'osm-descriptors.R' 'source.R' 'osmar-plotting.R'
'as-osmar-elements.R' 'as-osmar.R' 'as-osm.R' 'as-sp.R' 'get.R'
'osmar-subsetting.R' 'osmar-finding.R' 'osmar.R' 'source-api.R'
'source-file.R' 'source-osmosis.R' 'as-osmar-sp.R'
'as-igraph.R'
ouchtree.R ape2ouch.R glssoln.R rmvnorm.R brown.R hansen.R
methods.R paint.R plot.R
'auxClasses.R' 'generics.R' 'obkContacts.R' 'obkSequences.R'
'obkData.R' 'obkData_Accessors.R' 'subset.R' 'obkData_basics.R'
'uniqSequences.R' 'dna2uniqSequences.R' 'make.phylo.R'
'make.attributes.R' 'get.incidence.R' 'phylo2ggphy.R'
'plotggMST.R' 'plotggphy.R' 'plot.R' 'plotIndividualTimeline.R'
'simuEpi.R' 'utils.R' 'plotGeo.R' 'annotatedTreeReader.R'
'importFromJSON.R' 'zzz.R' 'OutbreakTools.R'
global.R ClassU.R ClassV.R ClassW.R ClassV-ClassW.R
classCreator.R codeVerif.R
'network.R' 'paleobioDB-package.R' 'pbdb_cache.R'
'pbdb_geographic_functions.R' 'pbdb_querys.R'
'rest_api_tools.R' 'pbdb_rest_api_setup.R'
'pbdb_taxonomic_functions.R' 'pbdb_temporal_functions.R'
'helpers.R' 'labels.R' 'summarize.R' 'plot.R' 'prettify.R'
'toLatex.R'
p-cmaes.R p-classes.R p-constraints.R p-timeseries.R p-fun.R
p-Utility.R p-MILP.R p-NLP.R p-GNLP.R p-LP.R p-QP.R p-SOCP.R
p-main.R p-methods.R Socp.R zzz.R
'CrossValidation.R' 'GeneRank.R' 'GraphSVM.R'
'networkBasedSVM.R' 'PathwayMethods.R'
'RecursiveFeatureElimination.R' 'RRFE.R' 'SpanBound.R' 'SVMs.R'
'arrow.R' 'draw.R' 'latent.R' 'manifest.R' 'wall.R'
'fix.pathmox.R' 'fix.techmox.R' 'pathmox-internal.R'
'pathmox.R' 'plot.bootnodes.R' 'plot.treemox.R'
'plot.treemox.pls.R' 'plspmox.R' 'print.bootnodes.R'
'print.treemox.R' 'print.treemox.pls.R' 'techmox.R'
'treemox.boot.R' 'treemox.pls.R' 'get_fix_xexeloa.R'
'get_nominal_split.R' 'get_ordinal_split.R' 'get_pls_basic.R'
'get_xexeloa.R' 'test_sanchez_aluja.R' 'check_pathmox_args.r'
'cornish_fisher.r' 'edgeworth.r' 'gram_charlier.r' 'moments.r'
'PDQutils.r'
'AI_EM_mixed.R' 'ClusProc.R' 'cnvlmm_plot.R' 'silWidth.R'
'docs.R' 'score_test.R' 'AssoTestProcCS_mix.R'
onattach.R penfit.R breslow.R penalized.R core.R checkinput.R
cvl.R contrasts.R Brent.R plotpath.R cox.R logit.R linear.R
poisson.R
'phmm-package.R' 'formula.R' 'linear.predictors.R' 'phmm.R'
'pseudoPoisPHMM.R' 'traceHat.R'
'oldclasses-class.R' 'internal-constructors.R'
'phylo4-methods.R' 'RcppExports.R' 'checkdata.R'
'phylo4-class.R' 'getNode-methods.R' 'formatData.R'
'phylo4d-class.R' 'phylo4d-methods.R' 'MRCA-methods.R'
'addData-methods.R' 'ancestors.R' 'phylo4-accessors.R'
'root-methods.R' 'nodeId-methods.R' 'edgeLength-methods.R'
'setAs-methods.R' 'extractTree.R' 'labels-methods.R'
'multiphylo4-class.R' 'pdata.R' 'phylo4d-accessors.R'
'phylobase-package.R' 'phylobase.options.R' 'phylomats-class.R'
'print-methods.R' 'readNCL.R' 'reorder-methods.R'
'shortestPath-methods.R' 'subset-methods.R' 'summary-methods.R'
'tbind.R' 'tdata-methods.R' 'treePlot.R' 'treestruc.R' 'zzz.R'
'load.tree.R' 'as_tree.R' 'get.descendants.R'
'nodes.branches.R' 'phylocanvas.R'
'utility.R' 'tokenize.R' 'newick.R' 'nexus.R' 'format.R'
'mbattrs.R'
'capital.R' 'compat-purrr.R' 'dim.R' 'extent.R' 'multi.R'
'ornament.R' 'pillar-package.R' 'pillar.R' 'rowid-capital.R'
'rowid-data.R' 'rowid-title.R' 'rowid-type.R' 'scientific.R'
'shaft.R' 'shaft-simple.R' 'sigfig.R' 'spark-bar.R'
'spark-line.R' 'strrep.R' 'styles.R' 'testthat.R' 'tick.R'
'title.R' 'type-sum.R' 'type.R' 'utils.R' 'width.R' 'zzz.R'
'CreateScoreFun.R' 'DysData.R' 'EngelData.R' 'Estimators.R'
'pim.poset-class.R' 'pim.environment-class.R' 'pim-package.R'
'pim.formula-class.R' 'pim-class.R' 'pim.summary-class.R'
'Extract.pim.summary.R' 'FEVData.R' 'Getters.R' 'Getters_pim.R'
'Getters_pim.formula.R' 'InternalFunctions.R'
'InternalObjects.R' 'LR.R' 'MHData.R' 'P.R' 'SUData.R'
'add.poset.R' 'as.data.frame.pim.environment.R'
'as.matrix.pim.summary.R' 'coef.R' 'confint.pim.R'
'create.poset.R' 'formula.R' 'has.intercept.R' 'is.complete.R'
'make.posfun.R' 'model.matrix.pim.R' 'new.pim.R'
'new.pim.env.R' 'new.pim.formula.R' 'new.pim.poset.R' 'nobs.R'
'penv.R' 'pim.R' 'pim.fit.R' 'pimdata.R' 'poset.R' 'print.R'
'response.R' 'sandwich.estimator.R' 'summary.R' 'vcov.R'
'vcov.estimators.R' 'vcov.internal.R' 'zzz.R'
'utils.R' 'logging.R' 'unitTests.R' 'data.R' 'namespace.R'
'devutils.R' 'package.R' 'options.R' 'is.R' 'registry.R'
'bibtex.R' 'packages.R' 'vignette.R' 'files.R'
'package-extra.R' 'colors.R' 'graphics.R' 'rd.R' 'project.R'
'CLI.R' 'knitr.R' 'repositories.R'
global.r psnCdd.r psnBootstrap.r functionOK.r proto.r ggobi.r
function.r handler.r PKgraphlayout.r PKgraph.r
'nipals.R' 'plot.nipals.R' 'plot.plsca.R' 'plot.plsreg1.R'
'plot.plsreg2.R' 'plsca.R' 'plsreg1.R' 'plsreg2.R'
'print.nipals.R' 'print.plsca.R' 'print.plsreg1.R'
'print.plsreg2.R' 'interbat.R' 'plot.interbat.R'
'print.interbat.R' 'print.simpls.R' 'print.simplsca.R'
'simpls.R' 'simplsca.R' 'plot.simpls.R' 'plot.simplsca.R'
'plspm.r' 'auxiliar.r' 'check_arguments.r'
'check_specifications.r' 'get_alpha.r' 'get_ave.r'
'get_boots.r' 'get_dummies.r' 'get_effects.r' 'get_generals.r'
'get_gof.r' 'get_inner_summary.r' 'get_manifests.r'
'get_metric.r' 'get_nom_scale.r' 'get_num_scale.r'
'get_ord_scale.r' 'get_path_scheme.r' 'get_paths.r'
'get_plsr1.r' 'get_PLSRdoubleQ.r' 'get_rank.r' 'get_rho.r'
'get_scores.r' 'get_treated_data.r' 'get_unidim.r'
'get_weights.r' 'get_weights_nonmetric.r' 'innerplot.r'
'outerplot.r' 'plot.plspm.r' 'rescale.r' 'summary_plspm.r'
'test_manifest_scaling.r' 'test_null_weights.r'
'unidimensionality.r' 'test_factors.r' 'russett-data.r'
'plspm.fit.r' 'plspm.groups.r' 'test_dataset.r' 'get_GQI.r'
'get_locals_test.r' 'get_scaled_data.r' 'it.reb.r'
'local.models.r' 'print.rebus.r' 'rebus.pls.r' 'rebus.test.r'
'res.clus.r' 'get_PLSR.r' 'get_PLSR_NA.r' 'quantiplot.r'
'plspm-package.r'
generic.R UtilPolychaos.R analyticsPolyLeg.R PCEdesign-class.R
PCEpoly-class.R PLSPCE-class.R regpls2.R polyLeg.R forward.R
calcPLSPCE.R
'content-types.R' 'cookie-parser.R' 'parse-globals.R'
'images.R' 'parse-block.R' 'globals.R' 'serializer-json.R'
'shared-secret-filter.R' 'post-body.R' 'query-string.R'
'plumber.R' 'default-handlers.R' 'digital-ocean.R'
'find-port.R' 'includes.R' 'paths.R' 'plumber-static.R'
'plumber-step.R' 'response.R' 'serializer-content-type.R'
'serializer-html.R' 'serializer-htmlwidget.R'
'serializer-xml.R' 'serializer.R' 'session-cookie.R'
'swagger.R'
'accessors.r' 'bindings.r' 'coercion.r' 'constructor.r'
'dimensions.r' 'events.r' 'linking.r' 'mutalist.R' 'names.r'
'print.r' 'proxy-filter.r' 'selection.r' 'utils.r' 's4.r'
'import.r' 'globals.r'
'create.R' 'holdout.R' 'kernel.R' 'learn.R' 'pnn-package.r'
'data-norms.R' 'guess-category.R' 'guess-probabilities.R'
'perf.r' 'guess.r' 'smooth.R' 'what-else.R'
'CQI.R' 'CQI.cqpserver.R' 'Corpus_class.R' 'Labels.R'
'Partition.R' 'RegistryFile.R' 'TermDocumentMatrix_methods.R'
'Textstat.R' 'TokenStream_class.R' 'adjustEncoding_method.R'
'generics.R' 'textstat_class.R' 'polmineR_package.R'
'partition_class.R' 'aggregate_method.R' 'bundle_class.R'
'features_class.R' 'context_class.R' 'partitionBundle_class.R'
'as.DocumentTermMatrix_method.R' 'as.VCorpus_method.R'
'as.markdown_method.R' 'contextBundle_class.R'
'cooccurrences_class.R' 'as.sparseMatrix_method.R'
'as.speeches_method.R' 'blapply_method.R' 'kwic_method.R'
'kwic_class.R' 'browse_method.R' 'chisquare_method.R'
'context_method.R' 'cooccurrences_method.R' 'corpus_method.R'
'dispersion_class.R' 'count_method.R' 'tempcorpus.R'
'cpos_method.R' 'decode_method.R' 'hits_class.R'
'dispersion_method.R' 'divide_method.R' 'dotplot_method.R'
'regions_class.R' 'encode_method.R' 'encoding_method.R'
'enrich_method.R' 'ngrams_method.R' 'features_method.R'
'freq_method.R' 'getEncoding_method.R' 'getTerms_method.R'
'getTokenStream_method.R' 'highlight_method.R' 'html_method.R'
'html_methods.R' 'install.corpus_function.R' 'label_method.R'
'll_method.R' 'mail_method.R' 'matches_method.R'
'means_method.R' 'noise_method.R' 'pAttributes_method.R'
'partitionBundle_method.R' 'sAttributes2cpos_method.R'
'partition_method.R' 'plot_method.R' 'pmi_method.R'
'read_method.R' 'registry.R' 'reindex_method.R' 'rustyard.R'
'sAttributes_method.R' 'scatterplot_method.R' 'size_method.R'
'tTest.R' 'templates_method.R' 'terms_method.R' 'trim_method.R'
'use_function.R' 'utils.R' 'view_method.R' 'weigh_method.R'
'zzz.R'
generic.R UtilPolychaos.R analyticsPolyLeg.R PCEdesign-class.R
PCEfit-class.R PCEpoly-class.R PCESI.R polyLeg.R
aaa.R authors.R bake.R generics.R eulermultinom.R csnippet.R
safecall.R pomp_fun.R plugins.R builder.R parmat.R logmeanexp.R
slice_design.R profile_design.R sobol.R bsplines.R sannbox.R
pomp_class.R load.R pomp.R pomp_methods.R rmeasure_pomp.R
rprocess_pomp.R initstate_pomp.R dmeasure_pomp.R
dprocess_pomp.R skeleton_pomp.R dprior_pomp.R rprior_pomp.R
simulate_pomp.R trajectory_pomp.R plot_pomp.R pfilter.R
pfilter_methods.R minim.R traj_match.R bsmc.R bsmc2.R kalman.R
kalman_methods.R mif.R mif_methods.R mif2.R mif2_methods.R
proposals.R pmcmc.R pmcmc_methods.R nlf_funcs.R nlf_guts.R
nlf_objfun.R nlf.R probe.R probe_match.R basic_probes.R spect.R
spect_match.R abc.R abc_methods.R covmat.R example.R
'utils.R' 'scheduler.R' 'pool.R' 'pool-methods.R' 'object.R'
'DBI.R' 'DBI-connection-interpolate.R' 'DBI-connection-quote.R'
'DBI-connection-sql.R' 'DBI-connection-transaction.R'
'DBI-connection.R' 'DBI-driver.R' 'DBI-object-deprecated.R'
'DBI-object.R' 'DBI-pool.R' 'dbplyr.R'
'aaa_all_classes.R' 'AllGenerics.R' 'bootstrap.R'
'bootstrap_p.R' 'checks.R' 'compare_distributions.R'
'ctn_helper_functions.R' 'data_help_files.R' 'def_conexp.R'
'def_conlorm.R' 'def_conpl.R' 'def_conweibull.R' 'def_disexp.R'
'def_dislnorm.R' 'def_displ.R' 'def_dispois.R' 'def_template.R'
'dist_data_cdf_methods.R' 'estimate_pars.R' 'estimate_xmin.R'
'get_n.R' 'get_ntail.R' 'lines_methods.R' 'plcon.R' 'pldis.R'
'plot_methods.R' 'points_methods.R' 'poweRlaw-package.R'
'show_methods.R' 'timer.R'
'adjective.R' 'adverb.R' 'exclamation.R' 'verb.R' 'rpackage.R'
'package.R'
'app.r' 'as.precintcon.annual.r' 'as.annual.r'
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'precintcon.deciles.analysis.r' 'as.deciles.r'
'as.precintcon.fd.r' 'precintcon.fd.r' 'as.fd.r'
'as.precintcon.monthly.r' 'as.monthly.r'
'as.precintcon.seasonal.r' 'as.seasonal.r'
'precintcon.ci.per.year.analysis.r' 'ci.per.year.r'
'precintcon.ci.analysis.r' 'ci.r' 'cv.r' 'ff.index.r'
'precintcon.limits.analysis.r' 'limits.r' 'pcd.r'
'precintcon.pci.analysis.r' 'pci.r' 'pci.seasonal.r'
'pci.supraseasonal.r' 'pcp.r' 'precintcon.pn.analysis.r' 'pn.r'
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'precintcon.plot.deciles.r' 'pplot.deciles.r'
'precintcon.plot.histogram.r' 'pplot.histogram.r'
'pplot.lorenz.r' 'pplot.pcd.r' 'precintcon.plot.pci.r'
'pplot.pci.r' 'pplot.pcp.r' 'precintcon.plot.pn.r' 'pplot.pn.r'
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'precintcon.min.r' 'precintcon.monthly.aggregation.r'
'precintcon.plot.lorenz.ungrouped.r' 'precintcon.pn.r'
'precintcon.r' 'precintcon.r.squared.r'
'precintcon.rai.analysis.r' 'precintcon.read.data.r'
'precintcon.spi.analysis.r'
'precintcon.spi.per.year.analysis.r'
'precintcon.stat.analysis.r' 'rai.r' 'read.data.r'
'spi.per.year.r' 'spi.r' 'stat.r' 'tii.r' 'trend.test.r'
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'03PreprocessorClass.R' '04GridClass.R'
'05PredictionControlClass.R' '06PreProCombClass.R'
'07AnalysisComponent.R' '08DefaultPreprocessorsAndPhases.R'
'00Utils.R' '01BaseClass.R' '02DefaultFeatures.R'
'03AnalysisClass.R' '04ControlClass.R' '05ReportingClass.R'
'06RunClass.R' 'DefaultControl.R'
'GKGamma.R' 'PResiduals-package.R' 'pgumbel.R' 'diagn.R'
'newPolr.R' 'cobot.R' 'cocobot.R' 'condis.R'
'conditional.Spearman.R' 'corTS.R' 'corr.R' 'countbot.R'
'getCI.R' 'kernel.function.R' 'lm.scores.R' 'megabot.R'
'nb.scores.R' 'orm.scores.R' 'partial.Spearman.R'
'plot.conditional.Spearman.R' 'poisson.scores.R' 'presid.R'
'print.cobot.R' 'print.cocobot.R'
'print.conditional.Spearman.R' 'print.partial.Spearman.R'
'prevR-package.r' 'setClass.prevR.r' 'is.prevR.r' 'as.prevR.r'
'onAttach.r' 'isInputOk.prevR.r' 'changeproj.prevR.r'
'colors.prevR.r' 'as.data.frame.prevR.r' 'plot.prevR.r'
'show.prevR.r' 'print.prevR.r' 'summary.prevR.r'
'rings.prevR.r' 'as.SpatialGrid.prevR.r'
'create.boundary.prevR.r' 'export.prevR.r' 'import.dhs.prevR.r'
'point.in.SpatialPolygons.prevR.r'
'NA.outside.SpatialPolygons.prevR.r' 'xyz2dataframe.prevR.r'
'init.exp.model.variogram.r' 'eyefit.prevR.r' 'krige.prevR.r'
'kde.prevR.r' 'Noptim.r' 'theme_prevR.r' 'direct.label_prevR.r'
'quick.prevR.r'
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'ArrayParameter-proto.R' 'Parameters-proto.R'
'ScalarParameter-proto.R' 'parameters.R' 'waiver.R'
'ConservationModifier-proto.R' 'Penalty-proto.R'
'Constraint-proto.R' 'Collection-proto.R'
'ConservationProblem-proto.R' 'Decision-proto.R' 'Id.R'
'Objective-proto.R' 'OptimizationProblem-proto.R'
'OptimizationProblem-methods.R' 'Portfolio-proto.R'
'RcppExports.R' 'Solver-proto.R' 'Target-proto.R'
'add_absolute_targets.R' 'add_binary_decisions.R'
'add_boundary_penalties.R' 'add_connected_constraints.R'
'add_connectivity_penalties.R' 'add_corridor_constraints.R'
'add_cuts_portfolio.R' 'add_default_decisions.R'
'add_default_objective.R' 'add_default_portfolio.R'
'add_default_solver.R' 'add_default_targets.R'
'add_feature_weights.R' 'add_gurobi_solver.R'
'intersecting_units.R' 'add_locked_in_constraints.R'
'add_locked_out_constraints.R' 'add_loglinear_targets.R'
'add_lpsymphony_solver.R' 'add_max_cover_objective.R'
'add_max_features_objective.R' 'add_max_phylo_objective.R'
'add_max_utility_objective.R' 'add_min_set_objective.R'
'add_neighbor_contraints.R' 'add_proportion_decisions.R'
'add_relative_targets.R' 'add_rsymphony_solver.R'
'add_semicontinuous_decisions.R' 'add_shuffle_portfolio.R'
'boundary_matrix.R' 'branch_matrix.R' 'compile.R'
'connected_matrix.R' 'connectivity_matrix.R' 'constraints.R'
'data.R' 'decisions.R' 'parallel.R' 'fast_extract.R'
'magrittr-operators.R' 'marxan_problem.R' 'misc.R'
'objectives.R' 'package.R' 'penalties.R' 'portfolios.R'
'problem.R' 'rij_matrix.R' 'run_calculations.R' 'simulate.R'
'solve.R' 'solvers.R' 'targets.R' 'zzz.R'
'api.R' 'compat-purrr.R' 'pprof-from-ds.R' 'rprof-read.R'
'pprof-read.R' 'pprof-to-ds.R' 'rprof-write.R' 'pprof-write.R'
'profile-package.R' 'proto.R' 'rprof-from-ds.R' 'rprof-to-ds.R'
'utils.R'
'misc.R' 'browsegWidgetsFiles.R' 'browseQtFiles.R'
'browseRGtk2Files.R' 'browseTclTkFiles.R' 'showGtkWidgetInfo.R'
'add.config.R' 'xport.reader.R' 'xlsx.reader.R' 'xls.reader.R'
'wsv.reader.R' 'url.reader.R' 'tsv.reader.R' 'systat.reader.R'
'stata.reader.R' 'require.package.R' 'sql.reader.R'
'spss.reader.R' 'rdata.reader.R' 'r.reader.R' 'ppm.reader.R'
'octave.reader.R' 'mtp.reader.R' 'mp3.reader.R' 'file.reader.R'
'epiinfo.reader.R' 'dbf.reader.R' 'db.reader.R' 'csv2.reader.R'
'csv.reader.R' 'arff.reader.R' 'preinstalled.readers.R'
'add.extension.R' 'cache.R' 'cache.name.R' 'cache.project.R'
'clean.variable.name.R' 'clear.R' 'clear.cache.R'
'translate.dcf.R' 'config.R' 'create.project.R'
'create.template.R' 'get.project.R' 'help.R' 'list.data.R'
'load.project.R' 'migrate.project.R' 'migrate.template.R'
'project.config.R' 'rds.reader.R' 'reload.project.R'
'run.project.R' 'show.project.R' 'stopifnotproject.R'
'stub.tests.R' 'test.project.R'
'data-00-selection-from-PD.r' 'data-01-consolidation.r'
'data-02-merging.r' 'data-03-analysis.r' 'zzz.R'
aa2mass.R deisotoper.R de_novo.R findNN.R fragmentIon.R
genMod.R iTRAQ2GroupAnalysis.R lcmsmap.R mascot.R mdp.R
parentIonMass.R peakplot.R pgImporter.R pgLFQ.R pp.R psm.R
PTM_MarkerFinder.R ssrc.R zzz.R RcppExports.R protViz-package.R
registry.R database.R dist.R similarities.R dissimilarities.R
util.R seal.R
'pssm.generate.data.R' 'llikef.R' 'rprog.R' 'rsurv.R'
'startv.R' 'generic_functions.R' 'pssm.R' 'pssm.survivalcurv.R'
'pssm.simulate.R' 'pssm.power.R'
'pssm.noninferiority.boundary.R'
'Abstracts-class.R' 'HGNC-class.R' 'Yearwise.R' 'Genewise.R'
'combineabs.R' 'gene_atomization.R' 'Find_conclusion.R'
'getabs.R' 'getabsT.R' 'gethgnc.R' 'ready.R' 'readabs.R'
'removeabs.R' 'searchabsL.R' 'searchabsT.R' 'sendabs.R'
'subabs.R' 'cleanabs.R' 'word_atomizations.R' 'SentenceToken.R'
'contextSearch.R' 'uniprotfun.R' 'local_uniprotfun.R'
'tdm_for_lsa.R' 'printabs.R' 'pubtator_function.R'
'cos_sim_calc.R' 'cos_sim_calc_boot.R' 'wordscluster.R'
'whichcluster.R' 'wordsclusterview.R' 'find_intro_conc_html.R'
'cluster_words.R' 'get_original_term.R' 'get_original_term2.R'
'input_for_find_intro_conc_html.R' 'xmlreadabs.R'
'xmlword_atomizations.R' 'xmlgene_atomizations.R'
'pubtator_result_list_to_table.R' 'genes_BWI.R' 'BWI.R'
'currentabs_fn.R' 'previousabs_fn.R' 'altnamesfun.R'
'subsetabs.R' 'prevsymbol_fn.R' 'alias_fn.R' 'get_NMids.R'
'get_PMCIDS.R' 'get_PMCtable.R' 'get_Sequences.R'
'Give_Sentences_PMC.R' 'head_abbrev.R' 'names_fn.R'
'official_fn.R' 'pmids_to_abstracts.R' 'get_gene_sentences.R'
'Give_Sentences.R' 'get_MedlinePlus.R'
'S.R' 'bind.R' 'bind_or.R' 'c.extracted.R' 'case.R' 'cheat.R'
'utils.R' 'rm_default.R' 'escape.R' 'explain.R' 'grab.R'
'group.R' 'group_or.R' 'is.regex.R' 'pastex.R'
'print.extracted.R' 'print.regexr.R' 'qdapRegex-package.R'
'rm_.R' 'rm_abbreviation.R' 'rm_between.R' 'rm_bracket.R'
'rm_caps.R' 'rm_caps_phrase.R' 'rm_citation.R'
'rm_citation_tex.R' 'rm_city_state.R' 'rm_city_state_zip.R'
'rm_date.R' 'rm_dollar.R' 'rm_email.R' 'rm_emoticon.R'
'rm_endmark.R' 'rm_hash.R' 'rm_nchar_words.R' 'rm_non_ascii.R'
'rm_non_words.R' 'rm_number.R' 'rm_percent.R' 'rm_phone.R'
'rm_postal_code.R' 'rm_repeated_characters.R'
'rm_repeated_phrases.R' 'rm_repeated_words.R' 'rm_tag.R'
'rm_time.R' 'rm_title_name.R' 'rm_url.R' 'rm_white.R'
'rm_zip.R' 'validate.R'
'aux-functions.R' 'generic-accessors.R' 'tsModel-class.R'
'Models.R' 'QPBoot-class.R' 'QPBoot-package.R' 'data.R'
'data.R' 'geom_qq_band.R' 'qqplotr.R' 'runShinyExample.R'
'stat_pp_band.R' 'stat_pp_line.R' 'stat_pp_point.R'
'stat_qq_line.R' 'stat_qq_band.R' 'stat_qq_point.R'
'RcppExports.R' 'View.R' 'bootstrap_dfm.R'
'casechange-functions.R' 'character-methods.R' 'convert.R'
'corpus-methods-base.R' 'corpus-methods-quanteda.R'
'corpus-methods-tm.R' 'corpus.R' 'corpus_reshape.R'
'corpus_sample.R' 'corpus_segment.R' 'corpus_subset.R'
'corpus_trim.R' 'corpuszip.R' 'data-deprecated.R'
'data-documentation.R' 'defunct-functions.R' 'dfm-classes.R'
'dfm-methods.R' 'dfm-print.R' 'dfm-subsetting.R' 'dfm.R'
'dfm_compress.R' 'dfm_group.R' 'dfm_lookup.R' 'dfm_replace.R'
'dfm_sample.R' 'dfm_select.R' 'dfm_sort.R' 'dfm_subset.R'
'dfm_trim.R' 'dfm_weight.R' 'dictionaries.R' 'docnames.R'
'docvars.R' 'fcm-classes.R' 'fcm-methods.R' 'fcm-subsetting.R'
'fcm.R' 'kwic.R' 'nfunctions.R' 'nscrabble.R' 'nsyllable.R'
'pattern2fixed.R' 'phrases.R' 'quanteda-documentation.R'
'quanteda_options.R' 'readtext-methods.R' 'settings.R'
'spacyr-methods.R' 'stopwords.R' 'textmodel-methods.R'
'textmodel_affinity.R' 'textmodel_ca.R' 'textmodel_lsa.R'
'textmodel_nb.R' 'textmodel_wordfish.R'
'textmodel_wordscores.R' 'textplot_influence.R'
'textplot_keyness.R' 'textplot_network.R' 'textplot_scale1d.R'
'textplot_wordcloud.R' 'textplot_xray.R' 'textstat-methods.R'
'textstat_collocations.R' 'textstat_dist.R'
'textstat_frequency.R' 'textstat_keyness.R' 'textstat_lexdiv.R'
'textstat_readability.R' 'textstat_simil.R' 'tokens.R'
'tokens_compound.R' 'tokens_group.R' 'tokens_lookup.R'
'tokens_ngrams.R' 'tokens_replace.R' 'tokens_segment.R'
'tokens_select.R' 'tokens_subset.R' 'utils.R' 'wordstem.R'
'zzz.R'
'Class-BootPos.R' 'generics.R' 'Class-LagOperator.R'
'Class-ClippedCov.R' 'Class-QSpecQuantity.R' 'aux-functions.R'
'Class-FreqRep.R' 'Class-ClippedFT.R' 'Class-QuantileSD.R'
'Class-IntegrQuantileSD.R' 'Class-Weight.R'
'Class-KernelWeight.R' 'Class-LagEstimator.R' 'kernels.R'
'Class-LagKernelWeight.R' 'Class-MovingBlocks.R'
'Class-QRegEstimator.R' 'Class-QuantilePG.R'
'Class-SmoothedPG.R' 'Class-SpecDistrWeight.R' 'RcppExports.R'
'data.R' 'deprecated.R' 'models.R' 'quantspec-package.R'
'environment.R' 'plotting-classes.R' 'plotting-colours.R'
'plotting-helpers.R' 'plotting-other.R' 'plotting.R'
'quickPlot-classes.R' 'quickPlot-package.R' 'zzz.R'
'GLSZM.R' 'MGLSZM.R' 'GLRLM.R' 'GLCM.R' 'CalculateFeatures.R'
'FirstOrder.R' 'GLCMFeatures.R' 'GLRLMFeatures.R'
'GLSZMFeatures.R' 'ImageQuantize.R' 'RcppExports.R' 'data.R'
'image.R' 'radiomics.R' 'startupMessage.R'
'base_data.R' 'base_startupMessage.R' 'chart_amBarplot.R'
'chart_amBoxplot.R' 'chart_amBullet.R' 'chart_amCandlestick.R'
'chart_amDataset.R' 'chart_amFloatingBar.R' 'chart_amFunnel.R'
'chart_amGauge.R' 'chart_amHist.R' 'chart_amMekko.R'
'chart_amOHLC.R' 'chart_amPie.R' 'chart_amPlot.R'
'chart_amRadar.R' 'chart_amTimeSeries.R' 'chart_amWaterfall.R'
'chart_amWind.R' 'utils_sharedGenerics.R' 'class_AmObject.R'
'class_AxisBase.R' 'class_ValueAxis.R'
'utils_basicClassUnions.R' 'class_TrendLine.R' 'class_Title.R'
'class_AmChart.R' 'class_StockPanel.R' 'class_StockEvent.R'
'class_PeriodSelector.R' 'class_Label.R' 'class_Guide.R'
'class_GaugeAxis.R' 'class_GaugeBand.R' 'class_GaugeArrow.R'
'class_DataSet.R' 'class_ChartScrollbar.R'
'class_ChartCursor.R' 'class_AmLegend.R' 'class_AmGraph.R'
'class_AmBalloon.R' 'classUnions.R'
'class_AmChart_constructors.R' 'class_CategoryAxis.R'
'class_AmChart_setters.R' 'class_AmChart_shinyUtils.R'
'class_AmStockChart.R' 'class_AmStockChart_setters.R'
'class_AxisBase_setters.R' 'class_ChartCursor_setters.R'
'class_ChartScrollbar_setters.R' 'class_DataSetSelector.R'
'class_DataSet_setters.R' 'class_GaugeArrow_setters.R'
'class_GaugeAxis_setters.R' 'class_Guide_setters.R'
'class_StockEvent_setters.R' 'class_StockPanel_setters.R'
'class_TrendLine_setters.R' 'shiny_examples.R'
'shiny_modules_export.R' 'shiny_modules_timeSeries.R'
'union_AmCharts.R' 'utils.R' 'utils_amOptions.R'
'utils_amTests.R'
'getDesign.R' 'randPar.R' 'abcdPar.R' 'randSeq.R' 'abcdSeq.R'
'getExpectation.R' 'normEndp.R' 'endpoint.R' 'util.R'
'getStat.R' 'power.R' 'imbalance.R' 'corGuess.R' 'doublyF.R'
'testDec.R' 'doublyT.R' 'chronBias.R' 'selBias.R' 'bias.R'
'issue.R' 'assess.R' 'bbcdPar.R' 'bbcdSeq.R' 'ebcPar.R'
'bsdPar.R' 'bsdSeq.R' 'chenPar.R' 'chenSeq.R'
'chronBiasStepT.R' 'combinedBias.R' 'compare.R' 'crPar.R'
'crSeq.R' 'createParam.R' 'derFunc.R' 'desFunc.R'
'getDesFunc.R' 'derringerLs.R' 'derringerRs.R' 'derringerTs.R'
'desScores.R' 'desirability.R' 'ebcSeq.R' 'evaluate.R'
'gbcdPar.R' 'gbcdSeq.R' 'hadaPar.R' 'hadaSeq.R' 'mpPar.R'
'mpSeq.R' 'pbrPar.R' 'pbrSeq.R' 'probUnDes.R' 'rtbdSeq.R'
'rpbrSeq.R' 'randomBlockSeq.R' 'randomizeROverview.R'
'randomizeRPackage.R' 'rarPar.R' 'rarSeq.R' 'rpbrPar.R'
'tbdPar.R' 'rtbdPar.R' 'saveAssess.R' 'saveRand.R' 'tbdSeq.R'
'udPar.R' 'udSeq.R'
'AAA.R' 'AllClasses.R' 'GUI.R' 'MapCanvas-methods.R'
'MapExport-methods.R' 'MapFetch-methods.R' 'MapPlot-methods.R'
'MapProcess-methods.R' 'MapRemove-methods.R'
'MapSave-methods.R' 'MapShow-methods.R' 'projectINI-methods.R'
'assemblageFetch-methods.R' 'bioSave-methods.R'
'metadataUpdate-methods.R' 'rangeFiles-methods.R'
'rangeMapper-package.R' 'rangeMapper-ramp.R' 'style.R'
'utils-spatial.R' 'utils.R'
'dependencies.R' 'RcppExports.R' 'raptr-internal.R'
'generics.R' 'DemandPoints.R' 'misc.R' 'PlanningUnitPoints.R'
'AttributeSpace.R' 'AttributeSpaces.R' 'GurobiOpts.R'
'ManualOpts.R' 'calcSpeciesAverageInPus.R' 'calcBoundaryData.R'
'RapData.R' 'RapReliableOpts.R' 'RapResults.R'
'RapUnreliableOpts.R' 'RapUnsolved.R' 'RapSolved.R'
'SpatialPolygons2PolySet.R' 'data.R' 'rap.R' 'raptr.R'
'rrap.proportion.held.R' 'sim.pus.R' 'sim.space.R'
'sim.species.R' 'urap.proportion.held.R' 'zzz.R'
'CvM.stat.R' 'H.cdf.R' 'RDperm.R' 'RPT.R' 'lee2008.R'
'plot.RDperm.R' 'summary.RDperm.R' 'summary.RPT.R'
'bef.options.R' 'zzz.R' 'rbefdata-package.R'
'bef.portal.get.dataset.R' 'bef.portal.get.proposal.R'
'bef.portal.get.metadata.R' 'bef.portal.get.keywords.R'
'bef.portal.get.categories.R' 'bef.portal.upload.R'
'bef.portal.api.R' 'bef.mediawiki.api.R' 'bef.tematres.api.R'
'helper.R' 'bef.portal.get.attachments.R'
'rbefdata-deprecated.R' 'rbefdata-defunct.R'
'bef.portal.vizalize.R'
'init.R' 'build.R' 'buildFPGrowth.R' 'classification.R'
'fpgrowth.R' 'pruning.R' 'utils.R'
'zzz.R' 'rChoiceDialogs-package.R' 'rFileChooser.R'
'rListChooser.R' 'rMiscFunctions.R'
'RcppExports.R' 'RClickhouse.R' 'ClickhouseDriver.R'
'ClickhouseConnection.R' 'ClickhouseResult.R'
'dbplyr-helpers.R' 'dplyr.R' 'zzz.R'
'accepted.R' 'as.clindex.data.frame.R' 'as.data.frame.R'
'climdex.data.frame.R' 'ks.test.R' 'RClimMAWGEN-package.R'
'temperature_max.R' 'temperature_min.R' 'wilcox.test.R'
'aaa-package.r' 'geom-stepribbon.r' 'guiparts-aaa.r'
'guiparts-checkboxes.r' 'guiparts-radioboxes.r'
'guiparts-textfield.r' 'guiparts-textfields.r'
'guiparts-toolbox.r' 'guiparts-variableboxes.r' 'data-aaa.r'
'plot-aaa.r' 'plot-factorise.r' 'plot-gcont.r' 'plot-gbox.r'
'plot-gdiscbar.r' 'plot-gdist.r' 'plot-gkm.r' 'plot-ghist.r'
'plot-gline.r' 'plot-gpie.r' 'plot-gqq.r' 'plot-gscat.r'
'plot-gscatmat.r' 'theme-natrisk.r' 'theme-natrisk21.r'
'theme-natriskbg.r' 'theme-simple.r' 'theme-wsj2.r'
'util-gettextKmg2.r' 'util-ggsaveKmg2.r' 'util-lastcom.r'
'util-nonFactors.r' 'zzz.r'
'RcppCWB_package.R' 'cl.R' 'cqp.R' 'checks.R' 'count.R'
'RcppExports.R' 'encode.R' 'decode.R' 'registry.R' 'cbow.R'
'region_matrix.R'
aclassesEnums.R bitClasses.R xbits.R base64.R binary.S
classes.S curl.S curlAuthConstants.R curlEnums.R curlError.R
curlInfo.S dynamic.R form.S getFormParams.R getURLContent.R
header.R http.R httpError.R httpErrors.R iconv.R info.S mime.R
multi.S options.S scp.R support.S upload.R urlExists.R zclone.R
zzz.R
'kernelwts.R' 'DCdensity.R' 'IKbandwidth.R' 'RDestimate.R'
'plot.RD.R' 'print.RD.R' 'rdd-package.R' 'summary.RD.R'
'attr_check.R' 'bw_ik09.R' 'bw_ik12.R' 'data.R' 'wt_kern.R'
'dc_test.R' 'treat_assign.R' 'mfrd_est.R' 'var_center.R'
'rd_est.R' 'mrd_est.R' 'mrd_impute.R' 'mrd_power.R'
'mrd_sens_bw.R' 'mrd_sens_cutoff.R' 'plot.mfrd.R'
'predict.rd.R' 'plot.rd.R' 'print.mfrd.R' 'print.rd.R'
'rd_impute.R' 'rd_power.R' 'rd_sens_bw.R' 'rd_sens_cutoff.R'
'rd_type.R' 'rddapp-package.R' 'sens_plot.R' 'shiny_run.R'
'summary.rd.R'
'ActivityGroupService.R' 'ActivityService.R'
'AdExclusionRuleService.R' 'AdRuleService.R'
'AudienceSegmentService.R' 'BaseRateService.R'
'CdnConfigurationService.R' 'CompanyService.R'
'ContactService.R' 'ContentBundleService.R'
'ContentMetadataKeyHierarchyService.R' 'ContentService.R'
'CreativeService.R' 'CreativeSetService.R'
'CreativeTemplateService.R' 'CreativeWrapperService.R'
'CustomFieldService.R' 'CustomTargetingService.R'
'ExchangeRateService.R' 'ForecastService.R'
'InventoryService.R' 'LabelService.R'
'LineItemCreativeAssociationService.R' 'LineItemService.R'
'LineItemTemplateService.R' 'LiveStreamEventService.R'
'MobileApplicationService.R' 'NativeStyleService.R'
'NetworkService.R' 'OrderService.R' 'PackageService.R'
'PlacementService.R' 'PremiumRateService.R'
'ProductPackageItemService.R' 'ProductPackageService.R'
'ProductService.R' 'ProductTemplateService.R'
'ProposalLineItemService.R' 'ProposalService.R'
'PublisherQueryLanguageService.R' 'RateCardService.R'
'ReconciliationLineItemReportService.R'
'ReconciliationOrderReportService.R'
'ReconciliationReportRowService.R'
'ReconciliationReportService.R' 'ReportService.R'
'SuggestedAdUnitService.R' 'TeamService.R' 'UserService.R'
'UserTeamAssociationService.R' 'WorkflowRequestService.R'
'dfp_auth.R' 'dfp_options_settings.R' 'dfp_service_endpoints.R'
'dfp_utils.R' 'dfp_utils_xml.R' 'rdfp.R'
'chainsaw.R' 'check_partition.R' 'check_similarity.R'
'class-metacommunity.R' 'class-powermean.R'
'class-relativeentropy.R' 'metacommunity.R'
'diversity-components.R' 'diversity-measures.R' 'get_title.R'
'hs_parameters.R' 'inddiv.R' 'metadiv.R' 'phy_abundance.R'
'phy_struct.R' 'plot_diversity.R' 'power_mean.R' 'powermean.R'
'qD.R' 'subdiv.R' 'qDZ.R' 'qDZ_single.R' 'qD_single.R'
'rdiversity-package.R' 'relativeentropy.R' 'repartition.R'
'similarity_shimatani.R' 'smatrix.R' 'summarise.R' 'tbar.R'
'zmatrix.R'
'RDML.types.R' 'RDML.R' 'RDML.AsDendrogram.R' 'RDML.AsTable.R'
'RDML.GetFData.R' 'RDML.Merge.R' 'RDML.SetFData.R'
'RDML.init.R' 'functional_wrappers.R' 'rdmlEdit.R'
'export-base.R' 'export-datetimes.R' 'export-numbers.R'
'export-unicode.R' 'imports.R' 'regex-package.R'
'alternation.R' 'regex-methods.R' 'backreferences.R'
'capture.R' 'internal.R' 'grouping-and-repetition.R'
'constants.R' 'class-groups.R' 'concatenation.R'
'compound-constants.R' 'escape_special.R' 'lookaround.R'
'misc.R' 'mode-modifiers.R'
'imports.R' 'unicode-classes.R' 'unicode-constants.R'
'unicode-general-category-classes.R'
'unicode-general-category-constants.R' 'unicode-operators.R'
register-S3-classes.r RLBigData-classes.r RLResult-class.r
accessor-methods.r evt.r classify.r classifySupv-methods.r
genSamples.r strcmp.r compare.r getPairs.r summary.r
em-methods.r internals.r em.r mygllm.r epilink-methods.r
phonetics.r onAttach.r getPairs-methods.r serialization.r
tools.r stochastic.r
'RcppExports.R' 'class.R' 'Survr.R' 'aic.R' 'baseline.R'
'coef.R' 'data.R' 'mcf-generic.R' 'mcf-formula.R'
'mcf-rateReg.R' 'mcfDiff.R' 'misc.R' 'plot.R' 'rateReg.R'
'reda.R' 'show.R' 'simEvent.R' 'summary.R'
'DataFunctions.R' 'aEddy.R' 'EddyGapfilling.R'
'EddyPartitioning.R' 'EddyPlotting.R'
'EddyUStarFilterChangePointDetection.R' 'EddyUStarFilterDP.R'
'Example.R' 'FileHandling.R' 'FileHandlingFormats.R'
'GeoFunctions.R' 'LRC_base.R' 'LRC_logisticSigmoid.R'
'LRC_nonrectangular.R' 'LRC_rectangular.R'
'PartitioningLasslop10.R' 'PartitioningLasslop10Nighttime.R'
'RcppExports.R' 'imports.R' 'variableNames.R' 'zzzDebugCode.R'
'redland.R' 'World.R' 'Node.R' 'Statement.R' 'Storage.R'
'Model.R' 'Parser.R' 'Query.R' 'QueryResults.R' 'Serializer.R'
'mergeNamespace_roclet.R' 'redland-package.R' 'util.R'
'Omegas.R' 'af.R' 'af_old.R' 'amc.R' 'ccb.fpc.R'
'create.prep.func.R' 'coefficients.pfr.R' 'dt_basis.R'
'irreg2mat.R' 'fbps.R' 'fgam.R' 'fosr.R' 'fosr.perm.R'
'fosr.perm.fit.R' 'fosr.perm.test.R' 'fosr.vs.R' 'fosr2s.R'
'fpc.R' 'fpca2s.R' 'fpca.sc.R' 'fpca.face.R' 'fpca.ssvd.R'
'fpcr.R' 'fpcr.setup.R' 'lf.R' 'lf_old.R' 'lf.vd.R' 'lofocv.R'
'lpeer.R' 'lpfr.R' 'quadWeights.R' 'lw.test.R' 'osplinepen2d.R'
'parse.predict.pfr.R' 'peer.R' 'peer_old.R' 'pffr-ff.R'
'pffr-ffpc.R' 'pffr-methods.R' 'pffr-pcre.R' 'pffr-robust.R'
'pffr-sff.R' 'pffr-utilities.R' 'pffr.R' 'pfr.R' 'pfr_old.R'
'pi_basis.R' 'plot.fosr.R' 'plot.fosr.perm.R' 'plot.fosr.vs.R'
'plot.fpcr.R' 'plot.lpeer.R' 'plot.peer.R' 'plot.pfr.R'
'poridge.R' 'postprocess.pfr.R' 'predict.fgam.R'
'predict.pfr.R' 'predict.pfr_old.R' 'preprocess.pfr.R'
'pspline.setting.R' 'pwcv.R' 'summary.pfr.R' 're.R'
'rlrt.pfr.R' 'vis.fgam.R' 'predict.fosr.vs.R' 'CD4-data.R'
'DTI-data.R' 'DTI2-data.R' 'PEER.Sim-data.R' 'gasoline-data.R'
'vis.pfr.R' 'GLS_CS.R' 'Gibbs_CS_FPCA.R' 'Gibbs_CS_Wish.R'
'Gibbs_Mult_FPCA.R' 'Gibbs_Mult_Wish.R' 'OLS_CS.R'
'VB_CS_FPCA.R' 'VB_CS_Wish.R' 'VB_Mult_FPCA.R' 'VB_Mult_Wish.R'
'XtSiginvX.R' 'bayes_fosr.R' 'f_sum.R' 'f_sum2.R' 'f_sum4.R'
'f_trace.R' 'mfpca.sc.R' 'fpca.lfda.R' 'predict.fbps.R'
'select_knots.R'
'decomp.R' 'decomp2d.R' 'decomp3d.R' 'reconstr.R'
'reconstr2d.R' 'reconstr3d.R' 'wcr.R' 'wcr.perm.R' 'wnet.R'
'wnet.perm.R' 'wUtil.R' 'plot.wnet-and-plot.wcr.R' 'Data2wd.R'
'wd2fhat.R'
'utils.R' 'SL-class.R' 'XL-class.R' 'QS-class.R'
'Claims-class.R'
'CA.R' 'clean.R' 'CW.R' 'delete.R' 'doc_temp.R' 'GQ.R' 'LL.R'
'mgsub.R' 'new_report.R' 'pad.R' 'paste2.R' 'presentation.R'
'reducer.R' 'reports-package.R' 'scrubber.R' 'strWrap.R'
'templates.R' 'tex2docx.R' 'Trim.R' 'unblanker.R'
'update_bib.R' 'wc.R' 'wheresPandoc.R' 'install_pandoc.R'
'repo2github.R' 'US.R' 'reveal.js.R' 'genX.R' 'genXtract.R'
'html5.R'
'generics.r' 'options.r' 'package.r' 'repr_datatable.r'
'utils.r' 'repr_list.r' 'repr_vector.r' 'repr_factor.r'
'repr_function.r' 'repr_help_files_with_topic.r'
'repr_htmlwidget.r' 'repr_matrix_df.r' 'repr_packageIQR.r'
'repr_recordedplot.r'
'RcppExports.R' 'cache-helpers.R' 'cache-tools.R'
'robustDigest.R' 'cache.R' 'consistentPaths.R' 'git.R'
'helpers.R' 'packages.R' 'pipe.R' 'reproducible-package.R'
'search.R'
CRUDProtocol-class.R CacheInfo-class.R Credentials-class.R
HTTP-class.R Media-class.R MediaCache-class.R RestUri-class.R
RestContainer-class.R test_restfulr_package.R utils.R
'utils.R' 'eutil-error.R' 'eutil.R' 'ecitmatch.R'
'parse-params.R' 'efetch.R' 'egquery.R' 'einfo.R' 'elink.R'
'epost.R' 'esearch.R' 'espell.R' 'esummary.R' 'parse-docsum.R'
'reutils-package.R' 'zzz.R'
'aaa.R' 'utils.R' 'escape.R' 'capture.R' 'character_class.R'
'counts.R' 'lookarounds.R' 'match.R' 'or.R' 'rex-mode.R'
'rex.R' 'shortcuts.R' 'wildcards.R' 'zzz.R'
'RForcecom.R' 'rforcecom.abortBulkJob.R' 'rforcecom.api.R'
'rforcecom.closeBulkJob.R' 'rforcecom.getBulkQueryResult.R'
'rforcecom.getBatchDetails.R' 'rforcecom.checkBatchStatus.R'
'rforcecom.submitBulkQuery.R' 'rforcecom.createBulkJob.R'
'rforcecom.bulkQuery.R' 'rforcecom.create.R'
'rforcecom.createBulkBatch.R' 'rforcecom.debug.R'
'rforcecom.delete.R' 'rforcecom.getObjectDescription.R'
'rforcecom.getObjectList.R' 'rforcecom.getServerTimestamp.R'
'rforcecom.insertBulkAttachments.R' 'rforcecom.login.R'
'rforcecom.logout.R' 'rforcecom.query.R'
'rforcecom.queryMore.R' 'rforcecom.retrieve.R'
'rforcecom.search.R' 'rforcecom.update.R' 'rforcecom.upsert.R'
'rforcecom.utils.R' 'rforcecom.write.csv.R'
'auth.R' 'utils.R' 'request.R' 'url.R' 'convert.R'
'get-data.R' 'mgmt.R' 'accounts.R' 'customDataSources.R'
'customDimensions.R' 'customMetrics.R' 'date-ranges.R'
'experiments.R' 'filters.R' 'profiles.R' 'query.R' 'report.R'
'ga.R' 'firstdate.R' 'goals.R' 'mcf.R' 'metadata.R' 'package.R'
'realtime.R' 'segments.R' 'unsampledReports.R' 'uploads.R'
'webproperties.R' 'zzz.R'
AAA.R options.R rgrass.R bin_link.R vect_link.R initGRASS.R
xml1.R
'rhoR.R' 'rho.R' 'rhoK.R' 'rhoSet.R' 'rhoCT.R' 'kappa.R'
'kappaSet.R' 'kappaCT.R' 'rhoMin.R' 'baserate.R'
'baserateSet.R' 'baserateCT.R' 'calcKappa.R' 'calcRho.R'
'checkBRPKcombo.R' 'contingencyTable.R' 'codeSet.R'
'contingencyToSet.R' 'createRandomSet.R'
'createSimulatedCodeSet.R' 'genPKcombo.R' 'genPcombo.R'
'generateKPs.R' 'getBootPvalue.R' 'getHandSet.R' 'getR.R'
'getTestSet.R' 'prset.R'
'comp_util.R' 'init_txt.R' 'init_pal.R' 'init_prob.R'
'comp_prob_prob.R' 'init_freq.R' 'comp_freq_prob.R'
'init_num.R' 'init_prob_num.R' 'init_freq_num.R'
'comp_freq_freq.R' 'comp_prob_freq.R' 'comp_xxxx_prob.R'
'comp_popu.R' 'comp_accu.R' 'plot_mosaic.R' 'plot_icons.R'
'plot_tree.R' 'plot_fnet.R' 'plot_curve.R' 'plot_plane.R'
'data.R' 'read_data.R' 'riskyr_class.R' 'start_riskyr.R'
readJSON.R asVars.R isValid.R json.R simpleHandler.R stream.R
RLumModel-package.R RcppExports.R calc_signal.R
calc_concentrations.R create_DRT.sequence.R
create_SAR.sequence.R extract_pars.R
model_LuminescenceSignals.R read_SEQ2R.R set_pars.R
simulate_CW_OSL.R simulate_heating.R simulate_illumination.R
simulate_irradiation.R simulate_LM_OSL.R simulate_pause.R
simulate_RF.R simulate_RF_and_heating.R simulate_TL.R
translate_sequence.R
'app_RLum.R' 'addin.R' 'chooser.R' 'jscolor.R' 'tooltip.R'
'popover.R' 'RLumShiny.R' 'zzz.R'
'MariaDBConnection.R' 'MariaDBDriver.R' 'MariaDBResult.R'
'RMariaDB.R' 'RcppExports.R' 'coerce.R' 'connect.R' 'default.R'
'export.R' 'query.R' 'quote.R' 'rownames.R' 'table.R'
'transaction.R' 'utils.R' 'zzz.R'
rmgarch-var.R rmgarch-functions.R rmgarch-classes.R
rmgarch-ica.R rmgarch-series.R rmgarch-mmean.R
gogarch-classes.R gogarch-distributions.R gogarch-main.R
gogarch-methods.R rdcc-classes.R rdcc-main.R rdcc-likelihoods.R
rdcc-plots.R fdcc-likelihoods.R fdcc-main.R rdcc-methods.R
rdcc-mdistributions.R rdcc-postestimation.R rdcc-solver.R
copula-classes.R copula-distributions.R copula-likelihoods.R
copula-solver.R copula-fn.R copula-transformations.R
copula-main.R copula-postestimation.R copula-methods.R
rmgarch-tests.R rmgarch-scenario.R zzz.R
'CI.R' 'STDERR.R' 'group.UCL.R' 'group.CI.R' 'group.STDERR.R'
'lr.glover.R' 'multiplot.R' 'panel.circle.R' 'rounder.R'
'rsi.R' 'summarySE.R'
'global.R' 'Model.R' 'Parameter.R' 'GaussianParameter.R'
'Strategy.R' 'MixmodResults.R' 'MixmodDAResults.R'
'MultinomialModel.R' 'GaussianModel.R' 'MultinomialParameter.R'
'Mixmod.R' 'CompositeModel.R' 'CompositeParameter.R'
'MixmodCluster.R' 'MixmodLearn.R' 'Rmixmod.R' 'MixmodPredict.R'
'zzz.R'
'mysql.R' 'driver.R' 'connection.R' 'data-type.R' 'default.R'
'escaping.R' 'result.R' 'extension.R' 'is-valid.R' 'table.R'
'transaction.R'
'AllGenerics.R' 'ng_data.R' 'ng_graph.R' 'ng_path.R'
'ng_image.R' 'SettingClasses.R' 'NavGraphHandler.R'
'GraphDisplay.R' 'Visualization.R' 'Visualization2D.R'
'Viz2D_Axis.R' 'Viz2D_tk2d.R' 'FunctionNavGraph.R'
'graphtools.R' 'navGraph.R' 'scagNav.R' 'zzz.R'
'Rnet_classes.R' 'Assign_Emetadata.R' 'Assign_Vmetadata.R'
'Clean_Sigma.R' 'Estimate_pCorrs.R' 'GenR.R'
'Gomez_modularity_v6_1.R' 'L1_selection.R' 'Rnet.R'
'Rnet_heatmap.r' 'Sq2L.R' 'data_e_attrs.R' 'data_ec_coords.R'
'data_narms_ec.r' 'data_v_attrs.R' 'methods-plot.R'
'methods-print.R' 'methods-summary.R'
'classes.R' 'add_basic_meta.R' 'add_characters.R' 'add_meta.R'
'add_namespaces.R' 'add_trees.R' 'character_classes.R'
'concatenate_nexml.R' 'deprecated.R' 'get_basic_metadata.R'
'get_characters.R' 'get_level.R' 'get_metadata.R'
'get_namespaces.R' 'get_rdf.R' 'get_taxa.R' 'get_taxa_meta.R'
'get_trees.R' 'internal_get_node_maps.R' 'internal_isEmpty.R'
'internal_name_by_id.R' 'internal_nexml_id.R' 'meta.R'
'nexmlTree.R' 'nexml_add.R' 'nexml_get.R' 'nexml_methods.R'
'nexml_publish.R' 'nexml_read.R' 'nexml_validate.R'
'nexml_write.R' 'simmap.R' 'taxize_nexml.R' 'tbl_df.R'
'GenUniGpois.R' 'CorrNNGpois.R' 'ComputeCorrGpois.R'
'ValidCorrGpois.R' 'CmatStarGpois.R' 'QuantileGpois.R'
'GenMVGPois.R' 'RNGforGPD-package.R'
'ghq.R' 'psiFunc2.R' 'AllClass.R' 'rlmer.R' 'accessors.R'
'fromLme4.R' 'DAS-scale.R' 'fit.effects.R' 'helpers.R'
'AllGeneric.R' 'lmer.R' 'mutators.R' 'plot.R'
AllGenerics.R AllClasses.R show-methods.R rococo.R
rococo.test-methods.R gauss.cor.R gauss.cor.test-methods.R
'ode_generics.R' 'ODESolver.R' 'ODE.R' 'AbstractODESolver.R'
'ODEAdaptiveSolver.R' 'DormandPrince45.R' 'Euler.R'
'EulerRichardson.R' 'ODESolverFactory.R' 'RK4.R' 'RK45.R'
'Verlet.R' 'rODE-package.r' 'utils.R'
'RcppExports.R' 'rosqp-package.R' 'solve.R' 'osqp.R'
'params.R'
'help.R' 'estimators.R' 'preliminary.R' 'parameterizations.R'
'distributions.R' 'kappa.R' 'regions.R' 'RcppExports.R'
'plot.R' 'grid-search.R' 'bayes.R' 'primatives.R' 'robust.R'
'init.R' 'classes.R' 'fit.R' 'drm.R' 'nrm.R' 'mcm.R' 'grm.R'
'LSAT.R' 'sample.R' 'dataframe.R' 'diagnose.R' 'science.R'
'kct.R' 'openmx.R' 'flexmirt.R' 'util.R' 'lmp.R'
'bgc.R' 'checkArgs.R' 'feat.R' 'hmm.R' 'listOfLists.R' 'msa.R'
'optim.R' 'phastCons.R' 'phyloFit.R' 'phyloP.R' 'plot.R'
'rphast.R' 'treeModel.R' 'trees.R' 'zzz.R'
'xml-lib.R' 'polyhedra-lib.R' 'ledger-lib.R' 'db-lib.R'
'zzz.R'
'PqDriver.R' 'PqConnection.R' 'PqResult.R' 'RPostgres-pkg.R'
'RcppExports.R' 'default.R' 'export.R' 'quote.R' 'tables.R'
'transactions.R' 'utils.R'
S4R.R zzz.R PostgreSQLSupport.R dbObjectId.R PostgreSQL.R
'rPref.r' 'RcppExports.R' 'pref-classes.r' 'base-pref.r'
'base-pref-macros.r' 'complex-pref.r' 'general-pref.r'
'pref-eval.r' 'show-pref.r' 'visualize.r' 'pred-succ.r'
'PrestoDriver.R' 'Presto.R' 'utility_functions.R'
'PrestoSession.R' 'PrestoConnection.R' 'PrestoCursor.R'
'PrestoResult.R' 'RcppExports.R' 'copy.to.src.presto.R'
'db.data.type.PrestoConnection.R' 'dbplyr_compatible.R'
'db.explain.PrestoConnection.R'
'db.query.rows.PrestoConnection.R' 'dbHasCompleted.R'
'dbClearResult.R' 'dbConnect.R' 'dbDataType.R' 'dbDisconnect.R'
'dbSendQuery.R' 'json.tabular.to.data.frame.R' 'extract.data.R'
'dbFetch.R' 'dbGetQuery.R' 'dbListTables.R' 'dbExistsTable.R'
'dbGetInfo.R' 'dbGetRowCount.R' 'dbGetStatement.R'
'dbIsValid.R' 'dbListFields.R' 'dbUnloadDriver.R'
'description_from_info.R' 'db_desc.PrestoConnection.R'
'db_query_fields.R' 'fetch.R' 'help.R'
'src.translate.env.src.presto.R' 'sql_translate_env.R'
'src.desc.src.presto.R' 'src.presto.R' 'tbl.src.presto.R'
'rrmake.R' 'criterion.R' 'file.R' 'has-file.R' 'root.R'
'rprojroot-package.R' 'shortcut.R' 'thisfile.R'
utility_functions.R reduce_rank_regression.R pca.R cva.R lda.R
rrr.R data.R
'gridtools.R' 'RSAGA-core.R' 'RSAGA-modules.R' 'RSAGA-utils.R'
'RSAGA-package.R' 'landslides.R'
'errorHandler.R' 'remoteDriver.R' 'rsDriver.R'
'selKeys-data.R' 'util.R' 'webElement.R' 'zzz.R'
'zzz.R' 'helper.R' 'sampling.R' 'models.R' 'rsig.R'
'evaluate.R' 'rsig.all.R'
utils.R pminmax.R Context-class.R DocCollection-class.R
Expression-class.R Facets-class.R FieldInfo-class.R
FieldType-class.R Promise-class.R SolrExpression-class.R
SolrQuery-class.R SolrSchema-class.R SolrCore-class.R
SolrResult-class.R SolrSummary-class.R Solr-class.R
SolrList-class.R SolrFrame-class.R SolrPromise-class.R
GroupedSolrFrame-class.R test.R zzz.R
'RcppExports.R' 'SQLiteConnection.R' 'SQLiteDriver.R'
'SQLiteResult.R' 'coerce.R' 'connect.R' 'copy.R' 'datasetsDb.R'
'deprecated.R' 'export.R' 'extensions.R' 'query.R' 'rownames.R'
'table.R' 'transactions.R' 'utils.R' 'zzz.R'
'Generics.R' 'Classifier.R' 'CrossValidation.R'
'LeastSquaresClassifier.R' 'EMLeastSquaresClassifier.R'
'NormalBasedClassifier.R' 'LinearDiscriminantClassifier.R'
'EMLinearDiscriminantClassifier.R' 'NearestMeanClassifier.R'
'EMNearestMeanClassifier.R' 'LogisticRegression.R'
'EntropyRegularizedLogisticRegression.R' 'Evaluate.R'
'GRFClassifier.R' 'GenerateSSLData.R' 'HelperFunctions.R'
'ICLeastSquaresClassifier.R' 'ICLinearDiscriminantClassifier.R'
'KernelLeastSquaresClassifier.R'
'KernelICLeastSquaresClassifier.R'
'LaplacianKernelLeastSquaresClassifier.R' 'LaplacianSVM.R'
'LearningCurve.R' 'LinearSVM.R' 'LogisticLossClassifier.R'
'MCLinearDiscriminantClassifier.R' 'MCNearestMeanClassifier.R'
'MCPLDA.R' 'MajorityClassClassifier.R' 'Measures.R'
'Plotting.R' 'QuadraticDiscriminantClassifier.R' 'RSSL.R'
'RcppExports.R' 'S4VM.R' 'SVM.R' 'SelfLearning.R' 'TSVM.R'
'USMLeastSquaresClassifier.R' 'WellSVM.R' 'scaleMatrix.R'
'svmd.R' 'svmlin.R' 'testdata-data.R'
'adj_risk_sim.R' 'freedom_functions_1.R'
'freedom_functions_2.R' 'n_rb2stage.R' 'prevalence_functions.R'
'risk_based_functions.R' 'sep_passive.R' 'sse_rb2stage.R'
'guessFileFormat.R' 'makeFileFormat.R' 'rtapeAdd.R'
'rtape_apply.R' 'rtapeAsList.R' 'rtapeFilter.R' 'rtapeLapply.R'
'rtapeRerecord.R' 'shared.R'
'rtematres.api.base.R' 'rtematres.api.convenience.R'
'rtematres.api.helpers.R' 'rtematres.options.R'
'rtematres-package.R' 'rtematres.helpers.R' 'zzz.R'
rugarch-imports.R rugarch-cwrappers.R rugarch-solvers.R
rugarch-lossfn.R rugarch-distributions.R rugarch-kappa.R
rugarch-helperfn.R rugarch-numderiv.R rugarch-series.R
rugarch-startpars.R rugarch-tests.R rugarch-armafor.R
rugarch-graphs.R rugarch-classes.R rugarch-sgarch.R
rugarch-figarch.R rugarch-csgarch.R rugarch-fgarch.R
rugarch-egarch.R rugarch-gjrgarch.R rugarch-aparch.R
rugarch-igarch.R rugarch-mcsgarch.R rugarch-realgarch.R
rugarch-multi.R rugarch-plots.R rugarch-rolling.R
rugarch-uncertainty.R rugarch-bootstrap.R rugarch-methods.R
rugarch-benchmarks.R arfima-classes.R arfima-multi.R
arfima-main.R arfima-methods.R rugarch-cv.R zzz.R
unuran_distr.R unuran_cont.R unuran_discr.R unuran_cmv.R
Runuran.R universal.R distributions.R deprecated.R zzz.R
AllGenerics.R AllClasses.R methods-pedigree.R methods-trio.R
methods-RVsharingProb.R functions.R approx2_functions.R
ped2trio.R PFU.R RVsharing.fn.R extract_carriers.R
