger
<cfuchsb@umich.edu>, Cristian Pattaro
<Cristian.Pattaro@eurac.edu>
Benjamin Stich <stich@mpipz.mpg.de>, Bettina Mueller
<b.mueller@uni-hohenheim.de>, Hans-Peter Piepho
<piepho@uni-hohenheim.de>
John Verzani. Based on  the iwidgets code of Simon Urbanek and suggestions by Simon Urbanek, Philippe Grosjean and Michael Lawrence
Binbin Lu[aut], Paul Harris[aut], Martin Charlton[aut], Chris Brunsdon[aut], Tomoki Nakaya[aut], Isabella Gollini[ctb]
A.J. Saez-Castillo <ajsaez@ujaen.es>, J. Rodriguez-Avi
<jravi@ujaen.es>, A. Conde-Sanchez <aconde@ujaen.es>, M.J.
Olmo-Jimenez <mjolmo@ujaen.es>, A.M. Martinez-Rodriguez
<ammartin@ujaen.es>
Ostap Okhrin <ostap.okhrin@wiwi.hu-berlin.de> and Alexander Ristig <ristigal@hu-berlin.de>
Andreas Dominik Cullmann [aut, cre],
Edgar Kublin [ctb]
K. Burkett <kburkett@sfu.ca>, B. McNeney <mcneney@sfu.ca>, J.
Graham <jgraham@stat.sfu.ca>, with code for case-control data
contributed by Zhijian Chen <z11chen@math.uwaterloo.ca>
S. Dejean, N. Oumouhou, D. Estivals, B. Mangin
Albart Coster [aut, cre],
John Bastiaansen [aut]
Pedro Rafael Diniz Marinho [aut, cre], Francisco Cribari Neto [aut, ctb]
Laurent Bergé, Charles Bouveyron and Stéphane Girard
Tarmo K. Remmel, Sandor Kabos, and Ferenc (Ferko) Csillag
Lukas Meier, Nicolai Meinshausen, Ruben Dezeure
Quentin Grimonprez [aut, cre],
Serge Iovleff [ctb]
Rob J Hyndman <Rob.Hyndman@monash.edu> with contributions from Jochen Einbeck and Matt Wand
Wei Qian <weiqian@stat.umn.edu>, Yi Yang <yiyang@umn.edu>, Hui Zou <hzou@stat.umn.edu>
Shilin Zhao, Yan Guo, Quanhu Sheng, Yu Shyr
Justin Esarey [aut, cre],
Andrew Pierce [aut],
Jericho Du [aut]
Juuso Parkkinen, Leo Lahti, Joona Lehtomaki
John Fox [aut], Michael Friendly [aut, cre], Georges Monette [ctb]
Michael Schweinberger <michael.schweinberger@rice.edu>, Mark S. Handcock <handcock@ucla.edu>
Dan Carr <dcarr@voxel.galaxy.gmu.edu>, ported by Nicholas
Lewin-Koh and Martin Maechler <maechler@stat.math.ethz.ch>,
contains copies of lattice function written by Deepayan Sarkar
<deepayan.sarkar@r-project.org>
The students of the `Advanced R Programming Course' Hannah
Frick, Ivan Kondofersky, Oliver S. Kuehnle, Christian
Lindenlaub, Georg Pfundstein, Matthias Speidel, Martin
Spindler, Ariane Straub, Florian Wickler, Katharina Zink under
the supervision of Manuel Eugster and Torsten Hothorn
Shachar Kaufman, based in part on an earlier implementation by Ruth Heller and Yair Heller.
Daniel C. Bowman [aut, cre],
Jonathan M. Lees [ctb]
Giovanni Petris <GPetris@Uark.edu> and Luca Tardella
<luca.tardella@@uniroma1.it>; original C code for ARMS by Wally
Gilks.
Hamada S. Badr [aut, cre],
Benjamin F. Zaitchik [aut],
Amin K. Dezfuli [aut]
Jason A. Osborne and Christopher T. Franck
Jonathan Cornelissen, Kris Boudt, Scott Payseur
Romain Francois [aut, cre],
Andre Simon [ctb]
Heidi Seibold <Heidi.Seibold@campus.lmu.de>, Monia Mahling
<monia.mahling@stat.uni-muenchen.de>
Michael Friendly [aut, cre],
Stephane Dray [ctb],
Hadley Wickham [ctb],
James Hanley [ctb],
Dennis Murphy [ctb]
Thoralf Mildenberger, Yves Rozenholc, David Zasada.
Murray Stokely [aut, cre],
Tim Hesterberg [ctb]
Ingo Feinerer [aut],
Stefan Theussl [aut, cre]
Bryan Hanson [aut, cre],
Vesna Memisevic [ctb],
Jonathan Chung [ctb]
Original S-PLUS code by Peter J. Rousseeuw and Andreas Christmann,
R port by Tobias Verbeke
Samrachana Adhikari, Brian Junker, Tracy Sweet, Andrew C. Thomas
Christoforos Anagnostopoulos
<christoforos.anagnostopoulos06@imperial.ac.uk> and David J.
Hand <d.j.hand@imperial.ac.uk>
Frank E Harrell Jr <f.harrell@vanderbilt.edu>, with
contributions from Charles Dupont and many others.
Scientific Software Development - Dr. Lin Himmelmann,
www.linhi.com
Rolf Turner <r.turner@auckland.ac.nz> and Limin Liu.
Colombi Roberto and Sabrina Giordano and Manuela Cazzaro, with contributions from Joseph Lang
Patricio S. La Rosa, Elena Deych, Berkley Shands, William D.
Shannon
Patricio S. La Rosa, Elena Deych, Berkley Shands, William D. Shannon
Skyler Cranmer, Jeff Gill, Natalie Jackson, Andreas Murr, Dave Armstrong
Yue Deng, Gang Wang, Xiaocheng Huang, Qinhao Wu , Yang Wang and Tingting Ma
Ghislain Vieilledent, Cory Merow, Jérôme Guélat, Andrew M. Latimer, Marc Kéry, Alan E. Gelfand, Adam M. Wilson, Frédéric Mortier and John A. Silander Jr.
Jan Bulla <bulla@math.unicaen.fr>, Ingo Bulla
<ingobulla@googlemail.com>
Mohammad Ferdosi <mferdosi@myune.edu.au>, Cedric Gondro <cgondro2@une.edu.au>
Guanhua Chen [aut, cre],
Michael Kosorok [aut],
Shannon Holloway [ctb]
"Markus Loecher, Berlin School of Economics and Law (BSEL)"
<markus.loecher@gmail.com>
Rob J Hyndman, Earo Wang and Alan Lee with contributions from Roman A Ahmed and Han Lin Shang to earlier versions of the package
Andrea Rau, Gilles Celeux, Marie-Laure Martin-Magniette, Cathy Maugis-Rabusseau
Andrea Rau, Marie-Laure Martin-Magniette, Cathy Maugis-Rabusseau
Hadley Wickham [cre, aut, cph],
RStudio [cph]
Tuo Zhao, Han Liu, Kathryn Roeder, John Lafferty, Larry
Wasserman
Natalia Novoselova,Junxi Wang,Jialiang Li, Frank Pessler,Frank Klawonn
John Maindonald, with \code{hwexact()} from Randall Johnson.
Jon Wakefield [aut, cre],
Martyn Plummer [ctb]
Michel Ballings, Dauwe Vercamer, and Dirk Van den Poel
Hugh Chipman, Rob Tibshirani, with tsvq code originally from
Trevor Hastie
Mauricio Zambrano-Bigiarini [aut, cre], Rodrigo Rojas [ctb]
Mauricio Zambrano-Bigiarini [aut, cre, cph]
Chengfeng Liu,Huiqing Liu,Yingyan Liang,Ruibin Feng
Spencer Maynes, Fan Yang, and Anne Parkhurst
Pierre-Andre Cornillon, Nicolas Hengartner, Eric Matzner-Lober
Antoine Tremblay, NeuroCognitive Imaging Lab, Dalhousie University
Alex Goldstein, Adam Kapelner, Justin Bleich
Itziar Irigoien [aut, cre],
Concepcion Arenas [aut]
Klaus Nordhausen, Hannu Oja, David E. Tyler
Klaus Nordhausen, Seija Sirkia, Hannu Oja, David E. Tyler
Christopher S. McMahan and  Lianming Wang
Francesca Mangili <francesca@idsia.ch>,
Alessio Benavoli <alessio@idsia.ch>,
Cassio P. de Campos <cassiopc@acm.org>,
Marco Zaffalon <zaffalon@idsia.ch>
Greg Jefferis with contributions from Thomas Braun
Bartek Rajwa, Murat Dundar, Allison Irvine, Tan Dang
Thomas J. Leeper [aut, cre],
Aaron Statham [ctb],
Yihui Xie [ctb]
Junghyun Namkung, Taeyoung Hwang, MinSeok Kwon, Sunggon Yi and
Wonil Chung
P Lafaye de Micheaux <lafaye@dms.umontreal.ca>, M Bilodeau
<bilodeau@dms.umontreal.ca>
Achim Zeileis [aut, cre],
Christian Kleiber [ctb]
Denis Talbot, Thierry Duchesne, Jacques Brisson, Nathalie
Vandal
Rense Nieuwenhuis, Ben Pelzer, Manfred te Grotenhuis
Virgilio Gomez Rubio <virgilio.gomez@uclm.es>, Roger Bivand <Roger.Bivand@nhh.no>
Oleg Sklyar, Duncan Murdoch, Mike Smith, Dirk Eddelbuettel, Romain Francois
Toby Dylan Hocking [aut, cre],
Keith Ponting [aut],
Thomas Wutzler [aut],
Philippe Grosjean [aut],
Markus Müller [aut],
R Core Team [ctb, cph]
Tal Galili [aut, cre, cph] (http://www.r-statistics.com),
Barry Rowlingson [ctb],
Boris Hejblum [ctb],
Dason [ctb],
Felix Schonbrodt [ctb],
G. Grothendieck [ctb],
GERGELY DAROCZI [ctb],
Heuristic Andrew [ctb],
James [ctb] (http://stackoverflow.com/users/269476/james),
Thomas Leeper [ctb] (http://thomasleeper.com/),
VitoshKa [ctb],
Yihui Xie [ctb] (http://yihui.name),
Michael Friendly [ctb] (http://datavis.ca/),
Kornelius Rohmeyer [ctb] (http://algorithm-forge.com/techblog/),
Dieter Menne [ctb],
Tyler Hunt [ctb] (https://github.com/JackStat),
Takekatsu Hiramura [ctb] (https://github.com/hiratake55)
Alicja Wolny--Dominiak and Michal Trzesiok
Edzer Pebesma <edzer.pebesma@uni-muenster.de>, Jon Olav Skoien <jon.skoien@gmail.com>  with contributions from Olivier Baume, A.Chorti, D.T. Hristopulos, H. Kazianka, S.J. Melles, G. Spiliopoulos
Edzer Pebesma <edzer.pebesma@uni-muenster.de>, Jon Skoien <jon.skoien@gmail.com> with contributions from Olivier Baume, A.Chorti, D.T. Hristopulos, S.J. Melles, G. Spiliopoulos
Volkmar Henschel [aut, cre], Ulrich Mansmann [aut], Christiane
Heiss [ctb]
Sebastien Dejean, Ignacio Gonzalez and Kim-Anh Le Cao
Aaron Fisher <afisher@jhsph.edu>, Michael Rosenblum
<mrosenbl@jhsph.edu>, Harris Jaffee<hj@jhu.edu>
Dominik Heider, PhD, University of Duisburg-Essen, Germany
Jorge Luis Ojeda Cabrera, <jojeda@unizar.es>
Daniel Caro <daniel.caro@education.ox.ac.uk>, Przemyslaw Biecek <przemyslaw.biecek@gmail.com>
Simon Urbanek <simon.urbanek@r-project.org>, Tobias Wichtrey <tobias@tarphos.de>
CJE Metcalf, SM McMahon, R Salguero-Gomez, E Jongejans, C Merow
Andrea Peters [aut],
Torsten Hothorn [aut, cre],
Brian D. Ripley [ctb],
Terry Therneau [ctb],
Beth Atkinson [ctb]
Emanuel P. Barbosa,
Flavio M. M. Barros <flaviomargarito@gmail.com>,
Elias de Jesus Goncalves <eliasjg@gmail.com>,
Daniela R. Recchia <daniela_recchia@yahoo.com.br>
Kristin A. Linn, Eric B. Laber, Leonard A. Stefanski
Manuel López-Ibáñez, Jérémie Dubois-Lacoste,
Thomas Stützle, Mauro Birattari, Eric Yuan and Prasanna Balaprakash
Antonio Mora <antoniocmora@gmail.com>, Ian Donaldson <ian.oslo@gmail.com>
Eufrasio de A. Lima Neto with contribution from Claudio A.
Vasconcelos
Matthias Gamer <m.gamer@uke.uni-hamburg.de>, Jim Lemon
<jim@bitwrit.com.au>, Ian Fellows <ifellows@uscd.edu> Puspendra
Singh <puspendra.pusp22@gmail.com>
Claus Ekstrom <ekstrom@sund.ku.dk> and Helle Sorensen <helle@math.ku.dk>
Ricardo Jorge de Almeida Queiroz Filho <rjaqfcin@gmail.com>,
Roberta Andrade de Araujo Fagundes <raaf@cin.ufpe.br>
Claudia van Borkulo, Sacha Epskamp, with contributions from Alexander Robitzsch
Gareth James, Daniela Witten, Trevor Hastie and Rob Tibshirani
Original S functions written by Janet E. Heffernan with R port
and R documentation provided by Alec G. Stephenson.
Byeong Yeob Choi, Jason P. Fine and M. Alan Brookhart
Sandra Barragán, Miguel A. Fernández and Cristina Rueda.
Christian Buchta [aut],
Kurt Hornik [aut, cre]
Patrick Mair [aut, cre], Jan De Leeuw [aut], Kurt Hornik [aut]
Mary C Meyer, Jiwen Wu, and Jean D. Opsomer
Uwe Block <u.block.mz@googlemail.com>, using an algorithm by
Hatto von Hatzfeld <hatto@salesianer.de>
Premal Shah, Benjamin Fitzpatrick, James Fordyce
Randy Lai [aut, cre], Martin Broadhurst [aut]
John A. Ramey <johnramey@gmail.com>,
Kayla Schaefer <kschaefer.tx@gmail.com>
Alex Goldstein (itree modifications) Terry Therneau, Beth
Atkinson, Brian Ripley (rpart)
Portfolio & Risk Advisory Group, Commerzbank Securities
Weihua An, Huizi Xu, and Zhida Zheng, Indiana University
Bloomington
Alex Lenkoski, Anna Karl, Andreas Neudecker
Paul R. Staab, Lisha Mathew and Dirk Metzler
Klaus Nordhausen, Jean-Francois Cardoso, Jari Miettinen, Hannu Oja, Esa Ollila, Sara Taskinen
Chaitanya R. Acharya and Andrew S. Allen
Markus Helbig <info@markushelbig.de>, Simon Urbanek, Ian Fellows
Pete Philipson, Ines Sousa, Peter Diggle, Paula Williamson,
Ruwanthi Kolamunnage-Dona, Robin Henderson
Feifei Zheng, Michael Leonard, Seth Westra
Ehsan Bahrami Samani and Zhale Tahmasebinejad
Jeroen Ooms, Duncan Temple Lang, Lloyd Hilaiel
Andrew Gelman, Gary King and Andrew C. Thomas
David B. Dahl [aut, cre], Scala developers [ctb] (see http://scala-lang.org/), beanshell2 developers [ctb] (see https://code.google.com/p/beanshell2/), jline2 developers [ctb] (see https://github.com/jline/jline2)
Michel Grabisch, Ivan Kojadinovic, Patrick Meyer.
Soo-Heang Eo [aut, cre],
Seung-Mo Hong [ctb],
HyungJun Cho [aut, ths]
Dave Zes, Jimmy Lewis, Dana Landis @ Korn/Ferry International
Bjorn Andersson, Kenny Branberg and Marie Wiberg
Alejandro Quintela del Rio and Graciela Estevez Perez
Alexandros Karatzoglou [aut, cre],
Alex Smola [aut],
Kurt Hornik [aut]
Matt Wand [aut],
Brian Ripley [trl, cre, ctb] (R port and updates)
Angelo Mazza, Antonio Punzo, Brian McGuire
Victor Moreno, Nilanjan Chatterjee, Bhramar Mukherjee
Johannes Ranke, with contributions from Katrin Lindenberger
Terry Therneau, Elizabeth Atkinson, Jason Sinnwell, Daniel Schaid, Shannon McDonnell
Christian Roever, Nils Raabe, Karsten Luebke, Uwe Ligges, Gero Szepannek, Marc Zentgraf
Yifan Yang <yifan.yang@uky.edu> , Mai Zhou<mai@ms.uky.edu>
Christophe Genolini [cre, aut],
Bruno Falissard [ctb]
Christophe Genolini [cre, aut],
Bruno Falissard [ctb],
Jean-Baptiste Pingault [ctb]
Mamoun O. Benghezal [aut, cre],
Christophe Genolini [ctb]
Original by Klein and Moeschberger, modifications by Jun Yan
<jun.yan@uconn.edu>
Adam Vogt [ctb],
Alastair Andrew [ctb],
Alex Zvoleff [ctb],
Ashley Manton [ctb],
Brian Diggs [ctb],
Cassio Pereira [ctb],
Fitch Simeon [ctb],
Hadley Wickham [ctb],
Heewon Jeon [ctb],
Jeff Arnold [ctb],
Jeremy Stephens [ctb],
Jim Hester [ctb],
Joe Cheng [ctb],
Jonathan Keane [ctb],
J.J. Allaire [ctb],
Johan Toloe [ctb],
Joseph Larmarange [ctb],
Kirill Mueller [ctb],
Kohske Takahashi [ctb],
Michael Friendly [ctb],
Michel Kuhlmann [ctb],
Nacho Caballero [ctb],
Nick Salkowski [ctb],
Noam Ross [ctb],
Ramnath Vaidyanathan [ctb],
Richard Cotton [ctb],
Romain Francois [ctb],
Sietse Brouwer [ctb],
Simon de Bernard [ctb],
Taiyun Wei [ctb],
Thibaut Lamadon [ctb],
Tom Torsney-Weir [ctb],
Trevor Davis [ctb],
Weicheng Zhu [ctb],
Wush Wu [ctb],
Yihui Xie [aut, cre]
Boxian Wei & Fan Yang & Xinmiao Wang & Yanni Ge
m.eik michalke [aut, cre],
Earl Brown [ctb],
Alberto Mirisola [ctb],
Alexandre Brulet [ctb],
Laura Hauser [ctb]
Clement Chevalier, Victor Picheny and David Ginsbourger
Jens Hainmueller (Stanford) Chad Hazlett (UCLA)
Youyi Fong <youyifong@gmail.com>, Saheli Datta, Krisztian Sebestyen
Christina Stahl [aut, cre],
David Meyer [aut]
Claus Ekstrom <ekstrom@sund.ku.dk>, Ib M. Skovgaard <ims@life.ku.dk>, Torben Martinussen <tma@sund.ku.dk>
Youyi Fong <yfong@fhcrc.org>, Krisztian Sebestyen <ksebestyen@gmail.com>
Brian Close <brian.close@gmail.com> and Igor Zurbenko <IZurbenko@albany.edu>
Wei Yang <peterwyang@gmail.com> and Igor Zurbenko
<igorg.zurbenko@gmail.com>
Derek Cyr <cyr.derek@gmail.com> and Igor Zurbenko <igorg.zurbenko@gmail.com>.
John Ehrlinger [aut, cre], Hemant Ishwaran [aut]
Vahid PARTOVI NIA, Anthony Davison, and Arpit Chaudhary
Stefano M.Iacus <stefano.iacus@unimi.it> and Guido Masarotto
<guido@sirio.stat.unipd.it>
Andreas Alfons [aut, cre],
Josef Holzer [aut],
Matthias Templ [aut]
Laercio Junio da Silva <laerciojunio@yahoo.com.br>
Michael Friendly [aut, cre], Dennis Murphy [ctb], Martin Monkman [ctb], Chris Dalzell [ctb]
Luke Winslow, Jake Zwart, Ryan Batt, Jessica Corman, Hilary Dugan, Paul
Hanson, Gordon Holtgrieve, Aline Jaimes, Jordan Read, Richard Woolway
Richard Zijdeman [aut, cre],
Josemiguel Lana-Berasain [ctb]
Weihua An and Xuefu Wang, Indiana University Bloomington
Trevor Hastie <hastie@stanford.edu> and Brad Efron
<brad@stat.stanford.edu>
Justin Lokhorst, Bill Venables and Berwin Turlach;
port to R, tests etc: Martin Maechler <maechler@stat.math.ethz.ch>
Pavel N. Krivitsky [aut, cre],
Mark S. Handcock [aut],
Susan M. Shortreed [ctb],
Jeremy Tantrum [ctb],
Peter D. Hoff [ctb]
Deepayan Sarkar <deepayan.sarkar@r-project.org>, Felix Andrews <felix@nfrac.org>
Douglas Nychka [aut, cre], Dorit Hammerling [aut], Stephan Sain [aut], Nathan Lenssen [aut]
Yves Rosseel [aut, cre],
Daniel Oberski [ctb],
Jarrett Byrnes [ctb],
Leonard Vanbrabant [ctb],
Victoria Savalei [ctb],
Ed Merkle [ctb],
Michael Hallquist [ctb],
Mijke Rhemtulla [ctb],
Myrsini Katsikatsou [ctb],
Mariska Barendse [ctb]
(in alphabetical order)
Joseph L. Gastwirth <jlgast@gwu.edu>;
Yulia R. Gel <ygl@math.uwaterloo.ca>;
W. L. Wallace Hui <wlwhui@uwaterloo.ca>;
Vyacheslav Lyubchich <vlyubchich@uwaterloo.ca>;
Weiwen Miao <miao@macalester.edu>;
Kimihiro Noguchi <kinoguchi@ucdavis.edu>
Mauro Birattari <mbiro@ulb.ac.be> and Gianluca Bontempi
<gbonte@ulb.ac.be>
Antonio Coppola [aut, cre, cph],
Brandon Stewart [aut, cph],
Naoaki Okazaki [aut, cph],
David Ardia [ctb, cph],
Dirk Eddelbuettel [ctb, cph],
Katharine Mullen [ctb, cph],
Jorge Nocedal [ctb, cph]
Pierre-Jerome Bergeron and Vahid Partovi Nia
Arafat TAYEB <arafat.tayeb@ircm.qc.ca>, Alexandre BUREAU
<alexandre.bureau@msp.ulaval.ca> and Aurelie Labbe
<aurelie.labbe@mcgill.ca>
Antoine Tremblay, Department of Psychology, Dalhousie University
Cecile Proust-Lima, Viviane Philipps, Amadou Diakite and Benoit Liquet
Charlie Casper and Oscar A. Perez.  Based on FORTRAN
program ld98.
David Desrousseaux, Florian Sandron, Aurelie Siberchicot,
Christine Cierco-Ayrolles and Brigitte Mangin
Daniel Taliun <Daniel.Taliun@eurac.edu>, Johann
Gamper<gamper@inf.unibz.it>, Cristian Pattaro
<Cristian.Pattaro@eurac.edu>
Ji-Hyung Shin <shin@sfu.ca>, Sigal Blay <sblay@sfu.ca>,
Nicholas Lewin-Koh <nikko@hailmail.net>, Brad McNeney
<mcneney@sfu.ca>, Jinko Graham <jgraham@sfu.ca>
Ehsan Masoudi, Majid Sarmad and Hooshang Talebi
Eero Liski [aut, cre],
Klaus Nordhausen [aut],
Hannu Oja [aut],
Anne Ruiz-Gazen [aut]
Christian Graul, with contributions from Francois Guillem
Yunting Sun <yunting.sun@gmail.com> , Nancy R.Zhang
<nzhang@stanford.edu>, Art B.Owen <owen@stanford.edu>
Thomas Lumley <tlumley@u.washington.edu> using Fortran code by
Alan Miller
Lafaye de Micheaux Pierre, Drouilhet Remy, Liquet Benoit
David W. Gerbing, School of Business Administration, Portland State University
Simen Gaure, Ragnar Frisch Centre for Economic Research
B. M. Taylor, T. M. Davies, B. S. Rowlingson, P. J. Diggle. Additional
code contributions from E. Pebesma.
Steffen Greilich and the libamtrack team
Thibault Helleputte <thibault.helleputte@dnalytics.com>; Pierre Gramme <pierre.gramme@dnalytics.com>
Ehsan Bahrami Samani and Parisa ParsaMaram
Giorgio Alfredo Spedicato with contributions from Reinhold Kainhofer and Kevin J. Owens
David Balding, Adrian Timpson, Christopher Steele, Mayeul d'Avezac, James Hetherington.
Karline Soetaert <karline.soetaert@nioz.nl>,
Dick van Oevelen<dick.vanoevelen@nioz.nl>
Karline Soetaert [aut, cre], Karel Van den Meersche [aut], Dick
van Oevelen [aut], LAPACK authors [cph]
Sriharsha Veeramachaneni and Ken Williams
Alex T. Kalinka <alex.t.kalinka@gmail.com>, with contributions from Alain Guenoche
Yi Xu <statxy@gmail.com> and Jixiang Wu <jixiang.wu@sdstate.edu>
Yannick Baraud, Christophe Giraud, Sylvie Huet
Graeme Blair <gblair@Princeton.edu>, Kosuke Imai
<kimai@Princeton.edu>, Bethany Park <bapark@princeton.edu>
Lars Kotthoff [aut,cre],
Bernd Bischl [aut],
Barry Hurley [ctb],
Talal Rahwan [ctb]
Holger Diedrich, Dr. Markus Abel (Department of Physics,
University Potsdam)
Marc Adams [aut, cre],
authors of R function 'lm' [ctb] (general interface),
authors of 'lm.gls' [ctb] (interface and R code for matrix weights),
U.S. NIST [ctb] (C++ code for TNT::Vector template)
Douglas Bates [aut], Martin Maechler [aut],
Ben Bolker [aut, cre], Steven Walker [aut],
Rune Haubo Bojesen Christensen [ctb],
Henrik Singmann [ctb], Bin Dai [ctb]
Antoine Tremblay, Dalhousie University, and Johannes Ransijn, University of Copenhagen
Alexandra Kuznetsova [aut, cre],
Per Bruun Brockhoff [aut, ths],
Rune Haubo Bojesen Christensen [aut]
Francesco Bartolucci, University of Perugia (IT),
http://www.stat.unipg.it/bartolucci
Jasmin Straube, Kim-Anh Le Cao and Emma Huang with contributions of
Dominique Gorse
Torsten Hothorn [aut],
Achim Zeileis [aut, cre],
Richard W. Farebrother [aut] (pan.f),
Clint Cummins [aut] (pan.f),
Giovanni Millo [ctb],
David Mitchell [ctb]
Claudio Agostinelli <claudio@unive.it>, Mario Romanazzi <romanaz@unive.it> and SLATEC Common Mathematical Library <www.netlib.org/slatec>
Walerian Sokolowski [aut, cre, cph],
Wit Jakuczun [aut, cph],
Natalia Okinczyc [aut],
Bogumil Kaminski [aut]
Bradley Efron, Brit B. Turnbull and Balasubramanian Narasimhan
John Myles White [aut, cph],
Kenton White [ctb],
Kirill Müller [ctb, cre]
Madeleine Cule, Robert Gramacy, Richard Samworth, Yining Chen
Dominic Schuhmacher <dominic.schuhmacher@mathematik.uni-goettingen.de>, Kaspar Rufibach <kaspar.rufibach@gmail.com>, Lutz Duembgen <duembgen@stat.unibe.ch>
Kaspar Rufibach <kaspar.rufibach@gmail.com> and Lutz Duembgen <duembgen@stat.unibe.ch>
Kaspar Rufibach <kaspar.rufibach@gmail.com> and Fadoua Balabdaoui <fadoua@ceremade.dauphine.fr> and Hanna Jankowski <hkj@mathstat.yorku.ca> and Kathrin Weyermann <kathrin.weyermann@bkw-fmb.ch>
Thomas L. Vincent <tlfvincent@gmail.com>, Peter J. Green and Derek N. Woolfson <D.N.Woolfson@bristol.ac.uk>
Charles Kooperberg <clk@fhcrc.org> and Ingo Ruczinski <ingo@jhu.edu>
Georg Heinze <georg.heinze@meduniwien.ac.at>, Meinhard Ploner,
Daniela Dunkler (former versions), Harry Southworth (former
versions)
Pei Wang <pwang@fhcrc.org>, Dennis Chao <dchao@fhcrc.org>, Li
Hsu <lih@fhcrc.org>
Eva Herrmann <eherrmann@mathematik.tu-darmstadt.de> (F77 & S original);
Packaged for R and enhanced by Martin Maechler
Thomas Ruf, partially based on C original by Press et al. (Numerical Recipes)
Stephen Tueller. Funded by the National Institute on Drug Abuse (NIDA) Award number 1R03DA030850 and the National Institute on Alcohol Abuse and Alcoholism (NIAAA) Award Number R03 AA019775.
Rainer Opgen-Rhein and Korbinian Strimmer.
S scripts originally by Jan Beran <jan.beran@uni-konstanz.de>;
Datasets via Brandon Whitcher <brandon@stat.washington.edu>.
Toplevel R functions and much more by Martin Maechler.
Michael C. Donohue, Anthony C. Gamst, Steven D. Edland
Seung W. Choi, with contributions from Laura E. Gibbons and
Paul K. Crane
John T. Ormerod <jormerod@sydney.edu.au>, M. P. Wand
<matt@maths.unsw.edua.au>
Burkhardt Seifert <seifert@ifspm.unizh.ch> (S original);
Packaged for R by Martin Maechler
lp_solve <http://lpsolve.sourceforge.net/>,
Kjell Konis <kjell.konis@me.com>.
Learned Pattern Similarity (LPS) for time series by Mustafa Gokce Baydogan,
Ensemble of regression trees by Andy Liaw and Matthew Wiener.
Ajith Dissanayake [aut, cre],
P. Wijekoon [aut],
R-core [cph]
Idris Eckley and Guy Nason, with contributions from Sarah Taylor and Matthew Nunes
Sarah Taylor [aut], Matt Nunes [aut, cre], Idris Eckley [ctb, ths]
Bjoern Schwalb, Achim Tresch, Romain Francois
Lin Huang <lh2123@columbia.edu>, Zhezhen Jin <zj7@columbia.edu>
Joshua Schwab [aut, cre],
Samuel Lendle [aut],
Maya Petersen [aut],
Mark van der Laan [aut],
Susan Gruber [ctb]
Betul Kan Kilinc [aut, cre], Ozlem Alpu [aut, cre]
Jin Chen, Jun Chen, Naresh Kumar, Dong Liang
Garrett Grolemund [aut, cre],
Hadley Wickham [aut],
Vitalie Spinu [ctb],
Imanuel Constigan [ctb],
Chel Hee Lee [ctb],
Richard Cotton [ctb],
Ian Lyttle [ctb],
Winston Chang [ctb]
Ji-Hyung Shin <shin@sfu.ca>, Brad McNeney
<mcneney@stat.sfu.ca>, Jinko Graham <jgraham@stat.sfu.ca>
Sebastian Kreutzer [aut, trl, cre],
Christoph Schmidt [aut],
Margret C. Fuchs [aut],
Michael Dietze [aut],
Manfred Fischer [aut, trl],
Christoph Burow [aut],
Markus Fuchs [ths]
Ehsan Bahrami Samani and Nourallah Tazikeh Miyandarreh
Yi Liu [aut, cre],
Richard A. Olshen [aut],
Andrew Z. Fire [aut],
Scott D. Boyd [aut]
Karl Kugler, Laurin Mueller, Simone Ecker, Armin Graber (with
contributions from Matthias Wieser)
Stefan Milton Bache <stefan@stefanbache.dk> and
Hadley Wickham <h.wickham@gmail.com>
Statistical Consulting - intelligent data analysis, stochastic modelling and statistical inference, Dr. Lin Himmelmann
Pedro Duarte Silva <psilva@porto.ucp.pt> Paula Brito <mpbrito.fep.up.pt>
Sebastian Gibb [aut, cre],
Korbinian Strimmer [ths]
Sebastian Gibb [aut, cre],
Pietro Franceschi [ctb]
Thomas Jaki <jaki.thomas@gmail.com>  and Dominic Magirr <d.magirr@gmail.co.uk>
Lauren L. Hulsman Hanna and David G. Riley
Nikolaos Kourentzes and Fotios Petropoulos
Original S code by Richard A. Becker and Allan R. Wilks.
R version by Ray Brownrigg <Ray.Brownrigg@ecs.vuw.ac.nz>.
Veronica Vieira, Scott Bartell, and Robin Bliss
Patrick Brown <patrick.brown@utoronto.ca> [aut, cre],  Barry Rowlingson [ctb]
Doug McIlroy.  Packaged for R by Ray Brownrigg and Thomas P
Minka, transition to Plan 9 codebase by Roger Bivand.
Original S code by Richard A. Becker and Allan R. Wilks.
R version by Ray Brownrigg <Ray.Brownrigg@ecs.vuw.ac.nz>.
Enhancements by Thomas P Minka <tpminka@media.mit.edu>
Roger Bivand [cre, aut],
Nicholas Lewin-Koh [aut],
Edzer Pebesma [ctb],
Eric Archer [ctb],
Adrian Baddeley [ctb],
Hans-Jörg Bibiko [ctb],
Steven Brey [ctb],
Jonathan Callahan [ctb],
German Carrillo [ctb],
Stéphane Dray [ctb],
David Forrest [ctb],
Michael Friendly [ctb],
Patrick Giraudoux [ctb],
Duncan Golicher [ctb],
Virgilio Gómez Rubio [ctb],
Patrick Hausmann [ctb],
Karl Ove Hufthammer [ctb],
Thomas Jagger [ctb],
Sebastian Luque [ctb],
Don MacQueen [ctb],
Andrew Niccolai [ctb],
Oscar Perpiñán Lamigueiro [ctb],
Tom Short [ctb],
Greg Snow [ctb],
Ben Stabler [ctb],
Murray Stokely [ctb],
Rolf Turner [ctb]
Denis White, Robert B. Gramacy <rbgramacy@chicagobooth.edu>
Karline Soetaert <karline.soetaert@nioz.nl>,
Thomas Petzoldt <thomas.petzoldt@tu-dresden.de>,
Filip Meysman <filip.meysman@nioz.nl>
Elisa Loza [aut, cre], David Phillips [aut, com]
JJ Allaire, Jeffrey Horner, Vicent Marti, and Natacha Porte
Jeff Laake <jeff.laake@noaa.gov>, Devin Johnson
<devin.johnson@noaa.gov>, Paul Conn <paul.conn@noaa.gov>
D. Commenges <Daniel.Commenges@isped.u-bordeaux2.fr>, M. Prague
<Melanie.Prague@isped.u-bordeaux2.fr> and A. Diakite
Eli Holmes, Eric Ward, and Kellie Wills, NOAA, Seattle, USA
Andreas Dominik Cullmann [aut, cre],
Daniel Mandallaz [ctb],
Alexander Francis Massey [ctb]
Brian Ripley [aut, cre, cph],
Bill Venables [ctb],
Douglas M. Bates [ctb],
Kurt Hornik [trl] (partial port ca 1998),
Albrecht Gebhardt [trl] (partial port ca 1998),
David Firth [ctb]
Gary King, Christopher Lucas, and Richard Nielsen
Daniel Ho <daniel.e.ho@gmail.com>, Kosuke Imai
<kimai@Princeton.Edu>, Gary King <king@harvard.edu>, Elizabeth
Stuart <stuart@stat.harvard.edu>
Original S code by Patrick J. Burns. Ported to R by Nick
Efthymiou. Adapted to new R releases by Claus Dethlefsen.
Ben Murrell, Dan Murrell & Hugh Murrell.
Douglas Bates <bates@stat.wisc.edu> and Martin Maechler
Douglas Bates <bates@stat.wisc.edu> and Martin Maechler
<maechler@stat.math.ethz.ch>
Henrik Bengtsson [aut, cre, cph], Hector Corrada Bravo [ctb], Robert Gentleman [ctb], Ola Hossjer [ctb], Harris Jaffee [ctb], Peter Langfelder [ctb]
Chih-Yi Chien, Chen-An Tsai, Ching-Wei Chang, and James J. Chen
Jianan Tian <jiant17@gmail.com>, Chenliang Xu
<lucky.random@gmail.com>
Ott Toomet <otoomet@gmail.com>,
Arne Henningsen <arne.henningsen@gmail.com>,
with contributions from Spencer Graves and Yves Croissant
Andrew O. Finley <finleya@msu.edu>, Sudipto Banerjee
<sudiptob@biostat.umn.edu>
Victor Urrea, Malu Calle, Kristel Van Steen, Nuria Malats
Torsten Hothorn [aut, cre],
Peter Buehlmann [aut],
Thomas Kneib [aut],
Matthias Schmid [aut],
Benjamin Hofner [aut],
Fabian Sobotka [ctb],
Fabian Scheipl [ctb]
Regis Pouillot [aut, cre],
Marie-Laure Delignette-Muller [ctb],
Jean-Baptiste Denis [ctb]
Gregory R. Warnes <greg@warnes.net>, Robert Burrows
Thierry Chenard [aut],
Rafael Najmanovich [aut, cre]
Minjeong Jeon, Cari Kaufman, and Sophia Rabe-Hesketh
Chris Fraley, Adrian Raftery and Luca Scrucca
Charles J. Geyer <charlie@stat.umn.edu> and Leif T. Johnson
<ltjohnson@google.com>
Andrew D. Martin, Kevin M. Quinn, and Jong Hee Park
S. McKay Curtis with contributions from Ilya Goldin
James M. Flegal <jflegal@ucr.edu> and John Hughes
<hughesj@umn.edu>
Olaf Mersmann [aut, cre],
Heike Trautmann [ctb],
Detlef Steuer [ctb],
Bernd Bischl [ctb],
Kalyanmoy Deb [cph]
Rob J Hyndman with assistance from Muhammad Akram and Christoph
Bergmeir.
Frank Schaarschmidt [aut, cre], Daniel Gerhard [aut], Martin Sill [aut]
Bjoern Bornkamp, Jose Pinheiro and Frank Bretz
Ekaterina Manuilova <ekaterina.manuilova@roche.com> Andre Schuetzenmeister <andre.schuetzenmeister@roche.com> Fabian Model <fabian.model@roche.com>
Christian P. Robert, Universite Paris Dauphine
S original by Trevor Hastie & Robert Tibshirani. Original R port by Friedrich Leisch, Kurt Hornik and Brian D. Ripley.
Glenn De'ath <g.death@aims.gov.au>; Code for mdm was adapted from multinom in the nnet package (Brian Ripley <ripley@stats.ox.ac.uk> and Bill Venables <Bill.Venables@csiro.au>)
Iadine Chades, Guillaume Chapron, Marie-Josee Cros, Frederick Garcia, Regis Sabbadin
Robert X.D. Hawkins <rxdh@stanford.edu>, Joe Houpt
<joseph.houpt@wright.edu>, Noah Silbert <noahpoah@gmail.com>, Leslie
Blaha <Leslie.Blaha@wpafb.af.mil>, Thomas D. Wickens
<twickens@socrates.berkeley.edu>
Hrishikesh D. Vinod <Vinod@fordham.edu> and Javier
López-de-Lacalle
Dustin Tingley <dtingley@gov.harvard.edu>,
Teppei Yamamoto <teppei@mit.edu>,
Kentaro Hirose <hirose@princeton.edu>,
Luke Keele <ljk20@psu.edu>,
Kosuke Imai <kimai@princeton.edu>
Adriano Polpo <polpo@ufscar.br>, Carlos Alberto de Braganca
Pereira <cpereira@ime.usp.br>
Jun Zhu <junzhu@cs.cmu.edu>, Aykut FIRAT <aykut@mit.edu>
Jose E. Lozano Alonso <lozalojo@jcyl.es>
Martin Elff (with contributions by Christopher N. Lawrence, Dave Atkins, Jason W. Morgan, and Achim Zeileis)
Douglas Bates <bates@stat.wisc.edu>,
Martin Maechler <maechler@R-project.org> and
Ben Bolker <bbolker@gmail.com>
Xiaoyue Cheng, Dianne Cook, Heike Hofmann
Nyamundanda Gift, Isobel Claire Gormley and Lorraine Brennan.
Sean C. Anderson [aut, cre],
Jonathan W. Moore [ctb],
Michelle M. McClure [ctb],
Nicholas K. Dulvy [ctb],
Andrew B. Cooper [ctb]
Thomas W. D. Möbius <kontakt@thomasmoebius.de>
Don Kang <donkang75@gmail.com> and George Tseng
<ctseng@pitt.edu>
Guillemette Marot, Florence Jaffrezic, Andrea Rau
Guido Schwarzer [aut, cre],
James Carpenter [aut],
Gerta Rücker [aut]
Oscar Perpinan Lamigueiro [cre, aut],
Marcelo Almeida [ctb]
Bendix Carstensen, Lyle Gurrin, Claus Ekstrom, Michal Figurski
Stephen L R Ellison <s.ellison@lgc.co.uk>.
Tim Bergsma, Bill Knebel, Leonid Gibiansky, Natalie Hsiang
Cherise R. Chin Fatt, Derek Beaton, Herve Abdi.
Wenxuan Zhong <wenxuan@illinois.edu>, Ping Ma
<pingma@illinois.edu>
Yiwen Zhang <yzhang31@ncsu.edu> and Hua Zhou <hua_zhou@ncsu.edu>
Jared O'Connell <jaredoconnell@gmail.com>, Søren Højsgaard
<sorenh@math.aau.dk>
Yves Croissant [aut, cre],
Fabrizio Carlevaro [aut],
Stephane Hoareau [aut]
Andrew Gelman <gelman@stat.columbia.edu>,
Jennifer Hill <jh1030@columbia.edu>,
Yu-Sung Su <suyusung@tsinghua.edu.cn>,
Masanao Yajima <my2167@columbia.edu>,
Maria Grazia Pittau <grazia@stat.columbia.edu>
Stef van Buuren [aut, cre],
Karin Groothuis-Oudshoorn [aut],
Alexander Robitzsch [ctb],
Gerko Vink [ctb],
Lisa Doove [ctb],
Shahab Jolani [ctb]
Arne Henningsen and Geraldine Henningsen
Olaf Mersmann [aut, cre],
Claudia Beleites [ctb],
Rainer Hurling [ctb],
Ari Friedman [ctb]
Carlos J. Gil Bellosta [aut, cre],
Carlos Neira [ctb],
José Luis Cañadas Reche [ctb]
Quinn Payton [aut],
Tony Olsen [aut],
Marc Weber [ctb],
Michael McManus [ctb],
Tom Kincaid [cre]
Dan Carr <dcarr@gmu.edu> and Jim Pearson <jpearson@statnetconsulting.com>,
with contributions from Linda Pickle <linda@statnetconsulting.com>.
Virmantas Kvedaras <virmantas.kvedaras@mif.vu.lt>, Vaidotas Zemlys
<vaidotas.zemlys@mif.vu.lt>
Robin Engler <robin.engler@gmail.com> and
Wim Hordijk <wim@WorldWideWanderings.net> and
Loic Pellissier <loic.pellissier@unil.ch>
Lajos Bálint <balint@demografia.hu> and Gergely Daróczi
<daroczig@rapporter.net>
Daniel Lee <bearlee@alum.mit.edu>,
Yu-Sung Su <suyusung@tsinghua.edu.cn>
Jeffrey Horner [ctb],
Yihui Xie [aut, cre]
Russell Steele, Naisyin Wang, and Adrian Raftery
Michele Filosi [aut, cre], Roberto Visintainer [aut], Davide
Albanese [aut], Samantha Riccadonna [ctb], Giuseppe Jurman
[ctb], Cesare Furlanello [ctb]
Revolution Analytics [aut],
Andrie de Vries [aut, cre]
Timur V. Elzhov, Katharine M. Mullen, Andrej-Nikolai Spiess, Ben Bolker
Douglas Bates, Katharine M. Mullen, John C. Nash, Ravi Varadhan
Shannon M. Bell <bell.shannonm@gmail.com>, Lyle D. Burgoon
<burgoon.lyle@epa.gov>
Phil Chalmers [aut, cre, cph],
Joshua Pritikin [ctb],
Mateusz Zoltak [ctb]
Stephan Artmann, Klaus Jung, Tim Beissbarth
Mortaza Jamshidian, Siavash Jalal, and Camden Jansen
Gilles Defaux, Bertrand Iooss, Vincent Moutoussamy, Clement Walter with contributions from Nicolas Bousquet and Paul Lemaitre
N. Basturk, L.F. Hoogerheide, A. Opschoor, H.K. van Dijk
Original by Joseph L. Schafer <jls@stat.psu.edu>.
Peter Macdonald <pdmmac@mcmaster.ca>, with contributions from
Juan Du <duduyy@hotmail.com>
Jean-Benoist Leger <jleger@agroparistech.fr>, ENS Cachan
Jean-Benoist Leger <jleger@agroparistech.fr>, INRA
Christophe Ambroise [aut], Gilles Grasseau [aut], Mark Hoebeke [aut], Pierre Latouche [aut,cre],  Vicent Miele [aut], Franck Picard [aut], LAPACK authors [aut] (copyrights apply to src/*.f)
John Lawson <lawson@byu.edu> and Cameron Willden <ccwillden@gmail.com>, with contributions from Greg Piepel
Cristina Tortora, Ryan P. Browne, Brian C. Franczak and Paul D. McNicholas.
Sebastien Dejean, Ignacio Gonzalez, Kim-Anh Le Cao with
contributions from Pierre Monget, Jeff Coquery, FangZou Yao,
Benoit Liquet, Florian Rohart
Volodymyr Melnykov [aut, cre],
Wei-Chen Chen [aut],
Ranjan Maitra [aut]
Marcos Prates <marcosop@est.ufmg.br>, Victor Lachos
<hlachos@ime.unicamp.br> and Celso Cabral
<celsoromulo@gmail.com>
Derek Young [aut, cre],
Tatiana Benaglia [aut],
Didier Chauveau [aut],
David Hunter [aut],
Ryan Elmore [ctb],
Thomas Hettmansperger [ctb],
Hoben Thomas [ctb],
Fengjuan Xuan [ctb]
Finlay Scott (JRC, IT) and Julia Blanchard (Sheffield University, UK)
and Ken Andersen (DTU Aqua, Copenhagen, DK)
Jeff A. Tracey, James Sheppard, Jun Zhu, Robert Sinkovts, Amit Chourasia, Glenn Lockwood, and Robert N. Fisher
Johannes Ranke [aut, cre, cph],
Katrin Lindenberger [ctb],
René Lehmann [ctb],
Eurofins Regulatory AG [cph]
Friedrich Leisch and Evgenia Dimitriadou.
Ken Knoblauch [aut, cre],
Laurence T. Maloney [aut]
Kenneth Knoblauch and Laurence T. Maloney,
based in part on C code written by Laurence T. Maloney
and J. N. Yang
Douglas Bates <bates@stat.wisc.edu>,
Martin Maechler <maechler@R-project.org> and
Ben Bolker <bolker@mcmaster.ca>
Bernd Bischl [aut, cre],
Michel Lang [aut],
Jakob Bossek [aut],
Leonard Judt [aut],
Jakob Richter [aut],
Tobias Kuehn [aut],
Erich Studerus [aut]
E. Lopez-Vizcaino, M.J. Lombardia and D. Morales
Sheng Luo, Yong Chen, Xiao Su, Haitao Chu
Lin Li, Tobias Guennel,Scott Marshall, Leo Wang-Kit Cheung
Asad Hasan, Wang Zhiyu, Alireza S. Mahani
Klaus Nordhausen, Jyrki Mottonen, Hannu Oja
Fortran code by Alan Genz, R code by Adelchi Azzalini
Kosuke Imai <kimai@princeton.edu>, David A. van Dyk
<dvd@uci.edu>.
Nikhil Garge, Barry Eggleston and Georgiy Bobashev
Kaspar Rufibach <kaspar.rufibach@gmail.com> and Guenther Walther <gwalther@stanford.edu>
Ivan Marin-Franch <imarinfr@indiana.edu>, Kamila Zychaluk
<kamila.zychaluk@liverpool.ac.uk>, and David H. Foster
<d.h.foster@manchester.ac.uk>
Torsten Hothorn, Friedrich Leisch, Achim Zeileis
Nicholas J. Matzke, Dept. of Integrative Biology, U.C. Berkeley
Peter Langfelder <Peter.Langfelder@gmail.com> and Steve Horvath
<SHorvath@mednet.ucla.edu>
L. Andries van der Ark <L.A.vanderArk@uva.nl>
David Rossell, John D. Cook, Donatello Telesca, P. Roebuck
Lukasz Komsta <lukasz.komsta@umlub.pl>, Frederick Novomestky
<fnovomes@poly.edu>
Vincent Bonhomme, Sandrine Picq, Julien Claude with
suggestions and contributions from Cedric Gaucherel, Sarah Ivorra, Ricardo Kriebel,
Neus Martinez, Marcelo Reginato, Evan Saitta, Norbert Telmon, Asher Wishkerman.
Hannes Muehleisen [aut, cre],
Thomas Lumley [ctb],
Anthony Damico [ctb]
Sasha D. Hafner <sdh11@cornell.edu> and Jon Katz <jonkatz4@gmail.com>, with code for the Fourier transform from the seewave package (by Jerome Sueur, Thierry Aubin, and Caroline Simonis), and code for the readMP3 function from the tuneR package (by Uwe Ligges).
Berwin A. Turlach [aut, cre],
Kevin Murray [ctb]
This version Kay Pilz and Steffanie Titoff. Earlier
developements by Holger Dette and Kay Pilz.
Matthias Geissbuehler <matthias.geissbuehler@a3.epfl.ch>, James Manton
<ajd.manton@googlemail.com>
Marie Laure Delignette-Muller [aut, cre], Philippe Ruiz [aut, cre], Sandrine Charles [aut], Wandrille Duchemin [ctb], Christelle Lopes [ctb], Philippe Veber [ctb]
Randall Pruim <rpruim@calvin.edu>, Daniel Kaplan
<kaplan@macalester.edu>, Nicholas Horton <nhorton@amherst.edu>
Birgit Erni <birgit.erni@uct.ac.za>. Based on models
developed by Underhill and Zucchini (1988, 1990).
Bart Kranstauber <bart.kranstauber@uni-konstanz.de>, Marco Smolla <marco.smolla@postgrad.manchester.ac.uk>
Kurt Hornik [aut, cre],
Bettina Grün [aut]
Daniel Hernando Rodriguez <dhrodriguezt@unal.edu.co> & Campo
Elias Pardo <cepardot@unal.edu.co>
Quentin Grimonprez with contributions from Guillemette Marot and Samuel
Blanck. Some functions use code created by Sjoerd Vosse, Mark van de Wiel,
Pierre Neuvial, Henrik Bengtsson.
Zhu Wang, with contributions from Achim Zeileis, Simon Jackman, Brian Ripley, Trevor Hastie, Rob Tibshirani, Balasubramanian Narasimhan, Gil Chu and Patrick Breheny
Kenneth Knoblauch and Laurence T. Maloney
Patrick Rubin-Delanchy <patrick.rubin-delanchy@bristol.ac.uk> and Nicholas A Heard <n.heard@imperial.ac.uk>
Guillaume Guenard, with contribution from Pierre Legendre
Henrik Singmann [aut, cre],
David Kellen [aut],
Quentin Gronau [aut],
Christian Mueller [ctb],
Akhil S Bhel [ctb]
Lyamine Hedjazi and Jean-Baptiste Cazier
Gemechis Dilba Djira, Mario Hasler, Daniel Gerhard, Frank
Schaarschmidt
Christian Ritz <ritz@life.ku.dk>, Mads Jeppe Tarp-Johansen,
Jeff Laake <jeff.laake@noaa.gov>, David Borchers
<dlb@mcs.st-and.ac.uk>, Len Thomas <len@mcs.st-and.ac.uk>, David
Miller <dave@ninepointeightone.net> and Jon Bishop <jonb@mcs.st-and.ac.uk>
Nicolas De Jay, Simon Papillon-Cavanagh, Catharina Olsen, Gianluca Bontempi, Benjamin Haibe-Kains
Y. Foucher <Yohann.Foucher@univ-nantes.fr>, K. Trebern-Launay <katygre@yahoo.fr>
Patrick Brandt [aut, cre],
W. Ryan Davis [cre]
Elodie Courtois <courtoiselodie@gmail.com>, Yann Guitton
<yann.guitton@gmail.com>, Florence Nicole
<florence.nicole@univ-st-etienne.fr>
Yann Guitton <yann.guitton@gmail.com> Cyrille Conord
<conord@emse.fr>
John Marden [aut, cph] and James Balamuta [cre, ctb, com]
Samuel Gerber <sgerber@cs.utah.edu>, Kristi Potter
<kpotter@sci.utah.edu>, Oliver Ruebel <ruebel1@llnl.gov>
Nicole Ferguson <nicole.ferguson@kennesaw.edu>, Somnath Datta
<somnath.datta@louisville.edu>, Guy Brock
<guy.brock@louisville.edu>
Xiaogang Su, Peter Calhoun, and Juanjuan Fan
Hein Putter, Liesbeth de Wreede, Marta Fiocco
Josep A. Sanchez-Espigares, Alberto Lopez-Moreno
Juhui WANG [aut, cre] (Software and Engineering), Hervé MONOD [aut] (Applications and Statistical Methods), Robert FAIVRE [ctb] (Applications and Statistical Methods), Hervé RICHARD [ctb] (Software and Engineering)
Washington Junger <wjunger@ims.uerj.br> and Antonio Ponce de
Leon <ponce@ims.uerj.br>
Thomas J. Leeper [aut, cre],
Solomon Messing [ctb],
Sean Murphy [ctb]
The students of the `Advanced R Programming Course' Basil Abou
El-Komboz, Andreas Bender, Abdelilah El Hadad, Laura Goeres,
Roman Hornung, Max Hughes-Brandl, Christian Lindenlaub,
Christina Riedel, Ariane Straub, Florian Wickler under the
supervision of Manuel Eugster and Torsten Hothorn
S original by Kenneth Hess, <khess@odin.mdacc.tmc.edu> R port
by R. Gentleman
Torsten Hothorn [aut, cre],
Frank Bretz [aut],
Peter Westfall [aut],
Richard M. Heiberger [ctb],
Andre Schuetzenmeister [ctb]
Spencer Graves, Hans-Peter Piepho and Luciano Selzer with help
from Sundar Dorai-Raj
Ana Belen Nieto Librero <ananieto@usal.es>, Nora Baccala
<norabaccala@gmail.com>, Purificacion Vicente Galindo
<purivg@usal.es>, Purificacion Galindo Villardon
<pgalindo@usal.es>
Eric Lunde, Mariza de Andrade, Beth Atkinson, Pat Votruba
James M. Curran, Aaron Williams, Dave Barber
Aida Eslami, El Mostafa Qannari, Stephanie Bougeard, Gaston Sanchez
Questions and comments go to Aida Eslami <aida.eslami@yahoo.fr> and
Stephanie Bougeard <stephanie.bougeard@anses.fr>
Francesco Bartolucci, Silvia Bacci, Michela Gnaldi - University of Perugia (IT)
Walter R. Mebane, Jr. <wrm1@cornell.edu>, Jasjeet Singh Sekhon
<sekhon@berkeley.edu>
Lachlan Coin, Paul O'Reilly, Yotsawat Pompyen, Clive Hoggart
and Federico Calboli
Stephan Ritter <sritter@berkeley.edu>, Alan Hubbard <hubbard@berkeley.edu>, Nicholas Jewell <jewell@berkeley.edu>
Matieyendou LAMBONI <clamboni@yahoo.fr>, Herve MONOD
<herve.monod@jouy.inra.fr>, Caroline BIDOT
<caroline.bidot@jouy.inra.fr>
Karim Rahim <karim.rahim@gmail.com>, Wesley Burr <wburr@mast.queensu.ca>
Nathaniel Graham and Mahmood Arai and Bj<c3><b6>rn Hagstr<c3><b6>mer
Alex Dmitrienko, Eric Nantz, and Gautier Paux, with
contributions by Thomas Brechenmacher
Edoardo Gaude, Francesca Chignola, Dimitrios Spiliotopoulos,
Silvia Mari, Andrea Spitaleri and Michela Ghitti
Jose Gama [aut, cre, trl],
Paul Centore [aut, cph],
Göran Högnäs [ths]
Ryan T. Moore <rtm@wustl.edu> and Andrew Reeves <areeves@bu.edu>
Algorithm by Robert C. Edgar. R port by Alex T. Kalinka.
Knut M. Wittkowski <kmw@rockefeller.edu> and Tingting Song
<ttsong@gmail.com>
MuToss Coding Team (Berlin 2010), Gilles Blanchard, Thorsten Dickhaus,
Niklas Hack, Frank Konietschke, Kornelius Rohmeyer,
Jonathan Rosenblatt, Marsel Scheer, Wiebke Werft
Yi Wang, Ulrike Naumann, Stephen Wright and David Warton
Andrew T. Karl (Adsurgo LLC), Jennifer Broatch (Arizona State University)
Julien Clavel, with contributions from Aaron King, and Emmanuel Paradis
Matteo Fasiolo, using the C++ parallel RNG of Thijs van den Berg and Ziggurat algorithm of Jens Maurer and Steven Watanabe (boost)
Kevin Gross, with help from Douglas Bates
Peter Filzmoser <P.Filzmoser@tuwien.ac.at> and
Moritz Gschwandtner <e0125439@student.tuwien.ac.at>
rpart by Terry M Therneau and Beth Atkinson <atkinson@mayo.edu>.
R port of rpart by Brian Ripley <ripley@stats.ox.ac.uk>.
Some routines from vegan -- Jari Oksanen <jari.oksanen@oulu.fi>
Extensions and adaptations of rpart to mvpart by Glenn De'ath <g.death@aims.gov.au>.
Marie-Helene Ouellette <marieheleneo@gmail.com>, with contributions from Pierre Legendre <pierre.legendre@umontreal.ca>.
Jean-Eudes Dazard [aut, cre],
Hua Xu [ctb],
Alberto Santana [ctb]
Alan Genz [aut],
Frank Bretz [aut],
Tetsuhisa Miwa [aut],
Xuefei Mi [aut],
Friedrich Leisch [ctb],
Fabian Scheipl [ctb],
Bjoern Bornkamp [ctb],
Martin Maechler [ctb],
Torsten Hothorn [aut, cre]
Ioannis Tsamardinos, Vincenzo Lagani, Giorgos Borboudakis, Giorgos Athineou
Deepika Kulshreshtha, Rupanjali Chaudhuri, S. Ramachandran
Stephane Mangenat (for libnabo), Gregory Jefferis
Marius Hofert <marius.hofert@math.ethz.ch> and Martin Maechler
Winston Chang, Daniel Adler, Hadley Wickham, Gregory R. Warnes,
R Core Team
Anton Gagin, Katharine Mullen, Igor Levin
Malika Charrad <malika.charrad.1@ulaval.ca> and
Nadia Ghazzali <nadia.ghazzali@uqtr.ca> and
Veronique Boiteau <veronique.boiteau.1@ulaval.ca> and
Azam Niknafs <azam.niknafs.1@ulaval.ca>
Yanming Di <diy@stat.oregonstate.edu>, Daniel W Schafer
<schafer@stat.oregonstate.edu>, with contributions from Jason S Cumbie
<cumbiej@onid.orst.edu> and Jeff H Chang <changj@cgrb.oregonstate.edu>.
David Bronaugh <bronaugh@uvic.ca> for the Pacific Climate Impacts
Consortium (PCIC)
Siomara Cristina Broch, Daniel Furtado Ferreira
Daniel Furtado Ferreira, Izabela Regina Cardoso de Oliveira and
Fernando Henrique Toledo
Antti Arppe [aut],
Peter Hendrix [aut],
Petar Milin [aut],
R. Harald Baayen [aut],
Cyrus Shaoul [aut, cre]
Skye Bender-deMoll [cre, aut],
Martina Morris [ctb]
Holger Hoefling, Ryan Tibshirani, Robert Tibshirani
Annegret Grimm, Bernd Gruber & Klaus Henle
Sebastien Bihorel, Michael Baudin (author of the original
module)
Mike Nowak <michael.nowak@gmail.com>, Solomon Messing
<solomon.messing@gmail.com>, Sean J Westwood
<seanjw@stanford.edu>, and Dan McFarland
<dmcfarla@stanford.edu>
Shannon M. Bell <bell.shannonm@gmail.com>, Lyle D. Burgoon
<burgoon.lyle@gmail.com>
Mike Nowak <michael.nowak@gmail.com>, Sean J Westwood
<seanjw@stanford.edu>, Solomon Messing
<solomon.messing@gmail.com>, and Dan McFarland
<dmcfarla@stanford.edu>
Karline Soetaert <karline.soetaert@nioz.nl>,
Julius Kipyegon Kones <jkones@uonbi.ac.ke>,
Dick van Oevelen <dick.vanoevelen@nioz.nl>
Gerta Rücker [aut],
Guido Schwarzer [aut, cre],
Ulrike Krahn [aut],
Jochem König [aut]
Michele Filosi [aut, cre], Roberto Visintainer [aut], Samantha Riccadonna [aut], Giuseppe Jurman [ctb], Cesare Furlanello [ctb]
Carter T. Butts [aut, cre],
David Hunter [ctb],
Mark Handcock [ctb],
Skye Bender-deMoll [ctb],
Jeffrey Horner [ctb]
Carter T. Butts [aut],
Ayn Leslie-Cook [aut],
Pavel N. Krivitsky [aut],
Skye Bender-deMoll [aut, cre],
Zack Almquist [ctb],
David R. Hunter [ctb],
Li Wang [ctb],
Kirk Li [ctb],
Steven M. Goodreau [ctb],
Martina Morris [ctb]
Ryan Admiraal <R.Admiraal@murdoch.edu.au> and Mark S. Handcock
<handcock@ucla.edu>
Alexander W Blocker, Paul Koullick, Edoardo Airoldi
Stefan Fritsch, Frauke Guenther <guenther@bips.uni-bremen.de>,
following earlier work by Marc Suling
Marijke Welvaert with contributions from Joke Durnez, Beatrijs
Moerkerke, Yves Rosseel and Geert Verdoolaege
Gilles Raiche (Universite du Quebec a Montreal) and David Magis (Universite de Liege)
Drew Schmidt [aut, cre], Christian Heckendorf [aut]
John Hughes <hughesj@umn.edu> and Xiaohui Cui <cuixx132@umn.edu>
Martin Lysy [aut, cre],
Ashley D. Stasko [aut, ctb],
Heidi K. Swanson [aut, ctb]
original from National Institutes for Standards and Technology
(NIST) http://www.itl.nist.gov/div898/strd/nls/nls_main.shtml R
port by Douglas Bates <bates@stat.wisc.edu>
José Pinheiro [aut] (S version),
Douglas Bates [aut] (up to 2007),
Saikat DebRoy [ctb] (up to 2002),
Deepayan Sarkar [ctb] (up to 2005),
EISPACK authors [ctb] (src/rs.f),
R-core [aut, cre]
Andrzej Galecki <agalecki@umich.edu>, Tomasz Burzykowski
<tomasz.burzykowski@uhasselt.be>
Jelmer Ypma <uctpjyy@ucl.ac.uk>, with contributions by
Hans W. Borchers <hwborchers@googlemail.com> and
Dirk Eddelbuettel <edd@debian.org>
S original by Alessandra R. Brazzale <alessandra.brazzale@unipd.it>
and Ruggero Bellio <ruggero.bellio@uniud.it>.  R port by Alessandra R.
Brazzale <alessandra.brazzale@unipd.it>, following earlier work by
Douglas Bates.
Zheng Sengui,Lu Xufen,Hou Qiongchen,Zheng Jianhui
Florent Baty <florent.baty@gmail.com> and Marie-Laure
Delignette-Muller <marielaure.delignettemuller@vetagro-sup.fr>
Aldo Garay <amedina@ime.usp.br>, Marcos Prates
<marcosop@est.ufmg.br> and Victor Lachos
<hlachos@ime.unicamp.br>
Florent Baty <florent.baty@gmail.com> and Marie-Laure
Delignette-Muller <marielaure.delignettemuller@vetagro-sup.fr>,
with contributions from Sandrine Charles, Jean-Pierre Flandrois,
and Christian Ritz
Will Beasley, Joe Rodgers, David Bard, Michael Hunter, and Kelly
Meredith
Katharine M. Mullen and Ivo H. M. van Stokkum
Arnaud Le Rouzic (2007-2010), Arne B. Gjuvsland (2010), Olivier
Ariste (2010)
Julio Cesar Hernandez Sanchez, Jose Luis Vicente-Villardon
Dong-hyun Oh <oh.donghyun77@gmail.com>, with Dukrok Suh
Heikki Tuomenvirta [aut], Achim Drebs [aut], Eirik Forland
[aut], Ole Einar Tveito [aut], Hans Alexandersson [aut], Ellen
Vaarby Laursen [aut], Trausti Jonsson [aut], Jose' Gama [cre]
Ported to R by Alvaro A. Novo <alvaro@novo-online.net>.
Original by Joseph L. Schafer <jls@stat.psu.edu>.
David Scott <d.scott@auckland.ac.nz>, Jason Shicong Fu and
Simon Potter
Juergen Gross <gross@statistik.uni-dortmund.de>, bug fixes by
Uwe Ligges
originally written by Subbiah M <sisufive@gmail.com> packaged
by Sumathi R with considerable contributions by Srinivasan M R
Jeffrey S. Racine [aut, cre],
Tristen Hayfield [aut]
Kimihiro Noguchi <kinoguchi@ucdavis.edu>, Mahbub Latif
<mlatif@gwdg.de>, Karthinathan Thangavelu <kthanga@gwdg.de>,
Frank Konietschke <fkoniet@gwdg.de>, Yulia R. Gel
<ygl@math.uwaterloo.ca>, Edgar Brunner
<brunner@ams.med.uni-goettingen.de>
Quanli Wang, Daniel Manrique-Vallier, Jerome P. Reiter and Jingchen Hu
Abdelaati Daouia <Abdelaati.Daouia@tse-fr.eu>, Thibault Laurent <thibault.laurent@univ-tlse1.fr>, Hohsuk Noh <word5810@gmail.com>
Yi Zheng [aut, cre],
Chia-Yi Chiu [aut],
Jeffrey A. Douglas [ctb]
María Oliveira, Rosa M. Crujeiras and Alberto Rodríguez-Casal
Emmanuelle Comets, Karl Brendel, Thi Huyen Tram Nguyen, France
Mentre.
Chao Cai, Songfeng Wang, Wenbin Lu, Jiajia Zhang
Robert V Baron <rvb5@pitt.edu>, Nandita Mukhopadhyay,
Xinyu Tang, Daniel E. Weeks <weeks@pitt.edu>
Frederic Commo [aut, cre],
Brian M. Bot [aut]
Chung-Hsing Chen <chchen@nhri.org.tw>, I-Shou Chang <ischang@nhri.org.tw> and Chao A. Hsiung <hsiung@nhri.org.tw>
Jochen Einbeck, Ross Darnell and John Hinde
R port by Douglas Bates <bates@stat.wisc.edu>
Maria Ortiz-Estevez <mortizest@gmail.com>, Ander Aranburu
<aaranburu@ceit.es>, Angel Rubio <arubio@ceit.es>
Christian Sigg [aut, cre],
R Core team [aut]
The Institute of Statistical Mathematics, based on the program by Ushio Tanaka
Grant Schneider, Eric Chicken, Rachel Becvarik
Alberto Viglione [aut, cre],
Jonathan R. M. Hosking [ctb],
Francesco Laio [ctb],
Alan Miller [ctb],
Eric Gaume [ctb],
Olivier Payrastre [ctb],
Jose Luis Salinas [ctb],
Chi Cong N'guyen [ctb],
Karine Halbert [ctb]
REvolution Computing <packages@revolution-computing.com> with
support and contributions from Pfizer, Inc.
Teresa Henriques, Luis Antunes and Cristina Costa-Santos
Keith Poole <ktpoole@uga.edu>, Jeffrey Lewis
<jblewis@ucla.edu>, James Lo <jameslo@princeton.edu> and Royce
Carroll <rcarroll@rice.edu>
Max Kuhn. Contributions from Steve Weston, Nathan Coulter, Patrick Lenon, Zekai Otles and the R Core Team
Daniel Fischer, Jyrki Möttönen, Klaus Nordhausen, Daniel Vogel
Brady Allred, Torre Hovick, Samuel Fuhlendorf
Rory Michelland <rory.michelland@gmail.com>,Laurent Cauquil
<laurent.cauquil@toulouse.inra.fr>
Phil Chalmers [cre, aut],
Carrie Smith [ctb],
Matthew Sigal [ctb]
Hae Kyung Im, Heather E. Wheeler, Keston Aquino Michaels, Vassily
Trubetskoy
Anja von Heydebreck <anja.von.heydebreck@merck.de>,
contributions from Christiane Heiss <christiane.heiss@web.de>
Sebastian Dümcke [aut, cre], Achim Tresch [ths]
Gabriel Margarido [aut], Marcelo Mollinari [aut, cre], Karl
Broman [ctb], Augusto Garcia [ctb]
Teemu Rintala, Einari Happonen, Jouni Tuomisto
Petr Simecek <simecek@gmail.com>,
Juergen Pilz <juergen.pilz@uni-klu.ac.at>,
Mingui Wang <mail.mwang@gmail.com>,
Albrecht Gebhardt <albrecht.gebhardt@uni-klu.ac.at>.
David Carslaw [aut, cre],
Karl Ropkins [aut]
David M Diez, Christopher D Barr, and Mine Cetinkaya-Rundel
Kurt Hornik [aut, cre],
The Apache Software Foundation [ctb, cph] (Apache OpenNLP Java
libraries),
JWNL development team [ctb, cph] (JWNL Java WordNet Library)
Ian Fellows, using the JMapViewer library by Jan Peter Stotz
Alexander Walker [aut, cre],
Luca Braglia [ctb]
Markus Goeker, with contributions by Benjamin Hofner, Lea A.I.
Vaas, Johannes Sikorski, Nora Buddruhs and Anne Fiebig
Markus Goeker, with contributions by Lea A.I. Vaas, Johannes
Sikorski and Maria del Carmen Montero-Calasanz
Monica Lopez-Raton, Maria Xose Rodriguez-Alvarez
John C Nash [aut, cre], Ravi Varadhan [aut], Gabor Grothendieck [ctb]
Bo Huang <bo.huang@pfizer.com> and Neal Thomas
<neal.thomas@pfizer.com>
Ben B. Hansen <ben.hansen@umich.edu>, Mark Fredrickson
<mark.m.fredrickson@gmail.com>, Josh Buckner, Josh Errickson, and Peter
Solenberger, with embedded Fortran code due to Dimitri P. Bertsekas
<dimitrib@mit.edu> and Paul Tseng
Trevor L Davis. Some documentation and examples ported from Allen Day's
getopt package. Some documentation from the optparse Python module by the
Python Software Foundation. Contributions from Steve Lianoglou, Jim
Nikelski, and Kirill Müller.
Adetayo Kasim, Martin Otava, Tobias Verbeke
Stefano Spada, Matteo Quartagno, Marco Tamburini
Jens J. Rogmann, University of Hamburg, Department of
Psychology, Germany
Rune Haubo Bojesen Christensen [aut, cre]
Kellie J. Archer, Jiayi Hou, Qing Zhou, Kyle Ferber, John G. Layne, Amanda Gentry
Fadoua Balabdaoui, Kaspar Rufibach, Filippo Santambrogio
Nathan G. Dodder, with code contributions from Katharine M. Mullen.
Tianqing Liu, Nan Lin, Ningzhong Shi and Baoxue Zhang
Fernando Fernandez-Palacin <fernando.fernandez@uca.es> and Manuel
Munoz-Marquez <manuel.munoz@uca.es>
Fernando Fernandez-Palacin <fernando.fernandez@uca.es> and
Manuel Munoz-Marquez <manuel.munoz@uca.es>
Eun-Kyung Lee, Sung Gon Yi, Yoojin Jung, Junghyun Namkung,
Taesung Park
Brandon Whitcher, Volker Schmid and Andrew Thornton, with
contributions by Karsten Tabelow and Jon Clayden
Sebastien Haneuse, Takumi Saegusa, Nilanjan Chaterjee, Norman Breslow, Elizabeth Smoot
Thomas Schlesinger and Manuel J. A. Eugster
Aaron A. King <kingaa@umich.edu> and Marguerite A. Butler <mbutler@hawaii.edu>
Thibaut Jombart <t.jombart@imperial.ac.uk>, Anne Cori, Xavier Didelot, Simon Cauchemez, Christophe Fraser, Neil Ferguson
The Hackout team (In alphabetic order: David Aanensen, Marc Baguelin, Paul Birrell, Simon Cauchemez, Anton Camacho, Caroline Colijn, Anne Cori, Xavier Didelot, Ken Eames, Christophe Fraser, Simon Frost, Niel Hens, Joseph Hugues, Thibaut Jombart, Lulla Opatowski, Oliver Ratmann, Samuel Soubeyrand, Marc Suchard, Jacco Wallinga, Rolf Ypma)
Soo-Heang Eo [aut, cre],
Huixia Judy Wang [ctb],
Lan Wang [ctb],
HyungJun Cho [aut]
Soo-Heang Eo [aut, cre],
HyungJun Cho [aut]
Jeremy M. Beaulieu <jbeaulieu@nimbios.org>, Brian O'Meara <bomeara@utk.edu>
Bill Venables [aut] (S original),
Kurt Hornik [aut, cre] (R port)
A.J. Perez-Luque; R. Moreno; R. Perez-Perez and F.J. Bonet
Luis Meira-Machado and Javier Roca-Pardinas <roca@uvigo.es>
Radhakrishnan Nagarajan <rnagarajan@uky.edu> and Marco Scutari <marco.scutari@gmail.com>
Kevin Ushey, Jonathan McPherson, Joe Cheng, JJ Allaire
Junwei Han,Yanjun Xu,Haixiu Yang,Chunquan Li and Xia Li
Cornelia Froemke, Ludwig Hothorn and Michael Schneider
Frank Schaarschmidt [aut, cre], Daniel Gerhard [aut]
Sara Varela [aut, cre],
Javier González Hernández [aut],
Luciano Fabris Sgarbi [aut]
Alexander Correa-Metrio, Dunia H. Urrego, Kenneth R. Cabrera,
Mark B. Bush
T. Hastie, R. Tibshirani, Balasubramanian Narasimhan, Gil Chu
Bernd Bischl <bernd_bischl@gmx.net>, Michel Lang
<lang@statistik.tu-dortmund.de>
Bernd Bischl [aut, cre],
Michel Lang [aut],
Jakob Bossek [aut],
Daniel Horn [aut]
Pengsheng Ji, Hussein Abdel-Haleem, H. Roger Boerma and Zenglu Li
Antonio Jose Saez-Castillo [aut, cre],
Faustino Prieto [aut],
Jose Maria Sarabia [aut]
Federico Rotolo <federico.rotolo@gustaveroussy.fr>
and Marco Munda <marco.munda@uclouvain.be>
Gabriele Sales <gabriele.sales@unipd.it>, Chiara Romualdi
<chiara.romualdi@unipd.it>
Annette Molinaro <annette.molinaro@ucsf.edu>,
Adam Olshen <olshena@biostat.ucsf.edu>,
Karen Lostritto <karen.lostritto@yale.edu>,
Gregory Ryslik <gregory.ryslik@yale.edu>,
Steve Weston <stephen.weston@yale.com>
Torsten Hothorn [aut, cre],
Kurt Hornik [aut],
Carolin Strobl [aut],
Achim Zeileis [aut]
Torsten Hothorn [aut, cre],
Achim Zeileis [aut]
Zhiqiu Hu; Shizhong Xu; Zhiquan Wang; Rongcai Yang
Philippe Grosjean [aut, cre],
Frederic Ibanez [aut],
Michele Etienne [ctb]
Klaas Hartmann, Gavin Thomas, Arne Mooers, Jeffrey Joy, Walter Jetz
Gaston Sanchez [aut, cre],
Tomas Aluja [aut, ths]
Tarmo K. Remmel, (Marie-Josee Fortin, Ferenc Csillag, Sandor Kabos)
Rafael Maia [aut, cre],
Chad Eliason [aut],
Pierre-Paul Bitton [aut]
Thomas Hoffmann <tjhoffm@gmail.com>, with contributions from
Christoph Lange <clange@hsph.harvard.edu>
Van Trung Pham and Gildas Mazo, with contributions from R Core team, Nash, Zhu, Byrd, Lu-Chen and Nocedal
Drew Schmidt [aut, cre],
Wei-Chen Chen [aut],
George Ostrouchov [aut],
Pragneshkumar Patel [aut]
Drew Schmidt [aut, cre],
Wei-Chen Chen [aut],
George Ostrouchov [aut],
Pragneshkumar Patel [aut],
R Core team [ctb] (some wrappers taken from the base and stats
packages)
Wei-Chen Chen [aut, cre],
George Ostrouchov [aut],
Drew Schmidt [aut],
Pragneshkumar Patel [aut],
Hao Yu [aut],
Christian Heckendorf [ctb] (FreeBSD),
Brian Ripley [ctb] (Windows HPC Pack 2012),
R Core team [ctb] (some functions are modified from the base packages)
Pragneshkumar Patel [aut, cre],
George Ostrouchov [aut],
Wei-Chen Chen [aut],
Drew Schmidt [aut],
David Pierce [aut]
Wei-Chen Chen [aut, cre],
Drew Schmidt [aut],
Gaurav Sehrawat [aut],
Pragneshkumar Patel [aut],
George Ostrouchov [aut]
Wei-Chen Chen [aut, cre],
Drew Schmidt [aut],
George Ostrouchov [aut],
Pragneshkumar Patel [aut],
Brian Ripley [ctb] (Solaris)
Fortran code by Alan Genz.  R code by Brenton Kenkel, based on
Adelchi Azzalini's 'mnormt' package.
Ulrich Halekoh <uhalekoh@health.sdu.dk> Søren Højsgaard <sorenh@math.aau.dk>
Jon T. Schnute <schnutej-dfo@shaw.ca>,
Rowan Haigh <rowan.haigh@dfo-mpo.gc.ca>,
Alex Couture-Beil <alex@mofo.ca>
Alex Couture-Beil <alex@mofo.ca>,
Jon T. Schnute <schnutej-dfo@shaw.ca>,
Rowan Haigh <rowan.haigh@dfo-mpo.gc.ca>,
Nicholas Boers <boersn@macewan.ca>,
Simon N. Wood, Ben J. Cairns
Jon T. Schnute <schnutej-dfo@shaw.ca>,
Nicholas Boers <boersn@macewan.ca>,
Rowan Haigh <rowan.haigh@dfo-mpo.gc.ca>,
Chris Grandin <chris.grandin@dfo-mpo.gc.ca>,
Angus Johnson, Paul Wessel, Franklin Antonio
Jon T. Schnute <schnutej-dfo@shaw.ca>,
Alex Couture-Beil <alex@mofo.ca>,
Rowan Haigh <rowan.haigh@dfo-mpo.gc.ca>,
A.R. (Rob) Kronlund <allen.kronlund@dfo-mpo.gc.ca>, and
Nicholas Boers <boersn@macewan.ca>
Sapan Jot <sapan.madaan@gmail.com> and Paul Brooks
<jpbrooks@vcu.edu>
Diego Colombo, Alain Hauser, Markus Kalisch, Martin Maechler
Marie Chavent and Vanessa Kuentz and Amaury Labenne and Benoit Liquet and Jerome Saracco
Carlos A. Arias <carias@icfes.gov.co> and Victor H. Cervantes
<vcervantes@icfes.gov.co>.
Peter Filzmoser, Heinrich Fritz, Klaudius Kalcher
Bin Yao <yaob@email.sc.edu> and Lianming Wang <wangl@stat.sc.edu>
David Bronaugh <bronaugh@uvic.ca> for the Pacific Climate
Impacts Consortium (PCIC); portions based on code written by
the R-Core team and Ulrich Drepper.
Lifeng Lin, Jing Zhang <zhan0979@umn.edu>, and Haitao Chu <chux0051@umn.edu>
Marlies Vervloet [aut, cre],
Henk Kiers [aut],
Eva Ceulemans [ctb]
Delia Bailey <delia.bailey@gmail.com> and Jonathan N. Katz
<jkatz@caltech.edu>
Adelchi Azzalini, Giovanna Menardi for the current version;
Adelchi Azzalini, Giovanna Menardi and Tiziana Rosolin up to version 0.1-13.
A. I. McLeod <aim@uwo.ca> and Mehmet Balcilar
<mehmet@mbalcilar.net>.
Meiling Liu, Sungho Won and Weicheng Zhu
Alessandro Samuel-Rosa [aut, cre],
Lucia H. C. Anjos [ths],
Gustavo M. Vasques [ths],
Gerard B. M. Heuvelink [ths]
Emmanuel Paradis [aut, cre, cph],
Klaus Schliep [aut, cph],
Alastair Potts [aut, cph],
David Winter [aut, cph]
Lin Chen <lchen@health.bsd.uchicago.edu> and Pei Wang <pwang@fhcrc.org>
Jelle Goeman <j.j.goeman@lumc.nl>, Rosa Meijer, Nimisha
Chaturvedi
Natalia Becker, Wiebke Werft, Axel Benner
L. Waldron, M. Pintilie, M.-S. Tsao, F. A. Shepherd, C. Huttenhower*, I. Jurisica* (*equal contribution)
Heike Hofmann, Eric Hare, ggobi Foundation
Daniel Osorio, Paola Rondon-Villarreal and Rodrigo Torres.
Anna Dudek, Harry Hurd and Wioletta Wojtowicz
Giorgio Valentini, Matteo Re -- Universita' degli Studi di Milano
Brian G. Peterson [cre, aut, cph],
Peter Carl [aut, cph],
Kris Boudt [ctb, cph],
Ross Bennett [ctb],
Joshua Ulrich [ctb],
Eric Zivot [ctb],
Matthieu Lestel [ctb],
Kyle Balkissoon [ctb],
Diethelm Wuertz [ctb]
Marie-Pierre Sylvestre, Thad Edens, Todd MacKenzie, Michal
Abrahamowicz
Ivo D. Shterev, Sin-Ho Jung, Stephen L. George and Kouros Owzar
Gavin L. Simpson [aut, cph, cre],
R Core Team [cph],
Douglas M. Bates [ctb],
Jari Oksanen [ctb]
Sadegh Rasoulinejad (R package and database version of the
dictionary) Manouchehr Aryanpour (for the original dictionary)
Joern Schulz <jschulz78@web.de> Peter Toft <pto@imm.dtu.dk>
Jesper James Jensen <jjj@oedan.dk> Peter Philipsen
<pap@imm.dtu.dk>
Xianyun Mao <xianyunmao@gmail.com> and Timothy Ryan
<tmr21@psu.edu>
Mohamed LAIB, Abla BOUDRAA and Zoubida Gheribi-Aoulmi
Paul McNicholas, Raju Jampani, Aaron McDaid, Brendan Murphy, Larry Banks
Kien Kieu <Kien.Kieu@jouy.inra.fr>, Marianne Mora <Marianne.Mora@u-paris10.fr>
Konrad Zych <k.zych@rug.nl> and Danny Arends <danny.arends@gmail.com>
Helios De Rosario-Martinez [aut, cre],
John Fox [ctb],
R Core Team [ctb]
S.R. Charlton, D.L. Parkhurst, and C.A.J. Appelo, with contributions
from D. Gillespie for Chipmunk BASIC and S.D. Cohen, A.C. Hindmarsh,
R. Serban, D. Shumaker, and A.G. Taylor for CVODE/SUNDIALS
J. Dustin Tracy, Zhiyi Xu, Leena Choi, Sari Acra, Kong Y. Chen, Maciej S. Buchowski
Wei-Chen Chen [aut, cre],
Karin Dorman [aut]
Pedro Tarroso, Guillermo Velo-Anton, Silvia Carvalho
R Hackathon et al. (alphabetically: Ben Bolker, Marguerite Butler,
Peter Cowan, Damien de Vienne, Dirk Eddelbuettel, Mark Holder, Thibaut
Jombart, Steve Kembel, Francois Michonneau, David Orme, Brian O'Meara,
Emmanuel Paradis, Jim Regetz, Derrick Zwickl)
Christoph Heibl, Clement Calenge (\code{import.ascii})
Lam Si Tung Ho, Cecile Ane, Robert Lachlan
Botond Sipos <sbotond@ebi.ac.uk>, Gregory Jordan <greg@ebi.ac.uk>
Jinlong Zhang, Nancai Pei, Xiangcheng Mi
Leena Choi, Zhouwen Liu, Charles E. Matthews, and Maciej S. Buchowski
Steven W. Kembel <steve.kembel@gmail.com>, David D. Ackerly <dackerly@berkeley.edu>, Simon P. Blomberg <s.blomberg1@uq.edu.au>, Will K. Cornwell <cornwell@zoology.ubc.ca>, Peter D. Cowan <pdc@berkeley.edu>, Matthew R. Helmus <mrhelmus@wisc.edu>, Helene Morlon <morlon.helene@gmail.com>, Campbell O. Webb <cwebb@oeb.harvard.edu>
Daniel G. Muenz, Ray Griner <rgriner@sdac.harvard.edu>, Huichao
Chen, Lijuan Deng, Sachiko Miyahara, and Scott R. Evans
<evans@sdac.harvard.edu>, with contributions from Lingling Li,
Hajime Uno, and Laura M. Smeaton.
Predictive Analytics Team at Pivotal Inc. <user@madlib.net>,
with contributions from Data Scientist Team at Pivotal Inc.
Roger Bivand, Friedrich Leisch and Martin Maechler
Thomas Jaki <jaki.thomas@gmail.com> and Martin J. Wolfsegger
<wolfseggerm@gmx.at>
Eun-Kyung Lee, Gyujeong Noh, Hyeong-Seok Lim
Anne Dubois, Julie Bertand, France Mentre and Douglas Bates
Florian Wickelmaier [cre, aut], Juergen Heller [aut],
Pasquale Anselmi [ctb]
Baptiste Auguie [aut, cre] (Some functions ported from the original
Matlab SPLAC code by E.C. Le Ru and P. G. Etchegoin),
Steven Johnson [aut, cph] (C code for the cubature library)
Hervé Monod, Annie Bouvier, André Kobilinsky
Yves Croissant [aut, cre],
Giovanni Millo [aut],
Arne Henningsen [ctb],
Ott Toomet [ctb],
Christian Kleiber [ctb],
Achim Zeileis [ctb]
Tomislav Hengl [cre, aut],
Pierre Roudier [ctb],
Dylan Beaudette [ctb],
Edzer Pebesma [ctb],
Michael Blaschek [ctb]
Arni Magnusson [aut, cre], Ian Stewart [aut]
Jim Lemon, Ben Bolker, Sander Oom,
Eduardo Klein, Barry Rowlingson,
Hadley Wickham, Anupam Tyagi,
Olivier Eterradossi, Gabor Grothendieck,
Michael Toews, John Kane, Rolf Turner,
Carl Witthoft, Julian Stander, Thomas Petzoldt,
Remko Duursma, Elisa Biancotto, Ofir Levy,
Christophe Dutang, Peter Solymos, Robby Engelmann,
Michael Hecker, Felix Steinbeck, Hans Borchers,
Henrik Singmann, Ted Toal, Derek Ogle
Bethany Kok [ctb],
Jolynn Pek [ctb],
Sonya Sterba [ctb],
Dan Bauer [ctb],
Phil Chalmers [cre, ctb]
German Aneiros Perez and Ana Lopez Cheda
Bjørn-Helge Mevik, Ron Wehrens and Kristian Hovde Liland
Anne-Laure Boulesteix <boulesteix@ibe.med.uni-muenchen.de>,
Sophie Lambert-Lacroix <Sophie.Lambert@imag.fr>, Julie Peyre
<Julie.Peyre@imag.fr>, and Korbinian Strimmer
<strimmer@uni-leipzig.de>.
Gaston Sanchez [aut, cre],
Laura Trinchera [aut],
Giorgio Russolillo [aut]
Frederic Bertrand <frederic.bertrand@math.unistra.fr>, Myriam Maumy-Bertrand <myriam.maumy-bertrand@math.unistra.fr>, Nicolas Meyer <Nicolas.Meyer@nmeyer@unistra.fr>.
Frederic Bertrand <frederic.bertrand@math.unistra.fr>, Nicolas Meyer <Nicolas.Meyer@nmeyer@unistra.fr>, Myriam Maumy-Bertrand <myriam.maumy-bertrand@math.unistra.fr>.
Eva Herrmann <eherrmann@mathematik.tu-darmstadt.de> (C original);
R interface etc by Martin Maechler
Cun-Hui Zhang and Ofer Melnik, with contributions from Yi Yu
and Stephanie Zhang
Daniela Witten and Rob Tibshirani and Sam Gross and
Balasubramanian Narasimhan
Wei-Chen Chen [aut, cre],
George Ostrouchov [aut]
John Verzani with contributions by Yvonnick Noel
Sarah Colby <colby@lunenfeld.ca>, Sophia Lee, Juan Pablo
Lewinger, Shelley Bull <bull@lunenfeld.ca>
Graham Williams, Tridivesh Jena, Wen Ching Lin, Michael Hahsler (arules),
Zementis Inc, Hemant Ishwaran, Udaya B. Kogalur, Rajarshi Guha
Tridivesh Jena, Wen Ching Lin (Zementis, Inc.)
Panagiotis Papastamoulis, Marie-Laure Martin-Magniette, Cathy Maugis-Rabusseau
David Pechel Cactcha, Laure Pauline Fotso and Celestin C
Kokonendji
Drew Linzer <drew@votamatic.org>,
Jeffrey Lewis <jblewis@ucla.edu>.
Eunjeong Park [aut, cre],
Jong Hee Park [aut]
Jeffrey B. Arnold [aut, cre],
Thomas J. Leeper [aut]
Glen Meeden <glen@stat.umn.edu> and Radu Lazar
<lazar@stat.umn.edu>
Angus Johnson. Ported to R by Adrian Baddeley and Brian Ripley.
Sebastian Meyer [aut, cre, trl],
Michael Hoehle [ths]
Bill Venables [aut] (S original),
Kurt Hornik [aut, cre] (R port),
Martin Maechler [aut] (R port)
Fawn Hornsby, Ryan Nielson, and Trent McDonald
(www.west-inc.com)
Aaron A. King [aut, cre],
Edward L. Ionides [aut],
Carles Breto [aut],
Stephen P. Ellner [ctb],
Matthew J. Ferrari [ctb],
Bruce E. Kendall [ctb],
Michael Lavine [ctb],
Dao Nguyen [ctb],
Daniel C. Reuman [ctb],
Helen Wearing [ctb],
Simon N. Wood [ctb]
Chris Stubben, Brook Milligan, Patrick Nantel
Iain Stott <iainmstott@gmail.com>, Dave Hodgson <D.J.Hodgson@exeter.ac.uk>, Stuart Townley <S.B.Townley@exeter.ac.uk>
Andrew C. Hooker [aut, cre, trl, cph],
Sebastian Ueckert [aut] (MATLAB version),
Marco Foracchia [aut] (O-Matrix version),
Joakim Nyberg [aut] (MATLAB version),
Eric Stroemberg [ctb] (MATLAB version)
Zhian N. Kamvar <kamvarz@science.oregonstate.edu>, Javier F. Tabima
<tabimaj@onid.orst.edu>, Niklaus J. Grunwald
<grunwaln@science.oregonstate.edu>
Lutz Hamel [aut, cre],
Benjamin Ott [aut],
Gregory Breard [aut]
Esam Mahdi, Ken Jinkun Xiao and A. Ian McLeod
Jeff Enos <jeff@kanecap.com> and David Kane <dave@kanecap.com>,
with contributions from Daniel Gerlanc <daniel@gerlanc.com> and
Kyle Campbell <Kyle.W.Campbell@williams.edu>
Jeff Enos <jeff@kanecap.com> and David Kane <dave@kanecap.com>,
with contributions from Kyle Campbell
<Kyle.W.Campbell@williams.edu>
Tamal Kanti Panja, Sourish Das, Rajeswaran Viswanathan.
Charles J. Geyer <charlie@stat.umn.edu> and Leif Johnson
<leif@stat.umn.edu>
Sundar Dorai-Raj <sdorairaj@gmail.com>, original Fortran from Mike Powell <mjdp@cam.ac.uk>
Pierre Lafaye de Micheaux <lafaye@dms.umontreal.ca>, Viet Anh Tran <vietanhtran184@yahoo.com>
Detlew Labes [aut, cre],
Helmut Schuetz [ctb]
Charles Kooperberg <clk@fhcrc.org> and Li Hsu <lih@fhcrc.org>
Weiliang Qiu <stwxq@channing.harvard.edu>, Jorge Chavarro
<JCHAVARR@hsph.harvard.edu>, Ross Lazarus
<ross.lazarus@channing.harvard.edu>, Bernard Rosner
<stbar@channing.harvard.edu>, Jing Ma
<jing.ma@channing.harvard.edu>.
Detlew Labes [aut, cre],
Helmut Schuetz [aut]
Yue Deng, Rongjie Shao, Gang Wang, Yuanjun Sun
Nicole Kraemer <kraemer_r_packages@yahoo.de> Anne-Laure Boulesteix
<boulesteix@ibe.med.uni-muenchen.de>
Christian Hennig <chrish@stats.ucl.ac.uk>, Bernhard Hausdorf
<Hausdorf@zoologie.uni-hamburg.de>
Richard Valliant, Jill A. Dever, Frauke Kreuter
Juan Carlos Martinez-Ovando, Sergio I. Olivares-Guzman, Adriana Roldan-Rodriguez.
Suman Kundu, Yurii S. Aulchenko, A. Cecile J.W. Janssens
Vladimir Vovk <vovk@cs.rhul.ac.uk> and Ilia Nouretdinov
<ilia@cs.rhul.ac.uk>
Dongwen Luo, Siva Ganesh and John Koolaard
Reinhold Hatzinger [aut],
Marco Johannes Maier [cre]
David I. Hastie <david.hastie@rsimony.com>, Silvia Liverani <liveranis@gmail.com>, Aurore J. Lavigne, Lucy Leigh, Lamiae Azizi, Sylvia Richardson <sylvia.richardson@mrc-bsu.cam.ac.uk>
Chao Deng, Timothy Daley and Andrew D. Smith
Charles Dupont, Jeffrey Horner, Chun Li, Bryan Shepherd
Jim Lemon <jim@bitwrit.com.au>,
Philippe Grosjean <phgrosjean@sciviews.org>
Brecht Devleesschauwer [aut, cre], Paul Torgerson [aut],
Johannes Charlier [aut], Bruno Levecke [aut], Nicolas Praet [aut],
Pierre Dorny [aut], Dirk Berkvens [aut], Niko Speybroeck [aut]
S original by Trevor Hastie <trevor@research.att.com> R port by
Andreas Weingessel <Andreas.Weingessel@ci.tuwien.ac.at>
Davor Cubranic, Jonathan Zhang, Nancy Heckman, Travis Gaydos,
and J.S. Marron
Raydonal Ospina <raydonal@de.ufpe.br>  and Leila D. Amorim <leiladen@ufba.br>.
Veronica J. Berrocal <veroberrocal@gmail.com>, Yulia Gel,
Adrian E. Raftery, Tilmann Gneiting
Chong Zhang, Seung Jun Shin, Junhui Wang, Yichao Wu, Hao Helen
Zhang, and Yufeng Liu
Xavier Robin, Natacha Turck, Alexandre Hainard, Natalia Tiberti, Frédérique Lisacek, Jean-Charles Sanchez and Markus Müller.
Okan Bulut <okanbulut84@gmail.com>, Christopher David Desjardins
<cddesjardins@gmail.com>
Y. Foucher <Yohann.Foucher@univ-nantes.fr> and C. Combescure <Christophe.Combescure@hcuge.ch>
Aleksandar Blagotic [ctb],
Diego Valle-Jones [ctb],
Jeffrey Breen [ctb],
Joakim Lundborg [ctb],
John Myles White [aut, cph],
Josh Bode [ctb],
Kenton White [ctb],
Kirill Mueller [ctb, cre],
Matteo Redaelli [ctb],
Noah Lorang [ctb],
Patrick Schalk [ctb]
John Michael O Ranola, Kenneth Lange, Steve Horvath, Peter Langfelder
Osama Mahmoud, Andrew Harrison, Aris Perperoglou, Asma Gul, Zardad Khan, Berthold Lausen
Antoine Stevens and Leonardo Ramirez-Lopez
Nan Xiao <road2stat@gmail.com>, Qingsong Xu <dasongxu@gmail.com>,
Dongsheng Cao <oriental-cds@163.com>
Christian Panse <cp@fgcz.ethz.ch>, Jonas Grossmann <jg@fgcz.ethz.ch>,
Simon Barkow-Oesterreicher <sb@fgcz.ethz.ch>
David Meyer [aut, cre],
Christian Buchta [aut]
Hadley Wickham [aut, cre],
R Core team [ctb] (Some code extracted from base R)
James E. Helmreich <James.Helmreich@Marist.edu> and Robert M.
Pruzek <RMPruzek@yahoo.com>.  We are grateful to KuangNan Xiong
for significant work on the functions new to version 2.0:
cstrata.psa, cv.bal.psa, and cv.trans.psa.
Kyu Ha Lee, Sounak Chakraborty, (Tony) Jianguo Sun
Henrik Bengtsson [aut, cre, cph],
Pierre Neuvial [aut],
Venkatraman E. Seshan [aut],
Adam B. Olshen [aut],
Paul T. Spellman [aut],
Richard A. Olshen [aut]
Simon Jackman, with contributions from
Alex Tahk, Achim Zeileis, Christina Maimone and Jim Fearon
Andre Chalom, Paulo Inacio Knegt Lopez de Prado
Maja Pohar Perme [aut], Mette Gerster [aut, cre]
Ben Ingram <bri@utalca.cl>, Remi Barillec <r.barillec@aston.ac.uk>
Xiaoxiao Xu, Alan B. Wells, David OBrien, Arye Nehorai, Joseph D. Dougherty
Stig Bousgaard Mortensen <stigbm@gmail.com> and Søren Klim
<soren@klimens.dk>
Baolin Wu [aut, cph],
Nianjun Liu [aut, cph],
Hongyu Zhao [aut, cph],
Jose Gama [cre]
S original by Jim Ramsey <ramsay@psych.mcgill.ca>.  R port by
Brian Ripley <ripley@stats.ox.ac.uk>.
Hannah Frick [aut, cre],
Carolin Strobl [aut],
Friedrich Leisch [aut],
Achim Zeileis [aut]
Achim Zeileis [aut, cre],
Carolin Strobl [aut],
Florian Wickelmaier [aut],
Basil Abou El-Komboz [aut],
Julia Kopf [aut]
Jose M. Maisog, Yuan Wang, George Luta, Jianfei Liu
Jan Gerretzen <j.gerretzen@science.ru.nl>, Paul Eilers
<p.eilers@erasmusmc.nl>, Hans Wouters, Tom Bloemberg
<t.bloemberg@science.ru.nl>, Ron Wehrens <ron.wehrens@fmach.it>
and The R Development Core Team
Jyoti Sharma, S.Ramachandran, Ab Rauf Shah
Niki Zumbrunnen <niki.zumbrunnen@stat.unibe.ch>,
Lutz Duembgen <lutz.duembgen@stat.unibe.ch>.
Ryota Suzuki <suzuki@ef-prime.com>, Hidetoshi Shimodaira
<shimo@is.titech.ac.jp>
Thiago Santos, Jose Alexandre Diniz-Filho, Thiago Rangel e Luis
Mauricio Bini
Achim Zeileis [aut, cre],
Guan Yang [aut]
Francisco J. Viciana [aut],
Carlos J. Gil Bellosta [cre, aut],
Oscar Perpinan Lamigueiro [aut],
Emilio Torres Manzanera [ctb],
Vicente David Canto Casasola [ctb],
Jorge Ivan Velez [ctb]
Adrian Dusa [aut, cre],
Alrik Thiem [aut]
This package is 99% based on the Rcmdr package written by John
Fox <jfox@mcmaster.ca>.  Some additional menus were adapted for
the QCA package by Adrian Dusa <dusadrian@unibuc.ro>
Miguel Flores <mflores@outlook.com>,
Salvador Naya <salva@udc.es>, Ruben Fernandez <ruben.fcasal@udc.es>
Bryan Goodrich [ctb],
Dason Kurkiewicz [ctb],
Tyler Rinker [aut, cre]
Jason Gray [ctb],
Tyler Rinker [aut, cre]
Bryan Goodrich [ctb],
Dason Kurkiewicz [ctb],
Kirill Muller [ctb],
Tyler Rinker [aut, cre]
Sacha Epskamp, Giulio Costantini, Angelique O. J. Cramer, Lourens J. Waldorp, Verena D. Schmittmann and Denny Borsboom
Jixiang Wu (South Dakota State University), Johnie N. Jenkins and Jack C. McCarty (USDA-ARS)
Jingyi Xin, Bibhas Chakraborty, and Eric B. Laber
Jichun Xie <jichun@mail.med.upenn.edu>, Justine Shults
<jshults@mail.med.upenn.edu>
Juan Gea Rosat, Ramon Martínez Coscollà .
Robert Castelo <robert.castelo@upf.edu>, Alberto Roverato
<alberto.roverato@unibo.it>
Bernhard Pfaff [aut, cre],
Marius Hofert [ctb],
Alexander McNeil [aut] (S-Plus original (QRMlib)),
Scott Ulmann [trl] (First R port as package QRMlib)
Karl W Broman <kbroman@biostat.wisc.edu> and Hao Wu, with
ideas from Gary Churchill and Saunak Sen and contributions from
Danny Arends, Timothee Flutre, Ritsert Jansen, Pjotr Prins, Laura
Shannon, and Brian Yandell
Brian S. Yandell <yandell@stat.wisc.edu> and Nengjun Yi
<nyi@ms.soph.uab.edu>, with contributions from Tapan Mehta,
Samprit Banerjee, Daniel Shriner and Ramprasad Venkataraman
(UA-Birmingham), Jee Young Moon, W. Whipple Neely (UW-Madison)
and Hao Wu and Randy von Smith (Jackson Laboratory).
Saunak Sen, Jaya Satagopan, Karl Broman, and Gary Churchill
Elias Chaibub Neto <echaibub@hotmail.com> and Brian S Yandell <byandell@wisc.edu>
Elias Chaibub Neto <echaibub@hotmail.com> and Brian S. yandell <byandell@wisc.edu>
S original by Berwin A. Turlach <Berwin.Turlach@gmail.com> R
port by Andreas Weingessel <Andreas.Weingessel@ci.tuwien.ac.at>
Konstantin Kashin <kkashin@fas.harvard.edu>, Adam Glynn <aglynn@emory.edu>, Nahomi Ichino <nichino@umich.edu>
K.G. van den Boogaart, Stefanie Rost and Thomas Petzoldt
Isabelle Charlier and Davy Paindaveine and Jerome Saracco
Roger Koenker [cre, aut],
Stephen Portnoy [ctb] (Contributions to Censored QR code),
Pin Tian Ng [ctb] (Contributions to Sparse QR code),
Achim Zeileis [ctb] (Contributions to dynrq code essentially identical
to his dynlm code),
Philip Grosjean [ctb] (Contributions to nlrq code),
Brian D Ripley [trl, ctb] (Initial (2001) R port from S (to my
everlasting shame -- how could I have been so slow to adopt R!) and
for numerous other suggestions and useful advice)
Steve Lund <lundsp@gmail.com> and Long Qu <long.qu@wright.edu>
Julien Barnier [aut, cre],
François Briatte [aut],
Joseph Larmarange [aut]
Cho-Jui Hsieh [aut], Matyas A. Sustik [aut, cre], Inderjit S.
Dhillon [aut], Pradeep Ravikumar [aut]
Elise Dusseldorp [aut, cre, cph],
Lisa Doove [aut],
Cor Ninaber [ctb] (supported with the plot function),
Iven Van Mechelen [aut, cph]
Reinhard Simon, Pablo Carhuapoma, Vilma Hualla, Stef de Haan, Marc
Ghislain, Jorge Nunez, Rene Gomez, Felipe de Mendiburu, William Roca,
Merideth Bonierbale
Wei-Chen Chen, partly adapted and using routines from LAPACK.
Nikolaus Umlauf [aut, cre],
Thomas Kneib [aut],
Stefan Lang [aut],
Achim Zeileis [aut]
The Cuba library has been written by Thomas Hahn
(http://wwwth.mppmu.mpg.de/members/hahn); Interface to R was
written by Annie Bouvier and Kiên Kiêu
Arni Magnusson [aut], Julian Burgos [aut, cre], Gregory R. Warnes [ctb]
Gabriel del Barrio, Juan Puigdefabregas, Maria E. Sanjuan &
Alberto Ruiz
Gianluca Campanella [cre],
Benoit Liquet [aut],
Marc Chadeau-Hyam [aut],
Leonardo Bottolo [ctb],
Sylvia Richardson [ctb]
Eric Lecoutre [aut],
Milan Bouchet-Valat [cre, ctb],
Thomas Friedrichsmeier [ctb]
Yu-Sung Su <suyusung@tsinghua.edu.cn>,
Masanao Yajima <yajima@stat.ucla.edu>,
Christophe Genolini, Bernard Desgraupes, Lionel Riou Franca
Zhengzheng Zhang [aut, cre],
Chris Charlton [aut],
Richard Parker [aut],
George Leckie [aut],
William Browne [aut]
originally written as R2WinBUGS by Andrew Gelman
<gelman@stat.columbia.edu>; changes and packaged by Sibylle
Sturtz <sturtz@statistik.tu-dortmund.de> and Uwe Ligges
<ligges@statistik.tu-dortmund.de>.  With considerable
contributions by Gregor Gorjanc <gregor.gorjanc@bfro.uni-lj.si>
and Jouni Kerman <kerman@stat.columbia.edu>.  Adapted to
R2OpenBUGS from R2WinBUGS by Neal Thomas.
Yixuan Qiu, Yihui Xie, Cameron Bracken and authors of included
software. See file AUTHORS for details.
originally written by Andrew Gelman <gelman@stat.columbia.edu>;
changes and packaged by Sibylle Sturtz
<sturtz@statistik.tu-dortmund.de> and Uwe Ligges
<ligges@statistik.tu-dortmund.de>.  With considerable
contributions by Gregor Gorjanc <gregor.gorjanc@bfro.uni-lj.si>
and Jouni Kerman <kerman@stat.columbia.edu>.  Ported to S-PLUS
by Insightful Corp.
Kevin P. Barry <ta0kira@users.berlios.de> with contributions from Avi Z.
Snyder <avi@npg.wustl.edu>
Ian Taylor, Ian Stewart, Allan Hicks, Tommy Garrison,
Andre Punt, John Wallace, Chantel Wetzel, James Thorson,
Yukio Takeuchi, Cole Monnahan, and other contributors.
Doug Nychka, Eric Gilleland, Liangying Zhang, Tim Hoar
Carel F.W. Peeters, Wessel N. van Wieringen
Jose' Gama [aut, cre, trl],
Vlad Maximov [aut],
Sebastian O.H. Madgwick [aut],
Alain Barraud [ctb],
Göran Högnäs [ths]
Charles Bouveyron, Yacine Jernite, Pierre Latouche, Laetitia Nouedoui
Zhiyong Zhang, Jack McArdle, Aki Hamagami, & Kevin Grimm
Brian J Smith [aut, cre],
Jun Yan [aut],
Mary Kathryn Cowles [aut]
Martin Schlather [aut, cre], Alexander Malinowski [aut], Marco Oesting [aut], Daphne Boecker [aut], Kirstin Strokorb [aut], Sebastian Engelke [aut], Johannes Martini [aut], Felix Ballani [aut], Peter Menck [aut], Sebastian Gross [aut], Ulrike Ober [ctb], Katharina Burmeister [ctb], Juliane Manitz [ctb], Paulo Ribeiro [ctb], Richard Singleton [ctb], Ben Pfaff [ctb], R Core Team [ctb]
Fortran original by Leo Breiman and Adele Cutler, R port by
Andy Liaw and Matthew Wiener.
Hemant Ishwaran <hemant.ishwaran@gmail.com>, Udaya B. Kogalur <ubk@kogalur.com>
Hemant Ishwaran <hemant.ishwaran@gmail.com>, Udaya B. Kogalur
<ubk@kogalur.com>
R port by Yohan Chalabi, Christophe Dutang, Petr Savicky and Diethelm Wuertz (underlying C codes of (i) the SFMT algorithm from M. Matsumoto and M. Saito, (ii) the Knuth-TAOCP RNG from D. Knuth).
Mihai Valcu  <valcu@orn.mpg.de>, James Dale  <J.Dale@massey.ac.nz>
Vasyl Pihur <vpihur@gmail.com>, Somnath Datta
<somnath.datta@louisville.edu>, Susmita Datta
<susmita.datta@louisville.edu>
Sunil Arya and David Mount (for ANN), Samuel E. Kemp, Gregory Jefferis
U.Sangeetha, M.Subbiah with considerable contribution from
M.R.Srinivasan
Markus Loecher <markus.loecher@gmail.com> and Madhav Kumar <madhavkumar2005@gmail.com>
Dirk Eddelbuettel, Junji Nakano, Ei-ji Nakama, and R Core (original code)
Hadley Wickham [trl, cre, cph],
RStudio [cph],
Sridhar Ratnakumar [aut],
Trent Mick [aut],
ActiveState [cph] (R/appdir.r, R/cache.r, R/data.r, R/log.r translated
from appdirs),
Eddy Petrisor [ctb],
Trevor Davis [trl, aut],
Gabor Csardi [ctb],
Gregory Jefferis [ctb]
Aleksandar Blagotić <alex@rapporter.net> and Gergely Daróczi
<daroczig@rapporter.net>
Yixuan Qiu and authors of the ARPACK library. See file AUTHORS for
details.
Reinhold Hatzinger, Patrick Mair, Norman D. Verhelst
Daniel Wiesmann <daniel.wiesmann@ist.utl.pt> and David Quinn
<djq@mit.edu>
Robert J. Hijmans [cre, aut],
Jacob van Etten [ctb],
Matteo Mattiuzzi [ctb],
Michael Sumner [ctb],
Jonathan A. Greenberg [ctb],
Oscar Perpinan Lamigueiro [ctb],
Andrew Bevan [ctb],
Etienne B. Racine [ctb],
Ashton Shortridge [ctb]
Oscar Perpinan Lamigueiro [cre, aut],
Robert Hijmans [aut]
Daniele Giardiello, Piero Quatto and Stefano Vigliani
Graham Williams [aut, cph, cre],
Mark Vere Culp [cph],
Ed Cox [ctb],
Anthony Nolan [ctb],
Denis White [cph],
Daniele Medri [ctb],
Akbar Waljee [ctb] (OOB AUC for Random Forest),
Brian Ripley [cph] (Author of original print.summary.nnet)
Giulio Vidotto <giulio.vidotto@unipd.it>, Stefano Noventa
<stefano.noventa@unipd.it>, Davide Massidda
<davide.massidda@unipd.it>, Marco Vicentini
<marco.vicentini@gmail.com>
Javier González Hernández <javigzz@yahoo.es>, Sara Varela <svarela@paleobiogeography.org>
Claas-Thido Pfaff [cre, aut],
Karin Nadrowski [aut],
Xingxing Man [aut]
Ching-Wei Cheng, Ying-Chao Hung, Narayanaswamy Balakrishnan
Scott Chamberlain [aut, cre],
Karthik Ram [aut]
Byoungnam Min, Byeonghyuk Park, Kyoung Heon Kim and In-Geol Choi
Jun Yan <jyan@stat.uconn.edu> and Marcos Prates
<marcosop@est.ufmg.br>
Annie Bouvier, Kien Kieu, Kasia Adamczyk, and Herve Monod
Louis-Paul Rivest and Sophie Baillargeon
Charles J. Geyer <charlie@stat.umn.edu> and
Glen D. Meeden <glen@stat.umn.edu>, incorporates code from
cddlib (ver 0.94f) written by Komei Fukuda <fukuda@ifor.math.ethz.ch>
Rajarshi Guha [aut, cre],
CDK Project [ctb, cph] (CDK Java library contained in multiple jar files)
Alex Lisovich [aut, cre],
Roger Day [aut],
Sun Microsystems, Inc [cph] (swing-layout-1.0.4.jar, download from
http://www.java2s.com/Code/Jar/s/Downloadswinglayout104jar.htm)
Michael Linderman [aut],
Robert Bruggner [cre]
John Fox [aut, cre],
Milan Bouchet-Valat [aut],
Liviu Andronic [ctb],
Michael Ash [ctb],
Theophilius Boye [ctb],
Stefano Calza [ctb],
Andy Chang [ctb],
Philippe Grosjean [ctb],
Richard Heiberger [ctb],
Kosar Karimi Pour [ctb],
G. Jay Kerns [ctb],
Renaud Lancelot [ctb],
Matthieu Lesnoff [ctb],
Uwe Ligges [ctb],
Samir Messad [ctb],
Martin Maechler [ctb],
Robert Muenchen [ctb],
Duncan Murdoch [ctb],
Erich Neuwirth [ctb],
Dan Putler [ctb],
Brian Ripley [ctb],
Miroslav Ristic [ctb],
Peter Wolf [ctb],
Kevin Wright [ctb]
John Fox [aut, cre],
Robert Muenchen [ctb],
Dan Putler [ctb]
Ulrike Groemping [aut, cre],
Fox John [ctb]
Maribel Pero <mpero@ub.edu>, David Leiva <dleivaur@ub.edu>,
Joan Guardia <jguardia@ub.edu>, Antonio Solanas <antonio.solanas@ub.edu>
Francois Husson, Julie Josse, Sebastien Le
Richard M. Heiberger, with contributions from Burt Holland
G. Jay Kerns [aut, cre],
Theophilus Boye [ctb],
Tyler Drombosky [ctb]
Andrew Heiss, Richard Payne, Christa Schank
Poul Svante Eriksen with contributions by Ege Rubak
Daniel-Corneliu Leucuta <danny.ldc@gmail.com>, Mihaela Hedesiu, Andrei Achimas, Oana Almasan
Antonio Solanas <antonio.solanas@ub.edu>, Rumen Manolov
<rrumenov13@ub.edu & Patrick Onghena
<patrick.onghena@ped.kuleuven.be>.
Milan Bouchet-Valat [aut, cre],
Gilles Bastin [aut]
Hector Corrada Bravo, with contributions from Stefan Theussl and Kurt Hornik
Dirk Eddelbuettel, Romain Francois, JJ Allaire, Kevin Ushey,
Douglas Bates, and John Chambers
Romain Francois [aut, cre],
Kevin Ushey [aut],
John Chambers [ctb]
Romain Francois, Dirk Eddelbuettel and Doug Bates
Dirk Eddelbuettel and Romain Francois, with contributions by David Reiss,
and based on code written during 2005 and 2006 by Dominick Samperi
Dirk Eddelbuettel and Romain Francois, based on code written
during 2005 and 2006 by Dominick Samperi
Dirk Eddelbuettel extending DEoptim (by David Ardia, Katharine Mullen,
Brian Peterson, Joshua Ulrich) which itself is based on DE-Engine (by Rainer Storn)
Douglas Bates, Romain Francois and Dirk Eddelbuettel;
the authors of Eigen for the included version of Eigen
JJ Allaire; Romain Francois; Intel, Inc.; Marcus Geelnard
Hector Corrada Bravo (CSDP by Brian Borchers)
Hadley Wickham [aut],
Yihui Xie [aut, cre]
Yaqian Guo <yaqiang@stat.stanford.edu> Trevor Hastie
<hastie@stat.stanford.edu> Robert Tibshirani
<tibs@stanford.edu>
Trent McDonald, with contributions from Ryan Nielson, James
Griswald, and Patrick McCann.
Konstantin A. Blagodatskikh [cre, aut],
Stefan Roediger [aut],
Michal Burdukiewicz [aut]
Sebastian Calonico <scalonico@bus.miami.edu>, Matias D. Cattaneo
<cattaneo@umich.edu>, Rocio Titiunik <titiunik@umich.edu>
Scott Chamberlain [aut, cre], Carl Boettiger [aut], Karthik Ram
[aut]
Mark S. Handcock [aut, cre],
Krista J. Gile [aut],
Ian E. Fellows [aut],
W. Whipple Neely [aut]
Karline Soetaert <karline.soetaert@nioz.nl>, Filip Meysman
<filip.meysman@nioz.nl>
Philip Reiss <phil.reiss@nyumc.org> and Lei Huang
<huangracer@gmail.com>
Rafael Maia [aut, cre],
Scott Chamberlain [aut],
Andy Teucher [aut]
Leonardo Dapporto, Matteo Ramazzotti, Simone Fattorini, Roger
Vila, Gerard Talavera, Roger H.L. Dennis
Yixuan Qiu, Chih-Jen Lin, Yu-Chin Juan, Yong Zhuang,
Wei-Sheng Chin and other contributors. See file AUTHORS for
details.
Benjamin Nutter. Initiated by Jeffrey Horner and Will Gray with contributions from Jeremy Stephens, and Will Beasley
Will Beasley [aut, cre],
David Bard [ctb],
Thomas Wilson [ctb],
John J Aponte [ctb],
Rollie Parrish [ctb]
Ciprian Crainiceanu [aut], Philip Reiss [aut], Jeff Goldsmith [aut],
Lei Huang [aut, cre], Lan Huo [aut], Fabian Scheipl [aut], Bruce Swihart
[ctb], Sonja Greven [ctb], Jaroslaw Harezlak [ctb], Madan Gopal Kundu
[ctb], Yihong Zhao [ctb], Matt McLean [ctb], Luo Xiao [ctb]
Weichao Bao, Xin Tong, Meredith Ray, Hongmei Zhang
David Clifford and Peter McCullagh. Additional contributions by
HJ Auinger.
Ulrike Groemping [aut, cre],
Lehrkamp Matthias [ctb]
David Meyer [aut],
Kurt Hornik [aut, cre],
Christian Buchta [ctb]
Vijay Kumar <vkgkp@rediffmail.com> and Uwe Ligges
<ligges@statistik.tu-dortmund.de>
David Firth with contributions from Heather Turner
Michael Hahsler [aut, cre, cph],
Margaret H. Dunham [aut, cph]
Jie Peng <jie@wald.ucdavis.edu>, Pei Wang <pwang@fhcrc.org>, Ji Zhu <jizhu@umich.edu>.
Tim Appelhans, Florian Detsch, Thomas Nauss
Yves Deville <deville.yves@alpestat.com>, IRSN <renext@irsn.fr>
Yves Deville <deville.yves@alpestat.com> and IRSN <renext@irsn.fr>
David Winter [aut, cre],
Scott Chamberlain [ctb]
Barb Banbury <darwinthesun@gmail.com>, Brian O'Meara <bomeara@utk.edu>
Natalia Novoselova,Frank Klawonn, Junxi Wang
Ruth Heller, Shachar Kaufman, Shay Yaacoby, Daniel Yekutieli
David Gohel [aut, cre],
Bootstrap [ctb, cph] (Bootstrap development team),
jQuery [ctb, cph] (The jQuery Foundation),
Dmitry Baranovskiy [ctb, cph] (raphael and g.raphael javascript
libraries)
David Gohel [aut, cre],
Jason Harrop [ctb, cph] (docx4j java library),
Apache Software Foundation [ctb, cph] (Apache Commons java libraries
and log4j),
SLF4J [ctb, cph] (Simple Logging Facade for Java)
Ryan Del Bel
<rdelbel@gmail.com>,
Wei Xu
<wxu@uhnres.utoronto.ca>
Daniel Fischer, Alain Berro, Klaus Nordhausen, Anne Ruiz-Gazen
Leonardo Ramirez-Lopez and Antoine Stevens
Subhash R. Lele, Jonah L. Keim, Peter Solymos
Steffen Liebscher and Thomas Kirschstein
Nicholas J. Matzke [aut, cre, cph],
Roger B. Sidje [aut, cph]
Robert M. Kirkpatrick <rkirkpatrick2@vcu.edu>, Xiang Li <lixxx554@umn.edu>, and Saonli Basu <saonli@umn.edu>.
Carl Boettiger [aut, cre],
Scott Chamberlain [aut],
Karthik Ram [aut],
Edmund Hart [aut]
Yohei Sato, Nobuaki Oshiro, Shinichi Takayanagi
Carl Boettiger [aut, cre],
Duncan Temple Lang [aut],
Peter C. Wainwright [aut]
Karthik Ram [aut, cre],
Carl Boettiger [aut],
Andrew Dyck [aut]
Fabienne Flessa, Alexandra Kehl, Mohammed Aslam Imtiaz, Matthias Kohl
Andrie de Vries [aut, cre],
Joris Meys [aut]
Artem Klevtsov [aut, cre],
Philipp Upravitelev [ctb],
Olga Shramko [ctb]
Matias Salibian-Barrera, Davor Cubranic, and Azadeh
Alimadad, with 'loess' code from Cleveland, Grosse, and Shyu
Scott Chamberlain [aut, cre],
Karthik Ram [aut],
Vijay Barve [aut],
Dan Mcglinn [aut]
Joseph Kelly, Hyungsuk Tak, and Carl Morris
Roger Bivand [cre, aut],
Tim Keitt [aut],
Barry Rowlingson [aut],
Edzer Pebesma [ctb],
Michael Sumner [ctb],
Robert Hijmans [ctb],
Even Rouault [ctb]
Walter R. Mebane, Jr. <wmebane@umich.edu>, Jasjeet Singh Sekhon
<sekhon@berkeley.edu>
Roger Bivand [cre, aut],
Colin Rundel [aut],
Edzer Pebesma [ctb],
Karl Ove Hufthammer [ctb]
George Vega Yon <george.vega@nodoschile.org> [aut], Jorge Fábrega Lacoa <jorge.fabrega@nodoschile.org> [ctb], Joshua B. Kunst <jbkunst@gmail.com> [ctb]
Duncan Temple Lang <duncan@research.bell-labs.com>,
Debby Swayne <dfs@research.att.com>,
Hadley Wickham <h.wickham@gmail.com>,
Michael Lawrence <michafla@gene.com>
María José Haro-Delicado, Virgilio Gómez-Rubio and Francisco
Parreño-Torres
Daniel Adler <dadler@uni-goettingen.de>, Duncan Murdoch <murdoch@stats.uwo.ca>, and others (see README)
Stefan Theussl [aut, cre],
Kurt Hornik [aut],
Christian Buchta [ctb],
Andrew Makhorin [cph],
Timothy A. Davis [cph],
Niklas Sorensson [cph],
Mark Adler [cph],
Jean-loup Gailly [cph]
Göran Högnäs [ths],
Nicolas Devillard [aut],
Mauricio Galo [ctb],
Patrick J. Bartlein [ctb],
Oscar Perpiñán Lamigueiro [ctb],
José Gama [aut, cre]
Michael Pearmain. Contributions from Nick Mihailowski,
Vignesh Prajapati, Kushan Shah and Nicolas Remy
Oliver Flasch, Olaf Mersmann, Thomas Bartz-Beielstein, Joerg Stork,
Martin Zaefferer
Michael Lawrence <michafla@gene.com> and Duncan Temple Lang <duncan@wald.ucdavis.edu>
Tom Taverner, with contributions from John Verzani, Iago Conde
and Liviu Andronic
G. Grothendieck. Author of h2 is Thomas Mueller.
Junji NAKANO <nakanoj@ism.ac.jp> and Eiji NAKAMA<nakama@com-one.com>
A. Mendez, L. Rodriguez-Linares, A. Otero, C.A. Garcia, X. Vila, M. Lado
Peter M. Aronow <peter.aronow@yale.edu> and Cyrus Samii
<cds2083@nyu.edu>
Pavel V. Moskalev, Alexey G. Bukhovets and Tatyana Ya.
Biruchinskay
ChungHa Sung, TaeJoon Song, Jae W. Lee, and Junghoon Lee
David C Sterratt [aut, cph, cre],
Mikko Vihtakari [aut, cph]
Vijay Raajaa, Austin Chungath Vincent, Sachin Sudarshana, Vikas Raguttahalli
Vijay Barve<vijay.barve@gmail.com>,
Edmund Hart <edmund.m.hart@gmail.com>
Dr. Nicola ZACCARELLI, Dr. Giorgio MANCINELLI, and Prof. Dan BOLNICK
Chung-hong Chan <chainsawtiney@gmail.com>, Geoffrey CH Chan
<gefchchan@gmail.com>
Talita Perciano [aut, cre],
Alejandro C Frery [ctb]
Thomas Alexander Gerds, Thomas Harder Scheike
Patrick J. Heagerty <heagerty@u.washington.edu>, packaging by
Paramita Saha-Chaudhuri <paramita.sahachaudhuri.work@gmail.com>
Jake Bowers <jwbowers@illinois.edu>, Mark Fredrickson
<mark.m.fredrickson@gmail.com>, and Ben Hansen <ben.hansen@umich.edu>
Gabriela Cohen-Freue and Davor Cubranic,
with contributions from B. Kaufmann and R.H. Zamar
Feng Mao <rivervis@outlook.com>, Yichuan Shi <yichuan@live.com>,
and Keith Richards <ksr10@cam.ac.uk>
Aleksander Mendyk [aut, cre], Sebastian Polak [aut]
Oscar Rueda <rueda.om@gmail.com> and Ramon Diaz-Uriarte
<rdiaz02@gmail.com>. zlib from Jean-loup Gailly and Mark Adler;
see README. Function "getHostname.System" from package R.utils
by Henrik Bengtsson.
Martyn Plummer [aut, cre],
Alexey Stukalov [ctb]
Sigal Blay, Jinko Graham, Brad McNeney, Annick Nembot-Simo
Attilio Mattiocco, Diana Nicoletti, Gianpaolo Lopez, Banca d'Italia
Aaron Schumacher <ajschumacher@gmail.com>, Håkon Malmedal
G. Grothendieck and Carlos J. Gil Bellosta (authors of Jython
itself are Jim Hugunin, Barry Warsaw, Samuele Pedroni, Brian
Zimmer, Frank Wierzbicki and others; Bob Ippolito is the author
of the simplejson Python module)
Ingo Feinerer [aut],
Kurt Hornik [aut, cre],
Eibe Frank [ctb, cph] (KEA Java library),
Olena Medelyan [ctb, cph] (KEA Java library),
The Apache Software Foundation [ctb, cph] (Apache Commons Logging and
Apache Jena Java libraries),
International Business Machines Corporation and others [ctb, cph]
(International Components for Unicode for Java (ICU4J)),
Martin Porter [ctb, cph] (Snowball base),
Richard Boulton [ctb, cph] (Snowball Java library),
University of Waikato [ctb, cph] (Weka Java library)
Luke Winslow, Jordan Read, Richard Woolway, Jennifer Brentrup, Taylor Leach, Jake Zwart
Hana Sevcikova <hanas@u.washington.edu>, Tony Rossini
<anthony.rossini@novartis.com>
Yusuf Bilgic, Herb Susmann and Joseph McKean
Tudor Bodea <tudor.bodea@ihg.com> & Dev Koushik
<dev.koushik@ihg.com> & Mark Ferguson
<mark.ferguson@mgt.gatech.edu>.
Christoph Bergmeir, Daniel Molina, José M. Benítez