pvalue_functions.R RVgene.R RVgene_allshare.R zzz.R
'methods_rxylib.R' 'rxylib.R' 'RcppExports.R' 'read_xyData.R'
'convert_xy2TKA.R'
'CalcAddon.R' 'CalcEAD.R' 'CalcPFE.R' 'CalcRC.R'
'ExampleBasisVol.R' 'ExampleComm.R' 'ExampleCredit.R'
'ExampleFX.R' 'ExampleIRD.R' 'ExampleIRDCommMargined.R'
'ExampleIRDCredit.R' 'HandleBasisVol.R' 'LoadSupervisoryData.R'
'runExampleCalcs.R' 'CalculateFactorMult.R'
'CreateTradeGraph.R' 'GroupCommTrades.R' 'GroupCreditTrades.R'
'GroupEquityTrades.R' 'GroupFXTrades.R' 'GroupIRDTrades.R'
'GroupTrades.R' 'SACCRCalculator.R' 'SingleTradeAddon.R'
'cobraInit.R' 'cobraPhaseI.R' 'cobraPhaseII.R' 'defaultRI.R'
'defaultSAC.R' 'defaultTR.R' 'drawSurrogate3d.R' 'fnArchive.R'
'initialHjkb.R' 'isres2.R' 'multiRunPlot.R' 'multiCOBRA.R'
'nmkb2.R' 'RbfInter.R' 'repairChootinan.R' 'repairInfeasRI2.R'
'SACOBRA.R' 'startCobra.R' 'trustRegion.R'
'cumulants.r' 'dnbeta.r' 'dneta.r' 'dnf.r' 'dnt.r' 'kprime.r'
'lambdap.r' 'moments.r' 'prodchisqpow.r' 'proddnf.r'
'prodnormal.r' 'runExample.r' 'sadists.r' 'sumchisqpow.r'
'sumlogchisq.r' 'upsilon.r' 'utils.r'
'aaa_generics.R' 'SaemixData.R' 'SaemixModel.R' 'SaemixRes.R'
'SaemixObject.R' 'compute_LL.R' 'func_FIM.R' 'func_aux.R'
'func_distcond.R' 'func_plots.R' 'func_simulations.R' 'main.R'
'main_estep.R' 'main_initialiseMainAlgo.R' 'main_mstep.R'
'zzz.R'
'NAMESPACE.R' 'RcppExports.R' 'bootstrap.R' 'check.R'
'correlation.R' 'fit.R' 'fixedPoint.R' 'helper.R' 'makeXY.R'
'mat.R' 'matModels.R' 'mse.R' 'plot.R' 'print.R'
'psiFunctions.R' 'rfh.R' 'robustEstimationEquations.R'
'robustObjectiveFunctions.R' 'testData.R' 'update.R'
'variance.R'
zzz.R transparentColorBase.R initRandomSeed.R boltDimensions.R
bboxCheck.R bboxToPoly.R bboxSum.R checkStemDimensions.R
defStemEnv.R transfMatrix.R spCircle.R sampleLogs.R
sampleTrees.R sampSurfClassUnions.R chainsawSliver.R
StemClass.R StemContainerClass.R TractClass.R
TractConstructors.R downLog.R downLogs.R standingTree.R
standingTrees.R ArealSamplingClass.R
ArealSamplingConstructors.R InclusionZoneClass.R
InclusionZoneConstructors.R izContainer.R
izContainerConstructors.R InclusionZoneGridClass.R
InclusionZoneGridConstructors.R izGridConstruct.R mirage.R
sampSurfClass.R sampSurfConstructors.R hist.R plotStem.R
plotStemContainer.R plotArealSampling.R plotInclusionZone.R
plotInclusionZoneGrid.R plotTract.R plotizContainer.R
plotSampSurf.R showArealSampling.R showInclusionZone.R
showInclusionZoneGrid.R showStem.R showTract.R showSampSurf.R
summaryStem.R summaryStemContainer.R summaryArealSampling.R
summaryInclusionZone.R summaryIZContainer.R
summaryInclusionZoneGrid.R summaryTract.R summarySampSurf.R
setAsStem.R setAsizContainer.R bbox.R perimeter.R area.R
heapIZ.R gridCellEnhance.R plot3D.R clipStemsToTract.R
monteClass.R monteConstructors.R summaryMonte.R showMonte.R
histMonte.R horizontalLineSampling.R criticalHeightSampling.R
antitheticCHS.R taperInterpolate.R getID.R segmentVolume.R
MonteCarloContainer.R MonteCarloClass.R
MonteCarloConstructors.R MonteCarloProxy.R showMonteCarlo.R
summaryMonteCarlo.R plotMonteCarlo.R
horizontalPointMCSampling.R smithPlot.R
utils.R filterClasses.R modelClasses.R sarima.R
autocovariances.R armacalc.R
'ModelPoset.R' 'MixtureModels.R' 'BinomialMixtures.R'
'FactorAnalyses.R' 'GaussianMixtures.R' 'LCAs.R'
'LatentForests-Helpers.R' 'LatentForests.R' 'RcppExports.R'
'ReducedRankRegressions.R' 'sBIC-Function.R' 'sbic.R'
'sbpiper.r' 'sbpiper_ggplot2_themes.r' 'sbpiper_plots.r'
'sbpiper_sim.r' 'sbpiper_ps1.r' 'sbpiper_ps2.r' 'sbpiper_pe.r'
'data.r'
tests.R post_hoc.R plotting.R scmamp.R latex.R
loading_functions.R data_manipulation.R
'agpop.R' 'agsrs.R' 'agstrat.R' 'anthrop.R' 'anthsrs.R'
'anthuneq.R' 'audit.R' 'books.R' 'certify.R' 'coots.R'
'counties.R' 'divorce.R' 'golfsrs.R' 'htpop.R' 'htsrs.R'
'htstrat.R' 'journal.R' 'lahiri.design.R' 'measles.R' 'ncvs.R'
'nybight.R' 'otters.R' 'ozone.R' 'samples.R' 'seals.R'
'selectrs.R' 'statepop.R' 'statepps.R' 'syc.R' 'teachers.R'
'teachmi.R' 'teachnr.R' 'winter.R'
'0classes.r' 'addGhostVars.R' 'addNoise.r' 'aux_functions.r'
'createNewID.R' 'dataGen.r' 'dataSets.R' 'dRisk.R' 'dRiskRMD.R'
'dUtility.R' 'freqCalc.r' 'globalRecode.R' 'groupAndRename.R'
'GUIfunctions.R' 'indivRisk.R' 'LLmodGlobalRisk.R'
'LocalRecProg.R' 'localSupp.R' 'localSuppression.R' 'mdav.R'
'measure_risk.R' 'methods.r' 'microaggregation.R' 'modRisk.R'
'muargus_compatibility_functions.R' 'plotFunctions.R'
'plotMicro.R' 'pram.R' 'rankSwap.R' 'RcppExports.R' 'report.R'
'riskyCells.R' 'sdcMicro-package.R' 'shuffle.R' 'suda2.R'
'timeEstimation.R' 'topBotCoding.R' 'valTable.R' 'zzz.R'
'printFunctions.R' 'mafast.R' 'maG.R' 'sdcApp.R'
'show_sdcMicroObj.R'
class.R def.R graph2catnet.R dags.R probs.R joint.prob.R
marginal.prob.R samples.R loglik.R entropy.R categor.R dist.R
plot.R find.R search.R eval.R predict.R chisq.R histo.R
cluster.R bif.R xdsl.R quant.R expres.r zzz.R
'HI.coast.r' 'WA.cities.r' 'WA.r' 'WY.r' 'SDraw-package.r'
'sdraw.SpatialPoints.r' 'sdraw.SpatialLines.r'
'sdraw.SpatialPolygons.r' 'sdraw.r' 'bas.line.r' 'bas.point.r'
'bas.polygon.r' 'extended.gcd.r' 'grts.equi.r' 'grts.line.r'
'grts.point.r' 'grts.polygon.r' 'hal.line.r' 'hal.point.r'
'hal.polygon.r' 'halton.frame.r' 'halton.indicies.CRT.r'
'halton.indicies.r' 'halton.indicies.vector.r'
'halton.lattice.polygon.r' 'halton.lattice.r' 'halton.r'
'plot.sdrawSample.R' 'primes.r' 'sss.point.r' 'sss.line.r'
'sss.polygon.r' 'srs.polygon.r' 'srs.line.r' 'srs.point.r'
'merge.lines.r' 'aprox.r' 'max.U.r' 'polygonArea.r'
'lineLength.r' 'voronoi.polygons.r' 'zzz.r'
'Class-Pattern.R' 'Class-PatternOrCharacter.R'
'Class-Searchables.R' 'Class-Searchable.R'
'Class-SearchableOrPattern.R' 'boundary.R' 'case.R' 'coll.R'
'detect.R' 'extract.R' 'fixed.R' 'invert.R' 'is.string.R'
'regex.R' 'reverse.lookup.R' 'searchable-package.R' 'std.R'
'stri_detect_std.R' 'zzz.R'
seismicRoll.R RcppExports.R findOutliers.R roll_hampel.R
roll_mean.R roll_median.R roll_sd.R roll_stalta.R
s4p.R points2polygrid.R SpatialPolygridDataFrame-class.R
SpatialPolygridDataFrame-methods.R
SpatialIndexDataFrame-class.R SpatialIndexDataFrame-methods.R
SpatialDataFrame-class.R subsetSDF.R areaSDF.R
Simulations-class.R Simulations-methods.R plotSimulations.R
cbindSimulations.R loadSimulations.R loadSimulations_raster.R
loadSimulations_scan.R extractSpatialDataFrame.R
SDF2simulations.R changeSimulationsPath.R subsetSimulations.R
summaryPlumes.R summaryLocations.R replaceDefault.R
simulationsApply.R fitMedianVariogram.R interpolate.R
interpolationErrorFunctions.R interpolationError.R
spatialSpreadFunctions.R spatialSpread.R
measurementsResultMap.R optimiseSD_greedy.R optimiseSD_ssa.R
optimiseSD_genetic.R completeSearch.R optimiseSD_global.R
coordCentral.R optimiseSD_manual.R optimiseSD.R plotSD.R
optimisationCurve.R postprocessing.R spplotLog.R
'RcppExports.R' 'alignment.R' 'seurat.R'
'cluster_determination.R' 'cluster_determination_internal.R'
'cluster_validation.R' 'data.R' 'deprecated_functions.R'
'differential_expression.R'
'differential_expression_internal.R' 'dimensional_reduction.R'
'dimensional_reduction_internal.R'
'dimensional_reduction_utilities.R' 'interaction.R'
'jackstraw.R' 'jackstraw_internal.R' 'multi_modal.R'
'plotting.R' 'plotting_internal.R' 'plotting_utilities.R'
'preprocessing.R' 'preprocessing_internal.R'
'printing_utilities.R' 'scoring.R' 'snn.R' 'spatial.R'
'spatial_internal.R' 'tSNE_project.R' 'utilities.R'
'utilities_internal.R'
RcppExports.R init.R crs.R bbox.R read.R db.R sfc.R sfg.R sf.R
bind.R wkb.R wkt.R plot.R geom.R transform.R sp.R grid.R
arith.R tidyverse.R cast_sfg.R cast_sfc.R graticule.R
datasets.R aggregate.R agr.R maps.R join.R sample.R valid.R
collection_extract.R jitter.R sgbp.R spatstat.R stars.R crop.R
gdal_utils.R deprecated.R
'SharpeR.r' 'utils.r' 'distributions.r' 'sr.r' 'estimation.r'
'tests.r' 'unified.r'
'Shazam.R' 'RegionDefinitions.R' 'Baseline.R' 'Core.R'
'DistToNearest.R' 'MutationDefinitions.R' 'MutationProfiling.R'
'Shmulate.R' 'TargetingModels.R'
'app.R' 'bookmark-state-local.R' 'stack.R' 'bookmark-state.R'
'bootstrap-layout.R' 'conditions.R' 'map.R' 'globals.R'
'utils.R' 'bootstrap.R' 'cache.R' 'diagnose.R' 'fileupload.R'
'graph.R' 'reactives.R' 'reactive-domains.R' 'history.R'
'hooks.R' 'html-deps.R' 'htmltools.R' 'image-interact-opts.R'
'image-interact.R' 'imageutils.R' 'input-action.R'
'input-checkbox.R' 'input-checkboxgroup.R' 'input-date.R'
'input-daterange.R' 'input-file.R' 'input-numeric.R'
'input-password.R' 'input-radiobuttons.R' 'input-select.R'
'input-slider.R' 'input-submit.R' 'input-text.R'
'input-textarea.R' 'input-utils.R' 'insert-tab.R' 'insert-ui.R'
'jqueryui.R' 'middleware-shiny.R' 'middleware.R' 'modal.R'
'modules.R' 'notifications.R' 'priorityqueue.R' 'progress.R'
'react.R' 'render-plot.R' 'render-table.R' 'run-url.R'
'serializers.R' 'server-input-handlers.R' 'server.R'
'shiny-options.R' 'shiny.R' 'shinyui.R' 'shinywrappers.R'
'showcase.R' 'snapshot.R' 'tar.R' 'test-export.R' 'timer.R'
'update-input.R'
'Announcement.R' 'ApplicationCharge.R' 'Article.R' 'Asset.R'
'Blog.R' 'CarrierService.R' 'Checkout.R' 'Collect.R'
'Comment.R' 'Country.R' 'CustomCollection.R' 'Customer.R'
'CustomerGroup.R' 'Event.R' 'Fulfillment.R'
'FulfillmentService.R' 'Location.R' 'MetaField.R' 'Order.R'
'OrderRisks.R' 'Page.R' 'Product.R' 'ProductImage.R'
'ProductVariant.R' 'Province.R' 'RecurringApplicationCharge.R'
'Redirect.R' 'Refund.R' 'ScriptTag.R' 'Shop.R' 'private.R'
'Webhook.R' 'Transaction.R' 'Theme.R' 'SmartCollection.R'
'ShopifyShop.R' 'shopfyr-package.R'
'categorize.R' 'mainFunctions.R' 'multipleFitFunction.R'
'sigmoidalFitFunctions.R' 'doublesigmoidalFitFunctions.R'
'normalizationFunction.R' 'sicegar.R' 'dataInputCheck.R'
'parameterCalculation.R' 'figureGeneration.R'
'sig.R' 'list_sigs.R' 'as.R' 'is.R' 'sig_report.R'
'indexing.R' 'utils.R' 'write_sigs.R'
'check_arguments.R' 'SimInf_events.R' 'SimInf_model.R'
'SEIR.R' 'SIR.R' 'SISe.R' 'SISe3.R' 'SISe3_sp.R' 'SISe_sp.R'
'SimInf.R' 'SimInf_mparse.R' 'distance.R' 'package_skeleton.R'
'run_outer.R'
'0classes.R' 'addKnownMargins.R' 'addWeights.r' 'calibPop.R'
'calibSample.R' 'calibVars.R' 'contingencyWt.R' 'correctHeap.R'
'fitGPD.R' 'getBreaks.R' 'getCat.R' 'ipu.r'
'numericalWeighting.R' 'ipu2.r' 'meanWt.R' 'printFunctions.R'
'quantileWt.R' 'sampHH.R' 'RcppExports.R' 'silcTools.R'
'silcTools2.R' 'simAnnealingDT.R' 'simCategorical.R'
'simComponents.R' 'simContinuous.R' 'simEUSILC.R' 'simGPD.R'
'simInitSpatial.R' 'simple_dis.R' 'simPop-package.R'
'simRelation.R' 'simStructure.R' 'spBwplot.R' 'spBwplotStats.R'
'spCdf.R' 'spCdfplot.R' 'spMosaic.R' 'spPredict.R' 'spSample.R'
'spTable.R' 'specifyInput.R' 'sprague.R' 'tableWt.R'
'utility.R' 'utils.R' 'whipple.R' 'dataSets.R' 'zzz.R'
'evals-class.R' 'output-class.R' 'component-class.R'
'model-class.R' 'draws-class.R' 'reference-classes.R'
'simulation-class.R' 'add-to-simulation.R' 'aggregator-class.R'
'utils.R' 'create.R' 'draws.R' 'evaluate.R' 'metric-class.R'
'method-class.R' 'examples.R' 'extended-method-class.R'
'get-from-simulation.R' 'import_from.R' 'load.R' 'manage.R'
'method-extension-class.R' 'methods.R' 'models.R'
'parallel-draws.R' 'parallel-methods.R' 'parallel.R'
'plot_eval.R' 'plot_eval_by.R' 'plot_evals.R' 'tables.R'
'zzz.R'
skeleSim-package.R skeleSim.classes.R fsc.classes.R
rms.classes.R skeleSim.internals.R skeleSimGUI.R
analysis.funcs.R fsc.loadScenario.R fsc.run.R
fsc.scenarioCheck.R rms.convert.R rms.init.land.function.R
rms.init.landscape.R rms.internals.R rms.run.R
rms.scenarioCheck.R runSim.R skeleSim.checks.R
getGammaMutRates.R
'skimr-package.R' 'formats.R' 'stats.R' 'functions.R'
'kable.R' 'options.R' 'pander.R' 'skim.R' 'skim_print.R'
'skim_v.R' 'summary.R' 'utils.R' 'wide.R'
'PowerLaw.R' 'createNodes.R' 'data_ODsample.R'
'data_airports.R' 'data_carriers.R' 'data_metro.R'
'disparityfilter.R' 'findAirport.R' 'fromto.R' 'convertRaw.R'
'netDir.R' 'netUnd.R' 'netImport.R' 'netInt.R' 'netPath.R'
'skynet.R' 'nodeStats.R' 'importT100.R' 'findCarrier.R'
'plotMap.R' 'skynet_example.R'
'alpha.fun.R' 'b.com.est.R' 'coef.sma.R' 'com.ci.R'
'defineAxis.R' 'elev.com.R' 'elev.test.R' 'fitted.sma.R'
'huber.M.R' 'line.cis.R' 'logLik_sma.R' 'lr.b.com.R' 'ma.R'
'makeLogMinor.R' 'meas.est.R' 'multcompmatrix.R' 'nicePlot.R'
'plot.sma.R' 'predict.sma.R' 'print.sma.R' 'residuals.sma.R'
'robust.factor.R' 'seqLog.R' 'shift.com.R' 'slope.com.R'
'slope.test.R' 'sma.R' 'summary.sma.R'
'customerAcquisition.R' 'acquisitionRetention.R'
'customerChurn.R' 'customerWinBack.R'
'customerRetentionDemographics.R'
'customerRetentionLifetimeDuration.R'
'customerRetentionTransactions.R'
'coefsmcure.R' 'em.R' 'plotpredictsmcure.R' 'predictsmcure.R'
'printsmcure.R' 'smcure.R' 'smrank.R' 'smsurv.R'
'fWilson.R' 'fFitKernelWeights.R' 'fCreateCashflowMatrix.R'
'fCreateTimeVector.R' 'fCreateKernelMatrix.R'
'fFitSmithWilsonYieldCurve.R' 'fFitYieldCurve.R'
'fFitSmithWilsonYieldCurveToInstruments.R' 'Utilities.R'
'SmithWilsonYieldCurve.R' 'plot.SmithWilsonYieldCurve.R'
'lines.SmithWilsonYieldCurve.R'
'points.SmithWilsonYieldCurve.R'
'onLoad.R' 'smoother-package.R' 'functions.R'
'smth-gaussian.R' 'smth.R'
'softclassval.R' 'make01.R' 'hardclasses.R' 'unittestdata.R'
'dev.R' 'factor2matrix.R' 'init.R' 'nsamples.R' 'postproc.R'
'operators.R' 'performance.R' 'unittests.R'
'package.R' 'datasets.R' 'data.lib.R' 'support.lib.R'
'solar.lib.R' 'assoc.lib.R' 'classSolarAssoc.R'
'classSolarPolygenic.R' 'plot.lib.R' 'polygenic.lib.R'
'solarAssoc.R' 'solarPolyAssoc.R' 'solarPolygenic.R'
'files.lib.R' 'classSolarMultipoint.R' 'solarMultipoint.R'
'multipoint.lib.R' 'transforms.lib.R' 'solarMultivar.R'
'solarBayesAvg.R' 'solarPar.R' 'modelPar.R'
solvebio.R annotation.R application.R beacon.R beacon_set.R
dataset.R dataset_commit.R dataset_export.R dataset_field.R
dataset_import.R dataset_migration.R dataset_template.R
object.R shiny.R task.R user.R utils.R vault.R zzz.R
'counting_function.R' 'f_ackley.R' 'f_bbob2009.R' 'f_branin.R'
'f_discus.R' 'f_double_sum.R' 'f_ellipsoidal.R'
'f_goldstein_price.R' 'f_griewank.R' 'f_kotancheck.R'
'f_mexican_hat.R' 'f_rastrigin.R' 'f_rosenbrock.R' 'f_sphere.R'
'f_weierstrass.R' 'generics.R' 'is_soo_function.R'
'persp3d_soo_function.R' 'plot_soo_function.R'
'print_soo_function.R' 'rotate_parameter_space.R'
'check-arguments.r' 'sortinghat-package.r'
'helper-partition-data.r' 'helper-which-min.r'
'simdata-normal.r' 'simdata-uniform.r' 'covariance-matrices.r'
'helpers.r' 'simdata-t.r' 'simdata-contaminated-normal.r'
'simdata-guo.r' 'simdata-friedman.r' 'simdata-wrapper.r'
'errorest-632.r' 'errorest-632plus.r' 'errorest-apparent.r'
'errorest-bcv.r' 'errorest-boot.r' 'errorest-cv.r'
'errorest-loo-boot.r' 'errorest-wrapper.r'
Constants.R Class-OWS.R Class-GML.R Class-SWE.R Class-OM.R
Class-SA.R Class-OGC.R Class-SOS.R Class-SML.R
Generic-methods.R OWS-methods.R OWS-methods-parsing.R
SOS-methods-parsing.R OM-methods.R OM-methods-coercion.R
OM-methods-parsing.R SA-methods.R GML-methods.R SWE-methods.R
SML-methods.R GML-methods-parsing.R SA-methods-parsing.R
SWE-methods-parsing.R OGC-methods.R SOS-methods-accessor.R
PrintShowStructureSummmary-methods.R SOS-methods-util.R
SOS-methods.R SOS-methods-plotting.R SOS-methods-coercion.R
SML-methods-util.R SML-methods-coercion.R Defaults.R
'NPC.R' 'PermSpace.R' 'RankResults.R' 'SOUP.R'
'multiplicity.R' 'simpleAD.R' 'simpleHotelling.R'
'simpleMeanDiff.R' 'simpleTtest.R' 'strataAD.R'
'strataLmCoef.R' 'strataMeanDiff.R' 'strataTtest.R' 't2p.R'
'utilities.R' 'PValueMat.R' 'SoupObject.R' 'rankingRule.R'
'iterNPC.r'
bpy.colors.R AAA.R Class-CRS.R CRS-methods.R Class-Spatial.R
Spatial-methods.R projected.R Class-SpatialPoints.R
SpatialPoints-methods.R Class-SpatialPointsDataFrame.R
SpatialPointsDataFrame-methods.R Class-SpatialMultiPoints.R
SpatialMultiPoints-methods.R
Class-SpatialMultiPointsDataFrame.R
SpatialMultiPointsDataFrame-methods.R Class-GridTopology.R
Class-SpatialGrid.R Class-SpatialGridDataFrame.R
Class-SpatialLines.R SpatialLines-methods.R
Class-SpatialLinesDataFrame.R SpatialLinesDataFrame-methods.R
Class-SpatialPolygons.R Class-SpatialPolygonsDataFrame.R
SpatialPolygons-methods.R SpatialPolygonsDataFrame-methods.R
GridTopology-methods.R SpatialGrid-methods.R
SpatialGridDataFrame-methods.R SpatialPolygons-internals.R
point.in.polygon.R SpatialPolygons-displayMethods.R zerodist.R
image.R stack.R bubble.R mapasp.R select.spatial.R gridded.R
asciigrid.R spplot.R over.R spsample.R recenter.R dms.R
gridlines.R spdists.R rbind.R flipSGDF.R chfids.R loadmeuse.R
compassRose.R surfaceArea.R spOptions.R subset.R disaggregate.R
sp_spat1.R merge.R aggregate.R
Class-xts.R Class-ST.R Class-STFDF.R Class-STSDF.R
Class-STIDF.R Class-STTDF.R interval.R endtime.R ST-methods.R
STFDF-methods.R STSDF-methods.R STIDF-methods.R STTDF-methods.R
apply.R coerce.R stconstruct.R plot.R stplot.R timematch.R
over.R aggregate.R eof.R mnf.R bind.R na.R raster.R tgrass.R
stinteraction.R
all_classes.R common.R moran.R SpawML.R Descriptives.R
ResExSpawML.R ResAggSpawML.R Weights.R ExplorSpawML.R
'environment.R' 'priority.R' 'module-dependencies-class.R'
'misc-methods.R' 'helpers.R' 'simList-class.R' 'POM.R'
'cache.R' 'check.R' 'checkpoint.R' 'copy.R' 'experiment.R'
'simulation-parseModule.R' 'simulation-simInit.R' 'load.R'
'module-define.R' 'module-dependencies-methods.R'
'moduleMetadata.R' 'module-repository.R' 'module-template.R'
'moduleCoverage.R' 'times.R' 'simList-accessors.R'
'plotting-diagrams.R' 'plotting.R' 'progress.R' 'save.R'
'simulation-spades.R' 'spades-classes.R'
'spades-core-package.R' 'zzz.R'
'RcppExports.R' 'heading.R' 'SELES.R'
'distanceFromEachPoint.R' 'downloadInputs.R' 'environment.R'
'gis.R' 'initialize.R' 'mapReduce.R' 'mergeRaster.R'
'movement.R' 'neighbourhood.R' 'numerical-comparisons.R'
'resample.R' 'rings.R' 'spades-tools-package.R' 'splitRaster.R'
'spread.R' 'spread2.R' 'zzz.R'
'schema.R' 'generics.R' 'jobj.R' 'column.R' 'group.R' 'RDD.R'
'pairRDD.R' 'DataFrame.R' 'SQLContext.R' 'WindowSpec.R'
'backend.R' 'broadcast.R' 'catalog.R' 'client.R' 'context.R'
'deserialize.R' 'functions.R' 'install.R' 'jvm.R'
'mllib_classification.R' 'mllib_clustering.R' 'mllib_fpm.R'
'mllib_recommendation.R' 'mllib_regression.R' 'mllib_stat.R'
'mllib_tree.R' 'mllib_utils.R' 'serialize.R' 'sparkR.R'
'stats.R' 'streaming.R' 'types.R' 'utils.R' 'window.R'
'call_all_functions.R' 'cov_estimation_tri.R'
'cov_estimation_tri_constr.R' 'cov_estimation_whole.R'
'fpc_estimation.R' 'fpc_famm_estimation.R'
'get_cross_products.R' 'mean_estimation.R'
'tri_constraint_constructor.R' 'useful_functions.R' 'zzz.R'
'tailIntEmp.r' 'gridUtilities.r' 'EstimationSmith.r'
'EstimationBR.r' 'Estimation.R' 'spatialTailDep-package.r'
'block_matrices.R' 'c_cov_matrices.R' 'c_F_mult.R'
'c_lin_alg.R' 'CV_aux.R' 'CV_estimate.R' 'CV_predict.R'
'deprecated.R' 'internal.R' 'loglikeST.R' 'loglikeST_aux.R'
'loglikeST_derivatives.R' 'MCMC.R' 'SpatioTemporal-data.R'
'SpatioTemporal-package.R' 'STdata.R' 'STdata_processing.R'
'STmodel.R' 'STmodel_combine.R' 'STmodel_estimate.R'
'STmodel_predict.R' 'STmodel_processing.R' 'STmodel_setup.R'
'STmodel_simulate.R' 'temporal_SVD_incomplete_data.R' 'utils.R'
'CUSUMlib.R' 'model.R' 'main.R' 'CUSUM.R' 'EWMAlib.R' 'EWMA.R'
'data.R' 'lm.R' 'logreg.R' 'shewhart.R'
class_Stratification.R class_CompactStratification.R
class_CompactStratificationEqualArea.R
class_CompactStratificationPriorPoints.R
class_SamplingPattern.R class_SamplingPatternPurposive.R
class_SamplingPatternCentroids.R
class_SamplingPatternPriorPoints.R
class_SamplingPatternRandom.R
class_SamplingPatternRandomSamplingUnits.R
class_SamplingPatternRandomComposite.R class_Statistic.R
class_SpatialMean.R class_SamplingVariance.R
class_StandardError.R class_SpatialVariance.R
class_SpatialCumulativeDistributionFunction.R
generic_estimate.R generic_getArea.R generic_getRelativeArea.R
generic_getAttributes.R generic_getCellSize.R
generic_getCentroid.R generic_getSampleSize.R
generic_getNumberOfCells.R generic_getNumberOfStrata.R
generic_getObjectiveFunctionValue.R generic_stratify.R
generic_plot.R methods_estimate.R
method_getArea-CompactStratification.R
method_getRelativeArea-CompactStratification.R
method_getCellSize-CompactStratification.R
method_getCellSize-SpatialPixels.R
method_getCentroid-CompactStratification.R
method_getNumberOfCells-CompactStratification.R
method_getNumberOfStrata-CompactStratification.R
method_getObjectiveFunctionValue-CompactStratification.R
method_getSampleSize-SamplingPattern.R
method_getSampleSize-SamplingPatternRandomComposite.R
method_initialize-CompactStratification.R
method_initialize-CompactStratificationPriorPoints.R
method_stratify-SpatialGrid.R method_stratify-SpatialPixels.R
method_stratify-SpatialPolygons.R methods_plot.R
methods_spsample.R methods_setAs.R method_show-Stratification.R
method_show-SamplingPattern.R method_show-Statistic.R onLoad.R
onAttach.R
misc-spd.R classes-spd.R methods-GPD.R methods-spdFit.R
methods-spdDistribution.R methods-spdPlots.R
'spectacles-package.R' 'AAA-Classes.R' 'Spectra-methods.R'
'SpectraDataFrame-methods.R' 'ZZZ.R' 'aggregate_spectra.R'
'australia.R' 'big_head.R' 'kenstone.R' 'performance.R'
'plot-methods.R' 'preprocessing.R' 'setters.R' 'splice.R'
AAA.R options.R spgrass6.R bin_link.R vect_link.R initGRASS.R
xml1.R
'utils.R' 'spikeAndSlab.R' 'terms.R' 'ssGAMDesign.R'
'spikeSlabGAM.R' 'bugs.R' 'plot.R' 'predict.R' 'summary.R'
'zzz.R'
Swrappers.R peaks.R bits.per.integer.R doTest.R muS2RC.R
showStructure.R sigseriesS3.R sigseriesS4.R splus2R_is.R
splus2R_pkg.R
'SDM.R' 'Algorithm.SDM.R' 'Ensemble.SDM.R' 'Env.R'
'Occurrences.R' 'PA.select.R' 'SSDM.R' 'Stacked.SDM.R'
'checkargs.R' 'data.values.R' 'ensemble.R' 'modelling.R'
'ensemble_modelling.R' 'evaluate.R' 'evaluate.axes.R'
'get_PA.R' 'get_model.R' 'gui.R' 'load_model.R' 'load_occ.R'
'load_var.R' 'mapDiversity.R' 'plot.model.R' 'project.R'
'save.model.R' 'stack_modelling.R' 'stacking.R'
'update.stack.R' 'zzz.R'
cte.R ExternalPackageInterface.R ToolsFct.R Interpolation.R
RegularInverse.R stableCF.R CFbasedMoment.R WeightingMatrix.R
eCFfirstZero.R tSchemes.R MultiDimIntegral.R InitialGuess.R
KoutParamsEstim.R MLParamsEstim.R GMMParamsEstim.R
CgmmParamsEstim.R OutputFileManip.R CheckPoint.R BestT_Class.R
Estim_Class.R Estim.R Simulation.R BestT.R
'create_generic.R' 'data.R' 'fun_table_per_dis.R'
'fun_write_monthly.R' 'fungraph.R' 'fungraph_glasseel.R'
'funstat.R' 'funstat_daily.R' 'funtable.R'
'interface_report_annual.R' 'interface_report_dc.R'
'interface_report_df.R' 'interface_report_env.R'
'interface_report_ge_weight.R' 'interface_report_mig.R'
'interface_report_mig_char.R' 'interface_report_mig_env.R'
'interface_report_mig_interannual.R'
'interface_report_mig_mult.R' 'interface_report_sample_char.R'
'interface_report_sea_age.R' 'interface_report_silver_eel.R'
'interface_report_species.R' 'ref_checkbox.R' 'ref_choice.R'
'ref_coe.R' 'ref_dc.R' 'ref_df.R' 'ref_env.R' 'ref_horodate.R'
'ref_list.R' 'ref_par.R' 'ref_parqual.R' 'ref_parquan.R'
'ref_period.R' 'ref_stage.R' 'ref_taxa.R' 'ref_textbox.R'
'ref_timestep.R' 'ref_timestep_daily.R' 'ref_year.R'
'report_annual.R' 'report_dc.R' 'report_df.R' 'utilities.R'
'report_env.R' 'report_ge_weight.R' 'report_ope.R'
'report_mig.R' 'report_sample_char.R' 'report_mig_char.R'
'report_mig_mult.R' 'report_mig_env.R'
'report_mig_interannual.R' 'report_sea_age.R'
'report_silver_eel.R' 'report_species.R' 'setAs.R' 'stacomi.R'
'ConnectionODBC.r' 'RequeteODBC.r' 'RequeteODBCwhere.r'
'RequeteODBCwheredate.r' 'utilities.r' 'stacomirtools.r'
'SolverOptions.R' 'addNamesToList.R' 'checkICs.R'
'checkSolution.R' 'deathFunction.R' 'reproductionFunction.R'
'immigrationFunction.R' 'emigrationFunction.R'
'durationFunction.R' 'developmentFunction.R'
'defaultRateFunctions.R' 'derivDede.R' 'derivPBS.R'
'genericPlot.R' 'initConditions.R' 'namingVariables.R'
'plotStrains.R' 'popModel.R' 'rateFuncCheck.R' 'rateFunction.R'
'runStagePopExample.R' 'stagePop-package.R' 'sumStrains.R'
'vecDivideFunc.R' 'vecToMatList.R'
'stutil.R' 'stwin.R' 'stsubpop.R' 'stmodelKM.R' 'stmodelCOX.R'
'stmodelCI.R' 'stmodelGLM.R' 'stmodel.R' 'steppes.R' 'stepp.R'
'init.R' 'sticky.R' 'append.R' 'extract1.R' 'print.R'
'subset.R' 'utils.R' 'zzz.R'
strataG-package.R gtypes.class.R gtypes.accessors.R
is.gtypes.R initialize.gtypes.R strataG-internal.R
as.array.gtypes.R as.matrix.gtypes.R as.data.frame.gtypes.R
alleleFreqFormat.R alleleFreqs.R alleleSplit.R
allelicRichness.R arlequin.R as.multidna.R baseFreqs.R clumpp.R
createConsensus.R dupGenotypes.R evanno.R expandHaplotypes.R
fasta.R fastsimcoal.input.R fastsimcoal.R fixedDifferences.R
fixedSites.R freq2GenData.R fusFs.R gelato.R genepop.R
heterozygosity.R hweTest.R iupacCode.R jackHWE.R
labelHaplotypes.R ldNe.R LDgenepop.R lowFreqSubs.R mRatio.R
maf.R mafft.R maverickRun.R mega.R mostDistantSequences.R
mostRepresentativeSequences.R neiDa.R nucleotideDivergence.R
nucleotideDiversity.R numAlleles.R numericSNPmat.R
numGenotyped.R numMissing.R permuteStrata.R phase.R
popGenEqns.R popStructStat.R popStructTest.R privateAlleles.R
propUniqueAlleles.R qaqc.R RcppExports.R readGenData.R
removeSequences.R sequenceLikelihoods.R sharedLoci.R
show.gtypes.R simGammaHaps.R strataGUI.R strataSplit.R
stratify.R structure.R structurePlot.R summarizeLoci.R
summarizeSamples.R summarizeSeqs.R summary.gtypes.R tajimasD.R
theta.R TiTvRatio.R trimNs.R validIupacCodes.R variableSites.R
write.gtypes.R write.nexus.snapp.R gtypes2genind.R
gtypes2loci.R gtypes2phyDat.R df2gtypes.R sequence2gtypes.R
'filterStream.R' 'parseTweets.R' 'sampleStream.R'
'userStream.R' 'streamR-package.R' 'readTweets.R'
'addins.R' 'communicate.R' 'compat-dplyr.R' 'compat-tidyr.R'
'expr-is.R' 'indent.R' 'initialize.R' 'nest.R'
'nested-to-tree.R' 'parse.R' 'reindent.R' 'token-define.R'
'relevel.R' 'rules-line-break.R' 'rules-other.R'
'rules-replacement.R' 'rules-spacing.R' 'serialize.R'
'set-assert-args.R' 'style-guides.R' 'styler.R' 'testing.R'
'token-create.R' 'transform-code.R' 'transform-files.R' 'ui.R'
'unindent.R' 'utils.R' 'vertical.R' 'visit.R' 'zzz.R'
fdr.est.R getBackground.R getEdgeLabel.R getEdgeLty.R
getInteGraphList.R GetK2riData.R getLayout.R getLocSubGraph.R
getOneNodePath.R getSymbolFromGene.R identifyGraph.R
initializeK2ri.R plotGraph.R printGraph.R updateOrgEnvir.R
mytriangle.R
ExpProcess.r getBackgroundLnc.r getEdgeLabel.r getEdgeLty.r
getEnvironmentData.r getInteGraphList.r getInteUMGraph.r
getKGeneFromGene.r getLayout.r getLncGenePairs.r
getExampleData.r getLocSubGraphLnc.r getNodeLabel.r
getOneNodePath.r getSymbolFromGene.r identifyLncGraphW.r
plotAnnGraph.r plotGraphL.r printGraphW.r
'package.R' 'readwrite.R' 'signals.R' 'subprocess.R' 'tests.R'
'utils.R'
'BMT.R' 'BRCAOV.survInfo.R' 'add_ggsurvplot.R' 'utilities.R'
'surv_summary.R' 'ggsurvtable.R' 'surv_pvalue.R'
'ggsurvplot_df.R' 'ggsurvplot_core.R' 'ggsurvplot_add_all.R'
'ggsurvplot_list.R' 'ggsurvplot_group_by.R' 'ggsurvplot.R'
'arrange_ggsurvplots.R' 'ggadjustedcurves.R'
'ggcompetingrisks.R' 'ggcoxdiagnostics.R' 'ggcoxfunctional.R'
'ggcoxzph.R' 'ggflexsurvplot.R' 'ggforest.R' 'ggsurvevents.R'
'ggsurvplot_combine.R' 'ggsurvplot_facet.R' 'ggsurvtheme.R'
'ggurvplot_arguments.R' 'myeloma.R' 'pairwise_survdiff.R'
'surv_cutpoint.R' 'surv_group_by.R' 'surv_fit.R'
'surv_median.R'
'sf.R' 'nc.R' 'ci.R' 'autoplotTAP.R' 'autoplotTen.R' 'print.R'
'asWide.R' 'ten.R' 'COV.R' 'predict.R' 'comp.R' 'cutp.R'
'gastric.R' 'gof.R' 'onAttach.R' 'plotSurv.R' 'profLik.R'
'rsq.R' 'survMisc_package.R' 'xtable.R'
generics.R validmodelorg.R validoptsol.R validreactId.R
validreactId_Exch.R validsysBiolAlg.R addAlgorithm.R
addExchReact.R addReact.R addSolver.R blockedReact.R
bracket_pairs.R ceilValues.R changeBounds.R changeGPR.R
changeObjFunc.R checkAlgorithm.R checkDefaultMethod.R
checkEmptyField.R checkReactId.R check_brackets.R
createReactionString.R deadEndMetabolite.R doInRound.R
doubleFluxDel.R doubleGeneDel.R doubleReact.R editEnvir.R
findExchReact.R floorValues.R fluxVar.R geneDel.R
geneDeletion.R generateFluxdels.R generateModKey.R generateWT.R
getsybilenv.R makeLPcompatible.R mod2irrev.R modelorg2ExPA.R
modelorg2text.R modelorg2tsv.R multiDel.R oneFluxDel.R
oneGeneDel.R onlyChangeGPR.R onlyCheckGPR.R optObj_basicfunc.R
optObj_lpSolveAPIcompat.R optimizer.R parseBoolean.R phpp.R
ppProcessing.R prepareSubSysMatrix.R printLogComment.R
printNamedList.R progress.R promptSysBiolAlg.R recodeMatrix.R
readTEXTmod.R readTSVmod.R reassignFwBwMatch.R rmReact.R
robAna.R settings.R singletonMetabolite.R sybilStack.R ypd.R
zzz.R modelorgClass.R modelorg_irrevClass.R optObj_pointer.R
optObjClass.R optObj_clpAPIClass.R optObj_cplexAPIClass.R
optObj_glpkAPIClass.R optObj_lpSolveAPIClass.R
sybilErrorClass.R ppProcClass.R netFluxClass.R
fluxDistributionClass.R reactIdClass.R reactId_ExchClass.R
optsolClass.R optsol_blockedReactClass.R
optsol_optimizeProbClass.R optsol_fluxVarClass.R
optsol_fluxdelClass.R optsol_robAnaClass.R optsol_phppClass.R
optsol_genedelClass.R checksolClass.R summaryOptsolClass.R
sysBiolAlgClass.R sysBiolAlg_fbaClass.R
sysBiolAlg_fbaEasyConstraintClass.R sysBiolAlg_fvClass.R
sysBiolAlg_lmomaClass.R sysBiolAlg_momaClass.R
sysBiolAlg_mtfClass.R sysBiolAlg_mtfEasyConstraintClass.R
sysBiolAlg_roomClass.R sybilLogClass.R upgradeModelorg.R
'00_environment.R' '01_class_01_kRp.hyph.pat.R'
'01_class_02_kRp.hyphen.R' '02_method_correct.R'
'02_method_hyphen.R' '02_method_kRp.hyphen.R'
'02_method_show.kRp.hyphen.R' '02_method_summary.kRp.hyphen.R'
'get.sylly.env.R' 'manage.hyph.pat.R' 'read.hyph.pat.R'
'set.hyph.support.R' 'set.sylly.env.R' 'sylly-internal.R'
'sylly-internal_langpack_generator.R' 'sylly-package.R'
'Other.R' 'helpFunctions.R' 'EstimationBR.R'
'EstimationGumbel.R' 'EstimationML.R'
'compile-cell.R' 'compile-operators.R' 'compile-post.R'
'parser.R' 'compile.R' 'compile-rms.R' 'compile-typing.R'
'helper-format.R' 'hmisc-cut2.R' 'hmisc-lm.fit.qr.bare.R'
'hmisc-impute.R' 'hmisc-biVar.R' 'render-html5.R'
'render-index.R' 'render-latex-map.R' 'render-latex.R'
'render-rmd.R' 'render-rtf.R' 'render-summary.R'
'transform-hmisc.R'
'NULLing.R''supporting_functions.R''taxlist-class.R''clean.R''as.list.R'
'taxon_names.R''taxon_relations.R''taxon_traits.R''taxon_views.R'
'levels.R''add_concept.R''add_synonym.R''add_trait.R''add_view.R'
'accepted_name.R' 'add_level.R''add_parent.R''get_children.R'
'change_concept.R''Extract.R''subset.R' 'merge_taxa.R'
'backup_object.R''summary.R'
'df2taxlist.R''tv2taxlist.R''tnrs.R' 'tax2traits.R'
'StartMessage.R'
'equal.lengths.r' 'is.wholenumber.r' 'qft.cens.r'
'qft.criteria.070.r' 'qft.criteria.cellestis.nomito.r'
'qft.criteria.cellestis.usa.r' 'trim.output.r'
'tbdiag-package.r' 'tspot.cens.r' 'tspot.criteria.10spot.r'
'tspot.criteria.oxford.global.r' 'tspot.criteria.oxford.usa.r'
'qft.interp.R' 'tspot.interp.R'
'has-dimension.r' 'has_factors.r' 'has-missing.r'
'has-names.r' 'is-class.r' 'is-dataframe.r' 'is-decimal.r'
'is-integer.r' 'is-matrix.r' 'is-multiple.r' 'is-natural.r'
'is-positive-negative.r' 'is-string.r' 'is-tabular.r'
'is-triangular.r' 'is-vector.r' 'is_square_matrix.r'
'list-of-vectors.r' 'list-with-vectors.r' 'odd-even.r'
'true-false.r' 'same-class.r' 'same-dim.r' 'same-length.r'
'same-mode.r' 'same-type.r' 'is-one-dim.r' 'is-single.r'
'is-scalar.r'
'auto-test.R' 'capture-condition.R' 'capture-output.R'
'colour-text.R' 'compare.R' 'compare-character.R'
'compare-numeric.R' 'compare-time.R' 'context.R' 'describe.R'
'evaluate-promise.R' 'expect-comparison.R' 'expect-equality.R'
'expect-inheritance.R' 'expect-known.R' 'expect-length.R'
'expect-logical.R' 'expect-named.R' 'expect-output.R'
'reporter.R' 'expect-self-test.R' 'expect-that.R'
'expectation.R' 'expectations-matches.R' 'make-expectation.R'
'mock.R' 'old-school.R' 'praise.R' 'recover.R'
'reporter-check.R' 'reporter-debug.R' 'reporter-fail.R'
'reporter-junit.R' 'reporter-list.R' 'reporter-location.R'
'reporter-minimal.R' 'reporter-multi.R' 'stack.R'
'reporter-progress.R' 'reporter-rstudio.R' 'reporter-silent.R'
'reporter-stop.R' 'reporter-summary.R' 'reporter-tap.R'
'reporter-teamcity.R' 'reporter-zzz.R' 'skip.R' 'source.R'
'teardown.R' 'test-compiled-code.R' 'test-directory.R'
'test-example.R' 'test-files.R' 'test-path.R' 'test-that.R'
'traceback.R' 'try-again.R' 'utils-io.R' 'utils.R' 'watcher.R'
'AIC.evm.R' 'Dcond.R' 'Profile_likelihood_HT_unc.R'
'Profile_likelihood_cd_nm_joint_D_KT.R'
'Profile_likelihood_cd_nm_joint_D_KT_neg.R' 'bootmex.R' 'chi.R'
'coef.evmSim.R' 'coefficients.evm.R' 'coefficients.migpd.R'
'constructObject.R' 'copulas.R' 'degp3.R' 'dgev.R' 'dgpd.R'
'efficient.closures.R' 'texmexFamily.R' 'egp3.R'
'egp3RangeFit.R' 'endPoint.R' 'estimate_HT.R'
'estimate_HT_KPT_joint_posneg_nm.R' 'evm.R' 'evm.simSetSeed.R'
'evmBoot.R' 'evmFit.R' 'evmSim.R' 'extremalIndex.R' 'gev.R'
'ggplot.bayes.R' 'ggplot.boot.R' 'ggplot.cluster.R'
'ggplot.evm.R' 'ggplot.mex.R' 'ggplot.migpd.R'
'ggplot.predict.R' 'ggplot.thresh.R' 'globalVariables.R'
'gpd.sandwich.R' 'gpd.info.R' 'gpd.R' 'gpdRangeFit.R'
'gumbel.R' 'hist.evm.R' 'initial_posneg.R'
'jointExceedanceCurves.R' 'mex.R' 'mexDependence.R'
'mexDependenceLowLevelFunctions.R' 'mexMonteCarlo.R'
'mexRangeFit.R' 'mexTransform.R' 'migpd.R' 'migpdCoefs.R'
'mrl.R' 'mspearman.R' 'multivariate.cauchy.R' 'pegp3.R'
'pgev.R' 'pgpd.R' 'plot.bootmex.R' 'plot.evm.R' 'plot.evmSim.R'
'plot.mex.R' 'plot.mexPrediction.R' 'plot.migpd.R'
'plot.predict.link.evm.R' 'plotrl.evm.R' 'ppevm.R'
'predict.evm.R' 'predict.mex.R' 'predictWorkers.R'
'print.bootmex.R' 'print.evm.R' 'print.evmSim.R'
'print.mexDependence.R' 'print.mexPrediction.R' 'print.migpd.R'
'profile_minmax_joint_posneg_KT.R' 'qegp3.R' 'qgev.R' 'qgpd.R'
'qgpd2.R' 'qqevm.R' 'rMaxAR.R' 'regp3.R' 'residuals.evm.R'
'revTransform.R' 'rgev.R' 'rgpd.R' 'roots.R' 'simulate.R'
'sombrero-internal.R' 'special.functions.R' 'summary.evm.R'
'summary.evmSim.R' 'summary.migpd.R' 'summary.predict.mex.R'
'texmex-package.R' 'texmexWorkers.R' 'thinAndBurn.evm.sim.R'
'u2gpd.R' 'weibull.info.R' 'weibull.R'
'Tier-class.R' 'IntervalTier-class.R'
'IntervalTier-accessors.R' 'IntervalTier-constructor.R'
'IntervalTier-utilities.R' 'PointTier-class.R'
'PointTier-accessors.R' 'PointTier-constructor.R'
'PointTier-utilities.R' 'TextGrid-class.R'
'TextGrid-accessors.R' 'TextGrid-constructor.R'
'TextGrid-utilities.R' 'Tier-accessors.R' 'Tier-utilities.R'
'finders.R' 'textgRid.R'
'add.R' 'as_tibble.R' 'check-names.R' 'compat-lazyeval.R'
'compat-purrr.R' 'enframe.R' 'exports.R' 'glimpse.R'
'has-name.R' 'tibble.R' 'lst.R' 'new.R' 'repair-names.R'
'rownames.R' 'strrep.R' 'tbl-df.r' 'tibble-package.R'
'tribble.R' 'type-sum.r' 'utils-format.r' 'utils.r' 'wrap.R'
'data.R' 'drop_cols.R' 'drop_rows.R' 'impute.R'
'make_impute.R' 'utils.R' 'impute_funs.R' 'na_predict.R'
'zzz.R'
'analyse_TL.MAAD.R' 'analyse_TL.SAR.R' 'analyse_TL.plateau.R'
'calc_TL.LxTx.R' 'calc_TL.MAAD.fit.I.R' 'calc_TL.MAAD.fit.Q.R'
'calc_TL.MAAD.separate.R' 'calc_TL.MAAD.average.R'
'calc_TL.SAR.fit.R' 'calc_TL.plateau.R' 'calc_TL.temperature.R'
'mod_align.peaks.R' 'mod_extract.TL.R' 'mod_extract.aliquot.R'
'mod_extract.preheat.R' 'mod_update.dType.R'
'mod_update.error.R' 'mod_remove.preheat.R'
'mod_remove.aliquot.R' 'mod_substract.background.R'
'plot_align.peaks.R' 'plot_extract.TL.R'
'plot_remove.preheat.R' 'plot_substract.background.R'
'plot_TL.plateau.R' 'plot_TL.MAAD.R' 'plot_TL.SAR.R'
'script_TL.export.R' 'script_TL.import.R' 'script_TL.MAAD.R'
'script_TL.plateau.R' 'script_TL.pretreatment.R'
'script_TL.SAR.R' 'TLum-class.R' 'TLum.Data-class.R'
'TLum.BIN.File-class.R' 'TLum.Data.Curve-class.R'
'TLum.Analysis-class.R' 'TLum.Results-class.R'
'Risoe.BINfileData2TLum.BIN.File.R'
'TLum.BIN.File2Risoe.BINfileData.R'
'TLum.BIN.File2TLum.Analysis.R' 'RLum.Analysis2TLum.Analysis.R'
'TLum.BIN.File2TLum.Data.Curve.R'
'TLum.Analysis2TLum.BIN.File.R' 'TLum.Analysis2RLum.Analysis.R'
'template_DRAC4flint.R' 'use_DRAC4flint.R'
'template_DRAC4ceramic.R' 'use_DRAC4ceramic.R'
'template_DRAC4cave.R' 'use_DRAC4cave.R'
'template_DRAC4brick.R' 'use_DRAC4brick.R' 'TLdating-package.R'
'certification_movie_list.R' 'certification_tv_list.R'
'changes_movie.R' 'changes_person.R' 'changes_tv.R'
'collection.R' 'collection_images.R' 'company.R'
'company_movies.R' 'configuration.R' 'credit.R'
'discover_movie.R' 'discover_tv.R' 'find_tmdb.R'
'genres_movie_list.R' 'genres_movies.R' 'genres_tv_list.R'
'is.wholenumber.R' 'jobs.R' 'keyword.R' 'keyword_movies.R'
'list_get.R' 'list_item_status.R' 'movie.R'
'movie_alternative_title.R' 'movie_changes.R' 'movie_credits.R'
'movie_images.R' 'movie_keywords.R' 'movie_latest.R'
'movie_lists.R' 'movie_now_playing.R' 'movie_popular.R'
'movie_releases.R' 'movie_reviews.R' 'movie_similar.R'
'movie_top_rated.R' 'movie_translations.R' 'movie_upcoming.R'
'movie_videos.R' 'network.R' 'person_tmdb.R' 'person_changes.R'
'person_combined_credits.R' 'person_external_ids.R'
'person_images.R' 'person_latest.R' 'person_movie_credits.R'
'person_popular.R' 'person_tagged_images.R'
'person_tv_credits.R' 'review.R' 'search_collection.R'
'search_company.R' 'search_keyword.R' 'search_list.R'
'search_movie.R' 'search_multi.R' 'search_person.R'
'search_tv.R' 'timezone.R' 'tv.R' 'tv_airing_today.R'
'tv_alternative_title.R' 'tv_changes.R' 'tv_content_ratings.R'
'tv_credits.R' 'tv_episode.R' 'tv_episode_changes.R'
'tv_episode_credits.R' 'tv_episode_external_ids.R'
'tv_episode_images.R' 'tv_episode_videos.R' 'tv_external_ids.R'
'tv_images.R' 'tv_keywords.R' 'tv_latest.R' 'tv_on_the_air.R'
'tv_popular.R' 'tv_season.R' 'tv_season_changes.R'
'tv_season_credits.R' 'tv_season_external_ids.R'
'tv_season_images.R' 'tv_season_videos.R' 'tv_similar.R'
'tv_top_rated.R' 'tv_translations.R' 'tv_videos.R'
tmuxr.R server.R session.R window.R pane.R prompt.R send.R
'toaster.R' 'misc.R' 'utils.R' 'tools.R'
'computeCorrelations.R' 'computeHistogram.R' 'computeHeatmap.R'
'maps.R' 'showData.R' 'computeAggregates.R' 'computeBarchart.R'
'computeSample.R' 'computePercentiles.R' 'computeLm.R'
'computeTfIdf.R' 'textParsers.R' 'computeKmeans.R' 'plotting.R'
'plottingKmeans.R' 'showGraph.R' 'computeGraph.R'
'computeGraphClusters.R' 'validateGraph.R'
'gui_andrews.r' 'gui_density.r' 'gui_faces.r' 'gui_image.r'
'gui_pcp.r' 'gui_scatmat.r' 'gui_stars.r' 'gui_stereo.r'
'gui_tour.r' 'gui_xy.r' 'tour_andrews.r' 'tour_density.r'
'tour_faces.r' 'tour_pcp.r' 'tour_scatmat.r' 'tour_stars.r'
'tour_stereo.r' 'tour_xy.r'
'Classes.R' 'DocCollectionRef-methods.R' 'Generics.R'
'PlotWrapper.R' 'ObjFeatureSet.R' 'Trackr-main-API.R'
'abbrev.R' 'accessor-methods.R' 'autotrack.R' 'backend-impls.R'
'coercerender.R' 'convenience_constructors.R'
'ggplotversions.R' 'knitr.R' 'makeRStudioExtras.R'
'parseCode.R' 'shiny.R' 'showMethods.R'
'Swap.R' 'Vol.R' 'Option.R' 'Trade.R' 'IRD.R' 'Bond.R' 'CSA.R'
'Collateral.R' 'Commodity.R' 'Credit.R' 'Curve.R' 'Equity.R'
'FX.R' 'GetTradeDetails.R' 'HashTable.R' 'ParseTrades.R'
'helpers.R' 'base.R' 'model.R' 'view.R' 'controller.R'
'container.R' 'editor.R' 'itemgroup.R' 'dialog.R' 'items.R'
'itemlist.R' 'loadanimation.R' 'traitR-package.R'
Class-Tracks.R Tracks-methods.R generalize.R stcube.R stplot.R
difftrack.R compare-methods.R
'calc-methods.R' 'check_methods.R' 'cnvrt-methods.R'
'gen-methods.R' 'get-nd-methods.R' 'get-nds-methods.R'
'get-spcl-methods.R' 'manip-methods.R' 'ndlst-methods.R'
'ndmtrx-methods.R' 'node-declaration.R' 'read-write-methods.R'
'server-methods.R' 'set-methods.R' 'treeman-declaration.R'
'treemen-declaration.R' 'update-methods.R' 'viz-methods.R'
'zzz.R'
RcppExports.R metrics.R medTree.R treespace.R findGroves.R
plotGroves.R plotTreeDiff.R servers.R transmissionTrees.R
data.R makeCollapsedTree.R relatedTreeDist.R simulateIndTree.R
tipsMRCAdepths.R treeConcordance.R
'avatar.R' 'dashboard.R' 'def.postParams.R' 'follow.R'
'followers.R' 'http.connection.R' 'info.blog.R' 'like.post.R'
'likes.R' 'post.R' 'post.delete.R' 'post.edit.R'
'post.reblog.R' 'posts.R' 'posts.draft.R' 'posts.queue.R'
'posts.submission.R' 'tagged.R' 'unfollow.R' 'unlike.post.R'
'user.following.R' 'user.info.R' 'user.likes.R' 'compact.R'
'nonce.R' 'oauth.encode.R' 'oauth.encode1.R' 'sort.names.R'
'df_to_blocks.r' 'dummy_to_list.r' 'factor_to_dummy.r'
'from_to.r' 'funlist.r' 'indexify.r' 'lengths.r' 'list_ones.r'
'list_to_dummy.r' 'list_to_matrix.r' 'listify.r' 'listsize.r'
'matrix_to_blocks.r' 'vector_to_dummy.r' 'turner-package.r'
'RcppExports.R' 'aaa.R' 'tween_numeric.R' 'display_ease.R'
'interpolate.R' 'tween_colour.R' 'tween_datetime.R'
'tween_date.R' 'tween.R' 'tween_appear.R' 'tween_constant.R'
'tween_elements.R' 'tween_states.R' 'tweenr_package.R'
allGenerics.R base.R account.R statuses.R users.R trends.R
s4methods.R convert.R dm.R oauth.R comm.R followers.R search.R
db.R df_columns.R db_connections.R db_utils.R db_search.R
toys.R utils.R zzz.R
'class-01-Points.R' 'class-02-UncertainPoints.R'
'class-03-UncertainInterpolation.R'
'class-04-FuzzyInterpolation.R' 'class-05-Variogram.R'
'methods-as.data.frame.R' 'methods-as.UncertainPoints.R'
'methods-uncertaintyConstant.R' 'methods-uncertaintyError.R'
'methods-uncertaintyPercent.R'
'methods-uncertaintyRandomDeviate.R'
'methods-uncertaintyRandomNumber.R'
'methods-uncertaintyRandomPercent.R' 'methods-Grid.R'
'methods-idwUncertain.R' 'methods-splineUncertain.R'
'methods-krigingUncertain.R' 'methods-variogram.R'
'methods-plot.R'
'simRedCard.R' 'getLineup.R' 'formation.R'
'penaltyGoalsProb.R' 'summary.R' 'unitedRPackage.R'
'unitedRoverview.R' 'unitedSimClass.R' 'unitedSimResults.R'
'unitedSimOne.R' 'unitedSim.R'
'asciiml.R' 'capture.R' 'is.R' 'global.R' 'change.R'
'class_unions.R' 'list.R' 'conditions.R' 'item.R' 'deparse.R'
'text.R' 'item.sub.R' 'section.R' 'test.R' 'unitizer.R'
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'diff.R' 'faux_prompt.R' 'get.R' 'heal.R' 'load.R' 'ls.R'
'misc.R' 'search.R' 'options.R' 'onload.R' 'parse.R' 'rename.R'
'repairenvs.R' 'result.R' 'shims.R' 'size.R' 'state.R'
'state.compare.R' 'translate.R' 'unitize.R' 'unitize.core.R'
'unitizer-package.r' 'unitizer.add.R' 'upgrade.R'
'classes.R' 'unmarkedEstimate.R' 'mapInfo.R' 'unmarkedFrame.R'
'unmarkedFit.R' 'utils.R' 'getDesign.R' 'colext.R' 'distsamp.R'
'multinomPois.R' 'occu.R' 'occuRN.R' 'pcount.R' 'gmultmix.R'
'pcountOpen.R' 'gdistsamp.R' 'unmarkedFitList.R'
'unmarkedLinComb.R' 'ranef.R' 'boot.R' 'occuFP.R' 'gpcount.R'
'occuPEN.R' 'pcount.spHDS.R'
'utils.R' 'rawrd.R' 'future.R' 'checklist.R' 'components.R'
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'lumberjack.R' 'retailers.R' 'utils.R' 'yaml.R'
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'RcppExports.R' 'misc.R' 'utils.R'
'CheckInputs.R' 'data.R' 'model.R' 'param.R' 'results.R'
'ICLexact.R' 'DesignOutput.R' 'Summary.R' 'Print.R'
'VarSelLCM.R' 'RcppExports.R' 'Plot.R' 'Imputation.R'
'withoutmixture.R' 'VarSelShiny.R' 'ARI.R' 'VarSelConvert.R'
Class-Cartesian2DCoordinateTransformation.R
Cartesian2DCoordinateTransformation-methods.R
Class-SimilarityTransformation.R Class-AffineTransformation.R
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AffineTransformation-methods.R
'NULLing.R''supporting_functions.R'
'coverconvert-class.R''vegtable-class.R''shaker-class.R'
'clean.R''as.list.R''merge_taxa.R'
'header.R''Extract.R''veg_relation.R'
'vegtable_stat.R''df2vegtable.R''used_synonyms.R' 'subset.R'
'coverconvert-methods.R''names.R'
'tv2coverconvert.R''tv2vegtable.R'
'crosstable.R''aggregate.R''write_juice.R''read_juice.R''vegtable2kml.R'
'shaker-methods.R''make_cocktail.R' 'summary.R'
'StartMessage.R'