Jeff Laake <jeff.laake@noaa.gov> with code contributions from Eldar
Rakhimberdiev, Ben Augustine and Daniel Turek, and example data and
analysis from Bret Collier, Jay Rotella, David Pavlacky, and Andrew Paul.
JJ Allaire, Jonathan McPherson, Yihui Xie, Hadley Wickham, Joe Cheng,
Jeff Allen
Stefan Widgren [aut, cre] (Author of the R interface to the C-library
matio),
Christopher Hulbert [aut] (Author of the C-library matio,
http://sourceforge.net/projects/matio/)
Henrik Bengtsson [aut, cre, cph], Andy Jacobson [ctb] (Internal MAT v4 reader), Jason Riedy [ctb] (Support for reading compressed files (via 'Rcompression'), sparse matrices and UTF-encoded strings.)
Davood Tofighi <dtofighi@psych.gatech.edu> and David P. MacKinnon
<davidpm@asu.edu>
Allan Strand <stranda@cofc.edu>,
James Niehaus
Benjamin Auder [aut, cre],
Remi Lebret [aut],
Serge Iovleff [aut],
Florent Langrognet [aut],
C. Biernacki [ctb],
G. Celeux [ctb],
G. Govaert [ctb]
MongoDB, Inc. (mongo-c-driver) & Markus Schmidberger (mongosoup.de).
Formerly Gerald Lindsly.
C code by A. Canale and N. Lunardon, port and R code by A.
Canale
Iain M. Johnstone, Zongming Ma, Patrick O. Perry and Morteza
Shahram.
Michael U. Kemp <mukemp+RNCEP@gmail.com> with contributions
from E. Emiel van Loon, Judy Shamoun-Baranes, and Willem Bouten
Carl Boettiger [cre, aut],
Scott Chamberlain [aut],
Hilmar Lapp [aut],
Kseniia Shumelchyk [aut],
Rutger Vos [aut]
all C code by F. Panneton, P. L'Ecuyer and M. Matsumoto and R port by Christophe Dutang and Petr Savicky <savicky@cs.cas.cz>.
Oliver Granert <o.granert <at> neurologie.uni-kiel.de>
Jon Clayden; based on original code by Marc Modat and Pankaj Daga
Hart Edmund [aut, cre],
Scott Chamberlain [aut],
Karthik Ram [aut]
Claudia Vitolo <cvitolodev@gmail.com>, Matthew Fry <mfry@ceh.ac.uk>
Jeff Gentry <geoffjentry@gmail.com>, Duncan Temple Lang
<duncan@r-project.org>
Matthias Templ, Karel Hron, Peter Filzmoser
Roland Fried <fried@statistik.tu-dortmund.de>, Karen
Schettlinger <schettlinger@statistik.tu-dortmund.de> and
Matthias Borowski <borowski@statistik.tu-dortmund.de>
Anita M. Thieler, Jonathan Rathjens and Roland Fried, with contributions from Brenton R. Clarke, Uwe Ligges, Matias Salibian-Barrera, Gert Willems and Victor Yohai
Jiahui Wang,
Ruben Zamar <ruben@stat.ubc.ca>,
Alfio Marazzi <Alfio.Marazzi@inst.hospvd.ch>,
Victor Yohai <vyohai@dm.uba.ar>,
Matias Salibian-Barrera <matias@stat.ubc.ca>,
Ricardo Maronna <maron@mate.unlp.edu.ar>,
Eric Zivot <ezivot@u.washington.edu>,
David Rocke <dmrocke@ucdavis.edu>,
Doug Martin,
Martin Maechler <maechler@stat.math.ethz.ch>,
Kjell Konis <kjell.konis@me.com>.
Alfio Marazzi <Alfio.Marazzi@chuv.ch>, Jean-Luc Muralti
Original code by many authors, notably
Peter Rousseeuw and Christophe Croux, see file 'Copyrights';
Valentin Todorov <valentin.todorov@chello.at>,
Andreas Ruckstuhl <andreas.ruckstuhl@zhaw.ch>,
Matias Salibian-Barrera <matias@stat.ubc.ca>,
Tobias Verbeke <tobias.verbeke@openanalytics.eu>,
Manuel Koller <mkoller@ispm.unibe.ch>,
Martin Maechler
Raymond K. W. Wong, Fang Yao and Thomas C. M. Lee
Claudio Agostinelli <claudio@unive.it>, Alfio Marazzi <Alfio.Marazzi@chuv.ch>, Victor Yohai <vyohai@uolsinectis.com.ar> and Alex Randriamiharisoa <Alex.Randriamiharisoa@chuv.ch>
Claudio Agostinelli <claudio@unive.it> and Victor J. Yohai <victoryohai@gmail.com>
Werner Stahel, Martin Maechler and potentially others
Tobias Sing, Oliver Sander, Niko Beerenwinkel, Thomas Lengauer
Y. Foucher, K. Trebern-Launay and M. Lorent
Brian Ripley [aut, cre],
Michael Lapsley [aut] (1999 to Oct 2002)
Mateusz Zoltak [aut, cre],
Brian Ripley [aut],
Michael Lapsley [aut]
Pablo Tamayo <pablo.tamayo@oracle.com> and Ari Mozes
<ari.mozes@oracle.com>
Kurt Hornik [aut],
David Meyer [aut],
Stefan Theussl [aut, cre]
Matthias Kohl <Matthias.Kohl@stamats.de>, Peter Ruckdeschel
Denis Mukhin, David A. James and Jake Luciani
Nicola Lunardon, Giovanna Menardi, Nicola Torelli
Bryan Stanfill <bryan.stanfill@csiro.au>, Heike Hofmann
<hofmann@iastate.edu>, Ulrike Genschel <ulrike@iastate.edu>
Lala Septem Riza, Andrzej Janusz, Chris Cornelis, Francisco
Herrera, Dominik Slezak, and Jose Manuel Benitez
Hadley Wickham [aut, cre, cph],
Peter Danenberg [aut, cph],
Manuel Eugster [aut, cph],
RStudio [cph]
Terry Therneau [aut],
Beth Atkinson [aut],
Brian Ripley [aut, trl, cre] (author of initial R port)
Giuliano Galimberti, Gabriele Soffritti, Matteo Di Maso
Joshua Pritikin [cre, aut],
Jonathan Weeks [ctb],
Li Cai [ctb],
Carrie Houts [ctb],
Phil Chalmers [ctb]
Melissa Hubisz, Katherine Pollard, and Adam Siepel
Barb Banbury <bbanbury@utk.edu>, Kurt Michels
<kamichels@math.arizona.edu>, Jeremy M. Beaulieu
<jeremy.beaulieu@yale.edu>, Brian O'Meara <bomeara@utk.edu>
Scott Chamberlain [aut, cre],
Carl Boettiger [aut],
Karthik Ram [aut]
E. Andres Houseman, Sc.D. and Devin C. Koestler, Ph.D.
Joe Conway, Dirk Eddelbuettel, Tomoaki Nishiyama, Sameer Kumar
Prayaga (during 2008), Neil Tiffin
Heiko Mannsperger with contributions of Stephan Gade
Romain Francois, Dirk Eddelbuettel, Murray Stokely and Jeroen Ooms
Authors@R: c(person("Thomas J.", "Leeper", role = c("aut", "cre"),
email = "thosjleeper@gmail.com"))
Dirk Eddelbuettel <edd@debian.org> and Khanh Nguyen <knguyen@cs.umb.edu>
Torben Schulz-Streeck, Boubacar Estaghvirou, Frank Technow
Eiji NAKAMA <nakama@com-one.com>, Junji NAKANO <nakanoj@ism.ac.jp>
Fortran original by Leo Breiman and Adele Cutler, R port by
Andy Liaw and Matthew Wiener, Regularized random forest by
Houtao Deng.
Natalia Belgorodski [aut] (STAT-UP Statistical Consulting),
Matthias Greiner [aut, cre] (Federal Institute for Risk Assessment,
Germany),
Kristin Tolksdorf [aut] (Federal Institute for Risk Assessment,
Germany),
Katharina Schueller [aut] (STAT-UP Statistical Consulting)
Moritz Gschwandtner, Peter Filzmoser, Christophe Croux,
Gentiane Haesbroeck
Daniel W. Heck [aut, cre], Morten Moshagen [aut]
Oliver Flasch, Olaf Mersmann, Thomas Bartz-Beielstein
Oldemar Rodriguez R. with contributions from Olger Calderon and Roberto Zuniga
Nabil Mustafa <http://sma.epfl.ch/~moustafa/index.html>, Saurabh Ray <http://www.mpi-inf.mpg.de/~saurabh/>, Mudassir Shabbir <http://paul.rutgers.edu/~mudassir/>.
Emmanuel Blondel [aut, cre],
Matthieu Stigler [ctb]
Wolfgang Konen <wolfgang.konen@fh-koeln.de>, Martin Zaefferer,
Patrick Koch; Bug hunting and testing by Ayodele Fasika, Ashwin
Kumar, Prawyn Jebakumar
Jeff Dumont <Jeff.Dumont@rsginc.com>, Jeff Keller <Jeff.Keller@rsginc.com>, Chase Carpenter <Chase.Carpenter@rsginc.com>
Ruth Ripley, Krists Boitmanis, Tom A.B. Snijders
Sangkyun Lee <sangkyun.lee@tu-dortmund.de>, Michel Lang
<michellang@gmail.com>
Willem Paling, Randy Zwitch & Jowanza Joseph
Scott Chamberlain [aut, cre],
Kevin Ushey [aut]
Chanhee Yi, Alexander Sibley, and Kouros Owzar
Hugh J. Devlin, Ph. D. and Tom Schenk, Jr.
Michael King <wmichaelking1@gmail.com> and Fernando Cela Diaz <fcela@sloan.mit.edu>. Original sofia-ml code by D. Sculley <dsculley@google.com>.
Jose' Gama [aut, cre],
John Fuller [aut, cph],
Paolo Leva [aut, cph],
Göran Högnäs [ths]
David A. James, Seth Falcon, and (for the included SQLite
sources) the authors of SQLite
Anton Korobeynikov, Alex Shlemov, Konstantin Usevich, Nina Golyandina
Scott Chamberlain [aut, cre],
Thomas J. Leeper [ctb]
T Jake Luciani <jakeluciani@yahoo.com>, Matthieu Decorde
<matthieu.decorde@ens-lyon.fr>, Vaudor Lise <lise.vaudor@ens-lyon.fr>
Tony Plate <tplate@acm.org>, based on RSvgDevice by T Jake Luciani <jakeluciani@yahoo.com>
Reinhard Harter [aut],
Kurt Hornik [aut, cre],
Stefan Theussl [aut],
Cyrille Szymanski [ctb]
G Grothendieck (SymPy itself is by Ondrej Certik and others),
Contributors: Carlos J. Gil Bellosta
Christophe Dutang [aut, cre], Armelle Guillou [ctb], Yuri Goegebeur [ctb]
James Li and Jacob Bien and Martin Wells
Timothy P. Jurka, Loren Collingwood, Amber E. Boydstun,
Emiliano Grossman, Wouter van Atteveldt
Nicholas Peterson, Andre Martins, Melissa Hubisz, and Adam
Siepel
Jonathan Shewchuk, David C. Sterratt <david.c.sterratt@ed.ac.uk>
Winston Chang,
Andrew Weeks,
Frank M. Siegert,
Mark Heath,
Thomas Henlick,
Sergey Babkin,
Turgut Uyar,
Rihardas Hepas,
Szalay Tamas,
Johan Vromans,
Petr Titera,
Lei Wang,
Chen Xiangyang,
Zvezdan Petkovic,
Rigel,
I. Lee Hetherington
Matthias Burger <burgerm@users.sourceforge.net>, Klaus
Juenemann <k.junemann@gmx.net>, Thomas Koenig
<thomas.koenig@epigenomics.com>
Stefan Schlager; the authors of VCGLIB for the included version of the
code
Scott Chamberlain [aut, cre], Vijay Barve [aut]
Josef Leydold and Sougata Chaudhuri <sgtchaudhuri@gmail.com>
Roland Rau Cards were created by Byron Knoll and released into
the public domain.  They were downloaded from
http://code.google.com/p/vector-playing-cards/downloads/detail?name=PNG-cards-1.2.zip
on 2012-06-08.  Byron Knoll stated in his blog that the cards
were released by him into the public domain:
http://byronknoll.blogspot.de/2011/03/vector-playing-cards.html
The backside of the cards were created by Roland Rau using the
R-Logo.
Alexandre Bureau, Ingo Ruczinski, Samuel G. Younkin
S original by Rene Carmona <rcarmona@princeton.edu> and Bruno
Torresani <bruno.torresani@cmi.univ-mrs.fr>; R port by Brandon
Whitcher <bjw34032@users.sourceforge.net>; upgrade by Jonathan M. Lees<jonathan.lees@unc.edu>
Kurt Hornik [aut, cre],
Christian Buchta [ctb],
Torsten Hothorn [ctb],
Alexandros Karatzoglou [ctb],
David Meyer [ctb],
Achim Zeileis [ctb]
Kurt Hornik [aut, cre],
University of Waikato [ctb, cph] (Weka Java library)
Uwe Ligges <Uwe.Ligges@R-project.org>
with contributions (the original R-Sweave mode) from Gilbert Ritschard and Karl Koeller
Anthony Albano <tony.d.albano@gmail.com>, Ben Babcock
<ben.babcock@arrt.org>
D. James Greiner, Paul Baines, and Kevin M. Quinn
Patrick Meyer and Sebastien Bigaret, with contributions from Krzysztof Ciomek.
Rob Goedman <goedman@mac.com>, Gabor Grothendieck <ggrothendieck@gmail.com>, Søren Højsgaard <Soren.Hojsgaard@agrsci.dk>, Ayal Pinkus <apinkus@xs4all.nl>
Eliandro Ronael Gilbert, Mauricio Garcia de Camargo, Leonardo Sandrini-Neto
Andrew Balemi, Dineika Chandra, James Curran, Brant Deppa, Mike
Forster, Charlotte Maia, Brendan McArdle, Chris Wild
Nicolau Manubens Gil <nicolau.manubens@ic3.cat>, Virginie Guemas <virginie.guemas@ic3.cat>, Isabel Andreu-Burillo <isabel.andreu-burillo@ic3.cat>, Fabian Lienert <fabian.lienert@ic3.cat>, Javier Garcia-Serrano <jgarcia@ic3.cat>, Ludovic Auger <ludovic.auger@meteo.fr>
Paulo I. Prado and Murilo Dantas Miranda
Emmanuelle Comets, Audrey Lavenu, Marc Lavielle.
Maria Dolores Esteban Lefler, Domingo Morales Gonzalez, Agustin Perez Martin
Wolfgang Trutschnig <wolfgang@trutschnig.net>, Asun Lubiano
<lubiano@uniovi.es>
Tuo Zhao, Xingguo Li, Han Liu, and Kathryn Roeder
Madeleine Thompson, except dchud.f and dchdd.f, which were written by
G. W. Stewart.
Arne Henningsen <arne.henningsen@gmail.com>,
Ott Toomet <otoomet@ut.ee>
Wei Jiang, Jonathan Mahnken, Matthew Mayo
Lawrence Joseph <lawrence.joseph@mcgill.ca> and Patrick Belisle
<pbelisle@epimgh.mcgill.ca>
Lawrence Joseph <lawrence.joseph@mcgill.ca>, Roxane du Berger
and Patrick Belisle <pbelisle@epimgh.mcgill.ca>
Yves Tillé <yves.tille@unine.ch>, Alina Matei <alina.matei@unine.ch>
Juliane Manitz <manitzj@gmx.de>, contributions by Mark
Hempelmann <mark.hempelmann@o2online.de>, Goeran Kauermann
<gkauermann@wiwi.uni-bielefeld.de>, Helmut Kuechenhoff
<kuechenhoff@stat.uni-muenchen.de>, Shuai Shao
<shuai.shao@campus.lmu.de>, Cornelia Oberhauser
<conny.oberhauser@gmx.de>, Nina Westerheide
<nwesterheide@wiwi.uni-bielefeld.de>, Manuel Wiesenfarth
<m.wiesenfarth@uni-goettingen.de>
Giulio Barcaroli, Daniela Pagliuca and Egon Willighagen
Emilio Lopez Escobar [aut, cre], Ernesto Barrios Zamudio [ctb] <ebarrios@itam.mx>
R. Tibshirani, G. Chu, Balasubramanian Narasimhan, Jun Li
Noory Y. Kim. Advisors: Shrikant I. Bangdiwala, Gerald Gartlehner.
Thomas Lumley [aut],
Achim Zeileis [aut, cre]
The Institute of Statistical Mathematics
Original by Littell, Milliken, Stroup, and Wolfinger,
modifications by Douglas Bates <bates@stat.wisc.edu>, Martin Maechler,
Ben Bolker and Steven Walker
Pei Wang <pwang@fhcrc.org>, Yan Liu <yanliu@fhcrc.org>
Unless otherwise noted, the contents of this package were written by
Gregory R. Warnes <greg@warnes.net> and are provided under the
terms of the GNU General Public License, version 2.0 or later.
--
The files 'src/ieee2ibm.c' and 'src/ibm2ieee.c' were extracted from
BRL-CAD file /brlcad/src/libbu/htond.c written by Michael John
Muuss, Copyright (c) 2004-2007 United States Government as
represented by the U.S. Army Research Laboratory, and is utilized
and redistributed under the terms of the GNU Lesser General Public
License, version 2.1.
--
The files 'R/AFirst.lib.s', 'R/all.is.numeric.R',
'R/importConvertDateTime.R', 'R/in.opererator.R', 'R/makeNames.R',
'R/read.xport.R', and 'R/testDateTime.R' are copied or adapted from the
'Hmisc' package created by Frank E. Harrell, Jr.
<f.harrell@vanderbilt.edu>, and are utilized and redistributed under
the terms of the GNU General Public License, version 2.0 or later.
--
The files 'R/xport.R', 'src/SASxport.c', 'src/SASxport.h', and
'src/foreign.h' are copied or adapted from the R 'foreign' package
created by Douglas M. Bates <bates@stat.wisc.edu> and Saikat DebRoy
<saikat@stat.wisc.edu>, and are utilized and redistributed under the
terms of the GNU General Public License, version 2.0 or later.
--
The creation of this package was partially funded by Metrum Institute
<http://metruminstitute.org>.
Jesus Palomo [aut, cre],
Gonzalo Garcia-Donato [aut],
Rui Paulo [aut],
James Berger [ctb],
Maria Jesus Bayarri [ctb],
Jerome Sacks [ctb]
Paul Fischer [aut, cre, cph],
Astrid Hilbert [ctb, cph]
Valentin Rousson <rousson@ifspm.unizh.ch> and Martin Maechler
Lee Wilkinson <leland.wilkinson@gmail.com> and Anushka Anand
Uwe Ligges <ligges@statistik.tu-dortmund.de>, Martin Maechler, Sarah Schnackenberg
Matteo Dell'Omodarme [aut, cre], Giada Valle [aut]
Mortier F., Trottier C., Cornu G., Bry X.
Philipp Erb, David Luethi, Juri Hinz, Simon Otziger
Manuel Morales <mmorales@williams.edu>, with code developed by
the R Development Core Team and with general advice from the
R-help listserv community, especially Duncan Murdoch.
Craig T. Armstrong, Thomas L. Vincent <tlfvincent@gmail.com>, Peter J. Green and Derek N. Woolfson <D.N.Woolfson@bristol.ac.uk>
Enio Jelihovschi <eniojelihovs@gmail.com>
José Cláudio Faria <joseclaudio.faria@gmail.com>
Ivan Bezerra Allaman <ivanalaman@gmail.com>
Holger Schwender, with a contribution of Arno Fritsch
Adrian Baddeley <Adrian.Baddeley@uwa.edu.au>
with contributions from Vittorio Broglio,
Pedro Antonio Neves, Andrew Bassom and Peter Buzzacott
Miika Ahdesmaki, Verena Zuber, Sebastian Gibb, and Korbinian Strimmer
Matthias Templ, Alexander Kowarik, Bernhard Meindl
Alexander Kowarik, Matthias Templ, Bernhard Meindl, Francois Fonteneau
Luca Bagnato, Lucio De Capitani, Angelo Mazza and Antonio Punzo
Jeremy VanDerWal, Lorena Falconi, Stephanie Januchowski, Luke Shoo and
Collin Storlie
Heloise Lavigne, Jean-Marie Epitalon and Jean-Pierre Gattuso
Rutger M. van den Bor and Willem M. van der Wal
Jerome Sueur <sueur@mnhn.fr>, Thierry Aubin, Caroline Simonis, \cr
with contributions of Ethan C. Brown, Camille Desjonqueres,
Amandine Gasc, Stefanie LaZerte, Jonathan Lees, Laurent Lellouch,
Sandrine Pavoine, Luis J. Villanueva-Rivera, Zev Ross, Carl G. Witthoft.
Seong-Yun Hong [aut, cre],
David O'Sullivan [ctb]
Alice Cleynen, Guillem Rigaill, Michel Koskas
Xuefei Mi, H. Friedrich Utz, Albrecht E. Melchinger (Project contact person: Melchinger@uni-hohenheim.de )
Simon Potter [aut, trl, cre],
Simon Sapin [aut],
Ian Bicking [aut]
Mohammed Sedki, Gilles Celeux, Cathy Maugis-Rabusseau
John Fox [aut, cre],
Zhenghua Nie [aut],
Jarrett Byrnes [aut],
Michael Culbertson [ctb],
Saikat DebRoy [ctb],
Michael Friendly [ctb],
Richard H. Jones [ctb],
Adam Kramer [ctb],
Georges Monette [ctb],
R-Core [ctb]
Rina Foygel <rina@uchicago.edu>, Mathias Drton <md5@uw.edu>.
Giampiero Marra <giampiero.marra@ucl.ac.uk> and Rosalba Radice <r.radice@bbk.ac.uk>
Giampiero Marra, Rosalba Radice and Malgorzata Wojtys
Sunthud Pornprasertmanit [aut, cre], Patrick Miller [aut], Alex
Schoemann [aut], Yves Rosseel [aut], Corbin Quick [ctb],
Mauricio Garnier-Villarreal [ctb], James Selig [ctb], Aaron
Boulton [ctb], Kristopher Preacher [ctb], Donna Coffman [ctb],
Mijke Rhemtulla [ctb], Alexander Robitzsch [ctb], Craig Enders
[ctb], Ruber Arslan [ctb], Bell Clinton [ctb], Pavel Panko
[ctb]
Olaf Mersmann [aut, cre],
Quinn Weber [ctb]
Daniel P Gaile <dpgaile@buffalo.edu>, Lori A. Shepherd
<las65@buffalo.edu>, Lara Sucheston <lsuchest@buffalo.edu>,
Andrew Bruno <aebruno2@ccr.buffalo.edu>, Kenneth F. Manly
<manly@buffalo.edu>
Gilles Pujol, Bertrand Iooss, Alexandre Janon with contributions from Paul Lemaitre, Laurent Gilquin, Loic Le Gratiet, Taieb Touati, Bernardo Ramos, Jana Fruth and Sebastien Da Veiga
Francois Husson, Sebastien Le, Marine Cadoret
Brian C. Franczak, Ryan P. Browne and Paul D. McNicholas
Rune Haubo Bojesen Christensen [aut, cre],
Per Bruun Brockhoff [aut]
Brian D. Greenhill, Michael D. Ward and Audrey Sacks
Michal Juraska and Doug Grove, with contributions from Xuesong Yu and Peter B. Gilbert
Delphine Charif and Jean R. Lobry and Anamaria Necsulea and
Leonor Palmeira and Simon Penel and Guy Perriere
Arend Voorman, Jennifer Brody, Han Chen, Thomas Lumley
Xiaowei Zhan <zhanxw@gmail.com> and Dajiang Liu
<dajiang.liu@gmail.com>, with contributions of Jean-loup Gailly
<jloup@gzip.org> and Mark Adler
<madler@alumni.caltech.edu> (they wrote zlib and provide license
so we include zlib source code), Julian Seward
<jseward@bzip.org> (this author wrote bzip2 and with BSD
license we include bzip2 source codes) and Heng Li
<lh3@live.co.uk> (this author wrote tabix and with MIT license
we include tabix source codes).
Laurent Gatto [aut, cre],
Robert Stojnic [aut]
Francesco Favero, Andrea M. Marquard, Tejal Joshi, Aron C. Eklund
Michael Hahsler [aut, cre, cph],
Christian Buchta [aut, cph],
Kurt Hornik [aut, cph],
Fionn Murtagh [ctb, cph],
Michael Brusco [ctb, cph],
Stephanie Stahl [ctb, cph],
Hans-Friedrich Koehn [ctb, cph]
Eric Lamb [aut, cre],
Kerrie Mengersen [aut],
Katherine Stewart [aut],
Udayanga Attanayake [aut],
Steven Siciliano [aut]
David Meyer [aut, cre], Kurt Hornik [aut], Christian Buchta
[ctb]
Ariane Straub, under the supervision of Torsten Hothorn
Joe Houpt <joseph.houpt@wright.edu>, Leslie Blaha <Leslie.Blaha@wpafb.af.mil>
S original by James J. Majure <majure@iastate.edu> Iowa State
University, R port + extensions by Albrecht Gebhardt
<albrecht.gebhardt@uni-klu.ac.at>
Noah Simon, Jerome Friedman, Trevor Hastie, and Rob Tibshirani
Balasubramanian Narasimhan [aut, cre],
Mei-Chiung Shih [aut]
Irene Castro Conde and Jacobo de Una Alvarez.
Damian W. Betebenner [aut, cre],
Adam Van Iwaarden [aut],
Ben Domingue [aut]
Yixuan Qiu and authors/contributors of the
included software. See file AUTHORS for details.
Liran Lerman <llerman@ulb.ac.be>, Gianluca Bontempi
<gbonte@ulb.ac.be>, Olivier Markowitch
<olivier.markowitch@ulb.ac.be>
Uwe Ligges [aut, cre] (new maintainer),
Tom Short [aut] (port to R),
Paul Kienzle [aut] (majority of the original sources),
Sarah Schnackenberg [ctb] (various test cases and bug fixes),
David Billinghurst [ctb],
Hans-Werner Borchers [ctb],
Andre Carezia [ctb],
Pascal Dupuis [ctb],
John W. Eaton [ctb],
E. Farhi [ctb],
Kai Habel [ctb],
Kurt Hornik [ctb],
Sebastian Krey [ctb],
Bill Lash [ctb],
Friedrich Leisch [ctb],
Olaf Mersmann [ctb],
Paulo Neis [ctb],
Jaakko Ruohio [ctb],
Julius O. Smith III [ctb],
Doug Stewart [ctb],
Andreas Weingessel [ctb]
Michal Burdukiewicz [cre, aut],
Piotr Sobczyk [aut]
Amir B.K. Foroushani, Fiona S.L. Brinkman, David J. Lynn
Gerald Jurasinski <gerald.jurasinski@uni-rostock.de>, with
contributions from Vroni Retzer <vroni.retzer@gmx.de>
Axel Gandy <a.gandy@imperial.ac.uk> with contributions from Patrick Rubin-Delanchy <patrick.rubin-delanchy@imperial.ac.uk> and Georg Hahn <g.hahn11@imperial.ac.uk>
Arsalane Chouaib Guidoum, Kamal Boukhetala
Wolfgang Lederer <Wolfgang.Lederer@gmail.com>, Helmut
Küchenhoff <Kuechenhoff@stat.uni-muenchen.de>
Juan Xiong <xiongjuan2000@gmail.com>, Wenqing He <whe@stats.uwo.ca>, Grace Y. Yi<yyi@uwaterloo.ca>
Syed Haider, Michal Grzadkowski, Paul C. Boutros
Julien Chiquet <julien.chiquet@genopole.cnrs.fr>, Gilles
Grasseau <gilles.grasseau@genopole.cnrs.fr>, Camille
Charbonnier <camille.charbonnier@genopole.cnrs.fr>, Christophe
Ambroise <christophe.ambroise@genopole.cnrs.fr>.
Peng Zhang, Zhengguo Qiu and Chengchun Shi
John P. Nolan, with parts based on code by Alan Genz
Olivier Lepais [aut, cre],
Jason Weir [aut]
Marius Hofert and Martin Maechler <maechler@stat.math.ethz.ch>
Sunthud Pornprasertmanit [aut], Patrick Miller [aut], Alexander
Schoemann [aut], Corbin Quick [ctb], Terry Jorgensen [ctb]
Ricardo Pio Monti, Christoforos Anagnostopoulos and Giovanni Montana
Jianqing Fan, Yang Feng,  Diego Franco Saldana, Richard Samworth,  Yichao Wu
Emilio L. Cano, Javier M. Moguerza and Andrés Redchuk
Seunggeun Lee, Larisa Miropolsky and Micheal Wu
Arend Voorman, Jennifer Brody, Thomas Lumley
Len Stefanski, Yichao Wu, and Kyle White
David Scott <d.scott@auckland.ac.nz>, Fiona Grimson
Robert King, <Robert.King@newcastle.edu.au>, with contributions
from Emily Anderson <emily.anderson@newcastle.edu.au>.
Patchanok Srisuradetchai, John J. Borkowski
Kurt Hornik [aut, cre],
Ingo Feinerer [aut],
Martin Kober [aut]
Bob Rudis (@hrbrmstr) & Jay Jacobs (@jayjacobs)
Kurt Hornik [aut, cre],
David Meyer [aut],
Christian Buchta [aut]
Antonio Solanas, Rumen Manolov, and Patrick Onghena
Original by F.L. Ramsey and D.W. Schafer, modifications by
Daniel W. Schafer, Jeannie Sifneos and Berwin A. Turlach
Wesley Burr, with contributions from Karim Rahim
Adrian Bowman and Adelchi Azzalini.
Ported to R by B. D. Ripley <ripley@stats.ox.ac.uk> up to version 2.0,
version 2.1 by Adrian Bowman and Adelchi Azzalini,
version 2.2 by Adrian Bowman.
Patrick Mair [aut, cre], Jan De Leeuw [aut], Ingwer Borg [ctb], Patrick
J. F. Groenen [ctb]
Jun Yan <jun.yan@uconn.edu> and
Vladimir Pozdnyakov <vladimir.pozdnyakov@uconn.edu>
David Warton <David.Warton@unsw.edu.au>, Remko Duursma, Daniel Falster and Sara Taskinen.
Prof. Juan David Velasquez H. Universidad Nacional de Colombia
Tobias Verbeke, based on datasets provided on the book's website
Chao Cai, Yubo Zou, Yingwei Peng, Jiajia Zhang
Murray Aitkin, Brian Francis, John Hinde, Ross Darnell
<ross.darnell@csiro.au>
Alexey Shipunov (with help of Eugenij Altshuler)
Aldo M. Garay <medina_garay@yahoo.com>, Victor Lachos <hlachos@ime.unicamp.br>,
and Monique Bettio Massuia <moniquemassuia@gmail.com>
Celia Touraine, Pierre Joly, Thomas A. Gerds
Artur Agostinho Araujo and Luis Meira-Machado
<lmachado@math.uminho.pt>
Kaspar Ru{f}{i}bach <kaspar.rufibach@gmail.com> and Samuel Mueller <samuel.mueller@sydney.edu.au>
Ben Deivide de Oliveira Batista, Daniel Furtado Ferreira
Jeffrey B. Arnold [aut, cre],
Alan Agresti [cph],
Barbara Finlay [cph]
Bo Wang, Aziz Mezlini, Feyyaz Demir, Marc Fiume, Zhuowen Tu, Michael Brudno, Benjamin Haibe-Kains, Anna Goldenberg
Luke Tierney, A. J. Rossini, Na Li, H. Sevcikova
Juan R González, Lluís Armengol, Elisabet Guinó, Xavier Solé, and Víctor Moreno
Mickael Canouil [aut, cre],
Loic Yengo [ctb]
Stephen W. Erickson, PhD, with contributions from Joshua
Callaway, MPH
Augustin Luna <augustin@mail.nih.gov>, Kristin K. Nicodemus
<kristin.nicodemus@well.ox.ac.uk>, <nicodemusk@mail.nih.gov>
Alireza S. Mahani, Asad Hasan, Marshall Jiang, Mansour T.A. Sharabiani
Bill Venables, based on original code by David Brahm
Anton Grau Larsen, with contributions from Christoph Ellersgaard and
Stefan Andrade
Trevor Hastie <hastie@stanford.edu> and Rahul Mazumder
<rahul.mazumder@gmail.com>
Anderson Rodrigo da Silva, Renato Paiva de Lima
Carlos A. Sierra <csierra@bgc-jena.mpg.de>, Markus Mueller
<mamueller@bgc-jena.mpg.de>
Andrew Sila and Thomas Terhoeven-Urselmans
Julien MOEYS <jules_m78-soiltexture@yahoo.fr>, contributions from Wei Shangguan.
Jon Clayden; based on the work of Ivan Zelinka
Olaf Mersmann <olafm@p-value.net> Bernd Bischl
<bischl@statistik.tu-dortmund.de>
Lee S. McDaniel, Jonathan S. Schildcrout
Leo Lahti, Juuso Parkkinen, Joona Lehtomaki, Juuso Haapanen, Jussi
Paananen, Einari Happonen
Spencer Graves, Sundar Dorai-Raj, and Romain Francois
Leo Lahti, Einari Happonen, Juuso Parkkinen, Joona Lehtomaki
Luis J. Villanueva-Rivera and Bryan C. Pijanowski
Edzer Pebesma [aut, cre],
Roger Bivand [aut],
Barry Rowlingson [ctb],
Virgilio Gomez-Rubio [ctb]
Balasubramanian Narasimhan [aut, cre],
Mei-Chiung Shih [aut], Pei He [aut]
Jie Peng <jie@wald.ucdavis.edu> , Pei Wang <pwang@fhcrc.org>,
Nengfeng Zhou <nfzhou@umich.edu>, Ji Zhu <jizhu@umich.edu>.
Arjun M. Gopalaswamy, Andrew J. Royle, Michael E. Meredith, Pallavi
Singh, Devcharan Jathanna, N. Samba Kumar and K. Ullas Karanth
Edzer Pebesma [cre, aut],
Benedikt Graeler [ctb],
Tom Gottfried [ctb],
Robert J. Hijmans [ctb]
Jonathan Eastman, Timothy Paine, and Olivier Hardy
Till Junge [aut, cre],
Sandra Penic [aut],
Mathieu Cossuta [aut],
Guy Elcheroth [aut],
Stephanie Glaeser [ctb],
Davide Morselli [ctb]
Reinhard Furrer [aut, cre], Florian Gerber [ctb]
François Rousset [aut, cre, cph],
Jean-Baptiste Ferdy [aut, cph]
Alexander Kowarik, Bernhard Meindl, Matthias Templ
Thomas Leeper [aut, cre], Andy Barbour [ctb]
Willem Robert van Hage <willem.van.hage@synerscope.com>, with contributions from: Tomi Kauppinen, Benedikt Graeler, Christopher Davis, Jesper Hoeksema, Alan Ruttenberg, and Daniel Bahls.
T.M. Davies, M.L. Hazelton and J.C. Marshall
R interface code by Michael Braun
Original Fortran code by Thomas F. Coleman, Burton S. Garbow and
Jorge J. More.
Line Clemmensen <lhc@imm.dtu.dk>, contributions by Max Kuhn
Roger Koenker <rkoenker@uiuc.edu> and Pin Ng <Pin.Ng@NAU.EDU>
Rahul Mazumder, Trevor Hastie, Jerome Friedman
Kieran Alden, Mark Read, Paul Andrews, Jon Timmis, Henrique Veiga-Fernandes, Mark Coles
Fabio Sigrist, Hans R. Kuensch, Werner A. Stahel
Brian Ripley [aut, cre, cph],
Roger Bivand [ctb],
William Venables [cph]
Cici Chen <cicichen@u.washington.edu>,
Albert Y. Kim <albert@stat.washington.edu>,
Michelle Ross <micher3@u.washington.edu>,
Jon Wakefield <jonno@u.washington.edu>
Mathieu Ribatet, Richard Singleton, R Core team
Seija Sirkia, Jari Miettinen, Klaus Nordhausen, Hannu Oja, Sara Taskinen
Felipe Osorio <felipe.osorios@usm.cl> and Ronny Vallejos
<ronny.vallejos@usm.cl>, with contributions of Francisco Cuevas
and Diego Mancilla.
Stefan Wilhelm <wilhelm@financial.com> and Miguel Godinho de Matos <miguelgodinhomatos@cmu.edu>
Johan Lindstrom, Adam Szpiro, Paul D. Sampson, Silas Bergen, Assaf P.
Oron
Adrian Baddeley <Adrian.Baddeley@uwa.edu.au>,
Rolf Turner <r.turner@auckland.ac.nz>
and Ege Rubak <rubak@math.aau.dk>,
with substantial contributions of code by
Kasper Klitgaard Berthelsen;
Abdollah Jalilian;
Marie-Colette van Lieshout;
Dominic Schuhmacher;
and
Rasmus Waagepetersen.
Additional contributions
by Q.W. Ang;
S. Azaele;
M. Baddeley;
C. Beale;
M. Bell;
R. Bernhardt;
T. Bendtsen;
A. Bevan;
B. Biggerstaff;
L. Bischof;
R. Bivand;
J.M. Blanco Moreno;
F. Bonneu;
J. Burgos;
S. Byers;
Y.M. Chang;
J.B. Chen;
I. Chernayavsky;
Y.C. Chin;
B. Christensen;
J.-F. Coeurjolly;
R. Corria Ainslie;
M. de la Cruz;
P. Dalgaard;
S. Das;
P.J. Diggle;
P. Donnelly;
I. Dryden;
S. Eglen;
O. Flores;
E.D. Ford;
P. Forbes;
J. Franklin;
N. Funwi-Gabga;
O. Garcia;
A. Gault;
M. Genton;
J. Gilbey;
J. Goldstick;
P. Grabarnik;
C. Graf;
U. Hahn;
A. Hardegen;
M. Hering;
M.B. Hansen;
M. Hazelton;
J. Heikkinen;
K. Hornik;
P. Hunziker;
R. Ihaka;
A. Jammalamadaka;
R. John-Chandran;
D. Johnson;
M. Kuhn;
J. Laake;
F. Lavancier;
T. Lawrence;
R.A. Lamb;
J. Lee;
G.P. Leser;
H.T. Li;
G. Limitsios;
B. Madin;
J. Marcus;
K. Marchikanti;
R. Mark;
J. Mateu;
P. McCullagh;
U. Mehlig;
S. Meyer;
X.C. Mi;
L. De Middeleer;
R.K. Milne;
J. Moller;
E. Mudrak;
G.M. Nair;
L.S. Nielsen;
F. Nunes;
J. Oehlschlaegel;
T. Onkelinx;
S. O'Riordan;
E. Parilov;
J. Picka;
N. Picard;
M. Porter;
S. Protsiv;
A. Raftery;
S. Rakshit;
P. Ramon;
X. Raynaud,
M. Reiter;
T.O. Richardson;
B.D. Ripley;
E. Rosenbaum;
B. Rowlingson;
J. Rudokas;
J. Rudge;
C. Ryan;
F. Safavimanesh;
A. Sarkka;
K. Schladitz;
B.T. Scott;
G.C. Shen;
V. Shcherbakov;
I.-M. Sintorn;
Y. Song;
M. Spiess;
M. Stevenson;
K. Stucki;
M. Sumner;
P. Surovy;
B. Taylor;
T. Thorarinsdottir;
B. Turlach;
K. Ummer;
M. Uppala;
A. van Burgel;
T. Verbeke;
M. Vihtakari;
A. Villers;
F. Vinatier;
H. Wang;
H. Wendrock;
J. Wild;
S. Wong;
M.E. Zamboni
and
A. Zeileis.
Benjamin M. Taylor and Barry S. Rowlingson
Haiming Zhou <zhouh@email.sc.edu> and Tim Hanson <hansont@stat.sc.edu>
Axel Gandy <a.gandy@imperial.ac.uk> and Jan Terje Kvaloy <jan.t.kvaloy@uis.no>.
Dennis Walvoort [aut, cre, cph], Dick Brus [aut, cph], Jaap de
Gruijter [aut, cph]
Roger Bivand [cre, aut],
Micah Altman [ctb],
Luc Anselin [ctb],
Renato Assunção [ctb],
Olaf Berke [ctb],
Andrew Bernat [ctb],
Guillaume Blanchet [ctb],
Eric Blankmeyer [ctb],
Marilia Carvalho [ctb],
Bjarke Christensen [ctb],
Yongwan Chun [ctb],
Carsten Dormann [ctb],
Stéphane Dray [ctb],
Virgilio Gómez-Rubio [ctb],
Rein Halbersma [ctb],
Elias Krainski [ctb],
Pierre Legendre [ctb],
Nicholas Lewin-Koh [ctb],
Hongfei Li [ctb],
Jielai Ma [ctb],
Giovanni Millo [ctb],
Werner Mueller [ctb],
Hisaji Ono [ctb],
Pedro Peres-Neto [ctb],
Gianfranco Piras [ctb],
Markus Reder [ctb],
Michael Tiefelsdorf [ctb],
Danlin Yu [ctb]
Javier Martinez-Lopez <javi.martinez.lopez@gmail.com>, Babak Naimi
<naimi.b@gmail.com>, Julia Martinez-Fernandez <juliamf@um.es>
Piers K Dunstan, Scott D Foster and Ross Darnell
Michal Juraska <mjuraska@u.washington.edu>, with contributions
from Peter B. Gilbert <pgilbert@scharp.org>, Xiaomin Lu
<xlu2@phhp.ufl.edu>, Min Zhang <mzhangst@umich.edu>, Marie
Davidian <davidian@stat.ncsu.edu>, and Anastasios A. Tsiatis
<tsiatis@stat.ncsu.edu>
Santiago Beguería [aut, cre],
Sergio M. Vicente-Serrano [aut]
Maximilian Lange, Sven Lautenbach, Claudia Raedig
Hee-Seok Oh <heeseok.oh@gmail.com>, Donghoh Kim
<donghoh.kim@gmail.com>
Francesca Demichelis <demichelis@science.unitn.it>, Davide
Prandi <prandi@science.unitn.it>
Samuel Brown, Rupert Collins, Stephane Boyer, Marie-Caroline Lefort, Jagoba Malumbres-Olarte, Cor Vink, Rob Cruickshank
Giovanni Millo <giovanni_millo@generali.com>, Gianfranco Piras
<gpiras@mac.com>
Dongjun Chung <chungdon@stat.wisc.edu>, Hyonho Chun
<chun@stat.wisc.edu>, Sunduz Keles <keles@stat.wisc.edu>
William Constantine, Tim Hesterberg, Knut Wittkowski, Tingting
Song, Stephen Kaluzny
Moliere Nguile Makao <moliere.nguile@gmail.com> and Alexandre Bureau <alexandre.bureau@msp.ulaval.ca>
Scott Chamberlain [aut, cre],
Karthik Ram [aut],
Ted Hart [aut]
Jean Gaudart, Nathalie Graffeo, Guillaume Barbet, Bernard Fichet, Roch Giorgi (Aix-Marseille University)
Zhong Guan <zguan@iusb.edu>; Cheng Peng <cpeng@usm.maine.edu>
Manuel J. A. Eugster <manuel.eugster@stat.uni-muenchen.de>
T. Bartz-Beielstein with contributions from: J. Ziegenhirt, W.
Konen, O. Flasch, M. Friese, P. Koch, M. Zaefferer, B. Naujoks
University of Edinburgh SPRINT Team <sprint@ed.ac.uk>
Tom Kincaid [aut, cre],
Tony Olsen [aut],
Don Stevens [ctb],
Christian Platt [ctb],
Denis White [ctb],
Richard Remington [ctb]
Owen Jones, Robert Maillardet, Andrew Robinson, Olga Borovkova, and Steven Carnie
Frederick Kin Hing Phoa and Shu-Ching Lin
Sean Anderson [aut, cre],
Cole Monnahan [aut],
Kelli Johnson [aut],
Kotaro Ono [aut],
Juan Valero [aut],
Curry Cunningham [aut],
Felipe Hurtado-Ferro [aut],
Roberto Licandeo [aut],
Carey McGilliard [aut],
Melissa Muradian [ctb],
Cody Szuwalski [aut],
Katyana Vert-pre [aut],
Athol Whitten [aut]
Charles Auerbach,PHD Wendy Zeitlin Schudrich,PHD Wurzweiler School of Social Work
Mikhail Zhelonkin, Marc G. Genton, Elvezio Ronchetti
Luis Hernando Vanegas <hvanegasp@gmail.com> and Gilberto A. Paula
Rainer Opgen-Rhein, Verena Zuber, and Korbinian Strimmer.
Hsin-ya Lee, Yung-jin Lee <mobilepk@gmail.com>
Diethelm Wuertz, Martin Maechler and Rmetrics core team
members.
Mike Malecki <Mike.Malecki at yougov.com>
David Meyer [aut, cre],
Torsten Hothorn [aut],
Friedrich Leisch [aut]
Leo Lahti, Juuso Parkkinen, Joona Lehtomaki
Gordon Smyth [cre, aut], Yifang Hu [ctb], Peter Dunn [ctb], Belinda Phipson [ctb], Yunshun Chen [ctb]
Mark S. Handcock [aut],
David R. Hunter [aut],
Carter T. Butts [aut],
Steven M. Goodreau [aut],
Pavel N. Krivitsky [aut, cre],
Skye Bender-deMoll [aut],
Martina Morris [aut]
Wai-ki Yip <wkyip@hsph.harvard.edu>, with contributions from
Ann Lazar, David Zahrieh, Chip Cole, Ann Lazar, Marco Bonetti,
and Richard Gelber
Jinko Graham <jgraham@stat.sfu.ca>, Brad McNeney
<mcneney@sfu.ca>, Francoise Seillier-Moiseiwitsch
<seillier@math.umbc.edu>, R interface by Sigal Blay
<sblay@sfu.ca>
Roman Olson, Won Chang, Klaus Keller, and Murali Haran
Elise Dusseldorp [aut, cre, cph],
Claudio Conversano [aut, cph],
Cor Ninaber [ctb],
Kristof Meers [ctb],
Peter Neufeglise [trl]
Tomas Johannesson & Halldor Bjornsson, Icelandic Met. Office;
Gabor Grothendieck
Margaret E. Roberts, Brandon M. Stewart and Dustin Tingley
Edith Gabriel, Peter J Diggle, stan function by Barry Rowlingson
Soubeyrand, S., Tollenaere, C., Haon-Lasportes, E. and Laine, A.-L.
Mark A. Bell <mark.bell521@gmail.com> Graeme T. Lloyd <graemetlloyd@gmail.com>.
Sophie Baillargeon <Sophie.Baillargeon@mat.ulaval.ca> and
Louis-Paul Rivest <Louis-Paul.Rivest@mat.ulaval.ca>
Michael Hahsler [aut, cre, cph],
Matthew Bolanos [aut, cph],
John Forrest [aut, cph]
Gustavo Carvalho, Marco Batalha, and Owen Petchey.
Mark van der Loo [aut, cre],
Jan van der Laan [ctb]
Marek Gagolewski and Bartek Tartanus (stringi source code);
IBM and other contributors (ICU4C 52.1 source code);
Unicode, Inc. (Unicode Character Database)
Achim Zeileis [aut, cre],
Friedrich Leisch [aut],
Kurt Hornik [aut],
Christian Kleiber [aut],
Bruce Hansen [ctb]
Nathan Morris [aut, cre],
Yeunjoo Song [aut],
Stephen Cahn [ctb]
Maciej Eder, Jan Rybicki, Mike Kestemont
Mary C Meyer, Jeffrey G Snodgrass, Michael Lacy
Fengfeng Zhou, Youxi Luo, Qinghan Meng, Ruiquan Ge, Guoqin Mai, Jikui Liu
Jorge Orestes Cerdeira <orestes@isa.utl.pt>,
Pedro Duarte Silva <psilva@porto.ucp.pt>,
Jorge Cadima <jcadima@isa.utl.pt>,
Manuel Minhoto <minhoto@uevora.pt>
Erin LeDell, Stephanie Sapp, Mark van der Laan
Andrey Alexeyenko, Woojoo Lee and Yudi Pawitan
David Brahm <brahm@alum.mit.edu> and Greg Snow
<Greg.Snow@intermountainmail.org>, with contributions from Curt
Seeliger <Seeliger.Curt@epamail.epa.gov> and Henrik Bengtsson
<hb@maths.lth.se>.
Filippo Botta, Casper Eriksen, Gilles Guillot
Marcel Dettling <marcel.dettling@zhaw.ch> and Martin Maechler
Tsung-han Tsai <t.tsai@wustl.edu>, Jeff Gill <jgill@wustl.edu>
and Jonathan Rapkin <rapkin@wustl.edu>
Wim Van der Elst, Ariel Alonso & Geert Molenberghs
Yingye Zheng, Tianxi Cai, and Marshall Brown
Sergej Potapov, Werner Adler and Matthias Schmid.
Michael Höhle [aut, cre, ths], Sebastian Meyer [aut],
Michaela Paul [aut], Leonhard Held [ctb, ths],
Thais Correa [ctb], Mathias Hofmann [ctb], Christian Lang [ctb],
Juliane Manitz [ctb], Andrea Riebler [ctb], Daniel Sabanés Bové [ctb],
Maëlle Salmon [ctb], Dirk Schumacher [ctb], Stefan Steiner [ctb],
Mikko Virtanen [ctb], Valentin Wimmer [ctb], R Core Team [ctb]
(A few code segments are modified versions of code from base R)
Terry M Therneau [aut, cre],
Thomas Lumley [ctb, trl] (original S->R port and maintainer until 2009)
Dominique-Laurent Couturier, Jun Ma, Stephane Heritier.
Ignacio Lopez de Ullibarri and Maria Amalia Jacome
Juan R González, Edsel A Peña, and Robert L Strawderman
Stanislas Hubeaux <stanislas.hubeaux@epfl.ch> and Kaspar Rufibach <kaspar.rufibach@gmail.com>
David Moriña (Centre for Research in Environmental Epidemiology, CREAL) and Albert Navarro (Universitat Autònoma de Barcelona)
Kouros Owzar, Zhiguo Li, Nancy Cox, Sin-Ho Jung and Chanhee Yi
Sutirtha Chakraborty, Somnath Datta and Susmita Datta
Susanne Heim, with support from Paul Eilers, Thomas Kneib, and
Michael Kobl
Philippe Grosjean [aut, cre],
Romain Francois [ctb],
Kamil Barton [ctb]
Hoksan Yip, Patrick J.F. Groenen, Georgi Nalbantov
Philippe Grosjean [aut, cre],
Matthew Dowle [ctb]
Philippe Grosjean [aut, cre],
Romain Francois [aut]
Manuel Reif <Manuel.Reif@statistik.gv.at>, Jakob Peterbauer <Jakob.Peterbauer@statistik.gv.at>
Fuka, DR, Walter, MT, Steenhuis, TS, and Easton, ZM
Love Hansson, Thomas Reinholdsson, Mans Magnusson
Nick Carchedi [aut, cre],
Bill Bauer [aut],
Gina Grdina [aut],
Sean Kross [aut]
Camila de Souza <camila@stat.ubc.ca> and Davor Cubranic <cubranic@stat.ubc.ca>
Gabriel Gelius-Dietrich [aut, cre],
C. Jonathan Fritzemeier [ctb],
Rajen Piernikarczyk [ctb],
Marc Andre Daxer [ctb],
Benjamin Braasch [ctb],
Abdelmoneim Desouki [ctb],
Martin J. Lercher [ctb]
Gabriel Gelius-Dietrich [aut, cre],
Deya Alzoubi [ctb]
Andrzej Dudek, Marcin Pelka <marcin.pelka@ue.wroc.pl>, Justyna Wilk<justyna.wilk@ue.wroc.pl>
Valentin Wimmer, Hans-Juergen Auinger, Theresa Albrecht, Chris-Carolin Schoen with contributions by Larry Schaeffer, Malena Erbe, Ulrike Ober, Christian Reimer, Yvonne Badke and Peter VandeHaar
Valentin Wimmer, Theresa Albrecht, Hans-Juergen Auinger, Chris-Carolin Schoen with contributions by  Malena Erbe, Ulrike Ober and Christian Reimer
Matlab original by Eugene Brevdo; R port by Dongik Jang, Hee-Seok Oh and Donghoh Kim
Jens Hainmueller <jhain@stanford.edu> and Alexis Diamond <adiamond@fas.harvard.edu>
Beata Nowok, Gillian M Raab and Chris Dibben (first two authors in alphabetical order)
Yixuan Qiu and authors/contributors of the
included fonts. See file AUTHORS for details.
Thomas Kiefer [aut], Alexander Robitzsch [aut, cre], Margaret Wu [aut]
Baiyu Zhou <zhouby98@stanford.edu> and Weihong Xu
<weihongx@stanford.edu>
Edgar Kublin [aut],
Johannes Breidenbach [ctb, cre]
Francesco Calvori, Fabrizio Cipollini, Giampiero M. Gallo
Christian Buchta [aut],
Kurt Hornik [aut, cre],
Ingo Feinerer [aut],
David Meyer [aut]
Jake Anderson, Jonathan Lees; largely translated from the TTBOX
Matlab toolbox by Martin Knapmeyer
(http://www.dr-knapmeyer.de/downloads/)
Scott Chamberlain [aut, cre],
Eduard Szoecs [aut],
Carl Boettiger [aut],
Karthik Ram [aut],
Ignasi Bartomeus [aut],
John Baumgartner [aut]
Adriano Polpo <polpo@ufscar.br>, Cassio de Campos
<cassio@idsia.ch>, D. Sinha <sinhad@stat.fsu.edu>, Stuart
Lipsitz <slipsitz@partners.org>, Jianchang Lin
<jlin@stat.fsu.edu>
Miou-ting Chen, Yung-jin Lee <mobilepk@gmail.com>
Wolfgang Konen <wolfgang.konen@fh-koeln.de>, Patrick Koch
<patrick.koch@fh-koeln.de>
Hugo Andres Gutierrez Rojas <hugogutierrez@usantotomas.edu.co>
Yanfei Kang, Danijel Belusic and Kate Smith-Miles
K. Gerald van den Boogaart <boogaart@uni-greifswald.de>
Pavel N. Krivitsky [aut, cre],
Mark S. Handcock [aut, ths],
David R. Hunter [ctb],
Steven M. Goodreau [ctb, ths],
Martina Morris [ctb, ths],
Nicole Bohme Carnegie [ctb],
Carter T. Butts [ctb],
Ayn Leslie-Cook [ctb],
Skye Bender-deMoll [ctb]
Hadley Wickham [aut, cre],
RStudio [cph]
Derek Beaton, Jenny Rieck, Cherise R. Chin Fatt, Herve Abdi
Kurt Hornik [aut, cre],
Johannes Rauch [aut],
Christian Buchta [aut],
Ingo Feinerer [aut]
Sylvain Loiseau, Vaudor Lise, Matthieu Decorde, Serge Heiden
Robert B. Gramacy <rbgramacy@chicagobooth.edu> and Matt A.
Taddy <taddy@chicagobooth.edu>
Maria Antonietta Del Ferraro, Henk A.L. Kiers, Paolo Giordani
Rebekah Robinson <rrobinson20@humana.com> and Homer
White <hwhite0@georgetowncollege.edu>
George C. Tseng <ctseng@pitt.edu>, Wing H. Wong
<whwong@stanford.edu>
Charlie Sharpsteen [aut],
Cameron Bracken [aut],
Kirill Mueller [ctb, cre],
Yihui Xie [ctb]
Diethelm Wuertz, Yohan Chalabi and Martin Maechler with
contributions from Joe W. Byers, and others
Gianluca Campanella [aut, cre],
Marc Chadeau-Hyam [aut],
Maria De Iorio [ctb],
Ajay Jasra [ctb]
Thomas Scheike with contributions from Torben Martinussen and Jeremy
Silver
Katharine M. Mullen, Joris J. Snellenburg, Sergey P. Laptenok, David Nicolaides, Ivo H.M. van Stokkum
Josef Leydold, Carsten Botts and Wolfgang H\"ormann
Stefano Costa [aut, cre],
Luca Bianconi [aut],
Elisabetta Starnini [ctb]
P. Neytchev, P. Filzmoser, R. Patnaik, A. Eisl and R. Boubela
Larissa Matos <larissa.amatos@gmail.com>, Marcos Prates
<marcosop@ime.unicamp.br> and Victor Lachos
<hlachos@ime.unicamp.br>
S-PLUS original by Phil Everson; R port by Roger D. Peng
<rpeng@jhsph.edu>
Ingo Feinerer [aut, cre],
Kurt Hornik [aut],
Artifex Software, Inc. [ctb, cph] (pdf_info.ps taken from GPL
Ghostscript)
Susan Gruber, in collaboration with Mark van der Laan
Milan Bouchet-Valat [aut, cre],
Grigorij Ljubownikow [ctb]
Ingo Feinerer [aut, cre],
Wolfgang Mauerer [aut]
Stefan Wilhelm <wilhelm@financial.com> with contributions from Manjunath B G <bgmanjunath@gmail.com>
Bettina Grün [aut, cre],
Kurt Hornik [aut]
Michael G. Schimek, Medical University of Graz, Graz, Austria; Eva Budinska, Masaryk University, Brno, Czech Republic; Jie Ding, Harvard University, Cambridge, Massachusetts, USA;  Karl G. Kugler, Helmholtz Centre Munich, Neuherberg, Germany; Vendula Svendova, Medical University of Graz, Graz, Austria; Shili Lin, The Ohio State University, Columbus, Ohio, USA.
Dianne Cook <dicook@iastate.edu>, Hadley Wickham
<h.wickham@gmail.com>
Bei <beihuang@iastate.edu>, Dianne Cook <dicook@iastate.edu>,
Hadley Wickham <h.wickham@gmail.com>
Albert D. Shieh, Tatsunori B. Hashimoto, and Edoardo M. Airoldi
Artur Agostinho Araujo, Javier Roca-Pardinas <roca@uvigo.es>
and Luis Meira-Machado <lmachado@math.uminho.pt>
Edzer Pebesma [cre, aut],
Benedikt Klus [aut]
Alexis Gabadinho [aut, cph],
Matthias Studer [aut, cph],
Nicolas Muller [aut],
Reto Buergin [aut],
Gilbert Ritschard [aut, cre, cph]
Gilbert Ritschard [aut, cre, ths, cph],
Matthias Studer [aut],
Reto Buergin [aut],
Alexis Gabadinho [ctb],
Nicolas Muller [ctb],
Patrick Rousset [ctb]
Dominic Schuhmacher <dominic.schuhmacher@mathematik.uni-goettingen.de> with substantial contributions of code by Bjoern Baehre and Carsten Gottschlich
Nicole Bohme Carnegie, Masataka Harada, Jennifer Hill
Carl Boettiger [aut, cre],
Duncan Temple Lang [aut]
Christian Zang [aut, cre, cph, trl],
Franco Biondi [ctb, cph]
Ed Zhang ; Vicky Qian Wu ; Shein-Chung Chow ; Harry G.Zhang
(Quality check) <ed.zhang.jr@gmail.com>
Tristan Senga Kiess\'{e}, Silvio S. Zocchi, C\'{e}lestin C.
Kokonendji
Tristan Senga Kiess\'{e}, Francial G. Libengu\'e, Silvio S.
Zocchi, C\'{e}lestin C. Kokonendji
Yael Grushka-Cockayne, Victor Richmond R. Jose, Kenneth C. Lichtendahl Jr. and Huanghui Zeng, based on the source code from the randomForest package by Andy Liaw and Matthew Wiener and on the original Fortran code by Leo Breiman and Adele Cutler.
Ji-Hyung Shin <shin@sfu.ca>, Brad McNeney <mcneney@sfu.ca>,
Jinko Graham <jgraham@sfu.ca>
Michael D. Sumner [aut, cre],
Sebastian Luque [ctb]
Fortran code by R. J. Renka.
R functions by Albrecht Gebhardt <albrecht.gebhardt@uni-klu.ac.at>.
With contributions from Stephen Eglen <stephen@anc.ed.ac.uk>,
Sergei Zuyev <sergei@stams.strath.ac.uk> and
Denis White <white.denis@epamail.epa.gov>
Michael D. Sumner [aut, cre],
Simon Wotherspoon [ctb]
F. D. Schönbrodt, M. D. Back, S. C. Schmukle
Frederick Novomestky <fnovomes@poly.edu>, Saralees Nadarajah
<saralees.nadarajah@manchester.ac.uk>
Heike Trautmann <trautmann@statistik.uni-dortmund.de>
and Detlef Steuer <detlef.steuer@hsu-hamburg.de>
and Olaf Mersmann <olafm@statistik.uni-dortmund.de>
and Björn Bornkamp <bornkamp@statistik.tu-dortmund.de>
Yves Croissant [aut, cre],
Achim Zeileis [aut]
Anita Lindmark and Maria Karlsson, Department of Statistics, Umea University
Usue Mori, Alexander Mendiburu, J.A. Lozano
Antonio Fabio Di Narzo [aut, cre],
Jose Luis Aznarte [ctb],
Matthieu Stigler [aut, cre]
Adrian Trapletti [aut], Kurt Hornik [aut, cre], Blake LeBaron
[ctb] (BDS test code)
Paul Gilbert and Erik Meijer <pgilbert.ttv9z@ncf.ca>
Jun Sheng, Peihua Qiu <qiu@stat.umn.edu>, and Charles J. Geyer
<charlie@stat.umn.edu>
Roger D. Peng <rpeng@jhsph.edu>, with contributions from Aidan
McDermott
Michael Hahsler [aut, cre, cph],
Kurt Hornik [aut, cph]
Bernd Bischl <bernd_bischl@gmx.net>,
Jakob Bossek <jakob.bossek@tu-dortmund.de>,
Olaf Mersmann <olafm@p-value.net>
David DEVREKER [aut],
Alain LEFEBVRE [aut, cre]
Ian H. Hutchinson [aut] (author of tth/ttm C sources),
Friedrich Leisch [aut, cre] (author of R wrappers to tth/ttm),
Achim Zeileis [aut] (author of R wrappers to tth/ttm)
José Cláudio Faria <joseclaudio.faria@gmail.com>
Enio G. Jelihovschi <eniojelihovs@gmail.com>
Ivan Bezerra Allaman <ivanalaman@gmail.com>
Christoph Müssel, Ludwig Lausser, Hans Kestler
Uwe Ligges <ligges@statistik.tu-dortmund.de> with contributions
from Sebastian Krey, Olaf Mersmann, Sarah Schnackenberg, Guillaume Guenard,
Andrea Preusser, Anita Thieler, and Claus Weihs, as well as code fragments
and ideas from the former package 'sound' by Matthias Heymann
and functions from 'rastamat' by Daniel P. W. Ellis.
The included parts of the libmad MPEG audio decoder library are
authored by Underbit Technologies.
Jennifer F. Bobb [aut], Ravi Varadhan [aut, cre], Hui Zhao [ctb]
Anna Cena [aut],
Marek Gagolewski [aut],
Marcin Kosinski [aut],
Natalia Potocka [aut],
Barbara Zogala-Siudem [aut, cre]
Greg Ridgeway, Dan McCaffrey, Andrew Morral, Beth Ann, Lane Burgette
<burgette@rand.org>
Bruno Remillard and Jean-Francois Plante
Shawn Mankad, George Michailidis, Moulinath Banerjee
Radu V. Craiu, Thierry Duchesne, Daniel Fortin and Sophie
Baillargeon
Hans Bruun Nielsen and Stig Bousgaard Mortensen
Charles J. Geyer <charlie@stat.umn.edu> and
Glen D. Meeden <glen@stat.umn.edu>
Andrea Dal Pozzolo, Olivier Caelen and Gianluca Bontempi
Jamie Lentin [aut],
Anthony Hennessey [aut, cre]
Ian Fiske, Richard Chandler, David Miller, Andy Royle, Marc Kery
Niamh Russell, Laura Cribbin, Thomas Brendan Murphy
Bernhard Pfaff [aut, cre], Matthieu Stigler [ctb]
Paula M. Murray, Ryan P. Browne, and Paul D. McNicholas
Neil J. Gunther [aut],
Stefan Moeding [aut, cre]
Peter Reichert <peter.reichert@eawag.ch>
with contributions by Nele Schuwirth <nele.schuwirth@eawag.ch>
Simon Urbanek <Simon.Urbanek@r-project.org> (R package), Theodore Ts'o <tytso@thunk.org> (libuuid)
Justin Lessler <jlessler@jhsph.edu>, Jessica Metcalf
<cmetcalf@@princeton.edu>
Juris Breidaks [aut, cre], Martins Liberts [aut]
Saralees Nadarajah, Stephen Chan and Emmanuel Afuecheta
David Scott <d.scott@auckland.ac.nz> and Christine Yang Dong
<c.dong@auckland.ac.nz>
Bernhard Pfaff [aut, cre],
Matthieu Stigler [ctb]
Andrew Womack and Gopal, V. and Novelo, L. L. and Casella, G.
Joris Muller [aut, cre],
Erik-André Sauleau [ctb]
David Meyer [aut, cre],
Achim Zeileis [aut],
Kurt Hornik [aut],
Florian Gerber [ctb],
Michael Friendly [ctb]
Michael Friendly [aut, cre],
Heather Turner [ctb],
Achim Zeileis [ctb],
Duncan Murdoch [ctb],
David Firth [ctb]
Xiaowei Zhan <zhanxw@gmail.com> and Dajiang Liu
<dajiang.liu@gmail.com>, with contributions of Jean-loup Gailly
<jloup@gzip.org> and Mark Adler
<madler@alumni.caltech.edu>(they wrote zlib and provide license
so we include zlib source code), Julian Seward
<jseward@bzip.org> (this author wrote bzip2 and with BSD
license we include bzip2 source codes) and Heng Li
<lh3@live.co.uk> (this author wrote tabix and with MIT license
we include tabix source codes).
Reto Buergin [aut, cre, cph],
Gilbert Ritschard [ctb, ths]
Edward Grant, Xia Li,  Kenneth Lange, Tong Tong Wu
Ruiz-Cuetos J.C., Felicisimo A.M., Cuartero A., Polo M.E., Rodriguez P.G.
Juan Carlos Ruiz Cuetos, Maria Eugenia Polo Garcia, Pablo
Garcia Rodriguez
Jari Oksanen, F. Guillaume Blanchet, Roeland Kindt, Pierre Legendre,
Peter R. Minchin, R. B. O'Hara, Gavin L. Simpson, Peter Solymos,
M. Henry H. Stevens, Helene Wagner
Silvia Espinosa, Joaquin Ordieres, Antonio Bello, Jose Maria Perez
Matthias Templ, Andreas Alfons, Alexander Kowarik, Bernd Prantner
Daniel Schopfhauser, Matthias Templ, Andreas Alfons, Alexander Kowarik,
Bernd Prantner
Ulf Schepsmeier, Jakob Stoeber, Eike Christian Brechmann, Benedikt Graeler
Boris Leroy with help from C. N. Meynard, C. Bellard & F. Courchamp
Gregor Passolt, James J. Anderson, Ting Li, David H. Salinger
Drew Griffin Levy [aut],
David C. Norris [aut, cre]
Taryn B. T. Athey and Paul D. McNicholas
Seung Won Hyun, Weng Kee Wong, and Yarong Yang
Philip Reiss, Yin-Hsiu Chen, Lei Huang, Lan Huo, and Ruixin Tan
Bastien Lecigne, Sylvain Delagrange and Christian Messier
Robin Genuer, Jean-Michel Poggi and Christine Tuleau-Malot
Josue Moises Polanco-Martinez (a.k.a. jomopo)
Will Beasley [aut, cre], Joe Rodgers [aut], Matthew Schuelke [ctb],
Ronnie Coleman [ctb], Mark Joseph Lachowicz [ctb]
Marc Raimondo <marcr@maths.usyd.edu.au> and Michael Stewart
<michael.stewart@sydney.edu.au>
Evan Ray, Peter Krafft, John Staudenmayer
Graham Williams, Joshua Z Huang, Xiaojun Chen, Qiang Wang,
Longfei Xiao
Aristidis K. Nikoloulopoulos <A.Nikoloulopoulos@uea.ac.uk> and
Harry Joe <harry.joe@ubc.ca>.
Josh Pasek, with some assistance from Alex Tahk and some code modified from R-core; Additional contributions by Gene Culter and Marcus Schwemmle.
Karthik Ram [aut, cre],
Richards Clark [ctb]
In Song Kim <insong@mit.edu>, Kosuke Imai <kimai@Princeton.edu>
Julian Taylor <julian.taylor@adelaide.edu.au>, Simon Diffey <simon.diffey@dpi.nsw.gov.au>, Ari Verbyla <ari.verbyla@csiro.au> and Brian Cullis <bcullis@uow.edu.au>.
Peter Langfelder <Peter.Langfelder@gmail.com> and Steve Horvath <SHorvath@mednet.ucla.edu> with contributions by Chaochao Cai, Jun Dong, Jeremy Miller, Lin Song, Andy Yip, and Bin Zhang
Chris Wallace [aut, cre], Olly Burren [ctb]
Heather Stoll <hstoll@polsci.ucsb.edu>,
Gary King <king@harvard.edu>,
Langche Zeng <zeng@ucsd.edu>,
Mark Seligman, with contributions from Chris Fraley.
Claudio Agostinelli <claudio@unive.it>,  SLATEC Common Mathematical Library <www.netlib.org/slatec>
Ingo Feinerer [aut],
Kurt Hornik [aut, cre],
Mike Wallace [ctb, cph] (Jawbone Java WordNet API library)
Hana Sevcikova <hanas@uw.edu>, Patrick Gerland <gerland@un.org>
Hana Sevcikova (hanas@uw.edu), Patrick Gerland (gerland@un.org), Kirill Andreev (andreev@un.org), Nan Li (li32@un.org), Danan Gu (gud@un.org), Thomas Spoorenberg (spoorenberg@un.org)
Manuel Castejon Limas <manuel.castejon@unileon.es>, Joaquin
Ordieres Mere <joaquin.ordieres@dim.unirioja.es>, Francisco
Javier de Cos Juez <decos@api.uniovi.es>, Francisco Javier
Martinez de Pison <fjmartin@dim.unirioja.es>
Marc Schwartz <marc_schwartz@me.com> and various authors for Perl modules listed in each .pm file.
Graham Williams [aut],
Joshua Z Huang [aut],
Xiaojun Chen [aut],
Qiang Wang [aut],
Longfei Xiao [aut],
He Zhao [cre]
Qinghan Meng [aut],
He Zhao [aut, cre],
Graham Williams [aut],
Junchao Lv [ctb]
Shu Chen, Andrzej Galecki <agalecki@umich.edu>
Alexander Kowarik <alexander.kowarik@statistik.gv.at>, Angelika Meraner
Daniel Schopfhauser, Alexander Kowarik
<alexander.kowarik@statistik.gv.at>, Angelika Meraner
Nicolas Turenne <nturenne.inra@yahoo.fr>, Tien Phan  <phantien84@gmail.com>
Philip Leifeld [aut, cre],
Skyler J. Cranmer [aut],
Bruce A. Desmarais [aut]
Tianqi Chen <tianqi.tchen@gmail.com>, Tong He <hetong007@gmail.com>
Martin Maechler, originally packaged for R by Kurt Hornik
<Kurt.Hornik@R-project.org>, based on the S code in the XGobi
distribution.  Windows port based on this and earlier work by
Brian Ripley <ripley@stats.ox.ac.uk>.
Mirai Solutions GmbH [aut],
Martin Studer [cre],
The Apache Software Foundation [ctb, cph] (Apache POI, Apache Commons
Codec, XML Commons External Components XML APIs),
Stephen Colebourne [ctb, cph] (Joda-Time Java library),
Metastuff, Ltd. [ctb, cph] (dom4j)
Mirai Solutions GmbH [aut],
Martin Studer [cre],
The Apache Software Foundation [ctb, cph] (Apache POI, Apache XMLBeans)
Adrian A. Dragulescu [aut, cre],
The Apache Software Foundation Apache Commons Codec [ctb, cph] (Apache
POI, Apache XMLBeans),
MetaStuff, Ltd. [ctb, cph] (Dom4j)
Karl W Broman <kbroman@biostat.wisc.edu>, Il youp Kwak
Andrew Hooker <andrew.hooker@farmbio.uu.se>,
E. Niclas Jonsson <niclas.jonsson@farmbio.uu.se>,
Mats Karlsson <mats.karlsson@farmbio.uu.se>
Nicholas L. Crookston, Andrew O. Finley, John Coulston (Sunil Arya and David Mount for ANN)
Kim Nimon <kim.nimon@gmail.com>, Fred Oswald, and J. Kyle Roberts.
Greg Lamp <greg@yhathq.com>, Eric Chiang <eric@yhathq.com>
Federico Girosi <girosi@rand.org>
Gary King <king@harvard.edu>
Elena Villalon <evillalon@latte.harvard.edu>
Jon Bischof <jbischof@fas.harvard.edu>
Konstantin Kashin <kkashin@fas.harvard.edu>
Remko Duursma. QuasiMC by Mik Cieslak. Uses code by Robert
Pearcy (Yplant) and Belinda Medlyn (MAESTRA).
Kim-Anh Le Cao, Florian Rohart, Leo McHugh, Othmar Korn, Christine A. Wells
Javier Palarea-Albaladejo and Josep Antoni Martin-Fernandez
Francois Brun (ACTA), David Makowski (INRA), Daniel Wallach
(INRA), James W. Jones (U.of Florida).
Matt Owen <mowen@iq.harvard.edu>, Kosuke Imai
<kimai@Princeton.Edu>, Gary King <king@harvard.edu>, Olivia Lau
<olau@fas.harvard.edu>
James Honaker \email{zelig-zee@@iq.harvard.edu} Matt Owen, Kosuke Imai, Olivia Lau and Gary King
Matthew Owen, Skyler Cranmer, Olivia Lau, Kosuke Imai and Gary King
Matthew Owen, Ferdinand Alimadhi, Delia Bailey
Ming Yang, Gideon K. D. Zamba, and Joseph E. Cavanaugh
Stefan Evert <stefan.evert@uos.de>, Marco Baroni
<marco.baroni@unitn.it>
Fang Liu <fang.liu.131@nd.edu> with contributions YunChuan Kong <s1155014473@cuhk.edu.hk>
Achim Zeileis [aut, cre],
Gabor Grothendieck [aut],
Jeffrey A. Ryan [aut],
Felix Andrews [ctb]
Philippe Grosjean [aut, cre],
Kevin Denis [aut]
Corentin M Barbu [aut, cre],
Sebastian Gibb [ctb]
David Bronaugh <bronaugh@uvic.ca>, Arelia Werner <wernera@uvic.ca> for the Pacific Climate Impacts Consortium
Wim Van der Elst <Wim.vanderelst@gmail.com>
Wilfried Thuiller <wilfried.thuiller@ujf-grenoble.fr>, Damien
Georges <damien.georges2@gmail.com>
For any question or bug please contact CITR@ligue-cancer.net
GenABEL project developers <genabel.project at gmail.com>
Tan Dang <dangt@purdue.edu>, Bartek Rajwa
<rajwa@cyto.purdue.edu>
Predictive Analytics Team at Pivotal Inc. <user@madlib.net>
Austin Chungath Vincent <austincv@gmail.com>,Vikas
Raguttahalli <vikas.r@mu-sigma.com>, Sachin Sudarshana
<sachin.sudarshana@gmail.com>
"F. Mortier" <fmortier@cirad.fr> and "G. Cornu"
<gcornu@cirad.fr> and "C. Trottier"
<catherine.trottier@univ-montp3.fr> and "X. Bry"
<xavier.bry@univ-montp2.fr>
This package is Copyright (C) 2006-2012 Weierstrass
Institute for Applied Analysis and Stochastics.
The code for apriori and eclat in src/rapriori.c was
obtained from http://www.borgelt.net/ and is Copyright (C)
1996-2003 Christian Borgelt. All other code is Copyright (C)
Michael Hahsler, Christian Buchta, Bettina Gruen and Kurt
Hornik.
(C) 2011 Michael Hahsler and Sudheer Chelluboina
This package is Copyright (C) 2005-2014 Weierstrass
Institute for Applied Analysis and Stochastics.
This package is Copyright (C) 2012-14 Weierstrass Institute
for Applied Analysis and Stochastics.
(C) 2012 John W. Emerson and Walton A. Green and John A.
Hartigan
(C) 2014 Michael J. Kane, Bryan Lewis, and John W. Emerson
(C) 2013 John W. Emerson and Michael J. Kane
(C) 2013 John W. Emerson, Michael J. Kane, and Peter Haverty
(C) 2013 Michael J. Kane and John W. Emerson
Copyright (C) Collective, Inc. | file inst/COPYRIGHTS
MNI152_T1_2mm_brain.nii.gz from Copyright (C) 1993-2009
Louis Collins, McConnell Brain Imaging Centre, Montreal
Neurological Institute, McGill University 6th generation
non-linear symmetric brain
(c) 2012 NYU School of Medicine; (c) 2012-14 Jeff Binder
file inst/COPYRIGHTS for src/zgels.f obtained from LAPACK
(C) 2012 Decision Analyst, Inc. (ChoiceModelR is a trademark
of Decision Analyst, Inc.)
Instituto Colombiano para la Evaluacion de la Educacion -
ICFES
Authors and Aalto University (for work of M. Korpela)
This library is Copyright (C) 2006-2013 Weierstrass
Institute for Applied Analysis and Stochastics.
The R package and code, and the main programs, were written
by and are Copyright by David Bolin and Finn Lindgren, and are
redistributable under the GNU Public License, version 3 or
later.  The package also includes code from the libraries AMD,
CHOLMOD, COLAMD, and CAMD from the SuiteSparse collection of
Tim Davis, as well as functions from the GNU Scientific library
and the RngStreams library by Pierre L'Ecuyer.  For details see
the COPYRIGHTS file.
This package is Copyright (C) 2006-2014 Weierstrass
Institute for Applied Analysis and Stochastics.
ANN Copyright (c) 1997-2010 University of Maryland and Sunil
Arya and David Mount. All Rights Reserved.
Copyright (C) 2006 University of Duisburg-Essen, Essen,
Germany, University of Bristol, Bristol, United Kingdom
The Pennsylvania State University and Northeastern
University
see inst/COPYRIGHTS for the files in src/.
(C) 2014 John W. Emerson and Walton A. Green
Copyright 2010, Merck Research Laboratories
Under the allowance of MIT license, smhash
(https://code.google.com/p/smhasher/) codes have been adopted.
inria 2013-2014 for HDPenReg. Serge Iovleff is the copyright
holder of the c++ library STKpp.
RStudio, Inc.; Joyent, Inc.; Nginx Inc.; Igor Sysoev; Niels
Provos; Internet Systems Consortium, Inc.; Alexander Chemeris
(c) Centre for Bioinformation Science, John Curtin Research
School of Medical Sciences and Centre for Mathematics and Its
Applications, The Australian National University
'lm.br' uses the design and some R-code of 'lm' copyright
(C) 2013 The R Foundation for Statistical Computing, and of
'lm.gls' copyright (C) 1994-2005 W. N. Venables and B. D.
Ripley.
file inst/COPYRIGHTS for src/*.f obtained from LAPACK
libnabo is copyright 2010--2011, Stephane Magnenat, ASL,
ETHZ, Switzerland <stephane at magnenat dot net>
ANN library is copyright University of Maryland and Sunil
Arya and David Mount. R wrapper is based on the ANN library,
copyright Samuel Kemp 2005-9 and Gregory Jefferis 2009-2013.
See file COPYRIGHT for details
Nozzle is copyright 2013 The Fellows and President of
Harvard College and The Broad Institute, Inc. All rights
reserved. jQuery is licensed under the MIT license. Copyright
2012 jQuery Foundation. jQuery Tablesorter plugin is licensed
under the MIT license. Copyright 2008 Christian Bach.
The fortran routines for the function HW.original are from
Jonathan R. M. Hosking and are available at the url
http://www.research.ibm.com/people/h/hosking/lmoments.html. The
functions `MLlaio2004.R', `GOFlaio2004.R' and `MSClaio2008.R'
are derived from original Francesco Laio Matlab code (non
published material). The fortran routines for the function
'bestlm' are from Alan Miller and were obtained from his R
package 'leaps'.
2006-2011, Bank of Canada. 2012, Paul Gilbert
Copyright (c) 2010, David Weisman and Dan Simovici, All
rights reserved.
Most files are originally designed by David Pierce under GPL
(>= 3).
See pbdPROF/inst/fpmpi_LICENSE.txt for the files in
src/fpmpi/ and src/fpmpi_win.
See pbdSLAP/inst/ScaLAPACK_LICENSE.txt for the files in
src/BLACS/, src/PBLAS/, src/REDIST/, src/ScaLAPACK/, and
src/TOOLS/. See pbdSLAP/inst/LAPACK_LICENSE.txt for the files
in src/LAPACK/.
This package is Copyright (C) 2006-2010 by Joern Schulz and
Peter Toft.
See phyclust/inst/Documents/ for files in src/msdir/,
src/seq-gen/, src/paml_baseml, and R/ttzeng-*.r.
This package uses zlib, Tabix and BGZF. Zlib by Jean-loup
Gally <jloup@gzip> and Mark Adler <madler@alumni.caltech.edu>,
uses the Zlib licence. Tabix by Heng Li <lh3@live.co.uk> use
the MIT licence. BGZF by Bob Handsaker and modified by Heng Li
uses the MIT licence.
Copyright for the file src/approx.c is detailed in
inst/COPYRIGHT
See QZ/inst/LAPACK_LICENSE.txt for the files in src/qz/.
ANN library is copyright University of Maryland and Sunil
Arya and David Mount. See file COPYRIGHT for details.
Original python appdirs module copyright (c) 2010
ActiveState Software Inc. R port copyright Hadley Wickham,
RStudio. See file LICENSE for details.
(C) 2014 Coherent Logic Limited; ALL RIGHTS RESERVED.
The functionos in EZR package were programmed by Y. Kanda,
except for the following functions. dot.plot(), prop.conf(),
Cochran.Q.test(), pairwise.prop2.test(),
pairwise.pairedt.test(), pairwise.kruskal.test(),
pairwise.friedman.test(), pairwise.logrank.test(),
pairwise.gray.test(), Steel.Dwass(), Steel(), pdunnet(),
Mantel.Byar() were modified from the corresponding functions
distributed by professor Aoki
(http://aoki2.si.gunma-u.ac.jp/R). crr() and cuminc() were
derived from cmprsk package. nrisk() was modified from survplot
package (http://www.cbs.dtu.dk/~eklund/survplot/). epi.test()
and epi.kappa() were derived from epiR package.
jonckheere.test(), ph2simon() and ph2single() were derived from
clinfun package. Functions of meta-analysis were derived from
meta and metatest packages. Functions of matched-pair analysis
were derived from optmatch package, and its license restricts
to academic use. See
http://cran.r-project.org/web/packages/optmatch/LICENSE for
details. The package must be installed before performing
matched-pair analysis, assuming the user is covered by the
license. Functions of receiver-operating characteristics curve
analyses were derived from pROC package. Many dialog functions
in EZR were modified from the corresponding functions in Rcmdr
package and RcmdrPlugin.survivalT package.
See the file COPYRIGHTS for various Eigen copyright details
(C) Michael Hahsler (PCA and SVD implementation (C) Saurabh
Bathnagar)
Data is freely available for research use when acknowledged
with the following reference: Improving Recommendation Lists
Through Topic Diversification, Cai-Nicolas Ziegler, Sean M.
McNee, Joseph A. Konstan, Georg Lausen; Proceedings of the 14th
International World Wide Web Conference (WWW '05), May 10-14,
2005, Chiba, Japan. To appear.
Freely available for research use when acknowledged with the
following reference: Eigentaste: A Constant Time Collaborative
Filtering Algorithm. Ken Goldberg, Theresa Roeder, Dhruv Gupta,
and Chris Perkins. Information Retrieval, 4(2), 133-151. July
2001.
Code for the wavelet transform is based on Brandon
Whitcher's work. See file COPYRIGHT for details
Copyright (c) 2008-2014 LabKey Corporation
The package includes the source code of matio written by
Christopher Hulbert (http://sourceforge.net/projects/matio/)
(License: Simplified BSD). The matio io routines have been
adopted to use R printing and error routines.
Authors, and Mixmod use Newmat C++ library
Code is Copyright (C) Jan Wijffels and BNOSAC
The code in src/pcre is Copyright (c) 1997-2010 University
of Cambridge.  All other code is Copyright (c) 2002-2010
University of California, Cornell University.
Authors listed above, PostgreSQL Global Development Group,
and The Regents of the University of California
The code in src/pcre is Copyright (c) 1997-2010 University
of Cambridge.  All other code is Copyright (c) 2002-2011
University of California, Cornell University.
The code in src/distributions/jags/ (and elements elsewhere
where indicated) is Copyright (C) 2002-10 Martyn Plummer,
licensed under GPL-2.  All other code is Copyright (C) Matthew
Denwood, licensed under GPL-2.
see files COPYRIGHTS for detailed information
(c) 2010-2013 Madeleine Thompson, except dchud.f and
dchdd.f, which are from LINPACK and are in the public domain in
the United States.
Code copyright held by Evolving Logic.  Documentation
copyright held by the RAND Corporation.
We have used the following software and made minimal
necessary changes: Zlib, Jean-loup Gailly <jloup@gzip.org> and
Mark Adler <madler@alumni.caltech.edu> (License:
http://zlib.net/zlib_license.html). We have changed its source
code to not include stdio.h, and explicitly includes unistd.h
heade file; Bzip2, Julian Seward <jseward@bzip.org> (BSD
license). We have renamed compress.c to bzCompress.c to avoid
name collision with zlib.h, and its license files are renamed
to bzip2.README and bzip2.LICENSE; Tabix, Heng Li
<lh3@live.co.uk> (MIT license). We removed standard IO related
functions, e.g. printf, fprintf ; also changed its un-safe
pointer arithmetics.
The code in src/bea.f is Copyright (C) 1991 F. Murtagh;
src/bbwrcg.f, src/arsa.f and src/bburcg.f are Copyright (C)
2005 M. Brusco, H.F. Koehn, and S. Stahl. All other code is
Copyright (C) Michael Hahsler, Christian Buchta, and Kurt
Hornik.
Dr Martin Porter (2001) for the libstemmer C library, and
Milan Bouchet-Valat (2013) for the R package contents
MOA code in inst/java/moa.jar is Copyright (C) The
University of Waikato and distributed under the Apache License,
version 2.0. All other code is Copyright (C) Matthew Bolanos,
John Forrest and Michael Hahsler
Copyright 2012. The Regents of the University of California
(Regents). All Rights Reserved.
2007-2011 Bank of Canada.  2012-2013 Paul Gilbert.
2004-2011 Bank of Canada and Erik Meijer. 2012-2014 Paul
Gilbert and Erik Meijer
2007-2011 Bank of Canada.  2012 Paul Gilbert.
All code is Copyright (C) Michael Hahsler and Kurt Hornik.
We have used the following software and made minimal
necessary changes: Zlib, Jean-loup Gailly <jloup@gzip.org> and
Mark Adler <madler@alumni.caltech.edu> (License:
http://zlib.net/zlib_license.html). We have changed its source
code to not inlcuding stdio.h, and explicitly includes unistd.h
heade file; Bzip2, Julian Seward <jseward@bzip.org> (BSD
license). We have renamed compress.c to bzCompress.c to avoid
name collision with zlib.h, and its license files are renamed
to bzip2.README and bzip2.LICENSE; Tabix, Heng Li
<lh3@live.co.uk> (MIT license). We removed standard IO related
functions, e.g. printf, fprintf ; also changed its un-safe
pointer arithmetics.
2011-2012 Shenzhen Institutes of Advanced Technology Chinese
Academy of Sciences
This package uses zlib, Tabix, bgzf and faidx. Zlib by
Jean-loup Gally <jloup@gzip.org> and Mark Adler
<madler@alumni.caltech.edu>, uses the Zlib licence. Tabix and
FaIdx by Heng Li <lh3@live.co.uk> use the MIT licence. BGZF as
by Bob Handsaker and modified by Heng Li uses the MIT licence.
Anything else was written entirely by Ulrich Wittelsbuerger.
All third-party code has been slightly modified to conform to R
package development requirements such as removal or replacement
of printf, fprintf on stderr and functions that would terminate
a running R session by R-supplied equivalents as well as
modifications that eliminate most compiler warnings.
The copyright holders of the Perl files are listed in each
.pm file under the Perl directory.
1998-2004 E. Niclas Jonsson and Mats Karlsson. 2005-2008
Andrew C. Hooker, Justin J. Wilkins, Mats O. Karlsson and E.
Niclas Jonsson. 2009-2014 Andrew C. Hooker, E. Niclas Jonsson
and Mats O. Karlsson.
ANN library is copyright University of Maryland and Sunil
Arya and David Mount. See file COPYRIGHTS for details.
(C) 2012 John W. Emerson and Walton Green
(C) 2007-2013. Federico Girosi and Gary King.
This package supplies tools for tabulating and analyzing
the results of predictive models. The methods employed are
applicable to virtually any predictive model and make
comparisons between different methodologies straightforward.
The package implements several ABC algorithms for
performing parameter estimation, model selection, and goodness-of-fit.
Cross-validation tools are also available for measuring the
accuracy of ABC estimates, and to calculate the
misclassification probabilities of different models.
Functions for Constraint Based Simulation using Flux
Balance Analysis and informative analysis of the data generated
during simulation.
This package contains code to 1.) construct the summary
statistic needed to facilitate approximate Bayesian computing
for spatial extremes, and then 2.) run the ABC-Rejection method
to produce a set of draws from the ABC posterior.  The
intention is for the user to directly modify the command
>abc.rej() to suit his or her purposes.  In general, ABC
fitting of max-stable processes is very computationally
expensive, and requires advanced programming skills with
research computing.
An implementation of Karaboga (2005) Artificial Bee Colony Optimization algorithm. This (working) version is a Work-in-progress, which is why it has been implemented using pure R code. This was developed upon the basic version programmed in C and distributed at the algorithm's official website.
This package tests the goodness of fit of a distribution of offspring to the Normal, Poisson, and Gamma distribution and estimates the proportional paternity of the second male (P2) based on the best fit distribution.
Tools for approximate Bayesian computation including summary statistic selection and assessing coverage
The abd package contains data sets and sample code for The
Analysis of Biological Data by Michael Whitlock and Dolph Schluter (2009;
Roberts & Company Publishers). Some examples utilize the manipulate package,
which is available within RStudio (http://www.rstudio.com).
Loads ABF2 files containing (gap-free) data from electrophysiological recordings, as created by Axon Instruments/Molecular Devices software such as pClamp 10.
Combine multi-dimensional arrays into a single array.
This is a generalization of cbind and rbind.  Works with
vectors, matrices, and higher-dimensional arrays.  Also
provides functions adrop, asub, and afill for manipulating,
extracting and replacing data in arrays.
Additive Bayesian network models are equivalent to
Bayesian multivariate regression using graphical modelling.
This library provides routines to help determine optimal
Bayesian network models for a given data set, where these
models are used to identify statistical dependencies in messy,
complex data. The additive formulation of these models is
equivalent to multivariate generalised linear modelling
(including mixed models with iid random effects). The usual
term to describe this model selection process is structure
discovery. The core functionality is concerned with model
selection - determining the most robust empirical model of data
from interdependent variables. Laplace approximations are used
to estimate goodness of fit metrics and model parameters, and
wrappers are also included to the INLA library. A comprehensive
set of documented case studies, numerical accuracy/quality
assurance exercises, and additional documentation are available
from the abn website.
Fit and predict with the high-dimensional principal fitted
components model.
This package contains a collection of functions that perform operations on time-series accelerometer data, such as identify non-wear time, flag minutes that are part of an activity bout, and find the maximum 10-minute average count value. The functions are generally very flexible, allowing for a variety of algorithms to be implemented. Most of the functions are written in C++ for efficiency.
This package provides functionality for creating and
evaluating acceptance sampling plans. Plans can be single,
double or multiple sampling plans
The main function is plotLMA(sourcefile,header) that takes
a data set and plots the appropriate LMA and ACC graphs. If no
sourcefile (a string) was passed, a manual data entry window is
opened. The header parameter indicates by TRUE/FALSE (false by
default) if the source CSV file has a head row or not. The data
set should contain only one independent variable (X) and one
dependent varialbe (Y) and can contain a weight for each
observation
Subject recruitment for medical research is challenging. Slow patient accrual leads to delay in research. Accrual monitoring during the process of recruitment is critical. Researchers need reliable tools to manage the accrual rate. We developed a Bayesian method that integrates researcher's experience on previous trials and data from the current study, providing reliable prediction on accrual rate for clinical studies. In this R package, we present functions for Bayesian accrual prediction which can be easily used by statisticians and clinical researchers.
Package for visualizing data quality of partially accruing time series.
Categorical data analysis with complete or missing responses
ACE and AVAS methods for choosing regression
transformations.
Fragment lengths or molecular weights from pairs of lanes are
compared, and a number of matching bands are calculated using the
Align-and-Count Method.
Package for NMF summarization of SNP probes.
Archimax copulas are mixture of Archimedean and EV copulas. The package provides definitions of several parametric families of generator and dependence function, computes CDF and PDF, estimates parameters, tests for goodness of fit, generates random sample and checks copula properties for custom constructs. In 2-dimensional case explicit formulas for density are used, in the contrary to higher dimensions when all derivatives are linearly approximated. Several non-archimax families (normal, FGM, Plackett) are provided as well.
Package for time series analysis of count data
Convenience functions for data preparation and modeling often used in aCRM.
Provides a general toolkit for downloading, managing, analyzing, and presenting data from the U.S. Census American Community Survey (ACS).  Confidence intervals provided with the data are converted to standard errors to be bundled with estimates in complex acs objects.  Package provides new methods to conduct standard operations and tests of significance on acs objects in statistically appropriate ways. Current version is 1.2 +/- .05.
Main functionality is to provide the algorithmic complexity for
short strings, an approximation of the Kolmogorov Complexity of a short
string using the coding theorem method (see ?acss). The database containing
the complexity is provided in the data only package acss.data, this package
provides functions accessing the data such as prob_random returning the
posterior probability that a given string was produced by a random process.
In addition, two traditional (but problematic) measures of complexity are
also provided: entropy and change complexity.
Data only package providing the algorithmic complexity of short strings, computed using the coding theorem method. For a given set of symbols in a string, all possible or a large number of random samples of Turing machines (TM) with a given number of states (e.g., 5) and number of symbols corresponding to the number of symbols in the strings were simulated until they reached a halting state or failed to end. This package contains data on 4.5 million strings from length 1 to 12 simulated on TMs with 2, 4, 5, 6, and 9 symbols. The complexity of the string corresponds to the distribution of the halting states of the TMs.
This package includes functions used to perform cluster analysis for cognitive
diagnosis based on the Asymptotic Classification Theory (Chiu, Douglas & Li, 2009). Given the
sample statistic of sum-scores and the item-by-attribute association matrix (Q-matrix; Tatsuoka, 1985),
cluster analysis techniques can be used to classify examinees into latent
classes based on their attribute patterns. In addition to the algorithms used to classify data, three
labeling approaches are proposed to label clusters so that examinees' attribute profiles can be obtained.
This package implements functional linear modeling and
analysis for actigraphy data.
Provides functions to fit kernel density functions
to animal activity time data; plot activity distributions;
quantify overall levels of activity; statistically compare
activity metrics through bootstrapping; and evaluate variation
in linear variables with time (or other circular variables).
Additional actuarial science functionality, mostly in the
fields of loss distributions, risk theory (including ruin
theory), simulation of compound hierarchical models and
credibility theory.  The package also features 17 new
probability laws commonly used in insurance, most notably heavy
tailed distributions.
Computes the probability density function, hazard rate
function, integrated hazard rate function and the quantile
function for 44 commonly used survival models
Performs discrete, real, and gentle boost under both
exponential and logistic loss on a given data set.  The package
ada provides a straightforward, well-documented, and broad
boosting routine for classification, ideally suited for small
to moderate-sized data sets.  Please refer to the Url below for
more information.
This package implements Freund and Schapire's Adaboost.M1 algorithm and Breiman's Bagging algorithm
using classification trees as individual classifiers. Once these classifiers have been trained,
they can be used to predict on new data. Also, cross validation predictions can be done.
Since version 2.0 the function "margins" is available to calculate the margins for these classifiers.
Also a higher flexibility is achieved giving access to the "rpart.control" argument of "rpart".
Four important new features were introduced on version 3.0, AdaBoost-SAMME (Zhu et al., 2009) is implemented
and a new function "errorevol" shows the error of the ensembles as a function of the number of iterations.
In addition, the ensembles can be pruned using the option "newmfinal" in the predict.bagging and predict.boosting
functions and the posterior probability of each class for observations can be obtained. Version 3.1 modifies
the relative importance measure to take into account the gain of the Gini index given by a variable in each tree
and the weights of these trees.
The R package 'adagio' will provide methods and algorithms for
discrete optimization and (evolutionary) global optimization.
The package provides new approaches to variable selection for censored data, based on AFT models optimized using regularized weighted least squares. Namely, a weighted elastic net, an adaptive elastic net, and two of their extensions by adding censoring observations as constraints into their model optimization frameworks.
The adaptive mixture discriminant analysis (AMDA) allows to adapt a model-based classifier to the situation where a class represented in the test set may have not been encountered earlier in the learning phase.
Based on the function "spm" of the SemiPar package fits
semiparametric regression models with spatially adaptive
penalized splines.
Based on the function "asp" of the AdaptFit package fits semiparametric regression models with spatially adaptive penalized splines and computes simultaneous confidence bands.
Implements Figueiredo EM algorithm for adaptive sparsity (Jeffreys prior) (see Figueiredo, M.A.T.; , "Adaptive sparseness for supervised learning," Pattern Analysis and Machine Intelligence, IEEE Transactions on , vol.25, no.9, pp. 1150- 1159, Sept. 2003) and Wong algorithm for adaptively sparse gaussian geometric models (see Wong, Eleanor, Suyash Awate, and P. Thomas Fletcher. "Adaptive Sparsity in Gaussian Graphical Models." In Proceedings of the 30th International Conference on Machine Learning (ICML-13), pp. 311-319. 2013.)
Implements adaptive tau leaping to approximate the
trajectory of a continuous-time stochastic process as
described by Cao et al. (2007) The Journal of Chemical
Physics.  This package is based upon work supported by
NSF DBI-0906041 and NIH K99-GM104158 to Philip Johnson and
NIH R01-AI049334 to Rustom Antia.
This package provides an implementation of the generic
adaptive Monte Carlo Markov chain sampler proposed by Vihola
(2011).
This package contains R functions for estimating the blood
oxygenation level dependent (BOLD) effect by using functional
Magnetic Resonance Imaging (fMRI) data, based on adaptive Gauss
Markov random fields, for real as well as simulated data. The
implemented simulations make use of efficient Markov Chain
Monte Carlo methods.
The functions defined in this program serve for
implementing adaptive two-stage tests. Currently, four tests
are included: Bauer and Koehne (1994), Lehmacher and Wassmer
(1999), Vandemeulebroecke (2006), and the horizontal
conditional error function. User-defined tests can also be
implemented. Reference: Vandemeulebroecke, An investigation of
two-stage tests, Statistica Sinica 2006.
Implementation of the Tukey, Mandel, Johnson-Graybill, LBI, Tusell
and modified Tukey non-additivity tests.
Accelerated destructive degradation tests (ADDT) are often used to collect necessary data for assessing the long-term properties of polymeric materials. Based on the collected data, a thermal index (TI) is estimated. The TI can be useful for material rating and comparison. This package performs the least squares (LS) and maximum likelihood (ML) procedures for estimating TI for polymeric materials. The LS approach is a two-step approach that is currently used in industrial standards, while the ML procedure is widely used in the statistical literature. The ML approach allows one to do statistical inference such as quantifying uncertainties in estimation, hypothesis testing, and predictions. Two publicly available datasets are provided to allow users to experiment and practice with the functions.
Multivariate data analysis and graphical display.
a Tcl/Tk GUI for some basic functions in the ade4 package
Classes and functions for genetic data analysis within the multivariate framework.
A collection of tools for the analysis of habitat selection by animals
A collection of tools for the estimation of animals home range
A collection of tools for the analysis of habitat selection
A collection of tools for the analysis of animal movements
A collection of tools to deal with raster maps
Multivariate tools to analyze comparative data, i.e. a phylogeny and some traits measured for each taxa.
This package provides some useful functions for calculating quality
measures for
adjustment, including statistics Kolmogorov-Smirnov, Cramer-von Mises and Anderson-Darling.
These statistics are often used to compare models not equipped. Also provided are other measures
of fitness, such as AIC, CAIC, BIC and HQIC. The package also provides functions for generating
pseudo-random number of random variables that follow the following probability distributions: Kumaraswamy Birnbaum-Saunders, Kumaraswamy Pareto, Exponentiated Weibull
and Kumaraswamy Weibull.
Anderson-Darling GoF test with p-value calculation based on Marsaglia's 2004 paper "Evaluating the Anderson-Darling Distribution"
An ad hoc distance threshold can be calculated for each particular reference library of DNA barcodes. Specimen identification at this ad hoc distance threshold (using the best close match method) will produce identifications with an estimated relative error probability that can be fixed by the user (e.g. 5%)
This package implements tools for manipulation of digital
images and the Propagation Separation approach by Polzehl and
Spokoiny (2006) for smoothing digital images.
Adaptive Wavelet transforms for signal denoising
Robust change point detection using ADM3 algorithm.
This package provides functions to perform the fitting of an
adaptive mixture of Student-t distributions to a target density through its kernel function.
The mixture approximation can then be used as the importance density
in importance sampling or as the candidate density in the Metropolis-Hastings algorithm to
obtain quantities of interest for the target density itself.
Perform first- and second-order multi-scale analyses derived from Ripley K-function, for univariate,
multivariate and marked mapped data in rectangular, circular or irregular shaped sampling windows, with tests of
statistical significance based on Monte Carlo simulations.
Download and process real time trading prices and demand data
freely provided by the Australian Energy Market Operator (AEMO). Note that
this includes a sample data set.
Functions, data sets, examples, demos, and vignettes for the book
Christian Kleiber and Achim Zeileis (2008),
Applied Econometrics with R, Springer-Verlag, New York.
ISBN 978-0-387-77316-2. (See the vignette for a package overview.)
Provides convenience functions for analyzing factorial experiments
using ANOVA or mixed-models. ez.glm() and aov.car() allow convenient
calculation of between, within (i.e., repeated-measures), or mixed
between-within (i.e., split-plot) ANOVAs for data in the long format (i.e.,
one observation per row) wrapping car::Anova() (aggregating more then one
observation per individual and cell of the design), per default returning a
print ready ANOVA table. Function mixed() fits a mixed model using
lme4::lmer() and computes p-values for all effects in the model using
either the Kenward-Roger approximation of degrees of freedom (LMM only),
parametric bootstrap (LMMs and GLMMs) or likelihood ratio tests (LMMs and
GLMMs). afex uses type 3 sums of squares as default (imitating commercial
statistical software). Furthermore, compare.2.vectors() conveniently
compares two vectors using a variety of tests.
This package is developed in the Plant Reproductive Biology Lab (RNM-214) - University of Seville. It contains hybrid simulation functions for dominant genetic data. It also provides several genome scan methods.
This package features both rank-based estimates and least
square estimates to the Accelerated Failure Time (AFT) model.
For rank-based estimation, it provides approaches that include
the computationally efficient Gehan's weight and the general's
weight such as the logrank weight.
For the least square estimation, the estimating equation is
solved  with Generalized Estimating Equations (GEE).
Moreover, in multivariate cases, the dependence working
correlation structure can be specified in GEE's setting.
Tools for NIHES course EP18 'Analysis of Growth Data', May
22-23 2012, Rotterdam.
Tools supporting multi-criteria decision making, including
variable number of criteria, by means of aggregation operators
and preordered sets. Possible applications include, but are not
limited to, scientometrics and bibliometrics.
Plots for visualization and point and
interval estimates for different metrics of agreement.
This package computes several statistics for measuring
agreement, for example, mean square deviation (MSD), total
deviation index (TDI) or concordance correlation coefficient
(CCC). It can be used for both continuous data and categorical
data for multiple raters and multiple readings cases.
Agricolae was presented in the thesis "A statistical analysis tool for agricultural research" to obtain the degree of Master on science, mention Systems Engineering, Faculty of Industrial and Systems Engineering, National Engineering University (UNI), Lima-Peru. Original idea, support and data sets came from CIP (International Potato Center). Agricolae offers extensive functionality on experimental design especially for agricultural and plant breeding experiments, which can also be useful for other purposes. It supports planning of lattice, Alpha, Cyclic, Complete Block, Latin Square, Graeco-Latin Squares, augmented block, factorial, split and strip plot designs. There are also various analysis facilities for experimental data, e.g. treatment comparison procedures and several non-parametric tests comparison, biodiversity indexes and consensus cluster.
Datasets from books and papers related to agriculture.
Example analyses included.  Functions for plotting field designs and GGE biplots.
This package calculates agreement or consensus in ordered rating scales. It implements van der Eijk's (2001) measure of agreement A, which can be used to describe agreement, consensus, or polarization among respondents. It also implements measures of consensus (dispersion) by Leik, Tatsle and Wierman, Blair and Lacy, Kvalseth, and Berry and Mielke. Furthermore, an implementation of Galtungs AJUS-system is provided to classify distributions, as well as a function to identify the position of multiple modes.
Estimation in adaptive group sequential trials
High-featured panel functions for bwplot and xyplot,
some plot management helpers, various convenience functions
Computationally efficient procedures for regularized
estimation with the semiparametric additive hazards regression
model.
This package includes functions to create model selection tables based on Akaike's information criterion (AIC) and the second-order AIC (AICc), as well as their quasi-likelihood counterparts (QAIC, QAICc).  The package also features functions to conduct classic model averaging (multimodel inference) for a given parameter of interest or predicted values, as well as a shrinkage version of model averaging parameter estimates.  Other handy functions enable the computation of relative variable importance, evidence ratios, and confidence sets for the best model.  The present version works with Cox proportional hazards models and conditional logistic regression ('coxph' and 'coxme' classes), linear models ('lm' class), generalized linear models ('glm', 'vglm', and 'zeroinfl' classes), linear models fit by generalized least squares ('gls' class), linear mixed models ('lme' class), generalized linear mixed models ('mer' and 'merMod' classes), multinomial and ordinal logistic regressions ('multinom'}, 'polr', 'clm', and 'clmm' classes), robust regression models ('rlm' class), nonlinear models ('nls' class), and nonlinear mixed models ('nlme' and 'nlmer' classes).  The package also supports various models of 'unmarkedFit' and 'maxLikeFit' classes estimating demographic parameters after accounting for imperfect detection probabilities.  Some functions also allow the creation of model selection tables for Bayesian models of the 'bugs' and 'rjags' classes.
Includes a function to estimate the power transformation parameter and some datasets
Read objects from the AIDA file and make them available
as dataframes in R
R functions for adaptively constructing index models for
continuous, binary and survival outcomes. Implementation
requires loading R-pacakge "survival"
Linear or cubic spline interpolation for irregular gridded data
Adaptive K-means algorithm with various threshold settings.
It support two distance metric:
Euclidean distance, Cosine distance (1 - cosine similarity)
In version 1.1, it contains one more threshold condition.
Augmented Lagrangian Adaptive Barrier Minimization
Algorithm for optimizing smooth nonlinear objective functions
with constraints. Linear or nonlinear equality and inequality
constraints are allowed.
EM algorithm for estimation of parameters and other methods in a quantile regression.
Determination of absolute protein quantities is necessary for multiple applications, such as mechanistic modeling of biological systems. Quantitative liquid chromatography tandem mass spectrometry (LC-MS/MS) proteomics can measure relative protein abundance on a system-wide scale. To estimate absolute quantitative information using these relative abundance measurements requires additional information such as heavy-labeled references of known concentration. Multiple methods have been using different references and strategies; some are easily available whereas others require more effort on the users end. Hence, we believe the field might benefit from making some of these methods available under an automated framework, which also facilitates validation of the chosen strategy. We have implemented the most commonly used absolute label-free protein abundance estimation methods for LC-MS/MS modes quantifying on either MS1-, MS2-levels or spectral counts together with validation algorithms to enable automated data analysis and error estimation. Specifically, we used Monte-carlo cross-validation and bootstrapping for model selection and imputation of proteome-wide absolute protein quantity estimation. Our open-source software is written in the statistical programming language R and validated and demonstrated on a synthetic sample.