'helperGUI.R' 'TKRaggr.R' 'TKRdevice.R' 'TKRmarginmatrix.R'
'TKRmatrixplot.R' 'TKRparcoordMiss.R' 'TKRpbox.R'
'TKRscattmatrixMiss.R' 'utils.R' 'VIMGUI.R' 'vmGUImenu.R'
'zzz.R'
h.R hinverse.R Vine.R show.R dimnames.R vineParameters.R
vineIter.R vineFit.R vineFitML.R vinePIT.R vineGoF.R
vineOrder.R dvine.R pvine.R rvine.R
'dataPrep.R' 'vitality.4p.R' 'vitality.6p.R' 'vitality.k.R'
'vitality.ku.R' 'vitality.utils.R' 'data_documentation.R'
'density.R' 'mortality_rate.R' 'package_documentation.R'
'auth.R' 'database.R' 'friends.R' 'groups.R'
'igraph_gefx_exporter.R' 'likes.R' 'messages.R' 'network.R'
'queries.R' 'status.R' 'search.R' 'users.R' 'utils.R' 'wall.R'
'vkR.R'
'AllClasses.R' 'summary-methods.R' 'plot-methods.R' 'MOPT.R'
'ceff1.R' 'ceff2.R' 'Deff.R' 'S.Weight.R' 'ff.R'
'RcppExports.R'
'utils.R' 'r_sample.R' 'age.R' 'r_sample_factor.R' 'animal.R'
'r_sample_binary.R' 'answer.R' 'area.R' 'as_integer.R' 'car.R'
'children.R' 'coin.R' 'color.R' 'date_stamp.R'
'r_sample_logical.R' 'death.R' 'dice.R' 'dna.R' 'dob.R'
'dummy.R' 'education.R' 'employment.R' 'eye.R' 'grade.R'
'grade_level.R' 'group.R' 'hair.R' 'normal.R' 'height.R'
'hour.R' 'id.R' 'income.R' 'internet_browser.R' 'interval.R'
'iq.R' 'language.R' 'level.R' 'r_sample_ordered.R' 'likert.R'
'lorem_ipsum.R' 'marital.R' 'military.R' 'minute.R' 'month.R'
'r_sample_replace.R' 'wakefield-package.R' 'name.R' 'peek.R'
'political.R' 'probs.R' 'r_data.R' 'r_data_frame.R' 'r_dummy.R'
'seriesname.R' 'r_insert.R' 'r_list.R' 'r_na.R'
'r_sample_integer.R' 'r_series.R' 'race.R' 'relate.R'
'religion.R' 'sat.R' 'second.R' 'sentence.R' 'sex.R'
'sex_inclusive.R' 'smokes.R' 'speed.R' 'state.R' 'string.R'
'table_heat.R' 'time_stamp.R' 'upper.R' 'valid.R' 'variables.R'
'varname.R' 'year.R' 'zip_code.R'
'waterData-package.R' 'importDVs.R' 'fillMiss.R'
'summaryStats.R' 'compAnom.R' 'plotAnoms.R'
zzz.R WebGestaltR.R WebGestaltR_batch.R IDMapping.R
GOSlimSummary.R listGeneSet.R listIDType.R listOrganism.R
readGMT.R listReferenceSet.R listArchiveURL.R formatCheck.R
'Z.value.R' 'genperms.R' 'pairtest.R' 'plotting.R'
'scoretest.R' 'wilcoxon.R' 'wgsea-package.R'
'whisker.R' 'section.R' 'parseTemplate.R' 'partials.R'
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'rowsplit.R'
'local_.R' 'with_.R' 'collate.R' 'connection.R' 'db.R'
'defer.R' 'wrap.R' 'devices.R' 'dir.R' 'env.R' 'file.R'
'libpaths.R' 'locale.R' 'makevars.R' 'namespace.R' 'options.R'
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'utils.R' 'with.R'
wav_boot.R wav_coef.R wav_dict.R wav_fdp.R wav_filt.R
wav_gnrc.R wav_mrd.R wav_plot.R wav_shrk.R wav_sig.R wav_util.R
wav_var.R wav_wtmm.R wav_xform.R wav_pkg.R
'aaa-rextdata.R' 'timings.R' 'break_even.R' 'p_values_7.R'
'p_values_agg.R'
'class.R' 'crossVal-methods.R' 'env_functions.R' 'ggplot2Fn.R'
'parameter-methods.R' 'plot-methods.R' 'plotFunctions.R'
'readSpc.R' 'summary-methods.R' 'summary.output.x12.R'
'summary.x12.R' 'times.R' 'x12-methods.R' 'x12work.R'
'readx12Out.R' 'zzz.R'
'ClassMethod-XGR.r' 'xRDataLoader.r' 'xRdWrap.r' 'xFunArgs.r'
'xRd2HTML.r' 'xDAGanno.r' 'xDAGsim.r' 'xConverter.r'
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'xEnricherYours.r' 'xEnrichViewer.r' 'xEnrichBarplot.r'
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'xSocialiserDAGplotAdv.r' 'xSocialiserNetplot.r' 'xCircos.r'
'xSubneterGenes.r' 'xSubneterSNPs.r' 'xVisNet.r'
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'xSNP2nGenes.r' 'xSparseMatrix.r' 'xSM2DF.r'
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'xEnrichMatrix.r' 'xVolcano.r' 'xGraphML.r' 'xSimplifyNet.r'
'xHeatmap.r' 'xCorrelation.r' 'xPolarDot.r' 'xPolarBar.r'
'xEnricherGenesAdv.r' 'xHeatmapAdv.r' 'xGScore.r'
'xGScoreAdv.r' 'xEnrichForest.r' 'xGR2xGenes.r'
'xGR2xGeneScores.r' 'xGR2xNet.r' 'xGRviaGeneAnnoAdv.r'
'xBigraph.r' 'xBiheatmap.r' 'xAddCoords.r' 'xRegress.r'
'xGraphSplit.r' 'xGGnetwork.r' 'xOBOcode.r' 'xGR2xGeneAnno.r'
'xGR2xGeneAnnoAdv.r' 'xEnrichTreemap.r' 'xCrosstalk.r'
'xGRsort.r' 'xGRsep.r' 'xGRcse.r' 'xEnrichLadder.r' 'xHEB.r'
'xEnrichHeatmap.r' 'xGRoverlap.r' 'xGRtrack.r' 'xAggregate.r'
'Xmisc-package.R' 'imports.R' 'util.R' 'list.R' 'internal.R'
'generic.R' 'xrefclass.R' 'valueparser.R' 'argumentparser.R'
'logging.R' 'unittest.R'
AAA.R DTD.R DTDClasses.R DTDRef.R SAXMethods.S XMLClasses.R
applyDOM.R assignChild.R catalog.R createNode.R dynSupports.R
error.R flatTree.R nodeAccessors.R parseDTD.R schema.S
summary.R tangle.R toString.S tree.R version.R xmlErrorEnums.R
xmlEventHandler.R xmlEventParse.R xmlHandler.R
xmlInternalSource.R xmlOutputDOM.R xmlNodes.R xmlOutputBuffer.R
xmlTree.R xmlTreeParse.R htmlParse.R hashTree.R zzz.R
supports.R parser.R libxmlFeatures.R xmlString.R saveXML.R
namespaces.R readHTMLTable.R reflection.R xmlToDataFrame.R
bitList.R compare.R encoding.R fixNS.R xmlRoot.R serialize.R
xmlMemoryMgmt.R keyValueDB.R solrDocs.R XMLRErrorInfo.R
xincludes.R namespaceHandlers.R tangle1.R htmlLinks.R
htmlLists.R getDependencies.R getRelativeURL.R xmlIncludes.R
simplifyPath.R
'RcppExports.R' 'as_list.R' 'xml_parse.R' 'as_xml_document.R'
'classes.R' 'init.R' 'paths.R' 'utils.R' 'xml_attr.R'
'xml_children.R' 'xml_find.R' 'xml_modify.R' 'xml_name.R'
'xml_namespaces.R' 'xml_path.R' 'xml_schema.R'
'xml_serialize.R' 'xml_structure.R' 'xml_text.R' 'xml_type.R'
'xml_url.R' 'xml_write.R' 'zzz.R'
'CalcNGR.R' 'CalcPD.R' 'CalcSimulatedExposure.R' 'CalcVA.R'
'GenerateTimeGrid.R' 'calcCVACapital.R' 'calcDefCapital.R'
'calcEAD.R' 'calcEffectiveMaturity.R' 'calcKVA.R'
'xVACalculator.R' 'xVACalculatorExample.R'
'assertions.R' 'model-zelig.R' 'model-timeseries.R'
'model-ma.R' 'model-ar.R' 'model-arima.R' 'model-weibull.R'
'model-tobit.R' 'model-bayes.R' 'model-tobit-bayes.R'
'model-glm.R' 'model-binchoice.R' 'model-probit.R'
'model-probit-bayes.R' 'model-poisson.R'
'model-poisson-bayes.R' 'model-oprobit-bayes.R'
'model-normal.R' 'model-normal-bayes.R' 'model-mlogit-bayes.R'
'model-gamma.R' 'model-gee.R' 'model-logit.R'
'model-logit-bayes.R' 'model-factor-bayes.R'
'model-poisson-gee.R' 'model-normal-gee.R' 'model-gamma-gee.R'
'model-binchoice-gee.R' 'model-probit-gee.R'
'model-logit-gee.R' 'model-relogit.R' 'model-quantile.R'
'model-lognorm.R' 'model-exp.R' 'model-negbinom.R'
'model-ivreg.R' 'model-ls.R' 'utils.R' 'create-json.R'
'datasets.R' 'interface.R' 'model-survey.R'
'model-binchoice-survey.R' 'model-gamma-survey.R'
'model-logit-survey.R' 'model-normal-survey.R'
'model-poisson-survey.R' 'model-probit-survey.R' 'plots.R'
'wrappers.R'
'model-mlogit.R' 'model-obinchoice.R' 'model-oprobit.R'
'model-ologit.R' 'model-bbinchoice.R' 'model-bprobit.R'
'model-blogit.R' 'create-json.R'
'create-json.R' 'model-ei.R' 'model-rxc-ei.R'
'model-dynamic-ei.R' 'model-ml-ei.R' 'model-hier-ei.R'
Mike, Jay, and Charles <bigmemoryauthors@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
Marie-Laure Delignette-Muller <marielaure.delignettemuller at
vetagro-sup.fr> or Christophe Dutang <christophe.dutang at
ensimag.fr>
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@madlb.incubator.apache.org>
Minghui Wang <minghui.wang@mssm.edu>, Bin Zhang
<bin.zhang@mssm.edu>
This package is Copyright (C) 2006-2016 Weierstrass
Institute for Applied Analysis and Stochastics.
This software is in the public domain because it contains
materials that originally came from the United States
Geological Survey, an agency of the United States Department of
Interior. For more information, see the official USGS copyright
policy at http://www.usgs.gov/visual-id/
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The mdlp2.R script reuses parts of the code from the R
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The code for apriori and eclat in src/rapriori.c was
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This software is in the public domain because it contains
materials that originally came from the United States
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ANN library is copyright by University of Maryland, Sunil
Arya and David Mount. All other code is copyright by Michael
Hahsler and Matthew Piekenbrock.
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src/arms.c and src/arms.h are copyright Wally Gilks. All
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This library is Copyright (C) 2006-2016 Weierstrass
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This software is in the public domain because it contains
materials that originally came from the United States
Geological Survey, an agency of the United States Department of
Interior. For more information, see the official USGS copyright
policy at
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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 CAMD
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file.
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See the individual source files for copyrights information
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Copyright (C) 2006 University of Duisburg-Essen, Essen,
Germany, University of Bristol, Bristol, United Kingdom
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University
see inst/COPYRIGHTS for the files in src/.
(C) 2016 by Helmholtz Institute Freiberg for Resource
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Copyright 2010, Merck Research Laboratories
Original algorithms and 'Matlab'/C library (c) 2015-2017
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Under the allowance of MIT license, smhash
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Copyright holders of FFTW3: Copyright (c) 2003, 2007-11
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'lm.br' uses the design and some R-code of 'lm' copyright
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v2 or later), copyright (c) 2002-2005 Rudolf Schuerer and GNU
GSL (also distributed under the GNU GPL, v2 or later),
copyright (c) 1996-2000 Brian
Gough.(http://www.gnu.org/software/gsl/).
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Segments of the package are based upon Will G. Hopkins'
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All files are copyright Institut National de la Sante Et de
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libnabo is copyright 2010--2011, Stephane Magnenat, ASL,
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The fortran routines for the function HW.original are from
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src/fpmpi/ and src/fpmpi_win.
See pbdRPC/inst/putty_LICENCE for files in src/putty/.
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/'.
See files AUTHORS, COPYING, COPYING.LESSER, and MAINTAINERS
in 'pbdZMQ/inst/zmq_copyright/' for the 'ZeroMQ' source files
in 'src/zmq_src/' which are under GPL-3.
See the file COPYRIGHTS for copyright details on some of the
functions and algorithms used.
This package is Copyright (C) 2006-2018 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.
Implementation of the binary heap is by Hewlett-Packard
Company (1994), Silicon Graphics Computer Systems,
Inc.(1996-1999). Modifications to the code by Johnathan Wells
(2002)
See the file COPYRIGHTS for copyright details on some of the
functions.
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.
For the copyrights for the 'Corpus Workbench' (CWB) and
acknowledgement of authorship, see file COPYRIGHTS.
See the file COPYRIGHTS for various Eigen copyright details
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.
(C) 2015 Coherent Logic Limited; ALL RIGHTS RESERVED.
Gleb Beliakov. The 'lpsolve' library and its parts are
copyright to various holders, including Kjell Eikland, Michel
Berkelaar, University of Florida, National Institute of
Standards and Technology, Free Software Foundation, Inc.
MaxMind, Inc. for the underlying libmaxmminddb library,
IP2Location.com for libip2location, and the package authors for
all other content.
(C) 2018 Coherent Logic Limited; ALL RIGHTS RESERVED.
Code for the wavelet transform is based on Brandon
Whitcher's work. See file COPYRIGHT for details
Copyright (c) 2010-2018 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 data in star_groj0422.32 have been kindly provided by
the NASA. The data in Mrk421 and Mrk501 have been kindly
provided by the Deutsches Elektronen-Synchrotron. See the
respective documentation.
All code is Copyright (c) 2002-2018 University of
California, Cornell University, Cold Spring Harbor Laboratory.
Authors listed above, PostgreSQL Global Development Group,
and The Regents of the University of California
Original SNNS software Copyright (C) 1990-1995 SNNS Group,
IPVR, Univ. Stuttgart, FRG; 1996-1998 SNNS Group, WSI, Univ.
Tuebingen, FRG. R interface Copyright (C) DiCITS Lab, Sci2s
group, DECSAI, University of Granada.
This software is in the public domain because it contains
materials that originally came from the United States
Geological Survey (USGS), an agency of the United States
Department of Interior. For more information, see the official
USGS copyright policy at
https://www2.usgs.gov/visual-id/credit_usgs.html
Copyright (c) 2002-2018 University of California, Cornell
University, Cold Spring Harbor Laboratory.
Inria, University Lille 1 and specifically inst/COPYRIGHTS
for the STK++ library
see files COPYRIGHTS for detailed information
(c) 2010-2018 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.
(c) 2014, Pioneer Hi-Bred International, Inc.
The Rector and Visitors of the University of Virginia
We have used the following software and made minimal
necessary changes: 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
1991-3 Barry Rowlingson and Peter Diggle; 1997-1999 Advanced
Interfaces Group, University of Manchester; 1993, 1995, 1997,
1998, 2002, 2005 Jonathan Richard Shewchuk; 1970-2003, Wm.
Randolph Franklin; 2011-2017 David Sterratt
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.
Incorporates C code from: ape by Emmanuel Paradis; Morphy
Phylogenetic Library by Martin Brazeau
<https://github.com/mbrazeau/MorphyLib> (GPL3)
2007-2011 Bank of Canada.  2012-2015 Paul Gilbert.
2007-2011 Bank of Canada.  2012-2017 Paul Gilbert.
2004-2011 Bank of Canada and Erik Meijer. 2012-2014 Paul
Gilbert and Erik Meijer
2007-2011 Bank of Canada.  2012-2016 Paul Gilbert.
All code is Copyright (C) Michael Hahsler and Kurt Hornik.
The copyright holders of the Perl files are listed in each
.pm file under the Perl directory.
2011-2014 Shenzhen Institutes of Advanced Technology Chinese
Academy of Sciences
2009-2018 Andrew C. Hooker, E. Niclas Jonsson and Mats O.
Karlsson. 2005-2008 Andrew C. Hooker, Justin J. Wilkins, Mats
O. Karlsson and E. Niclas Jonsson. 1998-2004 E. Niclas Jonsson
and Mats Karlsson.
ANN library is copyright University of Maryland and Sunil
Arya and David Mount. See file COPYRIGHTS for details.
Original GPL-2 C++ code by Naoki Yoshinaga (see
http://www.tkl.iis.u-tokyo.ac.jp/~ynaga/yakmo/)
(C) 2014 John W. Emerson and Walton Green
Check NEWS file for changes: news(package='simSummary')
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.
Get text from images of text using Abbyy Cloud Optical Character
    Recognition (OCR) API. Easily OCR images, barcodes, forms, documents with
    machine readable zones, e.g. passports. Get the results in a variety of formats
    including plain text and XML. To learn more about the Abbyy OCR API, see 
    <http://ocrsdk.com/>.
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.
For a given data set, the package provides a novel method of computing precise limits to acquire subsets which are easily interpreted. Closely related to the Lorenz curve, the ABC curve visualizes the data by graphically representing the cumulative distribution function. Based on an ABC analysis the algorithm calculates, with the help of the ABC curve, the optimal limits by exploiting the mathematical properties pertaining to distribution of analyzed items. The data containing positive values is divided into three disjoint subsets A, B and C, with subset A comprising very profitable values, i.e. largest data values ("the important few"), subset B comprising values where the yield equals to the effort required to obtain it, and the subset C comprising of non-profitable values, i.e., the smallest data sets ("the trivial many"). Package is based on "Computed ABC Analysis for rational Selection of most informative Variables in multivariate Data", PLoS One. Ultsch. A., Lotsch J. (2015) <DOI:10.1371/journal.pone.0129767>.
Contains data which are used by functions of the 'abc' package.
Functions for Constraint Based Simulation using Flux
        Balance Analysis and informative analysis of the data generated
        during simulation.
An implementation of Karaboga (2005) Artificial Bee Colony
    Optimization algorithm <http://mf.erciyes.edu.tr/abc/pub/tr06_2005.pdf>.
    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.
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.
It aims to identify candidate genes that are “differentially
    methylated” between cases and controls. It applies Student’s t-test and delta beta analysis to
    identify candidate genes containing multiple “CpG sites”.
Performs Approximate Bayesian Computation (ABC) model choice and parameter inference via random forests.
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).
Performs augmented backward elimination and checks the stability of the obtained model. Augmented backward elimination combines significance or information based criteria with the change in estimate to either select the optimal model for prediction purposes or to serve as a tool to obtain a practically sound, highly interpretable model. More details can be found in Dunkler et al. (2014) <doi:10.1371/journal.pone.0113677>.
Loads ABF2 files containing gap-free data from electrophysiological recordings, as created by Axon Instruments/Molecular Devices software such as pClamp 10.
Easy to use functions to visualize marker data
    from biparental populations. Useful for both analyzing and
    presenting genotypes in the ABH format.
Combine multidimensional 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.
The Brazilian Jurimetrics Association (ABJ in Portuguese,
  see <http://www.abjur.org.br/en/> for more information) is a non-profit
  organization which aims to investigate and promote the use of
  statistics and probability in the study of Law and its institutions.
  This package implements general purpose tools used by ABJ, such as functions
  for sampling and basic manipulation of Brazilian lawsuits identification
  number. It also implements functions for text cleaning, such as accentuation removal.
Bayesian network analysis is a form of probabilistic  graphical models which derives from empirical data a directed acyclic graph, DAG, describing the dependency structure between  random variables. An additive Bayesian network model consists of a form of a DAG where each node comprises a generalized linear model, GLM. Additive Bayesian network models are equivalent to Bayesian multivariate regression using graphical modelling, they generalises the usual multivariable regression, GLM, to multiple dependent variables. 'abn' 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 package which can be obtained from <http://www.r-inla.org>. It is recommended the testing version, which can be downloaded by running: source("http://www.math.ntnu.no/inla/givemeINLA-testing.R"). A comprehensive set of documented case studies, numerical accuracy/quality assurance exercises, and additional documentation are available from the 'abn' website.
Contains the functions to implement the methodology and considerations laid out by Marks et al. in the manuscript Measuring Abnormality in High Dimensional Spaces: Applications in Biomechanical Gait Analysis. As of 2/27/2018 this paper has been submitted and is under scientific review. Using high-dimensional datasets to measure a subject’s overall level of abnormality as compared to a reference population is often needed in outcomes research. Utilizing applications in instrumented gait analysis, that article demonstrates how using data that is inherently non-independent to measure overall abnormality may bias results. A methodology is introduced to address this bias to accurately measure overall abnormality in high dimensional spaces. While this methodology is in line with previous literature, it differs in two major ways. Advantageously, it can be applied to datasets in which the number of observations is less than the number of features/variables, and it can be abstracted to practically any number of domains or dimensions. After applying the proposed methodology to the original data, the researcher is left with a set of uncorrelated variables (i.e. principal components) with which overall abnormality can be measured without bias. Different considerations are discussed in that article in deciding the appropriate number of principal components to keep and the aggregate distance measure to utilize.
Performs angle-based outlier detection on a given dataframe. Three methods are available, a full but slow implementation using all the data that has cubic complexity, a fully randomized one which is way more efficient and another using k-nearest neighbours. These algorithms are specially well suited for high dimensional data outlier detection.
An implementation of the Abnormal Blood Profile Score (ABPS,
    part of the Athlete Biological Passport program of the World Anti-Doping
    Agency), which combines several blood parameters into a single score in
    order to detect blood doping (Sottas et al. (2006)
    <doi:10.2202/1557-4679.1011>). The package also contains functions to
    calculate other scores used in anti-doping programs, such as the
    OFF-score (Gore et al. (2003) <http://www.haematologica.org/content/88/3/333>),
    as well as example data.
Gene-set enrichment analysis (GSEA) is popularly used to assess the enrichment of differential signal in a pre-defined gene-set without using a cutoff threshold for differential expression. The significance of enrichment is evaluated through sample- or gene-permutation method. Although the sample-permutation approach is highly recommended due to its good false positive control, we must use gene-permuting method if the number of samples is small. However, such gene-permuting GSEA (or preranked GSEA) generates a lot of false positive gene-sets as the inter-gene correlation in each gene set increases. These false positives can be successfully reduced by filtering with the one-tailed absolute GSEA results. This package provides a function that performs gene-permuting GSEA calculation with or without the absolute filtering. Without filtering, users can perform (original) two-tailed or one-tailed absolute GSEA.
Simulation methods for the evolution of antibody repertoires. The heavy and light chain variable region of both human and C57BL/6 mice can be simulated in a time-dependent fashion. Both single lineages using one set of V-, D-, and J-genes or full repertoires can be simulated. The algorithm begins with an initial V-D-J recombination event, starting the first phylogenetic tree. Upon completion, the main loop of the algorithm begins, with each iteration representing one simulated time step. Various mutation events are possible at each time step, contributing to a diverse final repertoire.
Fit and predict with the high-dimensional principal fitted
        components model.  This model is described by Cook, Forzani, and Rothman (2012)
	<doi:10.1214/11-AOS962>.
Infers directional conservative causal core (gene) networks. It is an advanced version of the algorithm C3NET by providing directional network. Gokmen Altay (2018) <doi:10.1101/271031>, bioRxiv.
Offers an interactive function for the detection of breakpoints in series.
Processes accelerometer data from uni-axial and tri-axial devices,
    and generates data summaries. Also includes functions to plot, analyze, and
    simulate accelerometer data.
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.
Imputation for the missing count values in accelerometer data. The methodology includes both parametric and semi-parametric multiple imputations under the zero-inflated Poisson lognormal model. This package also provides multiple functions to pre-process the accelerometer data previous to the missing data imputation. These includes detecting wearing and non-wearing time, selecting valid days and subjects, and creating plots.
Provides functionality for creating and
	evaluating acceptance sampling plans. Sampling plans can be single,
	double or multiple.
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 data.
Implementation of sparse linear discriminant analysis, which is a supervised
    classification method for multiple classes. Various novel optimization approaches to
    this problem are implemented including alternating direction method of multipliers (ADMM),
    proximal gradient (PG) and accelerated proximal gradient (APG) (See Atkins et al. <arXiv:1705.07194>).
    Functions for performing cross validation are also supplied along with basic prediction
    and plotting functions.
    Sparse zero variance discriminant analysis (SZVD) is also included in the package
    (See Ames and Hong, <arXiv:1401.5492>). See the github wiki for a more extended description.
Categorical data analysis with complete or missing responses
Package for Autoregressive Conditional Duration (ACD, Engle and Russell, 1998) models. Creates trade, price or volume durations from transactions (tic) data, performs diurnal adjustments, fits various ACD models and tests them.
Optimal Bayesian experimental design using the approximate coordinate exchange (ACE) algorithm.
Two nonparametric methods for multiple regression transform selection are provided.
  The first, Alternative Conditional Expectations (ACE), 
  is an algorithm to find the fixed point of maximal
  correlation, i.e. it finds a set of transformed response variables that maximizes R^2
  using smoothing functions [see Breiman, L., and J.H. Friedman. 1985. "Estimating Optimal Transformations
  for Multiple Regression and Correlation". Journal of the American Statistical Association.
  80:580-598. <doi:10.1080/01621459.1985.10478157>].
  Also included is the Additivity Variance Stabilization (AVAS) method which works better than ACE when
  correlation is low [see Tibshirani, R.. 1986. "Estimating Transformations for Regression via Additivity
  and Variance Stabilization". Journal of the American Statistical Association. 83:394-405. 
  <doi:10.1080/01621459.1988.10478610>]. A good introduction to these two methods is in chapter 16 of
  Frank Harrel's "Regression Modeling Strategies" in the Springer Series in Statistics.
Twin models that are able to estimate the dynamic behaviour of the variance components in the classical twin models with respect to age using B-splines and P-splines.
Functions for the analysis of income distributions for subgroups of the population as defined by a set of variables like age, gender, region, etc. This entails a Kolmogorov-Smirnov test for a mixture distribution as well as functions for moments, inequality measures, entropy measures and polarisation measures of income distributions. This package thus aides the analysis of income inequality by offering tools for the exploratory analysis of income distributions at the disaggregated level.
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.
We use a non-linear model, termed ACME,
        that reflects a parsimonious biological model for
        allelic contributions of cis-acting eQTLs.
        With non-linear least-squares algorithm we
        estimate maximum likelihood parameters. The ACME model
        provides interpretable effect size estimates and
        p-values with well controlled Type-I error.
        Includes both R and (much faster) C implementations.
        For more details see Palowitch et al. (2017) <doi:10.1111/biom.12810>.
Implementation of estimator ACME, described in Wolpert (2015), ACME: A  		Partially Periodic Estimator of Avian & Chiropteran Mortality at Wind
        Turbines (submitted). Unlike most other models, this estimator
        supports decreasing-hazard Weibull model for persistence;
        decreasing search proficiency as carcasses age; variable
        bleed-through at successive searches; and interval mortality
        estimates. The package provides, based on search data, functions
        for estimating the mortality inflation factor in Frequentist and
        Bayesian settings.
A summarization method to estimate allele-specific copy number signals for Affymetrix SNP microarrays using non-negative matrix factorization (NMF).
Provides SNP array data from different types of
    copy-number regions. These regions were identified manually by the authors
    of the package and may be used to generate realistic data sets with known
    truth.
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.
Make acoustic cues to use with the R packages 'ndl' or 'ndl2'. The package implements functions used
              in the PLoS ONE paper:
              Denis Arnold, Fabian Tomaschek, Konstantin Sering, Florence Lopez, and R. Harald Baayen (2017).
              Words from spontaneous conversational speech can be recognized with human-like accuracy by 
              an error-driven learning algorithm that discriminates between meanings straight from smart 
              acoustic features, bypassing the phoneme as recognition unit.  PLoS ONE 12(4):e0174623
              https://doi.org/10.1371/journal.pone.0174623
              More details can be found in the paper and the supplement.
              'ndl' is available on CRAN. 'ndl2' is available by request from <konstantin.sering@uni-tuebingen.de>.
Analysis of count data exhibiting autoregressive properties, using the Autoregressive Conditional Poisson model (ACP(p,q)) proposed by Heinen (2003).
Convenience functions for data preparation and modeling often used in aCRM.
Construct minimum-cost regular spanning subgraph as part of a
    non-parametric two-sample test for equality of distribution.
Functions for testing affine hypotheses on the regression coefficient vector in regression models with autocorrelated errors.
Provides a general toolkit for downloading, managing,
  analyzing, and presenting data from the U.S. Census
  (<https://www.census.gov/data/developers/data-sets.html>), including
  SF1 (Decennial short-form), SF3 (Decennial long-form), and the
  American Community Survey (ACS).  Confidence intervals provided with
  ACS data are converted to standard errors to be bundled with
  estimates in complex acs objects.  Package provides new methods to
  conduct standard operations on acs objects and present/plot data in
  statistically appropriate ways.
Compute and represent gene set enrichment or depletion from your
    data based on pre-saved maps from the Atlas of Cancer Signalling Networks (ACSN)
    or user imported maps. User imported maps must be complying with the GMT format
    as defined by the Broad Institute, that is to say that the file should be tab-
    separated, that the first column should contain the module name, the second
    column can contain comments that will be overwritten with the number of genes
    in the module, and subsequent columns must contain the list of genes (HUGO
    symbols; tab-separated) inside the module. The gene set enrichment can be run
    with hypergeometric test or Fisher exact test, and can use multiple corrections.
    Visualization of data can be done either by barplots or heatmaps.
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.
A book designed to meet the requirements of masters students. Tattar, P.N., Suresh, R., and Manjunath, B.G. "A Course in Statistics with R", J. Wiley, ISBN 978-1-119-15272-9.
Cluster analysis for cognitive diagnosis based on the Asymptotic Classification Theory (Chiu, Douglas & Li, 2009; <doi:10.1007/s11336-009-9125-0>). Given the sample statistic of sum-scores, 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.
Functional linear modeling and analysis for actigraphy data.
A mutation analysis tool that discovers cancer driver genes with frequent mutations in protein signalling sites such as post-translational modifications (phosphorylation, ubiquitination, etc). The Poisson generalised linear regression model identifies genes where cancer mutations in signalling sites are more frequent than expected from the sequence of the entire gene. Integration of mutations with signalling information helps find new driver genes and propose candidate mechanisms to known drivers. Reference: Systematic analysis of somatic mutations in phosphorylation signaling predicts novel cancer drivers. Juri Reimand and Gary D Bader. Molecular Systems Biology (2013) 9:637 <doi:10.1038/msb.2012.68>.
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).
Performs estimation of physical activity and sedentary behavior variables from activPAL (PAL Technologies, Glasgow, Scotland) events files. See <http://paltechnologies.com> for more information on the activPAL.
Read in activity measurements from standard file formats
  used by circadian rhythm researchers, currently only 'ClockLab' format,
  and process and plot the data.  The central type of plot is the actogram,
  as first described by in "Activity and distribution of certain wild mice 
  in relation to biotic communities" by MS Johnson (1926) <doi:10.2307/1373575>.
Functions and data sets for actuarial science:
  modeling of loss distributions; risk theory and ruin theory;
  simulation of compound models, discrete mixtures and compound
  hierarchical models; credibility theory. Support for many additional
  probability distributions to model insurance loss amounts and loss
  frequency: 19 continuous heavy tailed distributions; the
  Poisson-inverse Gaussian discrete distribution; zero-truncated and
  zero-modified extensions of the standard discrete distributions.
  Support for phase-type distributions commonly used to compute ruin
  probabilities.
This code provides a simple method for representing a visual scene as it may be seen by an animal with less acute vision. When using (or for more information), please cite the original publication.
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.
It 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 estimation of the error 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. Version 4.0 includes the margin-based ordered aggregation for Bagging pruning (Guo
	and Boukir, 2013) and a function to auto prune the 'rpart' tree. Moreover, three new plots are also 
	available importanceplot(), plot.errorevol() and plot.margins(). Version 4.1 allows to predict on 
	unlabeled data. Version 4.2 includes the parallel computation option for some of the functions.
The R package 'adagio' will provide methods and algorithms for
    discrete optimization and (evolutionary) global optimization.
Provides new approaches to variable selection for AFT model.
Tracks reading and writing within R scripts that are organized into
    a directed acyclic graph. Contains an interactive shiny application adaprApp().
    Uses git2r package, Git and file hashes to track version histories of input
    and output. See package vignette for how to get started. V1.02 adds parallel
    execution of project scripts and function map in vignette. Makes project
    specification argument last in order. V2.0 adds project specific libraries, packrat option, and adaprSheet().
For distributions whose probability density functions are log-concave, the adaptive rejection sampling algorithm can be used to build envelope functions for sampling. For others, we can use the modified adaptive rejection sampling algorithm, the concave-convex adaptive rejection sampling algorithm and the adaptive slice sampling algorithm. So we designed an R package mainly including 4 functions: rARS(), rMARS(), rCCARS() and rASS(). These functions can realize sampling based on the algorithms above.
Based on the function "spm" of the SemiPar package fits
        semiparametric regression models with spatially adaptive
        penalized splines.
Fits semiparametric regression models with spatially adaptive penalized splines and computes simultaneous confidence bands.
Multimodal distributions can be modelled as a mixture of components. The model is derived using the Pareto Density Estimation (PDE) for an estimation of the pdf. PDE has been designed in particular to identify groups/classes in a dataset. Precise limits for the classes can be calculated using the theorem of Bayes. Verification of the model is possible by QQ plot, Chi-squared test and Kolmogorov-Smirnov test. The package is based on the publication of Ultsch, A., Thrun, M.C., Hansen-Goos, O., Lotsch, J. (2015)  <DOI:10.3390/ijms161025897>.
Implements adaptive gPCA, as described in: Fukuyama, J. (2017)
    <arXiv:1702.00501>. The package also includes functionality for applying
    the method to 'phyloseq' objects so that the method can be easily applied
    to microbiome data and a 'shiny' app for interactive visualization.
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
        <doi:10.1063/1.2745299>. 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.
Enables sampling from arbitrary distributions if the log density is known up to a constant; a common situation in the context of Bayesian inference. The implemented sampling algorithm was proposed by Vihola (2012) <DOI:10.1007/s11222-011-9269-5> and achieves often a high efficiency by tuning the proposal distributions to a user defined acceptance rate.
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.
Existing adaptive design methods in clinical trials. The package
    includes power, stopping boundaries (sample size) calculation functions for
    two-group group sequential designs, adaptive design with coprimary endpoints,
    biomarker-informed adaptive design, etc.
Functions to fit the binomial and multinomial additive hazards models and to calculate the contribution of diseases/conditions to the disability prevalence, as proposed by Nusselder and Looman (2004) <DOI:10.1353/dem.2004.0017>.
Contains tools to fit the additive hazards model to data from a cohort,
    random sampling, two-phase Bernoulli sampling and two-phase finite population sampling,
    as well as calibration tool to incorporate phase I auxiliary information into the
    two-phase data model fitting.  This package provides regression parameter estimates and
    their model-based and robust standard errors. It also offers tools to make prediction of
    individual specific hazards.
Browse through a continuously updated list of existing RStudio 
    addins and install/uninstall their corresponding packages.
Provides functions for calculating efficiency with two types of additive Data Envelopment Analysis models: (i) Generalized Efficiency Measures: unweighted additive model (Cooper et al., 2007 <doi:10.1007/978-0-387-45283-8>), Range Adjusted Measure (Cooper et al., 1999, <doi:10.1023/A:1007701304281>), Bounded Adjusted Measure (Cooper et al., 2011 <doi:10.1007/s11123-010-0190-2>), Measure of Inefficiency Proportions (Cooper et al., 1999 <doi:10.1023/A:1007701304281>), and the Lovell-Pastor Measure (Lovell and Pastor, 1995 <doi:10.1016/0167-6377(95)00044-5>); and (ii) the Slacks-Based Measure (Tone, 2001 <doi:10.1016/S0377-2217(99)00407-5>). The functions provide several options: (i) constant and variable returns to scale; (ii) fixed (non-controllable) inputs and/or outputs; (iii) bounding the slacks so that unrealistically large slack values are avoided; and (iv) calculating the efficiency of specific Decision-Making Units (DMUs), rather than of the whole sample. Package additiveDEA also provides a function for reducing computation time when datasets are large.
Implementation of the Tukey, Mandel, Johnson-Graybill, LBI, Tusell
    and modified Tukey non-additivity tests.
Methods for fitting identity-link GLMs and GAMs to discrete data,
    using EM-type algorithms with more stable convergence properties than standard methods.
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 implements the traditional method based on the least-squares method, the parametric method based on maximum likelihood estimation, and the semiparametric method based on spline methods, and the corresponding methods for estimating TI for polymeric materials. The traditional approach is a two-step approach that is currently used in industrial standards, while the parametric method is widely used in the statistical literature. The semiparametric method is newly developed. Both the parametric and semiparametric approaches allow one to do statistical inference such as quantifying uncertainties in estimation, hypothesis testing, and predictions. Publicly available datasets are provided illustrations. More details can be found in Jin et al. (2017).
Tools for multivariate data analysis. Several methods are provided for the analysis (i.e., ordination) of one-table (e.g., principal component analysis, correspondence analysis), two-table (e.g., coinertia analysis, redundancy analysis), three-table (e.g., RLQ analysis) and K-table (e.g., STATIS, multiple coinertia analysis). The philosophy of the package is described in Dray and Dufour (2007) <doi:10.18637/jss.v022.i04>.
A Tcl/Tk GUI for some basic functions in the 'ade4' package.
Univariate and multivariate non-parametric kernel density
    estimation with adaptive bandwidth using a Bayesian approach to Abramson's
    square root law.
Toolset for the exploration of genetic and genomic data. Adegenet
    provides formal (S4) classes for storing and handling various genetic data,
    including genetic markers with varying ploidy and hierarchical population
    structure ('genind' class), alleles counts by populations ('genpop'), and
    genome-wide SNP data ('genlight'). It also implements original multivariate
    methods (DAPC, sPCA), graphics, statistical tests, simulation tools, distance
    and similarity measures, and several spatial methods. A range of both empirical
    and simulated datasets is also provided to illustrate various methods.
Graphical functionalities for the representation of multivariate data. It is a complete re-implementation of the functions available in the 'ade4' package.
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.
Contains a 'shiny' application called AdEPro (Animation of Adverse Event Profiles) which (audio-)visualizes adverse events occurring in clinical trials. As this data is usually considered sensitive, this tool is provided as a stand-alone application that can be launched from any local machine on which the data is stored.
The main application concerns to a new robust optimization package with two major contributions. The first contribution refers to the assessment of the adequacy of probabilistic models through a combination of several statistics, which measure the relative quality of statistical models for a given data set. The second one provides a general purpose optimization method based on meta-heuristics functions for maximizing or minimizing an arbitrary objective function.
Tools for the multiscale spatial analysis of multivariate data.
    Several methods are based on the use of a spatial weighting matrix and its
    eigenvector decomposition (Moran's Eigenvectors Maps, MEM).