Algorithmic experimental designs. Calculates exact and
approximate theory experimental designs for D,A, and I
criteria. Very large designs may be created. Experimental
designs may be blocked or blocked designs created from a
candidate list, using several criteria.  The blocking can be
done when whole and within plot factors interact.
A collection of functions that implement several algorithms for
generating age-length keys for fish populations from incomplete data.
Automated fitting of linear regression models and a
stepwise routine
A collection of tools for stochastic sensor error
characterization using the Allan Variance technique originally
developed by D. Allan.
This package contains tools for simulating alphanumeric alleles
from parent-offspring pedigree databases to later reconstruct the
corresponding non-recombinant haplotypes. Allelic simulations can be
implemented taking into account many different factors (such as number of
families, markers, alleles per marker, probability of missing genotypes,
recombination rate, etc). Haplotype reconstruction can be performed even
with missing data.
This package provides tools for the identification of unique of multilocus genotypes when both genotyping error and missing data may be present. The package is targeted at those working with large datasets and databases containing multiple samples of each individual, a situation that is common in conservation genetics, and particularly in non-invasive wildlife sampling applications. Functions explicitly incorporate missing data, and can tolerate allele mismatches created by genotyping error. If you use this tool, please cite the package using the journal article in Molecular Ecology Resources (Galpern et al., 2012).  Please use citation('allelematch') to find this. Due to changing CRAN policy, and the size and compile time of the vignettes, they can no longer be distributed with this package.  Please contact the package primary author, or visit the allelematch site for a complete vignette (http://nricaribou.cc.umanitoba.ca/allelematch/).  For users with access to academic literature, tutorial material is also available as supplementary material to the article describing this software.
Simulate the effect of management or demography on allele
retention and inbreeding accumulation in bottlenecked
populations of animals with overlapping generations.
This is the implementation in R+C of a new association
test described in "A fast, unbiased and exact allelic exact
test for case-control association studies" (Submitted).  It
appears that in most cases the classical chi-square test used
for testing for allelic association on genotype data is biased.
Our test is unbiased, exact but fast throught careful
optimization.
Contains functions possSpells.fnc and
batch.possSpells.fnc.
An R interface to the open source article level metrics API
created by the Public Library of Science (PLOS). A number of publishers are
using the open source app created by PLOS, so you can drop in a different
base URL to the functions in this package to get to not only PLOS data, but
data for Crossref, and more as the open source PLOS software is used.
Obtain rCharts at https://github.com/ramnathv/rCharts.
This package computes the alpha-shape and alpha-convex
hull of a given sample of points in the plane. The concepts of
alpha-shape and alpha-convex hull generalize the definition of
the convex hull of a finite set of points. The programming is
based on the duality between the Voronoi diagram and Delaunay
triangulation. The package also includes a function that
returns the Delaunay mesh of a given sample of points and its
dual Voronoi diagram in one single object.
The package alphashape3d presents the implementation in R of the alpha-shape of a finite set of points in the three-dimensional space. This geometric structure generalizes the convex hull and allows to recover the shape of non-convex and even non-connected sets in 3D, given a random sample of points taken into it. Besides the computation of the alpha-shape, the package alphashape3d provides users with functions to compute the volume of the alpha-shape, identify the connected components and facilitate the three-dimensional graphical visualization of the estimated set.
This package is a companion to the textbook S. Weisberg (2005),
"Applied Linear Regression," 3rd edition, Wiley. It includes all the
data sets discussed in the book (except one), and a few functions that
are tailored to the methods discussed in the book.  As of version 2.0.0,
this package depends on the car package. Many functions formerly
in alr3 have been renamed and now reside in car.
Data files have beeen lightly modified to make some data columns row labels.
This package is a companion to the textbook S. Weisberg (2014),
"Applied Linear Regression," 4rd edition, Wiley. It includes all the
data sets discussed in the book and one function to access the textbook's
website.
This package depends on the car package.  Many data files in this package
are included in the alr3 package as well, so only one of them should be
loaded.
Alternating least squares is often used to resolve
components contributing to data with a bilinear structure; the
basic technique may be extended to alternating constrained
least squares.  Commonly applied constraints include
unimodality, non-negativity, and normalization of components.
Several data matrices may be decomposed simultaneously by
assuming that one of the two matrices in the bilinear
decomposition is shared between datasets.
Using of the accelerated line search algorithm  for simultaneously diagonalize a set of symmetric positive definite matrices.
Tools for Clustering and Principal Component Analysis
(With robust methods, and parallelized functions).
An Approximated Most Average Powerful Test with Optimal
FDR Control with Application to RNA-seq Data
This package provides a flexible statistical framework for generating optimal
epidemiological interventions that are designed to minimize the total expected
cost of an emerging epidemic while simultaneously propagating uncertainty regarding
underlying disease parameters through to the decision process via Bayesian posterior
inference.  The strategies produced through this framework are adaptive: vaccination
schedules are iteratively adjusted to reflect the anticipated trajectory of the
epidemic given the current population state and updated parameter estimates.
Amelia II "multiply imputes" missing data in a single
cross-section (such as a survey), from a time series (like
variables collected for each year in a country), or from a
time-series-cross-sectional data set (such as collected by
years for each of several countries).  Amelia II implements our
bootstrapping-based algorithm that gives essentially the same
answers as the standard IP or EMis approaches, is usually
considerably faster than existing approaches and can handle
many more variables.  Unlike Amelia I and other statistically
rigorous imputation software, it virtually never crashes (but
please let us know if you find to the contrary!).  The program
also generalizes existing approaches by allowing for trends in
time series across observations within a cross-sectional unit,
as well as priors that allow experts to incorporate beliefs
they have about the values of missing cells in their data.
Amelia II also includes useful diagnostics of the fit of
multiple imputation models.  The program works from the R
command line or via a graphical user interface that does not
require users to know R.
This package computes Bayesian estimates for additive and
multiplicative effects (AME) models for networks and relational
data. The basic model includes regression terms, the covariance
structure of the social relations model (Warner, Kenny and
Stoto (1979), Wong (1982)), and multiplicative factor effects
(Hoff(2009)). Four different link functions accommodate
different relational data structures, including binary/network
data (bin), normal relational data (nrm), ordinal relational
data (ord) and data from fixed-rank nomination schemes (frn).
Several of these link functions are discussed in Hoff, Fosdick,
Volfovsky and Stovel (2013). Development of this software was
supported in part by NICHD grant R01HD067509-01A1.
This package includes a set of pricing functions for
American call options. The following cases are covered: Pricing
of an American call using the standard binomial approximation;
Hedge parameters for an American call with a standard binomial
tree; Binomial pricing of an American call with continuous
payout from the underlying asset; Binomial pricing of an
American call with an underlying stock that pays proportional
dividends in discrete time; Pricing of an American call on
futures using a binomial approximation; Pricing of a currency
futures American call using a binomial approximation; Pricing
of a perpetual American call. The user should kindly notice
that this material is for educational purposes only. The codes
are not optimized for computational efficiency as they are
meant to represent standard cases of analytical and numerical
solution.
AMGET allows to simply and rapidly creates highly informative diagnostic plots for ADAPT 5 models. Features include data analysis prior any modeling form either NONMEM or ADAPT shaped dataset, goodness-of-fit plots (GOF), posthoc-fits plots (PHF), parameters distribution plots (PRM) and visual predictive check plots (VPC) based on ADAPT output.
This package implements the adaptive mixed lasso (AML) method proposed by Wang et al.(2011).  AML applies adaptive lasso penalty to a large number of predictors,  thus producing a sparse model, while accounting for the population structure in the linear mixed model framework.  The package here is primarily designed for application to genome wide association studies or genomic prediction in plant breeding populations, though it could be applied to other settings of linear mixed models.
A function to calculate spatial clusters using the Getis-Ord local statistic. It searches
irregular clusters (ecotopes) on a map.
This package was born to release the TAO robust neural
network algorithm to the R users. It has grown and I think it
can be of interest for the users wanting to implement their own
training algorithms as well as for those others whose needs lye
only in the "user space".
Increasingly powerful techniques for high-throughput sequencing open the possibility to comprehensively characterize microbial communities, including rare species. However, a still unresolved issue are the substantial error rates in the experimental process generating these sequences. To overcome these limitations we propose an approach, where each sample is split and the same amplification and sequencing protocol is applied to both halves. This procedure should allow to detect likely PCR and sequencing artifacts, and true rare species by comparison of the results of both parts. The AmpliconDuo package, whereas amplicon duo from here on refers to the two amplicon data sets of a split sample, is intended to help interpret the obtained read frequency distribution across split samples, and to filter the false positive reads.
Performs simple and canonical CA (covariates on rows/columns) on a two-way frequency table (with missings) by means of SVD. Different scaling methods (standard, centroid, Benzecri, Goodman) as well as various plots including confidence ellipsoids are provided.
Fits Modern Analogue Technique and Weighted Averaging transfer
function models for prediction of environmental data from species
data, and related methods used in palaeoecology.
Functions for I/O, visualisation and analysis of
functional Magnetic Resonance Imaging (fMRI) datasets stored in
the ANALYZE or NIFTI format.
Bidirectional connector to Anametrix API
This package contains functions for
normalisation,differentially analysis of microarray data and
others functions implementing recent methods developed by the
Statistic and Genom Team from UMR 518 AgroParisTech/INRA Appl.
Math. Comput. Sc.
Tools for analyzing survey responses with anchors.
AODE achieves highly accurate classification by averaging over all
of a small space.
Andrews curves for visualization of multidimensional data
This package provides a comprehensive system for selecting
variables and weighting data to match the specifications of the
American National Election Studies.  The package includes
methods for identifying discrepant variables, raking data, and
assessing the effects of the raking algorithm.  It also allows
automated re-raking if target variables fall outside identified
bounds and allows greater user specification than other
available raking algorithms. A variety of simple weighted
statistics that were previously in this package (version .55
and earlier) have been moved to the package 'weights.'
2D and 3D animal tracking data can be used to reconstruct tracks through time/space with correction based on known positions. 3D visualization of animal position and attitude.
This package contains a variety of functions for animations in
statistics, covering areas such as probability theory, mathematical
statistics, multivariate statistics, nonparametric statistics, sampling
survey, linear models, time series, computational statistics, data mining
and machine learning. These functions might be helpful in teaching
statistics and data analysis. Also provided in this package are several
approaches to save animations to various formats, e.g. Flash, GIF, HTML
pages, PDF and videos (saveSWF(), saveGIF(), saveHTML(), saveLatex(), and
saveVideo() respectively). PDF animations can be inserted into Sweave/knitr
easily.
Annotate multiple lists from a specific annotation file.
Offers a complete and interactive GUI to work out Mean Measures of Divergence, especially for anthropologists.
This package brings together some statistical methodologies especially developed to analyze anthropometric data. These methods are aimed 		at providing effective solutions to some commons problems related to Ergonomics and Anthropometry. They are based on clustering, the 		statistical concept of data depth, the statistical shape analysis and the archetypal analysis.
A collection of tools for antitrust practitioners, including the ability to calibrate different consumer demand systems and simulate the effects mergers under different competitive regimes.
A complete programmatic interface to the AntWeb database from the
California Academy of Sciences.
This package provides a set of functions to analyse
overdispersed counts or proportions. Most of the methods are
already available elsewhere but are scattered in different
packages. The proposed functions should be considered as
complements to more sophisticated methods such as generalized
estimating equations (GEE) or generalized linear mixed effect
models (GLMM).
This package provides functions to analyse overdispersed
counts or proportions.  These functions should be considered as
complements to more sophisticated methods such as generalized
estimating equations (GEE) or generalized linear mixed effect
models (GLMM). aods3 is an S3 re-implementation of the
deprecated S4 package aod.
This package implements AO transformations for binary response variables in Generalized Linear Models (GLMs) and bounded response variables in Quantile Regression (QR) models. The Maximum Likelihood Estimate (MLE) of the transformation parameter is obtained using the profile log likelihood method
'Aoristic' is one of the past tenses in Greek and represents an
uncertain occurrence time.  Aoristic analysis suggested by Ratcliffe (2002)
is a method to analyze events that do not have exact times of occurrence
but have starting times and ending times.  For example, a property crime
database (e.g., burglary) typically has a starting time and ending time of
the crime that could have occurred.  Aoristic analysis allocates the
probability of a crime incident occurring at every hour over a 24-hour
period. The probability is aggregated over a study area to create an
aoristic graph.
Using crime incident data with lat/lon, DateTimeFrom, and
DateTimeTo, functions in this package create a total of three (3) kml
files and corresponding aoristic graphs: 1) density and contour; 2) grid
count; and 3) shapefile boundary. (see also:
https://sites.google.com/site/georgekick/software)
Functions for age-period-cohort analysis. The data can be organised in matrices indexed by age-cohort, age-period or cohort-period. The data can include dose and response or just doses.  The statistical model is a generalized linear model (GLM) allowing for 3,2,1 or 0 of the age-period-cohort factors. The canonical parametrisation of Kuang, Nielsen and Nielsen (2008) is used. Thus, the analysis does not rely on ad hoc identification.
The apcluster package implements Frey's and Dueck's
Affinity Propagation clustering in R.  The algorithms are
largely analogous to the Matlab code published by Frey and
Dueck. The package further provides leveraged affinity
propagation and an algorithm for exemplar-based agglomerative
clustering that can also be used to join clusters obtained
from affinity propagation. Various plotting functions are
available for analyzing clustering results.
ape provides functions for reading, writing, plotting, and manipulating phylogenetic trees, analyses of comparative data in a phylogenetic framework, ancestral character analyses, analyses of diversification and macroevolution, computing distances from allelic and nucleotide data, reading and writing nucleotide sequences, and several tools such as Mantel's test, generalized skyline plots, graphical exploration of phylogenetic data (alex, trex, kronoviz), estimation of absolute evolutionary rates and clock-like trees using mean path lengths and penalized likelihood. Phylogeny estimation can be done with the NJ, BIONJ, ME, MVR, SDM, and triangle methods, and several methods handling incomplete distance matrices (NJ*, BIONJ*, MVR*, and the corresponding triangle method). Some functions call external applications (PhyML, Clustal, T-Coffee, Muscle) whose results are returned into R.
set of functions for drawing some special plots:
stem.leaf plots a stem and leaf plot,
stem.leaf.backback plots back-to-back versions of stem and leafs,
bagplot plots a bagplot,
skyline.hist plots several histgramm in one plot of a one dimensional data set,
plotsummary plots a graphical summary of a data set with one or more variables,
plothulls plots sequentially hulls of a bivariate data set,
faces plots chernoff faces,
spin3R for an inspection of a 3-dim point cloud,
slider functions for interactive graphics.
apmsWAPP provides a complete workflow for the analysis of AP-MS data (replicate single-bait purifications including negative controls) based on spectral counts.
It comprises pre-processing, scoring and postprocessing of protein interactions.
A final list of interaction candidates is reported: it provides a ranking of the candidates according
to their p-values which allow estimating the number of false-positive interactions.
This package wraps Fortran code by F. D. Colavecchia and
G. Gasaneo for computing the Appell's F1 hypergeometric
function. Their program uses Fortran code by L. F. Shampine and
H. A. Watts. Moreover, the hypergeometric function with complex
arguments is computed with Fortran code by N. L. J. Michel and
M. V. Stoitsov or with Fortran code by R. C.  Forrey. See the
function documentations for the references and please cite them
accordingly.
Approximate Path for Penalized Likelihood Estimators for
Generalized Linear Models penalized by LASSO or MCP
A few functions and several data set for the Springer book 'Applied Predictive Modeling'
Performs Bayesian prediction of complex computer codes
when fast approximations are available: M. C. Kennedy and A. O'Hagan
2000, Biometrika 87(1):1-13
Assists the evaluation of whether and
where to focus code optimization, using Amdahl's law and visual aids
based on line profiling. Amdahl's profiler organises profiling output
files (will soon include memory profiling) in a visually appealing
way. It is meant to help to balance development vs. execution time
by helping to identify the most promising sections of code to
optimize and projecting potential gains. The package is an addition
to R's standard profiling tools and is not a wrapper for them.
The Agricultural Production Systems sIMulator (APSIM) is a widely used simulator of agricultural systems.  This package includes a few basic functions to create, edit and run APSIM simulations then visualize the results. For more on APSIM including download instructions go to www.apsim.info.
Formats latex tables from one or more model objects
side-by-side with standard errors below, not unlike tables
found in such journals as the American Political Science
Review.
This package focuses on asymmetric price transmission (APT) between two time series. It contains functions for linear and nonlinear threshold cointegration, and furthermore, symmetric and asymmetric error correction model. A graphical user interface is also included for major functions included in the package, so users can also use these functions in a more intuitive way.
apTreeshape is mainly dedicated to simulation and analysis
of phylogenetic tree topologies using statistical indices. It
is a companion library of the 'ape' package. It provides
additional functions for reading, plotting, manipulating
phylogenetic trees. It also offers convenient web-access to
public databases, and enables testing null models of
macroevolution using corrected test statistics.  Trees of class
"phylo" (from 'ape' package) can be converted easily.
This package contains functions to help display air quality model output and monitoring data, such as creating color scatterplots, color legends, etc.
A collection of algorithms related to modeling of soil resources, soil classification, soil profile aggregation, and visualization.
This R extension provides methods to use a standalone ActiveQuant
Master Server from within R. Currently available features include fetching
and storing historical data, receiving and sending live data.  Several
utility methods for simple data transformations are included, too. For
support requests, please join the mailing list at
https://r-forge.r-project.org/mail/?group_id=1518
AquaEnv is an integrated development toolbox for aquatic
chemical model generation focused on (ocean) acidification and
CO2 air-water exchange.  It contains all elements necessary to
model the pH, the related CO2 air-water exchange, as well as
aquatic acid-base chemistry in general for an arbitrary marine,
estuarine or freshwater system. Also chemical batches can be
modelled.  Next to the routines necessary to calculate desired
information, AquaEnv also contains a suite of tools to
visualize this information.  Furthermore, AquaEnv can not only
be used to build dynamic models of aquatic systems, but it can
also serve as a simple desktop tool for the experimental
aquatic chemist to generate and visualize all possible derived
information from a set of measurements with one single easy to
use R function.  Additionally, the sensitivity of the system to
variations in the input variables can be visualized. The
corresponding publication in Aquatic Geochemistry can be found
at http://www.springerlink.com/content/l4747417w0k50463/
This package corresponds to the implementation of the robust approach for estimating change-points in the mean of an AR(1) Gaussian process by using the methodology described in the paper arXiv 1403.1958
The archdata package provides several types of data that are typically used in archaeological research.
The main function archetypes implements a
framework for archetypal analysis supporting arbitrary
problem solving mechanisms for the different conceptual
parts of the algorithm.
Data exploration and modelling is a process in which a lot of data
artifacts are produced. Artifacts like: subsets, data aggregates, plots,
statistical models, different versions of data sets and different versions
of results. The more projects we work with the more artifacts are produced
and the harder it is to manage these artifacts. Archivist helps to store
and manage artifacts created in R. Archivist allows you to store selected
artifacts as a binary files together with their metadata and relations.
Archivist allows to share artifacts with others, either through shared
folder or github. Archivist allows to look for already created artifacts by
using it's class, name, date of the creation or other properties. Makes it
easy to restore such artifacts. Archivist allows to check if new artifact
is the exact copy that was produced some time ago. That might be useful
either for testing or caching.
Package ArDec implements autoregressive-based
decomposition of a time series based on the constructive
approach in West (1997). Particular cases include the
extraction of trend and seasonal components.
Activated Region Fitting (ARF) is an analysis method for fMRI data.
Simulates, fits, and predicts long-memory and anti-persistent time series, possibly mixed with ARMA, regression, transfer-function components. Exact methods (MLE, forecasting simulation) are used.
Functions to filters animal satellite tracking data
obtained from Argos. It is especially indicated for telemetry
studies of marine animals, where Argos locations are
predominantly of low-quality.
A command line parser to
be used with Rscript to write "#!" shebang scripts that gracefully
accept positional and optional arguments and automatically generate usage.
Cross-platform command-line argument parser written purely in R
with no external dependencies. This package is useful with the Rscript
front-end and facilitates turning an R script into an executable script.
R functions for processing lm, glm, svy.glm, merMod and polr outputs.
Arnold Schwarzenegger movie weekend box office records from
1982-2014
This package implements classes for files and sets of files for various Affymetrix file formats, e.g. AffymetrixCdfFile, AffymetrixCelFile, and AffymetrixCelSet.  These are designed to be memory efficient but still being fast.  The idea is to keep all data on file and only read data into memory when needed.  Clever caching mechanisms are used to minimize the overhead of data IO.  All of the above is hidden in the package API and for the developer (and the end user), the data is queried as if it lives in memory.  With this design it is only the disk space that limits the number of arrays that can be analyzed.
THIS PACKAGE HAS BEEN DEPRECATED. (The (in-house) APD file format was initially developed to store Affymetrix probe-level data, e.g. normalized CEL intensities.  Chip types can be added to APD file and similar to methods in the affxparser package, this package provides methods to read APDs organized by units (probesets).  In addition, the probe elements can be arranged optimally such that the elements are guaranteed to be read in order when, for instance, data is read unit by unit.  This speeds up the read substantially.  This package is supporting the Aroma framework and should not be used elsewhere.)
Methods for analyzing DNA copy-number data.  Specifically,
this package implements the multi-source copy-number normalization (MSCN)
method for normalizing copy-number data obtained on various platforms and
technologies.  It also implements the TumorBoost method for normalizing
paired tumor-normal SNP data.
This package contains core methods and classes used by
higher-level aroma.* packages part of the Aroma Project, e.g.
aroma.affymetrix and aroma.cn.  Its API is mostly of interest
to developers and is rarely used by end users directly.
ARPobservation provides a set of tools for simulating data based
on direct observation of behavior. It works by first simulating a behavior
stream based on an alternating renewal process, given specified
distributions of event durations and interim times. Different procedures for
recording data can then be applied to the simulated behavior stream.
Functions are provided for the following recording methods: continuous
duration recording, event counting, momentary time sampling, partial
interval recording, and whole interval recording.
Functions for Arps decline-curve analysis on oil and gas data. Includes exponential, hyperbolic, harmonic, and hyperbolic-to-exponential models as well as the preceding with initial curtailment. Functions included for computing rate, cumulative production, instantaneous decline, EUR, time to economic limit, and performing least-squares best fits.
Fast adaptive binarization for numeric data arrays,
particularly designed for high-throughput biological datasets.
Includes options to filter out rows of the array with
insufficient magnitude or variation (based on gap statistic).
Some convenient functions to work with arrays
Adaptive Rejection Sampling, Original version
A package for calculating gene and pathway p-values using the Adaptive Rank Truncated Product test
Provides the infrastructure for representing,
manipulating and analyzing transaction data and patterns (frequent
itemsets and association rules). Also provides interfaces to
C implementations of the association mining algorithms Apriori and Eclat
by C. Borgelt.
NBMiner is an implementation of the model-based mining algorithm
for mining NB-frequent itemsets presented in "Michael Hahsler. A
model-based frequency constraint for mining associations from
transaction data. Data Mining and Knowledge Discovery, 13(2):137-166,
September 2006." In addition an extension for NB-precise rules is
implemented.
Add-on for arules to handle and mine frequent sequences.
Provides interfaces to the C++ implementation of cSPADE by
Mohammed J. Zaki.
Various visualization techniques for association rules and itemsets. The packages also includes several interactive visualizations for rule exploration. This package extends package arules.
An interface to the API for arXiv, a repository of
electronic preprints for computer science, mathematics, physics,
quantitative biology, quantitative finance, and statistics.
Contains functions from: Aho, K. (2014) Foundational and Applied Statistics for Biologists using R.  CRC/Taylor and Francis, Boca Raton, FL.
Coerce R object to asciidoc, txt2tags, restructuredText,
org, textile or pandoc syntax.  Package comes with a set of
drivers for Sweave.
Uses nearest neighbor autocovariates with SCRDA, provides cross-validation functions for the delete-d method, a simulation method is provided for generating synthetic microarray data, a consistent and simple interface is provided for functions from other R packages for SCRDA, PAM, SVM, DLDA, kNN.
Package runs simulations for adaptive seamless designs with and without early outcomes
for treatment selection and subpopulation type designs.
Functions for (A)ccurate and (S)peedy linkage map construction, manipulation and diagnosis of Doubled Haploid, Backcross and Recombinant Inbred R/qtl objects. This includes extremely fast linkage map clustering and optimal marker ordering using MSTmap (see Wu et al.,2008).
A collection of functions for computing centrographic
statistics (e.g., standard distance, standard deviation
ellipse, standard deviation box) for observations taken at
point locations. Separate plotting functions have been
developed for each measure. Users interested in writing results
to ESRI shapefiles can do so by using results from aspace
functions as inputs to the convert.to.shapefile and
write.shapefile functions in the shapefiles library. The aspace
library was originally conceived to aid in the analysis of
spatial patterns of travel behaviour (see Buliung and Remmel,
2008). Major changes in the current version include (1) removal
of dependencies on several external libraries (e.g., gpclib,
maptools, sp), (2) the separation of plotting and estimation
capabilities, (3) reduction in the number of functions, and (4)
expansion of analytical capabilities with additional functions
for descriptive analysis and visualization (e.g., standard
deviation box, centre of minimum distance, central feature).
This package consists of two main functions: The first function is corAspect() performs optimal scaling by maximizing an aspect (i.e. a target function such as the sum of eigenvalues, sum of squared correlations, squared multiple correlations, etc.) of the corresponding correlation matrix. The second function is lineals() performs optimal scaling by minimization a non-correlational aspect based on pairwise correlations and correlation ratios. The resulting correlation matrix and category scores can be used for further multivariate methods such as SEM. A platform including related PsychoR packages is provided on R-Forge.
assertive contains lots of is_* functions to check the
state of your variables, and assert_* functions to throw errors
if they aren't in the right form.
assertthat is an extension to stopifnot() that makes it
easy to declare the pre and post conditions that you code should
satisfy, while also producing friendly error messages so that your
users know what they've done wrong.
Calculates the optimal price of assets (such as
airline flight seats, hotel room bookings) whose value
becomes zero after a fixed ``expiry date''.  Assumes
potential customers arrive (possibly in groups) according
to a known inhomogeneous Poisson process.  Also assumes a
known time-varying elasticity of demand (price sensitivity)
function.  Uses elementary techniques based on ordinary
differential equations.  Uses the package deSolve to effect
the solution of these differential equations.
R package with statistical tests and methods for genetic
association studies with emphasis on rare variants and binary (dichotomous)
traits
functions and datasets for Aster modeling (forest graph
exponential family conditional or unconditional canonical statistic models
for life history analysis)
Aster models are exponential family graphical models that
combine aspects of generalized linear models and survival
analysis.
The astro package provides a series of functions, tools and routines in everyday use within astronomy. Broadly speaking, one may group these functions into 7 main areas, namely: cosmology, FITS file manipulation, the Sersic function, plotting, data manipulation, statistics and general convenience functions and scripting tools.
Astrochonologic testing, astronomical time scale development, time series analysis
A collection of 19 datasets from contemporary astronomical research.  They are described the textbook `Modern Statistical Methods for Astronomy with R Applications' by Eric D. Feigelson and G. Jogesh Babu (Cambridge University Press, 2012, Appendix C) or on the website of Penn State's Center for Astrostatistics (http://astrostatistics.psu.edu/datasets).  These datasets can be used to exercise methodology involving: density estimation; heteroscedastic measurement errors; contingency tables; two-sample hypothesis tests; spatial point processes; nonlinear regression; mixture models; censoring and truncation; multivariate analysis; classification and clustering; inhomogeneous Poisson processes; periodic and stochastic time series analysis.
Miscellaneous astronomy functions, utilities, and data.
Several dozen low-level utilities and codes from the Interactive Data Language (IDL) Astronomy Users Library (http://idlastro.gsfc.nasa.gov) are implemented in R.  They treat: time, coordinate and proper motion transformations; terrestrial precession and nutation, atmospheric refraction and aberration, barycentric corrections, and related effects; utilities for astrometry, photometry, and spectroscopy; and utilities for planetary, stellar, Galactic, and extragalactic science.
Data sets and scripts for Time Series Analysis and Its Applications: With R Examples by Shumway and Stoffer, 3rd edition
A set of routines written in the S language
that calculate power and related quantities utilizing asymptotic
likelihood ratio methods.
A collection of statistical simulation and computation tools with a GTK GUI, to help teach statistical concepts and compute probabilities. Two domains are covered: I. Understanding (Central-Limit Theorem and the Normal Distribution, Distribution of a sample mean, Distribution of a sample variance, Probability calculator for common distributions), and II. Elementary Bayesian Statistics (bayesian inference on proportions, contingency tables, means and variances, with informative and noninformative priors).
Uses adaptive diagnostics to tune and run a random walk Metropolis MCMC algorithm, to converge to a specified target distribution and estimate means of functionals.
This package provides functions for smart sampling and sensitivity analysis for metrology applications, including computationally expensive problems.
Calculates acoustic traveltimes and ray paths in 1-D,
linear atmospheres.  Later versions will support arbitrary 1-D
atmospheric models, such as radiosonde measurements and
standard reference atmospheres.
This package includes functions to compute the area under the curve of selected measures: The area under the sensitivity curve (AUSEC), the area under the specificity curve (AUSPC), the area under the accuracy curve (AUACC), and the area under the receiver operating characteristic curve (AUROC). The curves can also be visualized. Support for partial areas is provided.
Variable selection using Random Forest based on optimizing
the area-under-the ROC curve (AUC) of the Random Forest.
Interfaces to audio devices (mainly sample-based) from R to allow recording and playback of audio. Built-in devices include Windows MM, Mac OS X AudioUnits and PortAudio (the last one is very experimental).
Creates audio representations of common plots in R
Two Bayesian methods for Accounting Populations
Classifies a data.frame such that row deletions and
additions are tracked.  A mechanism exists to give formal names to the row
subsets that are coming or going.  These names are used to populate a
directed graph giving an account of all the transactions contributing to
the state of the data.frame.  The generic as.audited() has a
method for keyed data.frames that creates an audited data.frame.
Methods exist that track row count changes for the generics: Ops, !, ^, |,
[, subset, head, tail, unique, cast, melt, aggregate, and merge.
audit() extracts the transaction table from the audited object, while
write.audit() and read.audit() control exchange with the
file system. An audit method for as.igraph() creates a graph object that
can be displayed with the corresponding plot method.  Use options(audit= )
to provide an extra level of classification. Use options(artifact=TRUE)
and as.xlsx() to save dropped records to file.
The package implements the sparse autoencoder in R environment, following the notes of Andrew Ng (http://www.stanford.edu/class/archive/cs/cs294a/cs294a.1104/sparseAutoencoder.pdf). The features learned by the hidden layer of the autoencoder (through unsupervised learning of unlabeled data) can be used in constructing deep belief neural networks.
This package performs an automatic interpolation by automatically estimating the variogram and then calling gstat.
An extension of the pls package with automated backward selection of predictors
Automated Multi-path General-to-Specific (GETS) model
selection of the mean and log-volatility specifications of a
log-ARCH-X model
Rank overlap is a metric to measure the relation between two sets of ranked items. This package is a convenience to calculate average rank overlap across different settings.
The package contains R-functions implementing the
Propagation-Separation Approach to adaptive smoothing as
described in J. Polzehl and V. Spokoiny (2006),
Propagation-Separation Approach for Local Likelihood Estimation,
Prob. Theory and Rel. Fields, 135(3):335--362.
and J. Polzehl and V. Spokoiny (2004) Spatially adaptive
regression estimation: Propagation-separation approach,
WIAS-Preprint 998.
The package defines method extract and provides openMP support needed in several packages
R package for Amazon Web Services EC2/S3
A generalization of Fisher's exact test that allows for
structural zeros.
This package fits the Bayesian two-Zone Models.
This package contains b6e6rl algorithm, adaptive
differential evolution for global optimization.
Implements babel routines for identifying unusual ribosome protected fragment counts given mRNA counts
The package contains two variants of Bayesian Bootstrap
Predictive Mean Matching to multiply impute missing data. The
first variant is a variable-by-variable imputation combining
sequential regression and Predictive Mean Matching (PMM) that
has been extended for unordered categorical data. The Bayesian
Bootstrap allows for generating approximately proper multiple
imputations. The second variant is also based on PMM, but the
focus is on imputing several variables at the same time. The
suggestion is to use this variant, if the missing-data pattern
resembles a data fusion situation, or any other
missing-by-design pattern, where several variables have
identical missing-data patterns. Both variants can be run as
'single imputation' versions, in case the analysis objective is
of a purely descriptive nature.
US baby names provided by the SSA. This package contains all
names used for at least 5 children of either sex.
The BACCO bundle of packages is replaced by the BACCO
package, which provides a vignette that illustrates the constituent
packages (emulator, approximator, calibrator) in use.
The backtest package provides facilities for exploring
portfolio-based conjectures about financial instruments
(stocks, bonds, swaps, options, et cetera).
The BACprior package provides an approximate sensitivity
analysis of the Bayesian Adjustment for Confounding (BAC)
algorithm (Wang et al., 2012) with regards to hyperparameter
omega. The method is based on Lefebvre, Atherton and Talbot
(2013).
Implements sample size calculations following the approach
described in "Bayesian Average Error Based Approach to
Hypothesis Testing and Sample Size Determination."
bagRboostR is a set of ensemble classifiers for multinomial
classification. The bagging function is the implementation of Breiman's
ensemble as described by Opitz & Maclin (1999). The boosting function is
the implementation of Stagewise Additive Modeling using a Multi-class
Exponential loss function (SAMME) created by Zhu et al (2006). Both bagging
and SAMME implementations use randomForest as the weak classifier and
expect a character outcome variable.  Each ensemble classifier returns a
character vector of predictions for the test set.
This package includes methods that can select balanced and spatially balanced probability samples in multi-dimensional spaces with any prescribed inclusion probabilities. It contains fast (C++ via Rcpp) implementations of the included sampling methods. The local pivotal method and spatially correlated Poisson sampling (for spatially balanced sampling) are included. Also the cube method (for balanced sampling) and the local cube method (for doubly balanced sampling) are included.
Books is "Bayesian Methods: A Social and Behavioral
Sciences Approach, Second Edition published by CRC Press, 2007"
Implementation of the methods described in the paper Li, Dahl, Vannucci, Joo, and Tsai (2014), Bayesian Model of Protein Primary Sequence for Secondary Structure Prediction, PLOS ONE, revise and resubmit.
Bayesian meta-analysis of diagnostic test data based on a
scale mixtures bivariate random-effects model. Summary
statistics are based on pooled and predictive sensitivities and
specificities. Model checking is used for diagnostic of
random-effects' misfit and predictive data is used to assess
models departures from observed data.
BAMMtools provides functions for analyzing and visualizing complex macroevolutionary dynamics on phylogenetic trees. It is a companion package to the command line program BAMM (Bayesian Analysis of Macroevolutionary Mixtures) and is entirely oriented towards the analysis, interpretation, and visualization of evolutionary rates. Functionality includes visualization of rate shifts on phylogenies, estimating evolutionary rates through time, comparing posterior distributions of evolutionary rates across clades, comparing diversification models using Bayes factors, and more.
Code and data to compare several change-point detection
models on DNA copy number profiles.
A set of functions for doing analysis of A/B split test data and web metrics in general.
BANOVA covers several Bayesian Analysis of Variance (BANOVA) models used in analysis of experimental designs in which both within- and between- subjects factors are manipulated. They can be applied to data that are common in the behavioral sciences and related fields. The package includes: Hierarchical Bayes ANOVA models with normal response, t response, Binomial(Bernoulli) response, Poisson response, ordered multinomial response and multinomial response variables. All models accommodate unobserved heterogeneity by including a normal distribution of the parameters across individuals. Outputs of the package include tables of sums of squares, effect sizes and p-values, and tables of means, which are easily interpretable for behavioral researchers. BANOVA uses JAGS as the computational platform.
This package includes the function \code{barcode()}, which
produces a histogram-like plot of a distribution that shows
granularity in the data.
Impelementation of BARK: Bayesian Additive Regression
Kernels with Feature Selection, Zhi Ouyang (2008), Ph.D.
thesis, chapter 3.
An advanced implementation of Bayesian Additive Regression Trees with expanded features for data analysis and visualization
Package for Bayesian Model Averaging in linear models
using stochastic or deterministic sampling without replacement
from posterior distributions.  Prior distributions on
coefficients are from Zellner's g-prior or mixtures of g-priors
corresponding to the Zellner-Siow Cauchy Priors or the Liang et
al hyper-g priors (JASA 2008). Other model selection criterian
include AIC and BIC. Sampling probabilities may be updated
based on the sampled models.  Allows uniform or beta-binomial
prior distributions on models.
This package provides tools for handling base64 encoding. It is more flexible than the orphaned base64 package.
Collection of baseline correction algorithms, along with a framework and a GUI for optimising baseline algorithm parameters.
BASIX provides some efficient C/C++ implementations to speed up calculations in R.
This package estimates survival and mortality with covariates from capture-recapture/recovery data in a Bayesian framework when many individuals are of unknown age. It includes tools for data checking, model diagnostics and outputs such as life-tables and plots.
This package introduces algorithms to assess alpha and beta
diversity in all their dimensions (taxon, phylogenetic and functional
diversity), whether communities are completely sampled or not. It allows
performing a number of analyses based on either species identities or
ultrametric trees depicting species relationships.
This package provides some utility functions (e.g HTML
report maker).
Functions to allow you to easily pass command-line
arguments into R, and functions to aid in submitting your R
code in parallel on a cluster and joining the results afterward
(e.g. multiple parameter values for simulations running in
parallel, splitting up a permutation test in parallel, etc.).
See `parseCommandArgs(...)' for the main example of how to use
this package.
Extends the BatchJobs package to run statistical experiments on
batch computing clusters. For further details see the project web page.
Provides Map, Reduce and Filter variants to generate jobs on batch
computing systems like PBS/Torque, LSF, SLURM and Sun Grid Engine.
Multicore and SSH systems are also supported. For further details see the
project web page.
batchmeans provides consistent batch means estimation of
Monte Carlo standard errors.
This package implements Bayesian bridge regression.
BayesComm fits Bayesian multivariate binary (probit)
regression models for analysis of ecological communities.
A set of functions to allow analysis of count data (such
as faecal egg count data) using Bayesian MCMC methods.  Returns
information on the possible values for mean count, coefficient
of variation and zero inflation (true prevalence) present in
the data.  A complete faecal egg count reduction test (FECRT)
model is implemented, which returns inference on the true
efficacy of the drug from the pre and post treatment data
provided, using non-parametric bootstrapping as well as using
Bayesian MCMC.  Functions to perform power analyses for faecal
egg counts (including FECRT) are also provided.  Requires Just
Another Gibbs Sampler (JAGS) for most functions (except power
analysis calculations), see:
http://www-fis.iarc.fr/~martyn/software/jags/.
Propose a robust parametric  model of censored linear regression models based on SMN distributions from a Bayesian perspective.
Functions for Bayesian Data Analysis, with datasets from
the book "Bayesian data Analysis (second edition)" by Gelman,
Carlin, Stern and Rubin. Not all datasets yet, hopefully
completed soon.
Provides Graphical user interface for the packages bayesTFR, bayesLife and bayesPop
The BayesFactor package is a suite of functions for computing various Bayes factors for simple designs, including contingency tables, one- and two-sample designs, one-way designs, general ANOVA designs, and linear regression.
This package provides the bayesGARCH function which performs the
Bayesian estimation of the GARCH(1,1) model with Student's t innovations.
This package contains functions to help users implement the Generalized Direct Sampling algorithm for Bayesian hierarchical models (Braun and Damien, 2013). GDS is useful for sampling from posterior distributions for which there is a large number of conditionally independent heterogeneous units.
This package allows obtain the statistical inference based on Bayesian approach for regression models under the assumption that independent additive errors follow normal, Student-t, slash, contaminated normal, Laplace or symmetric hyperbolic distribution, where both location and dispersion parameters of the response variable distribution include nonparametric additive components described by B-splines.
Bayesian Latent Class Analysis using several different
methods.
Making probabilistic projections of life expectancy for all countries of the world, using a Bayesian hierarchical model.
The BayesLogit package does posterior simulation for binomial and
multinomial logistic regression using the Polya-Gamma latent variable
technique. This method is fully automatic, exact, and fast.  A routine to
efficiently sample from the Polya-Gamma class of distributions is included.
bayesm covers many important models used in marketing and
micro-econometrics applications.  The package includes: Bayes
Regression (univariate or multivariate dep var), Bayes
Seemingly Unrelated Regression (SUR), Binary and Ordinal
Probit, Multinomial Logit (MNL) and Multinomial Probit (MNP),
Multivariate Probit, Negative Binomial (Poisson) Regression,
Multivariate Mixtures of Normals (including clustering),
Dirichlet Process Prior Density Estimation with normal base,
Hierarchical Linear Models with normal prior and covariates,
Hierarchical Linear Models with a mixture of normals prior and
covariates, Hierarchical Multinomial Logits with a mixture of
normals prior and covariates, Hierarchical Multinomial Logits
with a Dirichlet Process prior and covariates, Hierarchical
Negative Binomial Regression Models, Bayesian analysis of
choice-based conjoint data, Bayesian treatment of linear
instrumental variables models, and Analysis of Multivariate
Ordinal survey data with scale usage heterogeneity (as in Rossi
et al, JASA (01)).  For further reference, consult our book,
Bayesian Statistics and Marketing by Rossi, Allenby and
McCulloch.
This package provides various Markov Chain Monte Carlo
(MCMC) sampler for model-based clustering of discrete-valued
time series obtained by observing a categorical variable with
several states (in a Bayesian approach).  In order to analyze
group membership, we provide also an extension to the
approaches by formulating a probabilistic model for the latent
group indicators within the Bayesian classification rule using
a multinomial logit model.
Default Bayesian hypothesis tests for correlation, partial correlation, and mediation
Bayesian mixture models of univariate Gaussian distributions using JAGS
A Bayesian testing procedure for noninferiority trials
with binary endpoints. The prior is constructed based on
Bernstein polynomials with options for both informative and
non-informative prior. The critical value of the test statistic
(Bayes factor) is determined by minimizing total weighted error
(TWE) criteria
Fits the Bayesian penalized credible region variable and confounder selection methods.
The package allows to generate population projections for all countries of the world using several probabilistic components, such as total fertility rate (TFR) and life expectancy.
This program implements a hierarchical Bayesian analysis
of count data, such as preference experiments. It provides
population-level and individual-level preference parameter
estimates obtained via MCMC. It also allows for model
comparison using Deviance Information Criterion.
Bayesian quantile regression using the asymmetric Laplace distribution, both continuous as well as binary dependent variables are supported. The package consists of implementations of the methods of Yu & Moyeed (2001), Benoit & Van den Poel (2012) and Al-Hamzawi, Yu & Benoit (2012). To speed up the calculations, the Markov Chain Monte Carlo core of all algorithms is programmed in Fortran and called from R.
bayess contains a collection of functions that allows the
reenactment of the R programs used in the book "Bayesian
Essentials with R" (revision of "Bayesian Core") without
further programming. R code being available as well, they can
be modified by the user to conduct one's own simulations.
This package provides a variety of functions to deal with several specific small area area-
level models in Bayesian context. Models provided range from the basic Fay-Herriot model to
its improvement such as You-Chapman models, unmatched models, spatial models and so on.
Different types of priors for specific parameters could be chosen to obtain MCMC posterior
draws. The main sampling function is written in C with GSL lab so as to facilitate the
computation. Model internal checking and model comparison criteria are also involved.
The BayesSingleSub package is a suite of functions for computing various Bayes factors for interrupted time-series, based on the models described in de Vries and Morey (2013).
Making probabilistic projections of total fertility rate for all countries of the world, using a Bayesian hierarchical model.
This package fits a linear mixed model for ordinal
categorical responses using Bayesian inference via Monte Carlo
Markov Chains. Default is Nandran & Chen algorithm using
Gaussian link function and saving just the summaries of the
chains.  Among the options, package allow for two other options
of algorithms, for using Student's "t" link function and for
saving the full chains.
BayesValidate implements the software validation method
described in the paper "Validation of Software for Bayesian
Models using Posterior Quantiles" (Cook, Gelman, and Rubin,
2005).  It inputs a function to perform Bayesian inference as
well as functions to generate data from the Bayesian model
being fit, and repeatedly generates and analyzes data to check
that the Bayesian inference program works properly.
Within the context of the linear regression model, this package provides tools for the analysis of the variable selection problem from a Bayesian perspective. The default implementation takes advantage of a closed-form expression for the posterior probabilities that the prior proposed in Bayarri, Berger, Forte and Garcia-Donato (2012) produces. Alternatively, other priors, like Zellner (1986) g-prior, Zellner-Siow (1980,1984) or Liang, Paulo, Molina, Clyde and Berger (2008) can be used. BayesVarSel allows the calculations to be performed either exactly (sequential or parallel computation) or heuristically, using a Gibbs sampling algorithm studied in Garcia-Donato and Martinez-Beneito (2013).
This package provides functionality for exploring and visualising estimation results
obtained with the software package BayesX for structured additive regression. It also provides
functions that allow to read, write and manipulate map objects that are required in spatial analyses
performed with BayesX, a free software for estimating structured additive regression models
(http://www.bayesx.org).
BayesX performs Bayesian inference in structured additive regression (STAR) models.
The R package BayesXsrc provides the BayesX command line tool for easy installation.
A convenient R interface is provided in package R2BayesX.
The package BayHap performs simultaneous estimation of
uncertain haplotype frequencies and association with haplotypes
based on generalized linear models for quantitative, binary and
survival traits. Bayesian statistics and Markov Chain Monte
Carlo techniques are the theoretical framework for the methods
of estimation included in this package. Prior values for model
parameters can be included by the user. Convergence diagnostics
and statistical and graphical analysis of the sampling output
can be also carried out.
A suite of R functions for Bayesian estimation of smooth
hazard rates via Compound Poisson Process (CPP) and Bayesian
Penalized Spline (BPS) priors.
Functions and data used for Baylor University Educational
Psychology Quantitative Courses
Tools for fitting and simulating multi-optima Ornstein-Uhlenbeck
models to phylogenetic comparative data using Bayesian reversible-jump
methods.
This R package is the software implementation of the
algorithm BaySIC, a Bayesian approach toward analysis of
significantly mutated genes in cancer data.
The manuscript introduces the BAYSTAR package, which
provides the functionality for Bayesian estimation in
autoregressive threshold models.
Barzilai-Borwein spectral methods for solving nonlinear
system of equations, and for optimizing nonlinear objective
functions subject to simple constraints. A tutorial style
introduction to this package is available in a vignette on the
CRAN download page or, when the package is loaded in an R
session, with vignette("BB").
Estimating optimal bandwidths for the regression mean function approximated by the functional Nadaraya-Watson estimator and the error density approximated by a kernel density of residuals simultaneously in a scalar-on-function regression. As a by-product of Markov chain Monte Carlo, the optimal choice of semi-metric is selected based on largest marginal likelihood.
Bayesian bandwidth estimation for Nadaraya-Watson type
multivariate kernel regression with Gaussian error density
In this package we implemented a Bayesian-statistics approach for
subtraction of incoherent scattering from neutron total-scattering data.
In this approach, the estimated background signal associated with
incoherent scattering maximizes the posterior probability, which combines
the likelihood of this signal in reciprocal and real spaces with the prior
that favors smooth lines.
Miscellaneous helper functions for and from B. Bischl and
some other guys at TU Dortmund, mainly for package development.
Methods and functions for fitting maximum likelihood models in R.
This package modifies and extends the mle classes in the stats4 package.
The model provides an empirical estimate of a movement
path using discrete location data obtained at relatively short
time intervals.
This package provides an R implementation of
Biogeography-Based Optimization (BBO), originally invented by
Prof. Dan Simon, Cleveland State University, Ohio. This method
is an application of the concept of biogeography, a study of
the geographical distribution of biological organisms, to
optimization problems. More information about this method can
be found here: http://academic.csuohio.edu/simond/bbo/.
Model fitting of flexible behavioural recapture models based on conditional probability reparameterization and meaningful partial capture history quantification also referred to as meaningful behavioural covariate
This package implements the BC3NET algorithm for gene
regulatory network inference (de Matos Simoes and Frank
Emmert-Streib, Bagging Statistical Network Inference from
Large-Scale Gene Expression Data, PLoS ONE 7(3): e33624)
Underlying support functions for RcmdrPlugin.BCA and a
companion to the book Customer and Business Analytics: Applied
Data Mining for Business Decision Making Using R by Daniel S.
Putler and Robert E. Krider
This package is used to predict the discrete class labels
based on a selected subset of high-dimensional features, such
as expression levels of genes. The data are modeled with a
hierarchical Bayesian models using heavy-tailed t distributions
as priors. When a large number of features are available, one
may like to select only a subset of features to use, typically
those features strongly correlated with the response in
training cases. Such a feature selection procedure is however
invalid since the relationship between the response and the
features has be exaggerated by feature selection. This package
provides a way to avoid this bias and yield better-calibrated
predictions for future cases when one uses F-statistic to
select features.
Tools for Dating Business Cycles using Harding-Pagan (Quarterly Bry-Boschan) method and various plotting features.
This package provides Diagnostic tools based on two way
anova and median polish residual plots for Bicluster output
obtained from packages; "biclust" by Kaiser et al.(2008),"isa2"
by Csardi et al. (2010) and "fabia" by Hochreiter et al.
(2010). Moreover, It provides visualization tools for bicluster
output and corresponding non bicluster rows or columns
outcomes.  It has also extended the idea of Kaiser et al.(2008)
which is, extracting bicluster output in a text format, by
adding by adding two bicluster methods from fabia and isa2 r
packages.
Function to estimates taxonomic compositions from biomarker data, using a Bayesian approach.
Produces an economic evaluation of a Bayesian model in the form of MCMC simulations. Given suitable variables of cost and effectiveness / utility for two or more interventions, BCEA computes the most cost-effective alternative and produces graphical summaries and probabilistic sensitivity analysis
Implements a full Bayesian cost-effectiveness analysis in the case where the cost variable is characterised by structural zeros. The package implements the Gamma, log-Normal and Normal models for the cost variable and the Gamma, Beta, Bernoulli and Normal models for the measure of clinical effectiveness.
This package enables quick calibration of radiocarbon dates under various calibration curves (including user generated ones); Age-depth modelling as per the algorithm of Haslett and Parnell (2008); Relative sea level rate estimation incorporating time uncertainty in polynomial regression models; and non-parametric phase modelling via Gaussian mixtures as a means to determine the activity of a site (and as an alternative to the Oxcal function SUM).
This package takes pollen/time data from lake cores and produces a Bayesian posterior distribution of palaeoclimate from that location after fitting a non-linear non-Gaussian state-space model.
The package builds a dendrogram with log posterior as a
natural distance defined by the model. It is also capable to
computing Bayesian discrimination probabilities equivalent to
the implemented Bayesian clustering. Spike-and-Slab models are
adopted in a way to be able to produce an importance measure
for clustering and discriminant variables. The method works
properly for data with small sample size and high dimensions.
The model parameter estimation maybe difficult, depending on
data structure and the chosen distribution family.
This package provides a set of analytical tools that helps searching for sites in an alignment, which might be associated with the label of the organism (e.g. changes in columns that can be associated with the pathogenicity character of an organism)
This package provides an implementation of an
approximation to the Barry and Hartigan (1993) product
partition model for the normal errors change point problem
using Markov Chain Monte Carlo.  It also extends the
methodology to independent multivariate series with an assumed
common change point structure.
The Behavioral Change Point Analysis (BCPA) is a method of
identifying hidden shifts in the underlying parameters of a time series,
developed specifically to be applied to animal movement data which is
irregularly sampled.  The original paper on which it is based is: E.
Gurarie, R. Andrews and K. Laidre A novel method for identifying
behavioural changes in animal movement data (2009) Ecology Letters 12:5
395-408.
A Bayesian approach to detect mean shifts in AR(1) time series while accommodating metadata (if available). In addition, a linear trend component is allowed.
This package implements a wide variety of one and two-parameter Bayesian CRM designs. The program can run interactively, allowing the user to enter outcomes after each cohort has been recruited, or via simulation to assess operating characteristics.
This package implements methods for choosing the rank of
an SVD approximation via cross validation.  It provides both
Gabriel-style "block" holdouts and Wold-style "speckled"
holdouts.  Also included is an implementation of the SVDImpute
algorithm.  For more information about Bi-cross-validation, see
Owen & Perry's 2009 AOAS article (at
http://arxiv.org/abs/0908.2062) and Perry's 2009 PhD thesis (at
http://arxiv.org/abs/0909.3052).
This package collects algorithms/functions developed for
density estimation based on weighted or grouped (pre-binned) data.
This package provides a Bayesian methodology for structure learning in undirected graphical models. Our Bayesian methodology is based on birth-death Markov chain Monte Carlo (BDMCMC) algorithm which is the main function with the name 'bdgraph'. The main target of this package is high-dimensional data analysis wherein usually p >> n. The computation is memory-optimized using the sparse matrix output.
This package contains a function that will classify DNA
barcodes as well as a few test and reference data sets.
Computation of asymptotic confidence intervals for negative and positive predictive values in binary diagnostic tests in case-control studies. Experimental design for hypothesis tests on predictive values.
This is a special case of sparse matrices, used by coxme
Biodiversity data visualizations using R would be helpful to
understand completeness of biodiversity inventory, extent of geographical,
taxonomic and temporal coverage, gaps and biases in data.
The package bdynsys for panel/longitudinal data combines methods to model
changes in up to four indicators over times as a function of the indicators
themselves and up to three predictors using ordinary differential equations
(ODEs) with polynomial terms that allow to model complex and nonlinear
effects. A Bayesian model selection approach is implemented. The package
provides also visualisation tools to plot phase portraits of the dynamic
system, showing the complex co-evolution of two indicators over time with the
possibility to highlight trajectories for specified entities (e.g. countries,
individuals). Furthermore the visualisation tools allow for making
predictions of the trajectories of specified entities with respect to the
indicators.
This package contains functions to fit the filtering model
of Forcheh et al., (2012) which is used to derive the
intra-cluster correlation (ICC). Model fitting is done using
the modified version of the ``MLM.beadarray" function of Kim
and Lin (2011).
Imports bead-summary data from Illumina scanner.
Pre-processes using a suite of optional normalizations and
transformations. Clusters and automatically calls genotypes,
critically able to handle markers in duplicated regions of the
genome (multisite variants; MSVs). Interactive clustering if
needed. MSVs with variation in both paralogs may be resolved
and mapped to their respective chromosomes. Quality control
including pedigree checking and visual assessment of clusters.
Too large data-sets are handled by working on smaller subsets
of the data in sequence.
Plots univariate comparison graphs, an alternative to
boxplot/stripchart/violin plot.
Computing age specific nest survival rates based on a Bayesian hierarchical model. This package can handle unknown nest age, irregular visiting schedule and nest-specific covariates.
An average bioequivalence (ABE) and bioavailability data
analysis tool including sample size estimation, noncompartmental
analysis (NCA), ANOVA (lm) for a standard RT/TR 2x2x2 crossover
design or a parallel study. And linear mixed effect model (lme
of nlme) for a 2-treatment, 2-sequence with 2 periods or more
(i.e. 2x2x3/2x2x4) replicate crossover design.
This package provides functions that allow users to quantify the relative
contributions of geographic and ecological distances to empirical patterns of genetic
differentiation on a landscape.  Specifically, the package contains a function that
calls a custom Markov chain Monte Carlo (MCMC) algorithm, which is used to estimate
the parameters of the inference model, as well as functions for performing MCMC
diagnosis and assessing model adequacy.
This package contains one function, beep(), with one purpose: To
make it easy to play notification sounds on whatever platform you are on.
It is intended to be useful, for example, if you are running a long analysis
in the background and want to know when it is ready.
The bee swarm plot is a one-dimensional scatter plot like "stripchart", but with closely-packed, non-overlapping points.
Full implementation of the 28 distributions introduced as
benchmarks for nonparametric density estimation by Berlinet and
Devroye (1994). Includes densities, cdfs, quantile functions
and generators for samples as well as additional information on
features of the densities. Also contains the 4 histogram
densities used in Rozenholc/Mildenberger/Gather (2010).
The benchmark package provides a toolbox for setup, execution
and analysis of benchmark experiments. Main focus is the analysis of
data accumulating during the execution -- one primary objective is the
statistical correct computation of the candidate algorithms' order.
The package contains methods to support frontier analysis.
It covers Data Envelopment Analysis (DEA). DEA is supported
under different technology assumptions (fdh, vrs, drs, crs,
irs, add/frh, and fdh+), and using different efficiency
measures (input based, output based, hyperbolic graph,
additive, super, and directional efficiency). Peers and slacks
are available, partial price information can be included, and
optimal cost, revenue and profit can be calculated. Evaluation
of mergers is also supported.  Methods for graphing the
technology sets are also included. The package also support
comparative methods based on Stochastic Frontier Analyses
(SFA). In general, the methods can be used to solve not only
standard models, but also many other model variants. The
package complements the book, Bogetoft and Otto, Benchmarking
with DEA, SFA, and R, Springer-Verlag, 2011, but can of course
also be used as a stand-alone package.
The Benford Analysis package provides tools that make it easier to
validate data using Benford's Law.
This package contains seven specialized statistical tests and support functions
for determining if numerical data could conform to Benford's law.
This package contains two main interfaces for fitting and
diagnosing bent-cable regressions for autoregressive
time-series data or independent data (time series or
otherwise). The interfaces are 'bentcable.ar()' and
'bentcable.dev.plot()'. Some components in the package can also
be used as stand-alone functions. The bent cable
(linear-quadratic-linear) generalizes the broken stick
(linear-linear), which is also handled by this package. Version
0.2 corrects a glitch in the computation of confidence
intervals for the CTP. References updated from Versions 0.2.1
and 0.2.2 appear in Version 0.2.3.
Benthic Ecosystem Quality Index 2. This package facilitates the analysis of benthos data. It estimates several quality indices like the total abundance of species, species richness, Shannon index, AZTI Marine Biotic Index (AMBI), and the BEQI-2 index. The package includes two additional optional features that enhance data preprocessing: (1) genus to species conversion, i.e.,taxa counts at the taxonomic genus level can optionally be converted to the species level and (2) pooling: small samples are combined to bigger samples with a standardized size to (a) meet the data requirements of the AMBI, (b) generate comparable species richness values and (c) give a higher benthos signal to noise ratio.
The functions in this package remove batch effects from
microarrary normalized data. The expression levels of the genes
are represented in a matrix where rows correspond to
independent samples and columns to genes (variables). The
batches are represented by categorical variables (objects of
class factor). When further covariates of interest are
available they can be used to remove efficiently the batch
effects and adjust the data.
Set of tools to analyse Bayesian exponential random graph models
This package provides easy access to http://daten.berlin.de
It allows you to search through the data catalogue and to download
the data directly from within R.
collection of functions to: zoom in X11 graphics and ESRI shapefile plots, use the unit hydrograph in a linear storage cascade, compare several extreme-value-distributions at return periods, draw horizontal histograms and scatterplots colored by 3rd dimension, convert lists to data.frames, fit multiple functions (power, reciprocal, exponential, logarithmic, polynomial, rational) by regression.
Bessel Function Computations for complex and real numbers;
notably interfacing TOMS 644; approximations for large arguments,
experiments, etc.
An alternative to t-tests, producing posterior estimates
for group means and standard deviations and their differences and
effect sizes.
Best subset glm using AIC, BIC, EBIC, BICq or Cross-Validation. For the normal case, the 'leaps' is used. Otherwise, a slower exhaustive search. The 'xtable' package is needed for vignette 'SimExperimentBICq.Rnw' accompanying this package.
To detecting rare variants for quantitative traits using
nuclear families, the linear combination methods are proposed
using the estimated regression coefficients from the multiple
regression and regularized regression as the weights.
betapart allows computing pair-wise dissimilarities (distance matrices) and multiple-site dissimilarities, separating the turnover and nestedness-resultant components of taxonomic (incidence and abundance based), functional and phylogenetic beta diversity.
Permutational method to incorporate taxonomic uncertainty
and some functions to assess its effects on parameters of some
widely used multivariate methods in ecology
Beta regression for modeling beta-distributed dependent variables, e.g., rates and proportions.
In addition to maximum likelihood regression (for both mean and precision of a beta-distributed
response), bias-corrected and bias-reduced estimation as well as finite mixture models and
recursive partitioning for beta regressions are provided.
Simulation, estimation and forecasting of first-order Beta-Skew-t-EGARCH models with leverage (one-component, two-component, skewed versions).
The sample size according to the Bethel's procedure.
The bezier package is a toolkit for working with Bezier curves and splines. The package provides functions for point generation, arc length estimation, degree elevation and curve fitting.
This package provides model fitting for
several Bayesian factor models including Gaussian,
ordinal probit, mixed and semiparametric Gaussian
copula factor models under a range of priors.
BFAST integrates the decomposition of time series into trend,
seasonal, and remainder components with methods for detecting
and characterizing abrupt changes within the trend and seasonal
components. BFAST can be used to analyze different types of
satellite image time series and can be applied to other disciplines
dealing with seasonal or non-seasonal time series, such as hydrology,
climatology, and econometrics. The algorithm can be extended to
label detected changes with information on the parameters of the
fitted piecewise linear models. BFAST monitoring functionality is added
based on a paper that has been submitted to Remote Sensing of Environment.
BFAST monitor provides functionality to detect disturbance in near real-time based on BFAST-type models.
BFAST approach is flexible approach that handles missing data without interpolation.
Furthermore now different models can be used to fit the time series data and detect structural changes (breaks).
The package implements the Bayesian paradigm for fractional
polynomial models under the assumption of normally distributed error terms.
Routine for fitting regression models for binary rare
events with linear and nonlinear covariate effects when using
the quantile function of the Generalized Extreme Value random
variable.
The bgmm package implements two partially supervised
mixture modeling methods: soft-label and belief-based modeling.
For completeness, we equipped the package also with the
functionality of unsupervised, semi- and fully supervised
mixture modeling.  The package can be applied also to selection
of the best-fitting from a set of models with different
component numbers or constraints on their structures.
For detailed introduction see:
Przemyslaw Biecek, Ewa Szczurek, Martin Vingron, Jerzy
Tiuryn (2012), The R Package bgmm: Mixture Modeling with
Uncertain Knowledge, Journal of Statistical Software.
Computes the hazard rate estimate as described by Nieto-Barajas and Walker (2002) and Nieto-Barajas (2003)
Implement an efficient block Gibbs sampler with incomplete
data from a multinomial distribution taking values from the k
categories 1,2,...,k, where data are assumed to miss at random
and each missing datum belongs to one and only one of m
distinct non-empty proper subsets A1, A2,..., Am of 1,2,...,k
and the k categories are labelled such that only consecutive
A's may overlap.
Boost provides free peer-reviewed portable C++ source
libraries.  A large part of Boost is provided as C++ template code
which is resolved entirely at compile-time without linking.  This
package aims to provide the most useful subset of Boost libraries
for template use among CRAN package. By placing these libraries in
this package, we offer a more efficient distribution system for CRAN
as replication of this code in the sources of other packages is
avoided.
Functions for MLE, MCMC, CIs (originally in Fortran)
Functions and data sets reproducing some examples in Box,
Hunter and Hunter II
Performs BHMSMA of fMRI data (Sanyal & Ferreira, 2012) using wavelet based prior that borrows strength across subjects and returns posterior smoothed versions of the fMRI data and samples from the posterior distribution.
Bias correction of second order of the maximum likelihood
estimators of the parameters of the beta regression model.
Statistical models of biased sampling in the form of
univariate and multivariate noncentral hypergeometric distributions,
including Wallenius' noncentral hypergeometric distribution and
Fisher's noncentral hypergeometric distribution
(also called extended hypergeometric distribution).
See vignette("UrnTheory") for explanation of these distributions.
The main function biclust provides several algorithms to
find biclusters in two-dimensional data: Cheng and Church,
Spectral, Plaid Model, Xmotifs and Bimax. In addition, the
package provides methods for data preprocessing (normalization
and discretisation), visualisation, and validation of bicluster
solutions.
An S3 class with a method for calculates the bidimensional regression between two 2D configurations following the approach by Tobler (1965).
This package makes global and multiple inferences for
given bi- and trifactorial clinical trial designs using
bootstrap methods and a classical approach.
Some Tools for Survey Statistics in Educational Assessment,
Developed by BIFIE.
Bundesinstitut fuer Bildungsforschung,
Entwicklung und Innovation im oesterreichischen Schulwesen;
Federal Institute for Educational Research, Innovation and
Development of the Austrian School System;
Salzburg, Austria.
This package provides arithmetic functions for R matrix and big.matrix objects.
This package extends the bigmemory package with various
analytics.  Functions bigkmeans and binit may also be used with
native R objects.  For tapply-like functions, the bigtabulate
package may also be helpful.  For linear algebra support, see
bigalgebra.  For mutex (locking) support for advanced
shared-memory usage, see synchronicity.
The big data package is a collection of scalable methods
for large-scale data analysis.
bigGP distributes Gaussian process calculations across nodes
in a distributed memory setting, using Rmpi. The class provides high-level
methods for maximum likelihood with normal data, prediction, calculation of
uncertainty (i.e., posterior covariance calculations), and simulation of
realizations. In addition, bigGP provides an API for basic
matrix calculations with distributed covariance matrices, including
Cholesky decomposition, back/forwardsolve, crossproduct, and matrix
multiplication.
Least-angle regression, lasso and stepwise regression for
numeric datasets in which the number of observations is greater
than the number of predictors.  The functions can be used with
the ff library to accomodate datasets that are too large to be
held in memory.
Regression for data too large to fit in memory
Create, store, access, and manipulate massive matrices.
Matrices are allocated to shared memory and may use memory-mapped
files.  Packages biganalytics, bigtabulate, synchronicity, and
bigalgebra provide advanced functionality.
This package provides a shared resource interface for the bigmemory and synchronicity packages.
The bigml package contains bindings for the BigML API.
The package includes methods that provide straightforward
access to basic API functionality, as well as methods that
accommodate idiomatic R datatypes and concepts.
This package adds wrappers to add functionality for big.matrix objects
(see the bigmemory project). This allows fast scalable principle components analysis (PCA),
or singular value decomposition (SVD). There are also functions for transposing, using multicore
'apply' functionality, data importing and for compact display of big.matrix objects. Most functions
also work for standard matrices if RAM is sufficient.
This is an implementation of Leo Breiman's and Adele Cutler's
Random Forest algorithms for classification and regression, with optimizations
for performance and for handling of data sets that are too large to be
processed in memory. Forests can be built in parallel at two levels. First,
trees can be grown in parallel on a single machine using foreach. Second,
multiple forests can be built in parallel on multiple machines, then merged
into one. For large data sets, disk-based big.matrix's may be used for storing
data and intermediate computations, to prevent excessive virtual memory
swapping by the operating system. Currently, only classification forests with
a subset of the functionality in Breiman and Cutler's original code are
implemented. More functionality and regression trees may be added in the
future.
The package fits large-scale (generalized) ridge regression for various distributions of response. The shrinkage parameters (lambdas) can be pre-specified or estimated using an internal update routine (fitting a heteroscedastic effects model, or HEM). It gives possibility to shrink any subset of parameters in the model. It has special computational advantage for the cases when the number of shrinkage parameters exceeds the number of observations. For example, the package is very useful for fitting large-scale omics data, such as high-throughput genotype data (genomics), gene expression data (transcriptomics), metabolomics data, etc.
Fits smoothing spline regression models using scalable algorithms designed for large samples. Five marginal spline types are supported: cubic, different cubic, cubic periodic, cubic thin-plate, and nominal. Parametric predictors are also supported. Response can be Gaussian or non-Gaussian: Binomial, Poisson, Gamma, Inverse Gaussian, or Negative Binomial.
This package extends the bigmemory package with table- and split-like support for big.matrix objects.  The functions may also be used with regular R matrices for improving speed and memory-efficiency.
This package is trying to implement Top Scoring Pair based
methods for classification including LDCA, TSP-tree, TSP-random
forest and TSP gradient boosting algorithm.
bild performs logistic regression for binary longitudinal
data, allowing for serial dependence among observations from a
given individual and a random intercept term. Estimation is via
maximization of the exact likelihood of a suitably defined
model. Missing values and unbalanced data are allowed, with
some restrictions.
A power calculator for Genome-wide association studies
(GWAs) with combined gold (error-free) and silver (erroneous)
phenotyping per McDavid A, Crane PK, Newton KM, Crosslin DR, et
al. (2011)
The "binda" package implements functions for multi-class
discriminant analysis using binary predictors, for corresponding
variable selection, and for dichotomizing continuous data.
Generation of correlated artificial binary data.
Methods for model selection, model averaging, and calculating metrics, such as the Gini, Theil, Mean Log Deviation, etc, on binned income data where the topmost bin is right-censored.  We provide both a non-parametric method, termed the bounded midpoint estimator (BME), which assigns cases to their bin midpoints; except for the censored bins, where cases are assigned to an income estimated by fitting a Pareto distribution. Because the usual Pareto estimate can be inaccurate or undefined, especially in small samples, we implement a bounded Pareto estimate that yields much better results.  We also provide a parametric approach, which fits distributions from the generalized beta (GB) family. Because some GB distributions can have poor fit or undefined estimates, we fit 10 GB-family distributions and use multimodel inference to obtain definite estimates from the best-fitting distributions. We also provide binned income data from all United States of America school districts, counties, and states.
This package provides tools to analyze binary graph objects.
This package provides methods for estimation and
hypothesis testing of proportions in group testing designs. It
involves methods for estimating a proportion in a single
population (assuming sensitivity and specificity 1 in designs
with equal group sizes), as well as hypothesis tests and
functions for experimental design for this situation. For
estimating one proportion or the difference of proportions, a
number of confidence interval methods are included, which can
deal with various different pool sizes. Further, regression
methods are implemented for simple pooling and matrix pooling
designs.
Binomial Haar-Fisz transforms for Gaussianization
Asymptotic simultaneous confidence intervals for comparison of many treatments with one control,
for the difference of binomial proportions, allows for Dunnett-like-adjustment, Bonferroni or unadjusted intervals.
Simulation of power of the above interval methods, approximate calculation of any-pair-power, and sample size
iteration based on approximate any-pair power.
Exact conditional maximum test for many-to-one comparisons to a control.
Generating multiple binary and normal variables
simultaneously given marginal characteristics and association
structure based on the methodology proposed by Demirtas and
Doganay (2012).
Constructs confidence intervals on the probability of
success in a binomial experiment via several parameterizations
Binomial random numbers are generated via the perfect
sampling algorithm. At each iteration dual markov chains are
generated and coalescence is checked. In case coalescence
occurs, the resulting number is outputted. In case not, then
the algorithm is restarted from T(t)=2*T(t) until coalescence
occurs.
The R package contains different MCMC schemes to estimate the regression coefficients of a binomial (or binary) logit model within a Bayesian framework: a data-augmented independence MH-sampler, an auxiliary mixture sampler and a hybrid auxiliary mixture (HAM) sampler. All sampling procedures are based on algorithms using data augmentation, where the regression coefficients are estimated by rewriting the logit model as a latent variable model called difference random utility model (dRUM).
A suite of functions to compute confidence intervals and necessary
sample sizes for the parameter p of the Bernoulli B(p)
distribution under simple random sampling or under pooled
sampling. Such computations are e.g. of interest when investigating
the incidence or prevalence in populations.
The package contains functions to compute coverage probabilities and
coverage coefficients of the provided confidence intervals
procedures. Sample size calculations are based on expected length.
This package provides a range of diagnostic methods for
binomial regression models.
This package provides algorithms for cutting numerical values
exhibiting a potentially highly skewed distribution into evenly distributed
groups (bins). This functionality can be applied for binning discrete
values, such as counts, as well as for discretization of continuous values,
for example, during generation of features used in machine learning
algorithms.
For a series of binary responses, create stopping boundary with exact results after stopping, allowing updating for missing assessments.