Amiga Disk Files (ADF) are virtual
    representations of 3.5 inch floppy disks for the
    Commodore Amiga. Most disk drives from other systems
    (including modern drives) are not able to read these
    disks. To be able to emulate this system, the ADF
    format was created. This package enables you to read
    ADF files and import and export files from and to such
    virtual DOS-formatted disks.
Anderson-Darling GoF test with p-value calculation based on Marsaglia's 2004 paper "Evaluating the Anderson-Darling Distribution"
Computation of adherence to medications from Electronic Health care 
    Data and visualization of individual medication histories and adherence 
    patterns. The package implements a set of S3 classes and
    functions consistent with current adherence guidelines and definitions. 
    It allows the computation of different measures of
    adherence (as defined in the literature, but also several original ones), 
    their publication-quality plotting,
    the interactive exploration of patient medication history and 
    the real-time estimation of adherence given various parameter settings.
Two functions to calculate intra- and interspecific pairwise distances, evaluate DNA barcoding identification error and calculate an ad hoc distance threshold 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%).
Implements tools for manipulation of digital 
             		images and the Propagation Separation approach 
             		by Polzehl and Spokoiny (2006) <DOI:10.1007/s00440-005-0464-1> 
                        for smoothing digital images, see Polzehl and Tabelow (2007)
                        <DOI:10.18637/jss.v019.i01>.
Includes functions, data sets and examples for the calculation of various indices of biodiversity including species, functional and phylogenetic diversity. Part of the indices are expressed in terms of equivalent numbers of species. It also provides ways to partition biodiversity across spatial or temporal scales (alpha, beta, gamma diversities). In addition to the quantification of biodiversity, ordination approaches are available which rely on diversity indices and allow the detailed identification of species, functional or phylogenetic differences between communities.
Adjusted inference for Bayesian quantile regression based on
    asymmetric Laplace working likelihood, for details see Yang, Y., Wang, H.
    and He, X. (2015), Posterior inference in Bayesian quantile regression with
    asymmetric Laplace likelihood, International Statistical 
    Review, 2015 <doi:10.1111/insr.12114>.
Implements a constrained version of hierarchical agglomerative 
    clustering, in which each observation is associated to a position, and 
    only adjacent clusters can be merged. Typical application fields in 
    bioinformatics include Genome-Wide Association Studies or Hi-C data 
    analysis, where the similarity between items is a decreasing function of 
    their genomic distance. Taking advantage of this feature, the implemented 
    algorithm is time and memory efficient. This algorithm is described in 
    Chapter 4 of Alia Dehman (2015) 
    <https://hal.archives-ouvertes.fr/tel-01288568v1>.
Adjusts output of 'cranlogs' package to account for 'CRAN'-wide daily automated downloads and re-downloads caused by package updates.
Package for the access and distribution of Long-term lake datasets from lakes in the 
    Adirondack Park, northern New York state. Includes a wide variety of physical, chemical, and 
    biological parameters from 28 lakes. Data are from multiple collection organizations and have 
    been harmonized in both time and space for ease of reuse.
Adaptive wavelet lifting transforms for signal denoising using optimal local neighbourhood regression, from Nunes et al. (2006) <doi:10.1007/s11222-006-6560-y>.
Robust change point detection using ADM3 algorithm.
Provides functions to perform the fitting of an adaptive mixture
    of Student-t distributions to a target density through its kernel function as described in
    Ardia et al. (2009) <doi:10.18637/jss.v029.i03>. 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.
Implements tools for building and visualising admixture graphs
    and for extracting equations from them. These equations can be compared to f-
    statistics obtained from data to test the consistency of a graph against data --
    for example by comparing the sign of f_4-statistics with the signs predicted by
    the graph -- and graph parameters (edge lengths and admixture proportions) can
    be fitted to observed statistics.
Provides algorithms to solve popular optimization problems in statistics such as regression or denoising based on Alternating Direction Method of Multipliers (ADMM).
    See Boyd et al (2010) <doi:10.1561/2200000016> for complete introduction to the method.
Fit linear and cox models regularized with net (L1 and Laplacian), elastic-net (L1 and L2) or lasso (L1) penalty, and their adaptive forms, such as adaptive lasso and net adjusting for signs of linked coefficients. In addition, it treats the number of non-zero coefficients as another tuning parameter and simultaneously selects with the regularization parameter. The package uses one-step coordinate descent algorithm and runs extremely fast by taking into account the sparsity structure of coefficients.
Estimates a penalized precision matrix via the alternating direction method of multipliers (ADMM) algorithm. It currently supports a general elastic-net penalty that allows for both ridge and lasso-type penalties as special cases. This package is an alternative to the 'glasso' package.
    See Boyd et al (2010) <doi:10.1561/2200000016> for details regarding the estimation method.
Bayesian inference using the no-U-turn (NUTS) algorithm by 
 Hoffman and Gelman (2014) <http://www.jmlr.org/papers/v15/hoffman14a.html>. 
 Designed for 'AD Model Builder' ('ADMB') models,
 or when R functions for log-density and log-density gradient
 are available, such as 'Template Model Builder' ('TMB')
 models and other special cases. Functionality is similar to 'Stan', 
 and the 'rstan' and 'shinystan' packages are used for diagnostics and 
 inference.
An implementation of ADPclust clustering procedures (Fast
    Clustering Using Adaptive Density Peak Detection). The work is built and
    improved upon the idea of Rodriguez and Laio (2014)<DOI:10.1126/science.1242072>. 
    ADPclust clusters data by finding density peaks in a density-distance plot 
    generated from local multivariate Gaussian density estimation. It includes 
    an automatic centroids selection and parameter optimization algorithm, which 
    finds the number of clusters and cluster centroids by comparing average 
    silhouettes on a grid of testing clustering results; It also includes a user 
    interactive algorithm that allows the user to manually selects cluster 
    centroids from a two dimensional "density-distance plot". Here is the 
    research article associated with this package: "Wang, Xiao-Feng, and 
    Yifan Xu (2015)<DOI:10.1177/0962280215609948> Fast clustering using adaptive 
    density peak detection." Statistical methods in medical research". url:
    http://smm.sagepub.com/content/early/2015/10/15/0962280215609948.abstract.
This function takes a vector or matrix of data and smooths
    the data with an improved Savitzky Golay transform. The Savitzky-Golay
    method for data smoothing and differentiation calculates convolution
    weights using Gram polynomials that exactly reproduce the results of
    least-squares polynomial regression. Use of the Savitzky-Golay
    method requires specification of both filter length and
    polynomial degree to calculate convolution weights. For maximum
    smoothing of statistical noise in data, polynomials with
    low degrees are desirable, while a high polynomial degree
    is necessary for accurate reproduction of peaks in the data.
    Extension of the least-squares regression formalism with
    statistical testing of additional terms of polynomial degree
    to a heuristically chosen minimum for each data window leads
    to an adaptive-degree polynomial filter (ADPF). Based on noise
    reduction for data that consist of pure noise and on signal
    reproduction for data that is purely signal, ADPF performed
    nearly as well as the optimally chosen fixed-degree
    Savitzky-Golay filter and outperformed sub-optimally chosen
    Savitzky-Golay filters. For synthetic data consisting of noise
    and signal, ADPF outperformed both optimally chosen and
    sub-optimally chosen fixed-degree Savitzky-Golay filters. See Barak, P. (1995) <doi:10.1021/ac00113a006> for more information.
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.
Functions to compute upper Clopper-Pearson confidence limits of early life failure probabilities and required sample sizes of burn-in studies under further available information, e.g. from other products or technologies.
S4 Object Oriented for Advanced Fuzzy Clustering and Fuzzy COnsensus Clustering. Techniques that provided by this package are Fuzzy C-Means, Gustafson Kessel (Babuska Version), Gath-Geva, Sum Voting Consensus, Product Voting Consensus, and Borda Voting Consensus. This package also provide visualization via Biplot and Radar Plot.
This software solves an Advection Bi-Flux Diffusive Problem using the Finite Difference Method FDM. Vasconcellos, J.F.V., Marinho, G.M., Zanni, J.H., 2016, Numerical analysis of an anomalous diffusion with a bimodal flux distribution. <doi:10.1016/j.rimni.2016.05.001>. Silva, L.G., Knupp, D.C., Bevilacqua, L., Galeao, A.C.N.R., Silva Neto, A.J., 2014, Formulation and solution of an Inverse Anomalous Diffusion Problem with Stochastic Techniques. <doi:10.5902/2179460X13184>.
Implements wavelet-based approaches for describing population admixture. Principal Components Analysis (PCA) is used to define the population structure and produce a localized admixture signal for each individual. Wavelet summaries of the PCA output describe variation present in the data and can be related to population-level demographic processes. For more details, see Sanderson et al. (2015).
Package to incorporate change point analysis in ARIMA forecasting.
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 "AER" for a package overview.)
Estimates the attributable fraction in different sampling designs
    adjusted for measured confounders using logistic regression (cross-sectional
    and case-control designs), conditional logistic regression (matched case-control
    design), Cox proportional hazard regression (cohort design with time-to-
    event outcome) and gamma-frailty model with a Weibull baseline hazard. The variance of the estimator is obtained by combining the delta
    method with the the sandwich formula. Dahlqwist et al.(2016) <doi:10.1007/s10654-016-0137-7>.
This is an implementation of the Generalized Discrimination Score
    (also known as Two Alternatives Forced Choice Score, 2AFC) for various 
    representations of forecasts and verifying observations. The Generalized 
    Discrimination Score is a generic forecast verification framework which 
    can be applied to any of the following verification contexts: dichotomous, 
    polychotomous (ordinal and nominal), continuous, probabilistic, and ensemble.
    A comprehensive description of the Generalized Discrimination Score, including 
    all equations used in this package, is provided by Mason and Weigel (2009) 
    <doi:10.1175/MWR-D-10-05069.1>.
Active function cross-entropy clustering partitions the n-dimensional data into the clusters by finding the parameters of the mixed generalized multivariate normal distribution, that optimally approximates the scattering of the data in the n-dimensional space, whose density function is of the form: p_1*N(mi_1,^sigma_1,sigma_1,f_1)+...+p_k*N(mi_k,^sigma_k,sigma_k,f_k). The above-mentioned generalization is performed by introducing so called "f-adapted Gaussian densities" (i.e. the ordinary Gaussian densities adapted by the "active function"). Additionally, the active function cross-entropy clustering performs the automatic reduction of the unnecessary clusters. For more information please refer to P. Spurek, J. Tabor, K.Byrski, "Active function Cross-Entropy Clustering" (2017) <doi:10.1016/j.eswa.2016.12.011>.
Convenience functions for analyzing factorial
       experiments using ANOVA or mixed models. aov_ez(),
       aov_car(), and aov_4() allow specification of between,
       within (i.e., repeated-measures), or mixed between-within
       (i.e., split-plot) ANOVAs for data in long format (i.e.,
       one observation per row), aggregating multiple observations
       per individual and cell of the design. mixed() fits mixed
       models using lme4::lmer() and computes p-values for all
       fixed effects using either Kenward-Roger or Satterthwaite
       approximation for 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).
Computes the statistical indices of affluence (richness) and constructs bootstrap confidence intervals for these indices. Also computes the Wolfson polarization index.
Hybrid simulation functions for dominant genetic data and genome scan methods.
Provides Atomic Force Microscope images analysis such as Power
    Spectral Density, roughness against lengthscale, experimental variogram and variogram models,
    fractal dimension and scale. The AFM images can be exported to STL format for 3D
    printing.
Set of functions for analyzing Atomic Force Microscope (AFM) force-distance curves. It allows to obtain the contact and unbinding points, perform the baseline correction, estimate the Young's modulus, fit up to two exponential decay function to a stress-relaxation / creep experiment, obtain adhesion energies. These operations can be done either over a single F-d curve or over a set of F-d curves in batch  mode.
Allows estimation and modelling of flight costs in animal (vertebrate) flight,
    implementing the aerodynamic power model described in Klein Heerenbrink et al.
    (2015) <doi:10.1098/rspa.2014.0952>. Taking inspiration from the program
    'Flight', developed by Colin Pennycuick (Pennycuick (2008) "Modelling the flying
    bird". Amsterdam: Elsevier. ISBN 0-19-857721-4), flight performance is estimated
    based on basic morphological measurements such as body mass, wingspan and wing
    area. 'afpt' can be used to make predictions on how animals should adjust their
    flight behaviour and wingbeat kinematics to varying flight conditions.
A collection of methods for both the 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.
		 Details of the rank-based estimation can be found in
		 Chiou et al. (2014) <doi:10.1007/s11222-013-9388-2> and
		 Chiou et al. (2015) <doi:10.1002/sim.6415>.
		 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.
		 Details on the least-squares estimation can be found in
		 Chiou et al. (2014) <doi:10.1007/s10985-014-9292-x>.
Tools for NIHES course EP18 'Analysis of Growth Data', May 22-23
    2012, Rotterdam.
Convenience functions for aggregating data frame. Currently mean, sum and variance are supported. For Date variables, recency and duration are supported. There is also support for dummy variables in predictive contexts.
Contains functionality for performing the following methods of p-value aggregation: Fisher's method [Fisher, RA (1932, ISBN: 9780028447308)], the Lancaster method (weighted Fisher's method) [Lancaster, HO (1961, <doi:10.1111/j.1467-842X.1961.tb00058.x>)], and Sidak correction [Sidak, Z (1967, <doi:10.1080/01621459.1967.10482935>)].  Please cite Yi et al., the manuscript corresponding to this package [Yi, L et al., (2017), <doi:10.1101/190199>].
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.
Standardize the process of bringing various modes of output
    files into R. For more information, see:
    <https://actigraph.desk.com/customer/en/portal/articles/2515800-what-do-the-different-mode-numbers-mean-in-a-csv-or-dat-file->.
    Additionally, processes are provided to read and minimally pre-process
    raw data from primary accelerometer and inertial measurement unit files.
    ActiGraph monitors are used to estimate physical activity outcomes via
    body-worn sensors that measure (e.g.) acceleration or rotational velocity.
Bland-Altman plot and scatter plot with identity line 
             for visualization and point and 
             interval estimates for different metrics related to 
             reproducibility/repeatability/agreement including
             the concordance correlation coefficient, 
             intraclass correlation coefficient,
             within-subject coefficient of variation,
             smallest detectable difference, 
             and mean normalized smallest detectable difference.
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.
Original idea was presented in the thesis "A statistical analysis tool for agricultural research" to obtain the degree of Master on science, National Engineering University (UNI), Lima-Peru. Some experimental data for the examples come from the CIP and others research. 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, papers, and websites related to agriculture.
    Example graphics and analyses are included. Data come from small-plot trials,
    multi-environment trials, uniformity trials, yield monitors, and more.
Example software for the analysis of data from designed 
	experiments, especially agricultural crop experiments. The basics 
	of the analysis of designed experiments are discussed 
	using real examples from agricultural field trials. 
	A range of statistical methods using a range
	of R statistical packages are  exemplified . The experimental
	data is made available as separate data sets for each example
	and the R analysis code is made available as example code.
	The example code can be readily extended, as required.
Calculate agreement or consensus in ordered rating scales. The package implements van der Eijk's (2001) <DOI: 10.1023/A:1010374114305> 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, Berry and Mielke, and Garcia-Montalvo and Reynal-Querol. Furthermore, an implementation of Galtungs AJUS-system is provided to classify distributions, as well as a function to identify the position of multiple modes.
Calculation of repeated confidence intervals as well as confidence
    intervals based on the stage-wise ordering in group sequential designs and
    adaptive group sequential designs. For adaptive group sequential designs
    the confidence intervals are based on the conditional rejection probability
    principle. Currently the procedures do not support the use of futility
    boundaries or more than one adaptive interim analysis.
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.
Provides functions and data sets to accompany the book 'Applied Hierarchical Modeling in Ecology: Analysis of distribution, abundance and species richness in R and BUGS' by Marc Kery and Andy Royle. The first volume appeared early in 2016 (ISBN: 978-0-12-801378-6, <https://www.mbr-pwrc.usgs.gov/pubanalysis/keryroylebook>); the second volume is in  preparation and additional functions will be added to this package.
Implementation of the Artificial Hydrocarbon Networks for data
    modeling.
Aho-Corasick is an optimal algorithm for finding many
    keywords in a text. It can locate all matches in a text in O(N+M) time; i.e.,
    the time needed scales linearly with the number of keywords (N) and the size of
    the text (M). Compare this to the naive approach which takes O(N*M) time to loop
    through each pattern and scan for it in the text. This implementation builds the
    trie (the generic name of the data structure) and runs the search in a single
    function call. If you want to search multiple texts with the same trie, the
    function will take a list or vector of texts and return a list of matches to
    each text. By default, all 128 ASCII characters are allowed in both the keywords
    and the text. A more efficient trie is possible if the alphabet size can be
    reduced. For example, DNA sequences use at most 19 distinct characters and
    usually only 4; protein sequences use at most 26 distinct characters and usually
    only 20. UTF-8 (Unicode) matching is not currently supported.
Model and analyse complex decision making problems
    using the Analytic Hierarchy Process (AHP) by Thomas Saaty.
Methods for estimation of multivariate average hazard ratios as
    defined by Kalbfleisch and Prentice. The underlying survival functions of the
    event of interest in each group can be estimated using either the (weighted)
    Kaplan-Meier estimator or the Aalen-Johansen estimator for the transition
    probabilities in Markov multi-state models. Right-censored and left-truncated
    data is supported. Moreover, the difference in restricted mean survival can be
    estimated.
Functions to implement model selection and multimodel inference based on Akaike's information criterion (AIC) and the second-order AIC (AICc), as well as their quasi-likelihood counterparts (QAIC, QAICc) from various model object classes.  The package implements classic model averaging for a given parameter of interest or predicted values, as well as a shrinkage version of model averaging parameter estimates or effect sizes.  The package includes diagnostics and goodness-of-fit statistics for certain model types including those of 'unmarkedFit' 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.  Functions also implement model selection using BIC.  Objects following model selection and multimodel inference can be formatted to LaTeX using 'xtable' methods included in the package.
Performs Box-Cox power transformation for different purposes, graphical approaches, assesses the success of the transformation via tests and plots, computes mean and confidence interval for back transformed data.
Read objects from the AIDA (<http://aida.freehep.org/>) file and make them available as dataframes in R.
A collection of Automatic Item Generators used mainly for
    psychological research. This package can generate linear syllogistic reasoning,
    arithmetic and 2D/3D/Double 3D spatial reasoning items. It is recommended for research
    purpose only.
R functions for adaptively constructing index models for
        continuous, binary and survival outcomes. Implementation
        requires loading R-pacakge "survival"
Create a pie like plot to visualise if the aim or several aims of a
             project is achieved or close to be achieved i.e the aim is achieved when the point is at the
             center of the pie plot. Imagine it's like a dartboard and the center means 100%
             completeness/achievement. Achievement can also be understood as 100%
             coverage. The standard distribution of completeness allocated in the pie plot
             is 50%, 80% and 100% completeness.
Tools for downloading hourly averages, daily maximums and minimums from each of the 
    pollution, wind, and temperature measuring stations or geographic zones in the Mexico City 
    metro area. The package also includes the locations of each of the stations and zones. See 
    <http://aire.cdmx.gob.mx/> for more information.
Hydrological modelling tools developed at Irstea-Antony (HBAN Research Unit, France). The package includes several conceptual rainfall-runoff models (GR4H, GR4J, GR5J, GR6J, GR2M, GR1A), a snow accumulation and melt model (CemaNeige) and the associated functions for their calibration and evaluation. Use help(airGR) for package description.
Add-on package to the 'airGR' package that simplifies its use and is aimed at being used for teaching hydrology. The package provides 1) three functions that allow to complete very simply a hydrological modelling exercise 2) plotting functions to help students to explore observed data and to interpret the results of calibration and simulation of the GR ('Génie rural') models 3) a 'Shiny' graphical interface that allows for displaying the impact of model parameters on hydrographs and models internal variables.
Deals with many computations related to the thermodynamics of atmospheric processes. It includes many functions designed to consider the density of air with varying degrees of water vapour in it, saturation pressures and mixing ratios, conversion of moisture indices, computation of atmospheric states of parcels subject to dry or pseudoadiabatic vertical evolutions and atmospheric instability indices that are routinely used for operational weather forecasts or meteorological diagnostics.
A thin wrapper around the 'ajv' JSON validation package for
    JavaScript. See <http://epoberezkin.github.io/ajv/> for details.
Continuous and discrete (count or categorical) estimation of density, probability mass function (p.m.f.) and regression functions are performed using  associated kernels. The cross-validation  technique and the local Bayesian procedure are also implemented for bandwidth selection.
Several cubic spline interpolation methods of H. Akima for irregular and
  regular gridded data are available through this package, both for the bivariate case
  (irregular data: ACM 761, regular data: ACM 760) and univariate case (ACM 433 and ACM 697).
  Linear interpolation of irregular gridded data is also covered by reusing D. J. Renkas
  triangulation code which is part of Akimas Fortran code. A bilinear interpolator
  for regular grids was also added for comparison with the bicubic interpolator on
  regular grids.
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.
The Atlas of Living Australia (ALA) provides tools to enable users
    of biodiversity information to find, access, combine and visualise data on
    Australian plants and animals; these have been made available from
    <http://ala.org.au/>. ALA4R provides a subset of the tools to be
    directly used within R. It enables the R community to directly access data
    and resources hosted by the ALA. Our goal is to enable outputs (e.g.
    observations of species) to be queried and output in a range of standard
    formats.
Augmented Lagrangian Adaptive Barrier Minimization
        Algorithm for optimizing smooth nonlinear objective functions
        with constraints. Linear or nonlinear equality and inequality
        constraints are allowed.
Provides immunoglobulin (Ig) sequence lineage reconstruction,
    diversity profiling, and amino acid property analysis.
    Citations: 
    Gupta and Vander Heiden, et al (2017) <doi:10.1093/bioinformatics/btv359>,
    Stern, Yaari and Vander Heiden, et al (2014) <doi:10.1126/scitranslmed.3008879>.
Implements discrete time deterministic and stochastic age-structured population dynamics models described in Erguler and others (2016) <doi:10.1371/journal.pone.0149282> and Erguler and others (2017) <doi:10.1371/journal.pone.0174293>.
It provides the density, distribution function, quantile function, 
             random number generator, likelihood function, moments and Maximum Likelihood estimators for a given sample, all this for
             the three parameter Asymmetric Laplace Distribution defined 
             in Koenker and Machado (1999). This is a special case of the skewed family of distributions
             available in Galarza et.al. (2017) <doi:10.1002/sta4.140> useful for quantile regression.
EM algorithm for estimation of parameters and other methods in a quantile regression.
Visualizes the main effects of individual predictor variables and their second-order interaction effects in black-box supervised learning models. The package creates either Accumulated Local Effects (ALE) plots and/or Partial Dependence (PD) plots, given a fitted supervised learning model.
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.
Provides direct access to the ALFRED (<https://alfred.stlouisfed.org>) and FRED (<https://fred.stlouisfed.org>) databases.
    Its functions return tidy data frames for different releases of the specified time series. 
    Note that this product uses the FRED© API but is not endorsed or certified by the Federal Reserve Bank of St. Louis.
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.
Two unordered pairs of data of two different snips positions is haplotyped by resolving a small number ob closed equations.
The company, Algorithmia, houses the largest marketplace of online
    algorithms. This package essentially holds a bunch of REST wrappers that
    make it very easy to call algorithms in the Algorithmia platform and access
    files and directories in the Algorithmia data API. To learn more about the
    services they offer and the algorithms in the platform visit
    <http://algorithmia.com>. More information for developers can be found at
    <http://developers.algorithmia.com>.
algstat provides functionality for algebraic statistics in R.
    Current applications include exact inference in log-linear models for
    contingency table data, analysis of ranked and partially ranked data, and
    general purpose tools for multivariate polynomials, building on the mpoly
    package. To aid in the process, algstat has ports to Macaulay2, Bertini,
    LattE-integrale and 4ti2.
Methods for comparing two alternative multiple 
    sequence alignments (MSAs) to determine whether they align homologous residues in 
    the same columns as one another. It then classifies similarities and differences 
    into conserved gaps, conserved sequence, merges, splits or shifts of one MSA relative 
    to the other. Summarising these categories for each MSA column yields information 
    on which sequence regions are agreed upon my both MSAs, and which differ. Several 
    plotting functions enable easily visualisation of the comparison data for analysis.
Functions are provided to calculate the 'ALINE' Distance between words as per 
    (Kondrak 2000) and (Downey, Hallmark, Cox, Norquest, & Lansing, 2008, 
    <doi:10.1080/09296170802326681>). The score is based on phonetic features represented 
    using the Unicode-compliant International Phonetic Alphabet (IPA). Parameterized 
    features weights are used to determine the optimal alignment and functions are
    provided to estimate optimum values using a genetic algorithm and supervised 
    learning. See (Downey, Sun, and Norquest 2017, 
    <https://journal.r-project.org/archive/2017/RJ-2017-005/index.html>.
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.
Tools to simulate alphanumeric alleles, impute genetic missing data and reconstruct non-recombinant haplotypes from pedigree databases in a deterministic way. Allelic simulations can be implemented taking into account many factors (such as number of families, markers, alleles per marker,
    probability and proportion of missing genotypes, recombination rate, etc).
    Genotype imputation can be used with simulated datasets or real databases (previously loaded in .ped format). Haplotype reconstruction can be carried
    out even with missing data, since the program firstly imputes each family genotype (without a reference panel), to later reconstruct the corresponding
    haplotypes for each family member. All this considering that each individual (due to meiosis) should unequivocally have two alleles per marker (one inherited
    from each parent) and thus imputation and reconstruction results can be deterministically calculated.
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.
Creating alluvial diagrams (also known as parallel sets plots) for multivariate
  and time series-like data.
Provides algorithms for frequency-based pairing of alpha-beta T
  cell receptors.
Computation of 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.
Given the parameters of a distribution, the package uses the concept of alpha-outliers by Davies and Gather (1993) to flag outliers in a data set. See Davies, L.; Gather, U. (1993): The identification of multiple outliers, JASA, 88 423, 782-792, <doi:10.1080/01621459.1993.10476339> for details.
Implementation in R of the alpha-shape of a finite set of points in the three-dimensional space. The alpha-shape 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, this package 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.
Download data from the Alpha Vantage API (<https://www.alphavantage.co/>).
    Alpha Vantage is a RESTful API which provides various financial data, 
    including stock prices and technical indicators. 
    There is documentation for the underlying API available 
    here: <https://www.alphavantage.co/documentation/>. To get access to this API,
    the user needs to first claim an API key: <https://www.alphavantage.co/support/>.
Alpha Vantage has free historical financial information. 
    All you need to do is get a free API key at <https://www.alphavantage.co>.
    Then you can use the R interface to retrieve free equity information.
    Refer to the Alpha Vantage website for more information.
Data files used in the textbook S. Weisberg (2005), 
 'Applied Linear Regression,' 3rd edition, Wiley are presented. 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 been lightly modified to make some data columns row labels.
Datasets to Accompany S. Weisberg (2014, ISBN: 978-1-118-38608-8), 
 "Applied Linear Regression," 4th edition.  Many data files 
 in this package are included in the `alr3` package as well, so only one of them 
 should be used.
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.
Functions and Data set presented in Applied Linear Statistical Models Fifth Edition (Chapters 1-9 and 16-25), Michael H. Kutner; Christopher J. Nachtsheim; John Neter; William Li, 2005. (ISBN-10: 0071122214, ISBN-13: 978-0071122214) that do not exist in R, are gathered in this package. The whole book will be covered in the next versions.
A tool to access each of the 'Alteryx' Gallery 'API' endpoints.
    Users can queue jobs, poll job status, and retrieve application output as
    a data frame. You will need an 'Alteryx' Server license and have 'Alteryx'
    Gallery running to utilize this package. The 'API' is accessed through the
    'URL' that you setup for the server running 'Alteryx' Gallery and more
    information on the endpoints can be found at
    <https://gallery.alteryx.com/api-docs/>.
Provides alternative statistical methods for meta-analysis, including new heterogeneity tests and measures that are robust to outliers.
Creates the optimal (D, U and I) designs for the accelerated life
    testing with right censoring or interval censoring. It uses generalized 
    linear model (GLM) approach to derive the asymptotic variance-covariance 
    matrix of regression coefficients. The failure time distribution is assumed 
    to follow Weibull distribution with a known shape parameter and log-linear 
    link functions are used to model the relationship between failure time 
    parameters and stress variables. The acceleration model may have multiple 
    stress factors, although most ALTs involve only two or less stress factors. 
    ALTopt package also provides several plotting functions including contour plot,
    Fraction of Use Space (FUS) plot and Variance Dispersion graphs of Use Space
    (VDUS) plot.
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
Implements distributed transient groundwater modelling. The model
    is based on the groundwater flow equation solved numerically using the 
    finite difference explicit scheme.
Accompanies "Designing experiments and 
    analyzing data: A model comparison perspective" (3rd ed.) by 
    Maxwell, Delaney, & Kelley (forthcoming from Routledge). 
    Contains all of the data sets in the book's chapters and 
    end-of-chapter exercises. Information about the book is available at 
    <http://www.DesigningExperiments.com>.
Generate code for use with the Optical Mark Recognition free software Auto Multiple Choice (AMC). More specifically, this package provides functions that use as input the question and answer texts, and output the LaTeX code for AMC.
Annual macro-economic database provided by the European Commission.
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.
A tool that "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.
Implements anomaly detection as binary classification for cross-sectional data. Uses maximum likelihood estimates and normal probability functions to classify observations as anomalous. The method is presented in the following lecture from the Machine Learning course by Andrew Ng: <https://www.coursera.org/learn/machine-learning/lecture/C8IJp/algorithm/>, and is also described in: Aleksandar Lazarevic, Levent Ertoz, Vipin Kumar, Aysel Ozgur, Jaideep Srivastava (2003) <doi:10.1137/1.9781611972733.3>.
Analysis of dyadic network and relational data using additive and
    multiplicative effects (AME) models. The basic model includes
    regression terms, the covariance structure of the social relations model
    (Warner, Kenny and Stoto (1979) <DOI:10.1037/0022-3514.37.10.1742>, 
    Wong (1982) <DOI:10.2307/2287296>), and multiplicative factor
    models (Hoff(2009) <DOI:10.1007/s10588-008-9040-4>). 
    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) 
    <DOI:10.1017/nws.2013.17>. Development of this
    software was supported in part by NIH grant R01HD067509.
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.
Raw and processed versions of the data from De Cock (2011) <http://ww2.amstat.org/publications/jse> are included in the package.
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.
An implementation of alternating minimization induced active set (AMIAS) method for solving the L0 regularized learning problems. It includes a piecewise smooth estimator by minimizing the least squares function with constraints on the number of kink points in the discrete derivatives. It also includes generalized structural sparsity via composite L0 penalty. Both time series and image segmentation can be handled by this package.
Modern software often poorly support older file formats. This
    package intends to handle many file formats that were native to the
    antiquated Commodore Amiga machine. This package focuses on file types from
    the older Amiga operating systems (<= 3.0). It will read and write specific
    file formats and coerces them into more contemporary data.
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.
Helps enable adaptive management by codifying knowledge in the
    form of models generated from numerous analyses and data sets. Facilitates
    this process by storing all models and data sets in a single object that can
    be updated and saved, thus tracking changes in knowledge through time. A shiny
    application called AM Model Manager (modelMgr()) enables the use of these
    functions via a GUI.
Provides a function to calculate the concentration of un-ionized ammonia in the total ammonia in aqueous solution using the pH and temperature values.
Automated generation, running, and interpretation of moderated
    nonlinear factor analysis models for obtaining scores from observed
    variables. This package creates 'Mplus' input files which may be run iteratively
    to test two different types of covariate effects on items: 
    (1) latent variable impact (both mean and variance); and (2) differential 
    item functioning. After sequentially testing for all effects, it also creates a final
    model by including all significant effects after adjusting for multiple comparisons.
    Finally, the package creates a scoring model which uses the final values of parameter
    estimates to generate latent variable scores.
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".
Generates samples or complete list of Brazilian IBGE (Instituto Brasileiro de Geografia e Estatistica, see
  <http://www.ibge.gov.br/> for more information) census
    households, geocoding it by Google Maps. The package connects IBGE site and
    downloads maps and census data.
A method for automatic detection of peaks in noisy periodic and quasi-periodic signals. This method, called automatic multiscale-based peak detection (AMPD), is based on the calculation and analysis of the local maxima scalogram, a matrix comprising the scale-dependent occurrences of local maxima.
     For further information see  <doi:10.3390/a5040588>.
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.
Functions to simplify the analysis of Antimicrobial Resistance (AMR)
    of microbial isolates, by using new S3 classes and applying EUCAST expert rules
    on antibiograms according to Leclercq (2013)
    <doi:10.1111/j.1469-0691.2011.03703.x>.
Manage and analyze animal movement data. The functionality of 'amt' includes methods to calculate track statistics (e.g. step lengths, speed, or turning angles), prepare data for fitting habitat selection analyses (resource and step-selection functions), and simulation of space-use from fitted step-selection functions.
Predicts amyloid proteins using random forests trained on the
    n-gram encoded peptides. The implemented algorithm can be accessed from
    both the command line and shiny-based GUI.
Is a collection of models to analyze genome scale codon
        data using a Bayesian framework. Provides visualization
        routines and checkpointing for model fittings. Currently
        published models to analyze gene data for selection on codon
        usage based on Ribosome Overhead Cost (ROC) are: ROC (Gilchrist
        et al. (2015) <doi:10.1093/gbe/evv087>), and ROC with phi
        (Wallace & Drummond (2013) <doi:10.1093/molbev/mst051>). In
        addition 'AnaCoDa' contains three currently unpublished models.
        The FONSE (First order approximation On NonSense Error) model
        analyzes gene data for selection on codon usage against of
        nonsense error rates. The PA (PAusing time) and PANSE (PAusing
        time + NonSense Error) models use ribosome footprinting data to
        analyze estimate ribosome pausing times with and without
        nonsense error rate from ribosome footprinting data.
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.
Provides a set of functions for interacting with the 'Digital
    Ocean' API at <https://developers.digitalocean.com/documentation/v2>, including
    creating images, destroying them, rebooting, getting details on regions, and
    available images.
Fits Modern Analogue Technique and Weighted Averaging transfer 
  	     function models for prediction of environmental data from species 
	     data, and related methods used in palaeoecology.
Provides additional functionality for the analogue package
	     that is not required by all users of the main package.
Current version includes outlier detection in a fitted linear model, stationary bootstrap using a truncated geometric distribution, a comprehensive test for weak stationarity, column means by group, weighted biplots, and a heuristic to obtain a better initial configuration in non-metric MDS.
Class with methods to read and execute R commands described as steps in a CSV file.
Functions for I/O, visualisation and analysis of
        functional Magnetic Resonance Imaging (fMRI) datasets stored in
        the ANALYZE or NIFTI format.
Analyze fuzzy time series by Chen, Singh, Heuristic and Chen-Hsu models. The Abbasov-Mamedova and NFTS models is included as well.
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.
Assigns genetic ancestry to an individual and
    studies relationships between local and global populations.
Supplements the 'MAD#' software (see <http://mad.codeplex.com/>, 
    or Osorio-Murillo, et al. (2015) <doi:10.1016/j.envsoft.2015.01.002>) that
    implements the Method of Anchored Distributions for inferring geostatistical
    parameters (see Rubin, et al. (2010) <doi:10.1029/2009WR008799>). Reads 'MAD#' 
    result databases, performs dimension reduction on inversion data, calculates
    likelihoods and posteriors, and tests for convergence. Also generates plots 
    to summarize results.
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
The package implements ANFIS Type 3 Takagi and Sugeno's fuzzy
    if-then rule network with the following features: (1) Independent number of
    membership functions(MF) for each input, and also different MF extensible
    types. (2) Type 3 Takagi and Sugeno's fuzzy if-then rule (3) Full Rule
    combinations, e.g. 2 inputs 2 membership funtions -> 4 fuzzy rules (4)
    Hibrid learning, i.e. Descent Gradient for precedents and Least Squares
    Estimation for consequents (5) Multiple outputs.
Simulate an angler population, sample the simulated population with a user-specified survey times, and calculate metrics from a bus route-type creel survey.
Helper functions for working with Regional Ocean Modeling System 'ROMS' output. See
    <https://www.myroms.org/> for more information about 'ROMS'.
Provides: (1) Tools to infer dominance hierarchies based on calculating Elo scores, but with custom functions to improve estimates in animals with relatively stable dominance ranks. (2) Tools to plot the shape of the dominance hierarchy and estimate the uncertainty of a given data set.
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.
Provides functions for animations in statistics, covering topics
    in probability theory, mathematical statistics, multivariate statistics,
    non-parametric statistics, sampling survey, linear models, time series,
    computational statistics, data mining and machine learning. These functions
    may be helpful in teaching statistics and data analysis. Also provided in
    this package are a series of functions to save animations to various formats,
    e.g. Flash, 'GIF', HTML pages, 'PDF' and videos. 'PDF' animations can be
    inserted into 'Sweave' / 'knitr' easily.
Simple animated versions of basic R plots, using the 'animation'
    package. Includes animated versions of plot, barplot, persp, contour,
    filled.contour, hist, curve, points, lines, text, symbols, segments, and
    arrows.
Animation of observed trajectories using spline-based interpolation (see for example, Buderman, F. E., Hooten, M. B., Ivan, J. S. and Shenk, T. M. (2016), <doi:10.1111/2041-210X.12465> "A functional model for characterizing long-distance movement behaviour". Methods Ecol Evol). Intended to be used exploratory data analysis, and perhaps for preparation of presentations.
Library to sample and clean text data, build N-gram model, Backoff algorithm etc.
Computationally efficient method to estimate orthant probabilities of high-dimensional Gaussian vectors. Further implements a function to compute conservative estimates of excursion sets under Gaussian random field priors.
Training of general classification and regression neural networks using 
    gradient descent. Special features include a function for training replicator 
    neural networks and a function for training autoencoders. Multiple activation and 
    cost functions (including Huber and pseudo-Huber) are supported, as well as L1 
    and L2 regularization, momentum, early stopping and the possibility to specify 
    a learning rate schedule. The package contains a vectorized gradient descent 
    implementation which facilitates faster training through batch learning.
Extraction of subsequences into FASTA files from GenBank annotations where gene names may vary among accessions.
Annotate multiple lists from a specific annotation file.
The 'annovarR' package provides R functions as well as database resources which offer an integrated framework to annotate genetic variants from genome and transcriptome data. The wrapper functions of 'annovarR' unified the interface of many published annotation tools, such as 'VEP' (<http://asia.ensembl.org/info/docs/tools/vep/index.html>), 'ANNOVAR' (<http://annovar.openbioinformatics.org/>), 'vcfanno' (<https://github.com/brentp/vcfanno>) and 'AnnotationDbi' (<http://www.bioconductor.org/packages/release/bioc/html/AnnotationDbi.html>). It also simplified the use of some of the external annotation tools in R. Besides, massive published genetic variants annotation databases were integrated into 'annovarR'. For example, 'annovarR' provides a newly RNA-seq allele frequency database, BRVar, which built from total 1,285 cases public B-progenitor acute lymphoblastic leukemia (B-ALL) transcriptome data.
Annuity Random Interest Rates proposes different techniques for the approximation of the present and final value of a unitary annuity-due or annuity-immediate considering interest rate as a random variable. Cruz Rambaud et al. (2017) <doi:10.1007/978-3-319-54819-7_16>. Cruz Rambaud et al. (2015) <doi:10.23755/rm.v28i1.25>.
Provides ANOCVA (ANalysis Of Cluster VAriability), a non-parametric statistical test
    to compare clustering structures with applications in functional magnetic resonance imaging
    data (fMRI). The ANOCVA allows us to compare the clustering structure of multiple groups
    simultaneously and also to identify features that contribute to the differential clustering.
The tools in this package are intended to help researchers assess multiple treatment-covariate interactions with data from a parallel-group randomized controlled clinical trial.
Analysis of means (ANOM) as used in technometrical computing. The package takes results from multiple comparisons with the grand mean (obtained with 'multcomp', 'SimComp', 'nparcomp', or 'MCPAN') or corresponding simultaneous confidence intervals as input and produces ANOM decision charts that illustrate which group means deviate significantly from the grand mean.
The 'anomalize' package enables a "tidy" workflow for detecting anomalies in data.
    The main functions are time_decompose(), anomalize(), and time_recompose().
    When combined, it's quite simple to decompose time series, detect anomalies,
    and create bands separating the "normal" data from the anomalous data at scale (i.e. for multiple time series). 
    Time series decomposition is used to remove trend and seasonal components via the time_decompose() function
    and methods include seasonal decomposition of time series by Loess ("stl") and 
    seasonal decomposition by piecewise medians ("twitter"). The anomalize() function implements
    two methods for anomaly detection of residuals including using an inner quartile range ("iqr")
    and generalized extreme studentized deviation ("gesd"). These methods are based on
    those used in the 'forecast' package and the Twitter 'AnomalyDetection' package. 
    Refer to the associated functions for specific references for these methods.
Implements procedures developed by Gutierrez et al. (2017, <https://journal.r-project.org/archive/2017/RJ-2017-039/index.html>) 
  to aid in detecting network log anomalies. By combining various multivariate 
  analytic approaches relevant to network anomaly detection, it provides cyber 
  analysts efficient means to detect suspected anomalies requiring further evaluation.
Fits ideal point model described in Carroll, Lewis, Lo, Poole and Rosenthal (2013), "The Structure of Utility in Models of Spatial Voting," American Journal of Political Science 57(4): 1008--1028, <doi:10.1111/ajps.12029>.
Allows users to quickly and easily anonymize data containing
    Personally Identifiable Information (PII) through convenience functions.
Allows for the computation of a prior predictive p-value to test replication of relevant features of original ANOVA studies. Relevant features are captured in informative hypotheses. The package also allows for the computation of sample sizes for new studies, post-hoc power calculations, and comes with a Shiny application in which all calculations can be conducted as well.
Long formulas and complex computations are two disadvantage of the extended ANOVA approach where the observed data are fuzzy rather that crisp; for more details see Parchami et al. (2017) <doi:10.1007/s40747-017-0046-8>. Package 'ANOVA.TFNs' can help to the applied users to overcome these two challenges. The most important functions in package 'ANOVA.TFNs' is FANOVA() which easily draw all membership functions of fuzzy numbers, report observed statistics in fuzzy ANOVA table, calculate the p-value and final result for Fuzzy ANOVA test at the certain significance level.
Machine learning based package to predict anti-angiogenic peptides using heterogeneous sequence descriptors. 'AntAngioCOOL' exploits five descriptor types of a peptide of interest to do prediction including: pseudo amino acid composition, k-mer composition, k-mer composition (reduced alphabet), physico-chemical profile and atomic profile. According to the obtained results, 'AntAngioCOOL' reached to a satisfactory performance in anti-angiogenic peptide prediction on a benchmark non-redundant independent test dataset.
Process results generated by 'Antares', a powerful software developed by
    RTE (Réseau de Transport d’Électricité) to simulate and study electric power systems (more information about
    'Antares' here: <https://antares.rte-france.com>). This package provides
    functions to create new columns like net load, load factors, upward and
    downward margins or to compute aggregated statistics like economic surpluses
    of consumers, producers and sectors.
Import, manipulate and explore results generated by 'Antares', a 
    powerful software developed by RTE (Réseau de Transport d’Électricité) to simulate and study electric power systems
    (more information about 'Antares' here: <https://antares.rte-france.com>).
Offers a graphical user interface for the calculation of the mean measure of divergence, with facilities for trait selection and graphical representations.
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, statistical shape analysis and archetypal analysis.
A collection of tools for antitrust practitioners, including the ability to calibrate different consumer demand systems and simulate the effects of mergers under different competitive regimes.
Wraps the 'AntiWord' utility to extract text from Microsoft
    Word documents. The utility only supports the old 'doc' format, not the 
    new xml based 'docx' format. Use the 'xml2' package to read the latter.
A complete programmatic interface to the AntWeb database from the
    California Academy of Sciences.
Convert input in any one of character, integer, numeric, factor,
 or ordered type into 'POSIXct' (or 'Date') objects, using one of a number of
 predefined formats, and relying on Boost facilities for date and time parsing.
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.
Another implementation of object-orientation in R. It provides
    syntactic sugar for the S4 class system and two alternative new
    implementations. One is an experimental version built around S4
    and the other one makes it more convenient to work with lists as objects.
Provides tools for analyzing adverse outcome pathways
    (AOPs) for pharmacological and toxicological research. Functionality
    includes the ability to perform causal network analysis of networks
    developed in and exported from Cytoscape or existing as R graph objects, and
    identifying the point of departure/screening/risk value from concentration-
    response data.
A collection of functions to construct A-optimal block designs for comparing test treatments with one or more control(s). Mainly A-optimal balanced treatment incomplete block designs, weighted A-optimal balanced treatment incomplete block designs, A-optimal group divisible treatment designs and A-optimal balanced bipartite block designs can be constructed using the package. The designs are constructed using algorithms based on linear integer programming. To the best of our knowledge, these facilities to construct A-optimal block designs for comparing test treatments with one or more controls are not available in the existing R packages. For more details on designs for tests versus control(s) comparisons, please see Hedayat, A. S. and Majumdar, D. (1984) <doi:10.1080/00401706.1984.10487989> A-Optimal Incomplete Block Designs for Control-Test Treatment Comparisons, Technometrics, 26, 363-370 and Mandal, B. N. , Gupta, V. K., Parsad, Rajender. (2017) <doi:10.1080/03610926.2015.1071394> Balanced treatment incomplete block designs through integer programming. Communications in Statistics - Theory and Methods 46(8), 3728-3737.
'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)
Formatter functions in the 'apa' package take the return value of a
    statistical test function, e.g. a call to chisq.test() and return a string
    formatted according to the guidelines of the APA (American Psychological
    Association).
Provides capabilities to process Apache HTTPD Log files.The main functionalities are to extract data from access and error log files to data frames.
Most psychological journals require that tables in a manuscript
    comply to APA (American Association of Psychology) standards. Creating APA
    tables manually is often time consuming and prone to transcription errors.
    This package generates tables for MS Word ('.docx' extension) in APA format
    automatically with just a few lines of code.
A common task faced by researchers is the creation of APA style
    (i.e., American Psychological Association style) tables from statistical
    output. In R a large number of function calls are often needed to obtain all of
    the desired information for a single APA style table. As well, the process of
    manually creating APA style tables in a word processor is prone to transcription
    errors. This package creates Word files (.doc files) containing APA style tables
    for several types of analyses. Using this package minimizes transcription errors
    and reduces the number commands needed by the user.
Create APA style text from analyses for use within R Markdown documents. Descriptive statistics, confidence intervals, and cell sizes are reported.
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) <DOI:10.1093/biomet/asn026> is used. Thus, the analysis does not rely on ad hoc identification.
Analysis of  data from unreplicated orthogonal experiments such as 2-level factorial and fractional factorial designs and Plackett-Burman designs using the all possible comparisons (APC) methodology developed by Miller (2005) <doi:10.1198/004017004000000608>.
Implements Affinity Propagation clustering introduced by Frey and
	Dueck (2007) <DOI:10.1126/science.1136800>. 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.
An implementation of the additive polynomial (AP) design matrix. It
    constructs and appends an AP design matrix to a data frame for use with
    longitudinal data subject to seasonality.
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 DNA sequences, reading and writing nucleotide sequences as well as importing from BioConductor, 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, dating trees with non-contemporaneous sequences, translating DNA into AA sequences, and assessing sequence alignments. 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.
The goal is to print an "aperçu", a short view of a vector, a
    matrix, a data.frame, a list or an array. By default, it prints the first 5
    elements of each dimension. By default, the number of columns is equal to
    the number of lines. If you want to control the selection of the elements,
    you can pass a list, with each element being a vector giving the selection
    for each dimension.
Toolkit for the analysis of multiple gene data. Apex implements
    the new S4 classes 'multidna', 'multiphyDat' and associated methods to handle
    aligned DNA sequences from multiple genes.
Helper tools for facilitating basic geo-processing tasks, such as reading/writing
  Shapefiles, merging polygons or generating terrain contours.
Designed for the development and application of
    hidden Markov models and profile HMMs for biological sequence analysis. 
    Contains functions for multiple and pairwise sequence alignment, 
    model construction and parameter optimization, file import/export,
    implementation of the forward, backward and Viterbi algorithms for 
    conditional sequence probabilities, tree-based sequence weighting, 
    and sequence simulation. 
    Features a wide variety of potential applications including 
    database searching, gene-finding and annotation, phylogenetic 
    analysis and sequence classification.
    Based on the models and algorithms described in Durbin et 
    al (1998, ISBN: 9780521629713).
An unofficial companion to "Applied
    Logistic Regression" by D.W. Hosmer, S. Lemeshow and
    R.X. Sturdivant (3rd ed., 2013) containing the dataset used in the book.
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.
Fit linear, logistic and Cox models regularized with L0, lasso (L1), elastic-net (L1 and L2), or net (L1 and Laplacian) penalty, and their adaptive forms, such as adaptive lasso / elastic-net and net adjusting for signs of linked coefficients. It solves L0 penalty problem by simultaneously selecting regularization parameters and the number of non-zero coefficients. This augmented and penalized minimization method provides an approximation solution to the L0 penalty problem, but runs as fast as L1 regularization problem. The package uses one-step coordinate descent algorithm and runs extremely fast by taking into account the sparsity structure of coefficients. It could deal with very high dimensional data and has superior selection performance.
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.
Convert several png files into an animated png file.
  This package exports only a single function `apng'. Call the
  apng function with a vector of file names (which should be
  png files) to convert them to a single animated png file.
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.
Calculating predictive model performance measures adjusted for predictor distributions using density ratio method (Sugiyama et al., (2012, ISBN:9781139035613)). L1 and L2 error for continuous outcome and C-statistics for binomial outcome are computed.
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'
Amyloid propensity prediction neural network (APPNN) is an amyloidogenicity propensity predictor based on a machine learning approach through recursive feature selection and feed-forward neural networks, taking advantage of newly published sequences with experimental, in vitro, evidence of amyloid formation.
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
Tools for constructing a matched design with multiple comparison groups.
 Further specifications of refined covariate balance restriction and exact match on 
 covariate can be imposed. Matches are approximately optimal in  the sense that the 
 cost of the solution is at most twice the optimal cost, Crama and Spieksma (1992) 
 <doi:10.1016/0377-2217(92)90078-N>.
An unofficial companion to the textbook "Applied Regression
    Analysis" by N.R. Draper and H. Smith (3rd Ed., 1998) including all the
    accompanying datasets.
Tools to compare several model adjustment and validation methods prior to application in a final analysis.
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 organizes profiling output
    files (including 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.
Contains functions designed to facilitate the loading
    and transformation of 'Agricultural Production Systems Simulator'
    output files <https://www.apsim.info>. Input meteorological data
    (also known as "weather" or "met") files can also be generated
    from user supplied data.
The Agricultural Production Systems sIMulator (APSIM) is a widely
    used simulator of agricultural systems.  This package includes
    functions to create, edit and run APSIM simulations from R.  It
    also includes functions to visualize the results of an APSIM simulation
    and perform sensitivity/uncertainty analysis of APSIM either via functions
    in the sensitivity package or by novel emulator-based functions. 
    For more on APSIM including download instructions go to
    \url{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.
Asymmetric price transmission between two time series is assessed. Several functions are available 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.
Generates phylogenetic trees and distance matrices from a list of species name or from a taxon down to whatever lower taxon. It can do so based on two reference super trees: mammals and angiosperms.
We provide tools to estimate two prediction performance metrics,
    the average positive predictive values (AP) as well as the well-known AUC
    (the area under the receiver operator characteristic curve) for risk scores
    or marker. The outcome of interest is either binary or censored event time.
    Note that for censored event time, our functions estimate the AP and the
    AUC are time-dependent for pre-specified time interval(s). A function that
    compares the APs of two risk scores/markers is also included. Optional
    outputs include positive predictive values and true positive fractions at
    the specified marker cut-off values, and a plot of the time-dependent AP
    versus time (available for event time data).
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.
        Implements methods described in Bortolussi et al. (2005)
        <doi:10.1093/bioinformatics/bti798> and Maliet et al. (2017) 
        <doi:10.1101/224295>.
This package contains functions to help display air quality model output and monitoring data, such as creating color scatterplots, color legends, etc.
The Algorithms for Quantitative Pedology (AQP) project was started in 2009 to organize a loosely-related set of concepts and source code on the topic of soil profile visualization, aggregation, and classification into this package (aqp). Over the past 8 years, the project has grown into a suite of related R packages that enhance and simplify the quantitative analysis of soil profile data. Central to the AQP project is a new vocabulary of specialized functions and data structures that can accommodate the inherent complexity of soil profile information; freeing the scientist to focus on ideas rather than boilerplate data processing tasks <doi:10.1016/j.cageo.2012.10.020>. These functions and data structures have been extensively tested and documented, applied to projects involving hundreds of thousands of soil profiles, and deeply integrated into widely used tools such as SoilWeb <https://casoilresource.lawr.ucdavis.edu/soilweb-apps/>. Components of the AQP project (aqp, soilDB, sharpshootR, soilReports packages) serve an important role in routine data analysis within the USDA-NRCS Soil Science Division. The AQP suite of R packages offer a convenient platform for bridging the gap between pedometric theory and practice.
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
Toolbox for the experimental aquatic chemist, focused on 
        acidification and CO2 air-water exchange. It contains all elements to
        model the pH, the related CO2 air-water exchange, and
        aquatic acid-base chemistry for an arbitrary marine,
        estuarine or freshwater system. It contains a suite of tools for 
        sensitivity analysis, visualisation, modelling of chemical batches, 
        and can be used to build dynamic models of aquatic systems. 
        As from version 1.0-4, it also contains functions to calculate 
        the buffer factors.
In mathematics, 'rejection sampling' is a basic technique used to generate observations from a distribution. It is also commonly called 'the Acceptance-Rejection method' or 'Accept-Reject algorithm' and is a type of Monte Carlo method. 'Acceptance-Rejection method' is based on the observation that to sample a random variable one can perform a uniformly random sampling of the 2D cartesian graph, and keep the samples in the region under the graph of its density function. Package 'AR' is able to generate/simulate random data from a probability density function by Acceptance-Rejection method. Moreover, this package is a useful teaching resource for graphical presentation of Acceptance-Rejection method. From the practical point of view, the user needs to calculate a constant in Acceptance-Rejection method, which package 'AR' is able to compute this constant by optimization tools. Several numerical examples are provided to illustrate the graphical presentation for the Acceptance-Rejection Method.
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
Allows users to stem Arabic texts for text analysis.
Processes noble gas mass spectrometer data to determine the isotopic composition of argon (comprised of Ar36, Ar37, Ar38, Ar39 and Ar40) released from neutron-irradiated potassium-bearing minerals. Then uses these compositions to calculate precise and accurate geochronological ages for multiple samples as well as the covariances between them. Error propagation is done in matrix form, which jointly treats all samples and all isotopes simultaneously at every step of the data reduction process. Includes methods for regression of the time-resolved mass spectrometer signals to t=0 ('time zero') for both single- and multi-collector instruments, blank correction, mass fractionation correction, detector intercalibration, decay corrections, interference corrections, interpolation of the irradiation parameter between neutron fluence monitors, and (weighted mean) age calculation. All operations are performed on the logs of the ratios between the different argon isotopes so as to properly treat them as 'compositional data', sensu Aitchison [1986, The Statistics of Compositional Data, Chapman and Hall].
Implements the Classification-based on
    Association Rules (CBA) (Bing Liu, Wynne Hsu,	Yiming Ma (1999) <http://dl.acm.org/citation.cfm?id=3000292.3000305>) algorithm for association rule classification (ARC).