The Biodem package provides a number of functions for
Biodemographycal analysis.
This package provides a GUI (Graphical User Interface, via the R-Commander) and some utility functions (often based on the vegan package) for statistical analysis of biodiversity and ecological communities, including species accumulation curves, diversity indices, Renyi profiles, GLMs for analysis of species abundance and presence-absence, distance matrices, Mantel tests, and cluster, constrained and unconstrained ordination analysis. A book on biodiversity and community ecology analysis is available for free download from the website. In 2012, methods for (ensemble) suitability modelling and mapping were expanded in the package.
Biograph is designed to facilitate the descriptive and statistical analysis of life histories.  It follows a multistate perspective on the life course and conceptualizes the life course as a sequence of states and transitions between states (events). Transitions are governed by transition rates that depend on event counts and exposure times. Transition rates also depend on covariates. Biograph produces useful graphics and computes life-course indicators for individuals and groups. The package structures the data for multistate statistical and demographic modeling of life histories. The package accepts data in a Biograph format. The Doc subdirectory of the package contains a description of how to prepare Biograph objects. It includes several illustrations and R code.
Imports benthic count data, reformats this data, and
computes environmental inferences from this data.
This is an R package for interfacing with the
BIOM format. This package includes basic tools for
reading biom-format files, accessing and subsetting
data tables from a biom object (which is more complex
than a single table), as well as limited support for
writing a biom-object back to a biom-format file. The
design of this API is intended to match the python API
and other tools included with the biom-format project,
but with a decidedly "R flavor" that should be familiar
to R users. This includes S4 classes and methods, as
well as extensions of common core functions/methods.
The package provides biomarker selection for several classification methods: the lasso/elastic net, PCLDA, PLSDA, and several t-tests. Two approaches are provided, one based on the stability of model coefficients under perturbation, and the other on higher criticism.
Functions for species distribution modeling, calibration and evaluation, ensemble of models.
Provides utilities to facilitate import, export and computation with the
BIOM (Biological Observation Matrix) format (http://biom-format.org).
Data Package that includes several examples of chemical and biological data networks, i.e. data graph structured.
Biopara is a parallel system designed to be used with R.
Utilities and functions to investigate the relation
between biomolecular structures, their interactions, and the
evolutionary information revealed in sequence alignments of
these molecules.
bioPN is a package suited to perform simulation of deterministic and stochastic systems of biochemical reaction
networks.
Models are defined using a subset of Petri Nets, in a way that is close at how chemical reactions
are defined.
For deterministic solutions, bioPN creates the associated system of differential equations "on the fly", and
solves it with a Runge Kutta Dormand Prince 45 explicit algorithm.
For stochastic solutions, bioPN offers variants of Gillespie algorithm, or SSA.
For hybrid deterministic/stochastic,
it employs the Haseltine and Rawlings algorithm, that partitions the system in fast and slow reactions.
bioPN algorithms are developed in C to achieve adequate performance.
Bios2mds is primarily dedicated to the analysis of
biological sequences by metric MultiDimensional Scaling with
projection of supplementary data. It contains functions for
reading multiple sequence alignment files, calculating distance
matrices, performing metric multidimensional scaling and
visualizing results.
This packages provides datasets and functions for the book "Initiation à la Statistique avec R", Dunod, 2ed, 2014.
This package contains tools designed to perform and work with cluster analysis,
discriminant analysis and path analysis (standard and with collinearity), as well as
some useful miscellaneous tools for dealing with sample size and optimum plot size calculations.
Bipartite provides functions to visualise webs and calculate a series of indices commonly used to describe pattern in ecological webs. It focuses on webs consisting of only two trophic levels, e.g. pollination webs or predator-prey-webs. Visualisation is important to get an idea of what we are actually looking at, while the indices summarise different aspects of the webs topology.
A GUI with which the user can construct and interact with Bootstrap methods on Classical Biplots
Provides a GUI with which users can construct and interact
with biplots.
Bayesian parameter estimation for (partially observed)
two-dimensional diffusions.
The package contains methods and functions to analyse re-encounter data of birds marked by metal rings. For a tutorial, go to http://www.tandfonline.com/doi/full/10.1080/03078698.2014.933053.
This is a compilation of functions that I found useful to make. It currently includes a unit of measurement conversion function and a standard error function that behaves complementary to sd().
Tools to find bad commits with git bisect. See
https://github.com/wch/bisectr for examples and test script
templates.
Enables the user to infer potential synthetic lethal relationships by analysing relationships between bimodally distributed gene pairs in big gene expression datasets.  Enables the user to look for enrichment of resistance to a compound in individual bimodal genes.
bitmapped vectors of booleans (no NAs),
coercion from and to logicals, integers and integer subscripts;
fast boolean operators and fast summary statistics.
With 'bit' vectors you can store true binary booleans {FALSE,TRUE} at the
expense of 1 bit only, on a 32 bit architecture this means factor 32 less
RAM and ~ factor 32 more speed on boolean operations. Due to overhead of
R calls, actual speed gain depends on the size of the vector: expect gains
for vectors of size > 10000 elements. Even for one-time boolean operations
it can pay-off to convert to bit, the pay-off is obvious, when such
components are used more than once.
Reading from and writing to bit is approximately as fast as accessing
standard logicals - mostly due to R's time for memory allocation. The package
allows to work with pre-allocated memory for return values by calling .Call()
directly: when evaluating the speed of C-access with pre-allocated vector
memory, coping from bit to logical requires only 70% of the time for copying
from logical to logical; and copying from logical to bit comes at a
performance penalty of 150%. the package now contains further classes for
representing logical selections: 'bitwhich' for very skewed selections and
'ri' for selecting ranges of values for chunked processing. All three index
classes can be used for subsetting 'ff' objects (ff-2.1-0 and higher).
Package 'bit64' provides serializable S3 atomic 64bit (signed) integers
that can be used in vectors, matrices, arrays and data.frames. Methods are
available for coercion from and to logicals, integers, doubles, characters
and factors as well as many elementwise and summary functions. Many fast
algorithmic operations such as 'match' and 'order' support interactive data
exploration and manipulation and optionally leverage caching.
Functions for bitwise operations on integer vectors.
Implements sample size calculations for bivariate random
intercept regression model that are described in Comulada and
Weiss (2010)
This is a port of the WTC MATLAB package written by Aslak Grinsted and the wavelet program written by Christopher Torrence and Gibert P. Compo. This package can be used to perform univariate and bivariate (cross-wavelet, wavelet coherence, wavelet clustering) analyses.
Compute multivariate location, scale, and correlation
estimates based on Tukey's biweight M-estimator.
bizdays is a small set of functions to help with calculations
which make use of business days. It works based on a list of holidays
which is passed as an argument. Its interface is simple and flexible
operating on vector of dates.
bizdays can be fairly used for fixed income calculations.
Many countries make intense use of business days to price
financial instruments, like bonds and derivatives.
Fast and accurate calculation of
Blaker's binomial confidence limits
(and some related stuff)
Bland-Altman Plots using base graphics as well as ggplot2,
slightly extended by confidence intervals, with detailed return values and
a sunflowerplot option for data with ties.
An integrated set of tools to calculate and analyze Blau statuses quantifying social distance between individuals belonging to organizations. Relational (network) data may be incorporated for additional analyses.
Tools for assessing exotic species' contributions to
landscape homogeneity using average pairwise Jaccard similarity
and an analytical approximation derived in Harris et al. (2011,
"Occupancy is nine-tenths of the law," The American
Naturalist). Also includes a randomization method for assessing
sources of model error.
Function for drawing the coastline of the British Isles
This package is an extension to the "ergm" package which implements the block ergms.
Implements regression models for binary data on the absolute risk scale. These models are applicable to cohort and population-based case-control data.
Maximum a posteriori estimation for linear and generalized
linear mixed-effects models in a Bayesian setting. Extends
lme4 by Douglas Bates, Martin Maechler, Ben Bolker, and Steve Walker.
Simultaneous clustering of rows and columns, usually designated by
biclustering, co-clustering or block clustering, is an important technique
in two way data analysis. It consists of estimating a mixture model which
takes into account the block clustering problem on both the individual and
variables sets. The blockcluster package provides a bridge between the C++
core library and the R statistical computing environment. This package
allows to co-cluster binary, contingency, continuous and categorical
data-sets. It also provides utility functions to visualize the results.
This package may be useful for various applications in fields of Data
mining,Information retrieval, Biology, computer vision and many more. More
information about the project and comprehensive tutorial can be found on
the link mentioned in URL.
Some elementary matrix algebra tools are implemented to manage
block matrices or partitioned matrix, i.e. "matrix of matrices"
(http://en.wikipedia.org/wiki/Block_matrix). The block matrix is here
defined as a new S3 object. In this package, some methods for "matrix"
object are rewritten for "blockmatrix" object. New methods are implemented.
This package was created to solve equation systems with block matrices for
the analysis of environmental vector time series .
Bugs/comments/questions/collaboration of any kind are warmly welcomed.
Creates strings that show a text message in 8 by 8 block
letters
The package is primarly ment as an implementation of
Generalized blockmodeling for valued networks. In addition,
measurese of similarity or dissimilarity based on structural
equivalence and regular equivalence (REGE algorithem) can be
computed and partitioned matrices can be ploted.
Create randomizations for block random clinical trials.
Can also produce a pdf file of randomization cards.
Nested block designs for unstructured treatment sets where blocks
can be repeatedly nested and treatments can have different levels of
replication. Blocks strata are optimized hierarchically with each set of
nested blocks optimized within the levels of the preceding set. Block sizes
are equal if the number of blocks exactly divides the number of plots,
otherwise they differ by at most one plot. The design output is a data
table giving a randomised allocation of treatments to blocks together with
a plan table showing treatments in blocks and a set of blocks-by-treatments
incidence matrices, one for each blocks stratum.
Blocks units into experimental blocks, with one unit per treatment condition, by creating a measure of multivariate distance between all possible pairs of units.  Maximum, minimum, or an allowable range of differences between units on one variable can be set.  Randomly assign units to treatment conditions.  Diagnose potential interference between units assigned to different treatment conditions.  Write outputs to .tex and .csv files.
Optimize a function with constraints via a transformation of the
Lagrangian.
Package for Bayesian model averaging for linear models,
generalizable linear models and survival models (cox
regression).
Benchmark dose analysis for continuous and quantal
dose-response data.
Four methods for mediation analysis with missing data: Listwise deletion, Pairwise deletion, Multiple imputation, and Two Stage Maximum Likelihood algorithm. For MI and TS-ML, auxiliary variables can be included. Bootstrap confidence intervals for mediation effects are obtained. The robust method is also implemented for TS-ML. Since version 1.4, bmem adds the capability to conduct power analysis for mediation models.
The BMhyd package analyzes the phenotypic evolution of species of hybrid origin on a phylogenetic network. This package can detect the hybrid vigor effect, a burst of variation at formation, and the relative portion of heritability from its parents. Parameters are estimated by maximum likelihood technique. Users need to enter comparative data set and phylogenetic networks in eNewick format. The full model contains five parameters: the overall mean, the overall variance, the hybrid vigor, the hybrid variation and the proportion of heritability. We report four different models. All fix the heritability to equal contributions from each parent. Model 1 fixes the hybrid vigor and burst of variation at formation at 1 and 0; respectively. Model 2 fixes the hybrid vigor at 1 but allow the burst of variation at formation to vary; model 3 allows hybrid vigor to vary but fixes burst of variation at origin; and model 4 allows both to vary. Those models are compared under sample size correction Akaike Information Criterion (AICc). The model averaged parameter estimates are reported.
This is a bare-bones implementation of sampling algorithms
for a variety of Bayesian stick-breaking (marginally DP)
mixture models, including particle learning and Gibbs sampling
for static DP mixtures, particle learning for dynamic BAR
stick-breaking, and DP mixture regression.  The software is
designed to be easy to customize to suit different situations
and for experimentation with stick-breaking models.  Since
particles are repeatedly copied, it is not an especially
efficient implementation.
MCMC diagnostic package that contains tools to diagnose
convergence as well as to evaluate sensitivity studies,
Includes summary functions which output mean, median,
95percentCI, Gelman & Rubin diagnostics and the Hellinger
distance based diagnostics, Also contains functions to
determine when an MCMC chain has converged via Hellinger
distance, A function is also provided to compare outputs from
identically dimensioned chains for determining sensitivy to
prior distribution assumptions
This package implements approximate and exact methods for
pairwise binary markov models. The exact method uses an
implementation of the junction tree algorithm for binary
graphical models. For more details see the help files
Reads Windows BMP format images. Currently limited to 8 bit
greyscale images and 24,32 bit (A)RGB images. Pure R implementation without
external dependencies.
An implementation of "Bundle Methods for Regularized Risk Minimization" (BMRM) inspired by work of Teo et al., JMLR 2010. This universal data mining framework is particularly useful for structured prediction, classification and regression on big data. The package implements L1 and L2 regularization for: linear SVM, multiclass SVM, f-beta optimization, ROC optimization, ordinal regression, quantile regression, epsilon insensitive regression, least mean square, logistic regression, least absolute deviation (see package examples). Other extensions are possible by implementing custom loss functions.
Bayesian Model Averaging for linear models with a wide choice of (customizable) priors. Built-in priors include coefficient priors (fixed, flexible and hyper-g priors), 5 kinds of model priors, moreover model sampling by enumeration or various MCMC approaches. Post-processing functions allow for inferring posterior inclusion and model probabilities, various moments, coefficient and predictive densities. Plotting functions available for posterior model size, MCMC convergence, predictive and coefficient densities, best models representation, BMA comparison.
Bayesian network structure learning, parameter learning and
inference.
This package implements constraint-based (GS, IAMB, Inter-IAMB, Fast-IAMB,
MMPC, Hiton-PC), pairwise (ARACNE and Chow-Liu), score-based (Hill-Climbing
and Tabu Search) and hybrid (MMHC and RSMAX2) structure learning algorithms
for both discrete and Gaussian networks, along with many score functions
and conditional independence tests.
The Naive Bayes and the Tree-Augmented Naive Bayes (TAN) classifiers are
also implemented.
Some utility functions (model comparison and manipulation, random data
generation, arc orientation testing, simple and advanced plots) are
included, as well as support for parameter estimation (maximum likelihood
and Bayesian) and inference, conditional probability queries and
cross-validation. Development snapshots with the latest bugfixes are
available from www.bnlearn.com.
Ferguson-Klass type algorithm for posterior normalized random measures
Implements the Bayesian nonparametric monotonic regression
method described in Bornkamp & Ickstadt (2009), Biometrics, 65,
198-205.
A menu-driven program and library of functions for
carrying out convergence diagnostics and statistical and
graphical analysis of Markov chain Monte Carlo sampling output.
Generic Extraction of main text content from HTML files; removal
of ads, sidebars and headers using the boilerpipe Java library. The
extraction heuristics from boilerpipe show a robust performance for a wide
range of web site templates.
The Bayesian optimal interval (BOIN) design is a novel phase I clinical trial design for finding the maximum tolerated dose (MTD). The BOIN design is motivated by the top priority and concern of clinicians when testing a new drug, which is to effectively treat patients and minimize the chance of exposing them to subtherapeutic or overly toxic doses. The prominent advantage of the BOIN design is that it achieves simplicity and superior performance at the same time. The BOIN design is algorithm-based and can be implemented in a simple way similar to the traditional 3+3 design. The BOIN design yields average performance comparable to the continual reassessment method (CRM, one of the best model-based design) in terms of selecting the MTD, but has a substantially lower risk of assigning patients to subtherapeutic or overly toxic doses.
A programmatic interface to the Web Service methods provided by
Bold Systems for genetic barcode data. Functions include methods for
searching by sequences by taxonomic names, ids, collectors, and
institutions; as well as a function for searching for specimens, and
downloading trace files.
A set of R functions and data sets for the book
Introduction to Bayesian Statistics, Bolstad, W.M. (2007), John
Wiley & Sons ISBN 0-471-27020-2
A set of R functions and data sets for the book
Understanding Computational Bayesian Statistics, Bolstad, W.M.
(2009), John Wiley & Sons ISBN 978-0470046098
This package implements a
partial-observability procedure for testing Boolean
hypotheses that generalizes the binary response GLM as
outlined in Braumoeller (2003).
Provides methods to reconstruct and generate synchronous,
asynchronous, and probabilistic Boolean networks, and to
analyze and visualize attractors in Boolean networks
A C++ library for Bayesian modeling, with an emphasis on
Markov chain Monte Carlo.  Although boom contains a few R utilities
(mainly plotting functions), its primary purpose is to install the
BOOM C++ library on your system so that other packages can link
against it.
Spike and slab regression a la McCulloch and George (1997).
boostr provides a modular framework that return the focus of
ensemble learning back to 'learning' (instead of programming).
This package contains functionality to select a subsample
of a genotyped cohort e.g. from a GWAS that is preferential for
resequencing under the assumtion that causal variants share a
haplotype with the risk allele of associated variants. The
subsample is selected such that is contains risk alleles at
maximum frequency for all SNPs specified. Phentoypes can also
be included as additional variables to obtain a higher fraction
of extreme phenotypes. An arbitrary number of SNPs and/or
phentoypes can be specified for enrichment in a single
subsample.
functions and datasets for bootstrapping from the
book "Bootstrap Methods and Their Applications" by A. C. Davison and
D. V. Hinkley (1997, CUP).
Functions for calculating bootstrap effect sizes
To select a set of features used for successful classification of two or more groups of samples, multiple classification and feature selection algorithms are utilised. By combining the results of all methods and applying a bootstrapping approach a robust set of features with high power to distinguish the sample groups is selected.
This package computes appropriate confidence intervals for the
likelihood ratio tests commonly used in medicine/epidemiology.  It is
particularly useful when the sensitivity or specificy in the sample is
100%.  Note that this does not perform the test on nested models--for that,
see epicalc::lrtest.
Bias-Corrected Forecasting and Bootstrap Prediction Intervals for Autoregressive Time Series
Calculation of Bootstrapped Response and Correlation
Functions for Use in Dendroclimatology
This package provides a framework for testing the probability of ruin in the classical (compound Poisson) risk process. It also includes some procedures for assessing and comparing the performance between the bootstrap test and the test using asymptotic normality.
Bootstrap for testing the hypothesis that the spectral
densities of a number m, m>=2, not necessarily independent time
series are equal
Model selection by bootstrapping the stepAIC() procedure.
Software (bootstrap, cross-validation, jackknife) and data
for the book "An Introduction to the Bootstrap" by B. Efron and
R. Tibshirani, 1993, Chapman and Hall.
_____________________________________________________________
This package is primarily provided for projects already based
on it, and for support of the book.  New projects should
preferentially use the recommended package "boot".
Implements fast, exact bootstrap Principal Component Analysis and
Singular Value Decompositions for high dimensional data, as described in
(arxiv.org/abs/1405.0922).
Bayesian approaches for analyzing multivariate data in ecology. Estimation is performed using Markov Chain Monte Carlo (MCMC) methods via JAGS. Three types of models may be fitted: 1) With explanatory variables only, boral fits independent column GLMs to each column of the response matrix; 2) With latent variables only, boral fits a purely latent variable model for model-based unconstrained ordination; 3) With explanatory and latent variables, boral fits correlated column GLMs with latent variables to account for any residual correlation between the columns of the response matrix.
Boruta is an all-relevant feature selection wrapper algorithm.
It finds relevant features by comparing original attributes'
importance with importance achievable at random, estimated
using their permuted copies.
BOSS uses parameter estimates obtained without genotype to boost standard one-step approximations, and precomputes as much as possible without genotype (using boss.set) to minimize effort required.
Sort sequence from genbank. Retrieve sequence information
from genbank (designed for viruses sequences and retrieve
information such as isolation date and host). BLAST sequence
and accession numbers. Detect group of sequences presenting
phylogeography signal. Read PDB file (protein 3D structure
file).
This package is a collection of R functions implemented
from published and available analytic solutions for the
One-Dimensional Boussinesq Equation (ground-water). In
particular, the function "beq.lin" is the analytic solution of
the linearized form of Boussinesq Equation between two
different head-based boundary (Dirichlet) conditions;
"beq.song" is the non-linear power-series analytic solution of
the motion of a wetting front over a dry bedrock (Song at al,
2007, see complete reference on function documentation).
Bugs/comments/questions/collaboration of any kind are warmly
welcomed.
Correlation chart of two set (x and y) of data.
Using Quartiles with boxplot style.
Visualize the effect of factor.
Implements biplot (2d and 3d) of multivariate data based
on principal components analysis and diagnostic tools of the quality of the reduction.
Calculates nonparametric pointwise confidence intervals for the survival distribution for right censored data. Has two-sample tests for dissimilarity (e.g., difference, ratio or odds ratio) in survival at a fixed time. Especially important for small sample sizes or heavily censored data.
bPeaks is a simple approach to identify transcription factor binding sites from ChIP-seq data. Our general philosophy is to provide an easy-to-use tool, well-adapted for small eukaryotic genomes (< 20 Mb). bPeaks uses a combination of 4 cutoffs (T1, T2, T3 and T4) to mimic "good peak" properties as described by biologists who visually inspect the ChIP-seq data on a genome browser. For yeast genomes, bPeaks calculates the proportion of peaks that fall in promoter sequences. These peaks are good candidates as transcription factor binding sites.
Nonparametric multivariate kernel density \
estimation using a back-projected kernel.
QTL mapping toolkit for inbred crosses and recombinant
inbred lines. Includes maximum likelihood and Bayesian tools.
Specify and fit the Bradley-Terry model and structured versions
Blind useRs do not have access to the graphical output from R without printing the content of graph windows to an embosser of some kind. This is not as immediate as is required for efficient access to statistical output. The functions here are created so that blind people can make even better use of R.
This includes functions for creating 3D and 4D images using WebGL, RGL, and JavaScript Commands.  This package relies on the X ToolKit (XTK, https://github.com/xtk/X#readme).
This package computes the correlation matrix for each
scale of a wavelet decomposition, namely the one performed by
the R package waveslim (Whitcher, 2000). An hypothesis test is
applied to each entry of one matrix in order to construct an
adjacency matrix of a graph. The graph obtained is finally
analysed using the small-world theory (Watts and Strogatz,
1998) and using the computation of efficiency (Latora, 2001),
tested using simulated attacks. The brainwaver project is
complementary to the camba project for brain-data
preprocessing. A collection of scripts (with a makefile) is
avalaible to download along with the brainwaver package, see
information on the webpage mentioned below.
Select cost-efficient factors using Branching LARS method
Estimates geometry of SICM pipette tips by fitting a
physical model to recorded breakage-current data.
breakaway estimates and models total (observed plus unobserved) species richness. It is applicable to any situation where an unknown number of classes is to be estimated, but is especially suited to the microbial context. Microbial diversity datasets are characterized by a large number of rare species and a small number of highly abundant species. The class of models implemented by breakaway is flexible enough to model both these features. In the event that the practitioner does not believe or trust their singleton count, the procedure breakaway_nof1 estimates diversity in a way that does not require a singleton count. betta_basic runs a regression-type analysis of estimated total diversity, thus permitting accounting for unobserved taxa while also incorporating available covariate information. betta_basic does not enforce use of breakaway for diversity estimation.
Implements the cross-entropy (CE) method, which is a model based stochastic optimization technique to estimate both the number as well as the corresponding locations of break-points in biological sequences of continuous measurements (Priyadarshana and Sofronov (2014)).
A collection of functions to analyse, visualize and interpret wind data
and to calculate the potential energy production of wind turbines.
brew implements a templating framework for mixing text and
R code for report generation. brew template syntax is similar
to PHP, Ruby's erb module, Java Server Pages, and Python's psp
module.
Fit generalized linear models with binomial responses using either an adjusted-score approach to bias reduction or maximum penalized likelihood where penalization is by Jeffreys invariant prior. These procedures return estimates with improved frequentist properties (bias, mean squared error) that are always finite even in cases where the maximum likelihood estimates are infinite (data separation). Fitting takes place by fitting generalized linear models on iteratively updated pseudo-data. The interface is essentially the same as 'glm'.  More flexibility is provided by the fact that custom pseudo-data representations can be specified and used for model fitting. Functions are provided for the construction of confidence intervals for the reduced-bias estimates.
Decomposes the empirical Brier score into reliability, resolution and uncertainty. Two different estimators for the components are provided: The original estimators proposed by Murphy (1974), and the bias-corrected estimators proposed by Ferro and Fricker (2012). Sampling variances of all the components are estimated. This package applies only to probabilistic predictions of binary events.
Bayesian regularization for feed-forward neural networks
Handles very large numbers in R.  Real numbers are held
using their natural logarithms, plus a logical flag indicating
sign.  The package includes a vignette that gives a
step-by-step introduction to using S4 methods.
Bayesian estimation and variable selection for quantile
regression models.
Fully-interactive R interface to the OpenBUGS software for Bayesian analysis using MCMC sampling.  Runs natively and stably in 32-bit R under Windows.  Versions running on Linux and on 64-bit R under Windows are in "beta" status and less efficient.
Collection of functions, data sets and code examples
for evaluations of field trials with the objective of equivalence assessment.
Data sets for book "Basic Statistics and Data Analysis" by
Larry J. Kitchens
The function estimates the hazard function non parametrically from a survival object (possibly adjusted for covariates). The smoothed estimate is based on B-splines from the perspective of generalized linear mixed models. Left truncated and right censoring data are allowed.
Bayes screening and model discrimination follow-up designs.
Bayesian inference on the (discrete) power spectrum of
time series.
Some functions for nonparametric and semiparametric
Bayesian models for random effects meta-analysis
This package models the quantile process as a function of
predictors.
The package includes functions to compute the asymptotic covariance matrices of mixing and unmixing matrix estimates of the following blind source separation (BSS) methods: adaptive FastICA, FOBI, JADE, AMUSE and deflation-based and symmetric SOBI. Also functions to estimate these covariances based on data are available.
The package contains HingeBoost for binary and multi-class classification, with unequal misclassification costs for binary case. Functional gradient descent algorithm to optimize the hinge loss. The algorithm can fit linear and nonlinear classifiers.
Time series regression using dynamic linear models fit using MCMC.
Trend filtering uses the generalized
lasso framework to fit an adaptive polynomial of degree k to
estimate the function f_0 at each input x_i in the model: y_i =
f_0(x_i) + epsilon_i, for i = 1, ..., n, and epsilon_i
is sub-Gaussian with E(epsilon_i) = 0.  Bayesian trend filtering adapts
the genlasso framework to a fully Bayesian hierarchical model, estimating
the penalty parameter lambda within a tractable Gibbs sampler.
BTSPAS provides advanced Bayesian methods to estimate
abundance and run-timing from temporally-stratified
Petersen mark-recapture experiments. Methods include
hierarchical modelling of the capture probabilities
and spline smoothing of the daily run size. This version
uses JAGS to sample from the posterior distribution.
This package contains functions for data preparation, parameter estimation, scoring, and plotting for the BG/BB, BG/NBD and Pareto/NBD models.
Buckley-James regression for right-censoring survival data with high-dimensional covariates. Including L_2 boosting with componentwise linear least squares, componentwise smoothing splines, P-splines, regression trees and boosted MARS. Other high-dimensional tools include elastic net and MARS.
A suite of functions for finance, including the estimation
of variance matrices via a statistical factor model or
Ledoit-Wolf shrinkage.
corner, genetic optimization, permutation tests
An implementation of Jon Kleinberg's burst detection algorithm.  Uses an infinite Markov model to detect periods of increased activity in a series of discrete events with known times, and provides a simple visualization of the results.
R/C++ implementation of the model proposed by Primiceri ("Time Varying Structural Vector Autoregressions and Monetary Policy", Review of Economic Studies, 2005), with a focus on generating posterior predictive distributions.
This package implements a simple alternative to the
traditional Venn diagram. It depicts each overlap as a separate
bubble with area proportional to the overlap size. Relation of
the bubbles to input sets is shown by their their arrangement.
An R interface to the Stark-Parker implementation of an
algorithm for bounded-variable least squares
Functions that solve boundary value problems (BVP) of systems of ordinary
differential equations (ODE).
The functions provide an interface to the FORTRAN functionS
twpbvpC, colnew/colsys, and an R-implementation of the shooting method.
The functions in this package focus on analyzing
case-control association studies involving a group of genetic
variants.  In particular, we are interested in modeling the
outcome variable as a function of a multivariate genetic
profile using Bayesian model uncertainty and variable selection
techniques.  The package incorporates functions to analyze data
sets involving common variants as well as extensions to model
rare variants via the Bayesian Risk Index (BRI) as well as
haplotypes.  Finally, the package also allows the incorporation
of external biological information to inform the marginal
inclusion probabilities via the iBMU.
c060 provides additional functions to perform stability selection, model validation and parameter tuning for glmnet models
This package allows inferring gene regulatory networks
with direct physical interactions from microarray expression
data using C3NET.
C5.0 decision trees and rule-based models for pattern recognition
A package for computation and visualization of simple, multiple and joint correspondence analysis.
Performs correspondence analysis on a two-way contingency
table and produces bootstrap-based elliptical confidence
regions around the projected coordinates for the category
points. Includes routines to plot the results in a variety of
styles. Also reports the standard numerical output for
correspondence analysis.
Computes classification accuracy and consistency under Item Response Theory by the approach proposed by Lee, Hanson & Brennen (2002) and Lee (2010) or the approach proposed by Rudner (2001, 2005).
A probabilistic extension of the standard multi-layer
perceptron neural network
This package performs Hansen's (1995) Covariate-Augmented
Dickey-Fuller (CADF) test. The only required argument is y, the
Tx1 time series to be tested. If no stationary covariate X is
passed to the procedure, then an ordinary ADF test is
performed. The p-values of the test are computed using a
procedure proposed in Costantini, Lupi and Popp (2007),
illustrated in Lupi (2009).
Provides functions for the estimation of the conditional Akaike
information in generalized mixed-effects models fitted with (g)lmer
form lme4.
This package provides a Cairo graphics device that can be use to create high-quality vector (PDF, PostScript and SVG) and bitmap output (PNG,JPEG,TIFF), and high-quality rendering in displays (X11 and Win32). Since it uses the same back-end for all output, copying across formats is WYSIWYG. Files are created without the dependence on X11 or other external programs. This device supports alpha channel (semi-transparent drawing) and resulting images can contain transparent and semi-transparent regions. It is ideal for use in server environments (file output) and as a replacement for other devices that don't have Cairo's capabilities such as alpha support or anti-aliasing. Backends are modular such that any subset of backends is supported.
Cairo/GTK graphics device driver with output to screen,
file (png, svg, pdf, and ps) or memory (arbitrary GdkDrawable
or Cairo context). The screen device may be embedded into RGtk2
interfaces. Supports all interactive features of other graphics
devices, including getGraphicsEvent().
Functions to impute using Random Forest under Full Conditional Specifications (Multivariate Imputation by Chained Equations). The CALIBER programme is funded by the Wellcome Trust (086091/Z/08/Z) and the National Institute for Health Research (NIHR) under its Programme Grants for Applied Research programme (RP-PG-0407-10314). The author is supported by a Wellcome Trust Clinical Research Training Fellowship (0938/30/Z/10/Z).
Package for drawing calibrated scales with tick marks on (non-orthogonal)
variable vectors in scatterplots and biplots.
Performs Bayesian calibration of computer models as per
Kennedy and O'Hagan 2001.  The package includes routines to find the
hyperparameters and parameters; see the help page for stage1() for a
worked example using the toy dataset.  A tutorial is provided in the
calex.Rnw vignette; and a suite of especially simple one dimensional
examples appears in inst/doc/one.dim/.
A multi-array post-processing method of allele-specific copy-number estimates (ASCNs)
Tools for the analysis of finite semiparametric mixtures.
These are useful when data is heterogeneous, e.g. in
pharmacokinetics or meta-analysis. The NPMLE and VEM algorithms
(flexible support size) and EM algorithms (fixed support size)
are provided for univariate and bivariate data
The package "camel" provides the implementation of a family of high-dimensional calibrated machine learning tools, including (1) LAD, SQRT Lasso and Calibrated Dantzig Selector for estimating sparse linear models; (2) Calibrated Multivariate Regression for estimating sparse multivariate linear models; (3) Tiger, Calibrated Clime for estimating sparse Gaussian graphical models. We adopt the combination of the dual smoothing and monotone fast iterative soft-thresholding algorithm (MFISTA). The computation is memory-optimized using the sparse matrix output, and accelerated by the path following and active set tricks.
This is a package with functions for timing copy number changes using estimates of mutational allele frequency from resequencing of tumor samples.
This package includes functions for computing and
visualizing generalized canonical discriminant analyses and
canonical correlation analysis for a multivariate linear model.
Traditional canonical discriminant analysis is restricted to a
one-way MANOVA design and is equivalent to canonical
correlation analysis between a set of quantitative response
variables and a set of dummy variables coded from the factor
variable.  The candisc package generalizes this to multi-way
MANOVA designs for all factors in a multivariate linear model,
computing canonical scores and vectors for each term. The
graphic functions provide low-rank (1D, 2D, 3D) visualizations
of terms in an mlm via the plot.candisc and heplot.candisc
methods. Related plots are now provided for canonical
correlation analysis when all predictors are quantitative.
This package combines complementary information across multiple related phenotypes to infer directed epistatic interactions between genetic markers. The analysis in this package can be applied to a variety of engineered and natural populations.
Functions for performing phylogenetic comparative analyses.
Provides functions for quantitative companion animal population
management.
The package uses constrained l1 optimization to solve the
regression coefficient matrix A and the precision matrix Omega
in the model Y = XA + Z, where Z follows N(0,Omega^{-1}). Both
A and Omega are high-dimensional sparse matrices.
This package is devoted to the calibration of penalized
criteria for model selection. The calibration methods available
in this package are based on the slope heuristics.
Fits models from Miller et al. 2005 to estimate population
sizes from natural populations. Several models are implemented.
Package also includes functions to perform a likelihood ratio
test to choose between models, perform parametric bootstrapping
to obtain confidence intervals and multiple functions to
simulate data.
This package accompanies J. Fox and S. Weisberg, An R Companion to Applied Regression,
Second Edition, Sage, 2011.
This package implements Bayesian hierarchical spatial areal unit models. In such models the spatial correlation is modelled by  a set of random effects, which are assigned a conditional autoregressive (CAR) prior distribution. Examples of the models included are the BYM model as well as a recently developed localised spatial smoothing model.The creation of this package was supported by the Economic and Social Research Council (ESRC) grant RES-000-22-4256.
Implements a series of Poisson log-linear models with spatio-temporal random effects. The spatial and temporal autocorrelation in the random effects are induced using the class of Conditional AutoRegressive (CAR) priors, which are a special case of Gaussian Markov Random Fields (GMRF). All models are fitted in a Bayesian context using Markov chain Monte Carlo (McMC) simulation.
The number of bird or bat fatalities from collisions with buildings, towers or wind energy turbines can be estimated based on carcass searches and experimentally assessed carcass persistence times and searcher efficiency. This package provides diverse functions for estimating the probability that a bird or bat that died is found by a searcher. It further provides a function to obtain a posterior distribution of the number of fatalities based on the number of carcasses found and the estimated detection probability.
The package helps to identify cardinal dates
(begin, time of maximum, end of mass developments)
in ecological time series using fitted Weibull functions.
The "care" package implements the regression approach
of Zuber and Strimmer (2011) "High-dimensional regression and variable
selection using CAR scores" SAGMB 10: 34.
CAR scores measure the correlation between the response and the
Mahalanobis-decorrelated  predictors.  The squared CAR score is a
natural measure of variable  importance and provides a canonical
ordering of variables. This package provides functions for estimating
CAR scores, for variable selection using CAR scores, and for estimating
corresponding regression coefficients. Both shrinkage as well as
empirical estimators are available.
The R package CARE1, the first part of the program CARE
(Capture-Recapture) in
http://chao.stat.nthu.edu.tw/softwareCE.html, can be used to
analyze epidemiological data via sample coverage approach (Chao
et al. 2001a). Based on the input of records from several
incomplete lists (or samples) of individuals, the R package
CARE1 provides output of population size estimate and related
statistics.
Misc functions for training and plotting classification and regression models
This is a package for estimating the population size of
migratory caribou herds based on large scale aggregations
monitored by radio telemetry. It implements the methodology
found in the article by Rivest et al. (1998) about caribou
abundance estimation. It also includes a function based on the
Lincoln-Petersen Index as applied to radio telemetry data by
White and Garrott (1990).
Includes commands for bootstrapping and permutation tests, a command for created grouped bar plots, and a demo of the quantile-normal plot for data drawn from different distributions.
The caroline R library contains dozens of functions useful
for: database migration (dbWriteTable2), database style joins &
aggregation (nerge, groupBy & bestBy), data structure
conversion (nv, tab2df), legend table making (sstable &
leghead), plot annotation (labsegs & mvlabs), data
visualization (violins, pies & raPlot), character string
manipulation (m & pad), file I/O (write.delim), batch scripting
and more.  The package's greatest
contributions lie in the database style merge, aggregation and
interface functions as well as in it's extensive use and
propagation of row, column and vector names in most functions.
This package fits Bayesian conditional autoregressive
models for spatial and spatiotemporal data on a lattice.  It
uses Nvidia GPUs to perform rejection sampling to obtain
independent samples from the joint posterior distribution of
model parameters.
This package fits Bayesian conditional autoregressive models for spatial and spatiotemporal data on a lattice.  It uses OpenCL kernels running on GPUs to perform rejection sampling to obtain independent samples from the joint posterior distribution of model parameters.
Functions, data sets and exercises solutions for the book 'Séries temporelles avec R - Méthodes et cas' (Yves Aragon, Springer, 2011). For all chapters, a vignette is available with some additional material and exercises solutions.
This package allows users to identify similar cases for qualitative case studies using statistical matching methods.
Analysis of categorical-variable with missing values
This R-package contains examples from the book "Regression for Categorical Data", Tutz 2011, Cambridge University Press. The names of the examples refer to the chapter and the data set that is used.
Using high-frequency or medium-frequency catch in biomass or numbers, fishing nominal effort, and mean body mass by time step, from one or two fishing fleets, estimate stock abundance, natural mortality rate, and fishing operational parameters. It includes methods for data organization, plotting standard exploratory and analytical plots, predictions, for 57 types of catch dynamics models of increasing complexity, and two distributions for the catch data.
Gives methods to create a catenary object and then plot it
and get properties of it. Can construct from parameters or
endpoints. Also can get catenary fitted to data.
A package to detect and quantify community assembly processes
using trait values of individuals or populations, the T-statistics and
other metrics, and dedicated null models.
Functions designed to simulate data that conform to basic
unidimensional IRT models (for now 3-parameter binary response models
and graded response models) along with Post-Hoc CAT simulations of
those models with various item selection methods, ability estimation
methods, and termination criteria.
A package that handles discrete Bayesian network models and provides inference using the frequentist approach
Contains several basic utility functions including: moving
(rolling, running) window statistic functions, read/write for
GIF and ENVI binary files, fast calculation of AUC, LogitBoost
classifier, base64 encoder/decoder, round-off-error-free sum
and cumsum, etc.
The catR package allows the generation of response patterns under computerized adaptive testing (CAT) framework, with the choice of several starting rules, next item selection routines, stopping rules and ability estimators. Control methods for item exposure and content balancing are also included.
`ctab' creates (multiway) percentage tables. `sqtab'
contains a set of functions for estimating models for square
tables such as quasi-independence, symmetry, uniform
association. Examples show how to use these models in a
loglinear model using glm or in a multinomial logistic model
using mlogit or clogit
An implementation of the complete identification algorithm constructed by Ilya Shpitser and Judea Pearl (2006) for deriving expressions of joint interventional distributions in causal models, which contain unobserved variables and induce directed acyclic graphs.
This package implements various estimators for average
treatment effects---an inverse probability weighted (IPW)
estimator, an augmented inverse probability weighted (AIPW)
estimator, and a standard regression estimator---that make use
of generalized additive models for the treatment assignment
model and/or outcome model.
A package to study how various assumptions about selection
bias affects estimates of causal effects.
The Causata package provides utilities for
extracting data from the Causata application, training binary classification
models, and exporting models as PMML for scoring.
Six variants of two-way correspondence analysis can be performed,
simple ca
singly ordered ca
doubly ordered ca
non symmetrical ca
singly ordered non symmetrical ca
doubly ordered non symmetrical ca
Implements clustering techniques such as Proximus and Rock, utility functions for efficient computation of cross distances and data manipulation.
CBPS is an R package that implements the covariate balancing propensity score proposed by Imai and Ratkovic (2014; JRSSB). The propensity score is estimated such that it maximizes the resulting covariate balance as well as the prediction of treatment assignment. The method, therefore, avoids an iteration between model fitting and balance checking.  The package also implements several extensions of the CBPS beyond the cross-sectional, binary treatment setting.  The current version implements the CBPS for longitudinal settings so that it can be used in conjunction with marginal structural models (Imai and Ratkovic, 2013a), treatments with three- and four-valued treatment variables, continuous-valued treatments (Fong, Imai, and Ratkovic), and the situation with multiple distinct binary treatments administered simultaneously. In the future it will be extended to other settings including the generalization of experimental and instrumental variable estimates.
The package provide a set of functions that extend the
cancor function with new numerical and graphical outputs. It
also include a regularized extension of the cannonical
correlation analysis to deal with datasets with more variables
than observations.
Variational Bayesian algorithm for learning canonical
correlation analysis (CCA), inter-battery factor analysis
(IBFA), and group factor analysis (GFA)
Canonical correlation analysis and maximum correlation via
projection pursuit, as well as fast implementations of correlation
estimators, with a focus on robust and nonparametric methods.
Class Cover Catch Digraphs, neighborhood graphs, and
relatives.
ccChooser can be used to developing and evaluation of core
collections for germplasm collections (entire collection). This
package used to develop a core collection for biological
resources like genbanks. A core collection is defined as a
sample of accessions that represent, with the lowest possible
level of redundancy, the genetic diversity (the richness of
gene or genotype categories) of the entire collection. The
establishing a core collection that represents genetic
diversity of the entire collection with minimum loss of its
original diversity and minimum redundancies is an important
problem for gene-banks curators and crop breeders. ccChooser
establish core collection base on phenotypic data (agronomic,
morphological, phenological).
Concordance Correlation Coefficient for repeated measures.
This package was developed with the support of the Spanish MEyC
grant project CGL2008-05448-C02-02/BOS.
Jozsef Kovacs, Solt Kovacs, Norbert Magyar, Peter Tanos, Istvan Gabor Hatvani, Angela Anda(2014): Classification into homogeneous groups using combined cluster and discriminant analysis (CCDA). Environmental Modelling & Software. DOI: http://dx.doi.org/10.1016/j.envsoft.2014.01.010
Functions for estimating and simulating the family of the
CC-GARCH models.
Convex Clustering methods, including K-means algorithm,
On-line Update algorithm (Hard Competitive Learning) and Neural Gas
algorithm (Soft Competitive Learning), and calculation of several
indexes for finding the number of clusters in a data set.
Classification method that classifies a sample according
to the class with the maximum mean (or any other function of)
correlation between the test and training samples with known
classes.
Tools to simulate networks based on Congruence Class models.
Significance tests for canonical correlation analysis,
including asymptotic tests and a Monte Carlo method
Tests of association between SNPs or pairs of SNPs and binary phenotypes, in case-control / case-population / case-control-population studies.
A user-friendly GUI for the application of Cultural Consensus Theory (CCT) models and methods to consensus data. The models are applied using hierarchical Bayesian inference. The current package version supports binary, ordinal, and continuous data formats.
Solves the electromagnetic problem of coupled-dipoles (scattering
and absorption by a cluster of subwavelength particles in arbitrary 3D
configuration) by setting up the interaction matrix and solving the linear
system for multiple incident fields. Functions are provided for linear
polarisation as well as circular polarisation with full angular averaging
(optical activity). Retardation is included in the dipole-dipole
interaction.
A constant database is a data structure created by Daniel
J. Bernstein in his cdb package. Its format consists on a
sequence of (key,value)-pairs. This R package replicates the
basic utilities for reading (cdbget) and writing (cdbdump)
constant databases.
This package proposes a statistical downscaling method for
cumulative distribution functions (CDF), as well as the
computation of the Cram\`er-von Mises statistics U, and the
Kolmogorov-Smirnov statistics KS.
Coordinate Descent Algorithms for Lasso Penalized L1, L2,
and Logistic Regression
Functions for cognitive diagnosis modeling
and multidimensional item response modeling for dichotomous
and polytomous data. This package enables the estimation of the
DINA and DINO model, the multiple group (polytomous) GDINA model,
the multiple choice DINA model, the general diagnostic model (GDM),
the multidimensional linear compensatory item response model and
the structured latent class model (SLCA).
Components of Canadian Credit Aggregates and Monetary
Aggregates with continuity adjustments.
This package provides functions for statistical inference
of canonical vine (C-vine) and D-vine copulas. It contains
tools for bivariate exploratory data analysis and for bivariate
as well as vine copula selection. Models can be estimated
either sequentially or by joint maximum likelihood estimation.
Sampling algorithms and plotting methods are also included.
Data is assumed to lie in the unit hypercube (so-called copula
data).
This package is a wrapper for the C implementation of the
25 benchmark functions for the CEC 2005 Special Session on
Real-Parameter Optimization. The original C code by Santosh
Tiwari and related documentation are available at
http://www.ntu.edu.sg/home/EPNSugan/.
This package provides R wrappers for the C implementation
of 28 benchmark functions defined for the Special Session and
Competition on Real-Parameter Single Objective Optimization at
CEC-2013. The focus of this package is to provide an
open-source and multi-platform implementation of the CEC2013
benchmark functions, in order to make easier for researchers to
test the performance of new optimization algorithms in a
reproducible way. The original C code (Windows only) was
provided by Jane Jing Liang, while GNU/Linux comments were made
by Janez Brest. This package was gently authorised for
publication on CRAN by Ponnuthurai Nagaratnam Suganthan. The
official documentation is available at
http://www.ntu.edu.sg/home/EPNSugan/index_files/CEC2013/CEC2013.htm.
Bugs reports/comments/questions are very welcomed (in English,
Spanish or Italian).
This package includes a number of common astronomy conversion routines, particularly the HMS and degrees schemes, which can be fiddly to convert between on mass due to the textural nature of the former.
This package is an object-oriented implementation of one-dimensional cellular automata. It supports many of the features offered by Mathematica, including elementary rules, user-defined rules, radii, user-defined seeding, and plotting.
This package implements a methodology for using cell
volume distributions to estimate cell growth rates and division
times that is described in the paper entitled "Cell Volume
Distributions Reveal Cell Growth Rates and Division Times", by
Michael Halter, John T. Elliott, Joseph B. Hubbard, Alessandro
Tona and Anne L. Plant, which is in press in the Journal of
Theoretical Biology.  In order to reproduce the analysis used
to obtain Table 1 in the paper, execute the command
"example(fitVolDist)".
Implementation of the Coarsened Exact Matching algorithm
Estimation of censored regression (Tobit) models
with cross-section and panel data
Fit univariate censored linear regression model with
Normal or Student-t distribution.
Use pathway topology information to assign weight to
pathway nodes.
Functions and Data to support context epp.
This package provides the iterated RMCD method of Cerioli (2010)
for multivariate outlier detection via robust Mahalanobis distances. It also
provides the finite-sample RMCD method discussed in the paper, as well as
the methods provided in Hardin and Rocke (2005) and Green and Martin (2014).
Analysis of configuration frequencies for simple and
repeated measures, more sample CFa, hierarchical CFA,
bootstrap-CFA, functional CFA, Kieser-Victor CFA and various
plots, and Lindner's test using a conventional and an
accelerated algorithm
Estimate quantiles using formula (18) from
http://www.jaschke-net.de/papers/CoFi.pdf (Yaschke; 2001)
cg is comprehensive data analysis software, and stands for "compare groups." Its genesis and evolution are driven by common needs to compare administrations, conditions, etc. in medicine research &  development. The current version provides comparisons of unpaired samples, i.e. a linear model with one factor of at least two levels. It also provides comparisons of two paired samples. Good data graphs, modern statistical methods, and useful displays of results are emphasized.
In this package, a constrained generalized additive model is fitted by the cgam routine. Given a set of predictors with or without shape or order restrictions, the maximum likelihood estimator for the constrained generalized additive model is found using an iteratively re-weighted cone projection algorithm. The cone information criterion (CIC) may be used to select the best combination of variables and shapes. This package depends on the R package coneproj.
The cgAUC can calculate the AUC-type measure of Obuchowski(2006) when gold standard is continuous, and find the optimal linear combination of variables with respect to this measure.
The package provides a basic set of R functions for querying the Cancer Genomics Data Server (CGDS), hosted by the Computational Biology Center at Memorial-Sloan-Kettering Cancer Center (MSKCC).
Efficient procedures for fitting an entire regression
sequences with different model types.
Functions to analyze microarray comparative genome
hybridization data using the Smith-Waterman algorithm
Spatial smoothing and hot spot detection using the fused
lasso regression
cghseg is an R package dedicated to the analysis of CGH
profiles using segmentation models.
Fit composite Gaussian process (CGP) models as described in Ba and Joseph (2012) "Composite Gaussian Process Models for Emulating Expensive Functions", Annals of Applied Statistics. The CGP model is capable of approximating complex surfaces that are not second-order stationary.  Important functions in this package are CGP, print.CGP, summary.CGP, predict.CGP and plotCGP.
mostly Q&D tools.
ls{size,type,class,data} ,Q&D tools to get info about various collections of objects
askrm , an interactive tool to apply a function to each selected object
seqle, reverse.seqle , extension of rle to find linear sequences
resave , add objects to an existing .Rdata file   short , combine Unix 'head' and 'tail', with options
mystat , get basic statistics of a vector or array
splatnd , a tutorial bit of code on how to create your own zero-argument operators
The ChainLadder package provides various statistical
methods and models which are typically used for the estimation of
outstanding claims reserves in general insurance. The
package has implementations of the Mack, Munich, Bootstrap,
multivariate, and chain-ladder factor models (CLFM),
as well as the loss development factor curve fitting
methods of Dave Clark and generalised linear model
based reserving models.
The package is DEPRECATED. All the functionalities offered
by changeLOS can now be found in the etm package.
Implements various mainstream and specialised changepoint methods for finding single and multiple changepoints within data.  Many popular non-parametric and frequentist methods are included.  The cpt.mean, cpt.var, cpt.meanvar functions should be your first point of call.
This package provides functions to compute Marcus, Marcus-Levich-Jortner or Landau-Zener charge transfer rates. These rates can then be used to perform kinetic Monte Carlo simulations to estimate charge carrier mobilities in molecular materials. The preparation of this package was supported by the the Fondazione Cariplo (PLENOS project, ref. 2011-0349).
CHAT is a collection of tools developed for tumor subclonality analysis using high density DNA SNP array data and sequencing data. The pipeline consists of four major compartments: 1) tumor aneuploid genome proportion (AGP) calculation and ploidy estimation. 2) segment-specific AGP calculation and absolute copy number estimation for somatic CNAs. 3) cancer cell fraction correction for somatic SNVs in clonal or subclonal sCNA regions. 4) number of subclones estimation using Dirichlet process prior followed by MCMC approach.
A compilation of historical through contemporary climate
measurements scraped from the Environment Canada Website
Including tools for scraping data, creating metadata and
formating temperature files.
The package contains methods for creating multivariate Chebyshev
approximation of functions on a hypercube. Some methods for non-Chebyshev grids are also provided.
A set of functions to calculate check digits according to
various algorithms and to verify whether a string ends in a
valid check digit
Tests and assertions to perform frequent argument checks. A
substantial part of the package was written in C to minimize any worries
about execution time overhead.
Cheddar provides a flexible, extendable representation of an ecological community and a range of functions for analysis and visualisation, focusing on food web, body mass and numerical abundance data. It also allows inter-web comparisons such as examining changes in community structure over environmental, temporal or spatial gradients.
chemCal provides simple functions for plotting linear
calibration functions and estimating standard errors for measurements
according to the Handbook of Chemometrics and Qualimetrics: Part A
by Massart et al. There are also functions estimating the limit
of detection (LOD) and limit of quantification (LOQ).
The functions work on model objects from - optionally weighted - linear
regression (lm) or robust linear regression (rlm from the MASS package).
This package is the R companion to the book "Introduction
to Multivariate Statistical Analysis in Chemometrics" written
by K. Varmuza and P. Filzmoser (2009)
The package provides functions and scripts used in the book "Chemometrics with R - Multivariate Data Analysis in the Natural Sciences and Life Sciences" by Ron Wehrens, Springer (2011).
The package provides data sets used in the book
"Chemometrics with R - Multivariate Data Analysis in the
Natural Sciences and Life Sciences" by Ron Wehrens, Springer
(2011).
This package introduces a software tool for the design of
synthetic experiments in machine olfaction. The generator of gas sensor
signals can be used in applications related to test and benchmarking of
signal processing methods, neuromorphic simulations in machine olfaction
and educational tools. A so-called virtual sensor array features unlimited
number of sensors, support of multicomponent gas mixtures and full
parametric control of the noise in the system. The artificial (virtual)
sensor array is inspired from a reference database of seventeen polymeric
sensors with concentration profiles for three analytes.
A collection of functions for plotting spectra (NMR, IR etc) and carrying out various forms of top-down exploratory data analysis, such as HCA, PCA and model-based clustering.  The design allows comparison of data from samples which fall into groups such as treatment vs. control.  Robust methods appropriate for this type of high-dimensional data are available.  ChemoSpec is designed to be very user friendly and suitable for people with limited background in R.
This package implements a class of multiple testing
methods that does not prescribe the user what hypotheses to
reject, but instead allows the user to select the set of
selected hypotheses freely. The multiple testing procedure aids
this selection by providing an upper confidence bound for the
number of false rejections among the selected set. Because the
confidence bounds are simultaneous for all possible rejected
sets, the validity is not compromised by post hoc selection. Some
of the functions in this package are optionally enhanced by the gurobi
software and its accompanying R package. For installation of gurobi, follow the instructions at
http://pages.gurobi.com/DownloadRegistration.html?/download/gurobi-optimizer and
http://www.gurobi.com/documentation/5.6/quick-start-guide/r_installing_the_r_package.
Calculation of standard deviation scores from different
growth standards
This package can be used to do a range of analyses on
long-term weather records and - more importantly - on long-term
observational records of the phenology of temperate vegetation,
i.e. vegetation with a chilling and a heat requirement that
must be fulfilled in order to initiate spring phenology.
The chipPCR package is a toolkit of functions to pre-process
amplification curve data. Amplification data can be obtained from
conventional PCR reactions or isothermal amplification reactions. The
package contains functions to normalize and baseline amplification curves,
a routine to detect the start of an amplification reaction, several
smoothers for amplification data, a function to distinguish positive and
negative amplification reactions and a function to determine the
amplification efficiency. The smoothers are based on LOWESS, moving
average, cubic splines, Savitzky-Golay and others. In addition the first
approximate approximate derivative maximum (FDM) and second approximate
derivative maximum (SDM) can be calculated by a 5-point-stencil as
quantification points from real-time amplification curves. chipPCR contains
data sets of experimental nucleic acid amplification systems including the
VideoScan HCU and a capillary convective PCR (ccPCR) system. The
amplification data were generated by helicase dependent amplification (HDA)
or polymerase chain reaction (PCR) under various temperature conditions. As
detection system intercalating dyes (EvaGreen, SYBR Green) and hydrolysis
probes (TaqMan) were used. The latest source code is available via:
https://github.com/michbur/chipPCR
chngpt is a package for change point logistic regression
This package includes functions and data sets to support chemical thermodynamic
modeling in biochemistry and low-temperature geochemistry. The features include calculation
of the standard molal thermodynamic properties and chemical affinities of reactions involving
minerals and/or biomolecules; a database of thermodynamic properties of aqueous, crystalline
and gaseous species; amino acid group additivity for the standard molal thermodynamic
properties of neutral and ionized proteins; use of the revised Helgeson-Kirkham-Flowers
equations of state for aqueous species; construction of equilibrium activity diagrams as a
function of temperature, pressure, and chemical activities or fugacities of basis species.
Implements an MCMC algorithm to estimate a hierarchical
multinomial logit model with a normal heterogeneity
distribution. The algorithm uses a hybrid Gibbs Sampler with a
random walk metropolis step for the MNL coefficients for each
unit. Dependent variable may be discrete or continuous.
Independent variables may be discrete or continuous with
optional order constraints. Means of the distribution of
heterogeneity can optionally be modeled as a linear function of
unit characteristics variables.
Choplump Tests are Permutation Tests for Comparing Two Groups with Some Positive but Many Zero Responses
Maximum likelihood estimation in respondent driven samples.
choroplethr simplifies the creation of choropleths in R. A
choropleth is a thematic map where geographic regions such as states are
colored according to some metric, such as which political party the state
voted for. choroplethr supports four levels of geographic resolution and
two types of scales. Common problems such as matching county data with map
data, choosing and labeling discrete scales and creating a clean background
are handled automatically. choroplethr provides native support for viewing
data from the US Census's American Community Survey (ACS).
chromoR provides users with a statistical pipeline for analysing chromosomal interactions data (Hi-C data).It combines wavelet methods and a Bayesian approach for correction (bias and noise) and comparison (detecting significant changes between Hi-C maps) of Hi-C contact maps.In addition, it also support detection of change points in 1D Hi-C contact profiles.
Chronological objects which can handle dates and times
Functions that cluster 1-3 dimensional data into their
local modes.  Based on a convergent form of Choi and Hall's
(1999) data sharpening method.  Note: sharp3dB is much faster
than sharp3d if data is ordered in at least one dimension.
Generative models for complex networks with conditionally independent dyadic structure. Now supports directed arcs!
Estimate of cumulative incidence function in two samples. Provide weighted summary statistics based on various methods and weights.
Many experiments in neuroscience involve randomized and fast stimulation while the continuous outcome measures respond at much slower time scale, for example event-related fMRI.  This package provide valid statistical tools with causal interpretation under these challenging settings, without imposing model assumptions.
This package provides functions to compute a method for identifying the instar of Curculionid larvae from the observed distribution of the headcapsule size of mature larvae.
A package for detecting and visualizing high-order epistatic interactions
This package contains functions for fitting circumplex
structural models for correlation matrices (with negative
correlation) by the method of maximum likelihood.
circlize implements circular visualization
(http://circos.ca/) in R. Due to the natural born feature of R to draw statistical
graphics, this package can provide a more general and flexible way to
visualize huge information in a circular style.
Functions for the analysis of circular data using
distributions based on Nonnegative Trigonometric Sums (NNTS).
Functions for calculation of densities and distributions, the
estimation of parameters, plotting and more
Circular Statistics, from "Topics in circular Statistics"
(2001) S. Rao Jammalamadaka and A. SenGupta, World Scientific.
Circular Statistics, from "Topics in circular Statistics" (2001) S. Rao Jammalamadaka and A. SenGupta, World Scientific.
This package implements a formal statistical hypothesis test, resulting in a p-value, to quantify uncertainty in a causal inference pertaining to a measured factor, e.g. a molecular species, which potentially mediates a known causal association between a locus and a quantitative trait. The test is applicable to data that includes genotype (discrete), possible causal mediator such as gene expression (continuous) and an outcome of interest (continuous).
CITAN is a library of functions useful in - but not
limited to - quantitative research in the field of
scientometrics. It contains various tools for preprocessing
bibliographic data retrieved e.g. from Elsevier's SciVerse
Scopus and calculating bibliometric impact of individuals.
Also, many functions dealing with Pareto-Type II (GPD) and
Discretized Pareto-Type II statistical models are included
(e.g. Zhang-Stephens and MLE estimators, goodness-of-fit and
two-sample tests, confidence intervals for the theoretical
Hirsch index etc.). They may be used to describe and analyze
many phenomena encountered in the social sciences.
This package implements the approach to assign tumor gene expression dataset to the 6 CIT Breast Cancer Molecular Subtypes described in Guedj et al 2012.
This package implements the approach to assign tumor gene expression dataset to the 6 CIT Colon Cancer Molecular Subtypes described in Marisa et al 2013.
Input: a csv-file for each database table and a
controlfile describing relations between tables. Output: An
extended ER diagram
This package implements a dynamic programming algorithm to perform optimal one-dimensional k-means clustering, by minimizing the sum of squares of within-cluster distances. As an alternative to the standard heuristic k-means algorithm, this algorithm guarantees optimality and repeatability.
This package implements in C++/Rcpp various cladogenesis-related calculations that are slow in pure R.  These include the calculation of the probability of various scenarios for the inheritance of geographic range at the divergence events on a phylogenetic tree, and other calculations necessary for models which are not continuous-time markov chains (CTMC), but where change instead occurs instantaneously at speciation events.  Typically these models must assess the probability of every possible combination of (ancestor state, left descendent state, right descendent state).  This means that there are up to (# of states)^3 combinations to investigate, and in biogeographical models, there can easily be hundreds of states, so calculation time becomes an issue.  C++ implementation plus clever tricks (many combinations can be eliminated a priori) can greatly speed the computation time over naive R implementations.  CITATION INFO: This package is the result of my Ph.D. research, please cite the package if you use it!  Type: citation(package="cladoRcpp") to get the citation information.
CLAG (for CLusters AGgregation) is an unsupervised non hierarchical clustering algorithm designed to cluster a large variety of biological data and to provide a clustered matrix and numerical values indicating cluster strength. CLAG clusterizes correlation matrices for residues in protein families, gene-expression and miRNA data related to various cancer types, sets of species described by multidimensional vectors of characters, binary matrices. It does not ask to all data points to cluster and it converges yielding the same result at each run. Its simplicity and speed allows it to run on reasonably large datasets.
Construct directed graphs of S4 class hierarchies and
visualize them.  In general, these graphs typically are DAGs
(directed acyclic graphs), often simple trees in practice.
Given $p$-dimensional training data containing
$d$ groups (the design space), a classification
algorithm (classifier) predicts which group new data
belongs to.  Generally the input to these algorithms is
high dimensional, and the boundaries between groups
will be high dimensional and perhaps curvilinear or
multi-faceted. This package implements methods for
understanding the division of space between the groups.
IRT classification uses the probability that candidates of
a given ability, will answer correctly questions of a specified
difficulty to calculate the probability of their achieving
every possible score in a test. Due to the IRT assumption of
conditional independence (that is every answer given is assumed
to depend only on the latent trait being measured) the
probability of candidates achieving these potential scores can
be expressed by multiplication of probabilities for item
responses for a given ability. Once the true score and the
probabilities of achieving all other scores have been
determined for a candidate the probability of their score lying
in the same category as that of their true score
(classification accuracy), or the probability of consistent
classification in a category over administrations
(classification consistency), can be calculated.
A package for choosing univariate class intervals for mapping or other graphics purposes
A collection of functions for the creation and application of highly optimised, robustly evaluated ensembles of support vector machines (SVMs). The package takes care of training individual SVM classifiers using a fast parallel heuristic algorithm, and combines individual classifiers into ensembles. Robust metrics of classification performance are offered by bootstrap resampling and permutation testing.
The R package clere implements an empirical Bayes approach for simultaneous variable clustering and regression.
This package implements the conditioned Latin hypercube sampling, as published by Minasny and McBratney (2006). This method proposes to stratify sampling in presence of ancillary data. An extension of this method, which propose to associate a cost to each individual and take it into account during the optimisation process, is also proposed (Roudier et al., 2012).
ClickClust allows clustering categorical sequences by means of finite mixtures with Markov model components. ClickClust also allows detecting blocks of equivalent states by forward and backward state selection procedures.
This package allows modeling clickstreams with Markov chains. It supports to model clickstreams as zero-order, first-order or higher-order Markov chains.
The climdex.pcic package provides PCIC's implementation of Climdex
routines for computation of climate indices.
A robust constrained L1 minimization method for estimating
a large sparse inverse covariance matrix (aka precision
matrix), and recovering its support for building graphical
models.  The computation uses linear programming.
Utilities to make your clinical collaborations easier if not fun.
Functions for calculating clinical significance.
This package provides the estimation of an index measuring
the clinical utility of DNA testing in the context of
gene-environment interactions on a disease. The corresponding
gene-environment interaction effect on the additive scale can
also be obtained. The estimation is based on case-control or
cohort data. The method was developed by Nguyen et al. 2013.
Tools for the fitting and cross validation of exact conditional logistic regression models with lasso and elastic net penalties. Uses cyclic coordinate descent and warm starts to compute the entire path efficiently.
Provides means of plots for comparing utilization data of compute systems.
R Interface to C API of COIN-OR Clp, depends on COIN-OR Clp Version >= 1.12.0
This package provides and implementation of a one step
smoothing newton method for the solution of second order cone
programming problems, originally described by Xiaoni Chi and
Sanyang Liu.
The package contains functions for automatically
estimating the number of clusters and getting the final cluster
partition without any input parameter except the stopping rule
for convergence. The package also provides functions to
evaluate and compare the performances of partitions of a data
set both numerically and graphically.
Cluster Analysis Methods.  Much extended the original from
Peter Rousseeuw, Anja Struyf and Mia Hubert,
based on Kaufman and Rousseeuw (1990)
A collection of data sets for teaching cluster analysis.
Visualise clustering algorithms with GGobi.  Contains both
general code for visualising clustering results and specific
visualisations for model-based, hierarchical and SOM clustering.
This package develops a genetic algorithm to build
hierarchical clusterings from a dataset. It additionally
provides a set of functions applied to cluster analysis.
The package contains functions for generating random
clusters, generating random covariance/correlation matrices,
calculating a separation index (data and population version)
for pairs of clusters or cluster distributions, and 1-D and 2-D
projection plots to visualize clusters.  The package also
contains a function to generate random clusters based on
factorial designs with factors such as degree of separation,
number of clusters, number of variables, number of noisy
variables.
The Partitioning Algorithm based on Recursive Thresholding
(PART) is used to recursively uncover clusters and subclusters
in the data. Functionality is also available for visualization
of the clustering.
This package enables researchers to calculate power for cluster-randomized crossover trials by employing a simulation-based approach.  A particular study design is specified, with fixed sample sizes for all clusters and an assumed treatment effect, and the empirical power for that study design is calculated by simulating hypothetical datasets.
A function for validating microarry clusters via
reproducibility
GDM Distance,
Sokal-Michener Distance,
Bray-Curtis Distance,
Calinski-Harabasz Index,
G2 Index,
G3 Index,
Silhouette Index,
Krzanowski-Lai Index,
Hartigan Index,
Gap Index,
DB Index,
Data Normalization,
HINoV method,
Replication analysis for cluster validation,
Clustering with several algorithms, distances,
Symbolic interval distances,
Plot functions,
Random cluster generation,
Linear ordering methods,
Comparing partitions,
Spectral clustering,
Ordinal to metric scale transformation,
Untypical data generation.
An R package that provides a suite of tools to evaluate
clustering algorithms, clusterings, and individual clusters.
Performs clustering of haplotypes that ought to share a
common ancestor. It is based on the similarity measures of
every pair of haplotypes using a sliding window along the
genome.
Model-based clustering of mixed data (i.e. data which consist of continuous, binary, ordinal or nominal variables) using a parsimonious mixture of latent Gaussian variable models.
Cluster analysis of a set of variables. Variables can be
quantitative, qualitative or a mixture of both.
A collection of methods for joint dimension reduction and
clustering
A complimentary package for use with hclust; simprof tests
to see which (if any) clusters are statistically different. The
null hypothesis is that there is no a priori group structure.
See Clarke, K.R., Somerfield, P.J., and Gorley R.N. 2008.
Testing of null hypothesis in exploratory community analyses:
similarity profiles and biota-environment linkage. J. Exp. Mar.
Biol. Ecol. 366, 56-69.
This package provides a set of functions that allows us to cluster
a set of variables around Latent Variables. Each group is associated with a
latent variable. External variables measured on the same observations
or/and additional information on the variables can be taken into account.
A function which implements variable selection methodology for model-based clustering which allows to find the (locally) optimal subset of variables  in a dataset that have group/cluster information. A greedy or headlong search can be used, either in a forward-backward or backward-forward direction, with or without sub-sampling at the hierarchical clustering stage for starting Mclust models. By default the algorithm uses a sequential search, but parallelization is also available.
Package contains most of the popular internal and external
cluster validation methods ready to use for the most of the
outputs produced by functions coming from package "cluster".
Package contains also functions and examples of usage for
cluster stability approach that might be applied to algorithms
implemented in "cluster" package as well as user defined
clustering algorithms.
Statistical and biological validation of clustering results.
Single objective optimization using a CMA-ES.
Calculation and plot of the stepwise Cronbach-Mesbah Curve
Collective matrix factorization (CMF) finds joint low-rank representations for a collection of matrices with shared row or column entities. This code learns variational Bayesian approximation for CMF, supporting multiple likelihood potentials and missing data, while identifying both factors shared by multiple matrices and factors private for each matrix.
Quite extensive package for the estimation of marginal
models for categorical data.
The main purpose of this package is to juxtapose the different control limits obtained by modelling a data set through the COM-Poisson distribution vs. the classical Poisson distribution. Accordingly, this package offers the ability to compute the COM-Poisson parameter estimates and plot associated Shewhart control charts for a given data set.
Estimation, testing and regression modeling of
subdistribution functions in competing risks, as described in Gray
(1988), A class of K-sample tests for comparing the cumulative
incidence of a competing risk, Ann. Stat. 16:1141-1154, and Fine JP and
Gray RJ (1999), A proportional hazards model for the subdistribution
of a competing risk, JASA, 94:496-509.
A collection of useful helper routines developed by
students of the Center for the Mathematical Research, Stankin,
Moscow.
various utilities for the complex multivariate Gaussian distribution
Provides a GUI with which users can construct and interact
with Canonical Non-symmetrical Correspondence Analysis.
Methods for assigning copy number states and breakpoints in resequencing experiments of prokaryotic organisms.
The package evaluates DNA copy number data, using both
their intitial form (copy number as a noisy function of genomic
position) and their approximation by a piecewise-constant
function (segmentation), for the purpose of identifying genomic
regions where the copy number differs from the norm.
This package carries out association analysis of common
copy number variants in population-based studies. This package
includes functions for analysing association under a series of
study designs (case-control, cohort, etc), using several
dependent variables (class status, censored data, counts) as
response, adjusting for covariates and considering various
inheritance models. It also includes functions for inferring
copy number (CNV genotype calling).  Various classes and
methods for generic functions (print, summary, plot, anova, ...
) have been created to facilitate the analysis.
This package contains example data sets with Copy Number Variants and imputed SNPs to be used by CNVassoc package.
coalescentMCMC provides a flexible framework for
coalescent analyses in R. It includes a main function running the
MCMC algorithm, auxiliary functions for tree rearrangement, and some
functions to compute population genetic parameters.
This package contains functions to analyze coarse data.
Specifically, it contains functions to (1) fit parametric accelerated
failure time models to interval-censored survival time data, and (2)
estimate the case-fatality ratio in scenarios with under-reporting.
This package's development was motivated by applications to infectious
disease: in particular, problems with estimating the incubation period and
the case fatality ratio of a given disease.  Sample data files are included
in the package.
This package performs prediction for regression-oriented problems, aggregating in a nonlinear scheme any basic regression machines suggested by the context and provided by the user. If the user has no valuable knowledge on the data, four defaults machines wrappers are implemented so as to cover a minimal spectrum of prediction methods. If necessary, the computations may be parallelized. The method is described in Biau, Fischer, Guedj and Malley (2013), "COBRA: A Nonlinear Aggregation Strategy".
Qualitatively Constrained (Regression) Smoothing via
Linear Programming and Sparse Matrices.
Qualitatively Constrained (Regression) Smoothing via
Linear Programming. OUTDATED version based on He & Ng (1999),
the modified "non-simplex" algorithm of Bartels and Conn
(1980).  Use 'cobs' instead, unless for historical comparisons.
This package provides functions to compare two correlations based
on either dependent or independent groups.
Fits predictive and symmetric co-correspondence analysis (CoCA) models to relate one data matrix
to another data matrix. More specifically, CoCA maximises the weighted covariance
between the weighted averaged species scores of one community and the weighted averaged species
scores of another community. CoCA attempts to find patterns that are common to both communitities.
This package provides functions to statistically compare two or
more alpha coefficients based on either dependent or independent groups of
individuals.
Output analysis and diagnostics for Markov Chain Monte
Carlo simulations.
Markov chain Monte Carlo burn-in based on "bridge" statistics, in the way of coda::heidel.diag, but including non asymptotic tabulated statistics.
Computation of Multiscale Codependence Analysis and spatial eigenvector maps, as an additional feature.
Cronbach's alpha is one of the most widely used
reliability or internal consistency measures in social,
behavioral and education sciences. Alpha is reported in nearly
every study that involves measuring a construct through
multiple test items. The package coefficientalpha calculates
Cronbach's coefficient alpha with missing data and non-normal
data. Robust standard error and confidence interval are also
provided.
Plots the coefficients from a model object
Simulate species occurrence and abundances (counts) along
gradients.
species coexistence modeling under asymmetric dispersal
and fluctuating source-sink dynamics;testing the proportion of
coexistence scenarios driven by neutral and niche processes
Copula based imputation method. A semiparametric imputation procedure for missing multivariate data based on conditional copula specifications.
Conditional inference procedures for the general independence
problem including two-sample, K-sample (non-parametric ANOVA), correlation,
censored, ordered and multivariate problems.
Methods for obtaining simultaneous confidence interval for
multinomial proportion have been proposed by many authors and
the present study include a variety of widely applicable
procedures. Seven classical methods (Wilson, Quesenberry and
Hurst, Goodman, Wald with and without continuity correction,
Fitzpatrick and Scott, Sison and Glaz) and Bayesian Dirichlet
models are included in the package. The advantage of MCMC pack
has been exploited to derive the Dirichlet posterior directly
and this also helps in handling the Dirichlet prior parameters.
This package is prepared to have equal and unequal values for
the Dirichlet prior distribution that will provide better scope
for data analysis and associated sensitivity analysis.
cold performs regression analysis for longitudinal count
data using a self-decomposable probability distribution,
a random intercept term is also allowed. Estimation is via maximization
of the exact likelihood of a suitably defined model. Missing values and
unbalanced data are allowed.
These functions implement collocation-inference
for continuous-time and discrete-time stochastic processes.
They provide model-based smoothing, gradient-matching,
generalized profiling and forwards prediction error methods.
Performs the colocalisation tests described in Plagnol et al
(2009), Wallace et al (2013) and Giambartolomei et al (2013).
Given 2-,3- or 4-dimensional data, plots a Venn diagram, i.e. 'crossing circles'. The user can specify values, labels for each circle-group and unique colors for each plotted part. Here is what it would look like for a 3-dimensional plot: http://elliotnoma.files.wordpress.com/2011/02/venndiagram.png. To see what the 4-dimensional plot looks like, go to http://elliotnoma.files.wordpress.com/2013/03/4dplot.png.
Methods and data for color science - color conversions by observer,
illuminant and gamma. Color matching functions and chromaticity diagrams.
Color indices, color differences and spectral data conversion/analysis.
Carries out mapping between assorted color spaces including
RGB, HSV, HLS, CIEXYZ, CIELUV, HCL (polar CIELUV),
CIELAB and polar CIELAB. Qualitative, sequential, and
diverging color palettes based on HCL colors are provided.
R package with handy functions to help users select and play with
color schemes in an HSV color model
Provides access to the COLOURlovers API, which provides color inspiration and color palettes.
Provides methods for the import/export and automated analysis of concept maps.
methods of construction of rectangular PBIB designs from a  rectangular association scheme and rectangular right angular PBIB(m) designs from rectangular right angular association scheme (m) (m= 4,5 and 7).
Functions for computing optimal convex combinations of
model selection criteria based on ranks, along with utility
functions for constructing model lists, MSCs, and priors on
model lists.
Computes community climate statistics for volume and mismatch using species' climate niches either unscaled or scaled relative to a regional species pool. These statistics can be used to describe biogeographic patterns and infer community assembly processes. Includes a vignette outlining usage.
An S4 representation of the Command design pattern. The
Operation class is a simple implementation using closures and supports
forward and reverse (undo) evaluation. The more complicated Protocol
framework represents each type of command (or analytical protocol) by
a formal S4 class. Commands may be grouped and consecutively executed
using the Pipeline class. Example use cases include logging, do/undo,
analysis pipelines, GUI actions, parallel processing, etc.
Useful libraries for building a Java based GUI under R
Directly extract and plot stochastic common trends from
a cointegration system using different approaches, currently
including Kasa (1992) and Gonzalo and Granger (1995).
The approach proposed by Gonzalo and Granger, also known as
Permanent-Transitory Decomposition, is widely used in
macroeconomics and market microstructure literature.
Kasa's approach, on the other hand, has a nice property that it only
uses the super consistent estimator: the cointegration vector
'beta'.
This package also provides functions calculate P-value
from Johansen Statistics according to the approximation method
proposed by Doornik (1998).
Update:
0.7-1: Fix bugs in calculation alpha. Add formulas and more explanations.
0.6-1: Rewrite the description file.
0.5-1: Add functions to calculate P-value from Johansen statistic, and vice versa.
The CommunityCorrelogram package is designed for the geostatistical analysis of ecological community datasets with either a spatial or temporal distance component.
Methods to categorize ICD-9-CM codes into sensible disease
categories have been developed and published by numerous
authors.  Two of the most widely used such methods are the Deyo
adaptation of the Charlson index and the Elixhauser index.
This package has functions to categorize comorbidites into the
Deyo-Charlson index, the original Elixhauser index of 30
comorbidities, and the AHRQ comorbidity index of 29 diagnoses
(an update to the original Elixhauser method).
Package that allow you to compare two ROC curves
throughout the methodology propose by Ana C. Braga
Creates empty plots with compact axis notation to which users can
add whatever they like (points, lines, text, etc.) The notation is more
compact in the sense that the axis-labels and tick-labels are closer to the
axis and the tick-marks are shorter. Also, if the plot appears as part of a
matrix, the x-axis notation is suppressed unless the plot appears along the
bottom row and the y-axis notation is suppress unless the plot appears
along the left column.
Functions for comparing vectors and data frames.
This package is designed to read, interpret, summarise, display and
analyse epidemiological data. Can be used to create data summaries for
quality control, extensive reports for exploring data, including
publication-ready univariate or bivariate tables in plain text HTML and
LaTeX, and a figures to quickly visualise the distribution of your data.
Depending on the nature of the variable, various statistics are
calculated (mean, median, frequencies, incidences) and appropriate
tests are performed (t-test, Analysis of variance, Kruskal-Wallis, Fisher,
log-rank, ...). The latest version includes functionality for summarising
genetic data, and for performing low-level quality control of your database
(see radiograph()) to help identify data-entry errors. This version has
also been implemented in a web-interface to make this package accessible
to non-R-users (see www.comparegroups.eu).
Input: 2 ODM files (ODM version 1.3) Output: list of
identical, matching, similar and differing data items
A standard test is observed on all specimens.  We treat
the second test (or sampled test) as being conducted on only a
stratified sample of specimens.  We treat the total sample as
stratified two-phase sampling and use inverse probability
weighting.  We estimate diagnostic accuracy (category-specific
classification probabilities; for binary tests reduces to
specificity and sensitivity) and agreement statistics (percent
agreement, percent agreement by category, Kappa (unweighted),
and symmetry tests (reduces to McNemar's test for binary
tests)).
Functions for calculating and evaluating likelihood ratios from uni/multivariate continuous observations
The package enables to compute event-specific incidence
rates for competing risks data, to compute rate ratios,
event-specific incidence proportions and cumulative incidence
functions from these, and to plot these in a comprehensive
multi-state type graphic.
Package for the systematic collection, storage and retrieval of gene expression data
via a MySQL database.
The package contains a function (which uses a similar interface to
the `glm' function) for the fitting of a Conway-Maxwell-Poisson GLM. There
are also various methods for analysis of the model fit. The package also
contains functions for the Conway-Maxwell-Poisson distribution in a similar
interface to functions `dpois', `ppois' and `rpois'. The functions are
generally quick, since the workhorse functions are written in C++ (thanks
to the Rcpp package).
Performs the complementary hierarchical clustering procedure and returns X' (the expected residual matrix) and a vector of the relative gene importances.
Compind package contains several functions to enhance approaches
to the Composite Indicators
(http://stats.oecd.org/glossary/detail.asp?ID=6278},
https://composite-indicators.jrc.ec.europa.eu/) methods, focusing, in
particular, on the normalisation and weighting-aggregation steps.
This package provides functionality to perform robust regression
on compositional data. To get information on the distribution of the
estimates, various bootstrapping methods are implemented for the
compositional as well as for standard robust regression models, to provide
a direct comparison between them.
Computes the probability density function, cumulative distribution function, quantile function, random numbers of any composite model based on the lognormal distribution.
Provides routines for density and moments of the
Conway-Maxwell-Poisson distribution as well as functions for
fitting the COM-Poisson model for over/under-dispersed count
data.
Fit a CMP regression model to count data. The code
provides functions for model estimation, dispersion testing,
and diagnostics.
The package provides functions for the consistent analysis of
compositional data (e.g. portions of substances) and positive numbers
(e.g. concentrations) in the way proposed by Aitchison and Pawlowsky-Glahn.
Calculate regression coefficients and their standard deviations under the Cox proportional hazard model with the large number of covariates. A shrinkage type estimation based on the compound covariate method (Emura, Chen & Chen, 2012 PLoS ONE) and a univariate gene selection method under dependent censoring (Emura & Chen, 2014 Statistical Methods in Medical Research) are implemented in this package.
Computing Continuous Distributions Obtained by Compounding
a Continuous and a Discrete Distribution
Computes the distribution function of quadratic forms in
normal variables using Imhof's method, Davies's algorithm,
Farebrother's algorithm or Liu et al.'s algorithm.
The aim of this package is to collect a set of procedures for the analysis of Random Fields by Composite Likelihood methods. Spatial analysis often involves dealing with large dataset. Therefore even simple studies may be too computationally demanding. Composite likelihood based methods are emerging as useful tools for mitigating such computational problems and show satisfactory results when compared with other techniques such as, for example the tapering method. Moreover, composite likelihood (and related quantities) have some good properties similar to those of the standard likelihood.
This package contains several functions for calculating the most
widely used effect sizes (ES), along with their variances, confidence
intervals and p-values.  The output includes ES's of d (mean difference), g
(unbiased estimate of d), r (correlation coefficient), z' (Fisher's z), and
OR (odds ratio and log odds ratio). In addition, NNT (number needed to
treat), U3, CLES (Common Language Effect Size) and Cliff's Delta are
computed. This package uses recommended formulas as described in The
Handbook of Research Synthesis and Meta-Analysis (Cooper, Hedges, &
Valentine, 2009).
Continuous convex piecewise linear (ccpl) resp. quadratic (ccpq) functions can be implemented with sorted breakpoints and slopes. This includes functions that are ccpl (resp. ccpq) on a convex set (i.e. an interval or a point) and infinite out of the domain. These functions can be very useful for a large class of optimisation problems. Efficient manipulation (such as log(N) insertion) of such data structure is obtained with map standard template library of C++ (that hides balanced trees). This package is a wrapper on such a class based on Rcpp modules.
The four functions svdcp (cp for column partitioned),
svdbip or svdbip2 (bip for bi-partitioned), and svdbips (s for
a simultaneous optimization of one set of r solutions),
correspond to a "SVD by blocks" notion, by supposing each block
depending on relative subspaces, rather than on two whole
spaces as usual SVD does. The other functions, based on this
notion, are relative to two column partitioned data matrices x
and y defining two sets of subsets xi and yj of variables and
amount to estimate a link between xi and yj for the pair (xi,
yj) relatively to the links associated to all the other pairs.
Higher order likelihood-based inference for logistic and
loglinear models
Solves for the mean parameters, the variance parameter, and their asymptotic variance in a conditional GEE for recurrent event gap times, as described by Clement and Strawderman (2009) in the journal Biostatistics. Makes a parametric assumption for the length of the censored gap time.
Conditional density estimation with mixtures for
heavy-tailed distributions
This package contains routines for cone projection and quadratic programming, as well as routines to do estimation and inference for constrained parametric regression and shape-restricted regression problems.
This small library contains a series of simple tools for
constructing and manipulating confounded and fractional
factorial designs.
Functions for estimating and reporting multiyear averages and corresponding confidence intervals and distributions. A potential use case is reporting the chemical and ecological status of surface waters according to the European Water Framework Directive.
Several functions for Configural Frequencies Analysis - introduced
by G. A. Lienert as 'Konfigurations Frequenz Analyse' (KFA)
Geometric circle fitting with Levenberg-Marquardt (a, b, R), Levenberg-Marquardt reduced (a, b), Landau, Spath and Chernov-Lesort. Algebraic circle fitting with Taubin, Kasa, Pratt and Fitzgibbon-Pilu-Fisher. Geometric ellipse fitting with ellipseLMG (geometric parameters) and conicLMA (algebraic parameters). Algebraic ellipse fitting with Fitzgibbon-Pilu-Fisher and Taubin.
plot conics (ellipses, hyperbolas, parabolas)
Conjoint is a simple package that implements a conjoint
analysis method to measure the preferences.
Implementation of a procedure (Domingue, 2012; see also
Karabatsos, 2001 and Kyngdon, 2011) to test the single and
double cancellation axioms of conjoint measure in data that is
dichotomously coded and measured with error.
ConnMatTools collects several different methods for analyzing and
working with connectivity matrices in R.  Though primarily oriented towards
marine larval dispersal, many of the methods are general and useful for
terrestrial systems as well.
The constrainedKriging package provides functions for
efficient computations of nonlinear spatial predictions with
local change of support. The package supplies functions for
tow-dimensional spatial interpolation by constrained,
covariance-matching constrained and universal (external drift)
kriging for points or block of any shape for data with a
nonstationary mean function and an isotropic weakly stationary
variogram. The linear spatial interpolation methods,
constrained and covariance-matching constrained kriging,
provide approximately unbiased prediction for nonlinear target
values under change of support. The constrainedKriging package
extends the range of geostatistical tools available in R and
provides a veritable alternative to conditional simulation for
nonlinear spatial prediction problems with local change of
support.
Various utilities for evaluating continued fractions
Bayesian analysis of complete and incomplete contingency tables.
Contrast methods, in the style of the Design package,
for fit objects produced by the lm, glm, gls, and geese functions.
Converts between the Date class and d/m/y for several
calendars, including Persian, Islamic, and Hebrew
This package provides a way to investigate various modes
of convergence of random variables.
This package contains functions to compute the
nonparametric maximum likelihood estimator (MLE) and the
nonparametric least squares estimator (LSE) of a convex hazard
function, assuming that the data is IID.
This R package applies the probabilistic model of species co-occurrence (Veech 2013) to a set of species distributed among a set of survey or sampling sites. The algorithm calculates the observed and expected frequencies of co-occurrence between each pair of species. The expected frequency is based on the distribution of each species being random and independent of the other species. The analysis returns the probabilities that a more extreme (either low or high) value of co-occurrence could have been obtained by chance. The package also includes functions for visualizing species co-occurrence results and preparing data for downstream analyses.
Computes several cooperative games and allocation rules associated with minimum cost spanning tree problems and minimum cost arborescence problems.
This is the package for selecting variables for SDR models
via correlation pursuit.
A logical workflow for the reading, processing, and visualization
of data obtained from the Union Biometrica Complex Object Parametric
Analyzer and Sorter (COPAS) platform large-particle flow cytometers and a
powerful suite of functions for the rapid processing and analysis of large
high-throughput screening data sets. It combines the speed of dplyr with
the elegance of ggplot2 to make analysis of COPAS data fast and painless.
The package implements extensive functions for copula computations and related mathematical operations oriented around the oft cited and fundamental copula theory described by Nelson (2006). The lower (W), upper (M), product (P), PSP, and Plackett (with parameter estimation) copulas are provided. Individual copula functional support is intentionally limited to keep the package focused on an API to general copula theory. Expressions available for an arbitrary copula include the diagonal of a copula, the survival copula, the dual of a copula, and co-copula. Level curves (sets) are available. Horizontal and vertical copula sections and derivatives of such sections also are supported. The numerical derivatives of a copula are provided as well as inverses, and thus functions for copula simulation also are supported. Composition of copulas is provided: (1) composition of a single copula using two composition parameters, (2) composition of two copulas using two composition parameters, (3) composition of two copulas using four composition parameters, and (4) composite copula random variates.  Characteristics of copulas include Kendall Tau, Spearman Rho, Gini Gamma, Blomqvist Beta, and Schweizer-Wolff Sigma. Logical evaluators of positively/negatively quadrant dependency, left-tail decreasing, and right-tail increasing also are available. Quantile and median regression for V with respect to U and U with respect to V is available. Empirical copulas (EC) are supported. ECs are dependent on gridded data structures for which generation capability is provided. The derivatives of the EC grid are computable. Also, the inverses of the derivatives, which are the conditional QDFs of copula sections also are computable. Median and quantile regression of an EC also is supported along with support for EC simulation of V conditional on U.
copCAR provides tools for fitting the copCAR regression model for discrete areal data. Three types of estimation are supported: continuous extension, composite marginal likelihood, and distributional transform.
Classes (S4) of commonly used elliptical, Archimedean, extreme
value and some more copula families.  Methods for density, distribution,
random number generation, bivariate dependence measures, perspective and
contour plots.  Fitting copula models including variance estimates.
Independence and serial (univariate and multivariate) independence tests,
and other copula related tests. Empirical copula and multivariate CDF.
Goodness-of-fit tests for copulas based on multipliers, the parametric
bootstrap with several transformation options.
Merged former package 'nacopula' for nested Archimedean copulas: Efficient
sampling algorithms, various estimators, goodness-of-fit tests and related
tools and special functions.
This package provides a platform where Estimation of Distribution Algorithms (EDAs) based on copulas can be implemented and studied. It contains complete implementations of various EDAs based on copulas and vines, and it also offers the possibility of implementing new algorithms. EDAs are implemented using S4 classes with generic functions for its main parts: seeding, selection, learning, sampling, replacement, local optimization, termination, and reporting. The package also provides a group of well-known benchmark problems and utility functions to study the behavior of EDAs.
This R-packages presents a bivariate, copula-based model
for the joint distribution of a pair of continuous and discrete
random variables. The two marginal random variables are modeled
via generalized linear models, and their joint distribution
(represented by a parametric copula family) is estimated using
maximum-likelihood techniques.
CopyDetect contains several IRT and non-IRT based statistical indices proposed in the literature for detecting answer copying on multiple-choice examinations. CopyDetect includes the indices that have been shown as effective and reliable based on the simulation studies. CopyDetect provides results for the Omega index (Wollack, 1996), Generalized Binomial Test (van der Linden & Sotaridona, 2006), K index (Holland, 1996), K1 and K2 indices (Sotaridona & Meijer, 2002), and S1 and S2 indices (Sotaridona & Meijer, 2003).
Perform a correlational class analysis of the data, resulting in a partition of the data into separate modules.
Generate high-dimensional correlated count random
variables with a prespecified Pearson correlation.
given a collection of intervals with integer start and end positions, find recurrently targeted regions and estimate the significance of finding. Randomization is implemented by parallel methods, either using local host machines, or submitting grid engine jobs.
CORElearn is machine learning suite ported to R from standalone C++ package.
It contains several model learning techniques in classification and regression,
for example classification and regression trees with optional constructive induction and models in the leafs,
random forests, kNN, naive Bayes, and locally weighted regression.
It is especially strong in feature evaluation where it contains several variants of Relief
algorithm and many impurity based attribute evaluation functions, e.g., Gini, information gain, MDL, DKM...
Its additional strength is ordEval algorithm and its visualization used for evaluation of data sets with ordinal features and class.
Several algorithms support parallel multithreaded execution via OpenMP.
The top level documentation is reachable through ?CORElearn.
Use to analysis case-parent trio sequencing studies. Test the compound heterozygous and recessive disease models
Fits a hidden rates model that allows different transition rate classes on different portions of a phylogeny by treating rate classes as hidden states in a Markov process and various other functions for evaluating models of binary character evolution.
We proposed a new model-based clustering method, called the clustering of
regression models method(CORM), which groups genes that share a similar
relationship to the covariate(s). This method provides a unified approach
for a family of clustering procedures and can be applied to data collected
with various experimental designs. This package includes the implementation
for two such clustering procedures: (1) the Clustering of Linear Models
(CLM) method, and (2) the Clustering of Linear Mixed Models (CLMM) method.
This package implements a James-Stein-type shrinkage estimator for
the covariance matrix, with separate shrinkage for variances and correlations.
The details of the method are explained in Sch\"afer and Strimmer (2005) and
Opgen-Rhein and Strimmer (2007).  The approach is both computationally as well
as statistically very efficient, it is applicable to "small n, large p" data,
and always returns a positive definite and well-conditioned covariance matrix.
In addition to inferring the covariance matrix the package also provides
shrinkage estimators for partial correlations and partial variances.
The inverse of the covariance and correlation matrix
can be efficiently computed, as well as any arbitrary power of the
shrinkage correlation matrix.  Furthermore, functions are available for fast
singular value decomposition, for computing the pseudoinverse, and for
checking the rank and positive definiteness of a matrix.
Utility functions and data sets for the statistical
analysis of corpus frequency data, used in the SIGIL statistics
course.
This package implements non-parametric analyses for clustered
binary data. The elements of the cluster are assumed exchangeable, and
identical joint distribution (also known as marginal compatibility, or
reproducibility) is assumed for clusters of different sizes. A trend test
based on stochastic ordering is implemented.
Linear regression based on a recursive structural equation model
(explicit correlations) found by a MCMC algorithm. It permits to face
highly correlated covariates. Variable selection is included (by lasso,
elasticnet, etc.). It also provides some graphical tools for basic
statistics.
Through permutations, obtain a multivariate distribution between univariate variables by specifying a correlation matrix.
Correlplot contains diverse routines for the construction of different plots for representing correlation matrices.
Calculates correlation of variables and displays the results graphically.
The corrplot package is a graphical display of a
correlation matrix, confidence interval. It also contains some
algorithms to do matrix reordering.
Statistical summary of Structure output.
Designed for analysis of the results of a Genome Wide Association Study. Includes tools to pull lists of Chromosome number and SNP position below a certain significance threshold, refine gene networks (including data I/O for Cytoscape), and check SNP base pair changes.
To identify the globally most discriminative subnetwork from gene
expression profiles using an optimization model and genetic algorithm
cosinor is a set of simple functions that transforms longitudinal
data to estimate the cosinor linear model as described in Tong (1976).
Methods are given to summarize the mean, amplitude and acrophase, to
predict the mean annual outcome value, and to test the coefficients.
Package encapsulates standard expressions for distances,
times, luminosities, and other quantities useful in
observational cosmology, including molecular line observations.
Currently coded for a flat universe only.
User-friendly interfaces to perform fast and reliable photometric
redshift estimation. The code makes use of generalized linear models and
can adopt gamma or inverse gaussian families, either from a frequentist or
a Bayesian perspective. The code additionally provides a Shiny application
providing a simple user interface.
This package aims at the easy production of descriptive and
bivariate statistics tables in HTML. It can load files in csv, xls/xlsx
formats, and read labels and formats from SAS or plain text files. It is
intended to be usable by absolute R beginners. Ce paquet permet la
production facile de tableaux HTML descriptifs et comparatifs, meme pour
les debutants sous R.
COSSO is a new regularization method that automatically
estimates and selects important function components by a
soft-thresholding penalty in the context of smoothing spline
ANOVA models. Implemented models include mean regression,
quantile regression, logistic regression and the Cox regression
models.
Contains functions that can determine whether a time series
is second-order stationary or not (and hence evidence for
locally stationarity). Given two non-stationary series (i.e.
locally stationary series) this package can then discover
time-varying linear combinations that are second-order stationary.
Implements cointegration/cotrending rank selection
algorithm in Guo and Shintani(2011). Paper: "Consistant
Cotrending rank selection when both stochastic and nonlinear
deterministic trends are present", Preprint, Feb 2011.
Functions, data and code for Hilbe, J.M. 2011. Negative Binomial Regression, 2-nd Edition (Cambridge University Press) and Hilbe, J.M. 2014. Modeling Count Data (Cambridge University Press).
Standardize country names, convert them into one of
eleven coding schemes, convert between coding schemes, and
assign region descriptors.
Modeling under- and over-dispersed count data using
extended Poisson process models (EPPM)
Estimation of latent class models with covariate effects
on underlying and measured variables. The measured variables
are dichotomous or polytomous, all with the same number of
categories.
This package fits a simultaneous regression model for the mean vectors and covariance matrices of multivariate response variables, as described in Hoff and Niu (2012). The explanatory variables can be continuous or discrete. The current version of the package provides the Bayesian estimates.
The cov.nnve() function for robust covariance estimation
by the nearest neighbor variance estimation (NNVE) method of
Wang and Raftery (2002,JASA)
Model-free selection of covariates under unconfoundedness for situations where the parameter of interest is an average causal effect. This package is based on  model-free backward elimination algorithms proposed in de Luna, Waernbaum and Richardson (2011). Marginal co-ordinate hypothesis testing is used in situations where all covariates are continuous while kernel-based smoothing appropriate for mixed data is used otherwise.
This package computes covariance test for the lasso.
This package provides routines for fitting Cox models by
likelihood based boosting for a single endpoint or in presence
of competing risks
Fits Cox-type models based on interval-censored data from a survival
or illness-death process
Cox proportional hazards models containing Gaussian random
effects, also known as frailty models.
Cox regression with Firth's penalized likelihood
This package implements weighted estimation in Cox regression as proposed by
Schemper, Wakounig and Heinze (Statistics in Medicine, 2009). Weighted Cox regression
provides unbiased average hazard ratio estimates also in case of non-proportional hazards.
Approximated generalized concordance probability an effect size measure for clear-cut
decisions can be obtained.
Additionally estimation of nonlinear effects using fractional polynomials similar to the
MFP algorithm (Royston, Sauerbrei, 2008) is provided. This feature can also be used to
estimate the interaction of a covariate with a nonlinear function of time.
A package for fitting Cox models with penalized ridge-type
partial likelihood. The package includes functions for fitting
simple Cox models with all covariates controlled by a ridge
penalty. The weight of the penalty is optimised by using a REML
type-algorithm. Models with time varying effects of the
covariates can also be fitted. Some of the covariates may be
allowed to be fixed and thus not controlled by the penalty.
There are three different penalty functions, ridge, dynamic and
weighted dynamic. Time varying effects can be fitted without
the need of an expanded dataset.
Fit robustly proportional hazards regression model
The package includes functions to test and compare causal models.
This package contains methods to perform Common Principal
Component Analysis (CPCA). The stepwise method by Trendafilov is published
in the current version. Please see Trendafilov (2010). Stepwise estimation
of common principal components. Computational Statistics & Data Analysis,
54(12), 3446-3457. doi:10.1016/j.csda.2010.03.010
Functions to calculate concordance probability estimates
in survival analysis
CpGassoc is designed to test for association between methylation at CpG sites across the genome and a phenotype of interest,
adjusting for any relevant covariates. The package can perform standard analyses of large datasets very quickly with no
need to impute the data. It can also handle mixed effects models with chip or batch entering the model as a random intercept.
CpGassoc also includes tools to apply quality control filters, perform permutation tests, and create QQ plots, manhattan plots,
and scatterplots for individual CpG sites.
This package computes the maximum likelihood estimator (MLE) of the shape-constrained hazard baseline and the effect parameters in the Cox proportional hazards model under IID sampling.  We assume that the data are continuous and allow for right censoring.  The function 'find.shapeMLE' allows for four different shape constraints: increasing, decreasing, unimodal, and u-shaped.
The package cpk provides simplified clinical pharmacokinetic functions for dose regimen design and modification at the point-of-care. Currently, the following functions are available: (1) ttc.fn for target therapeutic concentration, (2) dr.fn for dose rate, (3) di.fn for dosing interval, (4) dm.fn for maintenance dose, (5) bc.ttc.fn for back calculation, (6) ar.fn for accumulation ratio, (7) dpo.fn for orally administered dose, (8) cmax.fn for peak concentration, (9) css.fn for steady-state concentration, (10) cmin.fn for trough,(11) ct.fn for concentration-time predictions, (12) dlcmax.fn for calculating loading dose based on drug's maximum concentration, (13) dlar.fn for calculating loading dose based on drug's accumulation ratio, and (14) R0.fn for calculating drug infusion rate. Reference: Linares O, Linares A. Computational opioid prescribing: A novel application of clinical pharmacokinetics. J Pain Palliat Care Pharmacother 2011;25:125-135.
R Interface to C API of IBM ILOG CPLEX, depends on IBM ILOG CPLEX (>= 12.1)
Likelihood-based and Bayesian methods for various compound Poisson linear models.
We propose to determine the correction of the significance level after multiple coding of an explanatory variable in Generalized Linear Model. The different methods of correction of the p-value are the Single step Bonferroni procedure, and resampling based methods developped by P.H.Westfall in 1993. Resampling methods are based on the permutation and the parametric bootstrap procedure. If some continuous, and dichotomous transformations are performed this package offers an exact correction of the p-value developped by B.Liquet & D.Commenges in 2005. The naive method with no correction is also available.
Estimates the conditional probability function of a competing event, and fits, using the temporal process regression or the pseudo-value approach, a proportional-odds model to the conditional probability function
This package contains R-functions to perform power
calculation in a group sequential clinical trial with censored
survival data and possibly unequal patient allocation between
treatment and control groups. The fuctions can also be used to
determine the study duration in a clinical trial with censored
survival data as the sum of the accrual duration, which
determines the sample size in a traditional sense, and the
follow-up duration, which more or less controls the number of
events to be observed. This package also contains R functions
and methods to display the computed results.
R functions for cosmological research.
The main functions are similar to the python library, cosmolopy.
Using a sampling-based approach (either sequential importance sampling or explicit Monte Carlo), this package can be used to perform a probabilistic damage tolerance for aircraft structures. It can model a single crack, or two simultaneously growing fatigue cracks (the so-called continuing damage problem). With a single crack, multiple types of future repairs are possible.
Provides R routine for the so called two-sample
Cramer-Test.  This nonparametric two-sample-test on equality
of the underlying distributions can be applied to
multivariate data as well as univariate data. It offers two
possibilities to approximate the critical value both of which
are included in this package.
Functions for completing and recalculating rankings.
Various R tools for http://crantastic.org/
The (C)orrelated (RA)ndom (W)alk (L)ibrary of
R functions was designed for fitting continuous-time
correlated random walk (CTCRW) models with time indexed
covariates. The model is fit using the Kalman-Filter on
a state space version of the continuous-time stochastic
movement process.
Crayon adds support for colored terminal output on
terminals that support ANSI color and highlight codes.
ANSI color support is automatically detected.
Colors and highlighting can be combined and nested. New styles
can also be created easily. This package was inspired by the chalk
JavaScript project.
Implements a statistical test for comparing bar plots or
histograms of categorical data derived from a randomized block
repeated measures layout.
Different approaches to censored regression with conditional
heteroscedasticity are provided. First, continuous distributions can be used
for the (right and/or left censored) response with separate linear predictors
for the mean and variance. Second, cumulative link models for ordinal data
(obtained by interval-censoring continuous data) can be employed for
heteroscedastic extended logistic regression (HXLR). In the latter type of
models, the intercepts depend on the thresholds that define the intervals.
A set of functions for computing the CreditMetrics risk model
This package implements modeling and computational tools for
conditional random fields model (CRF) as well as other probabilistic
undirected graphical models of discrete data with pairwise and unary
potentials.
This package fits discrete mixtures of Zero-Inflated
Poisson (ZIP) models for analyzing criminal trajectories.
CRM simulator for Phase I Clinical Trials
Implements the Cross-contribution Compensating Multiple
standard Normalization (CCMN) method and other normalization
algorithms.
The crn package provides the core functions required to
download and format data from the Climate Reference Network.
Both daily and hourly data are downloaded from the ftp, a
consolidated file of all stations is created, station metadata
is extracted. In addition functions for selecting individual
variables and creating R friendly datasets for them is
provided.
Contains functions for the construction of carryover
balanced crossover designs. In addition contains functions to
check given designs for balance.
This package performs a test for comparing two
multivariate distributions by using the distance between
observations.  The input is a distance matrix and the labels of
the two groups to be compared, the output is the number of
cross-matches and a p-value.
Package Crossover provides different crossover designs from combinatorial or search algorithms as well as from literature and a GUI to access them.
This package provides functions to calculate confidence intervals for crossover points of two simple linear regression lines using
the non-linear regression, the delta method, the Fieller method, and the bootstrap methods.
This package contains generic functions for performing
cross validation and for computing diagnostic errors.
Modelling credit risks based on the concept of "CreditRisk+", First Boston Financial Products, 1997 and "CreditRisk+ in the Banking Industry", Gundlach & Lehrbass, Springer, 2003.
CRQA is a package to perform cross-recurrence quantification
analysis between two time-series, of either categorical or
continuous values. It provides different methods
for profiling cross-recurrence, i.e., only looking
at the diagonal recurrent points, as well as more
in-depth measures of the whole cross-recurrence plot,
e.g., recurrence rate.
Extension of cmprsk to Stratified and Clustered data.
Goodness of fit test for Fine-Gray model.
Performs forward and backwards stepwise regression for the Proportional subdistribution hazards model in competing risks (Fine & Gray 1999). Procedure uses AIC, BIC and BICcr as selection criteria. BICcr has a penalty of k = log(n*), where n* is the number of primary events.
This package provides for estimating regression splines that handle a mix of continuous and categorical (discrete) data often encountered in applied settings. I would like to gratefully acknowledge support from  the Natural Sciences and Engineering Research Council of Canada (NSERC:www.nserc.ca), the Social Sciences and Humanities Research Council of Canada (SSHRC:www.sshrc.ca), and the Shared Hierarchical Academic Research Computing Network (SHARCNET:www.sharcnet.ca).
This package contains basic tools for the purpose of sample size estimation in cluster (group) randomized trials.  The package contains traditional power-based methods, empirical smoothing (Rotondi and Donner, 2009), and updated meta-analysis techniques (Rotondi and Donner, 2012).
Exploratory tools to identify closely related subjects using autosomal genetic marker data.
Monte Carlo conditional inference for the parameters of a
linear nonnormal regression model
cSFM is a method to model skewed functional data when considering covariates via a copula-based approach.
Package for CShapes, a GIS dataset of country boundaries
(1946-2012). Includes functions for data extraction and the
computation of distance matrices and -lists.
This package contains functions for likelihood and
posterior analysis of conditionally specified logistic
regression models.  All calculus and simulation is done in
compiled FORTRAN.
This package provides functions for computing the density
and the log-likelihood function of closed-skew normal variates,
and for generating random vectors sampled from this distribution.
The package provides functions that facilitate extraction of information from an html document by using css selectors instead of xpath queries.
Cell-type specific differential expression of a microarray
experiment of heterogeneous tissue samples, using SAM.
Functions that allow a researcher to examine the robustness of the substantive significance of their findings. Implements ideas set out in Esarey and Danneman (2014).
This package provides functions for loading large (10M+ lines) CSV
and other delimited files, similar to read.csv, but typically faster and
using less memory than the standard R loader. While not entirely general,
it covers many common use cases when the types of columns in the CSV file
are known in advance. In addition, the package provides a class 'int64',
which represents 64-bit integers exactly when reading from a file. The
latter is useful when working with 64-bit integer identifiers exported from
databases. The CSV file loader supports common column types including
'integer', 'double', 'string', and 'int64', leaving further type
transformations  to the user.
Continuous Time Autoregressive Models and the Kalman Filter
Server-side and client-side tools for task views
to CRAN-style repositories
Adaptive multivariate integration over hypercubes
Estimating mutation and selection coefficients on synonymous
codon bias usage based on models of ribosome overhead cost (ROC).
Multinomial logistic regression and Markov Chain Monte Carlo are used to
estimate and predict protein production rates with/without the presence
of expressions and measurement errors. Work flows with examples for
simulation, estimation and prediction processes are also provided
with parallelization speedup. The whole framework is tested with
yeast genome and gene expression data of Yassour (2009).
Regression modeling using rules with added instance-based corrections
Compute Unified Device Architecture (CUDA) is a software
platform for massively parallel high-performance computing on
NVIDIA GPUs. This package provides a CUDA implementation of a
Bayesian multilevel model for the analysis of brain fMRI data.
A fMRI data set consists of time series of volume data in 4D
space. Typically, volumes are collected as slices of 64 x 64
voxels. Analysis of fMRI data often relies on fitting linear
regression models at each voxel of the brain. The volume of the
data to be processed, and the type of statistical analysis to
perform in fMRI analysis, call for high-performance computing
strategies. In this package, the CUDA programming model uses a
separate thread for fitting a linear regression model at each
voxel in parallel. The global statistical model implements a
Gibbs Sampler for hierarchical linear models with a normal
prior. This model has been proposed by Rossi, Allenby and
McCulloch in `Bayesian Statistics and Marketing', Chapter 3,
and is referred to as `rhierLinearModel' in the R-package
bayesm. A notebook equipped with a NVIDIA `GeForce 8400M GS'
card having Compute Capability 1.1 has been used in the tests.
The data sets used in the package's examples are available in
the separate package cudaBayesregData.
FMRI data sets used in the examples of "cudaBayesreg".
Data sets have been separated from the main package
"cudaBayesreg" for convenience.
Reconstruct individual-level values from aggregate-level
summaries.
Combining Univariate Association Test Results of Multiple
Phenotypes for Detecting Pleiotropy
Extends ddply to allow calculation of cumulative
quantities.
Estimation of number and location of change points in
mean-shift (piecewise constant) models. Particularly useful to
model genomic sequences of continuous measurements.
The currentSurvival package contains functions for the
estimation of the current cumulative incidence (CCI) and the
current leukaemia-free survival (CLFS). The CCI is the
probability that a patient is alive and in any disease
remission (e.g. complete cytogenetic remission in chronic
myeloid leukaemia) after initiating his or her therapy (e.g.
tyrosine kinase therapy for chronic myeloid leukaemia). The
CLFS is the probability that a patient is alive and in any
disease remission after achieving the first disease remission.
Test Equality of Curves with Homoscedastic or
Heteroscedastic Errors.
Filtering, also known as gating, of
flow cytometry samples using the curvHDR method.
This package provides a set of tools for spatio-temporal imputation
in R. It includes the implementation for then CUTOFF imputation method,
a useful cross-validation function that can be used not only by the
CUOTFF method but also by some other imputation  functions to help
choosing an optimal value for relevant parameters, such as the number
of k-nearest neighbors for the KNN imputation method, or the number of
components for the SVD imputation method. It also contains tools for
simulating data with missing values with respect to some specific
missing pattern, for example, block missing. Some useful visualisation
functions for imputation purposes are also provided in the package.
This package can be used to standardize abundance indices using the delta-GLM method and to model the English Channel cuttlefish stock using a two-stage biomass model
This package contains various tools for working with and
evaluating cross-validated area under the ROC curve (AUC)
estimators.  The primary functions of the package are ci.cvAUC
and ci.pooled.cvAUC, which report cross-validated AUC and
compute confidence intervals for cross-validated AUC estimates
based on influence curves for i.i.d. and pooled repeated
measures data, respectively.  One benefit to using influence
curve based confidence intervals is that they require much less
computation time than bootstrapping methods.  The utility
function, cvAUC, which computes cross-validated AUC, is a
wrapper for functions from the ROCR package.
Statistical inferences for estimating the calibration equation with error-in observations
Methods for color vision deficiencies (CVD), to help understanding and mitigating issues with CVDs and to generate tests for diagnosis and interpretation.
The package uses majorization minimization by coordinate
descent (MMCD) algorithm to compute the solution surface for
concave penalized logistic regression model.  The SCAD and MCP
(default) are two concave penalties considered in this
implementation.  For the MCP penalty, the package also provides
the local linear approximation by coordinate descant (LLA-CD)
and adaptive rescaling algorithms for computing the solutions.
The package also provides a Lasso-concave hybrid penalty for
fast variable selection. The hybrid penalty applies the concave
penalty only to the variables selected by the Lasso.  For all
the implemented methods, the solution surface is computed along
kappa, which is a more smooth fit for the logistic model.
Tuning parameter selection method by k-fold cross-validated
area under ROC curve (CV-AUC) is implemented as well.
The external prediction capability of quantitative structure-activity relationship (QSAR) models is often quantified using the predictive squared correlation coefficient. This value can be calculated with an external data set or by cross validation.
This package implements the fast cross-validation via sequential testing (CVST) procedure. CVST is an improved cross-validation procedure which uses non-parametric testing coupled with sequential analysis to determine the best parameter set on linearly increasing subsets of the data. By eliminating underperforming candidates quickly and keeping promising candidates as long as possible, the method speeds up the computation while preserving the capability of a full cross-validation. Additionally to the CVST the package contains an implementation of the ordinary k-fold cross-validation with a flexible and powerful set of helper objects and methods to handle the overall model selection process. The implementations of the Cochran's Q test with permutations and the sequential testing framework of Wald are generic and can therefore also be used in other contexts.
This package carries out level-dependent cross-validation
method for the selection of thresholding value in wavelet
shrinkage. This procedure is implemented by coupling a
conventional cross validation with an imputation method due to
a limitation of data length, a power of 2. It can be easily
applied to classical leave-one-out and k-fold cross validation.
Since the procedure is computationally fast, a level-dependent
cross validation can be performed for wavelet shrinkage of
various data such as a data with correlated errors.
Tools that allow developers to write functions for
cross-validation with minimal programming effort and assist
users with model selection.
This is a package for selecting tuning parameters based on cross-validation (CV) in regularized estimators of large covariance matrices. Four regularized methods are implemented: banding, tapering, hard-thresholding and soft-thresholding. Two types of matrix norms are applied: Frobenius norm and operator norm. Two types of CV are considered: K-fold CV and random CV. Usually K-fold CV use K-1 folds to train a model and the rest one fold to validate the model. The reverse version trains a model with 1 fold and validates with the rest with K-1 folds. Random CV randomly splits the data set to two parts, a training set and a validation set with user-specified sizes.
Alternating Minimization Algorithm (AMA) and Alternating Direction
Method of Multipliers (ADMM) splitting methods for convex clustering.
This package estimates gaussian cluster weighted linear regressions by EM algorithm.
extend cxxfunction by saving the dynamic shared objects
for reusing across R sessions
Tools for calculating coordinate representations of
hypocycloids, epicyloids, hypotrochoids, and epitrochoids
(altogether called 'cycloids' here) with different scaling
and positioning options. The cycloids can be visualised with
any appropriate graphics function in R.
This library contains a primary function that divides
chewing sequences in cycles and cycles into phases.  See
get.all.breaks for an example.
Calculates ecological diversity indices for a microbial
community
This packages is intended to make it easy to create D3 JavaScript
network, tree, dendrogram, and Sankey graphs from R using data frames.
various data sets used in examples and exercises in the
book Maindonald, J.H. and Braun, W.J. (2003, 2007, 2010) "Data
Analysis and Graphics Using R".
Data sets and functions, for the display of biological
expression array (microarray) data, and for demonstrations with
such data
various data sets used in additional exercises for
the book Maindonald, J.H. and Braun, W.J. (3rd edn 2010)
"Data Analysis and Graphics Using R", and for a
'Data Mining' course.  Note that a number of datasets
that were in earlier versions of this package have been
transferred to the DAAG package.
This package includes a number of functions that aid in
generating experimental designs and in examining their
canonical efficiency factors. It also includes functions that
facilitate diagnostic checking after an aov, especially when
the Error function has been used in the call to aov.
This package contains data frames and functions used in the book "Design and Analysis of Experiments with R"
Data and miscellanea to support the book "Introduction to
Data analysis with R for Forensic Scientists", Curran, J.M.
2010 CRC Press ISBN: 978-1-4200-8826-7
dagbag is a set of methods that learn DAGs via bootstrap aggregating.
Integrates the information from multiple linkage maps to
create a consensus directed graph, which is then linearized to
produce a consensus map.
Functions to draw, manipulate, evaluate directed
acyclic graphs and simulate corresponding data.
Several functions for evaluating the accuracy of
classification models. The package provides the following
performance measures: repeated k-fold cross-validation,
0.632 and 0.632+ bootstrap estimation of the misclassification rate,
sensitivity, specificity and AUC. If an application is
computationally intensive, parallel execution can be used
to reduce the computational effort.
Functions and an example dataset for the psychometric
theory of knowledge spaces.  This package implements data
analysis methods and procedures for simulating data and quasi
orders and transforming different formulations in knowledge
space theory.
The DALY Calculator is a free, open-source Graphical User
Interface (GUI) for stochastic disability-adjusted life year
(DALY) calculation.
This package contains a miscellaneous set of functions I
use in my teaching either at UWM or the ICPSR Summer Program.
Broadly, the functions help with presentation and
interpretation of GLMs.
The single largest source of dams in the United States is the
National Inventory of Dams (NID, http://nid.usace.army.mil) from the US
Army Corps of Engineers. Entire data from the NID cannot be obtained all at
once and NID's website limits extraction of more than a couple of thousand
records at a time. Moreover, selected data from the NID's user interface
cannot not be saved to a file. In order to make the analysis of this data
easier, all the data from NID was extracted manually. Subsequently, the raw
data was checked for potential errors and cleaned. This package provides
sample cleaned data from the NID and provides functionality to access the
entire cleaned NID data.
This package contains the function used to create the Dandelion Plot. Dandelion Plot is a visualization method for R-mode Exploratory Factor Analysis
The darch package is build on the basis of the code from G. E.
Hinton and R. R. Salakhutdinov (available under Matlab Code for deep belief
nets : last visit: 01.08.2013). This package is for generating neural
networks with many layers (deep architectures) and train them with the
method introduced by the publications "A fast learning algorithm for deep
belief nets" (G. E. Hinton, S. Osindero, Y. W. Teh) and "Reducing the
dimensionality of data with neural networks" (G. E. Hinton, R. R.
Salakhutdinov). This method includes a pre training with the contrastive
divergence method publishing by G.E Hinton (2002) and a fine tuning with
common known training algorithms like backpropagation or conjugate
gradient.
Are you aiming at the right spot in darts? Maybe not! Use
this package to find your optimal aiming location. For a better
explanation, go to
http://www-stat.stanford.edu/~ryantibs/darts/ or see the paper
"A Statistician Plays Darts".
a package providing an interface to the C++ FILEVECTOR
library facilitating analysis of large (giga- to tera-bytes) matrices;
matrix storage is organized in a way that either columns or rows are
quickly accessible; primarily aimed to support genome-wide association
analyses e.g. using GenABEL, MixABEL and ProbABEL
The package provides functionality to check variables against a
set of basic rules. A rule file can be accompanied by look-up tables. In
addition, the package provides some convenience testing functions that may
serve also as an example for defining clearer 'data rules' or tests. An
HTML based user interface facilitates initial exploration of the
functionality.
R tools for combining and cleaning data sets, including filling in
missing observations with data from another data frame and creating
lag/lead variables.
dataframes2xls writes data frames to xls files. It
supports multiple sheets and basic formatting.
datalist is an R package to 1) convert data sets into numeric data
lists for import into JAGS, WinBUGS and OpenBUGS and 2) generate data.frames
that can be passed to models for the purposes of predicting the effects of
particular variables with the other variables held constant.
datamap utilizes variable bindings and objects of class
"UserDefinedDatabase" to provide a simple mapping system to
foreign objects. Maps can be used as environments or attached
to the search path, and changes to either are persistent.
Mapped foreign objects are fetched in real-time and are never
cached by the mapping system.
Provides an S4 infrastructure for unified handling
of internal datasets and web based data sources. Examples include dbpedia,
eurostat and sourceforge.
This package is designed to simplify the problem of
combining overlapping and/or inconsistent data into a single
set.
The package performs variable level data quality checks including
missing values, unique values, frequency tables, and generates summary
statistics
Fast aggregation of large data (e.g. 100GB in RAM), fast ordered joins, fast add/modify/delete of columns by group using no copies at all, list columns and a fast file reader (fread). Offers a natural and flexible syntax, for faster development.
Support functions for packages VBmix, semisupKernelPCA,
and patchPlot.
This package provides clear, informative and colorful ways of
presenting the contents of data.frame, list, environment etc. in the
prompt. Ideally this package should be used in terminals supporting xterm
256 colour mode, e.g. the standard Ubuntu terminal. The Mac OS X terminal
(as of Snow Leopard) only support ANSI colour, which still works but
doesn't look as nice. On Windows systems no colouring is supported. If the
output of functions like 'whos' is a mess or somehow doesn't look like it
should check the help for the functions 'style' and 'style.mode' of the
'xtermStyle' package.
This package performs voxel-wise deconvolution analysis of
DCE-MRI contrast agent concentration versus time data and
generates the Impulse Response Function, which can be used to
approximate commonly utilized kinetic parameters such as Ktrans
and ve.  An interactive advanced voxel diagnosis tool (AVDT) is
also provided to facilitate easy navigation of voxel-wise data.
A collection of functions accompanying the book "Data Analysis in Vegetation Ecology", Second edition. 2013, Wiley-Blackwell, Chichester
Various utilities for the Davies distribution and the
generalized lambda distribution
This package performs linear and quadratic discriminant analysis with additional information expressed as inequality restrictions among the populations means and computes several estimations of the true error rate
Fits Bayesian additive regression trees (BART) while allowing the updating of predictors or response so that BART can be incorporated as a conditional model in a Gibbs/MH sampler. Also serves as a drop-in replacement for package BayesTree.
Using widgets provided by gWidgets this creates a nice
usable interface to help facilitate the learning of SQL.  It
also just gives a nice interface for more advanced users.  Not
all features are provided in the GUI but it is possible for
more advanced users to achieve these goals inside the GUI.
Goodness-of-fit and two sample comparison tests using sample entropy
Distance-based test of no difference between groups.
This package provides methods and examples to support a
method of Gene Set Analysis (GSA).  DBGSA is a novel
distance-based gene set enrichment analysis method. We consider
that, the distance between 2 groups with different phenotype by
focusing on the gene expression should be larger, if a certain
gene functional set was significantly associated with a
particular phenotype.
A database interface (DBI) definition for communication
between R and relational database management systems.  All
classes in this package are virtual and need to be extended by
the various R/DBMS implementations.
This package allows for nonparametric graduation of mortality rates using fixed or adaptive discrete beta kernel estimator.
Use EM algorithm to compute the NPMLE of CDF and also the
two censoring distributions. For doubly censored data (as
described in Chang and Yang (1987) Ann. Stat. 1536-47). You can
also specify a constraint, it will return the constrained NPMLE
and the -2 log empirical likelihood ratio. This can be used to
test the hypothesis about the constraint and find confidence
intervals for probability or quantile via empirical likelihood
ratio theorem. Influence function of hat F may also be
calculated (but may be slow).
This package allows simple computation of a full set of spatial statistic functions of distance, including classical ones (Ripley's K and others) and more recent ones used by spatial economists (Duranton and Overman's Kd, Marcon and Puech's M). It relies on spatstat for core calculation.
This package contains functions for distance-based prediction methods. These are methods for prediction where predictor information is coded as a matrix of distances between individuals. Distances can either be directly input as an interdistances matrix, a squared interdistances matrix, an inner-products matrix or computed from observed explanatory variables.
A collection of routines and documentation that allows one to
perform voxel-wise quantitative analysis of dynamic contrast-enhanced or
diffusion-weighted MRI data, with emphasis on oncology applications.
Functions for 1) gene filtration; 2) link filtration; 3) differential co-expression analysis: DCG (Differential Coexpressed
Gene) identification and DCL (Differentially Coexpressed Link)
identification; and 4) differential regulation analysis: DRG (Differential Regulated
Gene) identification, DRL (Differential Regulated Link)
identification, DRL visualization and regulator ranking.
The package is used to analyse domain-centric ontologies and annotations, particularly those in the dcGO database. The dcGO (http://supfam.org/SUPERFAMILY/dcGO) is a comprehensive domain-centric database for annotating protein domains using a panel of ontologies including Gene Ontology. With the package, users are expected to analyse and visualise domain-centric ontologies and annotations. Supported analyses include but are not limited to: easy access to a wide range of ontologies and their domain-centric annotations; able to build customised ontologies and annotations; domain-based enrichment analysis and visualisation; construction of a domain (semantic similarity) network according to ontology annotations; significance analysis for estimating a contact (statistical significance) network via Random Walk with Restart; and high-performance parallel computing.
Statistical modelling and forecasting in claims reserving in non-life insurance under the Double Chain Ladder framework by Martinez-Miranda, Nielsen and Verrall (2012).
Low level functions for implementing
maximum likelihood estimating procedures for
complex models using data cloning and Bayesian
Markov chain Monte Carlo methods.
Sequential and parallel MCMC support
for JAGS, WinBUGS and OpenBUGS.
A set of functions for the detection of spatial clusters
of disease using count data. Bootstrap is used to estimate
sampling distributions of statistics.
S4 classes around infrastructure
provided by the dclone package to make package
development with data cloning for hierarchical
models easy as a breeze.
Analysis of dichotomous response data to obtain attribute profile
estimates for respondents using Diagnostic Classification Model (DCM) and
Markov Chain Monte Carlo (MCMC) method. The estimation procedure uses a
loglinear cognitive diagnostic modeling (LDCM) framework that allows for
the estimation of a host of DCMs such as NIDO, NIDA, NC-RUM etc.
This package performs several conventional
Cross-validation statistical methods for climate-growth model
in the climate reconstruction from tree rings, including Sign
Test statistic, Reduction of Error statistic, Product Mean
Test, Durbin-Watson statistic etc. This package is at its
primary stage, the functions have not been tested exhaustively
and more functions would be added in the comming days.
Contains methods for depth classification based on the \eqn{\alpha}-procedure and for calculation of statistical depths
DDD implements maximum likelihood methods based on the diversity-dependent birth-death process to test whether speciation or extinction are diversity-dependent.
See Etienne et al. 2012, Proc. Roy. Soc. B 279: 1300-1309, doi: 10.1098/rspb.2011.1439.
DDD also implements a maximum likelihood method to detect key innovations defined as decoupling of diversity-dependence.
See Etienne & Haegeman 2012, Am. Nat. 180: E75-E89, doi: 10.1086/667574.
Finally, DDD contains a function to simulate the diversity-dependent process.
DDEPN (Dynamic Deterministic Effects Propagation Networks): Infer signalling networks for timecourse data. Given a matrix of high-throughput genomic or proteomic timecourse data, generated after external perturbation of the biological system, DDEPN models the time-dependent propagation of active and passive states depending on a network structure. Optimal network structures given the experimental data are reconstructed. Two network inference algorithms can be used: inhibMCMC, a Markov Chain Monte Carlo sampling approach and GA, a Genetic Algorithm network optimisation. Inclusion of prior biological knowledge can be done using different network prior models.
The package contains the normalizing and variance stabilizing
Data-Driven Haar-Fisz algorithm. Also contains related algorithms
for simulating from certain microarray gene intensity models and
evaluation of certain transformations.
This package provides useful functions for various DDI (Data Documentation Initiative) related outputs.
Bayesian networks with continuous and/or discrete
variables can be learned and compared from data.
deamer provides deconvolution algorithms for the
non-parametric estimation of the density f of an error-prone
variable x with additive noise e. The model is y = x + e where
the noisy variable y is observed, while x is unobserved.
Estimation may be performed for i) a known density of the error
ii) with an auxiliary sample of pure noise and iii) with an
auxiliary sample of replicate (repeated) measurements.
Estimation is performed using adaptive model selection and
penalized contrasts.
Debugger for R functions, with code display, graceful
error recovery, line-numbered conditional breakpoints, access
to exit code, flow control, and full keyboard input.
decctools provides functions for retrieving energy
statistics from the United Kingdom Department of Energy and Climate
Change and related data sources.  The current version focuses on
total final energy consumption statistics at the local authority, MSOA,
and LSOA geographies.  Methods for calculating the generation mix of
grid electricity and its associated carbon intensity are also provided.
Calculates various estimates for measures of educational
differentials, the relative importance of primary and secondary
effects in the creation of such differentials and compares the
estimates obtained from two datasets.
The R package "decompr" implements two export decomposition
algorithms. Firstly, the Wang-Wei-Zhu (Wang, Wei, and Zhu 2013) algorithm
splits bilateral gross exports into 16 value added components. Secondly,
the Source decomposition algorithm derives the value added origin of
exports by country and industry, which is also based on Wang, Wei, and Zhu
(2013).
This package contains a collection of functions to deal
with nonparametric measurement error problems using
deconvolution kernel methods. We focus two measurement error
models in the package: (1) an additive measurement error model,
where the goal is to estimate the density or distribution
function from contaminated data; (2) nonparametric regression
model with errors-in-variables. The R functions allow the
measurement errors to be either homoscedastic or
heteroscedastic. To make the deconvolution estimators
computationally more efficient in R, we adapt the "Fast Fourier
Transform" (FFT) algorithm for density estimation with
error-free data to the deconvolution kernel estimation. Several
methods for the selection of the data-driven smoothing
parameter are also provided in the package. See details in:
Wang, X.F. and Wang, B. (2011). Deconvolution estimation in
measurement error models: The R package decon. Journal of
Statistical Software, 39(10), 1-24.
An intuitive, cross-platform graphical data analysis system. It uses menus and dialogs to guide the user efficiently through the data manipulation and analysis process, and has an excel like spreadsheet for easy data frame visualization and editing. Deducer works best when used with the Java based R GUI JGR, but the dialogs can be called from the command line. Dialogs have also been integrated into the Windows Rgui.
Added functionality for Deducer. This package includes
additional dialogs for calculating distribution function
values, cluster analysis and more.
A example GUI plug-in package to serve as a template.
A Deducer plug-in for factor analysis, reliability
analysis and discriminant analysis, using psych, GPArotation
and mvnormtest packages.
A Deducer plug-in for spatial data analysis. Includes The
ability to plot and explore open street map and Bing satellite
images.
Adds Kaplan-Meier Survival Analysis to the Deducer GUI
A collection of methods for automated data cleaning where
all actions are logged.
Implement some deep learning architectures and neural network
algorithms, including BP,RBM,DBN,Deep autoencoder and so on.
Set, Get, and Import Global Function Defaults
Detection of differentially expressed genes between two
distinct groups of samples.
Likelihood-based inference for skewed count distributions
used in network modeling. "degreenet" is a part of the
"statnet" suite of packages for network analysis.
Provides probability mass, distribution, quantile, random variate generation, and method of moments parameter estimation functions for the Delaporte distribution.
Calculates the Delaunay triangulation and the Dirichlet
or Voronoi tessellation (with respect to the entire plane) of
a planar point set. Plots triangulations and tessellations in
various ways.  Clips tessellations to sub-windows. Calculates
perimeters of tessellations.
The deltaPlotR package proposes an implementation of Angoff's Delta Plot method to detect dichotomous DIF. Several detection thresholds are included, either from multivariate normality assumption or by prior determination. Item purification is supported.
Functions to calculate pairwise relatedness on diploid genetic datasets. Different estimators for relatedness can be combined with information on geographical distances. Information on heterozygosity, allele- and genotype diversity as well as genetic F-statistics are provided for each population.
This package allows to calculate the fixation index Gst
(Nei, 1973) and the differentiation index D (Jost, 2008) pairwise
between or averaged over several populations.  P-values, stating the
significance of differentiation, and 95 percent confidence intervals
can be estimated using bootstrap resamplings. In the case that more
than two populations are compared pairwise, the p-values are
adjusted by bonferroni correction and in several other ways due to
the multiple comparison from one data set.
DEMI method implementation as an R package
Generalized Deming regression, Theil-Sen regression and Passing-Bablock regression functions.
Functions for demographic analysis including lifetable
calculations; Lee-Carter modelling; functional data analysis of
mortality rates, fertility rates, net migration numbers; and
stochastic population forecasting.
Demonstration code showing how (univariate) kernel
density estimates are computed, at least conceptually,
and allowing users to experiment with different kernels,
should they so wish.  NOTE: the density function in the
stats package should be used for computational efficiency.
The dendextend package offers a set of functions for extending
dendrogram objects in R, letting you visualize and compare trees of
hierarchical clusterings. You can (1) Adjust a trees graphical parameters
- the color, size, type, etc of its branches, nodes and labels. (2)
Visually and statistically compare different dendrograms to one another.
This package offers faster manipulation of dendrogram objects in
R. A dendrogram object in R is a list structure with attributes in its
nodes and leaves. Working with dendrogram objects often require a function
to recursively go through all (or most) element in the list object.
Naturally, such function are rather slow in R, but can become much faster
thanks to Rcpp.
This package provides extra functions to manipulate dendrograms
that build on the base functions provided by the stats package. The main
functionality it is designed to add is the ability to colour all the edges
in an object of class dendrogram according to cluster membership i.e. each
subtree is coloured, not just the terminal leaves. In addition it provides
some utility functions to cut dendrogram and hclust objects and to set/get
labels.
Re-arranges a dendrogram to optimize visualisation-based cost functions
This package includes functions to optimize ordering of nodes in a dendrogram,
without affecting the meaning of the dendrogram. A dendrogram can be sorted based
on the average distance of subtrees, or based on the smallest distance value.
These sorting methods improve readability and interpretability of tree structure,
especially for tasks such as comparison of different distance measures or linkage
types and identification of tight clusters and outliers. As a result, it also
introduces more meaningful reordering for a coupled heatmap visualization.
The package provides tools to
(1) visualize multivariate density functions and density estimates
with level set trees,
(2) visualize level sets with shape trees,
(3) visualize multivariate data with tail trees,
(4) visualize scales of multivariate density estimates with
mode graphs and branching maps, and
(5) visualize anisotropic spread with 2D volume functions and
2D probability content functions.
Level set trees visualize mode structure,
shape trees visualize shapes of level sets of unimodal densities,
and tail trees visualize connected data sets.
The kernel estimator is implemented
but the package may be applied for visualizing other density estimates.
An implementation of the clustering algorithm described by Alex
Rodriguez and Alessandro Laio (Science, 2014 vol. 344), along with tools to
inspect and visualize the results.
Implementation in the density framework of the non-combinatorial algorithm and its greedy version, introduced by Magalhães and Rozenholc (2014), for T-estimation Hold-Out proposed in Birgé (2006, Section 9). The package provide an implementation which uses several families of estimators (regular and irregular histograms, kernel estimators) which may be used alone or combined. As a complement, provides also a comparison with other Held-Out derived from least-squares and maximum-likelihood. This package implements also the T-estimation Hold-Out derived from the test introduced in Baraud (2011).
Graphical methods for compactly illustrating probability distributions, including density strips, density regions, sectioned density plots and varying width strips.
This package provides the DEoptim function which performs
global optimization by differential evolution.
An implementation of the jDE variant of the Differential Evolution
stochastic algorithm for global optimization of nonlinear programming problems.
The package performs parametric and semiparametric inferences for models based on dependently truncated data. Semi-parametric approaches are based on Chaieb, Rivest and Abdous (2006 Biometrika) and Emura, Wang and Hung (2011 Statistica Sinica). Parametric approaches are based on Emura and Konno (2012 Statistical Papers).
Fit (multigroup) mixtures of latent Markov models on mixed categorical and continuous (timeseries) data. The Rdonlp2 package can optionally be used for optimization of the log-likelihood and is available from R-forge: https://r-forge.r-project.org/R/?group_id=156
Fit latent (hidden) Markov models on mixed categorical and continuous (time series)
data, otherwise known as dependent mixture models
Depth functions methodology applied to multivariate
analysis. Besides allowing calculation of depth values and
depth-based location estimators, the package includes functions
for drawing contour plots and perspective plots of depth
functions. Euclidian and spherical depths are supported.
depthTools is a package that implements different
statistical tools for the description and analysis of gene
expression data based on the concept of data depth, namely, the
scale curves for visualizing the dispersion of one or various
groups of samples (e.g. types of tumors), a rank test to decide
whether two groups of samples come from a single distribution
and two methods of supervised classification techniques, the DS
and TAD methods. All these techniques are based on the Modified
Band Depth, which is a recent notion of depth with a low
computational cost, what renders it very appropriate for high
dimensional data such as gene expression data.
Decompose a time series into seasonal, trend and irregular components using transformations to amplitude-frequency domain.
This package contains functions to describe weighted
categorical variables and functions to facilitate the character
encoding conversion of objects.
Produce publication quality graphics from output of GGobi's
describe display plugin
DescTools contains a bunch of basic statistic functions and convenience wrappers for efficiently describing data, creating specific plots, doing reports using MS Word, Excel or PowerPoint. The package's intention is to offer a toolbox, which facilitates the (notoriously time consuming) first descriptive tasks in data analysis, consisting of calculating descriptive statistics, drawing graphical summaries and reporting the results. Many of the included functions can be found scattered in other packages and other sources written partly by Titans of R. The reason for collecting them here, was primarily to have them consolidated in ONE instead of dozens of packages (which themselves might depend on other packages which are not needed at all), and to provide a common and consistent interface as far as function and arguments naming, NA handling, recycling rules etc. are concerned. Google style guides were used as naming rules (in absence of convincing alternatives). The 'camel style' was consequently applied to functions borrowed from contributed R packages as well.
Deseasonalize daily or monthly time series.
The package provides R scripts for designing genetical
genomics experiments.
S3 classes for multivariate optimization using the desirability function by Derringer and Suich (1980)
Harrington and Derringer-Suich type desirability functions
This package implements a statistical data mining method to
compare whole genome gene expression profiles, with respect to the presence
of a recurrent genetic disturbance event, to identify the affected target
genes.
Functions that solve initial value problems of a system
of first-order ordinary differential equations (ODE), of
partial differential equations (PDE), of differential
algebraic equations (DAE), and of delay differential
equations.  The functions provide an interface to the FORTRAN
functions lsoda, lsodar, lsode, lsodes of the ODEPACK
collection, to the FORTRAN functions dvode and daspk and a
C-implementation of solvers of the Runge-Kutta family with
fixed or variable time steps.  The package contains routines
designed for solving ODEs resulting from 1-D, 2-D and 3-D
partial differential equations (PDE) that have been converted
to ODEs by numerical differencing.
The package implements models to analyze
site occupancy and count data models with detection error.
The package development was supported by the
Alberta Biodiversity Monitoring Institute (ABMI)
and the Boreal Avian Modelling (BAM) Project.
Solvers and testset for stiff and nonstiff differential
equations, and differential algebraic equations
Simple GUI to perform some standard tree-ring analyses.
In the new era of population genomics, surveys of genetic
polymorphism ("genome scans") offer the opportunity to
distinguish locus-specific from genome wide effects at many
loci. Identifying presumably neutral regions of the genome that
are assumed to be influenced by genome-wide effects only, and
excluding presumably selected regions, is therefore critical to
infer population demography and phylogenetic history reliably.
Conversely, detecting locus-specific effects may help identify
those genes that have been, or still are, targeted by natural
selection. The software package DetSel has been developed to
identify markers that show deviation from neutral expectation
in pairwise comparisons of diverging populations.
Output graphics to EMF (enhanced metafile).
Data sets and sample analyses from Jay L. Devore (2008),
"Probability and Statistics for Engineering and the Sciences
(7th ed)", Thomson.
Collection of package development tools.
It handles numerics, characters, factors, and logicals.
This package provides functions to run the CRM and
TITE-CRM in phase I trials and calibration tools for trial
planning purposes.
Quickly and graphically show missing values and classes of each variable and each observation of a data.frame. The aim is to show pattern of missing values and if there is wrong class attribution.
A set of functions to perform Raju, van der Linden and Fleer's
(1995) Differential Item and Item Functioning (DFIT) analyses. It includes
functions to use the Monte Carlo Item Parameter Replication (IPR) approach
(Oshima, Raju, & Nanda, 2006) for obtaining the associated statistical
significance tests cut-off points. They may also be used for a priori and
post-hoc power calculations.
Derivative-Free optimization algorithms. These algorithms
do not require gradient information. More importantly, they can
be used to solve non-smooth optimization problems.
dglars implements the differential geometric extension
of the least angle regression method for logistic and Poisson
regression models. To compute the implicitly defined solution
curve, two different algorithms are implemented, i.e. the
predictor-corrector method and the cyclic coordinate descent
method.
Fitting double generalized linear models
Random data generation for PLS structural models.
This package contains a proposed revision to the
stats::ks.test() function and the associated ks.test.Rd help
page.  With one minor exception, it does not change the
existing behavior of ks.test(), and it adds features necessary
for doing one-sample tests with hypothesized discrete
distributions.  The package also contains cvm.test(), for doing
one-sample Cramer-von Mises goodness-of-fit tests.
The dhglm package fits double hierarchical generalized
linear models in which the mean, dispersion parameters for
variance of random effects, and residual variance
(overdispersion) can be further modeled as random-effect
models.
Visualises simple graphs (networks) based on a transition matrix, utilities to plot flow diagrams,
visualising webs, electrical networks, ...
Support for the books "A practical guide to ecological modelling -
using R as a simulation platform"
by Karline Soetaert and Peter M.J. Herman (2009). Springer.
and the book "Solving Differential Equations in R"
by Karline Soetaert, Jeff Cash and Francesca Mazzia. Springer.
Includes demo(flowchart), demo(plotmat), demo(plotweb)
The R package provides utilities to estimate and make inference on two summary measures for diagnostic tests when there are three ordinal groups ----volume under ROC surface (VUS) and the extended Youden index, assuming that diagnostic markers increase monotonically and stochastically with disease severity. Point estimates, variance, confidence interval and optimal cut-points both under the normal assumption and also the non-parametric method(s) will be provided for the summary measures. Statistical tests are implemented to compare multiple diagnostic tests and two diagnostic test.  Sample size is calculated to estimate the summary measures within a user-specified margin of error for future study planning.
Visualize one-factor data frame.
Beads plot consists of diamonds of each factor of each data series.
A diamond indicates average and range.
Look over a data frame with many numeric columns and a factor column.
This package provides utilities to calculate the probabilities of various dice-rolling events, such as the probability of rolling a four-sided die six times and getting a 4, a 3, and either a 1 or 2 among the six rolls (in any order); the probability of rolling two six-sided dice three times and getting a 10 on the first roll, followed by a 4 on the second roll, followed by anything but a 7 on the third roll; or the probabilities of each possible total of rolling five six-sided dice, dropping the lowest two rolls, and summing the remaining dice.
Space-Filling Designs and Uniformity Criteria.
Estimation, validation and prediction of metamodels
(linear models, additive models, MARS,PolyMARS and Kriging)
Estimation, validation and prediction of kriging models.
Important functions : km, print.km, plot.km, predict.km.
Expected Improvement. EGO algorithm. Multipoints EI and
parallelized versions of EGO: Constant Liars. Criteria and
algorithms for Noisy Kriging-based Optimization , including
AEI, AKG, EQI, and EI with plugin.
View 2D/3D sections or contours of computer experiments designs, surrogates or test functions.
Collapse red-green or green-blue distinctions to simulate
the effects of different types of color-blindness.
This package contains the dictionaries for reading microdata
from IBGE (Brazilian Institute of Geography and Statistics)
surveys PNAD, PME and POF.
Physical constants. Gold, silver and glass permittivities,
together with spline interpolation functions.
This package provides a wrapper to the FITPACK routines written by
Paul Dierckx. The original Fortran is available from
http://www.netlib.org/dierckx
2012.02.15:  This package is broken.
Currently, it seems NOT to have any users and is not worth fixing.
These FITPACK routines are over 20 years old and may be obsolete.
If you want to fix it, I will happily make you the maintainer.
Spencer Graves
The package includes functions to calculate confidence
intervals for the difference of dependent proportions. There
are two functions implemented to edit the data (dichotomising
with the help of cutpoints, counting accordance and discordance
of two tests or situations). For the calculation of the
confidence intervals entries of the fourfold table are needed.
Functions and examples from the book Solving Differential
Equations in R by Karline Soetaert, Jeff R Cash and Francesca
Mazzia.  Springer, 2012.
Package to fit diffusion-based IRT models to response and
response time data. Models are fitted using marginal maximum
likelihood. Parameter restrictions (fixed value and equality
constraints) are possible. In addition, factor scores (person drift
rate and person boundary separation) can be estimated. Model fit
assessment tools are also available. The traditional diffusion model
can be estimated as well.
Residual-based baseline identification and peak decomposition for x-ray diffractograms as introduced in Davies/Gather/Mergel/Meise/Mildenberger (2008).
Implements diffusion map method of data
parametrization, including creation and visualization of
diffusion map, clustering with diffusion K-means and
regression using adaptive regression model.
A package to perform DIFlasso, a method to detect DIF (Differential Item Functioning) in Rasch Models. It can handle settings with many covariates and also metric covariates.
The difR package contains several traditional methods to
detect DIF in dichotomously scored items. Both uniform and
non-uniform DIF effects can be detected, with methods relying
upon item response models or not. Some methods deal with more
than one focal group.
The digest package provides a function 'digest()' for the
creation of hash digests of arbitrary R objects (using the md5, sha-1,
sha-256 and crc32 algorithms) permitting easy comparison of R language
objects, as well as a function 'hmac()' to create hash-based message
authentication code.