    The package also contains several convenience methods that allow to automatically
    set CBA parameters (minimum confidence, minimum support) and it also natively
    handles numeric attributes by integrating a pre-discretization step.
    The rule generation phase is handled by the 'arules' package. 
    To further decrease the size of the CBA models produced by the 'arc' package, postprocessing by the 
    'qCBA' package is suggested.
It fits an univariate left or right censored linear regression model
    with autoregressive errors under the normal distribution. It provides estimates
    and standard errors of the parameters, prediction of future observations and
    it supports missing values on the dependent variable.
    It also performs influence diagnostic through local influence for three possible
    perturbation schemes.
Provides a list of functions for the Bayesian modeling of archaeological chronologies. The Bayesian models are implemented in 'JAGS' ('JAGS' stands for Just Another Gibbs Sampler. It is a program for the analysis of Bayesian hierarchical models using Markov Chain Monte Carlo (MCMC) simulation. See <http://mcmc-jags.sourceforge.net/> and "JAGS Version 4.3.0 user manual", Martin Plummer (2017) <https://sourceforge.net/projects/mcmc-jags/files/Manuals/>.). The inputs are measurements with their associated standard deviations and the study period. The output is the MCMC sample of the posterior distribution of the event date with or without radiocarbon calibration.
Provides a list of functions for the statistical analysis of archaeological dates and groups of dates. It is based on the post-processing of the Markov Chains whose stationary distribution is the posterior distribution of a series of dates. Such output can be simulated by different applications as for instance 'ChronoModel' (see <http://www.chronomodel.fr>), 'Oxcal' (see <https://c14.arch.ox.ac.uk/oxcal.html>) or 'BCal' (see <http://bcal.shef.ac.uk/>). The only requirement is to have a csv file containing a sample from the posterior distribution.
The archdata package provides several types of data that are typically used in archaeological research. It provides all of the data sets used in "Quantitative Methods in Archaeology Using R" by David L Carlson, one of the Cambridge Manuals in Archaeology.
The main function archetypes implements a
    framework for archetypal analysis supporting arbitrary
    problem solving mechanisms for the different conceptual
    parts of the algorithm.
Analysis of complex plant root system architectures (RSA) using the output files created by Data Analysis of Root Tracings (DART), an open-access software dedicated to the study of plant root architecture and development across time series (Le Bot et al (2010) "DART: a software to analyse root system architecture and development from captured images", Plant and Soil, <DOI:10.1007/s11104-009-0005-2>), and RSA data encoded with the Root System Markup Language (RSML) (Lobet et al (2015) "Root System Markup Language: toward a unified root architecture description language", Plant Physiology, <DOI:10.1104/pp.114.253625>). More information can be found in Delory et al (2016) "archiDART: an R package for the automated computation of plant root architectural traits", Plant and Soil, <DOI:10.1007/s11104-015-2673-4>.
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.
The extension of the 'archivist' package integrating the archivist with GitHub via GitHub API, 'git2r' packages and 'httr' package.
Set of functions to analyse and estimate Artificial Counterfactual models from Carvalho, Masini and Medeiros (2016) <DOI:10.2139/ssrn.2823687>.
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.
Plot stacked areas and confidence bands as filled polygons, or add
  polygons to existing plots. A variety of input formats are supported,
  including vectors, matrices, data frames, formulas, etc.
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.
	Bug reports should be done via GitHub (at
	<https://github.com/JQVeenstra/arfima>), where the development version
	of this package lives; it can be installed using devtools.
Functions to facilitate the estimation of Arfima-MLM models for repeated cross-sectional data and pooled cross-sectional time-series data (see Lebo and Weber 2015). The estimation procedure uses double filtering with Arfima methods to account for autocorrelation in repeated cross-sectional data followed by multilevel modeling (MLM) to estimate aggregate as well as individual-level parameters simultaneously.
Utilities for secure password hashing via the argon2 algorithm.
    It is a relatively new hashing algorithm and is believed to be very secure.
    The 'argon2' implementation included in the package is the reference
    implementation.  The package also includes some utilities that should be
    useful for digest authentication, including a wrapper of 'blake2b'.  For
    similar R packages, see sodium and 'bcrypt'.  See
    <https://en.wikipedia.org/wiki/Argon2> or
    <https://eprint.iacr.org/2015/430.pdf> for more information.
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. It is useful with the Rscript
    front-end and facilitates turning an R script into an executable script.
The typical process of checking arguments in functions is
    iterative.  In this process, an error may be returned and the user may fix
    it only to receive another error on a different argument.  'ArgumentCheck'
    facilitates a more helpful way to perform argument checks allowing the
    programmer to run all of the checks and then return all of the errors and
    warnings in a single message.
Perform the Adaptable Regularized Hotelling's T^2 test (ARHT) proposed by
    Li et al., (2016) <arXiv:1609.08725>. Both one-sample and two-sample mean test are available with
    various probabilistic alternative prior models. It contains a function to consistently
    estimate higher order moments of the population covariance spectral distribution using
    the spectral of the sample covariance matrix (Bai et al. (2010) <doi:10.1111/j.1467-842X.2010.00590.x>).
    In addition, it contains a function to sample from 3-variate chi-squared random vectors approximately 
    with a given correlation matrix when the degrees of freedom are large.
Create videos from 'R Markdown' documents, or images and audio
    files. These images can come from image files or HTML slides, and the audio
    files can be provided by the user or computer voice narration can be created
    using 'Amazon Polly'. The purpose of this package is to allow users to create
    accessible, translatable, and reproducible lecture videos. See
    <https://aws.amazon.com/polly/> for more information.
Functions to accompany A. Gelman and J. Hill, Data Analysis Using Regression and Multilevel/Hierarchical Models, Cambridge University Press, 2007.
Arnold Schwarzenegger movie weekend box office records from
    1982-2014
A cross-platform R framework that facilitates processing of any number of Affymetrix microarray samples regardless of computer system.  The only parameter that limits the number of chips that can be processed is the amount of available disk space.  The Aroma Framework has successfully been used in studies to process tens of thousands of arrays.  This package has actively been used since 2006.
DEPRECATED. Do not start building new projects based on this package. (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.
Core methods and classes used by higher-level 'aroma.*' packages
        part of the Aroma Project, e.g. 'aroma.affymetrix' and 'aroma.cn'.
Tools for geometric morphometric analysis. The package includes tools of virtual anthropology to align two not articulated parts belonging to the same specimen, to build virtual cavities as endocast (Profico et al, 2017 <doi:10.13140/RG.2.2.32524.00640>), and functions to import and export the coordinates of landmarks and 3D paths into 'landmarkAscii' and 'am' format files.
Tools for simulating data generated by direct observation
    recording. Behavior streams are simulated 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 streams. 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 or a period of linear rate buildup. Functions included for computing rate, cumulative production, instantaneous decline, EUR, time to economic limit, and performing least-squares best fits.
Fast generators and iterators for permutations, combinations
    and partitions. The iterators allow users to generate arrangements in 
    a memory efficient manner and the generated arrangements are in 
    lexicographical (dictionary) order. Permutations and combinations can 
    be drawn with/without replacement and support multisets. It has been 
    demonstrated that 'arrangements' outperforms most of the existing 
    packages of similar kind. Some benchmarks could be found at 
    <https://randy3k.github.io/arrangements/articles/benchmark.html>.
High performance variant of apply() for a fixed set of functions.
  Considerable speedup is a trade-off for universality, user defined
  functions cannot be used with this package. However, 20 most currently employed
  functions are available for usage. They can be divided in three types:
  reducing functions (like mean(), sum() etc., giving a scalar when applied to a vector),
  mapping function (like normalise(), cumsum() etc., giving a vector of the same length
  as the input vector) and finally, vector reducing function (like diff() which produces
  result vector of a length different from the length of input vector).
  Optional or mandatory additional arguments required by some functions
  (e.g. norm type for norm() or normalise() functions) can be
  passed as named arguments in '...'.
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
An Arsenal of 'R' functions for large-scale statistical summaries,
  which are streamlined to work within the latest reporting tools in 'R' and
  'RStudio' and which use formulas and versatile summary statistics for summary
  tables and models. The primary functions include tableby(), a Table-1-like
  summary of multiple variable types 'by' the levels of a categorical
  variable; modelsum(), which performs simple model fits on the same endpoint
  for many variables (univariate or adjusted for standard covariates);
  freqlist(), a powerful frequency table across many categorical variables;
  compare.data.frame(), the S3 method for comparing data.frames; and
  write2(), a function to output tables to a document.
An implementation of the Aligned Rank Transform technique for
    factorial analysis (see references below for details) including models with
    missing terms (unsaturated factorial models). The function first
    computes a separate aligned ranked response variable for each effect of the
    user-specified model, and then runs a classic ANOVA on each of the aligned
    ranked responses. For further details, see Higgins, J. J. and Tashtoush, S.
    (1994). An aligned rank transform test for interaction. Nonlinear World 1
    (2), pp. 201-211. Wobbrock, J.O., Findlater, L., Gergle, D. and
    Higgins,J.J. (2011). The Aligned Rank Transform for nonparametric factorial
    analyses using only ANOVA procedures. Proceedings of the ACM Conference on
    Human Factors in Computing Systems (CHI '11). New York: ACM Press, pp.
    143-146. <doi:10.1145/1978942.1978963>.
Fit and simulate ARTFIMA. Theoretical autocovariance function and spectral density function for stationary ARTFIMA.
Reversible Jump MCMC (RJ-MCMC)sampling for approximating the posterior 
        distribution of a time varying regulatory network, under the Auto Regressive TIme VArying
		(ARTIVA) model (for a detailed description of the algorithm, see Lebre et al. BMC Systems
		Biology, 2010). Starting from time-course gene expression measurements for a gene of 
		interest (referred to as "target gene") and a set of genes (referred to as "parent genes")
		which may explain the expression of the target gene, the ARTIVA procedure identifies
        temporal segments for which a set of interactions occur between the "parent genes" and the
		"target gene". The time points that delimit the different temporal segments are referred to
		as changepoints (CP).
The Aligned Rank Transform for nonparametric
    factorial ANOVAs as described by J. O. Wobbrock,
    L. Findlater, D. Gergle, & J. J. Higgins, "The Aligned
    Rank Transform for nonparametric factorial analyses
    using only ANOVA procedures", CHI 2011 <DOI:10.1145/1978942.1978963>.
For calculating gene and pathway p-values using the Adaptive Rank Truncated Product test.
Pathway and gene level association test using raw data or summary statistics.
Provides the infrastructure for representing,
    manipulating and analyzing transaction data and patterns (frequent
    itemsets and association rules). Also provides
    C implementations of the association mining algorithms Apriori and Eclat.
Provides a function to build an association rule-based classifier for data frames, and to classify incoming data frames using such a classifier.
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.
Extends package 'arules' with various visualization techniques for association rules and itemsets. The package also includes several interactive visualizations for rule exploration.
An interface to the API for 'arXiv'
    (<https://arxiv.org>), a repository of electronic preprints for
    computer science, mathematics, physics, quantitative biology,
    quantitative finance, and statistics.
Data sets are referred to in the text "Applied Survival Analysis Using R"
 by Dirk F. Moore, Springer, 2016, ISBN: 978-3-319-31243-9, <DOI:10.1007/978-3-319-31245-3>.
Contains functions from: Aho, K. (2014) Foundational and Applied Statistics for Biologists using R.  CRC/Taylor and Francis, Boca Raton, FL, ISBN: 978-1-4398-7338-0.
Coerce R object to asciidoc, txt2tags, restructuredText,
        org, textile or pandoc syntax.  Package comes with a set of
        drivers for Sweave.
An ASCII ruler is for measuring text and is especially useful for sequence analysis. Included in this package are methods to create ASCII rulers and associated GenBank sequence blocks, multi-column text displays that make it easy for viewers to locate nucleotides by position.
Lets you open an 'SPSS' or 'SAS' data file using a .txt file that has the data and a .sps or .sas file with setup instructions. This will only run in a txt-sps or txt-sas pair in which the setup file contains instructions to open that text file. It will NOT open other text files, .sav, .por, or 'SAS' files.
The as.color function takes an R vector of any class as an input,
    and outputs a vector of unique hexadecimal color values that correspond to the
    unique input values. This is most handy when overlaying points and lines for
    data that correspond to different levels or factors. The function will also
    print the random seed used to generate the colors. If you like the color palette
    generated, you can save the seed and reuse those colors.
Package runs simulations for adaptive seamless designs with and without early outcomes 
                           for treatment selection and subpopulation type designs.
A simple driver that reads binary data created by the ASD Inc.
    portable spectrometer instruments, such as the FieldSpec (for more information,
    see <http://www.asdi.com/products/fieldspec-spectroradiometers>). Spectral data
    can be extracted from the ASD files as raw (DN), white reference, radiance, or
    reflectance. Additionally, the metadata information contained in the ASD file
    header can also be accessed.
The Australian Statistical Geography Standard ('ASGS') is 
  a set of shapefiles by the Australian Bureau of Statistics. This package
  provides an interface to those shapefiles, as well as methods for converting
  coordinates to shapefiles.
David Scott's ASH routines ported from S-PLUS to R.
The R package 'ashr' implements an Empirical Bayes
    approach for large-scale hypothesis testing and false discovery
    rate (FDR) estimation based on the methods proposed in
    M. Stephens, 2016, "False discovery rates: a new deal",
    <DOI:10.1093/biostatistics/kxw041>. These methods can be applied
    whenever two sets of summary statistics---estimated effects and
    standard errors---are available, just as 'qvalue' can be applied
    to previously computed p-values. Two main interfaces are
    provided: ash(), which is more user-friendly; and ash.workhorse(),
    which has more options and is geared toward advanced users. The
    ash() and ash.workhorse() also provides a flexible modeling
    interface that can accomodate a variety of likelihoods (e.g.,
    normal, Poisson) and mixture priors (e.g., uniform, normal).
Some hypothesis test functions (sign test, median and other quantile tests, Wilcoxon signed rank test, coefficient of variation test, test of normal variance, test on weighted sums of Poisson, sample size for t-tests with different variances and non-equal n per arm, Behrens-Fisher test, nonparametric ABC intervals, Wilcoxon-Mann-Whitney test) with a focus on non-asymptotic methods that have matching confidence intervals.
'Asio' is a cross-platform C++ library for network and low-level
 I/O programming that provides developers with a consistent asynchronous model
 using a modern C++ approach.

    'Asio' is also included in Boost but requires linking when used with
 Boost. Standalone it can be used header-only provided a recent-enough
 compiler. 'Asio' is written and maintained by Christopher M. Kohlhoff.
 'Asio' is released under the 'Boost Software License', Version 1.0.
Provides an interface to the algorithm selection benchmark library
    at <http://www.aslib.net> and the 'LLAMA' package
    (<https://cran.r-project.org/web/packages/llama/index.html>) for building
    algorithm selection models.
Functions for Accurate and Speedy 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).
Implements several tools that are used in animal social network analysis. In particular, this package provides the tools to infer groups and generate networks from observation data, perform permutation tests on the data, calculate lagged association rates, and performed multiple regression analysis on social network data.
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 allows to make inference on the properties of causal genetic
             variants in linkage disequilibrium with genotyped markers. In a first step, 
			 we select a subset of variants using a score statistic for affected 
			 sib-pairs. In a second step, on the selected subset, we make 
             inference on causal genetic variants in the considered region.
The aSPC test is designed to test global association between two groups of variables potentially with moderate to high dimension (e.g. in hundreds). The aSPC is particularly useful when the association signals between two groups of variables are sparse.
Contains various functions for optimal scaling. One function 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. Another function performs implements the LINEALS approach for optimal scaling by minimization of an aspect based on pairwise correlations and correlation ratios. The resulting correlation matrix and category scores can be used for further multivariate methods such as structural equation models.
Tools for the analysis and visualization of bilateral asymmetry in parasitic infections.
R codes for the (adaptive) Sum of Powered Score ('SPU' and 'aSPU')
    tests, inverse variance weighted Sum of Powered score ('SPUw' and 'aSPUw') tests
    and gene-based and some pathway based association tests (Pathway based Sum of
    Powered Score tests ('SPUpath'), adaptive 'SPUpath' ('aSPUpath') test, 'GEEaSPU'
    test for multiple traits - single 'SNP' (single nucleotide polymorphism)
    association in generalized estimation equations, 'MTaSPUs' test for multiple
    traits - single 'SNP' association with Genome Wide Association Studies ('GWAS')
    summary statistics, Gene-based Association Test that uses an extended 'Simes'
    procedure ('GATES'), Hybrid Set-based Test ('HYST') and extended version
    of 'GATES' test for pathway-based association testing ('GATES-Simes'). ).
    The tests can be used with genetic and other data sets with covariates. The
    response variable is binary or quantitative. Summary; (1) Single trait-'SNP' set
    association with individual-level data ('aSPU', 'aSPUw', 'aSPUr'), (2) Single trait-'SNP'
    set association with summary statistics ('aSPUs'), (3) Single trait-pathway
    association with individual-level data ('aSPUpath'), (4) Single trait-pathway
    association with summary statistics ('aSPUsPath'), (5) Multiple traits-single
    'SNP' association with individual-level data ('GEEaSPU'), (6) Multiple traits-
    single 'SNP' association with summary statistics ('MTaSPUs'), (7) Multiple traits-'SNP' set association with summary statistics('MTaSPUsSet'), (8) Multiple traits-pathway association with summary statistics('MTaSPUsSetPath').
Assists in automating the testing of terms in mixed models when 'asreml' is used 
  to fit the models. The content falls into the following natural groupings: (i) Data, (ii) Object 
  manipulation functions, (iii) Model modification functions, (iv) Model testing functions, 
  (v) Model diagnostics functions, (vi) Prediction production and presentation functions, 
  (vii) Response transformation functions, and (viii) Miscellaneous functions. A history of the 
  fitting of a sequence of models is kept in a data frame. Procedures are available for choosing 
  models that conform to the hierarchy or marginality principle and for displaying predictions 
  for significant terms in tables and graphs. The package 'asreml' provides a computationally 
  efficient algorithm for fitting mixed models using Residual Maximum Likelihood. It can be 
  purchased from 'VSNi' <http://www.vsni.co.uk/> as 'asreml-R', who will supply a zip file for 
  local installation/updating.
Allows to correct bias in high-throughput screening (HTS) assays. It (1) Detects plate-specific spatial bias by identifying rows and columns of all plates of the assay affected by this bias (following the results of the Mann-Whitney U test) as well as assay-specific spatial bias by identifying well locations (i.e., well positions scanned across all plates of a given assay) affected by this bias (also following the results of the Mann-Whitney U test); (2) Allows one to correct plate-specific spatial bias using either of the three additive or either of the three multiplicative PMP (Partial Mean Polish) methods (the most appropriate spatial bias model can be either specified by the user or determined by the program following the results of the Kolmogorov-Smirnov, Anderson-Darling or Cramer-von-Mises two-sample test) to correct the assay measurements as well as to correct assay-specific spatial bias by carrying out robust Z-scores within each plate of the assay and then traditional Z-scores across well locations.
Simple, flexible, assertions on data.frame or data.table objects with verbose output for vetting. While other assertion packages apply towards more general use-cases, assertable is tailored towards tabular data. It includes functions to check variable names and values, whether the dataset contains all combinations of a given set of unique identifiers, and whether it is a certain length. In addition, assertable includes utility functions to check the existence of target files and to efficiently import multiple tabular data files into one data.table.
Lots of predicates (is_* functions) to check the state of your
    variables, and assertions (assert_* functions) to throw errors if they
    aren't in the right form.
A minimal set of predicates and assertions used by the assertive
    package.  This is mainly for use by other package developers who want to
    include run-time testing features in their own packages.  End-users will
    usually want to use assertive directly.
A set of predicates and assertions for checking the properties of
    code. This is mainly for use by other package developers who want to include
    run-time testing features in their own packages. End-users will usually want to
    use assertive directly.
A set of predicates and assertions for checking the properties of
    (country independent) complex data types.  This is mainly for use by other
    package developers who want to include run-time testing features in
    their own packages.  End-users will usually want to use assertive directly.
A set of predicates and assertions for checking the properties of
    UK-specific complex data types.  This is mainly for use by other package
    developers who want to include run-time testing features in their own
    packages.  End-users will usually want to use assertive directly.
A set of predicates and assertions for checking the properties of
    US-specific complex data types.  This is mainly for use by other package
    developers who want to include run-time testing features in their own
    packages.  End-users will usually want to use assertive directly.
A set of predicates and assertions for checking the properties of
    dates and times.  This is mainly for use by other package developers who
    want to include run-time testing features in their own packages.  End-users
    will usually want to use assertive directly.
A set of predicates and assertions for checking the properties of
    files and connections.  This is mainly for use by other package developers
    who want to include run-time testing features in their own packages.
    End-users will usually want to use assertive directly.
A set of predicates and assertions for checking the properties of
    matrices.  This is mainly for use by other package developers who want to
    include run-time testing features in their own packages.  End-users will
    usually want to use assertive directly.
A set of predicates and assertions for checking the properties of
    models.  This is mainly for use by other package developers who want to
    include run-time testing features in their own packages.  End-users will
    usually want to use assertive directly.
A set of predicates and assertions for checking the properties of
    numbers.  This is mainly for use by other package developers who want to
    include run-time testing features in their own packages.  End-users will
    usually want to use assertive directly.
A set of predicates and assertions for checking the properties of
    variables, such as length, names and attributes.  This is mainly for use by
    other package developers who want to include run-time testing features in
    their own packages.  End-users will usually want to use assertive directly.
A set of predicates and assertions for checking the state and
    capabilities of R, the operating system it is running on, and the IDE
    being used.  This is mainly for use by other package developers who
    want to include run-time testing features in their own packages.
    End-users will usually want to use assertive directly.
A set of predicates and assertions for checking the properties of
    sets.  This is mainly for use by other package developers who want to
    include run-time testing features in their own packages.  End-users will
    usually want to use assertive directly.
A set of predicates and assertions for checking the properties of
    strings.  This is mainly for use by other package developers who want to
    include run-time testing features in their own packages.  End-users will
    usually want to use assertive directly.
A set of predicates and assertions for checking the types of
    variables.  This is mainly for use by other package developers who want to
    include run-time testing features in their own packages.  End-users will
    usually want to use assertive directly.
Provides functionality to assert conditions
    that have to be met so that errors in data used in
    analysis pipelines can fail quickly. Similar to
    'stopifnot()' but more powerful, friendly, and easier
    for use in pipelines.
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.
Use Monte-Carlo and K-fold cross-validation coupled with machine-learning classification algorithms to perform population assignment, with functionalities of evaluating discriminatory power of independent training samples, identifying informative loci, reducing data dimensionality for genomic data, integrating genetic and non-genetic data, and visualizing results.
A comprehensive package for fitting various non-parametric/semi-parametric linear/nonlinear fixed/mixed smoothing spline models.
Clinical trial design for subgroup selection in three-stage group
    sequential trial. Includes facilities for design, exploration and analysis of
    such trials. An implementation of the initial DEFUSE-3 trial is also provided
    as a vignette.
When doing association analysis one does not always have the 
   genotypes for the control population.  In such cases it may be 
   necessary to fall back on frequency based tests using well known sources 
   for the frequencies in the control population, for instance, from the 
   1000 Genomes Project.  The Allele Frequency Comparison ('AssocAFC') package 
   performs multiple rare variant association analyses in both population and 
   family-based GWAS (Genome-Wide Association Study) designs. It includes 
   three score tests that are based on the difference of the sum of allele 
   frequencies between cases and controls.  Two of these tests, Wcorrected() 
   and Wqls(), are collapsing-based tests and suffer from having protective 
   and risk variants. The third test, afcSKAT(), is a score test that 
   overcomes the mix of SNP (Single-Nucleotide Polymorphism) effect 
   directions. For more details see 
   Saad M and Wijsman EM (2017) <doi:10.1093/bib/bbx107>.
Implements several new association indices that can control for various types of errors. Also includes existing association indices and functions for simulating the effects of different rates of error on estimates of association strength between individuals using each method.
Some procedures including EIGENSTRAT (a procedure for detecting and correcting 
	for population stratification through searching for the eigenvectors in genetic association 
	studies), PCoC (a procedure for correcting for population stratification through calculating 
	the principal coordinates and the clustering of the subjects), Tracy-Widom test (a procedure 
	for detecting the significant eigenvalues of a matrix), distance regression (a procedure for 
	detecting the association between a distance matrix and some independent variants of interest), 
	single-marker test (a procedure for identifying the association between the genotype at a 
	biallelic marker and a trait using the Wald test or the Fisher's exact test), MAX3 (a procedure 
	for testing for the association between a single nucleotide polymorphism and a binary phenotype 
	using the maximum value of the three test statistics derived for the recessive, additive, and 
	dominant models), nonparametric trend test (a procedure for testing for the association between 
	a genetic variant and a non-normal distributed quantitative trait based on the nonparametric 
	risk), and nonparametric MAX3 (a procedure for testing for the association between a biallelic 
	single nucleotide polymorphism and a quantitative trait using the maximum value of the three 
	nonparametric trend tests derived for the recessive, additive, and dominant models), which are 
	commonly used in genetic association studies.
Functions to calculate the assortment of vertices in social networks.  This can be measured on both weighted and binary networks, with discrete or continuous vertex values.
R package with statistical tests and methods for genetic
    association studies with emphasis on rare variants and binary (dichotomous)
    traits
Aster models are exponential family regression models for life
    history analysis.  They are like generalized linear models except that
    elements of the response vector can have different families (e. g.,
    some Bernoulli, some Poisson, some zero-truncated Poisson, some normal)
    and can be dependent, the dependence indicated by a graphical structure.
    Discrete time survival analysis, zero-inflated Poisson regression, and
    generalized linear models that are exponential family (e. g., logistic
    regression and Poisson regression with log link) are special cases.
    Main use is for data in which there is survival over discrete time periods
    and there is additional data about what happens conditional on survival
    (e. g., number of offspring).  Uses the exponential family canonical
    parameterization (aster transform of usual parameterization).
Aster models are exponential family regression models for life
    history analysis.  They are like generalized linear models except that
    elements of the response vector can have different families (e. g.,
    some Bernoulli, some Poisson, some zero-truncated Poisson, some normal)
    and can be dependent, the dependence indicated by a graphical structure.
    Discrete time survival analysis, zero-inflated Poisson regression, and
    generalized linear models that are exponential family (e. g., logistic
    regression and Poisson regression with log link) are special cases.
    Main use is for data in which there is survival over discrete time periods
    and there is additional data about what happens conditional on survival
    (e. g., number of offspring).  Uses the exponential family canonical
    parameterization (aster transform of usual parameterization).
    Unlike the aster package, this package does dependence groups (nodes of
    the graph need not be conditionally independent given their predecessor
    node), including multinomial and two-parameter normal as families.  Thus
    this package also generalizes mark-capture-recapture 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.
Routines for astrochronologic testing, astronomical time scale construction, and time series analysis. Also included are a range of statistical analysis and modeling routines that are relevant to time scale development and paleoclimate 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.
Included are data sets and scripts to accompany Time Series Analysis and Its Applications: With R Examples (4th ed), by R.H. Shumway and D.S. Stoffer. Springer Texts in Statistics, 2017. <DOI:10.1007/978-3-319-52452-8>.
Provides the ASUS procedure for estimating a high dimensional sparse parameter in the presence of auxiliary data that encode side information on sparsity.
    It is a robust data combination procedure in the sense that even when pooling non-informative auxiliary data ASUS would be at least as efficient as competing soft thresholding based methods that do not use auxiliary data.
    For more information, please see the website <http://www-bcf.usc.edu/~wenguans/Papers/ASUS.htm> and the accompanying paper.
The visual predictive checks are well-known method to validate the 
             nonlinear mixed effect model, especially in pharmacometrics area. 
             The average shifted visual predictive checks are the newly 
             developed method of Visual predictive checks combined with 
             the idea of the average shifted histogram.
Computes asymmetric LD measures (ALD) for multi-allelic genetic data. These measures are identical to the correlation measure (r) for bi-allelic data.
Multidimensional scaling models and methods for the visualization for asymmetric data. A matrix is asymmetric if the number of rows equals the number of columns, and these rows and columns refer to the same set of objects. An example is a  student migration table, where the rows correspond to the countries of origin of the students and the columns to the destination countries. This package provides the slide-vector model, a scaling model with unique dimensions and the asymscal model for asymmetric multidimensional scaling. Furthermore, a heat map for skew-symmetric data, and the decomposition of asymmetry are provided for the analysis of asymmetric tables.
Estimation of regression models for sparse asynchronous longitudinal observations, where time-dependent response and covariates are mismatched and observed intermittently within subjects. Kernel weighted estimating equations are used for generalized linear models with either time-invariant or time-dependent coefficients.
A set of routines written in the S language
             that calculate power and related quantities utilizing asymptotic
             likelihood ratio methods.
Nonparametric estimation and inference for average treatment effects based on covariate balancing.
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).
Atlantis is an end-to-end marine ecosystem modelling framework. It was originally developed in Australia by E.A. Fulton, A.D.M. Smith and D.C. Smith (2007) and has since been adopted in many marine ecosystems around the world (<http://atlantis.cmar.csiro.au>). The output of an Atlantis simulation is stored in various file formats like .netcdf and .txt and different output structures are used for the output variables like e.g. productivity or biomass. This package is used to convert the different output types to a unified format according to the "tidy-data" approach by H. Wickham (2014) <DOI:10.18637/jss.v059.i10>. Additionally, ecological metrics like for example spatial overlap of predator and prey or consumption can be calculated and visualised with this package. Due to the unified data structure it is very easy to share model output with each other and perform model comparisons.
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.
Plot party trees in left-right orientation instead of the
  classical top-down layout.
Contains some tools for testing, analyzing time series data and
    fitting popular time series models such as ARIMA, Moving Average and Holt
    Winters, etc. Most functions also provide nice and clear outputs like SAS
    does, such as identify, estimate and forecast, which are the same statements
    in PROC ARIMA in SAS.
Provides functions for retrieving time-series and related
    meta-data such as entities, metrics, and tags from the Axibase
    Time-Series Database (ATSD). ATSD is a non-relational clustered
    database used for storing performance measurements from IT infrastructure
    resources: servers, network devices, storage systems, and applications.
A friendlier condition handler, inspired by 'purrr' mappers and 
    based on 'rlang'.'attempt' extends and facilitates condition handling by 
    providing a consistent grammar, and provides a set of easy to use functions 
    for common tests and conditions. 'attempt' only depends on 'rlang', and 
    focuses on speed, so it can be easily integrated in other functions and 
    used in data analysis.
An implementation of tools for design of attribute 
  variable sampling interval cumulative sum chart. 
  It currently provides information for monitoring of mean increase such as 
  average number of sample to signal, average time to signal,
  a matrix of transient probabilities, suitable control limits when the data are
  (zero inflated) Poisson/binomial distribution. 
  Functions in the tools can be easily applied to other count processes.
  Also, tools might be extended to more complicated cumulative sum control chart.
  We leave these issues as our perpetual work.
Estimates population (etiological) attributable risk for
    unmatched, pair-matched or set-matched case-control designs and returns a
    list containing the estimated attributable risk, estimates of coefficients,
    and their standard errors, from the (conditional, If necessary) logistic
    regression used for estimating the relative risk.
Abridged data from the American Time Use Survey (ATUS) for years 2003-2016. The ATUS is an annual survey conducted on a sample of individuals across the United States studying how individuals spent their time over the course of a day. Individual respondents were interviewed about what activities they did, during what times (rounded to 15 minute increments), at what locations, and in the presence of which individuals. The activities are subsequently encoded based on 3 separate tier codes for classification. This package includes data from the multi-year ATUS Activities, ATUS-CPS, and ATUS Respondents files were included. Columns were selected based on completeness of data as well as presence on the Frequently Used Variables list provided by the ATUS website. All activity codes (other than code '50' for 'Unable to Code') were included. Permission was obtained from the Bureau of Labor Statistics for inclusion in this package. The full data can be obtained from <http://www.bls.gov/tus/>.
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.
Implements methods for identifying linear and nonlinear marker combinations that maximizes the Area Under the AUC Curve (AUC).
Variable selection using Random Forest based on optimizing
        the area-under-the ROC curve (AUC) of the Random Forest.
Performs statistical testing to compare predictive 
	models based on multiple observations of the A' statistic (also known as 
	Area Under the Receiver Operating Characteristic Curve, or AUC). 
	Specifically, it implements a testing method based on the equivalence 
	between the A' statistic and the Wilcoxon statistic. 
	For more information, see Hanley and McNeil (1982) <doi:10.1148/radiology.143.1.7063747>.
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
Implementation of the augmented
            Simulation-Extrapolation (SIMEX) algorithm proposed by Yi et al. (2015) <doi:10.1080/01621459.2014.922777>
            for analyzing the data with mixed measurement error and misclassification. The main
            function provides a similar summary output as that of glm() function. Both parametric and
            empirical SIMEX are considered in the package.
Extract and process bird sightings records from eBird 
    (<http://ebird.org>), an online tool for recording bird observations. 
    Public access to the full eBird database is via the eBird Basic Dataset 
    (EBD; see <http://ebird.org/ebird/data/download> for access), a downloadable 
    text file. This package is an interface to AWK for extracting data from the 
    EBD based on taxonomic, spatial, or temporal filters, to produce a 
    manageable file size that can be imported into R.
Provides tools for converting R objects and syntax into the Portable 
    Format for Analytics (PFA). Allows for testing validity and runtime behavior 
    of PFA documents through rPython and Titus, a more complete implementation of 
    PFA for Python. The Portable Format for Analytics is a specification for 
    event-based processors that perform predictive or analytic calculations and is aimed 
    at helping smooth the transition from statistical model development to large-scale 
    and/or online production. See <http://dmg.org/pfa> for more information.
Uses locality sensitive hashing and creates a neighbourhood graph for a data set and calculates the adjusted rank index value for the same. It uses Gaussian random planes to decide the nature of a given point. Datar, Mayur, Nicole Immorlica, Piotr Indyk, and Vahab S. Mirrokni(2004) <doi:10.1145/997817.997857>.
Estimate the AUC using a variety of methods as follows: 
             (1) frequentist nonparametric methods based on the Mann-Whitney statistic or kernel methods. 
             (2) frequentist parametric methods using the likelihood ratio test based on higher-order 
             asymptotic results, the signed log-likelihood ratio test, the Wald test, 
             or the approximate ''t'' solution to the Behrens-Fisher problem. 
             (3) Bayesian parametric MCMC methods.
Performs approximate unconditional and permutation testing for
    2x2 contingency tables. Motivated by testing for disease association with rare
    genetic variants in case-control studies. When variants are extremely rare,
    these tests give better control of Type I error than standard tests.
A framework for automated machine learning. Concretely, the focus is on the optimisation of bagging workflows. A bagging workflows is composed by three phases: (i) generation: which and how many predictive models to learn; (ii) pruning: after learning a set of models, the worst ones are cut off from the ensemble; and (iii) integration: how the models are combined for predicting a new observation. autoBagging optimises these processes by combining metalearning and a learning to rank approach to learn from metadata. It automatically ranks 63 bagging workflows by exploiting past performance and dataset characterization. A complete description of the method can be found in: Pinto, F., Cerqueira, V., Soares, C., Mendes-Moreira, J. (2017): "autoBagging: Learning to Rank Bagging Workflows with Metalearning" arXiv preprint arXiv:1706.09367.
An interface to the 'AutoDesk' 'API' Platform including the Authentication 
    'API' for obtaining authentication to the 'AutoDesk' Forge Platform, Data Management 
    'API' for managing data across the platform's cloud services, Design Automation 'API'
    for performing automated tasks on design files in the cloud, Model
    Derivative 'API' for translating design files into different formats, sending
    them to the viewer app, and extracting design data, and Viewer for rendering
    2D and 3D models (see <https://developer.autodesk.com> for more information).
Implementation of 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.
Automatic fixed rank kriging for (irregularly located)
    spatial data using a class of basis functions with multi-resolution features
    and ordered in terms of their resolutions. The model parameters are estimated
    by maximum likelihood (ML) and the number of basis functions is determined
    by Akaike's information criterion (AIC). For spatial data with either one
    realization or independent replicates, the ML estimates and AIC are efficiently
    computed using their closed-form expressions when no missing value occurs. Details 
    regarding the basis function construction, parameter estimation, and AIC calculation  
    can be found in Tzeng and Huang (2018) <doi:10.1080/00401706.2017.1345701>. For
    data with missing values, the ML estimates are obtained using the expectation-
    maximization algorithm. Apart from the number of basis functions, there are
    no other tuning parameters, making the method fully automatic. Users can also
    include a stationary structure in the spatial covariance, which utilizes
    'LatticeKrig' package.
Functions for displaying multiple images with a color 
    scale, i.e., heat maps, possibly with projected coordinates.  The
    package relies on the base graphics system, so graphics are
    rendered rapidly.
Parse R code in a given directory for R packages and attempt to install them from CRAN or GitHub. Optionally use a dependencies file for tighter control over which package versions to install.
This package performs an automatic interpolation by automatically estimating the variogram and then calling gstat.
A set of functions that automates the process and produces reasonable output for hierarchical multiple regression models.  It allows you to specify predictor blocks, from which it generates all of the linear models, and checks the assumptions of the model, producing the requisite plots and statistics to allow you to judge the suitability of the model.
Contains functions for autologistic variable selection and parameter estimation for spatially correlated categorical data (including k>2). The main function is MPLE. Capable of fitting the centered autologistic model described in Caragea and Kaiser (2009)<doi:10.1198/jabes.2009.07032>, as well as the traditional autologistic model of Besag (1974)<http://www.jstor.org/stable/2984812>.
PCA done by eigenvalue decomposition of a data correlation matrix, here it automatically determines the number of factors by eigenvalue greater than 1 and it gives the uncorrelated variables based on the rotated component scores, Such that in each principal component variable which has the high variance are selected. It will be useful for non-statisticians in selection of variables. For more information, see the <http://www.ijcem.org/papers032013/ijcem_032013_06.pdf> web page.
Functionalities to automatically generate interactive visualizations for
             statistical results supported by 'ggfortify', such as time series, PCA,
             clustering and survival analysis, with 'plotly.js' <https://plot.ly/>  and
             'ggplot2' style. The generated visualizations can also be easily extended
             using 'ggplot2' and 'plotly' syntax while staying interactive.
The image of the amino acid transform on the protein level is drawn, and the automatic routing of the functional elements such as the domain and the mutation site is completed.
Some convenience functions for pls regression, including backward 
        variable selection and validation procedures, image based predictions
		and plotting.
Consider autoregressive model of order p where the distribution function of innovation is unknown, but innovations are independent and symmetrically distributed. The package contains a function named ARMDE which takes X (vector of n observations) and p (order of the model) as input argument and returns minimum distance estimator of the parameters in the model.
General-to-Specific (GETS) modelling of the mean and variance of a regression. NOTE: The package has been succeeded by gets, also available on the CRAN, which is more user-friendly, faster and easier to extend. Users are therefore encouraged to consider gets instead.
Implements multiple heuristic search algorithms for
    automatically creating structural equation models.
Provides the 'ImageJ' 'Auto Threshold' plugin functionality to R users. 
    See <http://imagej.net/Auto_Threshold> and Landini et al. (2017) <DOI:10.1111/jmi.12474>.
Automatically find the best vector autoregression
    models and networks for a given time series data set. 'AutovarCore'
    evaluates eight kinds of models: models with and without log
    transforming the data, lag 1 and lag 2 models, and models with and
    without weekday dummy variables. For each of these 8 model configurations,
    'AutovarCore' evaluates all possible combinations for including
    outlier dummies (at 2.5x the standard deviation of the residuals)
    and retains the best model. Model evaluation includes the Eigenvalue
    stability test and a configurable set of residual tests. These eight
    models are further reduced to four models because 'AutovarCore'
    determines whether adding weekday dummies improves the model fit.
Check if a given package name is available to use. It checks the
  name's validity. Checks if it is used on 'GitHub', 'CRAN' and 'Bioconductor'. Checks
  for unintended meanings by querying Urban Dictionary, 'Wiktionary' and Wikipedia.
Average population attributable fractions are calculated for a set
    of risk factors (either binary or ordinal valued) for both prospective and case-
    control designs. Confidence intervals are found by Monte Carlo simulation. The
    method can be applied to either prospective or case control designs, provided an
    estimate of disease prevalence is provided. In addition to an exact calculation
    of AF, an approximate calculation, based on randomly sampling permutations has
    been implemented to ensure the calculation is computationally tractable when the
    number of risk factors is large.
Research of subgroups in random clinical trials with binary outcome and two treatments groups. This is an adaptation of the Jared Foster method (<https://www.ncbi.nlm.nih.gov/pubmed/21815180>).
Installs the compiled Java modules of the Amazon Web Services ('AWS') 'SDK' to be used in downstream R packages interacting with 'AWS'. See <https://aws.amazon.com/sdk-for-java> for more information on the 'AWS' 'SDK' for Java.
'RJDBC' based 'DBI' driver to Amazon Athena, which is an interactive
    query service to analyze data in Amazon 'S3' using standard 'SQL'.
Fetching data from Amazon 'Kinesis' Streams using the Java-based 'MultiLangDaemon' interacting with Amazon Web Services ('AWS') for easy stream processing from R. For more information on 'Kinesis', see <https://aws.amazon.com/kinesis>.
Encrypt plain text and 'decrypt' cipher text using encryption keys hosted at Amazon Web Services ('AWS') Key Management Service ('KMS'), on which see <https://aws.amazon.com/kms> for more information.
Collection of R-functions implementing the
    Propagation-Separation Approach to adaptive smoothing as
    described in "J. Polzehl and V. Spokoiny (2006)
    <DOI:10.1007/s00440-005-0464-1>"
    and "J. Polzehl and V. Spokoiny (2004) <DOI:10.20347/WIAS.PREPRINT.998>".
Use the Amazon Alexa Web Information Services API to 
    find information about domains, including the kind of content 
    that they carry, how popular are they---rank and traffic history, 
    sites linking to them, among other things. See <https://aws.amazon.com/awis/> 
    for more information.
A simple client package for the Amazon Web Services ('AWS') 'CloudTrail'
    'API' <https://aws.amazon.com/cloudtrail/>.
Client for 'AWS Comprehend' <https://aws.amazon.com/comprehend>, a cloud natural language processing service that can perform a number of quantitative text analyses, including language detection, sentiment analysis, and feature extraction.
Retrieve Amazon EC2 instance metadata from within the running instance.
A simple client for the Amazon Web Services ('AWS') Identity
    and Access Management ('IAM') 'API' <https://aws.amazon.com/iam/>.
Provides boilerplate access to all of the classes included in the 
    Amazon Web Services ('AWS') Java Software Development Kit (SDK) via 
    package:'rJava'.  According to Amazon, the 'SDK helps take the complexity 
    out of coding by providing Java APIs for many AWS services including 
    Amazon S3, Amazon EC2, DynamoDB, and more'.  You can read more about the 
    included Java code on Amazon's website: 
    <https://aws.amazon.com/sdk-for-java/>.
A simple client package for the Amazon Web Services ('AWS') Lambda 'API' <https://aws.amazon.com/lambda/>.
Defines the method extract and provides 'openMP' support as needed in several packages.
A bundle of all of 'cloudyr' project <http://cloudyr.github.io/> packages for Amazon Web Services ('AWS') <https://aws.amazon.com/>. It depends upon all of the 'cloudyr' project's 'AWS' packages. It is mainly useful for installing the entire suite of packages; more likely than not you will only want to load individual packages one at a time.
A client for AWS Polly <http://aws.amazon.com/documentation/polly>, a speech synthesis service.
A simple client package for the Amazon Web Services ('AWS') Simple
    Storage Service ('S3') 'REST' 'API' <https://aws.amazon.com/s3/>.
A simple client package for the Amazon Web Services (AWS) Simple
    Email Service (SES) <http://aws.amazon.com/ses/> REST API.
Generates version 2 and version 4 request signatures for Amazon Web Services ('AWS') <https://aws.amazon.com/> Application Programming Interfaces ('APIs') and provides a mechanism for retrieving credentials from environment variables, 'AWS' credentials files, and 'EC2' instance metadata. For use on 'EC2', users will need to install the suggested package 'aws.ec2metadata' <https://cran.r-project.org/package=aws.ec2metadata>.
A simple client package for the Amazon Web Services ('AWS') Simple
    Notification Service ('SNS') 'API' <https://aws.amazon.com/sns/>.
A simple client package for the Amazon Web Services ('AWS') Simple
    Queue Service ('SQS') <https://aws.amazon.com/sqs/> 'API'.
Client for 'AWS Transcribe' <https://aws.amazon.com/documentation/transcribe>, a cloud transcription service that can convert an audio media file in English and other languages into a text transcript.
A client for 'AWS Translate' <https://aws.amazon.com/documentation/translate>, a machine translation service that will convert a text input in one language into a text output in another language.
A generalization of Fisher's exact test that allows for
        structural zeros.
Functions and datasets to support Azure Machine Learning. This
    allows you to interact with datasets, as well as publish and consume R functions
    as API services.
This package fits the Bayesian two-Zone Models.
This package contains b6e6rl algorithm, adaptive
        differential evolution for global optimization.
Babar is designed to use nested sampling (a Bayesian analysis technique) to compare possible models for bacterial growth curves, as well as extracting parameters. It allows model evidence and parameter likelihood values to be extracted, and also contains helper functions for comparing distributions as well as direct access to the underlying nested sampling code.
Included here are babel routines for identifying unusual ribosome protected fragment counts given mRNA counts.
Included are 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.
R-based graphical tool to concisely visualise and compare biological annotations queried from the DAVID web service. It provides R functions to perform enrichment analysis (via DAVID - http://david.abcc.ncifcrf.gov) on several gene lists at once, and then visualizing all the results in one generated figure that allows R users to compare the annotations found for each list.
Can be used for simulation of organisms living in
    communities. Each organism is represented individually and genome scale
    metabolic models determine the uptake and release of compounds. Biological
    processes such as movement, diffusion, chemotaxis and kinetics are available
    along with data analysis techniques.
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.
Implements the Bayesian Augmented Control (BAC, a.k.a. Bayesian historical data borrowing) method under clinical trial setting by calling 'Just Another Gibbs Sampler' ('JAGS') software. In addition, the 'BACCT' package evaluates user-specified decision rules by computing the type-I error/power, or probability of correct go/no-go decision at interim look. The evaluation can be presented numerically or graphically. Users need to have 'JAGS' 4.0.0 or newer installed due to a compatibility issue with 'rjags' package. Currently, the package implements the BAC method for binary outcome only. Support for continuous and survival endpoints will be added in future releases. We would like to thank AbbVie's Statistical Innovation group and Clinical Statistics group for their support in developing the 'BACCT' package.
Provides convenience functions for the Backblaze B2 cloud storage
    API (see https://www.backblaze.com/b2/docs/). All B2 API calls are mapped
    to equivalent R functions. Files can be easily uploaded, downloaded and
    deleted from B2, all from within R programs.
Provides a backward-pipe operator for 'magrittr' (%<%) or 
    'pipeR' (%<<%) that allows for a performing operations from right-to-left. 
    This indispensable for writing clear code where there is natural 
    right-to-left ordering common with nested structures 
    and hierarchies such as trees/directories or markup languages such as HTML 
    and XML.
Implementations of functions which have been introduced in
    R since version 3.0.0. The backports are conditionally exported which
    results in R resolving the function names to the version shipped with R (if
    available) and uses the implemented backports as fallback. This way package
    developers can make use of the new functions without worrying about the
    minimum required R version.
Code for 'backShift', an algorithm to estimate the connectivity
    matrix of a directed (possibly cyclic) graph with hidden variables. The
    underlying system is required to be linear and we assume that observations
    under different shift interventions are available. For more details,
    see <arXiv:1506.02494>.
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 the
 hyperparameter omega. The package also provides functions to guide the user in their choice
 of an appropriate omega value. The method is based on Lefebvre, Atherton and Talbot (2014).
Estimating the average causal effect based on the Bayesian Adjustment for Confounding (BAC) algorithm.
Tired of copy and pasting almost identical markdown for badges in
    every new R package that you create on Github? 
    This package will search your DESCRIPTION file and extract the package name,
    licence, R-version, and current projectversion and transform that into 
    badges. It will also search for a .travis.yml file and create a 'Travis' badge,
    if you use 'Codecov.io' to check your code coverage after a 'Travis' build 
    this package will also build a 'Codecov.io'-badge. All the badges will be 
    placed below the top YAML content of your Rmarkdown file (Readme.Rmd). 
    Currently creates badges for  Projectstatus (Repostatus.org), licence
    travis build status, codecov, minimal R version, CRAN status, 
    current version of your package and last change of Readme.Rmd.
Query information and generate badge for using in README
    and GitHub Pages.
Implements sample size calculations following the approach
        described in "Bayesian Average Error Based Approach to
        Hypothesis Testing and Sample Size Determination."
This is the companion package of a PhD thesis entitled "Bases Giving Distances. A new paradigm for investigating functional data with applications for spectroscopy" by Timmermans (2012). See references for details and related publications. The core of the BAGIDIS methodology is a functional wavelet based semi-distance that has been introduced by Timmermans and von Sachs (2010, 2015) and Timmermans, Delsol and von Sachs (2013). This semi-distance allows for comparing curves with sharp local patterns that might not be well aligned from one curve to another. It is data-driven and highly adaptive to the curves being studied.  Its main originality is its ability to consider simultaneously horizontal and vertical variations of patterns, which proofs highly useful when used together with clustering algorithms or visualization method. BAGIDIS is an acronym for BAsis GIving DIStances. The extension of BAGIDIS to image data relies on the same principles and has been described in Timmermans and Fryzlewicz (2012), Fryzlewicz and Timmermans (2015).
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.
Bayesian estimation of the two and three parameter models of item response theory (IRT). Also, it is possible to use a web interactive application intended for the making of an MCMC estimation and model-fit of the IRT models.
Automated quantification of metabolites by targeting mass spectral/retention time libraries into full scan-acquired gas chromatography - mass spectrometry (GC-MS) chromatograms. Baitmet outputs a table with compounds name, spectral matching score, retention index error, and compounds area in each sample. Baitmet can automatically determine the compounds retention indexes with or without co-injection of internal standards with samples.
Two practical tests are provided for assessing whether multiple covariates in a treatment group and a matched control group are balanced in observational studies.
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.
Facilitates the evolutionary analysis and structure conservation study of specified amino acids in proteins.
Hypothesis tests and sure independence screening (SIS) procedure based on ball 
    statistics, including ball divergence, ball covariance, and ball correlation, 
    are developed to analyze complex data. The ball divergence and ball covariance based distribution-free 
    tests are implemented to examine equality of multivariate distributions and independence 
    between random vectors of arbitrary dimensions. Furthermore, a generic non-parametric SIS 
    procedure based on ball correlation and all of its variants are implemented to tackle the 
    challenge in the context of ultra high dimensional data.
Provides simple functions for accessing data/tables on
  <http://www.basketball-reference.com>.
Functions and datasets for Jeff Gill: "Bayesian Methods: A Social and Behavioral Sciences Approach". First, Second, and Third Edition. Published by Chapman and Hall/CRC (2002, 2007, 2014).
Fit (using Bayesian methods) and simulate mixtures of univariate and bivariate angular distributions. Chakraborty and Wong (2017) <arXiv:1708.07804> .
Implementation of the Bamboo methods described in Li, Dahl, Vannucci, Joo, and Tsai (2014) <DOI:10.1371/journal.pone.0109832>.
Functions for Bayesian meta-analysis of diagnostic test data which
    are based on a scale mixtures bivariate random-effects model.
Infrastructure for estimating probabilistic distributional regression models in a Bayesian framework.
  The distribution parameters may capture location, scale, shape, etc. and every parameter may depend
  on complex additive terms (fixed, random, smooth, spatial, etc.) similar to a generalized additive model.
  The conceptual and computational framework is introduced in Umlauf, Klein, Zeileis (2017)
  <doi:10.1080/10618600.2017.1407325>.
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.
A set of functions for doing analysis of A/B split test data and web metrics in general.
Contains functions for importing and working with the BANES car parking
  records and other related datasets. For the full version of the package, including
  all datasets, see the repo at <https://github.com/owenjonesuob/BANEScarparking>.
  The original dataset of parking records can be found at
  <https://data.bathhacked.org/Government-and-Society/BANES-Historic-Car-Park-Occupancy/x29s-cczc>.
Provides a full package of posterior inference, model comparison, and graphical illustration of model fitting. A parallel computing algorithm for the Markov chain Monte Carlo (MCMC) based posterior inference and an Expectation-Maximization (EM) based algorithm for posterior approximation are are developed, both of which greatly reduce the computational time for model inference.
Provides functions for the Bayesian analysis of some simple 
    commonly-used models, without using Markov Chain Monte Carlo (MCMC) 
    methods such as Gibbs sampling.  The 'rust' package  
    <https://cran.r-project.org/package=rust> is used 
    to simulate a random sample from the required posterior distribution.  
    At the moment three conjugate hierarchical models are available:
    beta-binomial, gamma-Poisson and a 1-way analysis of variance (ANOVA).
A convenience package for use while drafting code.
    It facilitates making stand-out comment lines decorated with
    bands of characters.  The input text strings are converted into
    R comment lines, suitably formatted. These are then displayed in
    a console window and, if possible, automatically transferred to a
    clipboard ready for pasting into an R script.  Designed to save
    time when drafting R scripts that will need to be navigated and
    maintained by other programmers.
It 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 and social sciences. 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 predictions, which are easily interpretable for behavioral and social researchers. The floodlight analysis and mediation analysis based on these models are also provided. BANOVA uses 'Stan' and 'JAGS' as the computational platform.
A client for the "Base Adresses Nationale" (BAN) API, which allows to (batch)
    geocode and reverse-geocode French addresses. For more information about the BAN and its API, please see <https://adresse.data.gouv.fr/api>.
Provides functions to scrape IQY calls to Bank of Mexico,
    downloading and ordering the data conveniently.
Various tools dealing with batch effects, in particular enabling the 
  removal of discrepancies between training and test sets in prediction scenarios.
  Moreover, addon quantile normalization and addon RMA normalization (Kostka & Spang, 
  2008) is implemented to enable integrating the quantile normalization step into 
  prediction rules. The following batch effect removal methods are implemented: 
  FAbatch, ComBat, (f)SVA, mean-centering, standardization, Ratio-A and Ratio-G. 
  For each of these we provide  an additional function which enables a posteriori 
  ('addon') batch effect removal in independent batches ('test data'). Here, the
  (already batch effect adjusted) training data is not altered. For evaluating the
  success of batch effect adjustment several metrics are provided. Moreover, the 
  package implements a plot for the visualization of batch effects using principal 
  component analysis. The main functions of the package for batch effect adjustment 
  are ba() and baaddon() which enable batch effect removal and addon batch effect 
  removal, respectively, with one of the seven methods mentioned above. Another 
  important function here is bametric() which is a wrapper function for all implemented
  methods for evaluating the success of batch effect removal. For (addon) quantile 
  normalization and (addon) RMA normalization the functions qunormtrain(), 
  qunormaddon(), rmatrain() and rmaaddon() can be used.
Bayesian estimation and prediction for stochastic processes based
    on the Euler approximation. Considered processes are: jump diffusion,
    (mixed) diffusion models, hidden (mixed) diffusion models, non-homogeneous
    Poisson processes (NHPP), (mixed) regression models for comparison and a
    regression model including a NHPP.
Tool to find where a function has its lowest value(minimum). The
    functions can be any dimensions. Recommended use is with eps=10^-10, but can be
    run with 10^-20, although this depends on the function. Two more methods are in
    this package, simple gradient method (Gradmod) and Powell method (Powell). These
    are not recommended for use, their purpose are purely for comparison.
This package includes the function \code{barcode()}, which
        produces a histogram-like plot of a distribution that shows
        granularity in the data.
To perform species identification using DNA barcodes.
Barnard's unconditional test for 2x2 contingency tables.
Bayesian Additive Regression Trees (BART) provide flexible nonparametric modeling of covariates for continuous, binary and time-to-event outcomes.  For more information on BART, see Chipman, George and McCulloch (2010) <doi:10.1214/09-AOAS285> and Sparapani, Logan, McCulloch and Laud (2016) <doi:10.1002/sim.6893>.
An advanced implementation of Bayesian Additive Regression Trees with expanded features for data analysis and visualization.
These are bartMachine's Java dependency libraries. Note: this package has no functionality of its own and should not be installed as a standalone package without bartMachine.
Computations of Sinkhorn Distances (upper bounds to the Wasserstein Distance a.k.a. Earth Mover's Distance) and regularized Wasserstein Barycenters. Relevant papers are Marco Cuturi (2013) <arXiv:1306.0895>, Marco Cuturi (2014) <arXiv:1310.4375> and Jason Altschuler et al. <arXiv:1705.09634>. To speed up the computation time the main iteration steps are based on 'RcppArmadillo'.
Package for Bayesian Variable Selection and  Model Averaging in linear models and
    generalized 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
    mixture of g-priors from Liang et al (2008)
    <DOI:10.1198/016214507000001337>
    for linear models or mixtures of g-priors in GLMs of Li and Clyde (2015)
    <arXiv:1503.06913>. Other model
    selection criteria include AIC, BIC and Empirical Bayes estimates of g.
    Sampling probabilities may be updated based on the sampled models
    using Sampling w/out Replacement or an efficient MCMC algorithm
    samples models using the BAS tree structure as an efficient hash table.
    Uniform priors over all models or beta-binomial prior distributions on
    model size are allowed, and for large p truncated priors on the model
    space may be used.  The user may force variables to always be included.
    Details behind the sampling algorithm are provided in
    Clyde, Ghosh and Littman (2010)   <DOI:10.1198/jcgs.2010.09049>.
    This material is based upon work supported by the National Science
    Foundation under Grant DMS-1106891.  Any opinions, findings, and
    conclusions or recommendations expressed in this material are those of
    the author(s) and do not necessarily reflect the views of the
    National Science Foundation.
Provides a Bayesian variable selection approach using continuous spike and slab prior distributions. The prior choices here are motivated by the shrinking and diffusing priors studied in Narisetty & He (2014) <DOI:10.1214/14-AOS1207>.
Convert base plot function call (using expression or formula) to 'grob' object that compatible to the 'grid' ecosystem. With this package, we are able to e.g. using 'cowplot' to align base plots with 'ggplot' objects and using 'ggsave' to export base plot to file.
Compatibility wrapper to replace the orphaned package by
    Romain Francois. New applications should use the 'openssl' or
    'base64enc' package instead.
This package provides tools for handling base64 encoding. It is more flexible than the orphaned base64 package.
In contrast to RFC3548, the 62nd character ("+") is replaced with
    "-", the 63rd character ("/") is replaced with "_". Furthermore, the encoder
    does not fill the string with trailing "=". The resulting encoded strings
    comply to the regular expression pattern "[A-Za-z0-9_-]" and thus are
    safe to use in URLs or for file names.
    The package also comes with a simple base32 encoder/decoder suited for
    case insensitive file systems.
A tool for gathering and analyzing data from the Baseball Databank <http://www.baseball-databank.org/>, which includes player performance statistics from major league baseball in the United States beginning in the year 1871.
Some very simple infrastructure for basis functions.
Collection of baseline correction algorithms, along with a framework and a GUI for optimising baseline algorithm parameters. Typical use of the package is for removing background effects from spectra originating from various types of spectroscopy and spectrometry, possibly optimizing this with regard to regression or classification results. Correction methods include polynomial fitting, weighted local smoothers and many more.
Conducts Aldrich-McKelvey and Blackbox Scaling (Poole et al 2016)
		<doi:10.18637/jss.v069.i07> to recover latent dimensions of judgment.
Easily create tables from data 
    frames/matrices.  Create/manipulate tables 
    row-by-row, column-by-column or cell-by-cell.  
    Use common formatting/styling to output 
    rich tables as 'HTML', 'HTML widgets' or to 
    'Excel'.
Add trendline of basic linear or nonlinear regression models and show equation to plot as simple as possible.
It makes the creation of networks from sequences of RNA, with this is done the abstraction of characteristics of these networks with a methodology of threshold for the purpose of making a classification between the classes of the sequences. There are two data present in the 'BASiNET' package, "sequences" and "sequences2" with 11 and 10 sequences respectively. These sequences were taken from the data set used in the article (LI, Aimin; ZHANG, Junying; ZHOU, Zhongyin, 2014) <doi:10.1186/1471-2105-15-311>. These sequences are used to run examples.
BASIX provides some efficient C/C++ implementations to speed up calculations in R.
Bayesian fitting and sensitivity analysis methods for adaptive
    spline surfaces. Built to handle continuous and categorical inputs as well as
    functional or scalar output. An extension of the methodology in Denison, Mallick
    and Smith (1998) <doi:10.1023/A:1008824606259>.
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.
Big data statistical analysis for high-dimensional models is made possible by modifying lasso.proj() in 'hdi' package by replacing its nodewise-regression with sparse precision matrix computation using 'BigQUIC'.
Includes 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
    phylogenetic/functional 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.
Makes it easy to download a large number of trade data from Yahoo or Google Finance.
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.
Algorithms that build on the 'OneMap' package to create linkage
    maps from high density data in outcrossing species in reasonable time frames.
Provides consistent batch means estimation of Monte
    Carlo standard errors.
As a successor of the packages 'BatchJobs' and 'BatchExperiments',
    this package provides a parallel implementation of the Map function for high
    performance computing systems managed by schedulers 'IBM Spectrum LSF'
    (<http://www-03.ibm.com/systems/spectrum-computing/products/lsf/>),
    'OpenLava' (<http://www.openlava.org/>), 'Univa Grid Engine'/'Oracle Grid
    Engine' (<http://www.univa.com/>), 'Slurm' (<http://slurm.schedmd.com/>),
    'TORQUE/PBS'
    (<http://www.adaptivecomputing.com/products/open-source/torque/>), or
    'Docker Swarm' (<https://docs.docker.com/swarm/>).
    A multicore and socket mode allow the parallelization on a local machines,
    and multiple machines can be hooked up via SSH to create a makeshift
    cluster. Moreover, the package provides an abstraction mechanism to define
    large-scale computer experiments in a well-organized and reproducible way.
BaTFLED is a machine learning algorithm designed to make predictions and determine interactions in data that varies along three independent modes. For example BaTFLED was developed to predict the growth of cell lines when treated with drugs at different doses. The first mode corresponds to cell lines and incorporates predictors such as cell line genomics and growth conditions. The second mode corresponds to drugs and incorporates predictors indicating known targets and structural features. The third mode corresponds to dose and there are no dose-specific predictors (although the algorithm is capable of including predictors for the third mode if present). See 'BaTFLED3D_vignette.rmd' for a simulated example.
Survey systems and other third-party data sources commonly use non-standard representations of logical values when
    it comes to qualitative data - "Yes", "No" and "N/A", say. batman is a package designed to seamlessly convert these into logicals.
    It is highly localised, and contains equivalents to boolean values in languages including German, French, Spanish, Italian,
    Turkish, Chinese and Polish.
Battery reduction is a method used in data reduction. It uses Gram-Schmidt orthogonal rotations to find out a subset of variables best representing the original set of variables.
Collection of tools to work with basketball data. Functions available are related to friendly 
  web scraping and visualization. Data were obtained from <http://www.euroleague.net/>, 
	<http://www.eurocupbasketball.com/> and <http://www.acb.com>, following the instructions of their 
	respectives robots.txt files. Tools for visualization include a population pyramid, 2D plots of 
	archetypoids and the rest of players, percentile circular plots, plots to analyze monthly evolution, 
	heatmaps, shooting barplots, four factor plots and maps of players' nationalities.
A Bayesian feature allocation model is implemented for inference on tumor heterogeneity using next-generation sequencing data. The model identifies the subclonal copy number and single nucleotide mutations at a selected set of loci and provides inference on genetic tumor variation.
A suite of functions that allow the user to analyze A/B test
    data in a Bayesian framework. Intended to be a drop-in replacement for
    common frequentist hypothesis test such as the t-test and chi-sq test.
Performs Bayesian estimation of the additive main effects and multiplicative interaction (AMMI) model. The method is explained in Crossa, J., Perez-Elizalde, S., Jarquin, D., Cotes, J.M., Viele, K., Liu, G. and Cornelius, P.L. (2011) (<doi:10.2135/cropsci2010.06.0343>).
Provides tools for carrying out a Bayesian analysis of image boundaries.  Functions are provided
  for both binary (Bernoulli) and continuous (Gaussian) images.  Examples, along with an interactive shiny function
  illustrate how to perform simulations, analyze custom data, and plot estimates and credible intervals.
Fully Bayesian inference for estimating the number of clusters and related parameters to heterogeneous binary data.
A hodgepodge of hopefully helpful functions. Two of these perform
    shrinkage estimation: one using a simple weighted method where the user can
    specify the degree of shrinkage required, and one using James-Stein shrinkage
    estimation for the case of unequal variances.
Functions for performing the Bayesian bootstrap as introduced by
    Rubin (1981) <doi:10.1214/aos/1176345338> and for summarizing the result.
    The implementation can handle both summary statistics that works on a
    weighted version of the data and summary statistics that works on a
    resampled data set.
Bayesian Inference on a GPU. The package currently supports sampling from PolyaGamma, Multinomial logit and Bayesian lasso.
Combine diverse evidence across multiple studies to test a high level scientific theory. The methods can also be used as an alternative to a standard meta-analysis.
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.
Propose a parametric fit for censored linear regression models based on SMSN distributions, from a Bayesian perspective. Also, generates SMSN random variables.
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.
Bayesian estimation of dynamic conditional correlation GARCH model for multivariate time series volatility (Fioruci, J.A., Ehlers, R.S. and Andrade-Filho, M.G., (2014), DOI:10.1080/02664763.2013.839635).
Definitive Screening Designs are a class of experimental designs that under factor sparsity have the potential to estimate linear, quadratic and interaction effects with little experimental effort. BAYESDEF is a package that performs a five step strategy to analyze this kind of experiments that makes use of tools coming from the Bayesian approach. It also includes the least absolute shrinkage and selection operator (lasso) as a check (Aguirre VM. (2016) <DOI:10.1002/asmb.2160>).
Provides graphical user interface for the packages 'bayesTFR', 'bayesLife' and 'bayesPop'.
Functions for data augmentation using the
    Bayesian discount prior function for 1 arm and 2 arm clinical trials.
Determines effective sample size of a parametric prior distribution
    in Bayesian models. To learn more about Bayesian effective sample size, see: Morita, S., Thall, P. F., & Muller, P. (2008) <https://www.jstor.org/stable/25502095>.
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.
Collection of procedures to perform Bayesian analysis on a variety
    of factor models. Currently, it includes: Bayesian Exploratory Factor
    Analysis (befa), an approach to dedicated factor analysis with stochastic
    search on the structure of the factor loading matrix. The number of latent
    factors, as well as the allocation of the manifest variables to the factors,
    are not fixed a priori but determined during MCMC sampling.
    More approaches will be included in future releases of this package.
Provides the bayesGARCH() function which performs the
    Bayesian estimation of the GARCH(1,1) model with Student's t innovations as described in Ardia (2008) <doi:10.1007/978-3-540-78657-3>.
Functions for implementing the Braun and Damien (2015) rejection
    sampling algorithm for Bayesian hierarchical models. The algorithm generates
    posterior samples in parallel, and is scalable when the individual units are
    conditionally independent.
Set of tools to perform the statistical inference based on the Bayesian approach for regression models under the assumption that independent additive errors follow normal, Student-t, slash, contaminated normal, Laplace or symmetric hyperbolic distributions, i.e., additive errors follow a scale mixtures of normal distributions. The regression models considered in this package are: (i) Generalized elliptical semi-parametric models, where both location and dispersion parameters of the response variable distribution include non-parametric additive components described by using B-splines; and (ii) Flexible measurement error models under the presence of homoscedastic and heteroscedastic random errors, which admit explanatory variables with and without measurement additive errors as well as the presence of a non-parametric components approximated by using B-splines.
A Bayesian data modeling scheme that performs four interconnected tasks: (i) characterizes the uncertainty of the elicited parametric prior; (ii) provides exploratory diagnostic for checking prior-data conflict; (iii) computes the final statistical prior density estimate; and (iv) executes macro- and micro-inference. Primary reference is Mukhopadhyay, S. and Fletcher, D. (2018, Technical Report, <arXiv:1802.00474>).
Functions to performs Bayesian regression model with mixture of two scaled inverse
             chi square as hyperprior distribution for variance of each regression coefficient.
Bayesian melding approach to combine the GPS observations and Dead-Reckoned path for an accurate animal's track, or equivalently, use the GPS observations to correct the Dead-Reckoned path. It can take the measurement errors in the GPS observations into account and provide uncertainty statement about the corrected path. The main calculation can be done by the BMAnimalTrack function.
The Epidemic Type Aftershock Sequence  (ETAS) model is one of the best-performing methods for modeling and forecasting earthquake occurrences. This package implements Bayesian estimation routines to draw samples from the full posterior distribution of the model parameters, given an earthquake catalog. The paper on which this package is based is Gordon J. Ross - Bayesian Estimation of the ETAS Model for Earthquake Occurrences (2016), available from the below URL.
Implements a data-augmented block Gibbs sampler for simulating the posterior distribution of concentration matrices for specifying the topology and parameterization of a Gaussian Graphical Model (GGM). This sampler was originally proposed in Wang (2012) <doi:10.1214/12-BA729>.
A 'Shiny' web application for creating interactive Bayesian Network models,
    learning the structure and parameters of Bayesian networks, and utilities for classic
    network analysis.
General-purpose MCMC and SMC samplers, as well as plot and
    diagnostic functions for Bayesian statistics, with a particular focus on
    calibrating complex system models. Implemented samplers include various
    Metropolis MCMC variants (including adaptive and/or delayed rejection MH), the
    T-walk, two differential evolution MCMCs, two DREAM MCMCs, and a sequential
    Monte Carlo (SMC) particle filter.
Various algorithms for segmentation of 2D and 3D images, such
    as computed tomography and satellite remote sensing. This package implements
    Bayesian image analysis using the hidden Potts model with external field
    prior of <doi:10.1016/j.csda.2014.12.001>.
    Latent labels are sampled using chequerboard updating or Swendsen-Wang.
    Algorithms for the smoothing parameter include pseudolikelihood, path sampling,
    the exchange algorithm, approximate Bayesian computation (ABC-MCMC and ABC-SMC),
    and Bayesian indirect likelihood (BIL). Refer to <doi:10.1007/s11222-014-9525-6>
    and <arXiv:1503.08066> for further details.
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 <doi:10.1007/s13524-012-0193-x>.
Efficient sampling for Gaussian linear regression with arbitrary priors.
The function MC3() searches for log-linear models with the highest posterior probability. The function gibbsSampler() is a blocked Gibbs sampler for sampling from the posterior distribution of the log-linear parameters. The functions findPostMean() and findPostCov() compute the posterior mean and covariance matrix for decomposable models which, for these models, is available in closed form.
Adjusts longitudinal regression models using Bayesian methodology 
            for covariance structures of composite symmetry (SC), 
            autoregressive ones of order 1 AR (1) and 
            autoregressive moving average of order (1,1) ARMA (1,1).
Natural history museums and herbaria collectively hold hundreds of millions of zoological, botanical, and paleontological specimens. These collections serve as the foundation for understanding the distribution of life on Earth and the basis for addressing loss of biodiversity, emerging diseases, and other pressing global problems as well as important question in ecology and evolution. One of the short comings of these kind of data is that the lack of evidence of the presence of a species in a certain region does not mean the species is truly absent there. Likewise, specimens are often misidentified, and therefore the report of a species in a locality is not always evidence that a viable population occurs there. The goal of this project is to develop a method which could be used to estimate the probability of presence of a species in a certain study region based on certain sampling effort and presence reports.
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,
  Analysis of Multivariate Ordinal survey data with scale
     usage heterogeneity (as in Rossi et al, JASA (01)),
  Bayesian Analysis of Aggregate Random Coefficient Logit Models as in BLP (see
  Jiang, Manchanda, Rossi 2009)
  For further reference, consult our book, Bayesian Statistics and
  Marketing by Rossi, Allenby and McCulloch (Wiley 2005) and Bayesian Non- and Semi-Parametric
  Methods and Applications (Princeton U Press 2014).
Calculating Bayesian sample sizes for multi-arm trials where several experimental treatments are compared to a common control, perhaps even at multiple stages.
Default Bayesian hypothesis tests for correlation, partial correlation, and mediation
A collection of functions allowing to derive the posterior distribution of the two parameters in a random-effects meta-analysis, and providing functionality to evaluate joint and marginal posterior probability distributions, predictive distributions, shrinkage effects, posterior predictive p-values, etc.
The fitting of finite mixture models of univariate
	     Gaussian distributions using JAGS within a Bayesian
	     framework is provided.
Bayesian Mixture Survival Models using Additive Mixture-of-Weibull Hazards, with Lasso Shrinkage and
        Stratification. As a Bayesian dynamic survival model, it relaxes the proportional-hazard assumption. Lasso shrinkage controls
        overfitting, given the increase in the number of free parameters in the model due to presence of two Weibull components
        in the hazard function.
Belief propagation methods in Bayesian Networks to propagate evidence through the network. The implementation of these methods are based on the article: Cowell, RG (2005). Local Propagation in Conditional Gaussian Bayesian Networks <http://www.jmlr.org/papers/volume6/cowell05a/>.
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 a piecewise exponential hazard to survival data using a
    Hierarchical Bayesian model with an Intrinsic Conditional Autoregressive
    formulation for the spatial dependency in the hazard rates for each piece.
    This function uses Metropolis- Hastings-Green MCMC to allow the number of split
    points to vary and also uses Stochastic Search Variable Selection to determine
    what covariates drive the risk of the event. This function outputs trace plots
    depicting the number of split points in the hazard and the number of variables
    included in the hazard. The function saves all posterior quantities to the
    desired path.
Fits a piecewise exponential hazard to survival data using a
    Hierarchical Bayesian model with an Intrinsic Conditional Autoregressive
    formulation for the spatial dependency in the hazard rates for each piece.
    This function uses Metropolis- Hastings-Green MCMC to allow the number of split
    points to vary. This function outputs graphics that display the histogram of
    the number of split points and the trace plots of the hierarchical parameters.
    The function outputs a list that contains the posterior samples for the number
    of split points, the location of the split points, and the log hazard rates
    corresponding to these splits. Additionally, this outputs the posterior samples
    of the two hierarchical parameters, Mu and Sigma^2.
Plotting functions for posterior analysis, model checking,
    and MCMC diagnostics. The package is designed not only to provide convenient
    functionality for users, but also a common set of functions that can be
    easily used by developers working on a variety of R packages for Bayesian
    modeling, particularly (but not exclusively) packages interfacing with 'Stan'.
Generating population projections for all countries of the world using several probabilistic components, such as total fertility rate 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) <doi:10.1016/S0167-7152(01)00124-9>, Benoit & Van den Poel (2012) <doi:10.1002/jae.1216> and Al-Hamzawi, Yu & Benoit (2012) <doi:10.1177/1471082X1101200304>. To speed up the calculations, the Markov Chain Monte Carlo core of all algorithms is programmed in Fortran and called from R.
Runs hierarchical linear Bayesian models. Samples from the posterior
    distributions of model parameters in JAGS (Just Another Gibbs Sampler;
	Plummer, 2017, <http://mcmc-jags.sourceforge.net>). Computes Bayes factors for group
	parameters of interest with the Savage-Dickey density ratio (Wetzels,
	Raaijmakers, Jakab, Wagenmakers, 2009, <doi:10.3758/PBR.16.4.752>).
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.
In p >> n settings, full posterior sampling using existing Markov chain Monte
    Carlo (MCMC) algorithms is highly inefficient and often not feasible from a practical
    perspective. To overcome this problem, we propose a scalable stochastic search algorithm that is called the Simplified Shotgun Stochastic Search (S5) and aimed at rapidly explore interesting regions of model space and finding the maximum a posteriori(MAP) model. Also, the S5 provides an approximation of posterior probability of each model (including the marginal inclusion probabilities). This algorithm is a part of an article titled Scalable Bayesian Variable Selection Using Nonlocal Prior Densities in Ultrahigh-dimensional Settings (2017+), by Minsuk Shin, Anirban Bhattachary, and Valen E. Johnson, accepted in Statistica Sinica.
Provides a variety of methods from Rao (2003, ISBN:0-471-41374-7)
  and some other research articles to deal with several specific small area area-
  level models in Bayesian framework. 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).
An implementation of methods for spectral analysis using the Bayesian framework. It includes functions for modelling spectrum as well as appropriate plotting and output estimates. There is segmentation capability with RJ MCMC (Reversible Jump Markov Chain Monte Carlo). The package takes these methods predominantly from the 2012 paper "AdaptSPEC: Adaptive Spectral Estimation for Nonstationary Time Series" <DOI:10.1080/01621459.2012.716340>.
Methods for generating Markov Chain Monte Carlo (MCMC) posterior samples of Bayesian linear regression model parameters that require only summary statistics of data as input. Summary statistics are useful for systems with very limited amounts of physical memory. The package provides two functions:  one function that computes summary statistics of data and one function that carries out the MCMC posterior sampling for Bayesian linear regression models where summary statistics are used as input. The function read.regress.data.ff utilizes the R package 'ff' to handle data sets that are too large to fit into a user's physical memory, by reading in data in chunks.
Contains Bayesian implementations of Mixed-Effects Accelerated Failure Time (MEAFT) models
             for censored data. Those can be not only right-censored but also interval-censored,
	     doubly-interval-censored or misclassified interval-censored.
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.
This is an implementation of BART:Bayesian Additive Regression Trees,
        by Chipman, George, McCulloch (2010).
Provides a way to simulate from the prior distribution of Bayesian trees by Chipman et al. (1998) <DOI:10.2307/2669832>. The prior distribution of Bayesian trees is highly dependent on the design matrix X, therefore using the suggested hyperparameters by Chipman et al. (1998) <DOI:10.2307/2669832> is not recommended and could lead to unexpected prior distribution. This work is part of my master thesis (expected 2016).
Bayesian analysis of item-level hierarchical twin data using an integrated item response theory model. Analyses are based on Schwabe & van den Berg (2014) <doi:10.1007/s10519-014-9649-7>, Molenaar & Dolan (2014) <doi:10.1007/s10519-014-9647-9>, Schwabe, Jonker & van den Berg (2016) <doi:10.1007/s10519-015-9768-9> and Schwabe, Boomsma & van den Berg (2016) <doi:10.1016/j.lindif.2017.01.018>.
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.
Conceived to calculate Bayes factors in linear models and then to provide a formal Bayesian answer to testing and variable selection problems. From a theoretical side, the emphasis in this package is placed on the prior distributions and it allows a wide range of them: Jeffreys (1961); Zellner and Siow(1980)<DOI:10.1007/bf02888369>; Zellner and Siow(1984); Zellner (1986)<DOI:10.2307/2233941>; Fernandez et al. (2001)<DOI:10.1016/s0304-4076(00)00076-2>; Liang et al. (2008)<DOI:10.1198/016214507000001337>  and Bayarri et al. (2012)<DOI:10.1214/12-aos1013>. The interaction with the package is through a friendly interface that syntactically mimics the well-known lm() command of R. The resulting objects can be easily explored providing the user very valuable information (like marginal, joint and conditional inclusion probabilities of potential variables; the highest posterior probability model, HPM; the median probability model, MPM) about the structure of the true -data generating- model. Additionally, this package incorporates abilities to handle problems with a large number of potential explanatory variables through parallel and heuristic versions of the main commands, Garcia-Donato and Martinez-Beneito (2013)<DOI:10.1080/01621459.2012.742443>.
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
Bayesian analysis of luminescence data and 14C age estimate. Bayesian models are based on the following publications: Combes, B. & Philippe, A. (2017) <doi:10.1016/j.quageo.2017.02.003> and Combes et al (2015) <doi:10.1016/j.quageo.2015.04.001>. This includes, amongst others, data import, export, application of age models and palaeodose model.
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.
Performs general Bayesian estimation method of linear–bilinear models for genotype × environment interaction. The method is explained in Perez-Elizalde, S., Jarquin, D., and Crossa, J. (2011) (<doi:10.1007/s13253-011-0063-9>).
The manuscript introduces the BAYSTAR package, which
        provides the functionality for Bayesian estimation in
        autoregressive threshold models.
A collection of miscellaneous functions for 
    copying objects to the clipboard ('Copy');
    manipulating strings ('concat', 'mgsub', 'trim', 'verlan'); 
    loading or showing packages ('library_with_dep', 'require_with_dep', 
    'sessionPackages'); 
    creating or testing for named lists ('nlist', 'as.nlist', 'is.nlist'), 
    formulas ('is.formula'), empty objects ('as.empty', 'is.empty'), 
    whole numbers ('as.wholenumber', 'is.wholenumber'); 
    testing for equality ('almost.equal', 'almost.zero') and computing 
    uniqueness ('almost.unique'); 
    getting modified versions of usual functions ('rle2', 'sumNA'); 
    making a pause or a stop ('pause', 'stopif'); 
    converting into a function ('as.fun'); 
    providing a C like ternary operator ('condition %?% true %:% false'); 
    finding packages and functions ('get_all_pkgs', 'get_all_funs');
    and others ('erase', '%nin%', 'unwhich', 'top', 'bot', 'normlize').
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
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, 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.
Provides a set of functions to fit general macroevolutionary models for continuous traits evolving in adaptive landscapes of any shape. This package implements the Fokker-Planck-Kolmogorov model (FPK), in which the trait evolves under random diffusion but is also subject to a force that pulls it towards specific values - this force can be of any shape. FPK has a version in which hard reflective bounds exist at the extremes of the trait interval: this second model is called BBMV.
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
The blocked weighted bootstrap (BBW) is an estimation technique 
    for use with data from two-stage cluster sampled surveys in which either 
    prior weighting (e.g. population-proportional sampling or PPS as used in 
    Standardized Monitoring and Assessment of Relief and Transitions or SMART 
    surveys) or posterior weighting (e.g. as used in rapid assessment method or
    RAM and simple spatial sampling method or S3M surveys). The method was 
    developed by Accion Contra la Faim, Brixton Health, Concern Worldwide, 
    Global Alliance for Improved Nutrition, UNICEF Sierra Leone,  UNICEF Sudan
    and Valid International. It has been tested by the Centers for Disease
    Control (CDC) using infant and young child feeding (IYCF) data. See Cameron
    et al (2008) <doi:10.1162/rest.90.3.414> for application of bootstrap
    to cluster samples. See Aaron et al (2016) <doi:10.1371/journal.pone.0163176> 
    and Aaron et al (2016) <doi:10.1371/journal.pone.0162462> for application 
    of the blocked weighted bootstrap to estimate indicators from two-stage 
    cluster sampled surveys.
Implementation of 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, <doi:10.1371/journal.pone.0033624>).
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
Fully Bayesian Classification with a 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.
Calculates the prices of European options based on the universal solution provided by Bakshi, Cao and Chen (1997) <doi:10.1111/j.1540-6261.1997.tb02749.x>. This solution considers stochastic volatility, stochastic interest and random jumps. Please cite their work if this package is used.
Tools for Dating Business Cycles using Harding-Pagan (Quarterly Bry-Boschan) method and various plotting features.
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 two bicluster methods from the 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, This package computes the most cost-effective alternative and produces graphical summaries and probabilistic sensitivity analysis.
Implementation of the Bayesian Causal Effect Estimation algorithm, 
 a data-driven method for the estimation of the causal effect of an exposure 
 on a continuous outcome. For more details, see Talbot et al. (2015) DOI:10.1515/jci-2014-0035.
Includes a set of functions to analyze for instance nucleotide frequencies as well as transition and  transversion. Can reconstruct germline sequences based on the international ImMunoGeneTics information system (IMGT/HighV-QUEST) outputs, calculate and plot the difference (%) of nucleotides at 6 positions around a mutation to identify and characterize hotspot motifs as well as calculate and plot average mutation frequencies of nucleotide mutations resulting in amino acid substitution.
Fits generalised partial linear regression models using a Bayesian approach, where shape and
			smoothness constraints are imposed on nonparametrically modelled predictors through shape-restricted splines, and 
			no constraints are imposed on optional parametrically modelled covariates. See Meyer et al. (2011) <doi/10.1080/10485252.2011.597852> 
			for more details. IMPORTANT: before installing 'bcgam', you need to install 'Rtools' (Windows) or 'Xcode' (Mac OS X). These are	
			required for the correct installation of 'nimble' (<https://r-nimble.org/download>).
Provides bias-corrected estimates for the regression coefficients of a marginal model estimated with generalized estimating equations. Details about the bias formula used are in Lunardon, N., Scharfstein, D. (2017) <doi:10.1002/sim.7366>.
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) <DOI:10.1111/j.1467-9876.2008.00623.x>; Relative sea level 
  rate estimation incorporating time uncertainty in polynomial regression 
  models (Parnell and Gehrels 2015) <DOI:10.1002/9781118452547.ch32>; 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; currently 
  unpublished), and reverse calibration of dates from calibrated into 
  un-calibrated years (also unpublished).
Takes pollen and chronology 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. For more details see the
    paper Parnell et al. (2015), Bayesian inference for palaeoclimate with
    time uncertainty and stochastic volatility. Journal of the Royal Statistical
    Society: Series C (Applied Statistics), 64: 115–138 <DOI:10.1111/rssc.12065>.
Builds a dendrogram using log posterior as a natural distance defined by the model and meanwhile waits the clustering variables. It is also capable to computing equivalent Bayesian discrimination probabilities. The adopted method suites small sample large dimension setting. The model parameter estimation maybe difficult, depending on data structure and the chosen distribution family.
Many clustering methods have been proposed, but
    most of them cannot work for longitudinal gene expression data. 
    'BClustLonG' is a package that allows us to perform clustering analysis for 
    longitudinal gene expression data. It adopts a linear-mixed effects framework 
    to model the trajectory of genes over time, while clustering is jointly 
    conducted based on the regression coefficients obtained from all genes. 
    To account for the correlations among genes and alleviate the 
    high dimensionality challenges, factor analysis models are adopted
    for the regression coefficients. The Dirichlet process prior distribution 
    is utilized for the means of the regression coefficients to induce clustering.
    This package allows users to specify which variables to use for clustering 
    (intercepts or slopes or both) and whether a factor analysis model is desired.
    More details about this method can be found in Jiehuan Sun, et al. (2017) <doi:10.1002/sim.7374>.
Provides access to various spatial layers for B.C., such as 
    administrative boundaries, natural resource management boundaries, etc. 
    All layers are imported from the 'bcmaps.rdata' package as 'sf' or 'Spatial' objects
    through function calls in this package. All layers are in B.C. 'Albers' equal-area projection 
    <http://spatialreference.org/ref/epsg/nad83-bc-albers/>, which is the B.C. 
    government standard.
Provides an implementation of 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 regression models on a connected
    graph (Wang and Emerson, 2015); this allows estimation of change point models
    with multivariate responses. Parallel MCMC, previously available in bcp v.3.0.0,
    is currently not implemented.
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 method is based on: 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.
Functions provide risk projections of invasive breast cancer based on Gail model according to National Cancer Institute's Breast Cancer Risk Assessment Tool algorithm for specified race/ethnic groups and age intervals.
Methods for advanced analysis of B cell receptor repertoire
    data, like gene usage, mutations, clones, diversity, distance measures and
    multidimensional scaling and their visualisation.
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.
The bias-corrected estimation methods for the receiver operating characteristics
  ROC surface and the volume under ROC surfaces (VUS) under missing at random (MAR)
  assumption.
Bindings to the 'blowfish' password hashing algorithm derived from
    the 'OpenBSD' implementation.
Gene expression profiles are commonly utilized to infer disease
             subtypes and many clustering methods can be adopted for this task.
             However, existing clustering methods may not perform well when
             genes are highly correlated and many uninformative genes are included
             for clustering. To deal with these challenges, we develop a novel
             clustering method in the Bayesian setting. This method, called BCSub,
             adopts an innovative semiparametric Bayesian factor analysis model
             to reduce the dimension of the data to a few factor scores for
             clustering. Specifically, the factor scores are assumed to follow
             the Dirichlet process mixture model in order to induce clustering.
Methods for choosing the rank of an SVD approximation via cross
    validation.  The package provides both Gabriel-style "block"
    holdouts and Wold-style "speckled" holdouts.  It also includes 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).
Functions for density estimation based on 
  grouped data, and next-generation gene expression 
  data.
A collection of S4 classes which implements different methods to estimate and deal with densities in bounded domains. That is, densities defined within the interval [lower.limit, upper.limit], where lower.limit and upper.limit are values that can be set by the user.
Provides statistical tools for Bayesian structure learning in undirected graphical models for continuous, discrete, and mixed data. The package is implemented the recent improvements in the Bayesian graphical models literature, including Mohammadi and Wit (2015) <https://doi:10.1214/14-BA889> and Mohammadi et al. (2017) <https://doi:10.1111/rssc.12171>. To speed up the computations, the BDMCMC sampling algorithms are implemented in parallel using OpenMP in C++.
The main function generateDataset() processes a user-supplied .R file that 
  contains metadata parameters in order to generate actual data. The metadata parameters 
  have to be structured in the form of metadata objects, the format of which is 
  outlined in the package vignette. This approach allows to generate artificial data 
  in a transparent and reproducible manner.
Analyze differences among time series curves with p-value adjustment for multiple comparisons introduced in Oleson et al (2015) <DOI:10.1177/0962280215607411>.
Optimisation of the expected utility in single-stage and multi-stage Bayesian decision problems. The expected utility is estimated by simulation. For single-stage problems, JAGS is used to draw MCMC samples.
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.
Provides a continuous date scale, omitting weekends and holidays.
This is a special case of sparse matrices, used by coxme.
Provides a set of functions to create basic visualizations to quickly
    preview different aspects of biodiversity information such as inventory 
    completeness, extent of coverage (taxonomic, temporal and geographic), gaps
    and biases.
A Bayesian regression model for discrete response, where the conditional distribution is modelled via a discrete Weibull distribution. This package provides an implementation of Metropolis-Hastings and Reversible-Jumps algorithms to draw samples from the posterior. It covers a wide range of regularizations through any two parameter prior. Examples are Laplace (Lasso), Gaussian (ridge), Uniform, Cauchy and customized priors like a mixture of priors. An extensive visual toolbox is included to check the validity of the results as well as several measures of goodness-of-fit.
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.
A platform is provided for interactive analyses with a goal of totally easy to develop, deploy, interact, and explore (TEDDIE). Using this package, users can create customized analyses and make them available to end users who can perform interactive analyses and save analyses to RTF or HTML files. It allows developers to focus on R code for analysis, instead of dealing with html or shiny code.
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.
Reproducible and automated analysis of multiplex bead assays such as CBA, 
    LEGENDplex, and MACSPlex. The package provides functions for streamlined reading of fcs 
    files, and identification of bead clusters and analyte expression. It eases the 
    calculation of a standard curve and the subsequent calculation of the analyte concentration.
Fast Bayesian inference of marginal and conditional independence structures between variables from high-dimensional data (Leday and Richardson (2018) <arXiv:1803.08155>).
Plots univariate comparison graphs, an alternative to
        boxplot/stripchart/violin plot.
It is vital to assess the heterogeneity of treatment effects
    (HTE) when making health care decisions for an individual patient or a group
    of patients. Nevertheless, it remains challenging to evaluate HTE based
    on information collected from clinical studies that are often designed and
    conducted to evaluate the efficacy of a treatment for the overall population.
    The Bayesian framework offers a principled and flexible approach to estimate
    and compare treatment effects across subgroups of patients defined by their
    characteristics. This package allows users to explore a wide range of Bayesian
    HTE analysis models, and produce posterior inferences about HTE.
Provides an R interface for the Bureau of Economic Analysis (BEA) 
		API (see <http://www.bea.gov/API/bea_web_service_api_user_guide.htm> for 
		more information) that serves two core purposes - 
    1. To Extract/Transform/Load data [beaGet()] from the BEA API as R-friendly 
		formats in the user's work space [transformation done by default in beaGet() 
		can be modified using optional parameters; see, too, bea2List(), bea2Tab()].
		2. To enable the search of descriptive meta data [beaSearch()].
		Other features of the library exist mainly as intermediate methods 
		or are in early stages of development.
		Important Note - You must have an API key to use this library.  
		Register for a key at <http://www.bea.gov/API/signup/index.cfm> .
Assume that a temporal process is composed of contiguous segments with differing slopes and replicated noise-corrupted time series measurements are observed. The unknown mean of the data generating process is modelled as a piecewise linear function of time with an unknown number of change-points. The package infers the joint posterior distribution of the number and position of change-points as well as the unknown mean parameters per time-series by MCMC sampling. A-priori, the proposed model uses an overfitting number of mean parameters but, conditionally on a set of change-points, only a subset of them influences the likelihood. An exponentially decreasing prior distribution on the number of change-points gives rise to a posterior distribution concentrating on sparse representations of the underlying sequence, but also available is the Poisson distribution. See Papastamoulis et al (2017) <arXiv:1709.06111> for a detailed presentation of the method.
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, we use 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.
A matrix-like data structure that allows for efficient,
    convenient, and scalable subsetting of binary genotype/phenotype files
    generated by PLINK (<https://www.cog-genomics.org/plink2>), the whole
    genome association analysis toolset, without loading the entire file into
    memory.
Genomic regions processing using open-source command line tools such as 'BEDTools', 'BEDOPS' and 'Tabix'. 
  These tools offer scalable and efficient utilities to perform genome arithmetic e.g indexing, formatting and merging.
  bedr API enhances access to these tools as well as offers additional utilities for genomic regions processing.
The sole function of this package is beep(), with the 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.
Useful functions for R beginners, including hints for the arguments of the 'plot()' function, self-defined functions for error bars, user-customized pair plots and hist plots, enhanced linear regression figures, etc.. This package could be helpful to R experts as well.
Implements an S3 class based on 'data.table' to store and process efficiently ethomics (high-throughput behavioural) data.
Tools for downloading historical financial data from the www.belex.rs.
Calculates the Boltzmann entropy of a landscape gradient.
    It uses the analytical method created by Gao, P., Zhang, H. and Li, Z., 2018
    (<doi:10.1111/tgis.12315>).
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.
Methods for frontier
	analysis, Data Envelopment Analysis (DEA), 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. There is 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. It 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.
Benchmark your CPU and compare against other CPUs. Also provides 
    functions for obtaining system specifications, such as
    RAM, CPU type, and R version.
Crowd sourced benchmarks from running the 'benchmarkme' package.
Provides infrastructure to accurately measure and compare
    the execution time of R expressions.
Provides tools that make it easier to validate data using Benford's Law.
Several specialized statistical tests and support functions 
			for determining if numerical data could conform to Benford's law.
Included are two main interfaces for fitting and diagnosing
	bent-cable regressions for autoregressive time-series data or
	independent data (time series or otherwise): '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 that were updated from Versions 0.2.1 and 0.2.2 appear
	in Version 0.2.3 and up. Version 0.3.0 improves robustness of the
	error-message producing mechanism. It is the author's intention to
	distribute any future updates via GitHub.
Preprocessing tools and biodiversity measures 
    (species abundance, species richness, population heterogeneity and 
    sensitivity) for analysing marine benthic data. See Van Loon et al.
    (2015) <doi:10.1016/j.seares.2015.05.002> for an application of 
    these tools.
Tool for analysing benthos data. It estimates several quality 
    indices like the total abundance of species, species richness, 
    Margalef's d, AZTI Marine Biotic Index (AMBI), and the BEQI-2 index. 
    Furthermore, additional (optional) features are provided 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.
Draw horizontal histograms, color scattered points by 3rd dimension,
    enhance date- and log-axis plots, zoom in X11 graphics, trace errors and warnings, 
    use the unit hydrograph in a linear storage cascade, convert lists to data.frames and arrays, 
    fit multiple functions.
An implementation of best subset selection in generalized linear model and Cox proportional hazard model via the primal dual active set algorithm proposed by Wen, C., Zhang, A., Quan, S. and Wang, X. (2017) <arXiv:1709.06254>. The algorithm formulates coefficient parameters and residuals as primal and dual variables and utilizes efficient active set selection strategies based on the complementarity of the primal and dual variables.
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 information criteria or cross-validation.
Estimate a suite of normalizing transformations, including 
    a new adaptation of a technique based on ranks which can guarantee 
    normally distributed transformed data if there are no ties: ordered 
    quantile normalization (ORQ). ORQ normalization combines a rank-mapping
    approach with a shifted logit approximation that allows
    the transformation to work on data outside the original domain. It is 
    also able to handle new data within the original domain via linear 
    interpolation. The package is built to estimate the best normalizing 
    transformation for a vector consistently and accurately. It implements 
    the Box-Cox transformation, the Yeo-Johnson transformation, three types 
    of Lambert WxF transformations, and the ordered quantile normalization 
    transformation.
Three games: proton, frequon and regression. Each one is a console-based data-crunching game for younger and older data scientists.
  Act as a data-hacker and find Slawomir Pietraszko's credentials to the Proton server.
  In proton you have to solve four data-based puzzles to find the login and password.
  There are many ways to solve these puzzles. You may use loops, data filtering, ordering, aggregation or other tools.
  Only basics knowledge of R is required to play the game, yet the more functions you know, the more approaches you can try.
  In frequon you will help to perform statistical cryptanalytic attack on a corpus of ciphered messages.
  This time seven sub-tasks are pushing the bar much higher. Do you accept the challenge?
  In regression you will test your modeling skills in a series of eight sub-tasks.
  Try only if ANOVA is your close friend.
  It's a part of Beta and Bit project.
  You will find more about the Beta and Bit project at <http://betabit.wiki>.
Fit beta calibration models and obtain calibrated probabilities from
    them.
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.
Measures of beta-diversity in networks, and easy visualization of why two networks are different.
Functions to compute 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.
Computes standardized beta coefficients and corresponding
    standard errors for the following models:
    linear regression models with numerical covariates only,
    linear regression models with numerical and factorial covariates,
    weighted linear regression models,
    all these linear regression models with interaction terms, and
    robust linear regression models with numerical covariates only.
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.
It provides access to and information about the most important
    Brazilian economic time series - from the Getulio Vargas Foundation <http://portal.fgv.br/en>,
    the Central Bank of Brazil <http://www.bcb.gov.br> and the Brazilian Institute of Geography
    and Statistics <http://www.ibge.gov.br>. It also presents tools for managing, analysing (e.g.
    generating dynamic reports with a complete analysis of a series) and exporting
    these time series.
A fast integrative genetic association test for rare diseases based on a model for disease status given allele counts at rare variant sites. Probability of association, mode of inheritance and probability of pathogenicity for individual variants are all inferred in a Bayesian framework - 'A Fast Association Test for Identifying Pathogenic Variants Involved in Rare Diseases', Greene et al 2017 <doi:10.1016/j.ajhg.2017.05.015>.
Implementations of a Bayesian parametric (autoregressive), a Bayesian nonparametric (Whittle likelihood with Bernstein-Dirichlet prior) and a Bayesian semiparametric (autoregressive likelihood with Bernstein-Dirichlet correction) procedure are provided. The work is based on the corrected parametric likelihood by C. Kirch et al (2017) <arXiv:1701.04846>. It was supported by DFG grant KI 1443/3-1.
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.
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).
Implements the Bayesian paradigm for fractional
 polynomial models under the assumption of normally distributed error terms, see
 Sabanes Bove, D. and Held, L. (2011) <doi:10.1007/s11222-010-9170-7>.
An umbrella package providing a phenotype/genotype data structure
    and scalable and efficient computational methods for large genomic datasets
    in combination with several other packages: 'BEDMatrix', 'LinkedMatrix',
    and 'symDMatrix'.
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.
Application of genome prediction for a continuous variable, focused 
             on genotype by environment (GE) genomic selection models (GS). It consists a group of functions 
             that help to create regression kernels for some GE genomic models proposed by Jarquín et al. (2014) <doi:10.1007/s00122-013-2243-1>
             and Lopez-Cruz et al. (2015) <doi:10.1534/g3.114.016097>. Also, it computes genomic predictions based on Bayesian approaches.
             The prediction function uses an orthogonal transformation of the data and specific priors
             present by Cuevas et al. (2014) <doi:10.1534/g3.114.013094>.
Implementation of Bayesian estimation in generalized linear models following Gamerman (1997).
A collection of box-geometry model (BGM) files for the Atlantis 
    ecosystem model. Atlantis is a deterministic, biogeochemical, 
    whole-of-ecosystem model (see <http://atlantis.cmar.csiro.au/> for more information).
Two partially supervised mixture modeling methods: 
        soft-label and belief-based modeling are implemented.
        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 
        <doi:10.18637/jss.v047.i03>.
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.
Fits a Bayesian group-sparse multi-task regression model using Gibbs
    sampling. The hierarchical prior encourages shrinkage of the estimated regression
    coefficients at both the gene and SNP level. The model has been applied
    successfully to imaging phenotypes of dimension up to 100; it can be used more
    generally for multivariate (non-imaging) phenotypes.
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. As of release 1.65.0-1, the following Boost libraries are
 included: 'algorithm' 'align' 'any' 'atomic' 'bimap' 'bind'
 'circular_buffer' 'compute' 'concept' 'config' 'container' 'date'_'time'
 'detail' 'dynamic_bitset' 'exception' 'filesystem' 'flyweight' 'foreach'
 'functional' 'fusion' 'geometry' 'graph' 'heap' 'icl' 'integer'
 'interprocess' 'intrusive' 'io' 'iostreams' 'iterator' 'math' 'move' 'mpl'
 'multiprcecision' 'numeric' 'pending' 'phoenix' 'preprocessor'
 'propery_tree' 'random' 'range' 'scope_exit' 'smart_ptr' 'sort' 'spirit'
 'tuple' 'type_traits' 'typeof' 'unordered' 'utility' 'uuid'.
Functions for MLE, MCMC, CIs (originally in Fortran)
Functions and data sets reproducing some examples in
             Box, Hunter and Hunter II.  Useful for statistical design
             of experiments, especially factorial experiments.
Contains tools to fit both predictive and prognostic biomarker effects using biomarker threshold models. Evaluate the treatment effect, biomarker effect and treatment-biomarker interaction using probability index measurement. Test for treatment-biomarker interaction using residual bootstrap method.
Performs Bayesian hierarchical multi-subject multiscale analysis of fMRI data as described in Sanyal & Ferreira (2012) <DOI:10.1016/j.neuroimage.2012.08.041> using wavelet based prior that borrows strength across subjects and returns posterior smoothed versions of the fMRI data and samples from the posterior distribution.
An implementation of the Bayesian Clearance Estimator (Fogarty et al. (2015) <doi:10.1111/biom.12307>).
            It takes serial measurements of a response on an individual (e.g., parasite load 
            after treatment) that is decaying over time and performs Bayesian hierarchical 
            regression of the clearance rates on the given covariates. This package provides 
            tools to calculate WWARN PCE (WorldWide Antimalarial Resistance Network's 
            Parasite Clearance Estimator) estimates of the clearance rates as well.
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.
A simple R wrapper for the Java BiBit algorithm from "A
    biclustering algorithm for extracting bit-patterns from binary datasets"
    from Domingo et al. (2011) <DOI:10.1093/bioinformatics/btr464>. An simple adaption for the BiBit algorithm which allows noise in the biclusters is also introduced as well as a function to guide the algorithm towards given (sub)patterns. Further, a workflow to derive noisy biclusters from discoverd larger column patterns is included as well.
Tool for quantitative research in scientometrics and bibliometrics.
    It provides various routines for importing bibliographic data from SCOPUS (<http://scopus.com>) and 
    Clarivate Analytics Web of Science (<http://www.webofknowledge.com/>) databases, performing bibliometric analysis 
    and building data matrices for co-citation, coupling, scientific collaboration and co-word analysis.
R class to access 'sqlite', 'BiblioSpec' generated, mass spectrometry search result files,
    containing detailed information about peptide spectra matches.
    Convert 'Mascot' '.dat' or e.g. 'comet' '.pep.xml' files with 'BiblioSpec' into 'sqlite' files and than 
    access them with the 'CRAN' 'bibliospec' package to analyse with the R-packages 'specL' to generate
    spectra libraries, 'protViz' to annotate spectra, or 'prozor' for false discovery rate 
    estimation and protein inference.
Currently, the package provides two functions for plotting and analyzing bibliometric data (JIF, Journal Impact Factor, and paper percentile values) and a plot function to visualize the result of a reference publication year spectroscopy (RPYS) analysis performed in the free software 'CRExplorer' (see <http://crexplorer.net>). Further extension to more plot variants is planned.
A tool for enumerating maximal complete bipartite graphs. The input should be a edge list file or a binary matrix file. 
             The output are maximal complete bipartite graphs. Algorithms used can be found in this paper Y Zhang et al. BMC Bioinformatics 2014 15:110 <doi:10.1186/1471-2105-15-110>.
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.
Implementation of a collection of MCMC methods for Bayesian structure learning
    of directed acyclic graphs (DAGs), both from continuous and discrete data. For efficient
    inference on larger DAGs, the space of DAGs is pruned according to the data. To filter
    the search space, the algorithm employs a hybrid approach, combining constraint-based 
    learning with search and score. A reduced search space is initially defined on the basis
    of a skeleton obtained by means of the PC-algorithm, and then iteratively improved with
    search and score. Search and score is then performed following two approaches:
    Order MCMC, or Partition MCMC.
    The BGe score is implemented for continuous data and the BDe score is implemented
    for binary data. The algorithms may provide the maximum a posteriori (MAP) graph or
    a sample (a collection of DAGs) from the posterior distribution given the data.
    References:
    N. Friedman and D. Koller (2003) <doi:10.1023/A:1020249912095>, 
    D. Geiger and D. Heckerman (2002) <doi:10.1214/aos/1035844981>, 
    J. Kuipers and G. Moffa (2016) <doi:10.1080/01621459.2015.1133426>, 
    M. Kalisch et al.(2012) <doi:10.18637/jss.v047.i11>.
An S3 class with a method for calculates the bidimensional regression between two 2D configurations following the approach by Tobler (1965).
Provides Tools for Accessing the Botanical Information and Ecology Network Database.  The BIEN database contains cleaned and standardized botanical data including occurrence, trait, plot and taxonomic data (See <http://Bien.nceas.ucsb.edu/bien/> for more Information).  This package provides functions that query the BIEN database by constructing and executing optimized SQL queries.
Estimates fixed effects binary choice models (logit and probit) with potentially many individual fixed effects and computes average partial effects. Incidental parameter bias can be reduced with a bias-correction proposed by Hahn and Newey (2004) <doi:10.1111/j.1468-0262.2004.00533.x>.
Contains tools for survey statistics (especially in educational
    assessment) for datasets with replication designs (jackknife, 
    bootstrap, replicate weights; see Kolenikov, 2010). 
    Descriptive statistics, linear and logistic regression, 
    path models for manifest variables with measurement error 
    correction and two-level hierarchical regressions for weighted 
    samples are included. Statistical inference can be conducted for 
    multiply imputed datasets and nested multiply imputed datasets.    
    This package is developed by BIFIE (Federal Institute for 
    Educational Research, Innovation and Development of the Austrian 
    School System; Salzburg, Austria).
An implementation of Bayesian Aggregation in Genomic Applications (BiG), where BiG is
    a Bayesian latent variable approach to aggregation of partial and top ranked lists 
    (Li et. al in preparation). It provides implementations for three different prior setups
	for variance/standard deviation parameters: diffuse inverse gamma (IG), diffuse uniform, half-t.
This package provides arithmetic functions for R matrix and big.matrix objects.
Extend 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'.
Data sets and functions for chi-squared Hardy-Weinberg and case-control association tests of highly polymorphic genetic data [e.g., human leukocyte antigen (HLA) data]. Performs association tests at multiple levels of polymorphism (haplotype, locus and HLA amino-acids) as described in Pappas DJ, Marin W, Hollenbach JA, Mack SJ (2016) <doi:10.1016/j.humimm.2015.12.006>. Combines rare variants to a common class to account for sparse cells in tables as described by Hollenbach JA, Mack SJ, Thomson G, Gourraud PA (2012) <doi:10.1007/978-1-61779-842-9_14>.
A reimplementation of the fastLm() functionality of 'RcppEigen' for
    big.matrix objects for fast out-of-memory linear model fitting.
Distributes Gaussian process calculations across nodes
    in a distributed memory setting, using Rmpi. The bigGP 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.
Features the multiple polynomial quadratic sieve algorithm
    for factoring large integers and a vectorized factoring function that
    returns the complete factorization of an integer. Utilizes the C
    library GMP (GNU Multiple Precision Arithmetic) and classes created
    by Antoine Lucas et al. found in the 'gmp' package.
Functions for Kernel-Regularized Least Squares optimized for speed and memory usage are provided along with visualization tools. For working papers, sample code, and recent presentations visit <https://sites.google.com/site/petemohanty/software/>. bigKRLS, as well its dependencies, require current versions of R and its compilers (and RStudio if used). For details, see <https://github.com/rdrr1990/bigKRLS/blob/master/INSTALL.md>.
Response surface methods for drug synergy analysis. Available
    methods include generalized and classical Loewe formulations as well as Highest
    Single Agent methodology. Response surfaces can be plotted in an interactive
    3-D plot and formal statistical tests for presence of synergistic effects are
    available. Implemented methods and tests are described in the article 
    "BIGL: Biochemically Intuitive Generalized Loewe null model for prediction 
    of the expected combined effect compatible with partial agonism and antagonism"
    by Koen Van der Borght, Annelies Tourny, Rytis Bagdziunas, Olivier Thas, 
    Maxim Nazarov, Heather Turner, Bie Verbist & Hugo Ceulemans (2017) 
    <doi:10.1038/s41598-017-18068-5>.
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.
Extend lasso and elastic-net model fitting for ultrahigh-dimensional, 
    multi-gigabyte data sets that cannot be loaded into memory. It's much more 
    memory- and computation-efficient as compared to existing lasso-fitting packages 
    like 'glmnet' and 'ncvreg', thus allowing for very powerful big data analysis 
    even with an ordinary laptop.
Calculation of physical (e.g. aerodynamic conductance, surface temperature), 
             and physiological (e.g. canopy conductance, water-use efficiency) ecosystem properties
			 from eddy covariance data and accompanying meteorological measurements. Calculations
			 assume the land surface to behave like a 'big-leaf' and return bulk ecosystem/canopy variables.
Regression for data too large to fit in memory
Implements optimal matching with near-fine balance in large observational studies with the use of optimal calipers to get a sparse network. The caliper is optimal in the sense that it is as small as possible such that a matching exists. 
    Glover, F. (1967).  <DOI:10.1002/nav.3800140304>.
    Katriel, I. (2008). <DOI:10.1287/ijoc.1070.0232>.
    Rosenbaum, P.R. (1989). <DOI:10.1080/01621459.1989.10478868>.
    Yang, D., Small, D. S., Silber, J. H., and Rosenbaum, P. R. (2012). <DOI:10.1111/j.1541-0420.2011.01691.x>.
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 data types and concepts.
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.