The md5 algorithm by Ron Rivest is specified in RFC 1321, the sha-1
and sha-256 algorithms are specified in FIPS-180-1 and FIPS-180-2,
and the crc32 algorithm is described in
ftp://ftp.rocksoft.com/cliens/rocksoft/papers/crc_v3.txt.

For md5, sha-1, sha-256 and aes, this package uses a small standalone
implementations that were provided by Christophe Devine. For crc32, code
from the zlib library is used. For sha-512, an implementation by Aaron
D. Gifford is used.

Please note that this package is not meant to be deployed for
cryptographic purposes for which more comprehensive (and widely
tested) libraries such as OpenSSL should be used.
Digiroo2 is an R package developed by researchers at the
University of Queensland to investigate association patterns
and social structure in wild animal populations. Proximity
between individuals is generally considered to be an
appropriate proxy for associations and pairwise association
indices are the most widely used technique for analysing animal
social structure. However, little attention is given to
identifying how patterns of spatial overlap affect these
association patterns. For example, do individuals associate
randomly with others with whom they share home ranges, or do
some individuals go out of their way to associate with or avoid
particular individuals? This program builds a null model of
random associations based on an individual's space use
determined using home range methodologies. Random points may be
generated within a specified home range contour or according to
the Utilization Distribution (UD). Expected associations of
individuals are extracted based on probability of occurrence
and the proximity between home range weighted random points.
Association matrices can be generated from multiple
permutations for analysis using SOCPROG 2.4 (Whitehead 2009) to
create 'expected' pairwise half-weight association indices
(HWIs). These may be compared with the 'observed' HWIs from
field observations to reveal whether pairs of animals associate
more (= attraction) or less (= avoidance) than expected by
chance.
Combine multiple-relationship networks into a single weighted
network.  The approach is similar to factor analysis in the that
contribution from each constituent network varies so as to maximize the
information gleaned from the multiple-relationship networks.
This implementation uses Principal Component Analysis calculated using
'prcomp' with bootstrap subsampling.  Missing links are imputed using
the method of Chen et al.
(2012).
A robust differential identification method that considers an ensemble of finite mixture models combined with a local false discovery rate (fdr) to analyze ChIP-seq (high-throughput genomic)data comparing two samples allowing for flexible modeling of data.
This function implements the DiNAMIC procedure for
assessing the statistical significance of recurrent DNA copy
number aberrations (Bioinformatics (2011) 27(5) 678 - 685).
Compute Hartigan's dip test statistic for unimodality
Implements the clustering method in Fu, Russell, Bray and
Tavare: Bayesian clustering with the Dirichlet-process prior.
Estimates the number of clusters from the data.  Data are
replicated time series or replicated measurements from multiple
experimental conditions.
An extensible framework
for automatically placing direct labels onto multicolor lattice or
ggplot2 plots.
Label positions are described using Positioning Methods
which can be re-used across several different plots.
There are heuristics for examining "trellis" and "ggplot" objects
and inferring an appropriate Positioning Method.
A package for pathway analysis in experiments with multiple perturbation designs.
Implements the Dirichlet Regression in R
Estimate parameters in Dirichlet-Multinomial and compute
profile log-likelihoods.
Probability mass function estimation is performed through three standard discrete associated kernels: Binomial,  Poisson and Negative binomial kernels.  The cross validation method for bandwidth selection is also implemented.
Discrete Laplace exponential family for models such as a generalized linear model
disclapmix makes inference in a mixture of Discrete Laplace distributions using the EM algorithm. This can e.g. be used for modelling the distribution of Y chromosomal haplotypes as described in [1, 2] (refer to the URL section).
DiscML performs rate estimation using maximum likelihood with the
options to correct for unobservable data, to implement a Gamma-distribution
for rate variation, and to estimate the prior root probabilities from the
empirical data.
Probability mass function, distribution function, quantile
function, random generation and parameter estimation for the
discrete inverse Weibull distribution
Density, distribution function, quantile function, random
generation and estimation for the skew discrete Laplace
distribution
Multiple testing procedures for discrete test statistics,
that use the known discrete null distribution of the p-values
for simultaneous inference.
discreteRV implements a set of functions that allow computations
involving discrete random variables.  It uses a syntax which is familiar to
that which is used in mathematical statistics and probability courses.
Probability mass function, distribution function, quantile function, random generation and parameter estimation for the type I and III discrete Weibull distributions
This package is a collection of supervised discretization
algorithms. It can also be grouped in terms of top-down or
bottom-up, implementing the discretization algorithms.
'discrimARTs' discriminates with explicit confidence the alternative reproductive tactics (ARTs) in dimorphic systems with bimodal traits by computing the maximum likelihood estimate of a mixture of distributions of a measured ARTs trait. Supported distributions include a mixture of 2 normal distributions and a mixture of 2 facing gamma distributions.
Functions for Discriminant Analysis and Classification purposes
covering various methods such as descriptive, geometric, linear, quadratic,
PLS, as well as qualitative discriminant analyses
Tools for calculating rates and Standardized Incidence Ratios from case and population data.
Disc Memoisation for R, enabling faster computation by avoiding repeated re-evaluation of function calls.
Functions for species distribution modeling, that is, predicting entire geographic distributions form occurrences at a number of sites.
An implementation of two exact algorithms for testing the
Hausdorff and simplex dispersion orderings
A package containing R functions for displaying data and
results from high-throughput screening experiments.
Description: Functions for modelling dispersion in GLM.
This package has extensible C++ code for computing dissimilarities between vectors. It also has a number of C++ functions for assembling collections of dissimilarities. In particular, it lets you find a matrix of dissimilarities between the rows of two input matrices. There are also functions for finding the nearest neighbors of each row of a matrix, either within the matrix itself or within another matrix.
A library that provides a simple way of fitting detection
functions to distance sampling data for both line and point transects.
Adjustment term selection, left and right truncation as well as
monotonicity constraints and binning are supported. Abundance and density
estimates can also be calculated if survey area information is provided.
Implement DiSTATIS and CovSTATIS (three-way multidimensional scaling). For the  analysis of  multiple distance/covariance matrices collected on the same set of observations
An R package that is developed for constructing confidence
regions without the need to know the sampling distribution of
bivariate data.
Some very simple method functions for confidence interval
calculation and to distill pertinent information from a
potentially complex object; primarily used in common with
packages extRemes and SpatialVx.
Geodesic distance between phylogenetic trees and
associated functions.
S4 Classes and Methods for distributions
provides documentation in form of a common vignette to packages distr, distrEx,
distrMod, distrSim, distrTEst, distrTeach, and distrEllipse
Distribution (S4-)classes for elliptically contoured distributions (based on
package distr)
Extensions of package distr and some additional functionality
This package contains utilities which are of use in the
packages I have developed for dealing with distributions.
Currently these packages are GeneralizedHyperbolic,
VarianceGamma, and SkewHyperbolic and NormalLaplace. Each of
these packages requires DistributionUtils. Functionality
includes sample skewness and kurtosis, log-histogram, tail
plots, moments by integration, changing the point about which a
moment is calculated, functions for testing distributions using
inversion tests and the Massart inequality. Also includes an
implementation of the incomplete Bessel K function.
Object oriented implementation of probability models based on packages 'distr' and
'distrEx'
Gamma-lasso path estimation for a multinomial logistic regression factorized into independent  Poisson log regressions.  See the textir package for applications in multinomial inverse regression analysis of text.
(S4-)distribution classes based on package distr for distributions from packages
fBasics and fGarch
Simulation (S4-)classes based on package distr
Evaluation (S4-)classes based on package distr for evaluating procedures
(estimators/tests) at data/simulation in a unified way.
Provides tools for quality checks of georeferenced plant
species accessions.
R-package DivE contains functions for the DivE estimator (Laydon, D. et al., Quantification of HTLV-1 clonality and TCR diversity, PLOS Comput. Biol. 2014). The DivE estimator is a heuristic approach to estimate the number of classes or the number of species (species richness) in a population.
Utilities to represent, visualize, filter, analyse, and summarize
time-depth recorder (TDR) data.  Miscellaneous functions for
handling location data are also provided.
Contains a number of comparative phylogenetic methods,
mostly focusing on analysing diversification and character
evolution.  Contains implementations of BiSSE (Binary State
Speciation and Extinction; Maddison et al. 2007: Syst. Biol. 56:
701) and its unresolved tree extensions (FitzJohn et al. 2009:
Syst. Biol. 58: 595), MuSSE (Multiple State Speciation and
Extinction), QuaSSE (Quantitative State Speciation and Extinction;
FitzJohn 2010: Syst. Biol. 59: 619), GeoSSE (Geographic State
Speciation and Extinction; Goldberg et al. 2011: Syst. Biol. 60:
451), and BiSSE-ness (Magnuson-Ford and Otto 2012: Am. Nat.)
Other included methods include Markov models of discrete and
continuous trait evolution and constant rate speciation and
extinction.
diveRsity is a package which allows the calculation of both genetic
diversity partition statistics, genetic differentiation statistics and locus
informativeness for ancestry assignment. It also provides users with various
option to calculate bootstrapped 95% confidence intervals both across loci,
for pairwise population comparisons and to plot these results interactively.
V1.2 and up provides parallel computing capabilities and pairwise results
without bootstrapping. Weir and Cockerham's 1984 F-statistics are now also
calculated. Various plotting features are also provided. Chi-square tests of
genetic heterogeneity are also provided. Functionality for the calculation
of various diversity parameters is also possible for RAD-seq derived SNP
data sets containing thousands of marker loci. A shiny application for the
development of microsatellite multiplexes is also available
There are two functions in this package, which can be used
together to estimate the Shannon's Diversity index at different
levels of sample size. A Monte-Carlo procedure is used to
re-sample a given observation at each level of sampling. The
expectation being that the mean of the re-sampling will
approach Shannon's diversity index at that sample level.
This package has tools for analyzing DNA melting data to
generate HRM scores, the DNA diversity measure output of the
HRM Diversity Assay.  For additional documentation visit
http://code.google.com/p/divmelt/.
Function to test spatial segregation and association based
in contingency table analysis of nearest neighbour counts
following Dixon (2002). Some fortran code has been included to
the original dixon2002 function of the ecespa package to
improve speed.
Compute diffusion kernels on DNA polymorphisms, including
SNP and bi-allelic genotypes.
Maximum likelihood, Kalman filtering and smoothing, and Bayesian
analysis of Normal linear State Space models, also known as
Dynamic Linear Models
QTL mapping in a mixed model framework with separate
detection and localization stages. The first stage detects the
number of QTL on each chromosome based on the genetic variation
due to grouped markers on the chromosome; the second stage uses
this information to determine the most likely QTL positions.
The mixed model can accommodate general fixed and random
effects, including spatial effects in field trials and pedigree
effects. Applicable to backcrosses, doubled haploids,
recombinant inbred lines, F2 intercrosses, and association
mapping populations.
dlmodeler is a set of user-friendly functions to simplify the state-space modelling, fitting, analysis and forecasting of Generalized Dynamic Linear Models (DLMs). It includes functions to name and extract individual components of a DLM, build classical seasonal time-series models (monthly, quarterly, yearly, etc. with calendar adjustments) and provides a unified interface compatible with other state-space packages including: dlm, FKF and KFAS.
Simulation testing and implementation of data-limited fishery stock assessment methods
The package dlnm contains functions to specify and run distributed lag linear and non-linear models.
Dynamic model averaging for binary and continuous
outcomes.
A backward selection procedure called delete or merge
regressors (DMR) combines deleting continuous variables with
merging levels of factors. The method assumes greedy search
among linear models with set of constraints of two types:
either a parameter for a continuous variable is set to zero or
parameters corresponding to two levels of a factor are
compared. DMR is a stepwise regression procedure, where in each
step a new constraint is added according to ranking of the
hypotheses based on squared t-statistics. As a result a nested
family of linear models is obtained and the final decision is
made according to minimization of the generalized information
criterion (GIC, default BIC). The main function of the package
is DMR, which is based on hierarchical clustering. Moreover,
other functions for extensions of DMR method are given, such as
stepDMR which is based on recalculation of t-statistics in each
step and function DMR4glm for generalized linear models.
This package includes functions and data accompanying the book
"Data Mining with R, learning with case studies" by Luis Torgo, CRC Press 2010.
Package for conducting differential network analysis from
microarray data.
Methods for studying forensic DNA profiling, in particular familial database searches.
Computationally efficient tools for comparing all pairs of profiles in a DNA database. The expectation and covariance of the summary statistic
is implemented for fast computing. Routines for estimating proportions of close related individuals are available. The use of wildcards (also
called F-designation) is implemented. Dedicated functions ease plotting the results.
The package involves two kinds of functions. One is to
solve cubic and quartic equations. The other is to analyse the
distribution of a set of given data.  The function Cubic and
Quart are for solving equations. The function which has the
form as "is.xxdistribution" is for analysing whether the data
obeys the xxdistrbution. If you don not know which distribution
to judge, use function is.dt.
This package is initiated to fill in the need of an open-source tool for high-throughput omics data in an integrative manner in terms of network, evolution and ontology. More specifically, dnet intends to analyse the biological network whose nodes/genes are associated with digitised information such as expression levels across samples. To help make sense of identified networks, enrichment analysis is also supported using a wide variety of pre-compiled ontologies and phylostratific gene age information in major organisms including human, mouse, rat, chicken, C.elegans, fruit fly, zebrafish and arabidopsis. Add-on functionalities are supports for semantic similarity calculation between ontology terms (and genes), and network affinity calculation using Random Walk with Restart; both can be done via high-performance parallel computing. In summary, dnet aims to deliver an eye-intuitive tool with rich visuals but less inputs.
Frequentist (EM) and Bayesian  (MCMC) Methods for Inference of  Birth-Death-Immigration Markov Chains
doBy originally contained facilities to 'do' something to data stratified 'by' some variables. doBy contains a
variety of utilities including:

1) Facilities for groupwise computations of summary statistics and
other facilities for working with grouped data.

2) LSmeans (least-squares means), general linear contrasts.

3) Miscellaneous other utilities.
docopt lets you define a command-line interface by just giving it
a description in the specific format.
Production of R packages from tagged comments introduced within the code
and a minimum of additional documentation files.
Various sampling plans are able to be compared using evaluations of their OC, AOQ, ATI etc.
This package creates full factorial experimental designs and designs based on orthogonal arrays for (industrial) experiments. Additionally, it provides some utility functions used also by other DoE packages.
This package creates various kinds of designs for
(industrial) experiments. It uses, and sometimes enhances,
design generation routines from other packages.
So far, response surface designs from package rsm, latin hypercube
samples from packages lhs and DiceDesign, and
D-optimal designs from package AlgDesign have been implemented.
Provides a parallel backend for the %dopar% function using
the multicore functionality of the parallel package..
The package can calculate ADI (Average Dominance Index) and can build social network graphs with dual directions, can build a Music Notation Graph
The Domino R package is a wrapper on top of the Domino command-line client. It lets you run  Domino commands (e.g., "run", "upload", "download") directly from your R environment. Under the hood, it uses R's system function to run the Domino executable, which must be installed as a prerequisite.
Provides a parallel backend for the %dopar% function using
the Rmpi package.
Provides a parallel backend for the %dopar% function using
the parallel package.
A Redis parallel backend for the %dopar% function
This package provides functions to perform
reproducible parallel foreach loops, using independent
random streams as generated by L'Ecuyer's combined
multiple-recursive generator [L'Ecuyer (1999)]. It
enables to easily convert standard %dopar% loops into
fully reproducible loops, independently of the number
of workers, the task scheduling strategy, or the chosen
parallel environment and associated foreach backend.
The DoseFinding package provides functions for the design and analysis
of dose-finding experiments (with focus on pharmaceutical Phase
II clinical trials). It provides functions for: multiple contrast
tests, fitting non-linear dose-response models (using Bayesian and
non-Bayesian estimation), calculating optimal designs and an
implementation of the MCPMod methodology.
Provides a parallel backend for the %dopar% function using
Luke Tierney's snow package.
It estimates a dose-response relation from either a single or
multiple summarized data. The trend estimation takes into account the
correlation among sets of log relative risks and use it to efficiently
estimate the dose-response relation. To obtain a pooled functional
relation, the study-specific trends are combined according to principles of
multivariate random-effects meta-analysis.
A small package containing helper utilities for creating function
for computing statistics.
Load configuration from a .env file, that is
in the current working directory, into environment variables.
Performs hypothesis tests concerning a regression function in a least-squares model, where the null is a parametric function, and the alternative is the union of large-dimensional convex polyhedral cones.
Differential Exon Usage Test for RNA-Seq data via Empirical Bayes Shrinkage of the Dispersion Parameter
This package provides a wrapper for the download.file
function, making it possible to download files over https on
Windows, Mac OS X, and other Unix-like platforms. The RCurl
package provides this functionality (and much more) but can be
difficult to install because it must be compiled with external
dependencies. This package has no external dependencies, so it
is much easier to install.
A GUI or command-line operated data analysis tool, for
analyzing time-dependent simulation data in which multiple
instantaneous or time-lagged relations are assumed. This
package uses Structural Equation Modeling (the sem package). It
is aimed to deal with time-dependent data and estimate whether
a causal diagram fits data from an (agent-based) simulation
model.
Analysis, visualisation and simulation of digital PCR experiments.
fits the primal graphical lasso, via one-at-a-time
block-coordinate descent.
This package contains functions for performing tree-ring
analyses, IO, and graphics.
A fast, consistent tool for working with data frame like objects,
both in memory and out of memory.
The package implements a Dirichlet Process Mixture (DPM)
model for clustering and image segmentation.  The DPM model is
a Bayesian nonparametric methodology that relies on MCMC
simulations for exploring mixture models with an unknown number
of components.  The code implements conjugate models with
normal structure (conjugate normal-normal DP mixture model).
The package's applications are oriented towards the
classification of magnetic resonance images according to tissue
type or region of interest.
This package contains functions to perform inference via
simulation from the posterior distributions for Bayesian
nonparametric and semiparametric models. Although the name of
the package was motivated by the Dirichlet Process prior, the
package considers and will consider other priors on functional
spaces. So far, DPpackage includes models considering Dirichlet
Processes, Dependent Dirichlet Processes, Dependent Poisson-
Dirichlet Processes, Hierarchical Dirichlet Processes, Polya
Trees, Linear Dependent Tailfree Processes, Mixtures of
Triangular distributions, Random Bernstein polynomials priors
and Dependent Bernstein Polynomials.  The package also includes
models considering Penalized B-Splines. Currently the package
includes semiparametric models for marginal and conditional
density estimation, ROC curve analysis, interval censored data,
binary regression models, generalized linear mixed models, IRT
type models, and generalized additive models. The package also
contains functions to compute Pseudo-Bayes factors for model
comparison, and to elicitate the precision parameter of the
Dirichlet Process. To maximize computational efficiency, the
actual sampling for each model is done in compiled FORTRAN. The
functions return objects which can be subsequently analyzed
with functions provided in the coda package.
The purpose of the functions implemented in this package is to find a subset containing the weakest species with some acceptable certainty. Suppose we have sampled some quality measures T_{k,j} (>=0) from K species (k=1,...,K, j=1,...,m_k= sample size of kth species), and wish to find the species with the smallest alpha quantile (say, 5th percentile) of T (namely the weakest species). Due to the small sample size (m_k) or large number of species to be considered (K), we might not be able to select a single species as a weakest one with acceptably large certainty. Instead of selecting a single species with a low certainty, we propose to select a subset of species which contains the weakest species with predetermined certainty.
Functions, methods, and datasets for fitting dimension
reduction regression, using slicing (methods SAVE and SIR), Principal
Hessian Directions (phd, using residuals and the response), and an
iterative IRE.  Partial methods, that condition on categorical
predictors are also available.  A variety of tests, and stepwise
deletion of predictors, is also included.  Also included is
code for computing permutation tests of dimension.  Adding additional
methods of estimating dimension is straightforward.
For documentation, see the vignette in the package.   With version 3.0.4,
the arguments for dr.step have been modified.
Graphical display of R expression, showing the
interpretation of an expression by R and the various kind of R
data structure. The steps of the interpretation of an
expression are obtained through the parsed tree.
Analysis of dose-response curves in biology, environmental sciences, medicine, pharmacology, toxicology
drfit provides basic and easy-to-use functions for fitting
dose-response curves to dose-response data, calculating some
(eco)toxicological parameters and plotting the results. Functions that are
fitted are the cumulative density function of the lognormal distribution
(probit fit), of the logistic distribution (logit fit), of the weibull
distribution (weibull fit) and a linear-logistic model ("linlogit" fit),
derived from the latter, which is used to describe data showing stimulation
at low doses (hormesis). In addition, functions checking, plotting and
retrieving dose-response data retrieved from a database accessed via RODBC
are included. As an alternative to the original fitting methods, the
algorithms from the drc package can be used.
The package provides functions to estimate parameters quantifying
an association between an exposure and an outcome in a generalized
estimating equations model in the presence of nuisance variables. Three methods are implemented:
outcome nuisance model based estimation where a model for the association
between the nuisance factors and the outcome is assumed, exposure nuisance model
based estimation where a model for the association between the nuisance factors
and the exposure is assumed and doubly robust estimation where both models are
used. In doubly robust estimation, the estimates will be consistent when
at least one of the models are correctly specified, not necessarily both.
Integrative analysis of DNA copy number and gene
expression data described in Salari et al 2009
Likelihood-based marginal regression and association
modelling for repeated, or otherwise clustered, categorical
responses using dependence ratio as a measure of the
association
Dual empirical likelihood (DEL) inference under semiparametric density ratio models (DRM) in the presence of multiple samples, including population cumulative distribution function estimation, quantile estimation and comparison, density estimation, composite hypothesis testing for DRM parameters which encompasses testing for changes in population distribution functions as a special case, etc.
Drsmooth provides tools for assessing the shape of a dose-response
curve by testing linearity and non-linearity at user-defined cut-offs. It
also provides two methods of estimating a threshold dose, or the dose at
which the dose-response function transitions to significantly increasing:
bi-linear (based on pkg:segmented) and smoothed with splines (based on
pkg:mgcv).
The package performs various analyzes of descriptive statistics, including correlations
The package provides two discretization-based Monte Carlo
algorithms, namely the Fu-Wang algorithm and the Wang-Lee
algorithm, for random sample generation from a high dimensional
distribution of complex structure. The normalizing constant of
the target distribution needs not to be known.
Calculate posterior modes and credible intervals of parameters of the Dixon-Simon model for subgroup analysis (with binary covariates) in clinical trials.
Package dse provides tools for multivariate, linear,
time-invariant, time series models. It includes ARMA and
state-space representations, and methods for converting between
them. It also includes simulation methods and several
estimation functions. The package has functions for looking at
model roots, stability, and forecasts at different horizons.
The ARMA model representation is general, so that VAR, VARX,
ARIMA, ARMAX, ARIMAX can all be considered to be special cases.
Kalman filter and smoother estimates can be obtained from the
state space model, and state-space model reduction techniques
are implemented.  An introduction and User's Guide is available
in a vignette.
This package is only to aid transition to the unbundled dse
package. It has no functions, but simply requires package EvalEst.
A package providing functions and methods for handling distributed storage (DStorage) and lists (DLlist). It implements an abstract DList class for storing large list-type objects in a distributed manner. In doing so it uses a well defined storage backend implemented based on the DStorage class. It allows for processing such DLists on distributed systems efficiently.
Dynamic slicing is a method designed for dependency detection between a categorical variable and a continuous variable. It could be applied for non-parametric hypothesis testing and gene set enrichment analysis.
This library implements density surface modelling of line
transect data, based on the methods of Hedley et al. (2004). Some
recent developments in the literature have also be incorporated.
Outputs are point and interval estimates of population abundance
and density.
Please note that this version of dsm WILL NOT work with the Windows
package DISTANCE earlier than version 6.2. For a version that works with
DISTANCE 6.1, please go to:
https://github.com/DistanceDevelopment/dsm-distance-6.1
For the latest version of dsm, please use the github version at the URL
listed below.
Miller, D. L., M. L. Burt, E. Rexstad and L. Thomas. 2013. Spatial
models for distance sampling data: recent developments and future
directions. Methods in Ecology and Evolution
Hedley, S.L.,  S.T. Buckland and D.L. Borchers. 2004. "Spatial
distance sampling methods" pp 48-70 in Advanced Distance Sampling,
Buckland, S.T. et al. (eds). Oxford University Press.
Provides functions for fitting spatial models to line
transect sampling data and to estimate abundance within a
region.
Performs distance sampling simulations. It repeatedly
generates instances of a user defined population within a given survey
region, generates realisations of a survey design (currently these must
be pre-generated using Distance software) and simulates the detection
process. The data are then analysed so that the results can be compared for
accuracy and precision across all replications. This will allow users to
select survey designs which will give them the best accuracy and precision
given their expectations about population distribution. Any uncertainty in
population distribution or population parameters can be included by running
the different survey designs for a number of different population
descriptions. An example simulation can be found in the help file
for make.simulation.
Building discrete-time survival trees and bagged trees based on
the functionalities of the rpart package. Splitting criterion maximizes the
likelihood of a covariate-free logistic discrete time hazard model.
This package contains functions to compare the accuracy of two binary diagnostic tests in a "paired" study design, i.e. when each test is applied to each subject in the study.
This package implements different algorithms for analyzing
randomly truncated data, one-sided and two-sided (i.e. doubly)
truncated data. Two real data sets are included.
Diffusion Weighted Imaging (DWI) is a Magnetic Resonance Imaging
modality, that measures diffusion of water in tissues like the human
brain. The package contains R-functions to process diffusion-weighted
data. The functionality includes diffusion tensor imaging (DTI),
diffusion kurtosis imaging (DKI), modeling for high angular resolution
diffusion weighted imaging (HARDI) using Q-ball-reconstruction and
tensor mixture models, several methods for structural adaptive
smoothing including POAS and msPOAS, and a streamline fiber tracking
for tensor and tensor mixture models.
The package provides functionality to manipulate and visualize results
in 2D and 3D.
This package was created to analyze multi-level one-way
experimental designs. It is designed to handle vectorized
observation and factor data where there are unequal sample
sizes and population variance homogeneity can not be assumed.
To conduct the Dunnett modified Tukey-Kramer test (a.k.a. the
T3 Procedure), create two vectors: one for your observations
and one for the factor level of each observation. The function,
gl.unequal, provides a means to more conveniently produce a
factor vector with unequal sample sizes. Next, use the DTK.test
function to conduct the test and save the output as an object
to input into the DTK.plot function, which produces a
confidence interval plot for each of the pairwise comparisons.
Lastly, the function TK.test conducts the original Tukey-Kramer
test.
A series of functions which aid in both simulating and
determining the properties of finite, discrete-time, discrete
state markov chains.  Two functions (DTMC, MultDTMC) produce n
iterations of a Markov Chain(s) based on transition
probabilities and an initial distribution.  The function FPTime
determines the first passage time into each state.  The
function statdistr determines the stationary distribution of a
Markov Chain.
Estimation and comparison of survival distributions of dynamic treatment regimes (DTRs) from sequentially randomized clinical trials
This package provides functions for 1D and 2D Discrete
Cosine Transform (DCT), Discrete Sine Transform (DST) and
Discrete Hartley Transform (DHT).
A comprehensive implementation of dynamic time warping (DTW) algorithms in R.  DTW computes the optimal (least cumulative distance) alignment between points of two time series.  Common DTW variants covered include local (slope) and global (window) constraints, subsequence matches, arbitrary distance definitions, normalizations, minimum variance matching, and so on.  Provides cumulative distances, alignments, specialized plot styles, etc.
Functions to analyze multiple choice data using Dual Scaling
Expands factors, characters and other eligible classes
into dummy/indicator variables.
For the implementation of the step-down or step-up Dunnett testing procedures, the package includes R functions to calculate critical constants and R functions to calculate adjusted P-values of the test statistics. In addition, the package also contains functions to evaluate testing powers and hence the necessary sample sizes specially for the classical problem of comparisons of several treatments with a control.
dunn.test computes Dunn's test (1964) for stochastic dominance and reports the results among multiple pairwise comparisons after a Kruskal-Wallis test for stochastic dominance among k groups (Kruskal and Wallis, 1952). The interpretation of stochastic dominance requires an assumption that the CDF of one group does not cross the CDF of the other. dunn.test makes k(k-1)/2 multiple pairwise comparisons based on Dunn's z-test-statistic approximations to the actual rank statistics. The null hypothesis for each pairwise comparison is that the probability of observing a randomly selected value from the first group that is larger than a randomly selected value from the second group equals one half; this null hypothesis corresponds to that of the Wilcoxon-Mann-Whitney rank-sum test. Like the rank-sum test, if the data can be assumed to be continuous, and the distributions are assumed identical except for a difference in location, Dunn's test may be understood as a test for median difference. dunn.test accounts for tied ranks.
Inference of population sizes using a binomial likelihood and least informative discrete uniform priors.
Hurst exponent estimation of a fractional Brownian motion
by using discrete variations methods in presence of outliers
and/or an additive noise
Provides access to The Dataverse Network APIs via the RCurl package, enabling access to archived data (and metadata), and the ability to create and manipulate studies in a user's dataverse(s).
Time series regression.  The dyn class interfaces ts,
irts, its, zoo and zooreg time series classes to lm, glm,
loess, quantreg::rq, MASS::rlm, MCMCpack::MCMCregress,
quantreg::rq, randomForest::randomForest and other regression
functions allowing those functions to be used with time series
including specifications that may contain lags, diffs and
missing values.
The package is aimed at dynamically visualizing probability
distributions and their moments and all the commonly used distributions are
included.
Interactive graphical tool for manipulating graphs
Contains methods for detection of clusters in hierarchical clustering dendrograms.
a native R implementation and enhancement of Dynamic TOPMODEL, Beven and Freers' (2001) extension to the semi-distributed hydrological model TOPMODEL (Beven and Kirkby, 1979).
Inference by sequential Monte Carlo for
dynamic tree regression and classification models
with hooks provided for sequential design and optimization,
fully online learning with drift, variable selection, and
sensitivity analysis of inputs.  Illustrative
examples from the original dynamic trees paper are facilitated
by demos in the package; see demo(package="dynaTree")
A GUI to solve Dynamic Biplots, classic Biplot and HJ-biplot. Try matrices of 2-way and 3-way. Multilanguage.
DynClust is a two-stage procedure for the denoising and clustering of stack of noisy images acquired over time. Clustering only assumes that the data contain an unknown but small number of dynamic features. The method first denoises the signals using local spatial and full temporal information. The clustering step uses the previous output to aggregate voxels based on the knowledge of their spatial neighborhood. Both steps use a single keytool based on the statistical comparison of the difference of two signals with the null signal. No assumption is therefore required on the shape of the signals. The data are assumed to be normally distributed (or at least follow a symmetric distribution) with a known constant variance. Working pixelwise, the method can be time-consuming depending on the size of the data-array but harnesses the power of multicore cpus.
Computes dynamical correlation estimates and percentile
bootstrap confidence intervals for pairs of longitudinal
responses, including consideration of lags and derivatives.
Fit dynamic intervention model using the arima() function.
Dynamic linear models and time series regression.
An R implementation of dynamic simulations of autoregressive
relationships
Functions to fit time-varying coefficient models for interval
censored and right censored survival data. Three major
approaches are implemented: 1) Bayesian Cox model with
time-independent, time-varying or dynamic coefficients for
right censored and interval censored data; 2) Spline based
time-varying coefficient Cox model for right censored data;
3) Transformation model with time-varying coefficients for
right censored data using estimating equations.
Functions for latent class analysis, short time Fourier
transform, fuzzy clustering, support vector machines,
shortest path computation, bagged clustering, naive Bayes
classifier, ...
Plots of the empirical attainment function for two
objectives.
The Early-Warning-Signals Toolbox provides methods for estimating
statistical changes in timeseries that can be used for identifying nearby
critical transitions. Based on Dakos et al (2012) Methods for Detecting
Early Warnings of Critical Transitions in Time Series Illustrated Using
Simulated Ecological Data. PLoS ONE 7(7):e41010
Build regression models using the techniques in Friedman's
papers "Fast MARS" and "Multivariate Adaptive Regression
Splines". (The term "MARS" is copyrighted and thus not used in
the name of the package.)
Forms queries to submit to the Cleveland Federal Reserve Bank web
site's credit easing policy tools data site.  Provides various query functions
according to the easing data desired.  By default the download includes
easing tool weekly time series data starting January 2007.  The functions
return a class of type easing which contains a list of items related to the
query and its graphical presentation.  The list includes the time series
data as an xts object.  The package provides two lattice time series plots
to render the data in a manner similar to the bank's own presentation.
The package EasyABC enables to launch a series of simulations of a computer code from the R platform, and to retrieve the simulation outputs in an appropriate format for post-processing treatments. Four sequential sampling schemes and three coupled-to-MCMC schemes are implemented.
Perform analysis of variance and other important complementary analyzes. The functions are easy to use. Performs analysis in various designs, with balanced and unbalanced data.
It is a package that can be used to send HTML reports easily.
Contains a few utility functions for working with capture-history data, a function for simulating capture-history data, and a function to fit this data, using a Gibbs sampler.
The package fit and plot some nonlinear models
This is a pipelining tool that facilitates
evaluation and visualisation of stratified genome-wide
association meta-analyses (GWAMAs) results data. It
provides (i) statistical methods to test and to account
for between-strata difference and to clump genome-wide
results into independent loci and (ii) extended graphical
features (e.g., Manhattan, Miami and QQ plots) tailored
for stratified GWAMA results.
Fitting and testing multi-attribute probabilistic choice
models, especially the Bradley-Terry-Luce (BTL) model (Bradley
& Terry, 1952; Luce, 1959), elimination-by-aspects (EBA) models
(Tversky, 1972), and preference tree (Pretree) models (Tversky
& Sattath, 1979).
Package implements entropy balancing, a data preprocessing procedure that allows users to reweight a dataset such that the covariate distributions in the reweighted data satisfy a set of user specified moment conditions. This can be useful to create balanced samples in observational studies with a binary treatment where the control group data can be reweighted to match the covariate moments in the treatment group. Entropy balancing can also be used to reweight a survey sample to known characteristics from a target population.
This package carries out Empirical Bayes thresholding
using the methods developed by I. M. Johnstone and B. W.
Silverman. The basic problem is to estimate a mean vector given
a vector of observations of the mean vector plus white noise,
taking advantage of possible sparsity in the mean vector.
Within a Bayesian formulation, the elements of the mean vector
are modelled as having, independently, a distribution that is a
mixture of an atom of probability at zero and a suitable
heavy-tailed distribution.  The mixing parameter can be
estimated by a marginal maximum likelihood approach.  This
leads to an adaptive thresholding approach on the original
data.  Extensions of the basic method, in particular to wavelet
thresholding, are also implemented within the package.
This package is used to infer the adjacency matrix of a
network from time course data using an empirical Bayes
estimation procedure based on Dynamic Bayesian Networks.
Empirical Bayesian Elastic Net algorithms.
This package performs an exact Bayesian segmentation on data and returns the probabilities of breakpoints, an ICL criteria, comparison of change-point location, etc
Genotyping and SNP calling tool for single-sample next generation sequencing data analysis using an empirical Bayes method.
Some wrappers, functions and data sets for for spatial
point pattern analysis (mainly based on spatstat), used in the
book "Introduccion al Analisis Espacial de Datos en Ecologia y
Ciencias Ambientales: Metodos y Aplicaciones".
Functions to update datasets in Ecdat and to create,
manipulate, plot and analyze those and similar datasets.
Provides a function that quickly computes the fine structure
isotope patterns of a set of chemical formulas to a given degree of
accuracy (up to the limit set by errors in floating point arithmetic). A
data-set comprising the masses and isotopic abundances of individual
elements is also provided.
Dissimilarity-based analysis functions including ordination and Mantel test functions, intended for use with spatial and community data.
The ecoengine (http://ecoengine.berkeley.edu/) provides access to
more than 2 million georeferenced specimen records from the Berkeley
Natural History Museums. http://bnhm.berkeley.edu/
This package provides a flexible foundation for scientists,
engineers, and policy makers to base teaching exercises as well as for
more applied use to model complex eco-hydrological interactions.
Figures, data sets and examples from the book "A practical guide to ecological modelling - using R as a simulation platform" by Karline Soetaert and Peter MJ Herman (2009). Springer.
All figures from chapter x can be generated by "demo(chapx)", where x = 1 to 11.
The R-scripts of the model examples discussed in the book are in subdirectory "examples", ordered per chapter.
Solutions to model projects are in the same subdirectories.
Estimating individual-level covariate-outcome associations
using aggregate data ("ecological inference") or a combination
of aggregate and individual-level data ("hierarchical related
regression").
Provides an R interface to the EcoData Retriever
(http://ecodataretriever.org/) via the EcoData Retriever's
command line interface. The EcoData Retriever automates the
tasks of finding, downloading, and cleaning ecological datasets,
and then stores them in a local database.
Classes and methods for implementing aquatic ecosystem models,
for running these models, and for visualizing their results.
Miscellaneous methods and utilities for spatial ecology analysis, written by current and former members and collaborators of the ecospat group of Prof. Antoine Guisan, Department of Ecology and Evolution (DEE) & Institute of Earth Surface Dynamics (IDYST), University of Lausanne, Switzerland.
EcoTroph is an approach and software for modelling marine and freshwater ecosystems. It is articulated entirely around trophic levels. EcoTroph's key displays are bivariate plots, with trophic levels as the abscissa, and biomass flows or related quantities as ordinates. Thus, trophic ecosystem functioning can be modelled as a continuous flow of biomass surging up the food web, from lower to higher trophic levels, due to predation and ontogenic processes. Such an approach, wherein species as such disappear, may be viewed as the ultimate stage in the use of the trophic level metric for ecosystem modelling, providing a simplified but potentially useful caricature of ecosystem functioning and impacts of fishing. This version contains catch trophic spectrum analysis (CTSA) function and corrected versions of the mf.diagnosis and create.ETmain functions.
A package for simulations of classical ecological models as a learning resource.
This package performs hierarchical change point analysis
through the use of U-statistics. Both agglomerative
and divisive procedures return the set of change points estimates,
and other summary information.
This package provides a unified approach for Economic
Design of Control Charts. The main purpose of this package is
to find out the optimal parameters to minimize the ECH
(Expected Cost per Hour) of the process.
This package will allow to connect with email server through Internet Message Access Protocol (IMAP) and extract header information e.g. from, to, cc, subject, date and time. User will supply their email address and password along with other options. Initially this package is developed only for Gmail. To run the functions from this package user have to have IMAP enabled Gmail account.
Extends edgeR functionality by improving on exactTest using an unconditional exact test of negative binomial means.
Package EDISON (Estimation of Directed Interactions from
Sequences Of Non-homogeneous gene expression) runs an MCMC
simulation to reconstruct networks from time series data, using
a non-homogeneous, time-varying dynamic Bayesian network.
Networks segments and changepoints are inferred concurrently,
and information sharing priors provide a reduction of the
inference uncertainty.
Facilitates reading and manipulating (multivariate) data
restrictions (edit rules) on numerical and categorical data.
Rules can be defined with common R syntax and parsed to an
internal (matrix-like format). Rules can be manipulated with
variable elimination and value substitution methods, allowing
for feasibility checks and more. Data can be tested against the
rules and erroneous fields can be found based on Fellegi and
Holt's generalized principle. Rules dependencies can be
visualized with using the igraph package.
The library contains R-functions to estimate the effective
dimension reduction space in multi-index regression models.
This package comes to illustrate the articles "A graphical
tool for selecting the number of slices and the dimension of
the model in SIR and SAVE approaches" and "Comparison of sliced
inverse regression approaches for underdetermined cases"
Package with tools for classification of electroencephalography (EEG) data. Feature extraction techniques such as Fourier Transform and Continuous Wavelet Transform (CWT) are available. Support Vector Machines (SVM) can be used to classify the extracted features. An algorithm using Analysis of Variance (ANOVA), False Discovery Rate (FDR), and SVM is available to feature selection. Additionally, the package contains functions to plot data and features.
Analysis and visualization tools for electroencephalography (EEG) data. Includes functions for plotting (a) EEG caps, (b) single- and multi-channel EEG time courses, and (c) EEG spatial maps. Also includes smoothing and Independent Component Analysis functions for EEG data analysis.
Contains the example EEG data used in the package eegkit. Also contains code for easily creating larger EEG datasets from the EEG Database on the UCI Machine Learning Repository.
Collection of convenience functions to make working with
administrative records easier and more consistent. Includes functions to
clean strings, identify cutpoints, and quickly combine shapefiles and
dataframes for plotting. Includes four alternative themes for ggplot2 as
well as a wrapper for exporting graphics for inclusion in MS Office
products. Also includes three example datasets of administrative education
records for learning how to process records with errors.
Graphical and tabular effect displays, e.g., of interactions, for
various statistical models with linear predictors.
Functions to test for changes of a response to a stimulus grouping relative
to a background or baseline response.
The package provides functions to visualize regression models with categorical response. The effects of the covariates are plotted with star plots in order to allow for an optical impression of the fitted model.
This package contains the functions to compute the standardized
effect sizes for experiments (Cohen d, Hedges g, Cliff delta, Vargha and Delaney A).
The computation algorithms have been optimized to allow efficient computation even
with very large data sets.
This package provides an easy-to-use implementation of the Engle-Granger
two-step procedure for identifying pairs of cointegrated series.  It is geared towards
the analysis of pairs of securities.  Summary and plot functions are provided,
and the package is able to fetch closing prices of securities from Yahoo.
A variety of unit root tests are supported, and an improved unit root test is included.
This package implements hierarchical models
for faecal egg count data to assess anthelmintic
efficacy. Bayesian inference is done via MCMC sampling.
A small tool for Social Network Analysis, dealing with
ego-centric network measures, including Burt's effective size
and aggregate constraint and an import code suitable for a
large number of adjacency matrices. A free web application is
also available on http://www.egonet.associazionerospo.org
Event history analysis: Sampling of risk sets in Cox
regression, selections in the Lexis diagram, bootstrapping.
Parametric proportional hazards fitting with left truncation
and right censoring for common families of distributions,
piecewise constant hazards, and discrete models. AFT regression
for left truncated and right censored data. Binary and Poisson
regression for clustered data, fixed and random effects with
bootstrapping.
Hierarchical logistic species response curves along one-dimensional gradients including no response, monotone, plateau, unimodal and bimodal models.
An R wrapper to allow the user to query categories and Series IDs, and import data, from the EIA's API.
Solves the ``inverse eigenvalue problem'' which is to
generate a real-valued matrix that has the specified real
eigenvalue spectrum.  It can generate infinitely many dense
matrices, symmetric or asymmetric, with the given set of
eigenvalues.  Algorithm can also generate stochastic and doubly
stochastic matrices.
This package estimates the parameters of a model for
symmetric relational data (e.g., the above-diagonal part of a
square matrix), using a model-based eigenvalue decomposition
and regression. Missing data is accomodated, and a posterior
mean for missing data is calculated under the assumption that
the data are missing at random. The marginal distribution of
the relational data can be arbitrary, and is fit with an
ordered probit specification.
Computes confidence intervals for the proportion explained by the first 1,2,k principal components, and computes confidence intervals for each eigenvalue. Both computations are done via nonparametric bootstrap.
Implementation of Efficient Inference of Local Ancestry
using fused quantile regression and k-means classifier
Provides methods for analyzing RxC ecological contingency
tables using the extreme case analysis, ecological regression,
and Multinomial-Dirichlet ecological inference models.  Also
provides tools for manipulating higher-dimension data objects.
EIVE performs a compact genetic algorithm search to reduce errors-in-variables bias in linear regression.
This package allows to use the hybrid Multinomial-Dirichlet-Model
of Ecological Inference for estimating inner Cells of RxC-Tables. This was
already implemented in the eiPack-package. eiwild-package now has the
possibility to use individual level data to support the aggregate level
data and using different Hyperpriori-Distributions.
Empirical likelihood (EL) inference for two-sample problems. The following statistics are included: the difference of two-sample means, smooth Huber estimators, quantile (qdiff) and cumulative distribution functions (ddiff), probability-probability (P-P) and quantile-quantile (Q-Q) plots as well as receiver operating characteristic (ROC) curves.
This package provides functions for fitting the entire
solution path of the Elastic-Net and also provides functions
for estimating sparse Principal Components. The Lasso solution
paths can be computed by the same function. First version:
2005-10.
This is a bizzare collection of functions written to do
various sorts of statistical election audits.  There are also
functions to generate simulated voting data, and simulated
"truth" so as to do simulations to check charactaristics of
these methods.
An extension of the elec package intended for use on
election audits using stratified random samples.  Includes
functions to obtain conservative and exact p-values, and
functions that give sample sizes that may make election audits
more efficient.
Useful when reading the book above mentioned, in the
documentation referred to as `the book'.
Correlation chart of two set (x and y) of data.
Using Quantiles.  Visualize the effect of factor.
This package contains various routines for drawing
ellipses and ellipse-like confidence regions, implementing the
plots described in Murdoch and Chow (1996), A graphical display
of large correlation matrices, The American Statistician 50,
178-180. There are also routines implementing the profile plots
described in Bates and Watts (1988), Nonlinear Regression
Analysis and its Applications.
A suite of elliptic and related functions including Weierstrass and
Jacobi forms.  Also includes various tools for manipulating and
visualizing complex functions.
Training and predict functions for SLFN ( Single
Hidden-layer Feedforward Neural Networks ) using the ELM
algorithm. ELM algorithm differs from the traditional
gradient-based algorithms for very short training times ( it
doesn't need any iterative tuning, this makes learning time
very fast ) and there is no need to set any other parameters
like learning rate, momentum, epochs, etc.
Calculate Elo ratings as means to describe animal dominance hierarchies
elrm implements a Markov Chain Monte Carlo algorithm to
approximate exact conditional inference for logistic regression
models. Exact conditional inference is based on the
distribution of the sufficient statistics for the parameters of
interest given the sufficient statistics for the remaining
nuisance parameters. Using model formula notation, users
specify a logistic model and model terms of interest for exact
inference.
Flexible implementation of Elston-Stewart algorithm
The package is meant to help actuaries to build experience life tables. It contains functions to calculate exposure and number of deaths, smoothing following four different methods, and validation following three criteria.
We propose both a clear analysis strategy and a selection of tools to investigate microarray gene expression data. The most usual and relevant existing R functions were discussed, validated and gathered in an easy-to-use R package (EMA) devoted to gene expression microarray analysis. These functions were improved for ease of use, enhanced visualisation and better interpretation of results.
Tools to analyze the thermal reaction norm of embryo growth
random walk Metropolis, Metropolis Hasting, parallel tempering, evolutionary Monte Carlo, temperature ladder construction and placement
evolutionary Monte Carlo methods for clustering,
temperature ladder construction and placement. For mclust see
http://cran.r-project.org/web/packages/mclust/LICENSE
EMCluster provides EM algorithms and several efficient
initialization methods for model-based clustering of finite
mixture Gaussian distribution with unstructured dispersion
in both of unsupervised and semi-supervised learning.
This package carries out empirical mode decomposition and Hilbert spectral
analysis. For usage of EMD, see Kim and Oh, 2009 (Kim, D and Oh, H.-S. (2009) EMD: A Package for Empirical
Mode Decomposition and Hilbert Spectrum, The R Journal, 1, 40-46).
The EMDAT database provides valuable information on human
and financial losses from natural disasters around the world. Some
of the issues with the EMDAT data are lack of entire data
accessibility, static and inconsistent summary reports, and the
lack of auxiliary financial and demographic data. The emdatr
package addresses some of these issues. Data from EMDAT was downloaded
via the web (http://www.emdat.be/database) and cleaned and
enhanced. emdatr provides a sample of this cleaned and
enhanced data. Also, emdatr provides functionality to access
the entire cleaned and enhanced EMDAT data. The examples provided in
the vignette demonstrate the functionality provided by emdatr.
The goal of emdatr is to promote the use of EMDAT data, bring
transparency to the data, shed light on the limitations of the data,
and make the analysis of the data easier through the R language and
the plethora of open source packages built around it.
Auxiliary functions and data sets for _Ecological Models
and Data_, a book presenting maximum likelihood estimation and
related topics for ecologists (ISBN 978-0-691-12522-0)
Package providing calculation of Earth Mover's Distance
(EMD).
A toolbox for designing and evaluating predictive models with
resampling methods. The aim of this package is to provide a simple and
efficient general framework for working with any type of prediction
problem, be it classification, regression or survival analysis, that is
easy to extend and adapt to your specific setting. Some commonly used
methods for classification, regression and survival analysis are included.
Calculates parameters for Jump Diffusion processes via
EM-Algorithm. The jumps-times are considered to be bernoulli
distributed with normally distributed jump-sizes.
The evolutionary model-based multiresponse approach (EMMA)
is a novel methodology to process optimisation and product
improvement. The approach is suitable to contexts in which the
experimental cost and/or time limit the number of implementable
trials.
This package provides a set of functions for convenient end-member modelling analysis of grain-size data (EMMAgeo).
This package includes functions to read and write to an
EMME/2 databank
For forming contrasts in the mixed effects for mixtures of linear mixed models fitted to the gene profiles.
EM algorithm for Mixture of Multivariate Skew Normal/T Distributions
Functions to fit finite mixture of unrestricted multivariate skew t (FM-uMST) model, random sample generation, discriminant analysis, 2D and 3D contour plots
The main functions are emmreml, and emmremlMultiKernel.emmreml solves a mixed model with known covariance structure using the EMMA algorithm . emmremlMultiKernel is a wrapper for emmreml to handle multiple random components with known covariance structures. The function emmremlMultivariate solves a multivariate gaussian mixed model with known covariance structure using the ECM algorithm.
Collection of building blocks for the design and analysis
of evolutionary multiobjective optimization algorithms.
Functions for estimating EMP (Expected Maximum Profit Measure) in
Credit Scoring, according to Verbraken et al (2014).
empirical likelihood ratio tests for means/quantiles/hazards
from possibly censored and/or truncated data. Now does regression too.
Calculates the p-value for a mean-type hypothesis (or
multiple mean-type hypotheses) based on two samples.
The package provides functions to carry out a
Goodness-of-fit test for discrete multivariate data. It is
tested if a given observation is likely to have occurred under
the assumption of an ab-initio model. A p-value can be
calculated using different distance measures between observed
and expected frequencies. A Monte Carlo method is provided to
make the package capable of solving high-dimensional problems.
This package allows one to estimate the output of a computer program,
as a function of the input parameters, without actually running it.
The computer program is assumed to be a Gaussian process, whose
parameters are estimated using Bayesian techniques that give a PDF of
expected program output.  This PDF is conditional on a ``training set''
of runs, each consisting of a point in parameter space and the model
output at that point.  The emphasis is on complex codes that take
weeks or months to run, and that have a large number of undetermined
input parameters; many climate prediction models fall into this
class.  The emulator essentially determines Bayesian posterior
estimates of the PDF of the output of a model, conditioned on results
from previous runs and a user-specified prior linear model.  A
working example is given in the help page for function `interpolant()',
which should be the first point of reference.
Contains logic for the data-driven optimization of annotations via minimization of the entropy of variable group members over discrete variable clusters.
Ensemble network aggregation is an approach which leverages the
inverse-rank-product (IRP) method to combine networks. This package
provides the capabilities to use IRP to bootstrap a dataset using a single
method, to aggregate the networks produced by multiple methods, or to
aggregate the networks produced on different datasets.
Functions for the analysis of ecological networks
endogMNP is an R package that fits a Bayesian multinomial
probit model with endogenous selection, which is sometimes
called an endogenous switching model.  This can be used to
model discrete choice data when respondents select themselves
into one of several groups.  This package is based on the MNP
package by Kosuke Imai and David A. van Dyk.  This package
modifies their code.
This R package implements the statistical model proposed by Bullock, Imai, and Shapiro (2011; Political Analysis) to analyze endorsement experiments.  Endorsement experiments are a survey methodology for eliciting truthful responses to sensitive questions.  This methodology is helpful when measuring support for socially sensitive political actors such as militant groups.  The model is fitted with the Markov chain Monte Carlo algorithm and produces the output containing draws from the posterior distribution.
E-statistics (energy) tests and statistics for comparing
distributions: multivariate normality, multivariate distance
components and k-sample test for equal distributions,
hierarchical clustering by e-distances, multivariate
independence tests, distance correlation, goodness-of-fit
tests. Energy- statistics concept based on a generalization of
Newton's potential energy is due to Gabor J. Szekely.
Allow numbers to be presented in an English language
version, one, two, three, ...
Datasets from Nelson, Coffin and Copeland "Introductory
Statistics for Engineering Experimentation" (Elsevier, 2003)
with sample code.
Automatically partitions data into evaluation bins, executes ecological niche models across a range of settings, and calculates a variety of evaluation statistics.  Current version only implements ENMs with Maxent (Phillips et al. 2006).
The ENmisc package contains utility function for different
purposes: mtapply and mlapply (multivariate version of tapply
and lapply), wtd.boxplot (a boxplot with weights), and a visual
interface to restructuring mosaic plots.
enRich is an R package for joint statistical modelling of ChIP-seq data, accounting for technical/biological replicates, multiple conditions and different ChIP efficiencies of the individual experiments.
These programs are used for calculating enrichment
factors, drawing enrichment curves to evaluate virtual
screening approaches.
Bayesian Model Averaging to create probabilistic forecasts
from ensemble forecasts and weather observations.
Ensemble Model Output Statistics to create probabilistic
forecasts from ensemble forecasts and weather observations.
Measurement and partitioning of diversity, based on Tsallis entropy.
This package implements various estimators of entropy, such
as the shrinkage estimator by Hausser and Strimmer, the maximum likelihood
and the Millow-Madow estimator, various Bayesian estimators, and the
Chao-Shen estimator.  It also offers an R interface to the NSB estimator.
Furthermore, it provides functions for estimating Kullback-Leibler divergence,
chi2-squared, mutual information, and chi2-squared statistic of independence.
In addition there are functions for discretizing continuous random variables.
Contains methods for the estimation of Shannon's entropy, variants of Renyi's entropy, mutual information, Kullback-Leibler divergence, and generalized Simpson's indices. The estimators used have a bias that decays exponentially fast.
Fast and very memory-efficient calculation of isotope patterns,
subsequent convolution to theoretical envelopes (profiles) plus valley
detection and centroidization or intensoid calculation. Batch processing,
resolution interpolation, wrapper, adduct calculations and molecular
formula parsing.
Sequential partitioning, clustering and peak detection of
centroided LC-MS mass spectrometry data (.mzXML). Interactive result and raw
data plot.
Companion code to the book by Nhu D Le and James V Zidek, Springer (2006)
Plot environmental conditions with a high number of species, create boxplots with a range of environmental variables under different environmental conditions selected by the user, list species under the different environmental conditions selected by the user and create a summary of the environmental conditions, using an output file of ModestR (www.ipez.es/ModestR).
Functions and datasets to perform analyses of environmental data.
A collection of nice plotting functions directly from a
data.frame with limited customisation possibilities.
Functions for demographic and epidemiological analysis in
the Lexis diagram, i.e. register and cohort follow-up data, in
particular representation and manipulation of multistate data - the
Lexis suite of functions.
Includes interfaces to mstate, etm and cmprsk packages.
Also contains functions for Age-Period-Cohort modeling and a
function for interval censored data and some useful functions for
tabulation and plotting, as well some epidemiological datasets.
The 'epi2loc' package provides a set of utilities for using common models of epistasis. Interactions between two biallelic loci (e.g. SNPs) can easily be represented using GLM regression parameters, penetrance models, or variance components. Functions are included for conversion among these common interaction models, as well as data generation, power analysis, and effect size measures.
This package contains elementary tools for analysis of
common epidemiological problems, ranging from sample size
estimation, through 2x2 contingency table analysis and basic
measures of agreement (kappa, sensitivity/specificity).
Appropriate print and summary statements are also written to
facilitate interpretation wherever possible.  Source code is
commented throughout to facilitate modification.  The target
audience includes advanced undergraduate and graduate students
in epidemiology or biostatistics courses, and clinical
researchers.
Functions making R easy for epidemiological calculation.
Routines for epidemiological contact tracing
and visualisation of network of contacts.
Mathematical models of infectious diseases in humans and animals.
Both, deterministic and stochastic models can be simulated and plotted.
This package provides tools to quantify transmissibility
throughout an epidemic from the analysis of time series of
incidence.
Tools for simulating mathematical models of infectious disease.
Epidemic model classes include deterministic compartmental models,
stochastic individual contact models, and stochastic network models.
Disease types include SI, SIR, and SIS epidemics with and without
demography, with tools available for expansion to model complex epidemic
processes.
A collection of epidemic/network-related tools. Simulates transmission of diseases through contact networks. Performs Bayesian inference on network and epidemic parameters, given epidemic data.
An R package for the analysis of epidemiological data. Contains functions for directly and indirectly adjusting measures of disease frequency, quantifying measures of association on the basis of single or multiple strata of count data presented in a contingency table, and computing confidence intervals around incidence risk and incidence rate estimates. Miscellaneous functions for use in meta-analysis, diagnostic test interpretation, and sample size calculations.
EpiTools: R Package for Epidemiologic Data and Graphics
Aim: Adjust the graphical parameters to create nicer
longitudinal series plots. The default set of graphical parameters is very
general, and can be improved upon when we are interested in plotting data
points observed over time. Functions facilitate plotting those kind of
series, univariate plots, bivariate plots (with vertical axis on both left
and right hand sides), multivariate plots and plots which allow to examine
whether a new observation is 'unusual' via construction and visualization
of prediction intervals around it.
Estimates sparse matrices A or G using fast lasso regression from mRNA transcript levels Y and CNA profiles U. Two models are provided, EPoC A where
AY + U + R = 0
and EPoC G where
Y = GU + E,
the matrices R and E are so far treated as noise. For details see the reference and the manual page of `lassoshooting'.
The package performs analysis of polynomial regression in simple designs with quantitative treatments
Computation of the EQL for a given family of variance
functions, Saddlepoint-approximations and related auxiliary
functions (e.g. Hermite polynomials)
Transitioning from EQS to R for structural equation modeling (SEM)
is made easier with a set of functions to convert .out files into R code.
The EQS output can be converted into lavaan syntax and run in the R
environment.  Other functions parse descriptive statistics and the covariance matrix
from an EQS .out file.  A heat map plot of a covariance matrix is also
included.
Analysis of experimental crosses to identify genes (called
quantitative trait loci, QTLs) contributing to variation in
quantitative traits.
The equate package contains methods for
observed-score linking and equating under the single-
group, equivalent-groups, and nonequivalent-groups
designs. Equating types include identity, mean, linear,
general linear, equipercentile, circle-arc, and
composites of these. Equating methods include synthetic,
nominal weights, Tucker, Levine observed score, Levine
true score, Braun/Holland, frequency estimation, and
chained equating. Plotting and summary methods, and
methods for univariate and bivariate presmoothing are
also provided.
This package computes direct, chain and average (bisector) equating coefficients with standard errors using Item Response Theory (IRT) methods for dichotomous items.
This package provides some statistical tests and graphics
for assessing tests of equivalence.  Such tests have similarity
as the alternative hypothesis instead of the null.  Sample
data sets are included.
Expectile regression is a nice tool for estimating the conditional expectiles of a response variable given a set of covariates. This package implements a regression tree based gradient boosting estimator for nonparametric multiple expectile regression.
Functions, datasets, and sample codes related to the book of 'Empirical Research in Economics: Growing up with R' by Dr. Changyou Sun are included. The book is still under preparation as of August 2014. Marginal effects for binary or ordered choice models can be calculated. Static and dynamic Almost Ideal Demand System (AIDS) models can be estimated. A typical event analysis in finance can be conducted with several functions included.
An integrated set of tools to analyze and simulate networks based on exponential-family random graph models (ERGM). "ergm" is a part of the "statnet" suite of packages for network analysis.
A set of extensions for the ergm package to fit weighted networks whose edge weights are counts.
A package integrating graphlet statistics based model terms
for use in "ergm" models as part of the "statnet" suite of packages.
Data for use with the Sage Introduction to Exponential
Random Graph Modeling text by Jenine K. Harris.  Network data
set consists of 1283 local health departments and the
communication links among them along with several attributes.
A template package to demonstrate the use of user-specified statistics for use in "ergm" models as part of the "statnet" suite of packages.
eRm fits Rasch models (RM), linear logistic test models (LLTM), rating scale model (RSM), linear rating scale models (LRSM), partial credit models (PCM), and linear partial credit models (LPCM).  Missing values are allowed in the data matrix.  Additional features are the ML estimation of the person parameters, Andersen's LR-test, item-specific Wald test, Martin-Loef-Test, nonparametric Monte-Carlo Tests, itemfit and personfit statistics including infit and outfit measures, various ICC and related plots, automated stepwise item elimination, simulation module for various binary data matrices.  An eRm platform is provided at R-forge (see URL).
The functions provided in the package ERP are designed for the significance analysis of ERP data in a linear model framework. The possible procedures are either the collection of FDR or FWER controlling methods available in the generic function p.adjust, the same collection combined with a factor modeling of the time dependence among tests (see Sheu, Perthame, Lee and Causeur, 2014) and the Guthrie and Buchwald (1991) test.
This package is dedicated to the analysis of event-related potentials (ERPs). Event-related potentials are the measured brain responses associated with a specific sensory, cognitive, or motor event and are obtained from electroencephalographic (EEG) signal. The erpR package contains a series of functions for importing ERP data, computing traditional ERP measures, exploratory ERP analyses and plotting.
Implementation of the Edge Selection Algorithm
The package presents a "Scenarios" class containing
general parameters, risk parameters and projection results.
Risk parameters are gathered together into a ParamsScenarios
sub-object. The general process for using this package is to
set all needed parameters in a Scenarios object, use the
customPathsGeneration method to proceed to the projection, then
use xxx_PriceDistribution() methods to get asset prices.
Toolkit for Monte Carlo simulations of financial assets and
interest rates models, involved in an Economic Scenario Generator (ESG).
The underlying simulation loops have been implemented in C++.
Source code from esotericR articles at lemnica.com
The EstCRM package estimates item and person parameters
for the Samejima's Continuous Response Model (CRM), computes
item fit residual statistics, draws empirical 3D item category
response curves, draws theoretical 3D item category response
curves, and generates data under the CRM for simulation
studies.
This package is intended to speedup the process of
creating model-comparing tables common in Macroeconomics. The
function collection stores the estimates of several models and
formats it to a table of the form estimate starred and std.err.
below. The default output is LaTeX but output to CSV for later
editing in a spreadsheet tool is possible as well. It works for
linear models (lm) and panel models from the "plm"-package
(plm). Two further implemented functions "descsto" and
"desctab" enable you to export descriptive statistics of
data-frames and single variables to LaTeX and CSV.
A table function for descriptive statistics in tabular format, using variables in a data.frame. You can create simple or highly customized tables.
A package for fitting the ETAS model to earthquake
catalogoues ETAS model is a spatio-temporal marked point
process model and a special case of the Hawcks process
Estimation of the components of an ETAS model for earthquake description.
Non-parametric background seismicity can be estimated through FLP (Forward Likelihood Predictive), while parametric components are estimated through maximum likelihood.
The two estimation steps are alternated until convergence is obtained.
For each event the probability of being a background event is estimated and used as a weight for declustering steps. Many options to control the estimation process are present. Some descriptive functions for earthquakes catalogs are  present; also plot, print, summary, profile methods are defined for main output (objects of class "etasclass").
The package allows selecting those treatments of a one-way layout
being equivalent to a control. Bonferroni adjusted "two one-sided t-tests"
(TOST) and related simultaneous confidence intervals are given for both
differences or ratios of means of normally distributed data. For the case of
equal variances and balanced sample sizes for the treatment groups, the
single-step procedure of Bofinger and Bofinger (1995) can be chosen. For
non-normal data, the Wilcoxon test is applied.
Provides functions to facilitate the use of the ff package
in interaction with bigdata in SQL databases (e.g. in
Oracle/MySQL/PostgreSQL/Hive) by allowing easy importing
directly into ffdf objects using DBI, RODBC and RJDBC. Also
contains some basic utility functions to do fast left outer
join merging based on match and basic recoding.
Matrix of transition probabilities for any time-inhomogeneous multistate model with finite state space
An eulerian path is a path in a graph which visits every edge exactly once. This package provides methods to handle eulerian paths or cycles.
EvalEst provides functions for evalating (time series)
model estimation methods. These facilitate monte carlo
experiments of repeated simulations and estimations. The
package also provides methods for looking at the distribution
of the results from these experiments, including model roots
(which are an equivalence class invariant).
Parsing and evaluation tools that make it easy to recreate the
command line behaviour of R.
Uses data and constants to calculate potential evapotranspiration (PET) and actual evapotranspiration (AET) from 17 different formulations including Penman, Penman-Monteith FAO 56, Priestley-Taylor and Morton formulations.
Package for combining pieces of evidence
Extends simulation, distribution, quantile and density
functions to univariate and multivariate parametric extreme
value distributions, and provides fitting functions which
calculate maximum likelihood estimates for univariate and
bivariate maxima models, and for univariate and bivariate
threshold models.
Provides functions for the bayesian analysis of extreme
value models, using MCMC methods.
Compute a matrix comparison of lists (from 2 to infinite)
and plot the results in a Venn diagram if (N<=4) with
regulation details. It allows to produce a complete annotated
file, merging the annotations of the compared lists. It is also
possible to compute an overlaps table to show the overlaps
proportions of all the couples of lists and draw proportional
Venn diagrams.
Functions for analysis of rate changes in sequential events
Stores, manipulates, aggregates and otherwise messes with event
data from KEDS/TABARI or any other extraction tool with similar output
Implementation of short and long term event study
methodology
Delete-A-Group Jackknife replication. Calibration of
replicate weights. Estimates, standard errors and confidence
intervals for: totals, means, absolute and relative frequency
distributions, contingency tables, ratios, quantiles and
regression coefficients. Estimates, standard errors and
confidence intervals for user-defined estimators (even
non-analytic). Domain (subpopulation) estimation.
Functions for extreme value theory, which may be divided
into the following groups; exploratory data analysis, block
maxima, peaks over thresholds (univariate and bivariate), point
processes, gev/gpd distributions.
The usual distribution functions, maximum likelihood inference and
model diagnostics for univariate stationary extreme value mixture models
are provided. Kernel density estimation including various boundary
corrected kernel density estimation methods and a wide choice of kernels,
with cross-validation likelihood based bandwidth estimator.
Reasonable consistency with the base functions in the evd package is
provided, so that users can safely interchange most code.
evobiR is a collection of tools for use in evolutionary biology
EVORA is an adaptive index classification algorithm which
selects features based on differential variability and is aimed
at DNA methylation array studies.
Uses maximum likelihood to estimate rates of trait evolution across environmental gradients.
Computes extreme value index (EVI) estimate for heavy tailed models by Mean of order p (MOP)
and peaks over random threshold (PORT) Hill methodologies.
Besides, also computes moment, generalised Hill and mixed moment estimates for EVI.
Compute high quantile or value-at-risk (VaR) based on above EVI estimates.
Commonly used classification and regression tree methods like the CART algorithm
are recursive partitioning methods that build the model in a forward stepwise search.
Although this approach is known to be an efficient heuristic, the results of recursive
tree methods are only locally optimal, as splits are chosen to maximize homogeneity at
the next step only. An alternative way to search over the parameter space of trees is
to use global optimization methods like evolutionary algorithms. The evtree package
implements an evolutionary algorithm for learning globally optimal classification and
regression trees in R. CPU and memory-intensive tasks are fully computed in C++ while
the partykit package is leveraged to represent the resulting trees in R, providing
unified infrastructure for summaries, visualizations, and predictions.
This package contains a large number of the goodness-of-fit tests for the Exponential and Weibull distributions classified into families: the tests based on the empirical distribution function, the tests based on the probability plot, the tests based on the normalized spacings, the tests based on the Laplace transform and the likelihood based tests.
Unconditional exact tests for 2x2 contingency tables
Calculates Fisher's exact test, Blaker's exact test, or the exact McNemar's test with appropriate matching confidence intervals. Provides power and sample size calculations. Also gives melded confidence intervals for the binomial case.
Calculates exact tests and confidence intervals for one-sample binomial and one- or two-sample Poisson cases.
This is a package for exact Confidence Intervals for the
difference between two independent or dependent proportions.
Monte Carlo and MCMC goodness of fit tests for log-linear
models
Perform exact fixed effect meta analysis for rare events data without the need of artificial continuity correction.
ExactPath implements an algorithm for exact LASSO
solution. Two methods are provided to print and visualize the
whole solution paths. Use ?ExactPath to see an introduction.
Packages ncvreg and lars are required so that their data sets
can be used in examples.
Computes exact conditional p-values and quantiles using an
implementation of the Shift-Algorithm by Streitberg & Roehmel.
Sweave-based automatic generation of exams including multiple-choice
questions and arithmetic problems. Exams can be produced in various formats, including
PDF, HTML, Moodle XML, QTI 1.2 (for OLAT/OpenOLAT), QTI 2.1.
Tools for constructing confidence regions for exceedance regions
and contour lines.
Allow dynamic access to data in Microsoft Excel. (e. g.
xl[a1]=xl[b2]*3 and so on). Also class provided which allows treat data on
Excel sheet similar to data.frame (indexing, subsetting). Microsoft Excel
is required for this package.
This package contains functions that compute probabilistic excursion sets, contour credibility regions, and contour avoiding regions for latent gaussian random processes and fields. The function excursions.inla calculates these quantities for models estimated with the INLA package.
Calls copy number variants (CNVs) from targeted sequence data
Expanding Ploidy and Allele Frequency on Nested Subpopulations (ExPANdS) characterizes coexisting subpopulations in a tumor using copy number and allele frequencies derived from exome- or whole genome sequencing input data (http://www.ncbi.nlm.nih.gov/pubmed/24177718). The model amplifies the statistical power to detect coexisting genotypes, by exploiting run-specific tradeoffs between depth of coverage and breadth of coverage. ExPANdS predicts the number of clonal expansions, the size of the resulting subpopulations in the tumor bulk, the mutations specific to each subpopulation and tumor purity. The main function runExPANdS provides the complete functionality needed to predict coexisting subpopulations from single nucleotide variations (SNVs) and associated copy numbers.  The robustness of the subpopulation predictions by ExPANdS increases with the number of mutations provided. It is recommended that at least 200 mutations are used as an input to obtain stable results. Updates include: (1) Additional optional parameter "min_CellFreq" provided for function runExPANdS, specifying the minimum cellular prevalence of mutations to be included for subpopulation predictions.(2) Filtered loci with high-level amplifications, according to max_PM setting. This reduces unnecessary processing time, as assignment of mutations within amplified regions to subpopulations is not successful. (3) Additional function "buildMultiSamplePhylo" available, which integrates the subpopulations predicted in multiple, geographically distinct tumor samples into one common phylogeny.
Package for analysis of simple experimental designs (CRD,
RBD and LSD), experiments in double factorial schemes (in CRD
and RBD), experiments in a split plot in time schemes (in CRD
and RBD), experiments in double factorial schemes with an
additional treatment (in CRD and RBD), experiments in triple
factorial scheme (in CRD and RBD) and experiments in triple
factorial schemes with an additional treatment (in CRD and
RBD), performing the analysis of variance and means comparison
by fitting regression models until the third power
(quantitative treatments) or by a multiple comparison test,
Tukey test, test of Student-Newman-Keuls (SNK), Scott-Knott,
Duncan test, t test (LSD) and Bonferroni t test (protected LSD)
- for qualitative treatments.
Pacote destinado a analise de delineamentos experimentais
simples (DIC, DBC e DQL), experimentos em esquema de fatorial
duplo (em DIC e DBC), experimentos em esquema de parcelas
subdivididas no tempo (em DIC e DBC), experimentos em esquema
de fatorial duplo com um tratamento adicional (em DIC e DBC),
experimentos em esquema de fatorial triplo (em DIC e DBC) e
experimentos em esquema de fatorial triplo com um tratamento
adicional (em DIC e DBC); realizando a analise de variancia e
comparacao de medias pelo ajuste de modelos de regressao ate o
terceiro grau (tratamentos quantitativos) ou por testes de
comparacao multipla: teste de Tukey, teste de
Student-Newman-Keuls (SNK), teste de Scott-Knott, teste de
Duncan, teste t (LSD), teste t de Bonferroni (LSD protegido) e
teste Bootstrap - tratamentos qualitativos.
Expectile and quantile regression of models with nonlinear effects
e.g. spatial, random, ridge using least asymmetric weighed squares / absolutes
as well as boosting; also supplies expectiles for common distributions.
The package provides various statistical methods for
designing and analyzing randomized experiments. One main
functionality of the package is the implementation of
randomized-block and matched-pair designs based on possibly
multivariate pre-treatment covariates. The package also
provides the tools to analyze various randomized experiments
including cluster randomized experiments, randomized
experiments with noncompliance, and randomized experiments with
missing data.
Expert opinion (or judgment) is a body of techniques to
estimate the distribution of a random variable when data is scarce
or unavailable. Opinions on the quantiles of the distribution are
sought from experts in the field and aggregated into a final
estimate. The package supports aggregation by means of the Cooke,
Mendel-Sheridan and predefined weights models.
Computation of the matrix exponential and related quantities.
An R-interface to the Fortran package Expokit.
ExPosition is for descriptive (i.e., fixed-effects) multivariate analysis with the singular value decomposition.
Calculates the density dependent likelihood of a phylogenetic tree. It takes branching and sampling times as an argument and integrates the likelihood function over the whole tree.
Tools and data to accompany Schlicht, Valcu and Kempenaers "Spatial patterns of extra-pair paternity: beyond paternity gains and losses"
Data sets from the book "Forecasting with exponential
smoothing: the state space approach" by Hyndman, Koehler, Ord
and Snyder (Springer, 2008).
Tests for the composite hypothesis of exponentiality
The package provides some tools for botanists, plant
taxonomists, curators of plant genebanks and perhaps other
biological collections.
The package provides a consistent, unified and extensible
framework for parameter estimation of probability distributions; it extends
parameter estimation procedures to allow for weighted samples; moreover, it
extends the gallery of available distributions.
This is a package containing the function extfunnel()
which produces a funnel plot including additional augmentations
such as statistical significance contours and heterogeneity
contours.
The package estimates coefficients of extended LASSO penalized linear regression and generalized linear models. Currently lasso and elastic net penalized linear regression and generalized linear models are considered. The package currently utilizes an accurate approximation of L1 penalty and then a modified Jacobi algorithm to estimate the coefficients. There is provision for plotting of the solutions and predictions of coefficients at given values of lambda. The package also contains functions for cross validation to select a suitable lambda value given the data. The package also provides a function for estimation in fused lasso penalized linear regression.
This package tests for differences in minor allele
frequency between groups and is based on an extra-binomial
variation model for pooled sequencing data.
Categorical Data Analysis and Visualization
Tools to using fonts other than the standard PostScript fonts.
This package makes it easy to use system TrueType fonts and with PDF or
PostScript output files, and with bitmap output files in Windows. extrafont
can also be used with fonts packaged specifically to be used with, such as
the fontcm package, which has Computer Modern PostScript fonts with math
symbols. See https://github.com/wch/extrafont for instructions and
examples.
Package for holding the database for the extrafont package
Classification and regression based on an ensemble of decision trees. The package also provides extensions of ExtraTrees to multi-task learning and quantile regression. Uses Java implementation of the method.
An implementation of Extreme Bounds Analysis (EBA), a global sensitivity analysis that examines the robustness of determinants in regression models. The package supports both Leamer's and Sala-i-Martin's versions of EBA, and allows users to customize all aspects of the analysis.
Functions for performing extreme value analysis.
Detect extreme values in onedimensional data
This package implements the subjectively censored Weibull MLE and censored Weibull mixture
for the lower quantile estimation. It also includes functions to evaluation the standard error of the resulting
quantile estimates. Also, the methods here can be used to fit the Weibull or Weibull mixture for
the Type-I or Type-II right censored data.
Misc function for working with eyetracking data
This package facilitates easy analysis of factorial experiments, including purely within-Ss designs (a.k.a. "repeated measures"), purely between-Ss designs, and mixed within-and-between-Ss designs. The functions in this package aim to provide simple, intuitive and consistent specification of data analysis and visualization. Visualization functions also include design visualization for pre-analysis data auditing, and correlation matrix visualization. Finally, this package includes functions for non-parametric analysis, including permutation tests and bootstrap resampling. The bootstrap function obtains predictions either by cell means or by more advanced/powerful mixed effects models, yielding predictions and confidence intervals that may be easily visualized at any level of the experiment's design.
This package implements a simplified version of least
squares, and logistic regression for efficiently selecting the
significant non-additive interactions between two variables.
ezsim provides a handy way to run simulation and examine its result
This method tries to explain the gene-wise treatment response ratios in terms of the latent pathways. It uses bayesian sparse factor modeling to infer the loadings (weights) of each pathway on its associated probesets as well as the latent factor activity levels for each treatment.
The package contains methods using stochastic approximation
to analyse (big) data (streams).
The package estimates Factor Mixture Analysis via the EM
algorithm
Multivariate exploration of a data table with factorial
analysis and cluster methods.
an R package for exploratory data analysis
Calculate, print, summarize and plot pairwise differences from GLMs, GLHT or Multinomial Logit models
Package to fit Bayesian quantile regression models that
assume a factor structure for at least part of the design
matrix.
Per the Factual.com website, "Factual is a platform where
anyone can share and mash open, living data on any subject."
The data is in the form of tables and is accessible via REST
API.  The factualR package is a thin wrapper around the
Factual.com API, to make it even easier for people working with
R to explore Factual.com data sets.
The functions provided in the FADA (Factor Adjusted Discriminant Analysis) package aim at performing
supervised classification models and variable selection on dependent
covariates. The classification procedures are combined with a factor
modeling of dependence among covariates. The available procedures are Lasso
regularized logistic model (see Friedman et al. (2010)), sparse linear
discriminant analysis (see Clemmensen et al. (2011)), shrinkage linear and
diagonal discriminant analysis (see M. Ahdesmaki et al. (2010)).
This package contains several distributions that are
sometimes useful in hydrology
Data and functions for the book "Multivariate Statistical
Modelling Based on Generalized Linear Models", version 1, by
Ludwig Fahrmeir and Gerhard Tutz
More comfortable interface to work with a directory of R data or
source files
This package estimates factor analysis models using a
genetic algorithm, which permits a general mechanism for
restricted optimization with arbitrary restrictions that are
chosen at run time with the help of a GUI. Importantly,
inequality restrictions can be imposed on functions of multiple
parameters, which provides a new avenues for testing and
generating theories with factor analysis models. This package
also includes an entirely new estimator of the common factor
analysis model called semi-exploratory factor analysis, which
is a general alternative to exploratory and confirmatory factor
analysis. Finally, this package integrates a lot of other
packages that estimate sample covariance matrices and thus
provides a lot of alternatives to the traditional sample
covariance calculation. Note that you need to have the Gtk run
time library installed on your system to use this package; see
the URL below for detailed installation instructions. Most
users would only need to understand the first twenty-four pages
of the PDF manual.
This is a method for Allele-specific DNA Copy Number Profiling using Next-Generation Sequencing.  Given the allele-specific coverage at the variant loci, this program segments the genome into regions of homogeneous allele-specific copy number.  It requires, as input, the read counts for each variant allele in a pair of case and control samples. For detection of somatic mutations, the case and control samples can be the tumor and normal sample from the same individual.
A value is falsy if it is NULL, FALSE, the empty string,
zero, or an empty vector or list. Other values are truthy. The new
%&&% and %||% operators work with falsy and truthy values and
can be used for concise conditional execution.
This package represents an interface to the core Familias functions (www.familias.name),
which are programmed in C++. The interface is completely changed from version 1.2 of this
package, so programs dependent on the old functions should continue to use that version.
The method proposed in this package takes into account the impact of dependence on the multiple testing procedures for high-throughput data as proposed by Friguet et al. (2009). The common information shared by all the variables is modeled by a factor analysis structure. The number of factors considered in the model is chosen to reduce the false discoveries variance in multiple tests. The model parameters are estimated thanks to an EM algorithm. Adjusted tests statistics are derived, as well as the associated p-values. The proportion of true null hypotheses (an important parameter when controlling the false discovery rate) is also estimated from the FAMT model. Graphics are proposed to interpret and describe the factors.
An R package "fanc" computes the penalized maximum
likelihood estimates of factor loadings and unique variances
for various tuning parameters. The pathwise coordinate descent
along with EM algorithm is used.  This package also includes a
new graphical tool which outputs path diagram, goodness-of-fit
indices and model selection criteria for each regularization
parameter. The user can change the regularization parameter by
manipulating scrollbars, which is helpful to find a suitable
value of regularization parameter.
This package contains a collection of R functions to
perform nonparametric analysis of covariance for regression
curves or surfaces. Testing the equality or parallelism of
nonparametric curves or surfaces is equivalent to analysis of
variance (ANOVA) or analysis of covariance (ANCOVA) for
one-sample functional data. Three different testing methods are
available in the package, including one based on L-2 distance,
one based on an ANOVA statistic, and one based on variance
estimators.
Estimation and plotting of a function's FANOVA graph to identify the interaction structure and fitting, prediction and simulation of a kriging model modified by the identified structure. The interactive function plotManipulate can only be run on the RStudio IDE with RStudio's package manipulate loaded. RStudio is freely available (www.rstudio.org), and includes package manipulate. The equivalent function plotTk bases on CRAN Repository packages only.
Visualise sequential distributions using a range of plotting styles. Sequential distribution data can be input as either simulations or values corresponding to percentiles over time (columns). Plots are added to existing graphic devices using the fan function. Users can choose from four different styles, including fan chart type plots, where a set of coloured polygon, with shadings corresponding to the percentile values are layered to represent different uncertainty levels.
The package hosts a list of functions to download, manipulate,
construct and aggregate agricultural statistics provided by the FAOSTAT
database of the Food and Agricultural Organization of the United Nations.
Tools for detecting and summarize influential cases that
can affect exploratory and confirmatory factor analysis models as well as
structural equation models more generally.
Modelizations and previsions functions for Functional
AutoRegressive processes using nonparametric methods:
functional kernel, estimation of the covariance operator in a
subspace, ...
Books are "Practical Regression and ANOVA in R" on CRAN, "Linear Models with R" published 1st Ed. August 2004, 2nd Ed. July 2014 by CRC press and "Extending the Linear Model with R" published by CRC press in December 2005.
Environment for teaching "Financial Engineering and
Computational Finance"
Allow numbers to be presented in an persian language
version
Environment for teaching
"Financial Engineering and Computational Finance".
The Fourier Amplitude Sensitivity Test (FAST) is a method to determine global sensitivities of a model on parameter changes with relatively few model runs. This package implements this sensitivity analysis method.
The package "fastclime" provides a method of recover the
precision matrix efficiently by applying parametric simplex
method.  The computation is based on a linear optimization
solver. It also contains a generic LP solver and a parameterized LP solver using parametric simplex method.
This is a two-in-one package which provides interfaces to
both R and Python. It implements fast hierarchical, agglomerative
clustering routines. Part of the functionality is designed as drop-in
replacement for existing routines: “linkage” in the SciPy package
“scipy.cluster.hierarchy”, “hclust” in R's “stats” package, and the
“flashClust” package. It provides the same functionality with the
benefit of a much faster implementation. Moreover, there are
memory-saving routines for clustering of vector data, which go beyond
what the existing packages provide. For information on how to install
the Python files, see the file INSTALL in the source distribution.
We implement a cocktail algorithm, a good mixture of coordinate decent, the majorization-minimization principle and the strong rule, for computing the solution paths of the elastic net penalized Cox's proportional hazards model.
The FastHCS outlyingness index is a method for finding outliers in high dimensional data-sets.
This package implements HICA (Hierarchical Independent Component Analysis) algorithm. This approach, obtained through the integration between treelets and Independent Component Analysis, is able to provide a multi-scale non-orthogonal data-driven basis, whose elements have a phenomenological interpretation according to the problem under study.
Implementation of FastICA algorithm to perform Independent
Component Analysis (ICA) and Projection Pursuit.
TrainFastImputation uses training data to describe a
multivariate normal distribution that the data approximates or
can be transformed into approximating and stores this information
as an object of class FastImputationPatterns. The FastImputation
function uses this FastImputationPatterns object to impute (make
a good guess at) missing data in a single line or a whole dataframe
of data.  This approximates the process used by Amelia
[http://gking.harvard.edu/amelia/] but is much faster when
filling in values for a single line of data.
The package implements the new algorithm for fast computation of M-scatter matrices using a partial Newton-Raphson procedure for several estimators.
Package providing a fast match() replacement for cases
that require repeated look-ups. It is slightly faster that R's
built-in match() function on first match against a table, but
extremely fast on any subsequent lookup as it keeps the hash
table in memory.
The FastPCS outlyingness index is an algorithm for finding outliers for the multivariate elliptical model.
Data sets and utilities for Foundations and Applications of
Statistics:an Introduction using R by R Pruim
The FastRCS outlyingness index is an algorithm for finding outliers for the linear regression model.
This package provides a way of creating rich web contents
using fast-response R
Functions for computing spillover measures, especially
spillover tables and spillover indices, as well as their
average, minimal, and maximal values.
Performs family-based association tests with a polytomous outcome under 2-locus and 1-locus models
defined by some design matrix.
A set of functions to manipulate power hyperbolas, power
hyperbolic functions and Kiener distributions of type I, II, III and IV
which exhibit left and right fat tails like those that occur in financial
markets. These distributions can be used to estimate with a high
accuracy market risks and value-at-risk.
This package provides functions and datasets from the book
"Forest Analytics with R".
Environment for teaching "Financial Engineering and
Computational Finance" NOTE: SEVERAL PARTS ARE STILL
PRELIMINARY AND MAY BE CHANGED IN THE FUTURE. THIS TYPICALLY
INCLUDES FUNCTION AND ARGUMENT NAMES, AS WELL AS DEFAULTS FOR
ARGUMENTS AND RETURN VALUES. Please donate, www.rmetrics.org,
to support future activities of the Rmetrics association.
Does family-based gene by environment interaction tests, joint gene, gene-environment interaction test, and a test of a set of genes conditional on another set of genes.
We propose an objective Bayesian algorithm for searching
the space of Gaussian directed acyclic graphical models when
the variables are assumed to satisfy a given ordering. The
approach used is based on non-local parameter priors and thus
it is suitable for learning sparse graphs. The algorithm is
implemented in C++ using the open-source library Armadillo.
Normalizes the data from a file containing the raw values
of the SNP probes of microarrray data by using the FISH probes
and their corresponding CNs.
This package uses time dependent Poisson regression and a record of goals scored in matches to rank teams via estimated attack and defense strengths.  The statistical model is based on Dixon and Coles (1997) Modeling Association Football Scores and Inefficiencies in the Football Betting Market, Applied Statistics, Volume 46, Issue 2, 265-280.  The package has a some webscrapers to assist in the development and updating of a match database.  If the match database contains unconnected clusters (i.e. sets of teams that have only played each other and not played teams from other sets), each cluster is ranked separately relative to the median team strength in the cluster.  The package contains functions for predicting and simulating tournaments and leagues from estimated models. The package allows fitting via the glm(), speedglm(), and glmnet() functions.  The latter allows fast and efficient fitting of very large numbers of teams.  The fitting algorithm will analyze the match data and determine which teams form a cluster (a set of teams where there is a path of matches connecting every team) and fit each cluster separately.
Efficient procedures for community detection in network studies, especially for sparse networks with not very obvious community structure. The algorithms impose penalties on the differences of the coordinates which represent the community labels of the nodes.
Collection of pricing by duplication methods for popular structured products ("Zertifikate")
Algorithms for fuzzy clustering and cluster validity indices
A fold change ranks ordering statistics based method for detecting differentially
expressed genes. The variation in levels of fold change or ratios calculated using
biological conditions is used to associate statistic to genes.
FD is a package to compute different multidimensional FD indices. It implements a distance-based framework to measure FD that allows any number and type of functional traits, and can also consider species relative abundances. It also contains other useful tools for functional ecology.
These functions were developed to support functional data
analysis as described in Ramsay, J. O. and Silverman, B. W.
(2005) Functional Data Analysis. New York: Springer.  They were
ported from earlier versions in Matlab and S-PLUS.  An
introduction appears in Ramsay, J. O., Hooker, Giles, and
Graves, Spencer (2009) Functional Data Analysis with R and
Matlab (Springer). The package includes data sets and script
files working many examples including all but one of the 76
figures in this latter book.  Matlab versions of the code and
sample analyses are no longer distributed through CRAN, as they
were when the book was published.  For those, ftp from
http://www.psych.mcgill.ca/misc/fda/downloads/FDAfuns/
There you find a set of .zip files containing the functions and
sample analyses, as well as two .txt files giving instructions for
installation and some additional information.
The changes from Version 2.4.1 are fixes of bugs in density.fd and
removal of functions create.polynomial.basis, polynompen, and
polynomial. These were deleted because the monomial basis
does the same thing and because there were errors in the code.
The package fdakma jointly performs clustering and alignment of a multidimensional or unidimensional functional dataset.
Likelihood based analysis of 1-dimension functional data
in a mixed-effects model framework. Matrix computation are
approximated by semi-explicit operator equivalents with linear
computational complexity.
A library for functional data analysis using the square root
velocity framework
The package implements the Interval Testing Procedure for functional data in different frameworks (i.e., one or two-population frameworks) by means of different basis expansions (i.e., B-spline, Fourier, and phase-amplitude Fourier). The current version of the package requires functional data evaluated on a uniform grid; it automatically projects each function on a chosen functional basis; it performs the entire family of multivariate tests; and, finally, it provides the matrix of the p-values of the previous tests and the vector of the corrected p-values. The functional basis, the coupled or uncoupled scenario, and the kind of test can be chosen by the user. The package provides also a plotting function creating a graphical output of the procedure: the p-value heat-map, the plot of the corrected p-values, and the plot of the functional data.
The R package fda.usc carries out exploratory and descriptive analysis of functional data such as depth measurements or functional atypical curves detection, functions to compute functional regression models with a scalar response, supervised and unsupervised classification methods and functional analysis of variance.
FDR functions for permutation-based estimators, including pi0 as well as FDR
confidence intervals. The confidence intervals account for dependencies between
tests by the incorporation of an overdispersion parameter, which is estimated
from the permuted data.
Tools for FDR problems, including false discovery rate regression.
See corresponding paper: "False discovery rate regression: application to
neural synchrony detection in primary visual cortex."  James G. Scott, Ryan
C. Kelly, Matthew A. Smith, Robert E. Kass.
This package allows to estimate both tail area-based false
discovery rates (Fdr) as well as local false discovery rates (fdr) for a
variety of null models (p-values, z-scores, correlation coefficients,
t-scores).  The proportion of null values and the parameters of the null
distribution are adaptively estimated from the data.  In addition, the package
contains functions for non-parametric density estimation (Grenander estimator),
for monotone regression (isotonic regression and antitonic regression with weights),
for computing the greatest convex minorant (GCM) and the least concave majorant (LCM),
for the half-normal and correlation distributions, and for computing
empirical higher criticism (HC) scores and the corresponding decision threshold.
Perform frequency distribution tables, associated histograms
and poligons from vector, data.frame and matrix objects.
Feature significance for multivariate kernel density estimation
Discretely-sampled function is first smoothed.  Features
of the smoothed function are then extracted.  Some of the key
features include mean value, first and second derivatives,
critical points (i.e. local maxima and minima), curvature of
cunction at critical points, wiggliness of the function, noise
in data, and outliers in data.
Functions and example datasets for Fechnerian scaling of
discrete object sets.  It computes Fechnerian distances among
objects representing subjective dissimilarities, and other
related information.  See package?fechner for an overview.
Access data from the Federal Register API
Analysis of fragmented time directionality to investigate feedback in time series. Tools provided by the package allow the analysis of feedback for a single time series and the analysis of feedback for a set of time series collected across a spatial domain.
Collection of pricing by duplication and Monte Carlo methods for Express Certificates products (also known as Autocallables)
Basic (statistical) functionality for package ff
Functions, S4 classes/methods and a graphical user
interface (GUI) to design surveys to substantiate freedom from
disease using a modified hypergeometric function (see Cameron
and Baldock, 1997). Herd sensitivities are computed according
to sampling strategies "individual sampling" or "limited
sampling" (see M. Ziller, T. Selhorst, J. Teuffert, M. Kramer
and H. Schlueter, 2002). Methods to compute the a-posteriori
alpha-error are implemented. Risk-based targeted sampling is
supported.
Force field simulation of interaction of set of points.
Very useful for placing text labels on graphs, such as
scatterplots.
This package performs general linear modeling with
multiple responses (MANCOVA).  An overall p-value for each
model term is calculated by the 50-50 MANOVA method, which
handles collinear responses.  Rotation testing is used to
compute adjusted single response p-values according to
familywise error rates and false discovery rates.
Provides a simple and efficient wrapper around the fastest
Fourier transform in the west (FFTW) library.
This package is a wrapper for several fftw functions. Please see the fftw package for additional tools. This package provides access to the two-dimensional fft, the multivariate fft, and the one-dimensional real to complex fft using fftw3. The package includes the functions fftw and mvfftw which are designed to mimic the functionality of the R functions fftw and mvfft. The fft functions have a parameter that allows them to not return the redundant complex conjugate when the input is real data. Please see the package fftw for general one-dimensional use of fftw, the discrete cosine transform, and for more detailed use of performance enhancing plans when using fftw. The calls to fftw and mvfftw in this package mimic the standard routines in R.
Bi-variate data fitting is done by two stochastic
components: the marginal distributions and the dependency
structure. The dependency structure is modeled through a
copula. An algorithm was implemented considering seven families
of copulas (Generalized Archimedean Copulas), the best fitting
can be obtained looking all copula's options (totally positive
of order 2 and stochastically increasing models).
This is a package for implementation of Flury-Gautschi
algorithms.
Exact MLE and Whittle MLE estimation for power law decay models.
Goodness-of-fit test with multiplier or parametric
bootstrap.
FGSG package implements algorithms for feature grouping and selection over an undirected graph.
Rapidly create a GUI interface for a function you created
by automatically creating widgets for arguments of the
function. Automatically parses help routines for
context-sensative help to these arguments. The interface
essentially a wrapper to some tcltk routines to both simplify
and facilitate GUI creation. More advanced tcltk routines/GUI
objects can be incorporated into the interface for greater
customization for the more experienced.
FHtest offers several tests for the comparison of two or more survival curves:
a) The Fleming-Harrington test for right-censored data based on permutations and on counting processes.
b) Extension of the Fleming-Harrington test for interval-censored data based on a permutation distribution and on a score vector distribution.
Provide functions for forest inventory calculations.
Common volumetric equations (Smalian, Newton and Huber) as well
stacking factor and form
The package includes classic symmetric and deflation-based FastICA algorithms, reloaded deflation-based FastICA and an algorithm for adaptive deflation-based FastICA using multiple contrast functions.
Fields is for curve, surface and function
fitting with an emphasis on splines, spatial data and spatial
statistics. The major methods include cubic, and thin plate splines,
Kriging and compact covariances for large data sets. The splines and
Kriging methods are supporting by functions that can determine the
smoothing parameter (nugget and sill variance) by cross validation
and also by restricted maximum likelihood.  A major feature is that
any covariance function implemented in R  and following a simple fields
format can be used for spatial prediction. Some tailored optimization
functions are supplied for finding the MLEs for the Matern family of
covariances.  There are also many useful functions for plotting and
working with spatial data as images. This package also contains an
implementation of sparse matrix methods for large spatial data sets and
currently requires the sparse matrix (spam) package.
But spam is not required for the standard
spatial functions. Use help(fields) to get started and for an
overview.  The fields source code is heavily commented and
provides useful explanations of numerical details in addition to the
manual pages.
This package provides routines for simulate random fields.
A collection of functions that assist in data
manipulation analysis and plotting.
Simple key-value database using SQLite as the backend
Time value of money, cash flows and other financial
functions.
Infrastructure for defining meta-data and relationships
for financial instruments.
This package accomplishes two tasks: a) it classifies
implicit trading activity from quotes in OTC markets using the
algorithm of Lee and Ready (1991); b) based on information for
trade initiation, the package computes the probability of
informed trading of Easley and O'Hara (1987).
Package for time value of money calculation, time series analysis
and computational finance
Find all root(s) of the equation,including complex
roots;Find root(s) of the equation by dichotomy.Besides,in
dichotomy, more than one interval can be given at a time.
FindIt is an R package that implements the heterogeneous
treatment effect estimation procedure proposed by Imai and
Ratkovic (2013).  The proposed method is applicable, for
example, when selecting a small number of most (or least)
efficacious treatments from a large number of alternative
treatments as well as when identifying subsets of the
population who benefit (or are harmed by) a treatment of
interest. The method adapts the Support Vector Machine
classifier by placing separate LASSO constraints over the
pre-treatment parameters and causal heterogeneity parameters of
interest. This allows for the qualitative distinction between
causal and other parameters, thereby making the variable
selection suitable for the exploration of causal heterogeneity.
Creates models from all combinations of a list of variables and sorts by minimum IC (information criterion).
Package designed to find an acceptable python binary.
This package contains functions to manipulate binary fingerprints
of arbitrary length. A fingerprint is represented by an object of S4 class 'fingerprint'
which is internally represented a vector of integers, such
that each element represents the position in the fingerprint that is set to 1.
The bitwise logical functions in R are overridden so that they can be used directly
with 'fingerprint' objects. A number of distance metrics are also
available (many contributed by Michael Fadock). Fingerprints
can be converted to Euclidean vectors (i.e., points on the unit hypersphere) and
can also be folded using OR.  Arbitrary fingerprint formats can be handled via line
handlers. Currently handlers are provided for CDK, MOE and BCI fingerprint data.
In the Cramér–Lundberg risk process perturbed by a Wiener
process, this packages provides approximations to the probability of
ruin within a finite time horizon.  Currently, there are three methods
implemented: The first one uses saddlepoint approximation (two
variants are provided), the second one uses importance sampling and
the third one is based on the simulation of a dual process.  This last
method is not very accurate and only given here for completeness.
R companion to Tsay (2005)
Analysis of Financial Time Series, 2nd ed. (Wiley).
Includes data sets, functions and script files
required to work some of the examples.  Version 0.3-x
includes R objects for all data files used in the text
and script files to recreate most of the analyses in
chapters 1-3 and 9 plus parts of chapters 4 and 11.
The FisherEM package provides an efficient algorithm for
the unsupervised classification of high-dimensional data. This
FisherEM algorithm models and clusters the data in a
discriminative and low-dimensional latent subspace. It also
provides a low-dimensional representation of the clustered
data. A sparse version of Fisher-EM algorithm is also provided.
fisheyeR provides tools for creating Interactive Data
Visualizations by implementing ideas from Furnas, Munzner,
Costa and Venturini.
FisHiCal integrates Hi-C and FISH data, offering a modular and easy-to-use tool for chromosomal spatial analysis.
Fishery science methods and models from literature and colleague contributions
Fits models to catch and effort data. Single-species models are 1) delta log-normal, 2) Tweedie, or 3) Poisson-gamma (GL)Ms.
Functions to predict fish movement parameters based on multiple regression and plotting leptokurtic fish dispersal kernels (see Radinger and Wolter, 2013: Patterns and predictors of fish dispersal in rivers. Fish and Fisheries.)
Comprehensive model building function for identification,
estimation and diagnostic checking for AR and subset AR models.
Two types of subset AR models are supported.  One family of
subset AR models, denoted by ARp, is formed by taking subet of
the original AR coefficients and in the other, denoted by ARz,
subsets of the partial autocorrelations are used. The main
advantage of the ARz model is its applicability to very large
order models.
Implements fast maximum likelihood algorithm for fitting ARMA time series. Uses S3 methods print, summary, fitted, residuals. Fast exact Gaussian ARMA simulation.
Extends the fitdistr function (of the MASS package) with several functions to help the fit of a parametric distribution to non-censored or censored data. Censored data may contain left censored, right censored and interval censored values, with several lower and upper bounds. In addition to maximum likelihood estimation method the package provides moment matching, quantile matching and maximum goodness-of-fit estimation methods (available only for non-censored data).
Fits Density Ratio Classes to elicited
probability-quantile points or intervals
Utilities to read and write files in the FITS (Flexible
Image Transport System) format, a standard format in astronomy.
Present low-level routines allow: reading, parsing, and modifying
FITS headers; reading FITS images (multi-dimensional arrays);
reading FITS binary tables; and writing FITS images
(multi-dimensional arrays).  Higher-level functions allow: reading
files composed of one or more headers and a single (perhaps
multidimensional) image or single bintable; reading bintables into
data frames; generating vectors for image array axes; scaling and
writing images as 16-bit integers.  Known incompletenesses are
reading FITS ASCII table and random group extensions, as well as
bit, complex, and array descriptor data types in binary tables.
Package fitTetra contains three functions that can be used
to assign genotypes to a collection of tetraploid samples based
on bialleleic marker assays. Functions fitTetra (to fit several
models for one marker from the data and select the best
fitting) or saveMarkerModels (calls fitTetra for multiple
markers and saves the results to files) will probably be the
most convenient to use. Function CodomMarker offers more
control and fits one specified model for a given marker.
This is a fast and flexible implementation of the Kalman
filter, which can deal with NAs. It is entirely written in C
and relies fully on linear algebra subroutines contained in
BLAS and LAPACK. Due to the speed of the filter, the fitting of
high-dimensional linear state space models to large datasets
becomes possible. This package also contains a plot function
for the visualization of the state vector and graphical
diagnostics of the residuals.
The package "flare" provides the implementation of a family of Lasso variants including Dantzig Selector, LAD Lasso, SQRT Lasso, Lq Lasso for estimating high dimensional sparse linear model. We adopt the alternating direction method of multipliers and convert the original optimization problem into a sequential L1 penalized least square minimization problem, which can be efficiently solved by linearization algorithm. A multi-stage screening approach is adopted for further acceleration. Besides the sparse linear model estimation, we also provide the extension of these Lasso variants to sparse Gaussian graphical model estimation including TIGER and CLIME using either L1 or adaptive penalty. Missing values can be tolerated for Dantzig selector and CLIME. The computation is memory-optimized using the sparse matrix output.
Fast implementation of hierarchical clustering
The main function kcca implements a general framework for
k-centroids cluster analysis supporting arbitrary distance
measures and centroid computation. Further cluster methods
include hard competitive learning, neural gas, and QT
clustering. There are numerous visualization methods for
cluster results (neighborhood graphs, convex cluster hulls,
barcharts of centroids, ...), and bootstrap methods for the
analysis of cluster stability.
FlexMix implements a general framework for finite
mixtures of regression models using the EM algorithm.
FlexMix provides the E-step and all data handling, while the M-step can be
supplied by the user to easily define new models. Existing drivers
implement mixtures of standard linear models, generalized linear
models and model-based clustering.
selfStart functions and model selection tools to fit
parametric curves in nls, nlsList and nlme frameworks.
Flexible parametric models for time-to-event data, including the Royston-Parmar spline model, generalized gamma and generalized F distributions.   Any user-defined parametric distribution can be fitted, given at least an R function defining the probability density or hazard.  There are also tools for fitting and predicting from fully parametric multi-state models.
FLIM fits linear models for the observed increments in a longitudinal dataset, and imputes missing values according to the models.
It implements many univariate and multivariate permutation (and rotation) tests. Allowed tests: the t one and two samples, ANOVA, linear models, Chi Squared test, rank tests (i.e. Wilcoxon, Mann-Whitney, Kruskal-Wallis), Sign test and McNemar. Test on Linear Models are performed also in presence of covariates (i.e. nuisance parameters). The permutation and the rotation methods to get the null distribution of the test statistics are available. It also implements methods for multiplicity control such as Westfall-Young minP procedure and Closed Testing (Marcus, 1976) and k-FWER. Moreover, it allows to test for fixed effects in mixed effects models.
Fits the Fused Lasso Latent Feature model, which is used for modeling multi-sample aCGH data to identify regions of copy number variation (CNV).  Produces a set of features that describe the patterns of CNV and a set of weights that describe the composition of each sample.  Also provides functions for choosing the optimal tuning parameters and the appropriate number of features, and for estimating the false discovery rate.
Tools to quickly compile taxonomic and distribution data from
the Brazilian Flora Checklist. No internet connection needed.
Flow field forecasting draws information from an interpolated flow field of the observed time series to incrementally build a forecast.
This package implements a path algorithm for the Fused
Lasso Signal Approximator. For more details see the help files
Solve an approximate Subset Sum Problem by introducing a recursive
compression algorithm
Simulate and analyse fluorescence data sets obtained from
calcium imaging experiments in living cells (such as neurons).
The package provide tools to estimate background-subtracted
fluorescence transients and build meaningful confidence
intervals on these estimations. This covers both the case of
homogeneous background fluorescence and that of Regions Of
Interest (ROI) displaying background inhomogeneities.
Background-subtracted fluorescence transients can be fitted
with monoexponential models or estimated pointwise.
Contains data sets from Bernard Flury (1997) A First
Course in Multivariate Statistics, Springer NY
Functions for the calculation of greenhouse gas flux rates
from closed chamber concentration measurements. The package follows
a modular concept: Fluxes can be calculated in just two simple steps
or in several steps if more control in details is wanted. Additionally
plot and preparation functions as well as functions for modelling
gpp and reco are provided.
All data sets from "Forecasting: methods and applications"
by Makridakis, Wheelwright & Hyndman (Wiley, 3rd ed., 1998).
Provides functions to help in fitting models to data, to
perform Monte Carlo, sensitivity and identifiability analysis. It is
intended to work with models be written as a set of differential
equations that are solved either by an integration routine from
package deSolve, or a steady-state solver from package
rootSolve. However, the methods can also be used with other types of
functions.
The package contains R-functions to perform an fmri analysis
as described in
K. Tabelow, J. Polzehl, H.U. Voss, and V. Spokoiny,
Analysing fMRI experiments with structure adaptive smoothing procedures,
NeuroImage, 33:55-62 (2006),
J. Polzehl, H.U. Voss, K. Tabelow, Structural adaptive segmentation
for statistical parametric mapping, NeuroImage, 52:515-523 (2010)
and
K. Tabelow, J. Polzehl, Statistical Parametric Maps for
Functional MRI Experiments in {R}: The Package {fmri}},
Journal of Statistical Software, 44(11):1--21 (2011).
Several utility functions for the book entitled
"Practices of Medical and Health Data Analysis using R"
(Pearson Education Japan, 2007) with Japanese demographic
data and some demographic analysis related functions.
This package implements some basic procedures for dealing
with log maximally skew stable distributions, which are also
called finite moment log stable distributions.
This package computes posterior residual variances to be
used in the denominator of a moderated t-statistic from a
linear model analysis of microarray data.  It is an extension
of the moderated t-statistic original proposed by Smyth (2004).
LOESS local regression and empirical Bayesian method are used
to estimate gene specific prior degrees of freedom and prior
variance based on average gene intensity level. The posterior
residual variance in the denominator is a weighted average of
prior and residual variance and the weights are prior degrees
of freedom and residual variance degrees of freedom. The
degrees of freedom of the moderated t-statistic is simply the
sum of prior and residual variance degrees of freedom.
Environment for teaching
"Financial Engineering and Computational Finance"
Cover-tree and kd-tree fast k-nearest neighbor search algorithms and related applications
including KNN classification, regression and information measures are implemented.
foba is a package that implements forward, backward, and foba sparse learning algorithms for ridge regression, described in the paper "Adaptive Forward-Backward Greedy Algorithm for Learning Sparse Representations".
Computer Modern font for use with extrafont package
Calculates twelve commonly-used, basic measures of food
web network structure from binary, predator-prey matrices:
species richness, connectance, total number of links, link
density, number of trophic positions, predator:prey ratio, and
fraction of carnivores, herbivores, top species and
intermediate species.  Employs food web language in the code
and output, translates between a couple of common food web
formats, can handle food webs consisting of multiple levels,
and can automate the analysis for a large number of webs. The
program produces 3-dimensional graphs of high quality that can
be customized by the user.
SHE, FORAM Index and ABC Method analyses and custom plot
functions for community data.
Support for the foreach looping construct.  Foreach is an
idiom that allows for iterating over elements in a collection,
without the use of an explicit loop counter.  This package in
particular is intended to be used for its return value, rather
than for its side effects.  In that sense, it is similar to the
standard lapply function, but doesn't require the evaluation
of a function.  Using foreach without side effects also
facilitates executing the loop in parallel.
Forecastable Component Analysis (ForeCA) is a novel dimension
reduction (DR) technique for temporally dependent signals. Contrary to
other popular DR methods, such as PCA or ICA, ForeCA explicitly searches
for the most ''forecastable'' signal. The measure of forecastability is
based on negative Shannon entropy of the spectral density of the
transformed signal. This R package provides the main algorithms and
auxiliary function(summary, plotting, etc) to apply ForeCA to multivariate
data (time series).
Methods and tools for displaying and analysing
univariate time series forecasts including exponential smoothing
via state space models and automatic ARIMA modelling.
Functions for reading and writing data stored by some versions of
Epi Info, Minitab, S, SAS, SPSS, Stata, Systat and Weka
and for reading and writing some dBase files.
The statistical evaluation of DNA mixtures, DNA profile
match probability
Statistical methods and simulation tools for the
interpretation of forensic DNA mixtures
Imputation of missing values in datasets of ordinal
variables through a forward imputation algorithm
This package provides estimates of tree volume and biomass from
Italian NFI models
These functions provides simple wrappers around the Unix
process management API calls: fork, signal, wait, waitpid,
kill, and _exit.  This enables construction of programs that
utilize and mange multiple concurrent processes.
Implemented, addition, subtracking, multiplication,
division in formal series rings of any number of variables
(except division is only to 3 variables). Also are available
"[" "[<-" operators.
This package provides a function tidy_source() to format R source
code. Spaces and indent will be added to the code automatically, and
comments will be preserved under certain conditions, so that R code will be
more human-readable and tidy. There is also a Shiny app as a user interface
in this package (see tidy_app()).
Infrastructure for extended formulas with multiple parts on the
right-hand side and/or multiple responses on the left-hand side.
This package provides a number of useful utilities for
working with formulas, expressions, calls, names, symbols and
other R objects.  This package was designed to greatly
facilitate symbolic manipulation using native R objects.
Forward search approach to robust analysis in linear and
generalized linear regression models.
Forward Search analysis of time series regressions. Implements the asymptotic theory developed in Johansen and Nielsen (2013, 2014).
A set of analytical tools useful in analysing ecological
and geographical data sets, both ancient and modern. The
package includes functions for estimating species richness
(Chao 1 and 2, ACE, ICE, Jacknife), shared species/beta
diversity, species area curves and geographic distances and
areas.
A game for two players: Who gets first four in a row
(horizontal, vertical or diagonal) wins. Published by Milton
Bradley, designed by Howard Wexler and Ned Strongin.
Various methods for clustering and cluster validation.
Fixed point clustering. Linear regression clustering. Clustering by
merging Gaussian mixture components. Symmetric
and asymmetric discriminant projections for visualisation of the
separation of groupings. Cluster validation statistics
for distance based clustering including corrected Rand index.
Cluster-wise cluster stability assessment. Methods for estimation of
the number of clusters: Calinski-Harabasz, Tibshirani and Walther's
prediction strength, Fang and Wang's bootstrap stability.
Gaussian/multinomial mixture fitting for mixed
continuous/categorical variables. Variable-wise statistics for cluster
interpretation. DBSCAN clustering. Interface functions for many
clustering methods implemented in R, including estimating the number of
clusters with kmeans, pam and clara. Modality diagnosis for Gaussian
mixtures. For an overview see package?fpc.
A geometric approach to MLE for functional principal
components
Probabilistic distance clustering (PD-clustering) is an iterative, distribution free, probabilistic clustering method. PD-clustering assigns units to a cluster according to their probability of membership, under the constraint that the product of the probability and the distance of each point to any cluster centre is a constant. PD-clustering is a flexible method that can be used with non-spherical clusters, outliers, or noisy data. Facto PD-clustering (FPDC) is a recently proposed factor clustering method that involves a linear transformation of variables and a cluster optimizing the PD-clustering criterion. It allows clustering of high dimensional data sets.
Returns the noncentrality parameter of the noncentral F
distribution if probability of type I and type II error,
degrees of freedom of the numerator and the denominator are
given.  It may be useful for computing minimal detectable
differences for general ANOVA models.  This program is
documented in the paper of A. Baharev, S. Kemeny, On the
computation of the noncentral F and noncentral beta
distribution; Statistics and Computing, 2008, 18 (3), 333-340.
All data sets required for the examples and exercises in
the book "Forecasting: principles and practice" by Rob J
Hyndman and George Athanasopoulos. All packages required to run
the examples are also loaded.
This package contains several functions for an efficient
approximation of first-passage-time densities for diffusion
processes based on the First-Passage-Time Location (FPTL)
function.
Maximum likelihood estimation of the parameters of a
fractionally differenced ARIMA(p,d,q) model (Haslett and
Raftery, Appl.Statistics, 1989).
Stochastic fractal and deterministic chaotic time series
analysis.
Implements various methods for estimating fractal dimension of time series and 2-dimensional data.
The basic principle driving fractal generation of time
series is that data is generated iteratively based on
increasing levels of resolution.  The initial series is defined
by a so-called initiator pattern and then generators are used
to replace each segment of the initial pattern. Regular,
repeatable patterns can be produced by using the same seed and
generators.  By using a set of generators, non-repeatable time
series can be produced.  This technique is the basis of the
fractal time series process in this package.
This is the package which can help you turn
numeric,dataframe,matrix into fraction form.
The frailtyHL package implements the h-likelihood
estimation procedures for frailty models. The package fits
Cox's proportional hazards models with random effects (or
frailties). For the frailty distribution lognormal and gamma
are allowed. The h-likelihood uses the Laplace approximation
when the numerical integration is intractable, giving a
statistically efficient estimation in frailty models.
Frailtypack fits several classes of frailty models using a penalized likelihood
estimation on the hazard function but also a parametric estimation.
1) A shared frailty model (with gamma or log-normal frailty distribution) and Cox
proportional hazard model. Clustered and recurrent survival times can be studied.
2) Additive frailty models for proportional hazard models with two correlated
random effects (intercept random effect with random slope).
3) Nested frailty models for hierarchically clustered data (with 2 levels of
clustering) by including two iid gamma random effects.
4) Joint frailty models in the context of joint modelling for recurrent events
with terminal event for clustered data or not.
Prediction values are available. Left-truncated (not for Joint model),
right-censored data, interval-censored data (only for Cox proportional hazard
and shared frailty model) and strata are allowed. In each model,
the random effects have a gamma distribution, but you can switch to a log-normal
in shared and joint models. Now, you can also consider time-varying covariates
effects in Cox, shared and joint models. The package includes concordance measures
for Cox proportional hazards models and for shared frailty models.
A package for functional response analysis.
Point and interval estimation in dual frame surveys. In contrast
to classic sampling theory, where only one sampling frame is considered,
dual frame methodology assumes that there are two frames available for
sampling and that, overall, they cover the entire target population. Then,
two probability samples (one from each frame) are drawn and information
collected is suitably combined to get estimators of the parameter of
interest.
Accompanying package of the book 'Financial Risk Modelling
and Portfolio Optimisation with R'. The data sets used in the
book are contained in this package.
This package performs robust inference based on applying
Fast and Robust Bootstrap on robust estimators. Available
methods are multivariate regression, PCA and Hotelling tests.
This package implements functionality and various algorithms to
build and use fuzzy rule-based systems (FRBSs). FRBSs are based on the
concept of fuzzy sets, proposed by Zadeh in 1965, which aims at
representing the reasoning of human experts in a set of IF-THEN rules, to
handle real-life problems in, e.g., control, prediction and inference, data
mining, bioinformatics data processing, and robotics. FRBSs are also known
as fuzzy inference systems and fuzzy models. During the modeling of an
FRBS, there are two important steps that need to be conducted: structure
identification and parameter estimation. Nowadays, there exists a wide
variety of algorithms to generate fuzzy IF-THEN rules automatically from
numerical data, covering both steps. Approaches that have been used in the
past are, e.g., heuristic procedures, neuro-fuzzy techniques, clustering
methods, genetic algorithms, squares methods, etc. This package aims to
implement the most widely used standard procedures, thus offering a
standard package for FRBS modeling to the R community.
This package implements the functions associated with Fast
Regularized Canonical Correlation Analysis.
This package is for fitting free-knot splines for data
with one independent variable and one dependent variable.  Four
free-knot spline algorithms are provided for the case where the
number of knots is known in advance.  A knot-search algorithm
is provided for the case where the number of knots is not known
in advance.  In addition, methods are available to compute the
fitted values, the residuals, and the coefficients of the
splines, and to plot the results, along with a method to
summarize the results.
The package FreeSortR provides tools for describing and analysing free sorting data. Main methods are computation of consensus partition and factorial analysis of the dissimilarity matrix between stimuli (using multidimensional scaling approach).
A collections of useful statistical functions used in Columbia
course W4240/W4400.
Real capture frequencies will be fitted to various distributions which provide the basis of estimating population sizes, their standard error, and symmetric as well as asymmetric confidence intervalls.
A frequency moving average plot (MAP) is estimated from a
multinomial data and a continuous covariate. The frequency MAP
is a moving average estimate of category frequencies, where
frequency means and posterior bounds are estimated. Comparisons
of two frequency MAPs as well as odds ratios can be plotted.
New approaches to parallel coordinates plots for
multivariate data visualization, including applications to clustering,
outlier hunting and regression diagnostics.  Includes general functions
for multivariate nonparametric density and regression estimation,
using parallel computation.
The frequency of a particular data value is the number of times it
occurs. A frequency table is a table of values with their corresponding
frequencies. Frequency weights are integer numbers that indicate how many
cases each case represents. This package provides some functions to work
with such type of collected data.
FRESA.CAD provides a set of functions and feature selection algorithms for building Computer Aided Diagnosis Models
This package creates regular and non-regular Fractional
Factorial designs. Furthermore, analysis tools for Fractional
Factorial designs with 2-level factors are offered (main
effects and interaction plots for all factors simultaneously,
cube plot for looking at the simultaneous effects of three
factors, full or half normal plot, alias structure in a more
readable format than with the built-in function alias). The
package is still under development. While much of the intended
functionality is already available, some changes and
improvements are still to be expected. Suggestions are welcome.
This package provides catalogues of resolution IV regular
fractional factorial designs in 128 runs for up to 33 2-level
factors. The catalogues are complete, excluding resolution IV
designs without 5-letter words, because these do not add value
for a search for clear designs. The previous package version
1.0 with complete catalogues up to 24 runs (24 runs and a
namespace added later) can be downloaded from the authors
website.
This package estimates one- and two-part fractional regression models, tests the models' specification and calculates partial effects
A collection of R and C++ functions to work with the
generalized hyperbolic distribution, related distributions and
their applications in financial risk management and
quantitative analysis.
Maximum Likelihood Estimation of
Stochastic Frontier Production and Cost Functions.
Two specifications are available:
the error components specification with time-varying efficiencies
(Battese and Coelli, 1992)
and a model specification in which the firm effects are directly
influenced by a number of variables (Battese and Coelli, 1995).
It calculates the alpha-quantile and order-m efficiency score in multi-dimension and computes several summaries and representation of the associated frontiers in 2d and 3d.
Automated feature selection using variety of models
provided by caret package
This package provides functions for selecting attributes
from a given dataset. Attribute subset selection is the process
of identifying and removing as much of the irrelevant and
redundant information as possible.
Wrapper functions that interface with FSL
(http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/), a powerful and commonly-used
neuroimaging software, using system commands. The goal is to be able to
interface with FSL completely in R, where you pass R nifti objects and the
function executes an FSL command and returns an R nifti object if desired.
Fuzzy set ordination is a multivariate analysis used in
ecology to relate the composition of samples to possible
explanatory variables.  While differing in theory and method,
in practice, the use is similar to 'constrained ordination.'
The package contains plotting and summary functions as well as
the analyses
The package computes Social Relations Analysis with roles ("Family
SRM"), using a structural equation modeling approach. Groups ranging from
three members up to an unlimited number of members are supported and the
mean structure can be computed. Means and variances can be compared between
different groups of families and between roles.
This package was developed partially with funding from the
NIH Training Program in Biomolecular Technology
(2-T32-GM08799).
The package contains R-functions to perform the methods in
nonparametric regression and density estimation, described in
Davies, P. L. and Kovac, A. (2001) Local Extremes, Runs,
Strings and Multiresolution (with discussion) Annals of
Statistics. 29. p1-65 Davies, P. L. and Kovac, A. (2004)
Densities, Spectral Densities and Modality Annals of
Statistics. Annals of Statistics. 32. p1093-1136 Kovac, A.
(2006) Smooth functions and local extreme values. Computational
Statistics and Data Analysis (to appear) D\"umbgen, L. and
Kovac, A. (2006) Extensions of smoothing via taut strings
Davies, P. L. (1995) Data features. Statistica Neerlandica
49,185-245.
Fuel economy data from the EPA, 1985-2015, conveniently
packaged for consumption by R users.
This is an evolutionary algorithm for fuzzy systems, a
genetic algorithm is used to construct a fuzzy system able to
fit the given training data.  This fuzzy system can then be
used as a prediction model, it's composed of fuzzy logic rules
that provide a good lingustic representation.
A set of functions for the analysis of climate/weather
fire danger
This is a collection of R games and other funny stuff,
such as the classical Mine sweeper and sliding puzzles.
The package implements non-constant functional chi-square tests (Zhang and Song, 2013). Such tests offer an advantage over regression analysis when a parametric form cannot be reliably assumed for the underlying function. It requires data from two or more variables be formatted as a contingency table. Continuous variables must be discretized first which can be done by using the Ckmeans.1d.dp function.
FunCluster performs a functional analysis of microarray
expression data based on Gene Ontology & KEGG functional
annotations. From expression data and functional annotations
FunCluster builds classes of putatively co-regulated biological
processes through a specially designed clustering procedure.
This packages proposes a model-based clustering algorithm for
multivariate functional data. The parametric mixture model, based on the
assumption of normality of the principal components resulting from a
multivariate functional PCA, is estimated by an EM-like algorithm. The main
advantage of the proposed algorithm is its ability to take into account the
dependence among curves.
FuncMap analyzes the function calls in an R package and
creates a hive plot of the calls, dividing them among functions
that only make outgoing calls (sources), functions that have
only incoming calls (sinks), and those that have both incoming
calls and make outgoing calls (managers).  Function calls can
be mapped by their absolute numbers, their normalized absolute
numbers, or their rank.  FuncMap should be useful for comparing
packages at a high level for their overall design.  Plus, it's
just plain fun.  The hive plot concept was developed by Martin
Krzywinski (www.hiveplot.com) and inspired this package.
Curry, Compose, and other higher-order functions
R package providing functions to perform gene and gene set network inference.
The funFEM algorithm (Bouveyron et al., 2014) allows to cluster functional data by modeling the curves within a common and discriminative functional subspace.
The package provides the funHDDC algorithm (Bouveyron & Jacques, 2011) which allows to cluster functional data by modeling each group within a specific functional subspace.
This package performs functional regression, and some related
approaches, for intensive longitudinal data (see Walls & Schafer, 2006)
which is not necessarily observed on an equally spaced grid of times.  The
approach generally follows the ideas of Goldsmith, Bobb, Crainiceanu,
Caffo, and Reich (2011) and the approach taken in their sample code, but
with some modifications to make it more feasible to use with long rather
than wide, non-rectangular longitudinal datasets with unequal and
potentially random measurement times.  It also allows easy plotting of the
correlation between the smoothed covariate and the outcome as a function of
time, which can add additional insights on how to interpret a functional
regression.  Additionally, it also provides several permutation tests for
the significance of the functional predictor.  The heuristic interpretation
of ``time'' is used to describe the index of the functional predictor, but
the same methods can equally be used for another unidimensional continuous
index, such as space along a north-south axis.  The development of this
package was part of a research project supported by Award R03 CA171809-01
from the National Cancer Institute and Award P50 DA010075 from the National
Institute on Drug Abuse. The content is solely the responsibility of the
authors and does not necessarily represent the official views of the
National Institute on Drug Abuse, the National Cancer Institute, or the
National Institutes of Health.
Efficient procedures for community detection in network studies, especially for sparse networks. The algorithms impose penalties on the differences of the coordinates which represent the community labels of the nodes.
A collection of utility functions that provide
polymorphism over certain data types
Provides a simple yet powerful logging utility. Based loosely on
log4j, futile.logger takes advantage of R idioms to make logging a
convenient and easy to use replacement for cat and print statements.
A collection of functions for manipulating matrices and generating ensembles of random matrices
Provides dispatching implementations suitable for
functional programming paradigms. The framework provides a
mechanism for attaching guards to functions similar to Erlang,
while also providing the safety of assertions reminiscent of
Eiffel.
Exact calculation of fuzzy decision rules for multiple
testing. Choose to control FDR (false discovery rate) using the
Benjamini and Hochberg method, or FWER (family wise error rate)
using the Bonferroni method. Kulinsakaya and Lewin (2007).
Implements a fuzzy-logic-based map-matching algorithm used to match GPS
trajectories to the OpenStreetMap digital road network.
The FuzzyNumbers package provides S4 classes and methods
to deal with Fuzzy Numbers that allow for computations of arithmetic
operations, approximation by trapezoidal and piecewise linear FNs,
visualization, etc.
An implementation of a method for computing fuzzy numbers
representing stationary probabilities of an unknown Markov
chain, from which a sequence of observations along time has
been obtained. The algorithm is based on the proposal presented
by James Buckley in his book on Fuzzy probabilities (Springer,
2005), chapter 6. Package FuzzyNumbers is used to represent the
output probabilities.
A custom framework for working with Type 1 Fuzzy Logic,
produced by the University of Nottingham IMA Group.
fwdmsa performs the Forward Search for Mokken scale
analysis. It detects outliers, it produces several types of
diagnostic plots.
FWDselect package introduces a simple method to select the
best model or best subset of variables using different types of
data (continuous, binary or poisson) and applying it in
different contexts (parametric or nonparametric).
This package provides three new functions to calculate the outputs of the two main components of the Canadian Forest Fire Danger Rating System (CFFDRS): the Fire Weather Index (FWI) System and the Fire Behaviour Prediction (FBP) System.
Simulates a population under the Fisher-Wright model (fixed or stochastic population size) with a one-step neutral mutation process (stepwise mutation model, logistic mutation model and exponential mutation model supported). The stochastic population sizes are random Poisson distributed and different kinds of population growth are supported. For the stepwise mutation model, it is possible to specify locus and direction specific mutation rate (in terms of upwards and downwards mutation rate). Intermediate generations can be saved in order to study e.g. drift.
Exchange rate regression and structural change tools
for estimating, testing, dating, and monitoring
(de facto) exchange rate regimes.
G1DBN performs DBN inference using 1st order conditional
dependencies.
gives full descriptive statistics and a
physical description of sediment obtained with metric or phi
sieves according to the grain size distribution.
An R package for optimization using genetic algorithms. The package provides a flexible general-purpose set of tools for implementing genetic algorithms search in both the continuous and discrete case, whether constrained or not. Users can easily define their own objective function depending on the problem at hand. Several genetic operators are available and can be combined to explore the best settings for the current task. Furthermore, users can define new genetic operators and easily evaluate their performances. GAs can be run sequentially or in parallel.
This is a Genetic Algorithm package for the determination
of the stratum boundaries and sample sizes of each stratum in
stratified sampling
Generalized subspace pattern mining in data arrays, using a genetic algorithm framework.
This package analyses complex ANOVA models with any
combination of orthogonal/nested and fixed/random factors, as
described by Underwood (1997). There are two restrictions: (i)
data must be balanced; (ii) fixed nested factors are not
allowed. Homogeneity of variances is checked using Cochran's C
test and 'a posteriori' comparisons of means are done using
Student-Newman-Keuls (SNK) procedure.
This package constructs gains tables and lift charts for prediction algorithms. Gains tables and lift charts are commonly used in direct marketing applications.
GAIPE implements graphical representation of accuracy in
parameter estimation (AIPE) on RMSEA for sample size planning
in structural equation modeling. Sample sizes suggested by
RMSEA with AIPE method and power analysis approach can also be
obtained separately using the provided functions.
This package includes the ga.lts function that estimates
LTS (Least Trimmed Squares) parameters using genetic algorithms
and C-steps. ga.lts() constructs a genetic algorithm to form a
basic subset and iterates C-steps as defined in Rousseeuw and
van-Driessen (2006) to calculate the cost value of the LTS
criterion. OLS(Ordinary Least Squares) regression is known to
be sensitive to outliers. A single outlying observation can
change the values of estimated parameters. LTS is a resistant
estimator even the number of outliers is up to half of the
data. This package is for estimating the LTS parameters with
lower bias and variance in a reasonable time. Version 1.3
included the function medmad for fast outlier detection in
linear regression.
Functions for fitting and working with generalized
additive models, as described in chapter 7 of "Statistical Models in
S" (Chambers and Hastie (eds), 1991), and "Generalized Additive
Models" (Hastie and Tibshirani, 1990).
Data sets and scripts used in the book "Generalized
Additive Models: An Introduction with R", Wood (2006) CRC.
This package provides functions for fitting the gambin distribution to
species-abundance distributions from ecological data. The main function is fitGambin, which
estimates the 'alpha' parameter of the gambin distribution using maximum likelihood. Functions
are also provided to generate the gambin distribution and for calculating likelihood
statistics.
This package provides routines for fitting generalized
linear and and generalized additive models by likelihood based
boosting, using penalized B-splines
Boosting models for fitting generalized additive models for
location, shape and scale (GAMLSS) to potentially high dimensional
data.
Provides features to use function gamboost() from  package mboost for estimation of multistate models
Modern regression extends to include classification and multivariate exploration
Provides estimation and analysis functions for
strategic statistical models.
This package implements the gamma lasso algorithm for regularization paths corresponding to a range of non-convex cost functions between L0 and L1 norms.  As much as possible, usage is analogous to that for the glmnet package (which does the same thing for penalization between L1 and L2 norms).
This is an add on package to GAMLSS. The purpose of this
package is to allow users to fit interval response variables in
GAMLSS models. The main function gen.cens() generates a
censored version of an existing GAMLSS family distribution.
This package contains the distributions for GAMLSS
modelling.
The main purpose of this package is to allow fitting of
mixture distributions with GAMLSS models.
This is an add on package to GAMLSS. It allows one extra
method for fitting GAMLSS models. The main function nlgamlss()
can fit any parametric (up to four parameter) GAMLSS
distribution.
This is an add on package to GAMLSS. The purpose of this
package is to allow users to defined truncated distributions in
GAMLSS models. The main function gen.trun() generates truncated
version of an existing GAMLSS family distribution.
Fit generalized additive mixed models via a version of
mgcv's gamm function, using lme4 for estimation.
This package performs gamma regression, where both mean and shape parameters follows lineal regression structures.
This package implements a network-based gene weighting
algorithm for pathways, as well as a gene-weighted gene set
analysis approach for microarray data pathway analysis.
This is a dataset package for GANPA, which implements a
network-based gene weighting approach to pathway analysis. This
package includes data useful for GANPA, such as a functional
association network, pathways, an expression dataset and
multi-subunit proteins.
Performs a Genetic Algorithm Optimization, given a
real-based or permutation-based function and the associated
search space.
It is designed as an integrated package for genetic data
analysis of both population and family data. Currently, it
contains functions for sample size calculations of both
population-based and family-based designs, probability of
familial disease aggregation, kinship calculation, statistics
in linkage analysis, and association analysis involving genetic
markers including haplotype analysis with or without environmental
covariates.
The gap encodes the distance between clusters and improves
interpretation of cluster heatmaps. The gaps can be of the same
distance based on a height threshold to cut the dendrogram. Another
option is to vary the size of gaps based on the distance between
clusters.
A collection difference measures for multivariate Gaussian
probability density functions, such as the Euclidea mean, the
Mahalanobis distance, the Kullback-Leibler divergence, the
J-Coefficient, the Minkowski L2-distance, the Chi-square
divergence and the Hellinger Coefficient.
A collection of functions to perform Gaussian quadrature
with different weight functions corresponding to the orthogonal
polynomials in package orthopolynom.  Examples verify the
orthogonality and inner products of the polynomials.
This package collects algorithms and functions for fitting data to a generalized lambda distribution via moment matching methods, and generalized bootstrapping.
GB2 is a simple package that explores the Generalized Beta distribution of the second kind. Density, cumulative distribution function, quantiles and moments of the distributions are given. Functions for the full log-likelihood, the profile log-likelihood and the scores are provided. Formulae for various indicators of inequality and poverty under the GB2 are implemented. The GB2 is fitted using the methods of maximum pseudo-likelihood estimation using the full and profile log-likelihood, and non-linear least squares estimation of the model parameters. Various plots for the visualization and analysis of the results are provided. Variance estimation of the parameters is provided for the method of maximum pseudo-likelihood estimation. A compound distribution based on the GB2 is presented. This compound distribution can be calculated by a left or right tail decomposition. Density, cumulative distribution function, moments and quantiles for the compound distribution can be calculated. The compound distribution is fitted using the method of maximum likelihood estimation. The fit can be also adapted for the use of auxiliary information.
This package implements extensions to Freund and
Schapire's AdaBoost algorithm and Friedman's gradient boosting
machine. Includes regression methods for least squares,
absolute loss, t-distribution loss, quantile regression,
logistic, multinomial logistic, Poisson, Cox proportional
hazards partial likelihood, AdaBoost exponential loss,
Huberized hinge loss, and Learning to Rank measures
(LambdaMart).
Provides utilities for processing Rd objects and files.
Extract argument descriptions and other parts of the help pages
of functions.
A collection of utilities for analyzing censored and
uncensored data from generalized Birnbaum-Saunders
distributions
Many inherited biases and effects exists in RNA-seq due to both biological and technical effects. We observed the biological variance of testing target transcripts can influence the yield of sequencing reads, which might indicate a resource competition existing in RNA-seq. We developed this package to capture the bias depending on local sequence and perform the correction of this type of bias by borrowing information from spike-in measurement.
These are two-sample tests for categorical data utilizing similarity information among the categories.  They are useful when there is underlying structure on the categories.
GPU/CPU Benchmarking on Debian-package based systems
This package benchmarks performance of a few standard linear algebra
operations (such as a matrix product and QR, SVD and LU decompositions)
across a number of different BLAS libraries as well as a GPU implementation.