Interface with 'Google BigQuery',
    see <https://cloud.google.com/bigquery/> for more information.
    This package uses 'googleAuthR' so is compatible with similar packages, 
    including 'Google Cloud Storage' (<https://cloud.google.com/storage/>) for result extracts.
Use Newton's method, coordinate descent, and METIS clustering
    to solve the L1 regularized Gaussian MLE inverse covariance
    matrix estimation problem.
Allows the user to carry out GLM on very large
    data sets. Data can be created using the data_frame() function and appended
    to the object with object$append(data); data_frame and data_matrix objects
    are available that allow the user to store large data on disk. The data is
    stored as doubles in binary format and any character columns are transformed
    to factors and then stored as numeric (binary) data while a look-up table is
    stored in a separate .meta_data file in the same folder. The data is stored in
    blocks and GLM regression algorithm is modified and carries out a MapReduce-
    like algorithm to fit the model. The functions bglm(), and summary()
    and bglm_predict() are available for creating and post-processing of models.
    The library requires Armadillo installed on your system. It probably won't 
    function on windows since multi-core processing is done using mclapply() 
    which forks R on Unix/Linux type operating systems.
Easily talk to Google's 'BigQuery' database from R.
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.
Construct large systems of structural equations using the two-stage penalized least squares (2SPLS) method proposed by Chen, Zhang and Zhang (2016).
Fits smoothing spline regression models using scalable algorithms designed for large samples. Seven marginal spline types are supported: linear, cubic, different cubic, cubic periodic, cubic thin-plate, ordinal, and nominal. Random effects and parametric effects are also supported. Response can be Gaussian or non-Gaussian: Binomial, Poisson, Gamma, Inverse Gaussian, or Negative Binomial.
Selecting linear and generalized linear models for large data sets
    using modified stepwise procedure and modern selection criteria (like
    modifications of Bayesian Information Criterion). Selection can be
    performed on data which exceed RAM capacity. Special selection strategy is
    available, faster than classical stepwise procedure.
Extend the bigmemory package with 'table', 'tapply', and 'split'
    support for 'big.matrix' objects. The functions may also be used with native R
    matrices for improving speed and memory-efficiency.
For studying recurrent disease and death with competing
    risks, comparisons based on the well-known cumulative incidence function
    can be confounded by different prevalence rates of the competing events.
    Alternatively, comparisons of the conditional distribution of the survival
    time given the failure event type are more relevant for investigating the
    prognosis of different patterns of recurrence disease. This package implements
    a nonparametric estimator for the conditional cumulative incidence function
    and a nonparametric conditional bivariate cumulative incidence function for the
    bivariate gap times proposed in Huang et al. (2016) <doi:10.1111/biom.12494>.
Estimation of large Vector AutoRegressive (VAR), Vector AutoRegressive with Exogenous Variables X (VARX) and Vector AutoRegressive Moving Average (VARMA) Models with Structured Lasso Penalties, see Nicholson, Bien and Matteson (2017) <arXiv:1412.5250v2> and Wilms, Basu, Bien and Matteson (2017) <arXiv:1707.09208>.
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.
Estimates VAR and VARX models with structured Lasso Penalties.
Download and aggregate data from all public hire bicycle systems
    which provide open data, currently including 'Santander' Cycles in London,
    U.K., and from the U.S.A., 'citibike' in New York City NY, 'Divvy' in
    Chicago IL, 'Capital Bikeshare' in Washington DC, 'Hubway' in Boston MA, 
    'Metro' in Los Angeles LA, and 'Indego' in Philadelphia PA.
Anonymised Bay Area bike share trip data for the year 2014. 
    Also contains additional metadata on stations and weather.
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.
Contains data sets regarding songs on the Billboard Hot 100 list
    from 1960 to 2016. The data sets include the ranks for the given year,
    musical features of a lot of the songs and lyrics for several of the songs
    as well.
Provides an 'htmlwidgets' interface to 'billboard.js', 
    a re-usable easy interface JavaScript chart library, based on D3 v4+.
    Chart types include line charts, scatterplots, bar/lollipop charts, histogram/density plots, pie/donut charts and gauge charts.
    All charts are interactive, and a proxy method is implemented to smoothly update a chart without rendering it again in 'shiny' apps.
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)
Estimates non-Gaussian mixture models of case-control data. The four types of models supported are binormal, two component constrained, two component unconstrained, and four component.  The most general model is the four component model, under which both cases and controls are distributed according to a mixture of two unimodal distributions.  In the four component model, the two component distributions of the control mixture may be distinct from the two components of the case mixture distribution.  In the two component unconstrained model, the components of the control and case mixtures are the same; however the mixture probabilities may differ for cases and controls.  In the two component constrained model, all controls are distributed according to one of the two components while cases follow a mixture distribution of the two components.  In the binormal model, cases and controls are distributed according to distinct unimodal distributions.  These models assume that Box-Cox transformed case and control data with a common lambda parameter are distributed according to Gaussian mixture distributions.   Model parameters are estimated using the expectation-maximization (EM) algorithm.  Likelihood ratio test comparison of nested models can be performed using the lr.test function. AUC and PAUC values can be computed for the model-based and empirical ROC curves using the auc and pauc functions, respectively. The model-based and empirical ROC curves can be graphed using the roc.plot function. Finally, the model-based density estimates can be visualized by plotting a model object created with the bimixt.model function.
Defines the functions used to compute the
  bimodal index as defined by Wang et al. (2009)
  <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2730180/>.
Provides methods for the binarization of one-dimensional data and some visualization functions.
Allows to view the optimal probability cut-off point at which the Sensitivity and Specificity meets and its a best way to minimize both Type-1 and Type-2 error for a binary Classifier in determining the Probability threshold.
Implements variable selection for high dimensional datasets with a binary response
    variable using the EM algorithm.  Both probit and logit models are supported.  Also included 
    is a useful function to generate high dimensional data with correlated variables.
Modeling under- and over-dispersed binary data using extended Poisson process models (EPPM) as in the article MJ Faddy and DM Smith (2012) <doi:10.1002/bimj.201100214> .
Allows the estimation and prediction for binary Gaussian process model. The mean function can be assumed to have time-series structure. The estimation methods for the unknown parameters are based on penalized quasi-likelihood/penalized quasi-partial likelihood and restricted maximum likelihood. The predicted probability and its confidence interval are computed by Metropolis-Hastings algorithm. More details can be seen in Sung et al (2017) <arXiv:1705.02511>.
Provides the binary S3 class. The instance of binary is used to convert a decimal number (Base10) to a binary number (Base2). The Class provides some features e.G. shift(), rotate(), summary(). Based on logical vectors.
Estimate the correlation between two irregular time series that are not necessarily sampled on identical time points. This program is also applicable to the situation of two evenly spaced time series that are not on the same time grid. 'BINCOR' is based on a novel estimation approach proposed by Mudelsee (2010, 2014) to estimate the correlation between two time series with different timescales. 'BINCOR' contains four functions: bin_cor() (the main function to build the binned time series), plot_ts() (to plot and compare the irregular and binned time series, cor_ts() (to estimate the correlation between the binned time series) and ccf_ts() (to estimate the cross-correlation between the binned time series).
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.
Provides a simple interface for creating active bindings where the
    bound function accepts additional arguments.
Provides an easy way to fill an environment with active bindings
    that call a C++ function.
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.
Binford's hunter-gatherer data includes more than 200 variables
    coding aspects of hunter-gatherer subsistence, mobility, and social organization
    for 339 ethnographically documented groups of hunter-gatherers.
Methods for estimation and hypothesis testing of proportions
 in group testing designs: methods for estimating a proportion in a single
        population (assuming sensitivity and specificity equal to 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.
        Methods for identification of positive specimens in group
        testing designs: methods for identification of positive 
        specimens in a number of group testing algorithms. Hierarchical and
        array-based algorithms are implemented and used to find the 
        optimal testing configuration and calculate operating characteristics
        for testing configurations across a wide variety of situations.
Binomial Haar-Fisz transforms for Gaussianization
Tools and functions for managing the download of binary files.
    Binary repositories are defined in 'YAML' format. Defining new 
    pre-download, download and post-download templates allow additional 
    repositories to be added.
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.
Kernel density and distribution estimation for interval-grouped data (Reyes, Francisco-Fernandez and Cao 2016, 2017) <doi:10.1080/10485252.2016.1163348>, <doi:10.1007/s11749-017-0523-9>, (Gonzalez-Andujar, Francisco-Fernandez, Cao, Reyes, Urbano, Forcella and Bastida 2016) <doi:10.1111/wre.12216>
    and nonparametric estimation of seedling emergence indices (Cao, Francisco-Fernandez, Anand, Bastida and Gonzalez-Andujar 2011) <doi:10.1017/S002185961100030X>.
A spectral binning approach for high resolution flow infusion mass
    spectrometry data.
Generation of multiple binary and continuous non-normal variables simultaneously 
             given the marginal characteristics and association structure based on the methodology 
             proposed by Demirtas et al. (2012).
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
Includes functions for working with taxonomic data,
    including functions for combining, separating, and filtering
    taxonomic groups by any rank or name. Allows standard ('SE')
    and non-standard evaluation ('NSE').
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.
Generation of samples from a mix of binary, ordinal and continuous random variables with a pre-specified correlation matrix and marginal distributions.
Identify peaks in ChIP-seq data with biological replicates using a one-sided quasi-likelihood ratio test in quasi-Poisson or quasi-negative binomial models.
Implementation of 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.
Provides several methods for generating density functions
    based on binned data. Data are assumed to be nonnegative, but the bin widths
    need not be uniform, and the top bin may be unbounded. All PDF smoothing methods
    maintain the areas specified by the binned data. (Equivalently, all CDF
    smoothing methods interpolate the points specified by the binned data.) An
    estimate for the mean of the distribution may be supplied as an optional
    argument, which greatly improves the reliability of statistics computed from
    the smoothed density functions. Methods include step function, recursive
    subdivision, and optimized spline.
Various supervised and unsupervised binning tools
    including using entropy, recursive partition methods
    and clustering.
Utilities to process, organize and explore protein structure,
    sequence and dynamics data. Features include the ability to read and write
    structure, sequence and dynamic trajectory data, perform sequence and structure
    database searches, data summaries, atom selection, alignment, superposition,
    rigid core identification, clustering, torsion analysis, distance matrix
    analysis, structure and sequence conservation analysis, normal mode analysis,
    principal component analysis of heterogeneous structure data, and correlation
    network analysis from normal mode and molecular dynamics data. In addition,
    various utility functions are provided to enable the statistical and graphical
    power of the R environment to work with biological sequence and structural data.
    Please refer to the URLs below for more information.
Implement in 'R' interactive Circos-like visualizations of genomic data, to map information
	such as genetic variants, genomic fusions and aberrations to a circular genome, as proposed by the
	'JavaScript' library 'BioCircos.js', based on the 'JQuery' and 'D3' technologies. The output is by 
	default displayed in stand-alone HTML documents or in the 'RStudio' viewer pane. Moreover it can be
	integrated in 'R Markdown' documents and 'Shiny' applications.
Contains functions for the data analysis with the emphasis on biological data, including several algorithms for feature ranking, feature selection, classification
 algorithms with the embedded validation procedures.
 The functions can deal with numerical as well as with nominal features. Includes also the functions for calculation
 of feature AUC (Area Under the ROC Curve) and HUM (hypervolume under manifold) values and construction 2D- and 3D- ROC curves.
 Provides the calculation of Area Above the RCC (AAC) values and construction of Relative Cost Curves
 (RCC) to estimate the classifier performance under unequal misclassification costs problem.
 There exists the special function to deal with missing values, including different imputing schemes.
The Biodem package provides a number of functions for Biodemographic analysis.
Graphical User Interface (via the R-Commander) and 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.
Multilevel ecological data series (MEDS) are sequences of observations ordered according to temporal/spatial hierarchies that are defined by sample designs, with sample variability confined to ecological factors. Dendroclimatic MEDS of tree rings and climate are modeled into normalized fluctuations of tree growth and aridity.  Modeled fluctuations (model frames) are compared with Mantel correlograms on multiple levels defined by sample design. Package implementation can be understood by running examples in modelFrame(), and muleMan() functions.
Parse GenBank/GenPept records <https://www.ncbi.nlm.nih.gov/Sitemap/samplerecord.html>
    into native R objects to easily access and manipulate the sequence and annotation information.
The main drawback of the most common biodiversity indices is that different measures may lead to different rankings among communities. This instrument overcomes this limit using some functional tools with the diversity profiles. In particular, the derivatives, the curvature, the radius of curvature, the arc length, and the surface area are proposed. The goal of this method is to interpret in detail the diversity profiles and obtain an ordering between different ecological communities on the basis of diversity. In contrast to the typical indices of diversity, the proposed method is able to capture the multidimensional aspect of biodiversity, because it takes into account both the evenness and the richness of the species present in an ecological community.
High- and low-level functions for processing biogas data and predicting biogas production. Molar mass and calculated oxygen demand (COD') can be determined from a chemical formula. Measured gas volume can be corrected for water vapor and to (possibly user-defined) standard temperature and pressure. Gas quantity can be converted between volume, mass, and moles. Gas composition, cumulative production, or other variables can be interpolated to a specified time. Cumulative biogas and methane production (and rates) can be calculated using volumetric, manometric, or gravimetric methods for any number of reactors. With cumulative methane production data and data on reactor contents, biochemical methane potential (BMP) can be calculated and summarized, including subtraction of the inoculum contribution and normalization by substrate mass. Cumulative production and production rates can be summarized in several different ways (e.g., omitting normalization) using the same function. Biogas quantity and composition can be predicted from substrate composition and additional, optional data. Lastly, inoculum and substrate mass can be determined for planning BMP experiments.
Functions for error detection and correction in point data quality datasets that are used in species distribution modelling. Includes functions for parsing and converting coordinates into decimal degrees from various formats.
Tools for extraction and analysis of various
    n-grams (k-mers) derived from biological sequences (proteins
    or nucleic acids). Contains QuiPT (quick permutation test) for fast
    feature-filtering of the n-gram data.
Transition rates are computed from transitions and exposures.Useful graphics and life-course indicators are computed. The package structures the data for multistate statistical and demographic modeling of life histories.
Single linkage clustering and connected component analyses are often performed on biological images. 'Bioi' provides a set of functions for performing these tasks. This functionality is implemented in several key functions that can extend to from 1 to many dimensions. The single linkage clustering method implemented here can be used on n-dimensional data sets, while connected component analyses are limited to 3 or fewer dimensions.
Tools for 3D imaging, mostly for biology/microscopy. 
    Read and write TIFF stacks. Functions for segmentation, filtering and analysing 3D point patterns.
Prediction and adjustment to experimental data of microbial
    inactivation. Several models available in the literature are implemented.
Imports benthic count data, reformats this data, and
        computes environmental inferences from this data.
Can be used to install and download massive bioinformatics analysis softwares and databases, such as NGS reads mapping tools with its required databases.
Generate urls and hyperlinks to commonly used biological databases
    and resources based on standard identifiers. This is primarily useful when
    writing dynamic reports that reference things like gene symbols in text or
    tables, allowing you to, for example, convert gene identifiers to hyperlinks
    pointing to their entry in the NCBI Gene database. Currently supports NCBI
    Gene, PubMed, Gene Ontology, CRAN and Bioconductor.
Analysing and forecasting biological invasions time series
    with a stochastic, non-mechanistic approach that gives proper weight
    to the anthropic component, accounts for habitat suitability and
    provides measures of precision for its estimates.
Variable selection methods are provided for several classification methods: the lasso/elastic net, PCLDA, PLSDA, and several t-tests. Two approaches for selecting cutoffs can be used, one based on the stability of model coefficients under perturbation, and the other on higher criticism.
Perform metagenomic data retrieval and functional annotation
    retrieval. In detail, this package aims to provide users with a standardized
    way to automate genome, proteome, coding sequence ('CDS'), 'GFF', and metagenome
    retrieval from 'NCBI' and 'ENSEMBL' databases. Furthermore, an interface to the 'BioMart' database
    (Smedley et al. (2009) <doi:10.1186/1471-2164-10-22>) allows users to retrieve
    functional annotation for genomic loci. Users can download entire databases such
    as 'NCBI RefSeq' (Pruitt et al. (2007) <doi:10.1093/nar/gkl842>), 'NCBI nr',
    'NCBI nt' and 'NCBI Genbank' (Benson et al. (2013) <doi:10.1093/nar/gks1195>) as
    well as 'ENSEMBL' and 'ENSEMBLGENOMES' with only one command.
Contains functions to estimate aboveground biomass/carbon and its uncertainty in tropical forests. These functions allow to (1) retrieve and to correct taxonomy, (2) estimate wood density and its uncertainty, (3) construct height-diameter models, (4) estimate the above-ground biomass/carbon at the stand level with associated uncertainty. To cite BIOMASS, please use citation("BIOMASS").
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.
Extends the bioinactivation package with functions for Sensitivity
    Analysis and Optimum Experiment Design.
Prognostic Enrichment is a clinical trial strategy of evaluating an intervention in a patient population with a higher rate of the unwanted event than the broader patient population (R. Temple (2010) <DOI:10.1038/clpt.2010.233>). A higher event rate translates to a lower sample size for the clinical trial, which can have both practical and ethical advantages. This package is a tool to help evaluate biomarkers for prognostic enrichment of clinical trials.
Utilities and functions to investigate the relation
        between biomolecular structures, their interactions, and the
        evolutionary information revealed in sequence alignments of
        these molecules.
Visualization of complex biological datasets is
    essential to understand complementary spects of biology
    in big data era.
    In addition, analyzing of multiple datasets enables to
    understand biologcal processes deeply and accurately.
    Multiple datasets produce multiple analysis results, and
    these overlappings are usually visualized in Venn diagram.
    bioplots is a tiny R package that generates a heatmap to
    visualize overlappings instead of using Venn diagram.
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.
Utilities for computation and analysis of correlation/co-variation in multiple sequence alignments and in side chain motions during molecular dynamics simulations. Features include the computation of correlation/co-variation scores using a variety of scoring functions between either sequence positions in alignments or side chain dihedral angles in molecular dynamics simulations and to analyze the correlation/co-variation matrix through a variety of tools including network representation and principal components analysis.  In addition, several utility functions are based on the R graphical environment to provide friendly tools for help in data interpretation. Examples of sequence co-variation analysis and utility tools are provided in: Pele J, Moreau M, Abdi H, Rodien P, Castel H, Chabbert M. (2014) <doi:10.1002/prot.24570>. This work was supported by the French National Research Agency (Grant number: ANR-11-BSV2-026).
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.
Functions to help dealing with raw data from measurements, like
    reading and transforming raw values organized in matrices, calculating and
    converting concentrations and calculating precision of duplicates /
    triplicates / ... . It is compatible with and building on top of some
    'tidyverse'-packages.
Data processing tools to compute the rectified, integrated and the averaged EMG. Routines for automatic detection of activation phases. A routine to compute and plot the ensemble average of the EMG. An EMG signal simulator for general purposes.
Provides some tools for developing and validating prediction models, estimate expected survival of patients and visualize them graphically. Most of the implemented methods are based on penalized regressions such as: the lasso (Tibshirani R (1996)), the elastic net (Zou H et al. (2005) <doi:10.1111/j.1467-9868.2005.00503.x>), the adaptive lasso (Zou H (2006) <doi:10.1198/016214506000000735>), the stability selection (Meinshausen N et al. (2010) <doi:10.1111/j.1467-9868.2010.00740.x>), some extensions of the lasso (Ternes et al. (2016) <doi:10.1002/sim.6927>), some methods for the interaction setting (Ternes N et al. (2016) <doi:10.1002/bimj.201500234>), or others. A function generating simulated survival data set is also provided.
This packages provides datasets and functions for the book "Initiation Ã  la Statistique avec R", Dunod, 2ed, 2014.
Calculates a range of UK freshwater invertebrate biotic indices
    including BMWP, Whalley, WHPT, Habitat-specific BMWP, AWIC, LIFE and PSI.
Tools designed to perform and work with cluster analysis (including Tocher's algorithm), 
	discriminant analysis and path analysis (standard and under collinearity), as well as some 
	useful miscellaneous tools for dealing with sample size and optimum plot size calculations.
	Mantel's permutation test can be found in this package. A new approach for calculating its
	power is implemented. biotools also contains the new tests for genetic covariance components.
	An approach for predicting spatial gene diversity is implemented.
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 levels (bipartite), 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 web's topology.
A GUI with which the user can construct and interact with Bootstrap methods on Classical Biplots and with Clustering and/or Disjoint Biplot.
Provides a GUI with which users can construct and interact
        with biplots.
Bayesian parameter estimation for (partially observed)
    two-dimensional diffusions.
A connector to the API for 'Wordnik' <https://whoapi.com>, a dictionary service that also provides
             bigram generation, word frequency data, and a whole host of other functionality.
R functions to read EURING data and 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.
Collection of tools to make R more convenient. Includes tools to
    summarize data using statistics not available with base R and manipulate
    objects for analyses.
R functions for "The Basics of Item Response Theory Using R" by Frank B. Baker and Seock-Ho Kim (Springer, 2017, ISBN-13: 978-3-319-54204-1) including iccplot(), icccal(), icc(), iccfit(), groupinv(), tcc(), ability(), tif(), and rasch().  For example, iccplot() plots an item characteristic curve under the two-parameter logistic model.
Provides an interface to data provided by the Bank for International 
  Settlements <https://www.bis.org>, allowing for programmatic retrieval of a 
  large quantity of (central) banking data.
An implementation of Bisect, a method for inferring cell type composition of samples based on methylation sequencing data (Whole Genome Bisulfite Sequencing and Reduced Representation Sequencing). The method is specifically tailored for sequencing data, and therefore works better than methods developed for methylation arrays. It contains a supervised mode that requires a reference (the methylation probabilities in the pure cell types), and a semi-supervised mode, that requires cell counts for a subset of the samples, but does not require a reference.
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 visualise these candidate
    synthetic lethal relationships.
Provides functions for fitting Bayesian monotonic regression models to data.
Bisulfite-treated RNA non-conversion in a set of samples is
  analysed as follows : each sample's non-conversion distribution is
  identified to a Poisson distribution. P-values adjusted for multiple
  testing are calculated in each sample. Combined non-conversion P-values
  and standard errors are calculated on the intersection of the set of
  samples. For further details, see C Legrand, F Tuorto, M Hartmann, 
  R Liebers, D Jakob, M Helm and F Lyko (2017) <doi:10.1101/gr.210666.116>.
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. 
 These are useful for handling database keys and exact counting in +-2^63.
 WARNING: do not use them as replacement for 32bit integers, integer64 are not
 supported for subscripting by R-core and they have different semantics when 
 combined with double, e.g. integer64 + double => integer64. 
 Class integer64 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 inter-
 active data exploration and manipulation and optionally leverage caching.
Functions for bitwise operations on integer vectors.
Provides methods for the binarization and trinarization of one-dimensional data and some visualization functions.
MCMC methods to estimate transmission dynamics and infection routes in hospitals using genomic sampling data.
Perform competing risks analysis under bivariate Pareto models. See Shih et al. (2018) <doi:10.1080/03610926.2018.1425450> for details.
Parameter estimation of bivariate distribution functions
    modeled as a Archimedean copula function. The input data may contain
    values from right censored. Used marginal distributions are two-parameter.
    Methods for density, distribution, survival, random sample generation.
Implements sample size calculations for bivariate random
        intercept regression model that are described in Comulada and
        Weiss (2010)
Assess the agreement in method comparison studies by tolerance intervals and errors-in-variables regressions. The Ordinary Least Square regressions (OLSv and OLSh), the Deming Regression (DR), and the (Correlated)-Bivariate Least Square regressions (BLS and CBLS) can be used with unreplicated or replicated data. The BLS and CBLS are the two main functions to estimate a regression line, while XY.plot and MD.plot are the two main graphical functions to display, respectively an (X,Y) plot or (M,D) plot with the BLS or CBLS results. Assuming no proportional bias, the (M,D) plot (Band-Altman plot) may be simplified by calculating horizontal lines intervals with tolerance intervals (beta-expectation (type I) or beta-gamma content (type II)).
Generates bivariate residual plots with simulation polygons for any diagnostics and bivariate model from which functions to extract the desired diagnostics, simulate new data and refit the models are available.
Simulation of bivariate uniform data with a full range of correlations based on two beta densities and computation of the tetrachoric correlation (correlation of bivariate uniform data) from the phi coefficient (correlation of bivariate binary data) and vice versa.
Compute multivariate location, scale, and correlation
        estimates based on Tukey's biweight M-estimator.
Business days calculations based on a list of holidays and
    nonworking weekdays. Quite useful for fixed income and derivatives pricing.
Implementation of a statistical approach 
  for estimating the joint health effects of multiple 
  concurrent exposures.
Bayesian kernel projection classifier is a nonlinear multicategory classifier which performs the classification of the projections of the data to the principal axes of the feature space. A Gibbs sampler is implemented to find the posterior distributions of the parameters.
Performs prediction of a response function from simulated response values, allowing black-box optimization of functions estimated with some error. Includes a simple user interface for such applications, as well as more specialized functions designed to be called by the Migraine software (Rousset and Leblois, 2012 <doi:10.1093/molbev/MSR262>; Leblois et al., 2014 <doi:10.1093/molbev/msu212>; and see URL). The latter functions are used for prediction of likelihood surfaces and implied likelihood ratio confidence intervals, and for exploration of predictor space of the surface. Prediction of the response is based on ordinary Kriging (with residual error) of the input. Estimation of smoothing parameters is performed by generalized cross-validation.
Fast and accurate calculation of Blaker's binomial confidence limits (and some related stuff).
Bland-Altman Plots using either base graphics or ggplot2,
    augmented with confidence intervals, with detailed return values and
    a sunflowerplot option for data with ties.
Carries out Bland Altman analyses (also known as a Tukey
    mean-difference plot) as described by JM Bland and DG Altman in
    1986 <doi:10.1016/S0140-6736(86)90837-8>. This package was created in 
    2015 as existing Bland-Altman analysis functions did not calculate 
    confidence intervals. This package was created to rectify this, 
    and create reproducible plots.
Compose and send out responsive HTML email messages that render
    perfectly across a range of email clients and device sizes. Messages are
    composed using 'Markdown' and a text interpolation system that allows for
    the injection of evaluated R code within the message body, footer, and
    subject line. Helper functions let the user insert embedded images, web
    link buttons, and 'ggplot2' plot objects into the message body. Messages
    can be sent through an 'SMTP' server or through the 'Mailgun' API service
    <http://mailgun.com/>.
A wrapper around the 'Blat' command line SMTP mailer for Windows.
   'Blat' is public domain software, but be sure to read the license before use.
    It can be found at the Blat website http://www.blat.net.
An integrated set of tools to calculate and analyze Blau statuses quantifying social distance between individuals belonging to (covert) organizations. Relational (network) data can be incorporated for additional analyses. This project is supported by Defense Threat Reduction Agency (DTRA) Grant HDTRA-10-1-0043.
Fit a variety of Bayesian latent variable models, including confirmatory
   factor analysis, structural equation models, and latent growth curve models.
An implementation of the Black-Litterman Model and Atilio
    Meucci's copula opinion pooling framework.
A new link function that equals one specified link function up to a cutover then a linear rescaling of another specified link function. For use in glm() or glm2(). The intended use is in binary regression, in which case the first link should be set to "log" and the second to "logit". This ensures that fitted probabilities are between 0 and 1 and that exponentiated coefficients can be interpreted as relative risks for probabilities up to the cutoff.
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.
Package to perform a joint analysis of experiments with mixtures and random effects, assuming a process variable, represented by a covariate, Kalirajan K P (1990) <doi:10.1080/757582835>.
Function for drawing the coastline of the British Isles
An implementation of the model in Steorts (2015) <DOI:10.1214/15-BA965SI>, which performs Bayesian entity resolution for categorical and text data, for any distance function defined by the user. In addition, the precision and recall are in the package to allow one to compare to any other comparable method such as logistic regression, Bayesian additive regression trees (BART), or random forests. The experiments are reproducible and illustrated using a simple vignette.
Allows data to be processed by multiple machine learning algorithms
    at the same time, enables feature selection of data by single a algorithm or
    combinations of multiple. Easy to use tool for k-fold cross validation and
    nested cross validation.
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.
Data files and functions accompanying the book Korner-Nievergelt, Roth, von Felten, Guelat, Almasi, Korner-Nievergelt (2015) "Bayesian Data Analysis in Ecology using R, BUGS and Stan", Elsevier, New York.
Posterior distribution in the Black-Litterman model is computed from a prior distribution given in the form of a time series of asset returns and a continuous distribution of views provided by the user as an external function.
R's raw vector is useful for storing a single binary object.
    What if you want to put a vector of them in a data frame? The 'blob'
    package provides the blob object, a list of raw vectors, suitable for
    use as a column in data frame.
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 build on top of the 'STK++' 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.
An R implementation of an extension of the 'BayeScan' software (Foll, 2008) <DOI:10.1534/genetics.108.092221> for codominant markers, adding the option to group individual SNPs into pre-defined blocks. A typical application of this new approach is the identification of genomic regions, genes, or gene sets containing one or more SNPs that evolved under directional selection.
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
This is primarily meant as an implementation of Generalized blockmodeling for valued networks. In addition, measures of similarity or dissimilarity based on structural equivalence and regular equivalence (REGE algorithms) can be computed and partitioned matrices can be plotted.
Latent and Stochastic Block Model estimation by a Variational EM algorithm.
             Various probability distribution are provided (Bernoulli,
             Poisson...), with or without covariates.
Create randomizations for block random clinical trials.
        Can also produce a pdf file of randomization cards.
The 'blocksdesign' package constructs nested block and 
    D-optimal factorial designs for any unstructured or factorial
    treatment model of any size. The nested block designs can have repeated 
    nesting down to any required depth of nesting with either a simple set 
    of nested blocks or a crossed row-and-column blocks design at each level
    of nesting. The block design at each level of nesting is optimized for
    D-efficiency within the blocks of each preceding set of 
    blocks. Outputs include a table showing the allocation of 
    treatments to blocks, a plan layout showing the allocation of treatments
    within blocks (unstructured treatment designs only) the achieved
    D-efficiency factors for the block and treatment design 
    (factorial treatment designs only) and, where feasible,
    an A-efficiency factor and A-efficiency upper bound for the block design 
    (unstructured treatment designs only).
Segments a matrix in blocks with constant values.
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.
Write blog posts and web pages in R Markdown. This package supports
    the static site generator 'Hugo' (<https://gohugo.io>) best, and it also
    supports 'Jekyll' (<http://jekyllrb.com>) and 'Hexo' (<https://hexo.io>).
Provides the estimation algorithm to perform the demand estimation described in Berry, Levinsohn and Pakes (1995) <DOI:10.2307/2171802> . The routine uses analytic gradients and offers a large number of implemented integration methods and optimization routines.
Due to a limited availability of observed high-resolution precipitation records with adequate length, simulations with stochastic precipitation models are used to generate series for subsequent studies [e.g. Khaliq and Cunmae, 1996, <doi:10.1016/0022-1694(95)02894-3>, Vandenberghe et al., 2011, <doi:10.1029/2009WR008388>]. This package contains an R implementation of the original Bartlett-Lewis rectangular pulse model (BLRPM), developed by Rodriguez-Iturbe et al. (1987) <doi:10.1098/rspa.1987.0039>. It contains a function for simulating a precipitation time series based on storms and cells generated by the model with given or estimated model parameters. Additionally BLRPM parameters can be estimated from a given or simulated precipitation time series. The model simulations can be plotted in a three-layer plot including an overview of generated storms and cells by the model (which can also be plotted individually), a continuous step-function and a discrete precipitation time series at a chosen aggregation level.
Allows users to request data for one or multiple series through the
    U.S. Bureau of Labor Statistics API. Users provide parameters as specified in
    <https://www.bls.gov/developers/api_signature.htm> and the function returns a JSON
    string.
Scrapes various data from <https://www.bls.gov/>. The U.S. Bureau of Labor Statistics is the statistical branch of the United States Department of Labor. The package has additional functions to help parse, analyze and visualize the data.
Package for Bayesian model averaging and variable selection for linear models,
        generalized linear models and survival models (cox
        regression).
Toolkit for Bayesian estimation of the dependence structure
    in Multivariate Extreme Value parametric models.
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.
Provides a collection of functions for conducting meta-analyses under Bayesian context in R. The package includes functions for computing various effect size or outcome measures (e.g. odds ratios, mean difference and incidence rate ratio) for different types of data based on MCMC simulations. Users are allowed to fit fixed- and random-effects models with different priors to the data. Meta-regression can be carried out if effects of additional covariates are observed. Furthermore, the package provides functions for creating posterior distribution plots and forest plot to display main model output. Traceplots and some other diagnostic plots are also available for assessing model fit and performance.
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.
    Users need to enter a comparative data set, a phylogeny, and information on gene
    flow leading to hybrids. See Jhwueng and O'Meara (2017) 
    <DOI:10.1101/023986>.
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. Users need to enter a comparative data set, a phylogeny, and information on gene flow leading to hybrids.
These are miscellaneous functions for working with panel data, quantiles, and printing results.  For panel data, the package includes functions for making a panel data balanced (that is, dropping missing individuals that have missing observations in any time period), converting id numbers to row numbers, and to treat repeated cross sections as panel data under the assumption of rank invariance.  For quantiles, there are functions to make distribution functions from a set of data points (this is particularly useful when a distribution function is created in several steps) and to combine distribution functions based on some external weights; these distribution functions can easily be inverted to obtain quantiles.  Finally, there are several other miscellaneous functions for obtaining weighted means, weighted distribution functions, and weighted quantiles; to generate summary statistics and their differences for two groups; and to add or drop covariates from formulas.
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.
Provides statistical tools for Bayesian estimation for finite mixture of distributions, mainly mixture of Gamma, Normal and t-distributions. The package is implemented the recent improvements in Bayesian literature for the finite mixture of distributions, including Mohammadi and et al. (2013) <doi:10.1007/s00180-012-0323-3> and Mohammadi and Salehi-Rad (2012) <doi:10.1080/03610918.2011.588358>.
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
Easy estimation of Bayesian multilevel mediation models with Stan.
Counts occurrences of motifs in bipartite networks, as well as the number of
    times each node appears in each unique position within motifs. Intended for
    use in ecology, but its methods are general and can be applied to any
    bipartite network.
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.
Bundle methods for minimization of convex and non-convex risk
    under L1 or L2 regularization. Implements the algorithm proposed by Teo et
    al. (JMLR 2010) as well as the extension proposed by Do and Artieres (JMLR
    2012). The package comes with lot of loss functions for machine learning
    which make it powerful for big data analysis. The applications includes:
    structured prediction, linear SVM, multi-class SVM, f-beta optimization,
    ROC optimization, ordinal regression, quantile regression,
    epsilon insensitive regression, least mean square, logistic regression,
    least absolute deviation regression (see package examples), etc... all with
    L1 and L2 regularization.
Provides two Bayesian models for detecting the association between rare genetic variants and a trait that can be continuous, ordinal or binary. Bayesian latent variable collapsing model (BLVCM) detects interaction effect and is dedicated to twin design while it can also be applied to independent samples. Hierarchical Bayesian multiple regression model (HBMR) incorporates genotype uncertainty information and can be applied to either independent or family samples. Furthermore, it deals with continuous, binary and ordinal traits.
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.
Density, distribution, quantile function, random number generation for the BMT (Bezier-Montenegro-Torres) distribution. Torres-Jimenez C.J. and Montenegro-Diaz A.M. (2017) <arXiv:1709.05534>. Moments, descriptive measures and parameter conversion for different parameterizations of the BMT distribution. Fit of the BMT distribution to non-censored data by maximum likelihood, moment matching, quantile matching, maximum goodness-of-fit, also known as minimum distance, maximum product of spacing, also called maximum spacing, and minimum quantile distance, which can also be called maximum quantile goodness-of-fit. Fit of univariate distributions for non-censored data using maximum product of spacing estimation and minimum quantile distance estimation is also included.
Implements state-of-the art algorithms for learning discrete Bayesian network classifiers from data, including a number of those described in Bielza & Larranaga (2014) <doi:10.1145/2576868>, as well as functions for using these classifiers for prediction, assessing their predictive performance, and inspecting their properties.
Data generator based on Bayesian network model
Bayesian network structure learning, parameter learning and
  inference.
  This package implements constraint-based (PC, 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 discrete, Gaussian and conditional 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 <http://www.bnlearn.com>.
Perform Bayesian variable selection for high-dimensional nonlinear systems and also can be used to test nonlinearity for a general regression problem. The computation can be accelerated using multiple CPUs. You can refer to <doi:10.1080/01621459.2017.1409122> for more detail.
Implements a bootstrap aggregated (bagged) version of
    the k-nearest neighbors survival probability prediction method (Lowsky et
    al. 2013). In addition to the bootstrapping of training samples, the
    features can be subsampled in each baselearner to break the correlation
    between them. The Rcpp package is used to speed up the computation.
Implementation of Bayesian estimation in normal heteroscedastic nonlinear regression Models following Cepeda-Cuervo, (2001).
Bayesian nonparametric density estimation modeling mixtures by a Ferguson-Klass type algorithm for posterior normalized random measures.
Provides the Bayesian nonparametric inference for the causal effects of mediation. The function bnpmediation()
      gives the posterior means and credible intervals of the effects of mediation on the data. Full details: Kim, C., Daniels, M. J., Marcus, B. H. and Roy, J. A. (2017) <doi:10.1111/biom.12575>.
Bayesian nonparametric approach for clustering that is capable to combine different types of variables (continuous, ordinal and nominal) and also accommodates for different sampling probabilities in a complex survey design. The model is based on a location mixture model with a Poisson-Dirichlet process prior on the location parameters of the associated latent variables. The package performs the clustering model described in Carmona, C., Nieto-Barajas, L. E., Canale, A. (2016) <arXiv:1612.00083>.
Implements the Bayesian nonparametric monotonic regression
             method described in Bornkamp & Ickstadt (2009), Biometrics, 65, 198-205.
The Pritchard-Stephens-Donnelly (PSD) admixture model has k intermediate subpopulations from which n individuals draw their alleles dictated by their individual-specific admixture proportions.  The BN-PSD model additionally imposes the Balding-Nichols (BN) allele frequency model to the intermediate populations, which therefore evolved independently from a common ancestral population T with subpopulation-specific FST (Wright's fixation index) parameters.  The BN-PSD model can be used to yield complex population structures.  Method described in Ochoa and Storey (2016) <doi:10.1101/083923>.
Performs the algorithm for time series clustering described in Nieto-Barajas and Contreras-Cristan (2014).
From a given data frame, this package learns its Bayesian network structure based on a selected score.
Markov chain Monte Carlo algorithms for non- and semi-parametric models: 1. Dirichlet process mixtures & 2. spike-slab for variable selection in mean/variance regression models.
Allows spatial implementation of Bayesian networks and mapping in geographical space. It makes maps of expected value (or most likely state) given known and unknown conditions, maps of uncertainty measured as coefficient of variation or Shannon index (entropy), maps of probability associated to any states of any node of the network. Some additional features are provided as well: parallel processing options, data discretization routines and function wrappers designed for users with minimal knowledge of the R language. Outputs can be exported to any common GIS format. Development was funded by the European Union FP7 (2007-2013), under project ROBIN (<http://robinproject.info>).
Bayesian Network Structure Learning from Data with Missing Values.
    The package implements the Silander-Myllymaki complete search,
    the Max-Min Parents-and-Children, the Hill-Climbing, the
    Max-Min Hill-climbing heuristic searches, and the Structural
    Expectation-Maximization algorithm. Available scoring functions are
    BDeu, AIC, BIC. The package also implements methods for generating and using
    bootstrap samples, imputed data, inference.
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.
Tools for constructing board/grid based games, as well as readily available game(s) for your entertainment.
An overlap clustering algorithm for categorical ultra-dimension data.
This data package contains a subset of the Bodenmiller et al, Nat Biotech 2012 dataset for testing single cell, high dimensional analysis and visualization methods.
An implementation of three statistical tests for identification of COG (Cluster of Orthologous Groups) that are over represented among genes that show differential expression under conditions. It also provides tabular and graphical summaries of the results for easy visualisation and presentation.
Generic Extraction of main text content from HTML files; removal
    of ads, sidebars and headers using the boilerpipe 
    (http://code.google.com/p/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). It can be
    used to design both single-agent and drug-combination trials. 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 an average
    performance that is comparable to that of the continual reassessment method
    (CRM, one of the best model-based designs) 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 (<http://www.boldsystems.org/>) 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. (2017), John Wiley & Sons ISBN 978-1-118-09156-2.
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
Provides probability computation, data generation,
    and model estimation for fully-visible Boltzmann machines. It follows the methods
    described in Nguyen and Wood (2016a) <doi:10.1162/NECO_a_00813> 
    and Nguyen and Wood (2016b) <doi:10.1109/TNNLS.2015.2425898>.
Provides functions to interface with Australian Government Bureau
    of Meteorology (BOM) data, fetching data and returning a tidy data frame of
    précis forecasts, current weather data from stations, agriculture
    bulletin data, BOM 0900 or 1500 weather bulletins or a raster stack object
    of satellite imagery from GeoTIFF files. Data (c) Australian Government
    Bureau of Meteorology Creative Commons (CC) Attribution 3.0 licence or
    Public Access Licence (PAL) as appropriate. See
    <http://www.bom.gov.au/other/copyright.shtml> for further details.
An improved multiple testing procedure for controlling false discovery rates which is developed based on the Bonferroni procedure with integrated estimates from the Benjamini-Hochberg procedure and the Storey's q-value procedure. It controls false discovery rates through controlling the expected number of false discoveries.
Output formats and utilities for authoring books and technical documents with R Markdown.
A collection and selector of R 'bookdown' templates. 'bookdownplus' helps you write academic journal articles, guitar books, chemical equations, mails, calendars, and diaries. R 'bookdownplus' extends the features of 'bookdown', and simplifies the procedure. Users only have to choose a template, clarify the book title and author name, and then focus on writing the text. No need to struggle in 'YAML' and 'LaTeX'.
This package implements a
    partial-observability procedure for testing Boolean
    hypotheses that generalizes the binary response GLM as
    outlined in Braumoeller (2003).
Tools for optimal and approximate state estimation as well as
    network inference of Partially-Observed Boolean Dynamical Systems.
Provides methods to reconstruct and generate synchronous,
        asynchronous, probabilistic and temporal 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).
Implements Friedman's gradient descent boosting algorithm for longitudinal data using multivariate tree base learners.  A time-covariate interaction effect is modeled using penalized B-splines (P-splines) with estimated adaptive smoothing parameter.
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 Application" by A. C. Davison and 
  D. V. Hinkley (1997, CUP), originally written by Angelo Canty for S.
Implementation of the bootstrapping approach for the estimation of clustering stability on observation and cluster level, as well as its application in estimating the number of clusters.
Calculate robust measures of effect sizes using the bootstrap.
Computes appropriate confidence intervals for the likelihood ratio tests commonly used in medicine/epidemiology.  It is particularly useful when the sensitivity or specificity in the sample is 100%.  Note that this does not perform the test on nested models--for that, see 'epicalc::lrtest'.
Selection of informative features like genes, transcripts, RNA seq, etc. using Bootstrap Maximum Relevance and Minimum Redundancy technique from a given high dimensional genomic dataset. Informative gene selection involves identification of relevant genes and removal of redundant genes as much as possible from a large gene space. Main applications in high-dimensional expression data analysis (e.g. microarray data, NGS expression data and other genomics and proteomics applications).
Bootstrap methods to assess accuracy and stability of estimated network structures
              and centrality indices. Allows for flexible specification of any undirected network 
              estimation procedure in R, and offers default sets for 'qgraph', 'IsingFit', 'IsingSampler',
              'glasso', 'huge' and 'parcor' packages.
Bias-Corrected Forecasting and Bootstrap Prediction Intervals for Autoregressive Time Series
Calculation of Bootstrapped Response and Correlation
        Functions for Use in Dendroclimatology
We provide 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
Applicable to any classification problem with more than 2 classes. It relies on bootstrap subsamplings of sPLS-DA and provides tools to select the most stable variables (defined as the ones consistently selected over the bootstrap subsamplings) and to predict the class of test samples.
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
    <http://arxiv.org/abs/1405.0922>. For data matrices that are too large to operate
    on in memory, users can input objects with class 'ff' (see the 'ff'
    package), where the actual data is stored on disk. In response, this
    package will implement a block matrix algebra procedure for calculating the
    principal components (PCs) and bootstrap PCs. Depending on options set by
    the user, the 'parallel' package can be used to parallelize the calculation of
    the bootstrap PCs.
Used to create bootstrap tolerance levels for the Kolmogorov-Smirnov (KS) statistic, the area under receiver operator characteristic curve (AUROC) statistic, and the Gini coefficient for each score cutoff. Also provides a bootstrap alternative to the Vasicek test.
Main objective of a predictive model is to provide accurated predictions of a new observations. 
             Unfortunately we don't know how well the model performs. In addition, at the current era of omic
             data where p >> n, is not reasonable applying internal validation using data-splitting. Under this 
             background a good method to assessing model performance is applying internal bootstrap validation 
             (Harrell Jr, Frank E (2015) <doi:10.1007/978-1-4757-3462-1>.) This package provides bootstrap validation
             for the linear and logistic 'glmnet' models.
Provides significance tests for second-order stationarity
	for time series using bootstrap wavelet packet tests.
Bayesian approaches for analyzing multivariate data in ecology. Estimation is performed using Markov Chain Monte Carlo (MCMC) methods via Three. JAGS types of models may be fitted: 1) With explanatory variables only, boral fits independent column Generalized Linear Models (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.
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 (shadows).
Classical Boson Sampling using the algorithm of
 Clifford and Clifford (2017) <arXiv:1706.01260>. Also provides functions for
 generating random unitary matrices, evaluation of matrix permanents (both
 real and complex) and evaluation of complex permanent minors.
Reads and plots phylogenetic placements obtained using the 'epa', 'pplacer' and 'guppy' softwares.
Contains functions to convert binary (presence-absence) expert species range maps (like those found in a field guide) into continuous surfaces based on distance to range edge.  These maps can then be used in species distribution models such as Maximum Entropy (Phillips 2008 <DOI:10.1111/j.0906-7590.2008.5203.x>) using additional information (such as point occurrence data) to refine the expert map.
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.
Contains several plotting functions such as barplots, scatterplots, heatmaps, as well as functions to combine plots and assist in the creation of these plots. These functions will give users great ease of use and customization options in broad use for biomedical applications, as well as general purpose plotting. Each of the functions also provides valid default settings to make plotting data more efficient and producing high quality plots with standard colour schemes simpler. All functions within this package are capable of producing plots that are of the quality to be presented in scientific publications and journals.
Response transformations for overdispersed generalized linear
    models and variance component models using nonparametric profile maximum
    likelihood estimation. The main function is optim.boxcox().
Download daily box office information (how much each movie earned
    in theaters) using data from either Box Office Mojo (<http://www.boxofficemojo.com/>) or
   The Numbers (<http://www.the-numbers.com/>).
Correlation chart of two set (x and y) of data.  
  Using Quartiles with boxplot style. 
  Visualize the effect of factor.
An R interface for the remote file hosting service 'Box' 
    (<https://www.box.com/>). In addition to uploading and downloading files,
    this package includes functions which mirror base R operations for local 
    files, (e.g. box_load(), box_save(), box_read(), box_setwd(), etc.), as well
    as 'git' style functions for entire directories (e.g. box_fetch(), 
    box_push()).
Run basic pattern analyses on character sets, digits, or combined
    input containing both characters and numeric digits. Useful for data
    cleaning and for identifying columns containing multiple or nonstandard
    formats.
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. Includes mid-p options.
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.
Model-based clustering for phylogeographic data comprising mtDNA sequences and geographical locations along with optional environmental characteristics, aiming to identify migration events that led to homogeneous population clusters.
Nonparametric multivariate kernel density \
             estimation using a back-projected kernel.
Fitting Bayesian multiple and mixed-effect regression models for 
    circular data based on the projected normal distribution. Both continuous 
    and categorical predictors can be included. Sampling from the posterior is 
    performed via an MCMC algorithm. Posterior descriptives of all parameters, 
    model fit statistics and Bayes factors for hypothesis tests for inequality 
    constrained hypotheses are provided. See Cremers, Mulder & Klugkist (2018) 
    <doi:10.1111/bmsp.12108> and Nuñez-Antonio & Guttiérez-Peña (2014) 
    <doi:10.1016/j.csda.2012.07.025>.
Implements functions to update Bayesian Predictive Power Computations after not stopping a clinical trial at an interim analysis. Such an interim analysis can either be blinded or unblinded. Code is provided for Normally distributed endpoints with known variance, with a prominent example being the hazard ratio.
QTL mapping toolkit for inbred crosses and recombinant
        inbred lines. Includes maximum likelihood and Bayesian tools.
Specify and fit the Bradley-Terry model, including structured versions in which the parameters are related to explanatory variables through a linear predictor and versions with contest-specific effects, such as a home advantage.
Facilities are provided for fitting the simple, unstructured Bradley-Terry model to networks of binary comparisons. The implemented methods are designed to scale well to large, potentially sparse, networks. A fairly high degree of scalability is achieved through the use of EM and MM algorithms, which are relatively undemanding in terms of memory usage (relative to some other commonly used methods such as iterative weighted least squares, for example). Both maximum likelihood and Bayesian MAP estimation methods are implemented. The package provides various standard methods for a newly defined 'btfit' model class, such as the extraction and summarisation of model parameters and the simulation of new datasets from a fitted model. Tools are also provided for reshaping data into the newly defined "btdata" class, and for analysing the comparison network, prior to fitting the Bradley-Terry model. This package complements, rather than replaces, the existing 'BradleyTerry2' package. (BradleyTerry2 has rather different aims, which are mainly the specification and fitting of "structured" Bradley-Terry models in which the strength parameters depend on covariates.)
Provides functions to generate, format, and style surface plots for visualizing combined action data.  Also provides functions for reporting on a BRAID analysis, including plotting curve-shifts, calculating IAE values, and producing full BRAID analysis reports.
Contains functions for evaluating, analyzing, and fitting combined action dose response surfaces with the Bivariate Response to Additive Interacting Dose (BRAID) model of combined action.
Blind users do not have access to the graphical output from R
    without printing the content of graphics 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 the text descriptions of graphs, convenience functions
    to replace the functionality offered in many GUI front ends, and experimental
    functionality for optimising graphical content to prepare it for embossing as
    tactile images.
A set of tools for performing graph theory analysis of brain MRI
    data. It works with data from a Freesurfer analysis (cortical thickness,
    volumes, local gyrification index, surface area), diffusion tensor
    tractography data (e.g., from FSL) and resting-state fMRI data (e.g., from
    DPABI). It contains a graphical user interface for graph visualization and
    data exploration, along with several functions for generating useful
    figures.
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.
Simulation and parameter estimation of multitype Bienayme - Galton - Watson processes.
Tests the parallel regression assumption for ordinal logit models generated with the function polr() from the package 'MASS'.
Test the robustness of a user's Qualitative Comparative Analysis
    solutions to randomness, using the bootstrapped assessment: baQCA(). This
    package also includes a function that provides recommendations for improving
    solutions to reach typical significance levels: brQCA(). After applying recommendations from brQCA(), QCAdiff() shows which cases are excluded from the final result.
Obtain Brazilian map spatial objects of varying
    region types (e.g. cities, states, microregions, mesoregions).
    Convenience functions for plotting choropleths and working with
    the geographic codes are also provided.
A function to produce MCMC samples for posterior inference in semiparametric Bayesian discrete time competing risks recurrent events models.
Estimates geometry of SICM pipette tips by fitting a physical model to recorded breakage-current data.
Species richness estimation is an important problem in biodiversity analysis. This package provides methods for total species richness estimation (observed plus unobserved) and a method for modelling total diversity with covariates. breakaway() estimates total (observed plus unobserved) species richness. 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. breakaway_nof1() implements a similar procedure however does not require a singleton count. betta() provides a method for modelling total diversity with covariates in a way that accounts for its estimated nature and thus accounts for unobserved taxa, and betta_random() permits random effects modelling.
Break Down Plots are inspired by waterfall plots created by 'xgboostExplainer' package
    (see <https://github.com/AppliedDataSciencePartners/xgboostExplainer>). 
    The idea behind Break Down Plots it to decompose model prediction for a single observation. 
    Break Down Plots show the contribution of every variable present in the model. 
    Such plots will work for binary classifiers and general regression models.
The breakfast package performs multiple change-point detection in data
    sequences, or sequence segmentation, using computationally efficient multiscale
    methods. This version of the package implements the "Tail-Greedy Unbalanced Haar",
    "Wild Binary Segmentation" and "Adaptive Wild Binary Segmentation" change-point
    detection and segmentation methodologies. To start with, see the function
    segment.mean.
Implements the Cross-Entropy (CE) method, which is a model based stochastic optimization technique to estimate both the number and their corresponding locations of break-points in continuous and discrete measurements (Priyadarshana and Sofronov (2015), Priyadarshana and Sofronov (2012a), Priyadarshana and Sofronov (2012b)).
Reads several formats of 13C data (IRIS/Wagner, BreathID) and CSV.
        Creates artificial sample data for testing. 
        Fits Maes/Ghoos, Bluck-Coward self-correcting formula using 'nls', 'nlme'.
        See Bluck L J C and Coward W A 2006 <doi:10.1088/0967-3334/27/3/006>.
        Methods to 
        fit breath test curves with Bayesian Stan methods are refactored to 
        package 'breathteststan'. For a Shiny GUI, see 
        package 'dmenne/breathtestshiny' on github.
Stan-based curve-fitting function
  for use with package 'breathtestcore' by the same author.
  Stan functions are refactored here for easier testing.
Users can simulate their planned breeding schemes by using functions that do standard breeding tasks. Yabe, Iwata, & Jannink (2017) <doi:10.2135/cropsci2016.06.0538>.
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.
Estimation and inference from generalized linear models based on various methods for bias reduction. The 'brglmFit' fitting method can achieve reduction of estimation bias by solving either the mean bias-reducing adjusted score equations in Firth (1993) <doi:10.1093/biomet/80.1.27> and Kosmidis and Firth (2009) <doi:10.1093/biomet/asp055>, or the median bias-reduction adjusted score equations in Kenne et al. (2016) <arXiv:1604.04768>, or through the direct subtraction of an estimate of the bias of the maximum likelihood estimator from the maximum likelihood estimates as in Cordeiro and McCullagh (1991) <http://www.jstor.org/stable/2345592>. Estimation in all cases takes place via a quasi Fisher scoring algorithm, and S3 methods for the construction of of confidence intervals for the reduced-bias estimates are provided. In the special case of generalized linear models for binomial and multinomial responses, the adjusted score approaches return estimates with improved frequentist properties, that are also always finite, even in cases where the maximum likelihood estimates are infinite (e.g. complete and quasi-complete separation). 'brglm2' also provides pre-fit and post-fit methods for detecting separation and infinite maximum likelihood estimates in binomial response generalized linear models.
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.
An implementation of the bridge distribution with logit-link in
    R. In Wang and Louis (2003) <DOI:10.1093/biomet/90.4.765>, such a univariate
    bridge distribution was derived as the distribution of the random intercept that
    'bridged' a marginal logistic regression and a conditional logistic regression.
    The conditional and marginal regression coefficients are a scalar multiple
    of each other. Such is not the case if the random intercept distribution was
    Gaussian.
BRIC-seq is a genome-wide approach for determining RNA stability in mammalian cells.
    This package provides a series of functions for performing quality check of your BRIC-seq data,
    calculation of RNA half-life for each transcript and comparison of RNA half-lives between two conditions.