To do so, it takes advantage of the ability to 'plug and play' different
BLAS implementations easily on a Debian and/or Ubuntu system.  The current
version supports
- reference blas (refblas) which are unaccelerated as a baseline
- Atlas which are tuned but typically configure single-threaded
- Atlas39 which are tuned and configured for multi-threaded mode
- Goto Blas which are accelerated and multithreaded
- Intel MKL which are a commercial accelerated and multithreaded version.
As for GPU computing, we use the CRAN package
- gputools

For Goto Blas, the gotoblas2-helper script from the ISM in Tokyo can be
used. For Intel MKL we use the Revolution R packages from Ubuntu 9.10.
This package implements a generalized coordinate descent (GCD) algorithm for computing the solution path of the hybrid Huberized support vector machine (HHSVM) and its generalization, including the elastic net penalized least squares, the elastic net penalized SVM with the squared hinge loss and the elastic net penalized logistic regression.
Visualize cluster results and investigate additional
properties of clusters using interactive neighbourhood graphs.
By clicking on the node representing the cluster, information
about the cluster is provided using additional graphics or
summary statistics. For microarray data, tables with links to
genetic databases like NCBI Entrez Gene can be created for each
cluster.
Orders panels in scatterplot matrices and parallel
coordinate displays by some merit index. Package contains
various indices of merit, ordering functions, and enhanced
versions of pairs and parcoord which color panels according to
their merit level.
likelihood inference in Gaussian copula marginal
regression models
Estimates a sparse inverse covariance matrix from a convex
pseudo-likelihood function with L1 penalty
This package contains data sets used in the book "R
Graphics Cookbook" by Winston Chang, published by O'Reilly
Media.
Wrappers for the Geospatial Data Abstraction Library (GDAL)
Utilities
Various R programming tools for data manipulation
Create and maintain delayed-data packages (ddp's).  Data stored in
a ddp are available on demand, but do not take up memory until requested.
You attach a ddp with g.data.attach(), then read from it and assign to it in
a manner similar to S-PLUS, except that you must run g.data.save() to
actually commit to disk.
This package contains functions for 'specific' MCA (Multiple Correspondence Analysis),
'class specific' MCA, computing and plotting structuring factors and concentration ellipses,
'standardized' MCA, inductive tests and others tools for Geometric Data Analysis. It also provides functions
for the translation of logit models coefficients into percentages (forthcoming), weighted contingency tables and an association
measure - i.e. Percentages of Maximum Deviation from Independence (PEM).
The GDELT data set is over 60 GB now and growing 100 MB a month.
The number of source articles has increased over time and unevenly across
countries. This package makes it easy to download a subset of that data,
then normalize that data to facilitate valid time series analysis.
Diffusion anisotropy has been used to characterize
white matter neuronal pathways in the human brain, and infer global
connectivity in the central nervous system. The package implements
algorithms to estimate and visualize the orientation of neuronal
pathways in model-free methods (q-space imaging methods).
For estimating fibre orientations two methods have been
implemented.  One method implements fibre orientation detection
through local maxima extraction. A second more robust method
is based on directional statistical clustering of ODF voxel data.
Fibre orientations in multiple fibre voxels are estimated using
a mixture of von Mises-Fisher (vMF) distributions. This statistical
estimation procedure is used to resolve crossing fibre
configurations.
Reconstruction of orientation distribution function (ODF)
profiles may be performed using the standard generalized
q-sampling imaging (GQI) approach, Garyfallidis' GQI (GQI2)
approach, or Aganj's variant of the Q-ball imaging (CSA-QBI)
approach. Procedures for the visualization of RGB-maps,
line-maps and glyph-maps of real diffusion magnetic resonance
imaging (dMRI) data-sets are included in the package.
Calculate distances and routes on geographic grids.
This package provides a high-level R interface to CoreArray
Genomic Data Structure (GDS) data files, which are portable across
platforms and include hierarchical structure to store multiple scalable
array-oriented data sets with metadata information. It is suited for
large-scale datasets, especially for data which are much larger than the
available random-access memory. The gdsfmt package offers the efficient
operations specifically designed for integers with less than 8 bits, since
a single genetic/genomic variant, such like single-nucleotide polymorphism,
usually occupies fewer bits than a byte. Data compression and decompression
are also supported. It is allowed to read a GDS file in parallel with
multiple R processes supported by the parallel package.
Allows generalized estimating equation fits with user-specified link and variance functions.
Generalized estimating equations solver for parameters in
mean, scale, and correlation structures, through mean link,
scale link, and correlation link. Can also handle clustered
categorical responses.
Compute generalized eigenvalues and eigenvectors of a pair of matrices,
using routines from Lapack (dggev and zggev). Compute the
generalized Schur decomposition of a pair of matrices using Lapack routines.
In the latter case options for ordering the generalized eigenvalues are provided.
macroevolutionary simulation and parameter estimation
related to diversification from comparative
phylogenetic data
This package allows to simulating and analyzing multistate models with general hazard functions. It provides functionality for the preparation of hazard functions and parameters, simulation from a general multistate model and predicting future events. The multistate model is not required to be a Markov model and may take the history of previous events into account. In the basic version, it allows to simulate from transition-specific hazard function, whose parameters are multivariable normally distributed.
An R package for performing network meta-analyses (mixed treatment comparisons).
An R package providing the Java JAR for the gemtc package
a package for genome-wide association analysis between
quantitative or binary traits and single-nucleotide
polymorphisms (SNPs).
GenABEL.data package consists of a data set used by GenABEL functions
R based genetic algorithm for binary and floating point
chromosomes.
genasis package contains methods for air pollution assessment. Concerned on persistent organic pollutants, the package allows to compute trends of their concentrations, compare different datasets and estimate relations between values from active and passive air samplers.
Density, distribution function, quantile function and random generation for the Generalized Binomial Distribution. Functions to compute the Clopper-Pearson Confidence Interval and the required sample size. Enhanced model for burn-in studies, where failures are tackled by countermeasures.
Set of functions to create datasets using a correlation matrix.
Encodes gender based on names and dates of birth, using either the
Social Security Administration's data set of first names by year of birth
or Census Bureau data from 1789 to 1940, both from the United States of
America. By using these data sets instead of lists of male and female names,
this package is able to more accurately guess the gender of a name, and it
is able to report the probability that a name was male or female.
Functions and analytics for GENEA-compatible accelerometer
data into R objects. See topic 'GENEAread' for an introduction
to the package.
The GeneCycle package implements the approaches of Wichert
et al. (2004), Ahdesmaki et al. (2005) and Ahdesmaki et al.
(2007) for detecting periodically expressed genes from gene
expression time series data.
This package implements several generalized F-statistics.
The current version includes a generalized F-statistic based on
the flexible isotonic/monotonic regression or order restricted
hypothesis testing.
GeneFeST is a genome scan method to detect outlier loci (genes) which are under balancing or directional selection.
Stochastic simulation and MCMC inference of structure from genetic data.
"geneListPie" package is for mapping a gene list to
function categories defined in GOSlim or Kegg. The results can
be plotted as a pie chart to provide a quick view of the genes
distribution of the gene list among the function categories.
The gene list must contain a list of gene symbols. The package
contains a set of pre-processed gene sets obtained from Gene
Ontology and MSigDB including human, mouse, rat and yeast. To
provide a high level concise view, only GO slim and kegg are
provided. The gene sets are regulared updated. User can also
use customized gene sets. User can use the R Pie() or Pie3D()
function for plotting the pie chart. Users can also choose to
output the gene function mapping results and use external
software such as Excel(R) for ploting.
GeneNet is a package for analyzing gene expression
(time series) data with focus on the inference of gene networks.
In particular, GeneNet implements the methods of Schaefer and
Strimmer (2005a,b,c) and Opgen-Rhein and Strimmer (2006, 2007)
for learning large-scale gene association networks (including
assignment of putative directions).
Functions for Genetic Epi Methods Developed at MSKCC
This package provides functions for the hyperbolic and
related distributions. Density, distribution and quantile
functions and random number generation are provided for the
hyperbolic distribution, the generalized hyperbolic
distribution, the generalized inverse Gaussian distribution and
the skew-Laplace distribution. Additional functionality is
provided for the hyperbolic distribution, normal inverse
Gaussian distribution and generalized inverse Gaussian
distribution, including fitting of these distributions to data.
Linear models with hyperbolic errors may be fitted using
hyperblmFit.
GeneReg is an R package for inferring time delay gene
regulatory network using time course gene expression profiles.
The main idea of time delay linear model is to fit a linear
regression model using a set of putative regulators to estimate
the transcription pattern of a specific target gene.
A tool for finding an ensemble gene-signature by a steepest ascending algorithm partially supervised by survival time data.
Classes and methods for handling genetic data. Includes
classes to represent genotypes and haplotypes at single markers
up to multiple markers on multiple chromosomes. Function
include allele frequencies, flagging homo/heterozygotes,
flagging carriers of certain alleles, estimating and testing
for Hardy-Weinberg disequilibrium, estimating and testing for
linkage disequilibrium, ...
GeneticTools is a loose collection of tools for the analysis of expression and genotype data, currently with main focus on eQTL and MDR analysis.
This package computes the solution path for generalized lasso problems. Important use cases are the fused lasso over an arbitrary graph, and trend fitting of any given polynomial order. Specialized implementations for the latter two subproblems are given to improve stability and speed.
Genealogical data analysis including descriptive statistics (e.g., kinship and inbreeding coefficients) and gene-dropping simulations.
The genMOSS package implements the Mode Oriented Stochastic Search (MOSS) algorithm as well as a simple moving window approach to look for combinations of SNPs that are associated with a response.
This is a genMOSS package with additional datafile preprocessing functions. Performs genome-wide analysis of dense SNP array data using the mode oriented stochastic search (MOSS) algorithm in a case-control design. The MOSS algorithm is a Bayesian variable selection procedure that is applicable to GWAS data. It identifies combinations of the best predictive SNPs associated with the response. It also performs a hierarchical log-linear model search to identify the most relevant associations among the resulting subsets of SNPs. This package also includes preprocessing of the data from Plink format to the format required by the MOSS algorithm.
Circular genomic permutation approach uses GWAS results to
establish the significance of pathway/gene-set associations
whilst accounting for genomic structure. All SNPs in the GWAS
are placed in a 'circular genome' according to their location.
Then the complete set of SNP association p-values are permuted
by rotation with respect to the SNPs' genomic locations
genoPlotR draws gene or genome maps and comparisons between
these, in a publication-grade manner. Starting from simple, common files,
it will draw postscript or pdf files that can be sent as such to journals
The package implements a procedure for generating samples
from ordinal/discrete random variables with pre-specified
correlation matrix and marginal distributions.
The genridge package introduces generalizations of the
standard univariate ridge trace plot used in ridge regression
and related methods.  These graphical methods show both bias
(actually, shrinkage) and precision, by plotting the covariance
ellipsoids of the estimated coefficients, rather than just the
estimates themselves.  2D and 3D plotting methods are provided,
both in the space of the predictor variables and in the
transformed space of the PCA/SVD of the predictors.
This package searches for global minimum of a very complex non-linear objective function with a very large number of optima.
Generalized ensemble methods allowing arbitrary underlying
models to be used. Currently only bagging is supported.
Generation of survival data with one (binary)
time-dependent covariate.  Generation of survival data arising
from a progressive illness-death model.
Defines window or bin boundaries for the analysis of genomic data.
Boundaries are based on the inflection points of a cubic smoothing spline
fitted to the raw data. Along with defining boundaries, a technique to
evaluate results obtained from unequally-sized windows is provided.
Applications are particularly pertinent for, though not limited to, genome
scans for selection based on variability between populations (e.g. using
Wright's fixations index, Fst, which measures variability in subpopulations
relative to the total population).
This package provides functions to fit geostatistical
data. The data can be continuous, binary or count data and the
models implemented are flexible. Conjugate priors are assumed
on some parameters while inference on the other parameters can
be done through a full Bayesian analysis of by empirical Bayes
methods.
This package provides a variety of functions to analyze and model geostatistical count data with generalized linear spatial models, including
1) simulate and visualize the data;
2) posterior sampling with robust MCMC algorithms (in serial or parallel way);
3) perform prediction for unsampled locations;
4) conduct Bayesian model checking procedure to evaluate the goodness of fitting;
5) conduct transformed residual checking procedure.
In the package, seamlessly embedded C++ programs and parallel computing techniques are implemented to speed up the computing processes.
Given expression data this package calculate a multivariate
geometrical characterization of the differential expression and can also
perform gene-set enrichment.
Quantification of the effect of geographic versus environmental isolation on genetic differentiation
The package provides basic functions for global
positioning based on light intensity measurements over time.
Positioning process includes the determination of sun events, a
discrimination of residency and movement periods, the
calibration of period-specific data and, finally, the
calculation of positions.
Set of routines for making Map Projections (forward and inverse), Topographic Maps, Perspective plots, Geological Maps, geological map symbols, geological databases, interactive plotting and selection of focus regions.
Set of data for use in package GEOmap.  Includes world
map, USA map, Coso map, Japan Map, ETOPO5
This package makes the qhull library (www.qhull.org)
available in R, in a similar manner as in Octave and MATLAB. Qhull
computes convex hulls, Delaunay triangulations, halfspace
intersections about a point, Voronoi diagrams, furthest-site
Delaunay triangulations, and furthest-site Voronoi diagrams. It
runs in 2-d, 3-d, 4-d, and higher dimensions. It implements the
Quickhull algorithm for computing the convex hull. Qhull does not
support constrained Delaunay triangulations, or mesh generation of
non-convex objects, but the package does include some R functions
that allow for this. Currently the package only gives access to
Delaunay triangulation and convex hull computation.
Read, manipulate, and digitize landmark data, generate shape
variables via Procrustes analysis for points, curves and surfaces, perform
shape analyses, and provide graphical depictions of shapes and patterns of
shape variation
Code for querying the web service at www.geonames.org
Geophysics, Continuum Mechanics, Mogi Model, Okada Model, Gravity
Geostatistical analysis including traditional,
likelihood-based and Bayesian methods.
Functions for inference in generalised linear spatial models. The posterior and predictive inference is based on Markov chain Monte Carlo methods. Package geoRglm is an extension to the package geoR, which must be installed first.
The georob package provides functions for fitting linear models
with spatially correlated errors by robust and Gaussian Restricted
Maximum Likelihood and for computing robust and customary point
and block kriging predictions, along with utility functions for
cross-validation and for unbiased back-transformation of kriging
predictions of log-transformed data.
Function for adding the geological timescale to bivariate plots.
Generates distance matrices from shape files and represents
spatially weighted multilevel analysis results (see spacom)
Spherical trigonometry for geographic applications
This package contains functions for: estimation of the
variogram through trimmed mean, radial basis functions
(optimization, prediction and cross-validation), summary
statistics from cross-validation, pocket plot, and design of
optimal sampling networks through sequential and simultaneous
points methods
This package provides to geostatistical modelling facilities using raster and SpatialPoints objects.
geotopbricks: Analyzes raster maps and other information as
input/output files from the Hydrological Distributed Model GEOtop. It
contains functions and methods to import maps and other keywords from
geotop.inpts file. Any information about the GEOtop Distributed
Hydrological Model is available on www.geotop.org. The examples are tested
on two simulation cases run with GEOtop built 1.225-9 mostly developed by
Stefano Endrizzi. Bugs/comments/questions/collaboration of any kind are
warmly welcomed.
GeoXp is a tool for researchers in spatial statistics,
spatial econometrics, geography, ecology etc allowing to link
dynamically statistical plots with elementary maps. This
coupling consists in the fact that the selection of a zone on
the map results in the automatic highlighting of the
corresponding points on the statistical graph or reversely the
selection of a portion of the graph results in the automatic
highlighting of the corresponding points on the map. GeoXp
includes tools from different areas of spatial statistics
including geostatistics as well as spatial econometrics and
point processes. Besides elementary plots like boxplots,
histograms or simple scatterplot, GeoXp also couples with maps
Moran scatterplots, variogram cloud, Lorentz Curves,...In order
to make the most of the multidimensionality of the data, GeoXp
includes some dimension reduction techniques such as PCA.
The package allows geometric objects defined in geozoo to be
displayed in the R package tourr
The Gene Expression State Transformation (GESTr) models
the states of expression of genes across a compendium of
samples in order to provide a universal scale of gene
expression for all genes. TranSAM is a modification of the SAM
approach designed to utilise GESTr-transformed gene expression
data.
Package designed to be used with Rscript to write
``#!'' shebang scripts that accept short and long flags/options.
Many users will prefer using instead the packages optparse or argparse
which add extra features like automatically generated help option and usage,
support for default values, positional argument support, etc.
Parsing command-line arguments by Perl module Getopt::Long.
It also provides a simple way for variable interpolation in R.
The GetR package calculates Guttman error trees, which can
be used to find homogeneous subgroups regarding Guttman errors.
Getting to the Bottom is a companion package for the "Getting to
the Bottom" optimization methods series at Statisticsviews.com.  The
package contains data and code to reproduce the examples in the articles.
A flexible nonlinear modelling framework for nonstationary
generalized extreme value analysis in hydroclimatology
Package for ARMA-GARCH or ARMA-APARCH modelling with GEV and stable conditional distributions.
Perform statistical tests to unveil genomic clusters,
produces garphical interpretations of the statistical results
in pdf files, perform a Gene Ontology analysis and produces
graphic results in pdf files
The gfcanalysis package supports analyses using the Global
Forest Change dataset released by Hansen et al. 2013. gfcanalysis was
written by the Tropical Ecology Assessment and Monitoring (TEAM) Network
(http://www.teamnetwork.org). For additional details on the Global Forest
Change dataset, see: Hansen, M. et al. 2013.  "High-Resolution Global Maps
of 21st-Century Forest Cover Change." Science 342 (15 November): 850-53.
Data available on-line from:
http://earthenginepartners.appspot.com/science-2013-global-forest
GGally is designed to be a helper to ggplot2. It contains
templates for different plots to be combined into a plot matrix, a parallel
coordinate plot function, as well as a function for making a network plot.
This is a set of tools for dendrograms and
tree plots using ggplot2.  The ggplot2 philosophy is to
clearly separate data from the presentation.
Unfortunately the plot method for dendrograms plots
directly to a plot device without exposing the data.
The ggdendro package resolves this by making available
functions that extract the dendrogram plot data.
A GUI with which to construct and interact with GGE biplots.
Implements horizon graphs based on ggplot2.
GGIR is a tool to process multi-day raw acceleration data
This package implements a unified algorithm, blockwise-majorization-decent (BMD), for efficiently computing the solution paths of the group-lasso penalized least squares, logistic regression, Huberized SVM and squared SVM.
Functions for analyzing and fitting Graphical Markov models
ggmap allows for the easy visualization of spatial data
and models on top of Google Maps, OpenStreetMaps, Stamen Maps,
or CloudMade Maps using ggplot2.
ggmcmc is a tool for assessing and diagnosing convergence of
Markov Chain Monte Carlo simulations, as well as for graphically display
results from full MCMC analysis. The package also facilitates the graphical
interpretation of models by providing flexible functions to plot the
results against observed variables.
GGMselect is a package dedicated to graph estimation in Gaussian Graphical Models. The main functions return the adjacency matrix of an undirected graph estimated from a data matrix.
R package for creating hammock plots, parallel sets, and
common angle plots using the ggplot2 framework.
An implementation of the grammar of graphics
in R. It combines the advantages of both base and
lattice graphics: conditioning and shared axes are
handled automatically, and you can still build up a
plot step by step from multiple data sources. It also
implements a sophisticated multidimensional
conditioning system and a consistent interface to map
data to aesthetic attributes. See the ggplot2 website
for more information, documentation and examples.
ggsubplot makes it easy to embed customized
subplots within larger graphics. Subplots may be used
as a geom to explore interaction effects, spatial data,
and hierarchical data. Subplots can also be used to
explore big data without overplotting.
ggtern is a package that extends the functionality of ggplot2, giving the capability to plot ternary diagrams for (subset of) the ggplot2 geometries. Additionally, ggtern has implemented several NEW geometries which are unavailable to the standard ggplot2 release. For further examples and documentation, please proceed to the ggtern website.
Some extra themes, geoms, and scales for ggplot2.
ggvis implements a interactive grammar of graphics, taking the
best parts of ggplot2, combining them with shiny's reactive framework and
drawing web graphics using vega.
The GhcnDaily package provides the core functions required
to download and format the GHCN Daily Data (TMax and Tmin) into
Monthly datasets. It is currently restricted to operating with
temperature data from the daily files.
The GHQ function can be used to obtain the quadrature points and weights to approximate an integral in two or more dimensions. This function uses the pruning approach to eliminate that points that do not contribute to the approximation of the integral and increases computational cost. The advantage to conducting this elimination of points is the decrease in the number of times that the function of interest is evaluated. This advantage is crucial in mixed models in which we must address several integrations within an iterative process to obtain model parameters.
This package provides detailed functionality for working
with the univariate and multivariate Generalized Hyperbolic
distribution and its special cases (Hyperbolic (hyp), Normal
Inverse Gaussian (NIG), Variance Gamma (VG), skewed Student-t
and Gaussian distribution). Especially, it contains fitting
procedures, an AIC-based model selection routine, and functions
for the computation of density, quantile, probability, random
variates, expected shortfall and some portfolio optimization
and plotting routines as well as the likelihood ratio test. In
addition, it contains the Generalized Inverse Gaussian
distribution.
Gibbs sampler for one-way linear mixed-effects models
(ANOVA, ANCOVA) with homoscedasticity of errors and uniform
priors.
This package provides two generic functions for performing
Markov chain sampling in a naive way for a user-defined target
distribution, which involves only continuous variables. The
function "gibbs_met" performs Gibbs sampling with each
1-dimensional distribution sampled with Metropolis update using
Gaussian proposal distribution centered at the previous state.
The function "met_gaussian" updates the whole state with
Metropolis method using independent Gaussian proposal
distribution centered at the previous state. The sampling is
carried out without considering any special tricks for
improving efficiency. This package is aimed at only routine
applications of MCMC in moderate-dimensional problems.
Random variate generator for the GIG distribution
GillespieSSA provides a simple to use, intuitive, and
extensible interface to several stochastic simulation
algorithms for generating simulated trajectories of finite
population continuous-time model. Currently it implements
Gillespie's exact stochastic simulation algorithm (Direct
method) and several approximate methods (Explicit tau-leap,
Binomial tau-leap, and Optimized tau-leap). The package also
contains a library of template models that can be run as demo
models and can easily be customized and extended. Currently the
following models are included, decaying-dimerization reaction
set, linear chain system, logistic growth model, Lotka
predator-prey model, Rosenzweig-MacArthur predator-prey model,
Kermack-McKendrick SIR model, and a metapopulation SIRS model.
Maximum likelihood estimation under relational models, with or without the overall effect.
Supply classes and methods to represent and manipulate
graphs
Some mapping and spatial data manipulation tools - in particular
drawing choropleth maps with nice looking legends,  and aggregation of point
data to polygons.
gitter is a image analysis package for R. Its primary goal is to allow users to robustly and quickly grid and quantify biological images of pinned colonies. It includes several image pre-processing techniques, such as autorotation of plates, noise removal, contrast adjustment and image resizing.
`Giza' offers a simple way of creating multiple pyramid
plots in one graphics window, exploiting the power of the
lattice package. It is a handy way of visualizing longitudinal
grouped (i.e.: age- and education-structured) data.
This package provides functions for estimation, testing, diagnostic checking and forecasting of generalized linear autoregressive moving average (GLARMA) models for discrete valued time series with regression variables.  These are a class of observation driven non-linear non-Gaussian state space models. The state vector consists of a linear regression component plus an observation driven component consisting of an autoregressive-moving average (ARMA) filter of past predictive residuals. Currently three distributions (Poisson, negative binomial and binomial) can be used for the response series. Three options (Pearson, score-type and unscaled) for the residuals in the observation driven component are available. Estimation is via maximum likelihood (conditional on initializing values for the ARMA process) optimized using Fisher scoring or Newton Raphson iterative methods. Likelihood ratio and Wald tests for the observation driven component allow testing for serial dependence in generalized linear model settings. Graphical diagnostics including model fits, autocorrelation functions and probability integral transform residuals are included in the package. Several standard data sets are included in the package.
The  package glassomix provides a general
framework  for network recovering through
a model-based soft clustering. It provides
functions for parameter estimation via the
EM algorithm for Gaussian graphical mixture
models in high dimensional setting. The main
function is ``glasso.mix'' upon which a model
selection is performed. The package estimates
the optimum number of mixture components, K
and the tuning parameter, lambda, based on
the Extended Bayesian Information Criteria
(EBIC) via ``select.gm'' function.
The graphical structural of the K-networks
are also plotted through the function ``gm.plot''
Enables calculation of image textures derived from grey-level
co-occurrence matrices (GLCMs) in R. Supports processing images that cannot
fit in memory.
The generalised lambda distribution, or Tukey lambda distribution, provides a wide variety of shapes with one functional form.  This package provides random numbers, quantiles, probabilities, densities for four different parameterisations of the distribution.  It provides the density function, distribution function and Quantile-Quantile plots.  It implements a variety of estimation methods for the distribution, including diagnostic plots.  Estimation methods include the starship (all 4 parameterisations) and a number of methods for only the FKML parameterisation.  These include maximum likelihood, maximum product of spacings, Titterington's method, L moments, Trimmed L moments and Distributional Least Absolutes.
The fitting algorithms considered in this package have two major objectives. One is to provide a smoothing device to fit distributions to data using the weight and unweighted discretised approach based on the bin width of the histogram. The other is to provide a definitive fit to the data set using the maximum likelihood and quantile matching estimation. Other methods such as moment matching, starship method, L moment matching are also provided. Diagnostics on goodness of fit can be done via qqplots, KS-resample tests and comparing mean, variance, skewness and kurtosis of the data with the fitted distribution.
The generalized lambda distribution (GLD) is a versatile
distribution that can accommodate a wide range of shapes,
including fat-tailed and asymmetric distributions. This package
implements a more intuitive parameterization of the GLD that
expresses the location and scale parameters directly as the
median and inter-quartile range of the distribution. The
remaining two shape parameters characterize the asymmetry and
steepness of the distribution respectively. The fitting of the
GLD to empirical data can be reduced to a two-parameter
estimation problem where the location and scale parameters are
estimated by their robust sample estimators. Moreover, the
parameterization can be used to compare data sets in a
convenient asymmetry and steepness shape plot. The underline C
routines are written such that compilers that support
vectorized mathematical operations can automatically vectorize
the most time consuming loops (tested with icc 12.1.0). The
package includes the usual distribution functions, fitting
routines and a shape plot function.
Fits linear pairwise-interaction models that satisfy strong hierarchy: if an interaction coefficient is estimated to be nonzero, then its two associated main effects also have nonzero estimated coefficients. Accommodates categorical variables (factors) with arbitrary numbers of levels, continuous variables, and combinations thereof. Implements the machinery described in the paper below.
Routines for log-linear models of incomplete contingency tables,
including some latent class models, via EM and Fisher scoring
approaches.  Allows bootstrapping.
Fits generalized linear models using the same model specification as glm in the stats package, but with a modified default fitting method that provides greater stability for models that may fail to converge using glm
Fits generalized linear models where the parameters are
subject to linear constraints. The model is specified by giving
a symbolic description of the linear predictor, a description
of the error distribution, and a matrix of constraints on the
parameters.
This package contains functions to perform generalized
linear mixed Dirichlet models using posterior simulation.
Efficient algorithms for fitting regularization paths for
linear or logistic regression models penalized by LEP.
Write bugs model files for hierarchical and spatial models, arranges unbalanced data in ragged arrays, and creates starting values.
Solve Generalized Linear Mixed Models using Gauss-Seidel
algorithm
Variable selection for generalized linear mixed models by L1-penalized estimation
Binomial and Poisson regression for clustered data, fixed
and random effects with bootstrapping.
Extremely efficient procedures for fitting the entire lasso or elastic-net regularization path for linear regression, logistic and multinomial regression models, poisson regression and the Cox model. Two recent additions are the multiresponse gaussian, and the grouped multinomial. The algorithm uses cyclical coordinate descent in a pathwise fashion, as described in the paper listed below.
This packages includes functions for restructuring an ordinal response dataset for fitting continuation ratio models for datasets
where the number of covariates exceeds the sample size or when there is collinearity among the covariates. This package uses the glmnet package
fitting algorithm.
A path-following algorithm for L1 regularized generalized
linear models and Cox proportional hazards model
Provides a function for fitting a penalized constrained continuation ratio model using the glmpath algorithm and methods for extracting coefficient estimates, predicted class, class probabilities, and plots.
A permutation test is used for inference in generalized
linear models (glm). The procedure is useful when parameter
estimates in ordinary glm fail to converge or are unreliable
due to small sample size.
Automated model selection and model-averaging. Provides a
wrapper for glm and other functions, automatically generating
all possible models (under constraints set by the user) with
the specified response and explanatory variables, and finding
the best models in terms of some Information Criterion (AIC,
AICc or BIC). Can handle very large numbers of candidate
models. Features a Genetic Algorithm to find the best models
when an exhaustive screening of the candidates is not feasible.
Extended techniques for generalized linear models (GLMs), especially for binary responses,
including parametric links and heteroskedastic latent variables.
'globalboosttest' implements a permutation-based testing
procedure to globally test the (additional) predictive value of
a large set of predictors given that a small set of predictors
is already available. Currently, 'globalboosttest' supports
binary outcomes (via logistic regression) and survival outcomes
(via Cox regression). It is based on boosting regression as
implemented in the package 'mboost'.
permutation based global test with deviance as test statistic
Implementation of three different Gene set analysis (GSA) algorithms for combining the individual pvalues of a set of genetic variats (SNPs) in a gene level pvalue. The implementation includes the selection of the best inheritance model for each SNP.
Generate functions to get or set global options
This package makes available 50 objective functions for benchmarking the performance of global optimization algorithms
Tools for the generalized logistic distribution (Type I,
also known as skew-logistic distribution), encompassing
basic distribution functions (p, q, d, r, score), maximum
likelihood estimation, and structural change methods.
R Interface to C API of GLPK, needs GLPK Version >= 4.42
Performs linear regression with correlated predictors, responses and correlated measurement errors in predictors and responses, correcting for biased caused by these.
An R interface to the Gmail RESTful API.  Allows access to your
Gmail messages, threads, drafts and labels from R.
This package implements a general framework for utilizing R to harness the power of NVIDIA GPU's. The "gmatrix" and "gvector" classes allow for easy management of the separate device and host memory spaces. Numerous numerical operations are implemented for these objects on the GPU. These operations include matrix multiplication, addition, subtraction, the kronecker product, the outer product, comparison operators, logical operators, trigonometric functions, indexing, sorting, random number generation and many more.
Unsupervised Clustering and Meta-analysis using Gaussian Mixture Copula Models.
This package provides functions and a graphical user interface for graphical described multiple test procedures.
GMD is a package for non-parametric distance measurement between
two discrete frequency distributions.
This is a collection of functions for tables, plots, and more. For
tables you'll find the convenient htmlTable() that I use for advanced
markdown table layout. A major focus has been to have it compatible with
LibreOffice and MS Word - you can now copy-paste most tables directly into
your document from the viewer. The main plots in the package are the
transitionPlot() and forestplot2(), see the examples for demo. Apart from
those it is worth mentioning the getDescriptionStatsBy() and its associated
function that help you generate a descriptive table (i.e. the Table 1 in
most journals), the pvalueFormatter() that formats p-values, and
mergeLists() that provides a complex recursive list merge.
It is a complete suite to estimate models based on moment
conditions. It includes the two step Generalized method of
moments (GMM) of Hansen(1982), the iterated GMM and continuous
updated estimator (CUE) of Hansen-Eaton-Yaron(1996) and several
methods that belong to the Generalized Empirical Likelihood
(GEL) family of estimators, as presented by Smith(1997),
Kitamura(1997), Newey-Smith(2004) and Anatolyev(2005).
Likelihood-based Boosting for Generalized mixed models
Various R programming tools for model fitting
Multiple Precision Arithmetic (big integers and rationals,
prime number tests, matrix computation), "arithmetic without limitations"
using the C library GMP (GNU Multiple Precision Arithmetic).
Interface between the GMT map-making software and R,
enabling the user to manipulate geographic data within R and call GMT commands
to draw and annotate maps in postscript format. The 'gmt' package is about
interactive data analysis, rapidly visualizing subsets and summaries of
geographic data, while performing statistical analysis in the R console.
Generalized Mann-Whitney type tests based on probabilistic
indices and new diagnostic plots.
Provide functions to compute standard and generalized Nash Equilibria. The following optimization methods are available nonsmooth reformulation, fixed-point formulation, minimization problem and constrained-equation reformulation.
Functions to specify and fit generalized nonlinear models,
including models with multiplicative interaction terms such as
the UNIDIFF model from sociology and the AMMI model from crop
science, and many others.  Over-parameterized representations
of models are used throughout; functions are provided for
inference on estimable parameter combinations, as well as
standard methods for diagnostics etc.
This package performs generalized non-negative matrix factorization based on Renyi divergence.
Read data files readable by gnumeric into R. Can read
whole sheet or a range, from several file formats, including
the native format of gnumeric. Reading is done by using
ssconvert (a file converter utility included in the gnumeric
distribution http://projects.gnome.org/gnumeric/) to convert
the requested part to CSV. From gnumeric files (but not other
formats) can list sheet names and sheet sizes or read all
sheets.
A collection of functions to solve weighted and lexicographical
goal programming problems as specified by Lee (1972) and Ignizio (1976).
Implementation of model-checking techniques for generalized linear
models and linear structural equation models based on cumulative residuals
Tests of goodness-of-fit based on kernel smoothing of the data
This package implements some tests of fit for the normal, Gumbel (type I extreme
value distribution), multivariate normal and generalized Pareto distributions.
Cramer-Von Mises and Anderson-Darling tests of goodness-of-fit
for continuous univariate distributions, using
efficient algorithms.
Accounting for genes' functional-non-equivalence within pathways in classical Gene-set-analysis.
Implementation of the GO-GARCH model class
package provides a collection of functions to set up
Google Public Data Explorer data visualization tool with your
own data, building automaticaly the corresponding DSPL (XML)
metadata file jointly with the CSV files. All zipped up and
ready to be published at Public Data Explorer.
The googleVis package provides an interface between R
and the Google Charts API. It allows users to create web pages
with interactive charts based on R data frames, using the Google
Charts API. The package provides plot methods in R to display
the charts locally with the R HTTP help server without uploading
the data to Google. A modern browser with Internet connection is
required and for some charts Flash.
A suite of helper functions for obtaining data from the
Google Maps API JSON objects. Calls Google Maps API and returns
results as an R data frame.
Generalized Order-Restricted Information Criterion (GORIC)
value for a set of hypotheses in multivariate regression models
A package to visualise Gene Ontology (GO) enrichment analysis
results on gene lists arising from different analyses such clustering or
PCA. The significant GO categories are visualised as word clouds that can
be combined with different plots summarising the underlying data.
This package purposes to deal with public survey data of
Japanese government via their Application Programming Interface
(http://statdb.nstac.go.jp/)
Produces a generalized pairs (gpairs) plot.
Gradient Projection Algorithm Rotation for Factor
Analysis. See ?GPArotation.Intro for more details.
This package implements guided principal components analysis for the detection of batch effects in high-throughput data.
General polygon clipping routines for R based on Alan
Murta's C library
This package implements an extension of principal component analysis (PCA) tailored to handle multiple data tables. It can handle Big Data in the sense that the variation in massive data can be described by intervals [a, b] and multiple tables.
This package computes the bootstrap goodness-of-fit test
for the generalized Pareto distribution by Villasenor-Alva and
Gonzalez-Estrada (2009). The null hypothesis includes heavy and
non-heavy tailed gPd's. A function for fitting the gPd to data
using the parameter estimation methods proposed in the same
article is also provided.
Use functional regression as the mean structure and Gaussian Process as the covariance structure.
A computationally stable approach of fitting a Gaussian Process (GP) model to a deterministic simulator.
Collection of datasets as prepared by Profs. A.P. Gore, S.A. Paranjape, and M.B. Kulkarni of Department of Statistics, Poona University, India. With their permission, first letter of their names forms the name of this package, the package has been built by me and made available for the benefit of R users. This collection requires a rich class of models and can be a very useful building block for a beginner.
Functions for estimating generalized partial linear models
Various R programming tools for plotting data
Package for generalized partially linear tree-based regression model, combining a
generalized linear model with an additional tree part on the same scale.
This package contains tools for studying genotype-phenotype (GP) maps for bi-allelic loci underlying quantitative phenotypes. The 0.1 version is released in connection with the publication of Gjuvsland et al. (2003) and implements basic line plots and the monotonicity measures for GP maps presented in the paper. Reference: Gjuvsland AB, Wang Y, Plahte E and Omholt SW (2013) Monotonicity is a key feature of genotype-phenotype maps. Front. Genet. 4:216. doi: 10.3389/fgene.2013.00216 [\href{http://www.frontiersin.org/Journal/10.3389/fgene.2013.00216/full}{link}]
This package provides a minimalistic functionality necessary to apply Gaussian Process in R. They provide a selection of functionalities of GPML Matlab library.
A package for functional enrichment analysis via the
g:Profiler toolkit (http://biit.cs.ut.ee/gprofiler/).
Some functions for modeling sequence read counts as a
generalized poisson model and to use this model for detecting
differentially expressed genes in different conditions and
differentially spliced exons.
The gptk package implements a general-purpose toolkit for Gaussian
process regression with a variety of covariance functions (e.g. RBF, Mattern, polynomial, etc).
Based on a MATLAB implementation by Neil D. Lawrence. See inst/doc/index.html for more details.
This package provides R interfaces to a handful of common
functions implemented using the Nvidia CUDA toolkit. Some of
the functions require at least GPU Compute Capability 1.3.
Thanks to Craig Stark at UC Irvine for donating time on his
lab's Mac.
Maximum likelihood estimators are obtained via an EM algorithm
Provides functions for matching student-answers to teacher answers for a variety of data types.
Functions to fit, visualise and compare Gaussian random field species distribution models.
A package for probability propagation in graphical independence networks, also known as probabilistic expert systems or Bayesian networks.
Implementation of Context Free Grammar and Grammatical Evolution for R
Represents metagenomic samples on the Euclidean space to examine similarity amongst samples by studying clusters in the model. Given the matrix of metagenomic counts for samples, this package (1) quantifies dissimilarity between samples using Kendall's tau-distance, (2) constructs multidimensional models of different dimension, and (3) plots the models for visualization and comparison.
gRapfa is for modelling discrete longitudinal data using acyclic probabilistic finite automata (APFA). The package contains functions for constructing APFA models from a given data using penalized likelihood methods. For graphical display of APFA models, gRapfa depends on 'igraph package'. gRapfa also contains an interface function to Beagle software that implements an efficient model selection algorithm.
gRapHD is designed for efficient selection of high-dimensional undirected
graphical models. The package provides tools for selecting trees, forests
and decomposable models minimizing information criteria such as AIC or BIC,
and for displaying the independence graphs of the models. It has also some
useful tools for analysing graphical structures. It supports the use of
discrete, continuous, or both types of variables.
GrapheR is a multi-platform user interface for drawing highly customizable graphs in R. It aims to be a valuable help to quickly draw publishable graphs without any knowledge of R commands. Six kinds of graph are available: histogram, box-and-whisker plot, bar plot, pie chart, curve and scatter plot.
The package provides functions to generate
graphics files, compare them with ``model'' files,
and report the results.
This package implements a histogram principal components analysis. It provides numerical and graphical tools to deal with big data.
Optimizing a function F(U), where U is a semi-orthogonal matrix and F is invariant under an orthogonal transformation of U
The gRbase package provides certain general constructs
which are used by other graphical modelling packages, in particular
by the packages gRain, gRim and gRc.

gRbase contains several datasets relevant for use in connection with
graphical models. Almost all datasets used in the book Graphical
Models with R (2012) are contained in gRbase.

gRbase implements several graph algorithms (based mainly on
representing graphs as adjacency matrices - either in the form
of a standard matrix or a sparse matrix). Some graph
algorithms are:
(i) maximum cardinality search (for marked and unmarked graphs).
(ii) moralize.
(iii) triangulate.
(iv) junctionTree.

gRbase facilities for array operations,

gRbase implements functions for testing for conditional independence.

gRbase illustrates how hierarchical log-linear models (hllm) may be
implemented and describes concept of gmData (graphical meta
data). These features, however, are not maintained anymore and
remains in gRbase only because there exists a paper describing
these facilities: A Common Platform for Graphical Models in R:
The gRbase Package, Journal of Statistical Software, Vol 14,
No 17, 2005.
Estimation, model selection and other aspects of
statistical inference in Graphical Gaussian models with edge
and vertex symmetries (Graphical Gaussian models with colours)
The greport package contains many functions useful for
monitoring and reporting the results of clinical trials and other
experiments in which treatments are compared.  LaTeX is
used to typeset the resulting reports, recommended to be in the
context of knitr. The Hmisc and lattice packages are used by greport
for high-level graphics.
This package converts a spatial object from class
SpatialGridDataFrame to SpatialPolygonsDataFrame.
Functions for drawing scene trees representing scenes that
have been drawn using grid graphics.
Functions for drawing node-and-edge graphs that have been
laid out by graphviz.
The function GriegSmith accepts either quadrat count data,
a point process object(ppp) or a matrix of x and y coordinates.
The function calculates a nested analysis of variance and
simulation envelopes.
gRaphical Interaction Models:

Models for for contingency tables (i.e. log-linear models)

Graphical Gaussian models for multivariate normal data (i.e. covariance selection
models)

Mixed interaction models
Functions for converting, importing, and drawing PostScript
pictures in R plots.
The program GRNN implements the algorithm proposed by
Specht (1991).
Robust multiple or multivariate linear regression, nonparametric regression on orthogonal components, classical or robust partial least squares models
The package was developed to fit fit many growth curves
obtained under different conditions in order to derive a
conclusive dose-response curve, for instance for a compound
that potentially affects growth. grofit fits data to different
parametric models (function gcFitModel) and in addition
provides a model free spline fit (function gcFitSpline) to
circumvent systematic errors that might occur within
application of parametric methods.
Regression models for grouped and coarse data, under the
Coarsened At Random assumption.
The package provides a Tcl/Tk-based graphical user interface. It
enables the construction of group sequential designs based on normally
distributed test statistics, and particularly to compute critical
boundaries, power, drift, and confidence intervals for such designs. All
computations are done via the alpha spending approach by Lan-DeMets.
Various alpha spending functions are made available to the user for this
purpose.
Employs a non-parametric formulation for by-subject random effect
parameters to borrow strength over a constrained number of repeated
measurement waves in a fashion that permits multiple effects per subject.
One class of models employs a Dirichlet process (DP) prior for the subject
random effects and includes an additional set of random effects that
utilize a different grouping factor and are mapped back to clients through
a multiple membership weight matrix; e.g. treatment(s) exposure or dosage.
A second class of models employs a dependent DP (DDP) prior for the subject
random effects that directly incorporates the multiple membership pattern.
Estimates a collection of time-indexed functions under
either of Gaussian process (GP) or intrinsic Gaussian Markov
random field (iGMRF) prior formulations where a Dirichlet process
mixture allows sub-groupings of the functions to share the same
covariance or precision parameters.  The GP and iGMRF formulations
both support any number of additive covariance or precision terms,
respectively, expressing either or both of multiple trend and
seasonality.
A compilation of nonlinear growth models used in many areas
A nonparametric empirical Bayes method for recovering
gradients (or growth velocities) from observations of smooth
functions (e.g., growth curves) at isolated time points.
Fits user specified (GLM-) models with Group Lasso penalty
The package implements the concave 1-norm and 2-norm group penalty in linear and logistic regression.
The concave 1-norm group penalty includes 1-norm group SCAD and 1-norm group MCP.
The concave 1-norm group penalty has bi-level selection features. That is it selects variables at group and individual levels with proper tuning parameters.
The concave 1-norm group penalty is robust to mis-specified group information.
The concave 2-norm group penalty includes 2-norm group SCAD and 2-norm group MCP. The concave 2-norm group penalty select variable at group level only.
The package can also fit group Lasso, which is a special case of concave 2-norm group penalty when the regularization parameter kappa equals zero.
The highly efficient (block) coordinate descent algorithm (CDA) is used to compute the solutions for both penalties in linear models.
The highly stable and efficient (block) CDA and minimization-majorization approach are used to compute the solution for both penalties in logistic models.
In the computation of solution surface, the solution path along kappa is implemented.
This provides a better solution path compared to the solution path along lambda.
The package also provides a tuning parameter selection method based on cross-validation for both linear and logistic models.
Efficient algorithms for fitting the regularization path of
linear or logistic regression models with grouped penalties.  This
includes group selection methods such as group lasso, group MCP, and
group SCAD as well as bi-level selection methods such as the group
exponential lasso, the composite MCP, and the group bridge.
Functions to generate and analyze data for psychology
experiments based on the General Recognition Theory.
This package offers functions for the comparison of Gutenberg-Richter
b-values. Several functions in GRTo are helpful for the assessment of the
quality of seismicity catalogs.
GSAgm is an R package that completes a self-contained gene set analysis (GSA) for RNA-seq and SNP data using the Gamma Method.
Utility functions for GATK analyses of genome sequence
data
Write SARIMA models in (finite) AR representation and
simulate generalized multiplicative seasonal autoregressive
moving average (time) series with Normal / Gaussian, Poisson or
negative binomial distribution.
Group Sequential Operating Characteristics for Clinical,
Bayesian two-arm Trials with known Sigma and Normal Endpoints.
gsDesign is a package that derives group sequential designs and describes their properties.
Robust Estimation of Multivariate Location and Scatter in the Presence of Missing Data
This package provides an estimate of the change-point(s) and the corresponding p-values through graph-based approach.
calculate equivalence and futility boundaries based on the exact bivariate $t$ test statistics for group sequential designs in studies with equivalence hypotheses.
gsg (gam selection gradients) provides a unified approach
to the regression analysis of selection from longitudinal data
collected from natural populations.
Global Soil Information Facilities - tools (standards and functions) and sample datasets for global soil mapping.
Family based association test via GEE Kernel Machine score test
An R wrapper for the special functions and quasi random number
generators of the Gnu Scientific Library
(http://www.gnu.org/software/gsl/).  See gsl-package.Rd for details of
overall package organization, and Misc.Rd for some functions that are
widely used in the package, and some tips on installation.
This package implements a Bayesian approach for estimation of a mixture of gamma distributions in which the mixing occurs over the shape parameter.
This family provides a flexible and novel approach for modeling heavy-tailed distributions, it is computationally efficient, and it only requires to specify
a prior distribution for a single parameter.
Tools rewritten in C for various smoothing tasks
A comprehensive package for structural multivariate
function estimation using smoothing splines.
A collection of routines for the estimation of copula density
and 2-D hazard function using smoothing splines.
variogram modelling; simple, ordinary and universal point or block (co)kriging, sequential Gaussian or indicator (co)simulation; variogram and variogram map plotting utility functions.
This package contains classes and functions for
the spatial analysis of marker data to include structure
statistics, analyses based upon allele frequencies.
gsubfn is like gsub but can take a replacement function
or certain other objects instead of the replacement string.
Matches and back references are input to the replacement function and
replaced by the function output.   gsubfn can be used to split strings
based on content rather than delimiters and for quasi-perl-style string
interpolation. The package also has facilities for translating formulas
to functions and allowing such formulas in function calls instead of
functions.  This can be used with R functions such as apply, sapply,
lapply, optim, integrate, xyplot, Filter and any other function that
expects another function as an input argument or functions like cat
or sql calls that may involve strings where substitution is desirable.
Tools to make it easier to work with "tables" of grobs.
This package provides functions to calculate the
efficiencies (expected tests per unit) of hierarchical and
matrix group testing procedures.  Efficiencies can be
calculated in the presense of correlated responses under
multiple arrangements of clustesrs.  Efficiencies can also be
evaluated in the presense of test error.
Generalized Turnbull's estimator proposed by Dehghan and Duchesne
(2011).
Assorted tools for genetic association analyses.  The
current focus is on implementing (either exactly or
approximately) regression analyses using summary statistics
instead of using subject-specific data.  So far, functions
exist to support multi-SNP risk score analyses, multi-SNP
conditional regression analyses, and multi-phenotype analyses,
using summary statistics.  There are helper functions for
reading and manipulating subject-specific genotype data, which
provide a platform for calculating the summary statistics, or
for using R to conduct other analyses not supported by specific
GWAS analysis tools.
Provides an interface to the Open Platform's Content API of the Guardian Media Group. It retrieves content from news outlets The Observer, The Guardian, and guardian.co.uk from 1999 to current day
This package comprises maps of France in 1830, multivariate data from A.-M. Guerry and others, and statistical and
graphic methods related to Guerry's "Moral Statistics of France". The goal is to facilitate the exploration and
development of statistical and graphic methods for multivariate data in a geo-spatial context of historical interest.
The GUILDS package combines a range of sampling formulas for the unified neutral model of biogeography and biodiversity. Alongside the sampling formulas, it includes methods to perform maximum likelihood optimization of the sampling formulas, methods to generate data given the neutral model, and methods to estimate the expected species abundance distribution. Sampling formulas included in the GUILDS package are the Etienne Sampling Formula (Etienne 2005), the guild sampling formula, where guilds are assumed to differ in dispersal ability (Janzen 2014), and  the guilds sampling formula conditioned on guild size (Janzen 2014).
stand alone package providing R functions for the
Gumbel-Hougaard copula. We provide probability functions
(cumulative distribution and density functions), simulation
function (Gumbel copula multivariate simulation) and estimation
functions (Maximum Likelihood Estimation, Inference For
Margins, Moment Based Estimation and Canonical Maximum
Likelihood).
Generalized UniFrac distance for comparing microbial
communities. Permutational multivariate analysis of variance
using multiple distance matrices.
Given exposure and survival time series as well as parameter values, GUTS allows for the calculation of the survival probabilities as well as the logarithm of the corresponding likelihood.
Generalized structured regression models with regularized categorical effects, categorical effect modifiers, continuous effects and smooth effects.
Methods from the paper: Pena, EA and Slate, EH, "Global Validation of Linear Model Assumptions," J. American Statistical Association, 101(473):341-354, 2006.
Functions for genome-wide association/interaction analysis
and rare variant analysis on a continuous/dichotomous trait
using family data, and for making genome-wide p-value plot and
QQ plot.
This package contains a function to do exact
Hardy-Weinburg testing (using Fisher's test) for SNP genotypes
as typically obtained in a Genome Wide Association Study
(GWAS).
Controls for duplicated IDs, distribution of betas, SEs,
etc.
This package provides functions to calculate the
significance threshold for controlling the type I error rate in
mixed-model association mapping analyses.
Based on calculations of 758 women this package calculates
positive predictive values (PPV) and negative predictive values
(NPV) for inadequate and excessive gestational weight gain
(GWG) for different prevalences for different BMI categories.
gWidgets provides a toolkit-independent API for building interactive GUIs. At least one of the 'gWidgetsXXX packages', such as gWidgetstcltk, needs to be installed. Some icons are on loan from the scigraphica project http://scigraphica.sourceforge.net.
Re-implementation of gWidgets API. The API is
defined in this package. A second, toolkit-specific package is required to use
it. There are three in development: gWidgets2RGtk2, gWidgets2Qt, and
gWidgets2tcltk.
Port of the gWidgets2 API to the tcltk
package.
Port of the gWidgets API to the tcltk package. Requires Tk 8.5 or greater.
Functions for computing geographically weighted
models
GWRM is a package for fitting Generalized Waring
Regression Models. It includes the dataset and the example of
Rodriguez-Avi, J; Conde-Sanchez, A; Saez-Castillo, A.J.,
Olmo-Jimenez, M. J. and Martinez Rodriguez, A. M.(2009). A
generalized Waring regression model for count data.
Computational Statistics and Data Analysis, 53, pp. 3717-3725.
Fits geographically weighted regression (GWR) models and
has tools to diagnose and remediate collinearity in the GWR
models. Also fits geographically weighted ridge regression
(GWRR) and geographically weighted lasso (GWL) models.
Estimate genome-wide significance using extrapolation approach
Quantifying and testing gene-by-measured-environment interaction in behavior genetic designs.
A Haar-Fisz algorithm for Poisson intensity estimation
Package provides the estimation of the structure and the parameters, sampling methods and structural plots of Hierarchical Archimedean Copulae (HAC).
Provides a framework for writing map/reduce scripts for
use in Hadoop Streaming. Also facilitates operating on data in
a streaming fashion, without Hadoop.
This package offers helpful functions and algorithms for solving optimal matching tasks in the context of preclinical cancer studies. Furthermore, various helper and plotting functions are provided for unsupervised and supervised machine learning and longitudinal modeling of tumor response patterns.
(nothing but) an S4 implementation of Eq. (3) and Eq. (7)
by David J. Hand and Robert J. Till (2001). A Simple Generalisation of
the Area Under the ROC Curve for Multiple Class Classification Problems.
Machine Learning, 45 (2), p. 171--186.  DOI:10.1023/A:1010920819831.
Provides an R routine for a Cramer-von Mises type two sample test which is based on empirical Hankel transforms of the non-negative sample variables. The test is non-parametric and not distribution free. The exact value of the test statistic for univariate data as well as the p-value and the critical value are computed.
The following R functions are used for inference of trait
associations with haplotypes and other covariates in
generalized linear models.  The functions are developed
primarily for data collected in cohort or cross-sectional
studies. They can accommodate uncertain haplotype phase and
handle missing genotypes at some SNPs.
The package provides a set of functions whose aim is to
propose 4 methods of QTL detection. HAPimLD is an
interval-mapping method designed for unrelated individuals with
no family information that makes use of linkage disequilibrium.
HAPimLDL is an interval-mapping method for design of half-sib
families. It combines linkage analysis and linkage
disequilibrium. HaploMax is based on an analysis of variance
with a dose haplotype effect. HaploMaxHS is based on an
analysis of variance with a sire effect and a dose haplotype
effect in half-sib family design.  Fundings for the package
development were provided to the LDLmapQTL project by the ANR
GENANIMAL program and APIS-GENE.
Haplin performs a genetic association analysis of
case-parent triad (trio) data with multiple markers. It can
also incorporate complete or incomplete control triads, for
instance independent control children. Estimation is based on
haplotypes, for instance SNP haplotypes, even though phase is
not known from the genetic data. Haplin estimates relative risk
(RR + conf.int.) and p-value associated with each haplotype. It
uses maximum likelihood estimation to make optimal use of data
from triads with missing genotypic data, for instance if some
SNPs has not been typed for some individuals. Haplin also
allows estimation of effects of maternal haplotypes and
parent-of-origin effects, particularly appropriate in perinatal
epidemiology. Haplin allows special models, like
X-inactivation, to be fitted on the X-chromosome.
'haplo.ccs' estimates haplotype and covariate relative
risks in case-control data by weighted logistic regression.
Diplotype probabilities, which are estimated by EM computation
with progressive insertion of loci, are utilized as weights.
Simulate haplotypes through meioses. Allows specification
of population parameters.
A suite of R routines for the analysis of indirectly measured haplotypes. The statistical methods assume that all subjects are unrelated and that haplotypes are ambiguous (due to unknown linkage phase of the genetic markers). The main functions are: haplo.em, haplo.glm, haplo.score, haplo.power, and seqhap; all of which have detailed examples in doc/manualHaploStats.pdf.
Functions to perform ROR for sequence-based association
analysis
Package for haplotype data simulation. Haplotypes are
generated such that their allele frequencies and linkage
disequilibrium coefficients match those estimated from an input
data set.
Package HardyWeinberg is a package for exploring
bi-allelic marker data. It focuses on the graphical
representation of the results of tests for Hardy-Weinberg equilibrium
in a ternary plot. Routines for several tests for Hardy-Weinberg equilibrium
are included in the package. Procedures for handling missing genotype
data are included.
Functions for easy simulation and reproducability.
This package implements a data structure similar to hashes
in Perl and dictionaries in Python but with a purposefully R
flavor.  For objects of appreciable size, access using hashes
outperforms native named lists and vectors.
This package provides common non-cryptographic hash
functions for R. For example, SpookyHash, Murmur3Hash, Google
CityHash.
Drawing Hasse diagram - visualization of transitive reduction of a finite partially ordered set.
The package allows to simulate Hawkes process both in univariate and multivariate settings. It gives functions to compute different moments of the number of jumps of the process on a given interval, such as mean, variance or autocorrelation of process jumps on time intervals separated by a lag.
Damage Functions (DFs), also known as
Vulnerability Functions, associate the physical damage
to a building or a structure (and also its contents and
inventory) from natural disasters to financial damage.
The Federal Emergency Management Agency (FEMA) in USA
developed several thousand DFs and these serve as a
benchmark in natural catastrophe modeling, both in
academia and industry. However, these DFs and their
documentation are buried within the HAZUS software are
not easily accessible for analysis and visualization.
This package provides more than 1300 raw DFs used by FEMA's
HAZUS software and also functionality to extract and
visualize DFs specific to the flood hazard. The vignette
included with this package demonstrates its use.
Calculate expected relative risk and proportion protected assuming normally distributed log10 transformed antibody dose for several component vaccine. Uses Hill models for each component which are combined under Bliss independence.
Contains functions for fitting hierarchical versions of
EVSD, UVSD, DPSD, DPSD with d' restricted to be positive, and
our gamma signal detection model to recognition memory
confidence-ratings data.
Functions to compute small area estimates based on a basic
area or unit-level model. The model is fit using restricted
maximum likelihood, or in a hierarchical Bayesian way. In the
latter case numerical integration is used to average over the
posterior density for the between-area variance.  The output
includes the model fit, small area estimates and corresponding
MSEs, as well as some model selection measures. Additional
functions provide means to compute aggregate estimates and
MSEs, to minimally adjust the small area estimates to
benchmarks at a higher aggregation level, and to graphically
compare different sets of small area estimates.
This package fits Hierarchical Bayesian space-Time models for Gaussian data. Furthermore, its functions have been implemented for analysing the fitting qualities of those models.
A new diagnostic check for model adequacy in regression
and generalized linear models is implemented.
This package calculates the interval estimates for the parameters of
linear models heteroscedastic regression using bootstrap - (Wild Bootstrap) and double
bootstrap-t (Wild Bootstrap). It is also possible to calculate confidence intervals using
the percentile bootstrap and percentile bootstrap double. It is possible to calculate
consistent estimates of the covariance matrix of the parameters of linear regression models
with heteroskedasticity of unknown form. The package also provides function to calculate
consistently the covariance matrix of the parameters of linear models with heteroskedasticity
of unknown form.
Functions to perform dimensionality reduction for classification if the covariance matrices of the classes are unequal.
Discriminant analysis and data clustering methods for high
dimensional data, based on the assumption that high-dimensional
data live in different subspaces with low dimensionality
proposing a new parametrization of the Gaussian mixture model
which combines the ideas of dimension reduction and constraints
on the model.
Cross-validated linear discriminant calculations determine
the optimum number of features. Test and training scores from
successive cross-validation steps determine, via a principal
components calculation, a low-dimensional global space onto which test
scores are projected, in order to plot them. Further functions are
included that serve didactic purposes.
Facilitates discovery of hydrological data, non-programmatic
access to catalogues and databases.
A flexible and hierarchical framework for comparing categorical map composition and configuration (spatial pattern) along spatial, thematic, or external grouping variables.  Comparisons are based on measures of mutual information between thematic classes (colours) and location (spatial partitioning).  Results are returned in textual, tabular, and graphical forms.
Implementation of multiple approaches to perform inference in
high-dimensional models
Mimics the lm function found in the package stats to fit
high dimensional regression models with point estimates,
standard errors, and p-values. Methods for printing and
summarizing the results are given.
This package contains algorithms for lasso and fused-lasso
problems. It contains an implementation of the lars algorithm [1], for the
lasso and fusion penalization and EM-based algorithms for (logistic) lasso
and fused-lasso.
Computation of highest density regions in one and two dimensions,
kernel estimation of univariate density functions conditional on one covariate,
and multimodal regression.
This package implements a iteratively reweighed least square (IRLS) strategy that incorporates a blockwise majorization decent (BMD) method, for efficiently computing the solution paths of the (grouped) lasso and the (grouped) elastic net for the Tweedie model.
Timeseries analysis is conducted using Korean mortality and environmental variables
The heatex package calculates heat storage in the body and
the components of heat exchange (conductive, convective,
radiative, and evaporative) between the body and the
environment during physical activity based on the principles of
partitional calorimetry. The program enables heat exchange
calculations for a range of environmental conditions when
wearing various clothing ensembles.
heatmap3 is an improved heatmap package. It is completely
compatible with the original R function heatmap, and provides some more
powerful and convenient features.
Generates a fit plot for diagnosing misspecification in models of
binary dependent variables, and calculates the related heatmap fit
statistic (Esarey and Pierce, 2012).
Allows heatmap matrix to have non-identical X- and
Y-dimensions.  Allows multiple tracks of annotation for
RowSideColors and ColSideColors.
This package contains functions to perform robust estimation considering
heavy-tailed distributions. Currently, the package includes linear regression, linear
mixed-effect models, multivariate location and scatter estimation, penalized splines
and random variate generation.
A dummy package to demonstrate how to interface to a jar
file that resides inside an R package.
Tools for accessing various open data sources in the Helsinki
region in Finland. Current data sources include
the Real Estate Department (district maps),
the Environmental Services Authority,
Service Map API,
Linked Events API and
Helsinki Region Infoshare statistics API.
Provides HE plot functions for visualizing hypothesis tests in multivariate linear models. They represents sums-of-squares-and-products matrices for linear hypotheses and for error using ellipses (in two dimensions) and ellipsoids (in three dimensions).
The R package 'hergm' implements Hierarchical Exponential-Family Random Graph Models (HERGMs), which can be used to model a wide range of relational data (networks).  'hergm' implements both simulation and Bayesian inference.
Functions for the fitting and summarizing of heteroscedastic t-regression.
An implementation of White's Test for Heteroskedasticity
as outlined in Doornik (1996).
Binning and plotting functions for hexagonal bins. Now
uses and relies on grid graphics and formal (S4) classes and
methods.
Functions to view files in raw binary form like in a hex
editor.  Additional functions to specify and read arbitrary
binary formats.
A data only package containing commercial domestic flights that
departed Houston (IAH and HOU) in 2011.
High-dimensional additive models as introduced by Meier,
van der Geer and Buehlmann (2009).
Implements the hub graphical lasso and hub covariance graph proposal by Tan, KM., London, P., Mohan, K., Lee, S-I., Fazel, M., and Witten, D. (2014). Learning graphical models with hubs. To appear in Journal of Machine Learning Research. arXiv.org/pdf/1402.7349.pdf.
The hglm package is used to fit hierarchical generalized linear models. It can be used for linear mixed models and generalized linear mixed models with random effects for a variety of links and a variety of distributions for both the outcomes and the random effects. Fixed effects can also be fitted in the dispersion part of the mean model.
This data-only package was created for distributing data used in the examples of the hglm package.
Contains functions for
identifying and correcting HGNC gene symbols which have been converted
to date format by Excel, for reversibly converting between HGNC
symbols and valid R names, identifying invalid HGNC symbols and
correcting synonyms and outdated symbols which can be mapped to an
official symbol.
Support software for Statistical Analysis and Data Display (First Edition, Springer, ISBN 0-387-40270-5, 2004) and (Second Edition, Springer, ISBN x-xxx-xxxxx-x, anticipated 2015) by Richard M. Heiberger and Burt Holland.  This contemporary presentation of statistical methods features extensive use of graphical displays for exploring data and for displaying the analysis.  The second edition includes redesigned graphics and additional chapters. The authors emphasize how to construct and interpret graphs, discuss principles of graphical design, and show how accompanying traditional tabular results are used to confirm the visual impressions derived directly from the graphs. Many of the graphical formats are novel and appear here for the first time in print.  All chapters have exercises.  All functions introduced in the book are in the package.  R code for all examples, both graphs and tables, in the book is included in the scripts directory of the package.
Heller-Heller-Gorfine (HHG) tests are a set of powerful statistical tests of independence between two random vectors of arbitrary dimensions. For the case of testing independence between random variables (rather than vectors), the package also offers implementations of the DDP and ADP tests, which are consistent against all continuous alternatives but are distribution-free and thus much faster to apply.
This package builds on the EMD package to provide additional tools for empirical mode decomposition (EMD) and Hilbert spectral analysis. It also implements the ensemble empirical decomposition (EEMD) and the complete ensemble empirical mode decomposition (CEEMD) methods to avoid mode mixing and intermittency problems found in EMD analysis.  The package comes with several plotting methods that can be used to view intrinsic mode functions, the HHT spectrum, and the Fourier spectrum. To see the version history and download the bleeding-edge version (at your own risk!), see the project website at code.google.com below.  See the other links for PDF files describing numerical and exact analytical methods for determining instantaneous frequency, some examples of signals processed with this package, and some examples of the ensemble empirical mode decomposition method.
Simulation from distributions supported by nested
hyperplanes, using the algorithm described in Petris &
Tardella, "A geometric approach to transdimensional Markov
chain Monte Carlo", Canadian Journal of Statistics, v.31, n.4,
(2003).  Also random direction multivariate Adaptive Rejection
Metropolis Sampling.
HIBAG is a state of the art software package for imputing HLA
types using SNP data, and it uses the R statistical programming language.
HIBAG can be used by researchers with published parameter estimates instead
of requiring access to large training sample datasets. It combines the
concepts of attribute bagging, an ensemble classifier method, with
haplotype inference for SNPs and HLA types. Attribute bagging is a
technique which improves the accuracy and stability of classifier
ensembles using bootstrap aggregating and random variable selection.
HiClimR package modifies and improves hierarchical clustering in R
('hclust' function in 'stats' library), for climate regionalization. It adds a new
clustering method (called, regional linkage) to the set of available methods together
with several features including regridding, coarsening spatial resolution, geographic
masking, data thresholds, detrending and standardization preprocessing, faster correlation
function, hybrid hierarchical clustering, and cluster validation.
Badr et. al (2014) describes the regionalization algorithms and data processing tools
included in the package and presents a demonstration application in which the package
is used to regionalize Africa on the basis of interannual precipitation variability.
It is applicable to any correlation-based clustering.
This package allows you to detect domains in HiC data by rephrasing this problem as a two-dimensional segmentation issue.
Compute the maximal F-ratio and Bonferroni-adjusted pvalue for a test of no interaction between group and Factor 2 among all possible assignments of levels of Factor 1 into two groups.  Generate ANOVA table and interaction plot corresponding to this maximal configuration.
Contains functions for the analysis of Discrete Time Hidden Markov Models, Markov Modulated GLMs and the Markov Modulated Poisson Process. It includes functions for simulation, parameter estimation, and the Viterbi algorithm. See the topic "HiddenMarkov" for an introduction to the package, and "Change Log" for a list of recent changes. The algorithms are based of those of Walter Zucchini.
Functions for performing hierarchical analysis of distance
sampling data, with ability to use an areal spatial ICAR model
on top of user supplied covariates to get at variation in
abundance intensity.  The detection model can be specified as a
function of observer and individual covariates, where a
parametric model is supposed for the population level
distribution of covariate values.  The model uses data
augmentation and a reversible jump MCMC algorithm to sample
animals that were never observed.  Also included is the ability
to include point independence (increasing correlation multiple
observer's observations as a function of distance, with
independence assumed for distance=0 or first distance bin).
New in version 2.0 is the ability to model species
misclassification rates using a multinomial logit formulation
on data from double observers.
This R package allows the estimation of hierarchical F-statistics from haploid or diploid genetic data with any numbers of levels in the hierarchy, following the algorithm of Yang (Evolution, 1998, 52(4):950-956). Functions are also given to test via randomisations the significance of each F and variance components, using the likelihood-ratio statistics G -see Goudet etal (Genetics, 1996, 144(4): 1933-1940)
Fits sparse interaction models for continuous and binary responses subject to the strong (or weak) hierarchy restriction that an interaction between two variables only be included if both (or at least one of) the variables is included as a main effect.  For more details, see Bien, J., Taylor, J., Tibshirani, R., (2013) "A Lasso for Hierarchical Interactions." Annals of Statistics. 41(3). 1111-1141.
Variance partition of a multivariate data set
Uses likelihood to estimate ancestry and heterozygosity.
Evaluates simple hybrid classifications (parentals, F1, F2,
backcrosses). Estimates genomic clines.
The highfrequency package contains an extensive toolkit
for the use of highfrequency financial data in R. It contains
functionality to manage, clean and match highfrequency trades
and quotes data. Furthermore, it enables users to: calculate
easily various liquidity measures, estimate and forecast
volatility, and investigate microstructure noise and intraday
periodicity.
Syntax highlighter for R code based
on the results of the R parser. Rendering in HTML and latex
markup. Custom Sweave driver performing syntax highlighting
of R code chunks
This package provides syntax highlighting for R source code.
Currently it supports LaTeX and HTML output. Source code of other languages
can be supported via Andre Simon's Highlight package.
Functions for determining and evaluating high-risk zones and
simulating and thinning point process data, as described in 'Determining
high risk zones using point process methodology - Realization by building
an R package' (Seibold, 2012) and 'Determining high-risk zones for
unexploded World War II bombs by using point process methodology' (Mahling
et al., 2013).
Generate occupancy patterns based on arrival and departure timestamps
HiLMM provides estimation of heritability with confidence intervals in linear mixed models.
Hypergeometric Intersection distributions are a broad group of distributions that describe the probability of picking intersections when drawing independently from two (or more) urns containing variable numbers of balls belonging to the same n categories.
Provides multi-core or GPU support (or both if the system
has GPU and multi-core CPU) for the recommended R package,
Matrix.
Based on hierarchical modeling, this package provides a
few practical functions to find and present the optimal designs
for a pooled NGS design.
This package implements methods for hierarchical semiparametric regression models for test statistics. Specifically, test statistics given the null/alternative hypotheses are modeled parametrically, whereas the unobservable status of null/alternative hypotheses are modeled using nonparametric additive logistic regression over covariates.
The HistData package provides a collection of small data sets
that are interesting and important in the history of statistics and data visualization.
The goal of the package is to make these available, both for instructional use
and for historical research.  Some of these present interesting challenges for graphics
or analysis in R.
Automatic construction of regular and irregular histograms
as described in Rozenholc/Mildenberger/Gather (2009).
This package provides a number of utility functions useful for manipulating large histograms.  This includes methods to trim, subset, merge buckets, merge histograms, convert to CDF, and calculate information loss due to binning.  It also provides a protocol buffer representations of the default R histogram class to allow histograms over large data sets to be computed and manipulated in a MapReduce environment.
This package provides an implementation of the "Hit and Run" method for sampling uniformly from convex shapes. It further provides specialized implementation for sampling normalized weights with arbitrary linear constraints.
Hadoop InteractiVE, is an R extension facilitating distributed
computing via the MapReduce paradigm. It provides an easy to use
interface to Hadoop, the Hadoop Distributed File System (HDFS),
and Hadoop Streaming.
HiveR is an R package for creating and plotting 2D and 3D hive plots. Hive plots are a unique method of displaying networks of many types in which node properties are mapped to axes using meaningful properties rather than being arbitrarily positioned.  The hive plot concept was invented by Martin Krzywinski at the Genome Science Center (www.hiveplot.net/).  Keywords: networks, food webs, linnet, systems biology, bioinformatics.
The functions in this package produce a complete set of mortality rates as a function of a combination of HIV prevalence and either life expectancy at birth (e0), child mortality (5q0), or child mortality with adult mortality (45q15)
Conversion and Reverse Conversion between WGS84UTM, WGS84GEO, HK80UTM, HK80GEO and HK1980GRID Coordinate Systems. Algorithms follow Survey and Mapping Office Lands Department, Hong Kong Government (1995)
A suite of diagnostic tools for hierarchical
(multilevel) linear models. The package offers
not only leverage and traditional deletion diagnostics (Cook's
distance, covratio, covtrace, and MDFFITS) but also provides
convenience functions and graphics for residual analysis. The package
assumes that models were fit using the lme4 package.
Package implementing the methods described in
Rousseeuw and Christman (2003) to cope with separation issues
and outliers in logistic regression
Hierarchical latent space network model for ensemble of networks
Scalar performance metrics, including the H-measure, based
on classification scores for several classifiers applied to the
same dataset.
The Hmisc package contains many functions useful for data
analysis, high-level graphics, utility operations, functions for
computing sample size and power, importing datasets,
imputing missing values, advanced table making, variable clustering,
character string manipulation, conversion of R objects to LaTeX code,
and recoding variables.
Easy to use library to setup, apply and make inference
with discrete time and discrete space Hidden Markov Models
The package includes the functions designed to analyse continuous observations processes with the Hidden Markov Model approach. They include Baum-Welch and Viterbi algorithms and additional visualisation functions. The observations are assumed to have Gaussian distribution and to be weakly stationary processes. The package was created for analyses of financial time series, but can also be applied to any continuous observations processes.
Fits hidden Markov models with discrete non-parametric
observation distributions to data sets.  Simulates data
from such models.  Finds most probable underlying hidden
states, the most probable sequences of such states, and the
log likelihood of a collection of observations given the
parameters of the model.
Functions for specifying and fitting marginal models for contingency tables proposed
by Bergsma and Rudas (2002) here called hierarchical multinomial marginal models (hmmm) and their extensions presented by Bartolucci et al.
(2007); multinomial Poisson homogeneous (mph) models and homogeneous linear predictor (hlp) models for contingency
tables proposed by Lang (2004) and (2005); hidden Markov models where the distribution of the observed variables
is described by a marginal model.
Inequality constraints on the parameters are allowed and can be tested.
Analysing time-series accelerometer data to quantify length and intensity of physical activity.
This package provides several functions to perform formal
hypothesis testing, and power and sample size calculations for
human microbiome experiments. Dirichlet-Multinomial
distribution is proposed to model species abundance and ranked
abundance data.
In this package, we introduce Object Oriented Data Analysis (OODA) methods to analyze Human Microbiome taxonomic trees directly. We provide tools to model, compare, and visualize populations of taxonomic tree objects.
Statistical analysis of static chamber concentration data
for trace gas flux estimation
Information retrieval from NCBI databases, with main focus on Blast.
Simulates hands and tournaments of Texas Holdem.
Contains trading holiday and halfday data that is automatically registered when loaded.
This package performs a homogeneity analysis (multiple correspondence analysis) and various extensions. Rank restrictions on the category quantifications can be imposed (nonlinear PCA). The categories are transformed by means of optimal scaling with options for nominal, ordinal, and numerical scale levels (for rank-1 restrictions). Variables can be grouped into sets, in order to emulate regression analysis and canonical correlation analysis.
A collection of functions useful for the analysis of
building physics experiments.
A collection of homogeneity tests described in: Viglione
A., Laio F., Claps P. (2007) ``A comparison of homogeneity
tests for regional frequency analysis'', Water Resources
Research, 43, W03428, doi:10.1029/2006WR005095.  More on
Regional Frequency Analysis can be found in package nsRFA.
Performs multiple hot-deck imputation of categorical and continuous variables in a data frame.
This package provides hot deck imputation methods to resolve missing data.
A set of R functions and data sets which implements Hotelling's T^2 test, and some variants of it. Functions are also included for Aitchison's additive log ratio and centred log ratio transformations
Calculates a hot spot cutoff and associated analyses for
statistical populations
When testing multiple hypotheses simultaneously, this
package provides functionality to calculate a lower bound for
the number of correct rejections (as a function of the number
of rejected hypotheses), which holds simultaneously -with high
probability- for all possible number of rejections.  As a
special case, a lower bound for the total number of false null
hypotheses can be inferred.  Dependent test statistics can be
handled for multiple tests of associations. For independent
test statistics, it is sufficient to provide a list of
p-values.
This package provides all the functions necessary to
estimate the 8 parameters of the Heligman Pollard mortality
model using a Bayesian Melding procedure with IMIS as well as
to convert those parameters into age-specifc probabilities of
death and a corresponding life table
This package contains several models describing the behavior
of workload and queue on a High Performance Cluster and computing GRID
under FIFO service discipline basing on modified Kiefer-Wolfowitz
recursion. Also sample data for inter-arrival times, service times,
number of cores per task and waiting times of HPC of Karelian
Research Centre are included, measurements took place from 06/03/2009 to 02/30/2011.
A set of annotation maps describing the entire Human
Phenotype Ontology assembled using data from HPO(Data Version:
2013-05-05)
Functions for visualising a set of HPO-encoded phenotypes in a variety of ways
This package implements multiple similarity measures for HPO terms, genes and diseases.
It is aiming at phenotype analysis for gene sets and disease sets.
Functions for HPO enrichment analysis is also provided.
The disease-term and gene-term associations contained in this version
are based on the official HPO data released in January 2014.
This package contains some miscellaneous convenience functions,
to create a matrix of dummy columns from a factor,
to determine whether x lies in range [a,b],
to add a rectangular bracket to an existing plot,
and to convert frequencies between Hz, semitones, mel and Bark.
This package also contains an example data set of a stratified sample
of 80 talkers of Dutch.
Print beautiful horizontal rules in your R scripts.
Functions, data sets, analyses and examples from the book
`A Handbook of Statistical Analyses Using R' (Brian S. Everitt and Torsten
Hothorn, Chapman & Hall/CRC, 2006). The first chapter
of the book, which is entitled `An Introduction to R',
is completely included in this package, for all other chapters,
a vignette containing all data analyses is available.
Functions, data sets, analyses and examples from the
second edition of the book
`A Handbook of Statistical Analyses Using R' (Brian S. Everitt and Torsten
Hothorn, Chapman & Hall/CRC, 2008). The first chapter
of the book, which is entitled `An Introduction to R',
is completely included in this package, for all other chapters,
a vignette containing all data analyses is available. In addition,
the package contains Sweave code for producing slides for selected
chapters (see HSAUR2/inst/slides).
Functions, data sets, analyses and examples from the
third edition of the book
`A Handbook of Statistical Analyses Using R' (Torsten Hothorn and Brian S.
Everitt, Chapman & Hall/CRC, 2014). The first chapter
of the book, which is entitled `An Introduction to R',
is completely included in this package, for all other chapters,
a vignette containing all data analyses is available. In addition,
Sweave source code for slides of selected chapters is included in
this package (see HSAUR3/inst/slides).
hSDM is an R package for estimating parameters of hierarchical Bayesian species distribution models. Such models allow interpreting the observations (occurrence and abundance of a species) as a result of several hierarchical processes including ecological processes (habitat suitability, spatial dependence and anthropogenic disturbance) and observation processes (species detectability). Hierarchical species distribution models are essential for accurately characterizing the environmental response of species, predicting their probability of occurrence, and assessing uncertainty in the model results.
Canonical correlation analysis that extracts nonlinear
correlation through the use of Hilbert Schmidt Independence
Criterion and Centered Kernel Target Alignment.
A package for computation of hidden semi markov models
Identification of recombination events, haplotype reconstruction, sire imputation and pedigree reconstruction using half-sib family SNP data
This package implements a model for joint meta-analysis of sensitivity and specificity of the diagnostic test under evaluation, while taking into account the possibly imperfect sensitivity and specificity of the reference test. This hierarchical model accounts for both within and between study variability. Estimation is carried out using a Bayesian approach, implemented via a Gibbs sampler. The model can be applied in situations where more than one reference test is used in the selected studies.
HSSVD is a recently developed data mining tool for discovering subgroups of patients and genes which simultaneously display unusual levels of variability compared to other genes and patients. Previous biclustering methods were restricted to mean level detection, while the new method can detect both mean and variance biclusters.
Facilitates automated HTML report creation, in particular
framed HTML pages and dynamically sortable tables.
Methods for analysing and forecasting hierarchical and grouped time series.
This package implements a Poisson mixture model to cluster observations (e.g., genes) in high throughput sequencing data. Parameter estimation is performed using either the EM or CEM algorithm, and the slope heuristics are used for model selection (i.e., to choose the number of clusters).
This package implements a Poisson mixture model to identify differentially expressed genes from RNA-seq data.
HTTP Request protocols. Implements the GET, POST and multipart POST request.
httpuv provides low-level socket and protocol support for handling
HTTP and WebSocket requests directly from within R. It is primarily
intended as a building block for other packages, rather than making it
particularly easy to create complete web applications using httpuv alone.
httpuv is built on top of the libuv and http-parser C libraries, both of
which were developed by Joyent, Inc. (See LICENSE file for libuv and
http-parser license information.)
Provides useful tools for working with HTTP. The API is based
around http verbs (GET(), POST(), etc) with pluggable components to control
the request (authenticate(), add_headers() and so on).
The package "huge" provides a general framework for
high-dimensional undirected graph estimation. It integrates
data preprocessing, neighborhood screening, graph estimation,
and model selection techniques into a pipeline. In
preprocessing stage, the nonparanormal(npn) transformation is
applied to help relax the normality assumption. In the graph
estimation stage, the graph structure is estimated by
Meinshausen-Buhlmann graph estimation or the graphical lasso,
and both methods can be further accelerated by the lossy
screening rule preselecting the neighborhood of each variable
by correlation thresholding. We target on high-dimensional data
analysis usually d >> n, and the computation is
memory-optimized using the sparse matrix output. We also
provide a computationally efficient approach, correlation
thresholding graph estimation. Three
regularization/thresholding parameter selection methods are
included in this package: (1)stability approach for
regularization selection (2) rotation information criterion (3)
extended Bayesian information criterion which is only available
for the graphical lasso.
Tools for computing HUM (Hypervolume Under the Manifold) value to estimate features ability
to discriminate the class labels, visualizing the ROC curve for two or three class labels.
Format quantities of time or bytes into human-friendly strings.
Fits models for genotypic disequilibria, as described in
Huttley and Wilson (2000), Weir (1996) and Weir and Wilson (1986).
Contrast terms are available that account for first order interactions
between loci.
Also implements, for a single locus in a single population, a conditional
exact test for Hardy-Weinberg equilibrium
Estimation and testing of HWE using Bayesian methods.
Three models are currently considered: HWE, a model
parameterized in terms of the allele frequencies and a single
inbreeding coefficient f, and the saturated model. Testing is
based on Bayes factors.
This package implements the general (multiallelic)
Hardy-Weinberg equilibrium problem from an objective Bayesian
testing standpoint. This aim is achieved through the
identification of a class of priors specifically designed for
this testing problem. A class of intrinsic priors under the
full model is considered. This class is indexed by a tuning
quantity, the training sample size, as discussed in Consonni,
Moreno and Venturini (2010). These priors are objective,
satisfy Savage's continuity condition and have proved to behave
extremely well for many statistical testing problems.
HW.pval calculates plain and fully conditional
root-mean-square, chi-square, and log likelihood-ratio P-values
for the user-provided genotypic counts to be consistent with
the Hardy-Weinberg equilibrium model.  For further information
on the Hardy-Weinberg equilibrium model and the pseudocode,
refer to the paper "Testing Hardy-Weinberg equilibrium with a
simple root-mean-square statistic" by Rachel Ward.
Easy-to-use and versatile functions to output R objects in
HTML format
This package extends the package hwriter providing
facilities such as the inclusion of output from R, the
results of an R session, the display of mathematical
expressions using LaTeX notation and the incorporation of
SVG graphical objects.  It uses MathJax to display
mathematical expressions, in contrast to R2HTML which
uses ASCIIMathML.
Tests whether a set of genotype counts fits the HW expectations.
Exact tests performed by an efficient algorithm. Included test statistics
are likelihood ratio, probability, U-score and Pearsons X2.
This package contains functions to build and deploy an ensemble
consisting of six different sub-ensembles: Bagged Logistic Regressions,
Random Forest, Stochastic AdaBoost, Kernel Factory, Bagged Neural Networks,
Bagged Support Vector Machines. Functions to cross-validate the Hybrid
Ensemble and plot and summarize the results are also provided. There is
also a function to assess the importance of the predictors.
hybrid hierarchical clustering via mutual clusters
This package is an R implementation of the Hybrid Monte
Carlo and Multipoint Hybrid Monte Carlo sampling techniques
described in Liu (2001): "Monte Carlo Strategies in Computing".
Package providing tools for hydrological applications and models developed for regional analysis in Northwestern Italy.
Contains one function for drawing Piper diagrams (also
called Piper-Hill diagrams) of water analyses for major ions.
S3 functions implementing both statistical and graphical goodness-of-fit measures between observed and simulated values, mainly oriented to be used during the calibration, validation, and application of hydrological models. Missing values in observed and/or simulated values can be removed before computations. Comments / questions / collaboration of any kind are very welcomed.