Provides functions for estimating marginal likelihoods, Bayes
    factors, posterior model probabilities, and normalizing constants in general,
    via different versions of bridge sampling (Meng & Wong, 1996, 
    <http://www3.stat.sinica.edu.tw/statistica/j6n4/j6n43/j6n43.htm>).
A spatio-temporal exposure-hazard model for assessing biological
    risk and impact. The model is based on stochastic geometry for describing
    the landscape and the exposed individuals, a dispersal kernel for the
    dissemination of contaminants and an ecotoxicological equation.
Provides two main functions, il() and fil(). The il() function implements the EM algorithm developed by Ibrahim and Lipsitz (1996) <DOI:10.2307/2533068> to estimate the parameters of a logistic regression model with the missing response when the missing data mechanism is nonignorable. The fil() function implements the algorithm proposed by Maity et. al. (2017+) <https://github.com/arnabkrmaity/brlrmr> to reduce the bias produced by the method of Ibrahim and Lipsitz (1996) <DOI:10.2307/2533068>.
Fits novel models for the conditional relative risk, risk difference and odds ratio.
Fit Bayesian generalized (non-)linear multivariate multilevel models
    using 'Stan' for full Bayesian inference. A wide range of distributions 
    and link functions are supported, allowing users to fit -- among others -- 
    linear, robust linear, count data, survival, response times, ordinal, 
    zero-inflated, hurdle, and even self-defined mixture models all in a 
    multilevel context. Further modeling options include non-linear and 
    smooth terms, auto-correlation structures, censored data, meta-analytic 
    standard errors, and quite a few more. In addition, all parameters of the 
    response distribution can be predicted in order to perform distributional 
    regression. Prior specifications are flexible and explicitly encourage 
    users to apply prior distributions that actually reflect their beliefs.
    Model fit can easily be assessed and compared with posterior predictive 
    checks and leave-one-out cross-validation. References: Bürkner (2017)
    <doi:10.18637/jss.v080.i01>; Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>.
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.
Miscellaneous R functions, including functions related to
    graphics (mostly for base graphics), permutation tests, running
    mean/median, and general utilities.
Convert statistical analysis objects from R into tidy data frames,
    so that they can more easily be combined, reshaped and otherwise processed
    with tools like 'dplyr', 'tidyr' and 'ggplot2'. The package provides three
    S3 generics: tidy, which summarizes a model's statistical findings such as
    coefficients of a regression; augment, which adds columns to the original
    data such as predictions, residuals and cluster assignments; and glance, which
    provides a one-row summary of model-level statistics.
A lossless compressed data format that uses a combination of the
    LZ77 algorithm and Huffman coding. Brotli is similar in speed to deflate (gzip)
    but offers more dense compression.
An R interface for the Brown Dog which allows researchers to leverage Brown Dog Services 
    that provides modules to identify the conversion options for a file, to convert file to appropriate
    format, or to extract data from a file. See <http://browndog.ncsa.illinois.edu/> for more information.
Bayesian estimation and variable selection for quantile
 regression models.
Implementation of the Bayesian inference for the two independent Poisson samples model, using the semi-conjugate family of prior distributions.
Includes methods for fetching 'phylogenies' from a variety
    of sources, currently includes 'Phylomatic'
    (<http://phylodiversity.net/phylomatic>), with more in the future.
Analyses of large-scale -omics datasets commonly use p-values as the indicators of statistical significance. However, considering p-value alone neglects the importance of effect size (i.e., the mean difference between groups) in determining the biological relevance of a significant difference. Here, we present a novel algorithm for computing a new statistic, the biological relevance testing (BRT) index, in the frequentist hypothesis testing framework to address this problem.
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.
Inter-sample condition variability is a key challenge of normalising ChIP-seq data. This implementation uses either spike-in or a second factor as a control for normalisation. Input can either be from 'DiffBind' or a matrix formatted for 'DESeq2'. The output is either a 'DiffBind' object or the default 'DESeq2' output. Either can then be processed as normal. Supporting manuscript Guertin, Markowetz and Holding (2017) <doi:10.1101/182261>.
Collection of functions, data sets and code examples 
 for evaluations of field trials with the objective of equivalence assessment.
Tools to fit Bayesian state-space models to animal tracking data. Models are provided for location 
    filtering, location filtering and behavioural state estimation, and their hierarchical versions. 
    The models are primarily intended for fitting to ARGOS satellite tracking data but options exist to fit 
    to other tracking data types. For Global Positioning System data, consider the 'moveHMM' package. 
    Simplified Markov Chain Monte Carlo convergence diagnostic plotting is provided but users are encouraged 
    to explore tools available in packages such as 'coda' and 'boa'.
Contains functions to perform Bayesian inference
    using a spectral analysis of Gaussian process priors.
    Gaussian processes are represented with a Fourier series 
    based on cosine basis functions. Currently the package
    includes parametric linear models, partial linear additive
    models with/without shape restrictions, generalized linear
    additive models with/without shape restrictions, and  
    density estimation model. To maximize computational 
    efficiency, the actual Markov chain Monte Carlo sampling 
    for each model is done using codes written in FORTRAN 90.
    This software has been developed using funding supported by
    Basic Science Research Program through the National Research
    Foundation of Korea (NRF) funded by the Ministry of Education
    (no. NRF-2016R1D1A1B03932178 and no. NRF-2017R1D1A3B03035235).
Data sets for book "Basic Statistics and Data Analysis" by
    Larry J. Kitchens.
Exposes the binary search functions of the C++ standard library (std::lower_bound, std::upper_bound) plus other convenience functions, allowing faster lookups on sorted vectors.
The integration of Bayesian variable and sparse group variable selection approaches for regression models.
Bayesian survival model using Weibull regression on both scale and shape parameters. Dependence of shape parameter on covariates permits deviation from proportional-hazard assumption, leading to dynamic - i.e. non-constant with time - hazard ratios between subjects. Bayesian Lasso shrinkage in the form of two Laplace priors - one for scale and one for shape coefficients - allows for many covariates to be included. Cross-validation helper functions can be used to tune the shrinkage parameters. Monte Carlo Markov Chain (MCMC) sampling using a Gibbs wrapper around Radford Neal's univariate slice sampler (R package MfUSampler) is used for coefficient estimation.
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.
Bayesian synthetic likelihood (BSL, Price et al. (2018) <doi:10.1080/10618600.2017.1302882>)
 is an alternative to standard, non-parametric approximate Bayesian computation (ABC). BSL assumes a
 multivariate normal distribution for the summary statistic likelihood and it is suitable when
 the distribution of the model summary statistics is sufficiently regular. This package provides
 a Metropolis Hastings Markov chain Monte Carlo implementation of BSL and
 BSL with graphical lasso (BSLasso, An et al. (2018) <https://eprints.qut.edu.au/102263/>),
 which is computationally more efficient when the dimension of the summary statistic is high.
 Extensions to this package are planned.
Bayes screening and model discrimination follow-up designs.
The purpose of this package is to fit the three Spatial Econometric Models proposed
  in Anselin (1988, ISBN:9024737354) in the homoscedastic and the heteroscedatic case. The fit is made through MCMC algorithms
  and observational working variables approach.
Bayesian inference on the (discrete) power spectrum of time series.
Implementation of a Metropolis-within-Gibbs MCMC algorithm to flexibly estimate the spectral density of a stationary time series.  The algorithm updates a nonparametric B-spline prior using the Whittle likelihood to produce pseudo-posterior samples and is based on the work presented in Edwards, M.C., Meyer, R. and Christensen, N., Statistics and Computing (2018). <doi.org/10.1007/s11222-017-9796-9>.
The Bootstrap framework lets you add some JavaScript functionality to your web site by
  adding attributes to your HTML tags - Bootstrap takes care of the JavaScript
  <https://getbootstrap.com/javascript>. If you are using R Markdown or Shiny, you can
  use these functions to create collapsible sections, accordion panels, modals, tooltips,
  popovers, and an accordion sidebar framework (not described at Bootstrap site).
Some functions for nonparametric and semiparametric
        Bayesian models for random effects meta-analysis
This package models the quantile process as a function of
        predictors.
Functions to compute the asymptotic covariance matrices of mixing and unmixing matrix estimates of the following blind source separation (BSS) methods: symmetric and squared symmetric FastICA, regular and adaptive deflation-based FastICA, FOBI, JADE, AMUSE and deflation-based and symmetric SOBI. Also functions to estimate these covariances based on data are available. For details, see Miettinen et al. (2015) <doi:10.1214/15-STS520>, Miettinen et al. (2016) <doi:10.1111/jtsa.12159>, Miettinen et al. (2017) <doi:10.1016/j.sigpro.2016.08.028>, Miettinen et al. (2017) <doi:10.18637/jss.v076.i02>, and references therein.
Efficient methods for Bayesian inference of state space models 
    via particle Markov chain Monte Carlo and parallel importance sampling type weighted 
    Markov chain Monte Carlo (Vihola, Helske, and Franks, 2017, <arXiv:1609.02541>). 
    Gaussian, Poisson, binomial, or negative binomial 
    observation densities and basic stochastic volatility models with Gaussian state 
    dynamics, as well as general non-linear Gaussian models and discretised diffusion models 
    are supported.
It provides the density, distribution function, quantile function, random number generator, reliability function, failure rate, likelihood function,
             moments and EM algorithm for Maximum Likelihood estimators, also empirical quantile and generated envelope for a given sample, all this for the three parameter
             Birnbaum-Saunders model based on Skew-Normal Distribution.
             Additionally, it provides the random number generator for the mixture of Birnbaum-Saunders model based on Skew-Normal distribution.
Functional gradient descent algorithm for a variety of convex and non-convex loss functions, for both classical and robust regression and classification problems.
Time series regression using dynamic linear models fit using
  MCMC. See Scott and Varian (2014) <DOI:10.1504/IJMMNO.2014.059942>, among many
  other sources.
Kernel density estimation dedicated to urban geography.
Temporal Exponential Random Graph Models (TERGM) estimated by maximum pseudolikelihood with bootstrapped confidence intervals or Markov Chain Monte Carlo maximum likelihood. Goodness of fit assessment for ERGMs, TERGMs, and SAOMs. Micro-level interpretation of ERGMs and TERGMs.
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.
Performs 'BTLLasso' (Schauberger and Tutz, 2017: Subject-Specific Modelling of Paired Comparison Data - a Lasso-Type Penalty Approach), a method to include different types of variables in paired
    comparison models and, therefore, to allow for heterogeneity between subjects. Variables can be subject-specific, object-specific and subject-object-specific and
    can have an influence on the attractiveness/strength of the objects. Suitable L1 penalty terms are used 
    to cluster certain effects and to reduce the complexity of the models.
Tools for inferring asynchronous Boolean
    models from single-cell expression data.
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.
Provides advanced statistical methods to describe and predict customers'
  purchase behavior in a non-contractual setting. It uses historic transaction records to fit a
  probabilistic model, which then allows to compute quantities of managerial interest on a cohort-
  as well as on a customer level (Customer Lifetime Value, Customer Equity, P(alive), etc.). This
  package complements the BTYD package by providing several additional buy-till-you-die models, that
  have been published in the marketing literature, but whose implementation are complex and non-trivial.
  These models are: NBD, MBG/NBD, BG/CNBD-k, MBG/CNBD-k, Pareto/NBD (HB), Pareto/NBD (Abe) and Pareto/GGG.
Provides functions for various statistical techniques commonly used in the social sciences, including functions to compute clustered robust standard errors, combine results across multiply-imputed data sets, and simplify the addition of robust and clustered robust standard errors. The package was originally developed, in part, to assist porting of replication code from 'Stata' and attempts to replicate default options from 'Stata' where possible.
Implements a method of correcting for publication bias and
    uncertainty when planning sample sizes in a future study from an original study. See Anderson, Kelley, & Maxwell (submitted, revised and resubmitted).
Buckley-James regression for right-censoring survival data with high-dimensional covariates. Implementations for survival data include boosting with componentwise linear least squares, componentwise smoothing splines, regression trees and MARS. Other high-dimensional tools include penalized regression for survival data.
Analyze bullet lands using nonparametric methods. We provide a
    reading routine for x3p files (see <http://www.openfmc.org> for more
    information) and a host of analysis functions designed to assess the
    probability that two bullets were fired from the same gun barrel.
Helps in initial processing like converting text to lower case, removing punctuation, numbers, stop words, stemming, sparsity control and term frequency inverse document frequency processing. Helps in recognizing domain or corpus specific stop words. Makes use of 'ldatunig' output to pick optimal number of topics for topic modelling. Helps in topic modelling the content.
The bullwhipgame is an educational game that has as purpose the illustration and exploration of the bullwhip effect,i.e, the increase in demand variability along the supply chain. Marchena Marlene (2010) <arXiv:1009.3977>.
View and analyze data where bunching is expected. Estimate counter-
    factual distributions. For earnings data, estimate the compensated
    elasticity of earnings w.r.t. the net-of-tax rate.
All final tables of Germany's highest football (soccer!) league, the Bundesliga. Contains data from 1964 to 2016.
Comprehensive Business Process Analysis toolkit. Creates S3-class for event log objects, and related handler functions. Imports related packages for filtering event data, computation of descriptive statistics, handling of 'Petri Net' objects and visualization of process maps. See also packages 'edeaR','processmapR', 'eventdataR' and 'processmonitR'.
Basic tools to analyze forest fire history data (e.g. FHX) in R.
A suite of functions for finance, including the estimation
	of variance matrices via a statistical factor model or
	Ledoit-Wolf shrinkage.
Script search, corner, genetic optimization, permutation tests, write expect test.
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.
Calculates business duration between two dates. This excluding weekends, public holidays and non-business hours.
Implementation of the Generalized Pairwise Comparisons. This test
    enables to compare two groups of observations in randomized trials(e.g treated
    vs. control patients) on several prioritized outcomes. Pairwise comparisons
    require consideration of all possible pairs of individuals, one taken from the
    treatment group and the other taken from the control group. The outcomes of the
    two individuals forming a pair are compared. Thresholds of minimal clinically
    significant differences can be defined. It is possible to analyse simultaneously
    several outcomes by prioritizing the variables that capture them. The highest
    priority is assigned to the variable considered the most clinically relevant.
    A natural way of handling uninformative or neutral pairs is to consider the
    outcomes in descending order of priority: whenever a pair is uninformative or
    neutral for an outcome of higher priority, the outcomes of lower priority are
    examined In the case of time-to-event endpoint, four methods to handle censored
    observations are available in this package (Gehan, Peto, Efron, and Peron).
R/C++ implementation of the model proposed by Primiceri ("Time Varying Structural Vector Autoregressions and Monetary Policy", Review of Economic Studies, 2005), with functionality for computing posterior predictive distributions and impulse responses.
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') and differential algebraic equations ('DAE').
             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.
Variable/Feature selection in high or ultra-high dimensional
    settings has gained a lot of attention recently specially in cancer genomic
    studies. This package provides a Bayesian approach to tackle this problem,
    where it exploits mixture of point masses at zero and nonlocal priors to
    improve the performance of variable selection and coefficient estimation.
    product moment (pMOM) and product inverse moment (piMOM) nonlocal priors
    are implemented and can be used for the analyses. This package performs
    variable selection for binary response and survival time response datasets
    which are widely used in biostatistic and bioinformatics community.
    Benefiting from parallel computing ability, it reports necessary outcomes
    of Bayesian variable selection such as Highest Posterior Probability Model
    (HPPM), Median Probability Model (MPM) and posterior inclusion probability
    for each of the covariates in the model. The option to use Bayesian Model
    Averaging (BMA) is also part of this package that can be exploited for
    predictive power measurements in real datasets.
Whole-genome regression methods on Bayesian framework fitted via EM
    or Gibbs sampling, with optional sampling techniques and kernel term.
Performs the 'Baumgartner-Weiss-Schindler' two-sample test of equal
   probability distributions.
Shows statistics about bytes contained in a file 
  as a circle graph of deviations from mean in sigma increments. 
  The function can be useful for statistically analyze the content of files 
  in a glimpse: text files are shown as a green centered crown, compressed 
  and encrypted files should be shown as equally distributed variations with 
  a very low CV (sigma/mean), and other types of files can be classified between 
  these two categories depending on their text vs binary content, which can be 
  useful to quickly determine how information is stored inside them (databases, 
  multimedia files, etc).
c060 provides additional functions to perform stability selection, model validation and parameter tuning for glmnet models
Methods for detecting safety signals in clinical trials using groupings of adverse events by body-system or system organ class.The package title c212 is in reference to the original Engineering and Physical Sciences Research Council (UK) funded project which was named CASE 2/12.
Compare two classifications or clustering solutions that may or may
    not have the same number of classes, and that might have hard or soft
    (fuzzy, probabilistic) membership. Calculate various metrics to assess how
    the clusters compare to each other. The calculations are simple, but provide
    a handy tool for users unfamiliar with matrix multiplication. This package
    is not geared towards traditional accuracy assessment for classification/
    mapping applications - the motivating use case is for comparing a
    probabilistic clustering solution to a set of reference or existing class
    labels that could have any number of classes (that is, without having to
    degrade the probabilistic clustering to hard classes).
This package allows inferring gene regulatory networks
        with direct physical interactions from microarray expression
        data using C3NET.
Getting insight into a forest of classification trees, by calculating similarities
  between the trees, and subsequently clustering them. Each cluster is represented by it's most
  central cluster member. Sies, A & Van Mechelen, I. (paper submitted for publication).
C5.0 decision trees and rule-based models for pattern recognition that extend the work of Quinlan (1993, ISBN:1-55860-238-0).
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 indices under Item Response Theory. Implements the total score IRT-based methods in Lee, Hanson & Brennen (2002) and Lee (2010), the IRT-based methods in Rudner (2001, 2005), and the total score nonparametric methods in Lathrop & Cheng (2014). For dichotomous and polytomous tests.
Parameters of a user-specified probability distribution are modelled by a multi-layer perceptron artificial neural network. This framework can be used to implement probabilistic nonlinear models including mixture density networks, heteroscedastic regression models, zero-inflated models, etc. following Cannon (2012) <doi:10.1016/j.cageo.2011.08.023>.
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 the
        procedure illustrated in Lupi (2009).
Using Java GUI for R (JGR), CADStat provides a user
    interface for several statistical methods -
    scatterplot, boxplot, linear regression, generalized linear
    regression, quantile regression, conditional probability
    calculations, and regression trees.
Encrypts and decrypts strings using either the Caesar cipher or a
    pseudorandom number generation (using set.seed()) method.
Simulate plasma caffeine concentrations using population pharmacokinetic model described in Lee, Kim, Perera, McLachlan and Bae (2015) <doi:10.1007/s00431-015-2581-x>.
Provides functions for the estimation of the conditional Akaike
    information in generalized mixed-effect models fitted with (g)lmer() 
    from 'lme4'.
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.
This device uses Cairo and GTK to draw to the screen,
        file (png, svg, pdf, and ps) or memory (arbitrary GdkDrawable
        or Cairo context). The screen device may be embedded into RGtk2
        interfaces and supports all interactive features of other graphics
        devices, including getGraphicsEvent().
Functions for performing experimental comparisons of algorithms 
             using adequate sample sizes for power and accuracy.
Implements several string comparison algorithms, including calACS (count all common subsequences), lenACS (calculate the lengths of all common subsequences), and lenLCS (calculate the length of the longest common subsequence). Some algorithms differentiate between the more strict definition of subsequence, where a common subsequence cannot be separated by any other items, from its looser counterpart, where a common subsequence can be interrupted by other items. This difference is shown in the suffix of the algorithm (-Strict vs -Loose). For example, q-w is a common subsequence of q-w-e-r and q-e-w-r on the looser definition, but not on the more strict definition. calACSLoose Algorithm from Wang, H. All common subsequences (2007) IJCAI International Joint Conference on Artificial Intelligence, pp. 635-640.
Arithmetic operations scalar multiplication, addition, subtraction, multiplication and division of LR fuzzy numbers (which are on the basis of extension principle) have a complicate form for using in fuzzy Statistics, fuzzy Mathematics, machine learning, fuzzy data analysis and etc. Calculator for LR Fuzzy Numbers package relieve and aid applied users to achieve a simple and closed form for some complicated operator based on LR fuzzy numbers and also the user can easily draw the membership function of the obtained result by this package.
Contains greedy algorithms for coarse approximation linear
    functions.
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).
Automated parameter estimation for complex (ecological) models in R. 
  This package allows the parameter estimation or calibration of complex models, 
  including stochastic ones. It is a generic tool that can be used for fitting 
  any type of models, especially those with non-differentiable objective functions. 
  It supports multiple phases and constrained optimization. 
  It implements maximum likelihood estimation methods and automated construction 
  of the objective function from simulated model outputs. 
  See <http://roliveros-ramos.github.io/calibrar> for more details.
Package for drawing calibrated scales with tick marks on (non-orthogonal) 
             variable vectors in scatterplots and biplots.
Function to calculate weights and estimates for panel data with non-response.
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/.
Calibration of every computational code. It uses a Bayesian framework to rule the estimation. With a new data set, the prediction will create a prevision set taking into account the new calibrated parameters. The choices between several models is also available. The methods are described in the paper Carmassi et al. (2018) <arXiv:1801.01810>.
It is sometimes useful to perform a computation in a
    separate R process, without affecting the current R process at all.
    This packages does exactly that.
A multi-array post-processing method of allele-specific copy-number estimates (ASCNs).
The code takes an n x p data matrix and fits a Causal Additive Model (CAM) for estimating the causal structure of the underlying process. The output is a p x p adjacency matrix (a one in entry (i,j) indicates an edge from i to j). Details of the algorithm can be found in: P. Bühlmann, J. Peters, J. Ernest: "CAM: Causal Additive Models, high-dimensional order search and penalized regression", Annals of Statistics 42:2526-2556, 2014.
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.
Copernicus Atmosphere Monitoring Service (CAMS) radiations service 
    provides time series of global, direct, and diffuse irradiations on horizontal
    surface, and direct irradiation on normal plane for the actual weather 
    conditions as well as for clear-sky conditions.
    The geographical coverage is the field-of-view of the Meteosat satellite,
    roughly speaking Europe, Africa, Atlantic Ocean, Middle East. The time coverage
    of data is from 2004-02-01 up to 2 days ago. Data are available with a time step
    ranging from 15 min to 1 month. For license terms and to create an account,
    please see <http://www.soda-pro.com/web-services/radiation/cams-radiation-service>.
Management of and data extraction from camera trap photographs in wildlife studies. The package provides a workflow for storing and sorting camera trap photographs, computes record databases and detection/non-detection matrices for occupancy and spatial capture-recapture analyses with great flexibility. In addition, it provides simple mapping functions (number of species, number of independent species detections by station) and can visualise activity data.
Integrated, convenient, and uniform access to Canadian
    Census data and geography retrieved using the 'CensusMapper' API. This package produces analysis-ready 
    tidy data frames and spatial data in multiple formats, as well as convenience functions
    for working with Census variables, variable hierarchies, and region selection. API
    keys are freely available with free registration at <https://censusmapper.ca/api>.
    Census data and boundary geometries are reproduced and distributed on an "as
    is" basis with the permission of Statistics Canada (Statistics Canada 2006;
    2011; 2016).
Functions to perform the following analyses: i) inferring epistasis from RNAi double knockdown data; ii) identifying gene pairs of multiple mutation patterns; iii) assessing association between gene pairs and survival; and iv) calculating the smallworldness of a graph (e.g., a gene interaction network).  Data and analyses are described in Wang, X., Fu, A. Q., McNerney, M. and White, K. P. (2014). Widespread genetic epistasis among breast cancer genes. Nature Communications. 5 4828. <doi:10.1038/ncomms5828>.
Timing copy number changes using estimates of mutational allele frequency from resequencing of tumor samples.
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 higher-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.
A statistical framework and computational procedure for identifying
  the sub-populations within a tumor, determining the mutation profiles of each 
  subpopulation, and inferring the tumor's phylogenetic history. The input are 
  variant allele frequencies (VAFs) of somatic single nucleotide alterations 
  (SNAs) along with allele-specific coverage ratios between the tumor and matched
  normal sample for somatic copy number alterations (CNAs). These quantities can
  be directly taken from the output of existing software. Canopy provides a 
  general mathematical framework for pooling data across samples and sites to 
  infer the underlying parameters. For SNAs that fall within CNA regions, Canopy
  infers their temporal ordering and resolves their phase.  When there are 
  multiple evolutionary configurations consistent with the data, Canopy outputs 
  all configurations along with their confidence assessment.
Datasets are collected here for differentially (up- and down-)
        expressed proteins identified in proteomic studies of cancer and in cell
        culture experiments. Tables of amino acid compositions of proteins are
        used for calculations of chemical composition, projected into selected
        basis species. Plotting functions are used to visualize the compositional
        differences and thermodynamic potentials for proteomic transformations.
Extract CANSIM (Statistics Canada) tables and transform them into readily usable data in panel (wide) format. It can also extract more than one table at a time and produce the resulting merge by time period and geographical region.
Enables creation of visualizations using the CanvasXpress framework
    in R. CanvasXpress is a standalone JavaScript library for reproducible research
    with complete tracking of data and end-user modifications stored in a single
    PNG image that can be played back. See <http://canvasxpress.org> for more
    information.
Combines complementary information across multiple related
    phenotypes to infer directed epistatic interactions between genetic markers.
    This analysis can be applied to a variety of engineered and natural populations.
Functions for performing phylogenetic comparative analyses.
Quantitative analysis to support companion animal population
    management. Some functions assist survey sampling tasks (calculate sample 
    size for simple and complex designs, select sampling units and estimate 
    population parameters) while others assist the modelling of population 
    dynamics. For sampling methods see: Levy PS & Lemeshow S. (2013), 
    ISBN-10: 0470040076; Lumley (2010), ISBN: 978-0-470-28430-8. For 
    modelling of population dynamics see: Baquero et al (2016) 
    <https://doi.org/10.1016/j.prevetmed.2015.11.009>; Baquero et al (2016), 
    ISSN 1679-9216; Amaku et al (2010) 
    <http://dx.doi.org/10.1590/S1020-49892009000400003>.
Implements approximation methods for natural capital asset prices suggested by Fenichel and Abbott (2014) <doi:10.1086/676034> in Journal of the Associations of Environmental and Resource Economists (JAERE), Fenichel et al. (2016) <doi:10.1073/pnas.1513779113> in Proceedings of the National Academy of Sciences (PNAS), and Yun et al. (2017) in PNAS (accepted), and their extensions: creating Chebyshev polynomial nodes and grids, calculating basis of Chebyshev polynomials, approximation and their simulations for: V-approximation (single and multiple stocks, PNAS), P-approximation (single stock, PNAS), and Pdot-approximation (single stock, JAERE). Development of this package was generously supported by the Knobloch Family Foundation.
Provides a method for automatically numbering figures,
    tables, or other objects.  Captions can be displayed in full, or as citations.
    This is especially useful for adding figures and tables to R markdown
    documents without having to numbering them manually.
Get text from images of text using Captricity Optical Character
    Recognition (OCR) API. Captricity allows you to get text from handwritten
    forms --- think surveys --- and other structured paper documents. And it can
    output data in form a delimited file keeping field information intact. For more
    information, read <https://shreddr.captricity.com/developer/overview/>.
Calibration of penalized criteria for model selection. The calibration methods available 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.
Functions to Accompany J. Fox and S. Weisberg, 
  An R Companion to Applied Regression, Third Edition, Sage, in press.
Multi-objective optimizer initially developed for the calibration of hydrological models.
     The algorithm is a hybrid of the MEAS algorithm (Efstratiadis and Koutsoyiannis (2005) <doi:10.13140/RG.2.2.32963.81446>) by using the directional search method based on the simplexes of the objective space 
     and the epsilon-NGSA-II algorithm with the method of classification of the parameter vectors archiving management by epsilon-dominance (Reed and Devireddy <doi:10.1142/9789812567796_0004>).
Implements a class of univariate and multivariate spatial generalised linear mixed models for areal unit data, with inference in a Bayesian setting using Markov chain Monte Carlo (MCMC) simulation. The response variable can be binomial, Gaussian or Poisson, and spatial autocorrelation is modelled by  a set of random effects that are assigned a conditional autoregressive (CAR) prior distribution. A number of different models are available for univariate spatial data, including models with no random effects as well as random effects modelled by different types of CAR prior. Additionally,  a multivariate CAR (MCAR) model for multivariate spatial data is available, as is a two-level hierarchical model for individuals within areas. Full details are given in the vignette accompanying this package. The initial creation of this package was supported by the Economic and Social Research Council (ESRC) grant RES-000-22-4256, and on-going development has / is supported by the Engineering and Physical Science Research Council (EPSRC) grant EP/J017442/1, ESRC grant ES/K006460/1, and Innovate UK / Natural Environment Research Council (NERC) grant NE/N007352/1.
Spatio-temporal data from Scotland used in the vignettes accompanying the CARBayes (spatial modelling) and CARBayesST (spatio-temporal modelling) packages. For the CARBayes vignette the data include the Scottish lip cancer data and property price and respiratory hospitalisation data from the Greater Glasgow and Clyde health board. For the CARBayesST vignette the data include spatio-temporal data on property sales and respiratory hospitalisation and air pollution from the Greater Glasgow and Clyde health board.
Implements a class of spatio-temporal generalised linear mixed models for areal unit data, with inference in a Bayesian setting using Markov chain Monte Carlo (MCMC) simulation. The response variable can be binomial, Gaussian or Poisson, but for some models only the binomial and Poisson data likelihoods are available. The spatio-temporal autocorrelation is modelled by  random effects,  which are assigned conditional autoregressive (CAR) style prior distributions. A number of different random effects structures are available, including Bernardinelli et al. (1995) <doi:10.1002/sim.4780142112>, Rushworth et al. (2014) <doi:10.1016/j.sste.2014.05.001> and Lee et al. (2016) <doi:10.1214/16-AOAS941>. Full details are given in the vignette accompanying this package. The creation of this package was supported by the Engineering and Physical Sciences Research Council (EPSRC) grant EP/J017442/1 and the Medical Research Council (MRC) grant MR/L022184/1.
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. Functions for estimating the probability that a bird or bat that died is found by a searcher are provided. Further functions calculate the posterior distribution of the number of fatalities based on the number of carcasses found and the estimated detection probability.
Datasets to Accompany J. Fox and S. Weisberg, 
  An R Companion to Applied Regression, Third Edition, Sage (forthcoming).
Identification of cardinal dates
             (begin, time of maximum, end of mass developments)
             in ecological time series using fitted Weibull functions.
Includes over 100 mixed-effects model structures describing the relationship between drug concentration and QT interval, heart rate/pulse rate or blood pressure. Given an exposure-response dataset, the tool fits each model structure to the observed data.
Implements the regression approach 
  of Zuber and Strimmer (2011) "High-dimensional regression and variable 
  selection using CAR scores" SAGMB 10: 34, <DOI:10.2202/1544-6115.1730>.
  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.
Functions for creating ensembles of caret models: caretList
    and caretStack.  caretList is a convenience function for fitting multiple
    caret::train models to the same dataset. caretStack will make linear or
    non-linear combinations of these models, using a caret::train model as a
    meta-model, and caretEnsemble will make a robust linear combination of
    models using a glm.
We provide a toolbox to fit a continuous-time fractionally integrated ARMA process (CARFIMA) on univariate and irregularly spaced time series data via frequentist or Bayesian machinery. A general-order CARFIMA(p, H, q) model for p>q is specified in Tsai and Chan (2005)<doi:10.1111/j.1467-9868.2005.00522.x> and it involves p+q+2 unknown model parameters, i.e., p AR parameters, q MA parameters, Hurst parameter H, and process uncertainty (standard deviation) sigma. The package produces their maximum likelihood estimates and asymptotic uncertainties using a global optimizer called the differential evolution algorithm. It also produces their posterior distributions via Metropolis within a Gibbs sampler equipped with adaptive Markov chain Monte Carlo for posterior sampling. These fitting procedures, however, may produce numerical errors if p>2. The toolbox also contains a function to simulate discrete time series data from CARFIMA(p, H, q) process given the model parameters and observation times.
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.
Functions to calculate and plot ecological quality ratios (EQR) as specified by Ballesteros et al. 2007.
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.
Mainly used to build tables that are commonly presented for
    bio-medical/health research, such as basic characteristic tables or
    descriptive statistics.
CRISPR-Analyzer for pooled CRISPR screens (caRpools) provides an end-to-end analysis of CRISPR screens including quality control, hit candidate analysis, visualization and automated report generation using R markdown. Needs MAGeCK (http://sourceforge.net/p/mageck/wiki/Home/), bowtie2 for all functions. CRISPR (clustered regularly interspaced short palindromic repeats) is a method to perform genome editing. See <https://en.wikipedia.org/wiki/CRISPR> for more information on
  CRISPR.
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.
Predicts categorical or continuous outcomes while concentrating on four key points. These are Cross-validation, Accuracy, Regression and Rule of Ten (CARRoT). It performs the cross-validation specified number of times by partitioning the input into training and test set and fitting linear/multinomial/binary regression models to the training set. All regression models satisfying a rule of ten events per variable are fitted and the ones with the best predictive power are given as an output. Best predictive power is understood as highest accuracy in case of binary/multinomial outcomes, smallest absolute and relative errors in case of continuous outcomes. For binary case there is also an option of finding a regression model which gives the highest AUROC (Area Under Recever Operating Curve) value. The option of parallel toolbox is also available. Methods are described in Peduzzi et al. (1996) <doi:10.1016/S0895-4356(96)00236-3> and Rhemtulla et al. (2012) <doi:10.1037/a0029315>.
It implements the CARS procedure, which is a two-sample multiple testing procedure that utilizes an additional auxiliary variable to capture the sparsity information, hence improving power. The CARS procedure is shown to be asymptotically valid and optimal for FDR control. For more information, please see the website <http://www-bcf.usc.edu/~wenguans/Papers/CARS.html> and the accompanying paper.
Contains functions to estimate the Correlation-Adjusted Regression Survival (CARS) Scores. The method is described in Welchowski, T. and Zuber, V. and Schmid, M., (2018), Correlation-Adjusted Regression Survival Scores for High-Dimensional Variable Selection, <arXiv:1802.08178>.
Construct continuous and non-contiguous area cartograms.
Create and integrate maps in your R workflow. This package allows various cartographic representations such as proportional symbols, chroropleth, typology, flows or discontinuities maps. It also offers several features enhancing the graphic presentation of maps like cartographic palettes, layout elements (scale, north arrow, title...), labels, legends or access to some cartographic APIs. See Giraud and Lambert (2017) <doi:10.1007/978-3-319-57336-6_13>.
A censored time series class is designed. An estimation procedure
    is implemented to estimate the Censored AutoRegressive time series with
    eXogenous covariates (CARX), assuming normality of the innovations. Some other
    functions that might be useful are also included.
Functions, data sets and exercises solutions for the book 'Séries Temporelles Avec R' (Yves Aragon, edp sciences, 2016). For all chapters, a vignette is available with some additional material and exercises solutions.
Implements the case-base sampling approach of Hanley and Miettinen (2009) <DOI:10.2202/1557-4679.1125>, 
    Saarela and Arjas (2015) <DOI:10.1111/sjos.12125>, and Saarela (2015) <DOI:10.1007/s10985-015-9352-x>, for fitting flexible hazard 
    regression models to survival data with single event type or multiple competing causes via logistic and multinomial regression. 
    From the fitted hazard function, cumulative incidence, risk functions of time, treatment and profile 
    can be derived. This approach accommodates any log-linear hazard function of prognostic time, treatment, 
    and covariates, and readily allows for non-proportionality. We also provide a plot method for visualizing 
    incidence density via population time plots.
Allows users to identify similar cases for qualitative case studies using statistical matching methods.
Supporting functionality to run 'caret' with spatial or spatial-temporal data. 'caret' is a frequently used package for model training and prediction using machine learning. This package includes functions to improve spatial-temporal modelling tasks using 'caret'. It prepares data for Leave-Location-Out and Leave-Time-Out cross-validation which are target-oriented validation strategies for spatial-temporal models. To decrease overfitting and improve model performances, the package implements a forward feature selection that selects suitable predictor variables in view to their contribution to the target-oriented performance.
Efficient tree manipulations for large phylogenies, including pruning, rerooting, calculation of most-recent common ancestors, calculating distances from the tree root and calculating pairwise distances. Calculation of phylogenetic signal and mean trait depth (trait conservatism), ancestral state reconstruction and hidden character prediction of discrete characters. Simulating and fitting models of trait evolution, and generating random trees using birth-death models. Dating trees, comparing two trees, and reading/writing trees to a file. Citation: Louca, Stilianos and Doebeli, Michael (2017) <doi:10.1093/bioinformatics/btx701>.
Analysis of categorical-variable with missing values
Methods and utilities for classifying vectors as categorical 
    or continuous.
Categorical data analysis by AIC. The methodology is described in
 Sakamoto (1992) <ISBN 978-0-7923-1429-5>.
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.
Based on fishery Catch Dynamics instead of fish Population Dynamics (hence CatDyn) and using high-frequency or medium-frequency catch in biomass or numbers, fishing nominal effort, and mean fish body weight 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 77 types of models of increasing complexity, and 56 likelihood models for the data.
Provides several methods for factor analysis in high dimension (both n,p >> 1) and methods to adjust for possible confounders in multiple hypothesis testing.
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.
Contains some commonly used categorical variable encoders, such as 'LabelEncoder' and 'OneHotEncoder'. Inspired by the encoders implemented in Python 'sklearn.preprocessing' package (see <http://scikit-learn.org/stable/modules/preprocessing.html>).
A multi-factor dimensionality reduction based forward selection method for genetic association mapping.
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.
Calculates auto- and cross-correlation functions and plots an actogram and a smoothing function from a time series to identify and visualize periodic components. Tests presence of anticipated rhythm and estimates rhythm parameters; fits model consisting of multiple rhythmic components to data; performs least squares spectral analysis and other cosinor-based analyses, including population-mean cosinor (pmc).
Formal psychological models of categorization and learning, independently-replicated data sets against which to test them, and simulation archives.
Although many software tools can perform meta-analyses on genetic case-control data,
    none of these apply to combined case-control and family-based (TDT) studies. This package conducts
    fixed-effects (with inverse variance weighting) and random-effects [DerSimonian and Laird (1986)
    <DOI:10.1016/0197-2456(86)90046-2>] meta-analyses on combined genetic data. Specifically, this package
    implements a fixed-effects model [Kazeem and Farrall (2005) <DOI:10.1046/j.1529-8817.2005.00156.x>]
    and a random-effects model [Nicodemus (2008) <DOI:10.1186/1471-2105-9-130>] for combined studies.
Structure learning and parameter estimation of discrete Bayesian networks using likelihood-based criteria.  Exhaustive search for fixed node orders and stochastic search of optimal orders via simulated annealing algorithm are implemented.
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.
Allows the user to categorise a continuous predictor variable in a logistic or a Cox proportional hazards regression setting, by maximising the discriminative ability of the model. I Barrio, I Arostegui, MX Rodriguez-Alvarez, JM Quintana (2015) <doi:10.1177/0962280215601873>. I Barrio,  MX Rodriguez-Alvarez, L Meira-Machado, C Esteban, I Arostegui (2017) <https://www.idescat.cat/sort/sort411/41.1.3.barrio-etal.pdf>.
Provides routines for the generation of response patterns under unidimensional dichotomous and polytomous computerized adaptive testing (CAT) framework. It holds many standard functions to estimate ability, select the first item(s) to administer and optimally select the next item, as well as several stopping rules. Options to control for item exposure and content balancing are also available (Magis and Raiche (2012) <doi:10.18637/jss.v048.i08>).
`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
Provides methods of computerized adaptive testing for survey researchers. Includes functionality for data fit with the classic item response methods including the latent trait model, Birnbaum`s three parameter model, the graded response, and the generalized partial credit model.  Additionally, includes several ability parameter estimation and item selection routines.  During item selection, all calculations are done in compiled C++ code.
This function conducts the Cochran-Armitage trend test to a 2 by k contingency table. It will report the test statistic (Z) and p-value.A linear trend in the frequencies will be calculated, because the weights (0,1,2) will be used by default.
Provides functions for computing the one-sided p-values of the Cochran-Armitage trend test
  statistic for the asymptotic and the exact conditional test. The computation of the p-value for the exact test
  is performed using an algorithm following an idea by Mehta, et al. (1992) <doi:10.2307/1390598>.
Functions for dummy encoding, frequency encoding,
    label encoding, leave-one-out encoding, mean encoding,
    and one-hot encoding.
Functions and data to estimate causal dose response functions given continuous, ordinal, or binary treatments.
Functions for identification and transportation of causal effects. Provides a conditional causal effect identification algorithm (IDC) by Shpitser, I. and Pearl, J. (2006) <http://ftp.cs.ucla.edu/pub/stat_ser/r329-uai.pdf>, an algorithm for transportability from multiple domains with limited experiments by Bareinboim, E. and Pearl, J. (2014) <http://ftp.cs.ucla.edu/pub/stat_ser/r443.pdf> and a selection bias recovery algorithm by Bareinboim, E. and Tian, J. (2015) <http://ftp.cs.ucla.edu/pub/stat_ser/r445.pdf>. All of the previously mentioned algorithms are based on a causal effect identification algorithm by Tian , J. (2002) <http://ftp.cs.ucla.edu/pub/stat_ser/r309.pdf>.
Estimate causal effects of one variable on another, currently for
  binary data only. Methods include instrumental variable bounds, adjustment by a 
  given covariate set, adjustment by an induced covariate set using a variation of 
  the PC algorithm, and an effect bounding method (the Witness Protection Program) 
  based on covariate adjustment with observable independence constraints.
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. See: Glynn, Adam N.
  and Kevin M. Quinn. 2010. "An Introduction to the Augmented Inverse
  Propensity Weighted Estimator." Political Analysis. 18: 36-56.
Implements a Bayesian approach to causal impact estimation in time
  series, as described in Brodersen et al. (2015) <DOI:10.1214/14-AOAS788>.
  See the package documentation on GitHub
  <https://google.github.io/CausalImpact/> to get started.
Allows users to learn undirected and directed (causal) graphs over mixed data types (i.e., continuous and discrete variables). To learn a directed graph over mixed data, it first calculates the undirected graph (Sedgewick et al, 2016) and then it uses local search strategies to prune-and-orient this graph (Sedgewick et al, 2017). AJ Sedgewick, I Shi, RM Donovan, PV Benos (2016) <doi:10.1186/s12859-016-1039-0>. AJ Sedgewick, JD Ramsey, P Spirtes, C Glymour, PV Benos (2017) <arXiv:1704.02621>.
The causalsens package provides functions to perform sensitivity analyses and to study how various assumptions about selection bias affects estimates of causal effects.
Various estimation methods for causal inference based on inverse probability weighting. Specifically, the package includes methods for estimating average treatment effects as well as direct and indirect effects in causal mediation analysis. The models refer to the studies of Frölich (2007) <doi:10.1016/j.jeconom.2006.06.004>, Huber (2012) <doi:10.3102/1076998611411917>, Huber (2014) <doi:10.1080/07474938.2013.806197>, Huber (2014) <doi:10.1002/jae.2341>, and Frölich and Huber (2017) <doi:10.1111/rssb.12232>.
The Causata package provides utilities for 
    extracting data from the Causata application, training binary classification 
    models, and exporting models as PMML for scoring.
Provides six variants of two-way correspondence analysis (ca):
   simple ca, singly ordered ca, doubly ordered ca, non symmetrical ca,
   singly ordered non symmetrical ca, and doubly ordered non symmetrical
   ca.
Implements clustering techniques such as Proximus and Rock, utility functions for efficient computation of cross distances and data manipulation.
A set of functions that helps you to generate descriptive statistics based on the variable types.
Detect contextual anomalies in time-series data with Bayesian data
  analysis. It focuses on determining a normal range of target value, and
  provides simple-to-use functions to abstract the outcome.
Compare baseline characteristics between two or more groups. The variables being compared can be factor and numeric variables. The function will automatically judge the type and distribution of the variables, and make statistical description and bivariate analysis.
Classification performed on Big Data. It uses concepts from compressive sensing, and implements ensemble predictor (i.e., 'SuperLearner') and knockoff filtering as the main machine learning and feature mining engines.
Implementation of the d/p/q/r family of functions for a continuous analog to the standard discrete binomial with continuous size parameter and continuous support with x in [0, size + 1], following Ilienko (2013) <arXiv:1303.5990>.
The clustering of binary data with reducing the dimensionality (CLUSBIRD) proposed by Yamamoto and Hayashi (2015) <doi:10.1016/j.patcog.2015.05.026>.
Implements the covariate balancing propensity score (CBPS) proposed
    by Imai and Ratkovic (2014) <DOI:10.1111/rssb.12027>. 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 from Imai and Ratkovic
    (2015) <DOI:10.1080/01621459.2014.956872>, treatments with three- and four-
    valued treatment variables, continuous-valued treatments from Fong, Hazlett,
    and Imai (2015) <DOI:10.1214/17-AOAS1101>, 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. Recently we have added the 
    optimal CBPS which chooses the optimal balancing function and results in doubly 
    robust and efficient estimator for the treatment effect as well as high dimensional
    CBPS when a large number of covariates exist.
The data and meta data from Statistics
    Netherlands (www.cbs.nl) can be browsed and downloaded. The client uses
    the open data API of Statistics Netherlands.
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.
Client for Climate Change, Agriculture, and Food Security ('CCAFS')
    General Circulation Models ('GCM') data. Data is stored in Amazon 'S3', from
    which we provide functions to fetch data.
Variational Bayesian algorithms for learning canonical correlation analysis (CCA), inter-battery factor analysis (IBFA), and group factor analysis (GFA). Inference with several random initializations can be run with the functions CCAexperiment() and GFAexperiment().
Canonical correlation analysis and maximum correlation via
    projection pursuit, as well as fast implementations of correlation
    estimators, with a focus on robust and non-parametric 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).
Routines for solving convex optimization problems with cone constraints by means of interior-point methods. The implemented algorithms are partially ported from CVXOPT, a Python module for convex optimization (see <http://cvxopt.org> for more information).
Estimates the Concordance Correlation Coefficient to assess agreement. The scenarios considered are non-repeated measures, non-longitudinal repeated measures (replicates) and longitudinal repeated measures. The estimation approaches implemented are variance components and U-statistics approaches.
This package implements the combined cluster and discriminant analysis method for finding homogeneous groups of data with known origin as described in Kovacs et. al (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
Implementation of the CCDr (Concave penalized Coordinate Descent with reparametrization) structure learning algorithm as described in Aragam and Zhou (2015) <http://www.jmlr.org/papers/v16/aragam15a.html>. This is a fast, score-based method for learning Bayesian networks that uses sparse regularization and block-cyclic coordinate descent.
Contains methods for computing counterfactuals with a continuous treatment variable as in Callaway and Huang (2017) <https://ssrn.com/abstract=3078187>.  In particular, the package can be used to calculate the expected value, the variance, the interquantile range, the fraction of observations below or above a particular cutoff, or other user-supplied functions of an outcome of interest conditional on a continuous treatment.  The package can also be used for computing these same functionals after adjusting for differences in covariates at different values of the treatment.  Further, one can use the package to conduct uniform inference for each parameter of interest across all values of the treatment, uniformly test whether adjusting for covariates makes a difference at any value of the treatment, and test whether a parameter of interest is different from its average value at an value of the treatment.
Functions for estimating and simulating the family of the
        CC-GARCH models.
Contains a function, also called 'cchs', that calculates Estimator III of Borgan et al (2000), <DOI:10.1023/A:1009661900674>. This estimator is for fitting a Cox proportional hazards model to data from a case-cohort study where the subcohort was selected by stratified simple random sampling.
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 described in Dancik et al (2011) <doi:10.1158/0008-5472.CAN-11-2427> 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.
Estimate the direct and indirect (mediation) effects of treatment on the outcome when intermediate variables (mediators) are compositional and high-dimensional. Sohn, M.B. and Li, H. (2017). Compositional Mediation Analysis for Microbiome Studies. (AOAS: In revision).
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.
Implements a method for identifying and removing
				the cell-cycle effect from scRNA-Seq data. The description of the 
				method is in Barron M. and Li J. (2016) <doi:10.1038/srep33892>. Identifying and removing 
				the cell-cycle effect from single-cell RNA-Sequencing data. Submitted. 
				Different from previous methods, ccRemover implements a mechanism that
				formally tests whether a component is cell-cycle related or not, and thus
				while it often thoroughly removes the cell-cycle effect, it preserves
				other features/signals of interest in the data.
Implements the uniform scaled beta distribution and
  the continuous convolution kernel density estimator.
Consensus analysis, model-based clustering, and cultural consensus theory applications to response data (e.g. questionnaires). The models are applied using hierarchical Bayesian inference. The current package version supports binary, ordinal, and continuous data formats.
Supplies higher-order fluid data transform operators that include pivot and anti-pivot as special cases. 
    The methodology is describe in 'Zumel', 2018, "Fluid data reshaping with 'cdata'", <http://winvector.github.io/FluidData/FluidDataReshapingWithCdata.html> , doi:10.5281/zenodo.1173299 . 
    Based on the 'DBI' database interface.
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.
The 'U.S.' Centers for Disease Control ('CDC') maintains a portal
    <http://gis.cdc.gov/grasp/fluview/fluportaldashboard.html> for
    accessing state, regional and national influenza statistics as well as
    Mortality Surveillance Data. The web interface makes it difficult and 
    time-consuming to select and retrieve influenza data. Tools are provided 
    to access the data provided by the portal's underlying 'API'.
Gives conditional distance correlation and performs the conditional distance correlation sure independence screening procedure for ultrahigh dimensional data. The conditional distance correlation is a novel conditional dependence measurement of two random variables given a third variable. The conditional distance correlation sure independence screening is used for screening variables in ultrahigh dimensional setting.
CDEC maintains a set of web services at <http://cdec.water.ca.gov/queryTools.html>.
      In order to better interact and analyze the data this R packages allows
      users to quickly and easily retrieve data.
Designed by and for the community of differential privacy algorithm developers. It can be used to empirically evaluate and visualize Cumulative Distribution Functions incorporating noise that satisfies differential privacy, with numerous options made to streamline collection of utility measurements across variations of key parameters, such as epsilon, domain size, sample size, data shape, etc. Developed by researchers at Harvard PSI.
Employs a two-parameter family of
    distributions for modelling random variables on the (0, 1) interval by
    applying the cumulative distribution function (cdf) of one parent
    distribution to the quantile function of another.
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
Downloads USDA National Agricultural Statistics Service (NASS) 
    cropscape data for a specified state. Utilities for fips, abbreviation, 
    and name conversion are also provided. Full functionality requires an 
    internet connection, but data sets can be cached for later off-line use.
Functions for cognitive diagnosis modeling and multidimensional item response modeling 
    for dichotomous and polytomous item responses. This package enables the estimation of 
    the DINA and DINO model (Junker & Sijtsma, 2001, <doi:10.1177/01466210122032064>),
    the multiple group (polytomous) GDINA model (de la Torre, 2011, 
    <doi:10.1007/s11336-011-9207-7>), the multiple choice DINA model (de la Torre, 2009, 
    <doi:10.1177/0146621608320523>), the general diagnostic model (GDM; von Davier, 2008, 
    <doi:10.1348/000711007X193957>), the structured latent class model (SLCA; Formann, 1992, 
    <doi:10.1080/01621459.1992.10475229>) and regularized latent class analysis 
    (Chen, Li, Liu, & Ying, 2017, <doi:10.1007/s11336-016-9545-6>). 
    See George, Robitzsch, Kiefer, Gross, and Uenlue (2017) <doi:10.18637/jss.v074.i02> 
    for further details on estimation and the package structure.
    For tutorials on how to use the CDM package see 
    George and Robitzsch (2015, <doi:10.20982/tqmp.11.3.p189>) as well as
    Ravand and Robitzsch (2015).
Components of Canadian Credit Aggregates and Monetary Aggregates with continuity adjustments.
Wrapper functions to model and extract various quantitative information from absorption spectra of chromophoric dissolved organic matter (CDOM).
Parallel coordinate plotting with resolutions for large data sets
 and missing values.
Classification is based on the recently developed phylogenetic
    approach by Assis and Bachtrog (2013). The method classifies the
    evolutionary mechanisms retaining pairs of duplicate genes (conservation,
    neofunctionalization, subfunctionalization, or specialization) by comparing gene
    expression profiles of duplicate genes in one species to those of their single-
    copy ancestral genes in a sister species.
This is an implementation of constrained dual scaling for
    detecting response styles in categorical data, including utility functions. The
    procedure involves adding additional columns to the data matrix representing the
    boundaries between the rating categories. The resulting matrix is then doubled
    and analyzed by dual scaling. One-dimensional solutions are sought which provide
    optimal scores for the rating categories. These optimal scores are constrained
    to follow monotone quadratic splines. Clusters are introduced within which the
    response styles can vary. The type of response style present in a cluster can
    be diagnosed from the optimal scores for said cluster, and this can be used to
    construct an imputed version of the data set which adjusts for response styles.
Functions for statistical inference of canonical vine (C-vine)
        and D-vine copulas. Tools for bivariate exploratory data analysis and for bivariate
        as well as vine copula selection are provided. 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).
Provides tools for sampling from a conditional copula density decomposed via 
     Pair-Copula Constructions as C- or D- vine. Here, the vines which can be used for such a 
     sampling are those which sample as first the conditioning variables (when following the 
     sampling algorithms shown in Aas et al. (2009) <DOI:10.1016/j.insmatheco.2007.02.001>). 
     The used sampling algorithm is presented and discussed in Bevacqua et al. (2017) 
     <DOI:10.5194/hess-2016-652>, and it is a modified version of that from Aas et al. (2009) 
     <DOI:10.1016/j.insmatheco.2007.02.001>. A function is available to select the best vine 
     (based on information criteria) among those which allow for such a conditional sampling. 
     The package includes a function to compare scatterplot matrices and pair-dependencies of 
     two multivariate datasets.
Cross-Entropy Clustering (CEC) divides the data into Gaussian type clusters. It performs the automatic reduction of unnecessary clusters, while at the same time allows the simultaneous use of various type Gaussian mixture models.
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/index_files/CEC-05/CEC05.htm.
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).
Create and learn Chain Event Graph (CEG) models using a Bayesian 
    framework. It provides us with a Hierarchical Agglomerative algorithm to 
    search the CEG model space.
    The package also includes several facilities for visualisations of the
    objects associated with a CEG. The CEG class can represent a range of
    relational data types, and supports arbitrary vertex, edge and graph
    attributes. A Chain Event Graph is a tree-based graphical model that
    provides a powerful graphical interface through which domain experts can
    easily translate a process into sequences of observed events using plain
    language. CEGs have been a useful class of graphical model especially to
    capture context-specific conditional independences. References: Collazo R, 
    Gorgen C, Smith J. Chain Event Graph. CRC Press, ISBN 9781498729604, 2018
    (forthcoming); and Barday LM, Collazo RA, Smith JQ, Thwaites PA, Nicholson AE. 
    The Dynamic Chain Event Graph. Electronic Journal of Statistics, 9 (2) 2130-2169
    <doi:10.1214/15-EJS1068>.
Model building, surrogate model
    based optimization and Efficient Global Optimization in combinatorial
    or mixed search spaces.
Contains 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. It allows users to coordinate match datasets quickly. It also contains functions for various cosmological calculations.
Helper functions to work with spreadsheets and the "A1:D10" style
    of cell range specification.
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)".
Tools for detecting cellwise outliers and robust methods to analyze data which may contain them.
Implementation of the Coarsened Exact Matching algorithm.
Tools for climate- and ecological-niche factor analysis of spatial data, including methods for visualization of spatial variability of species sensitivity, exposure, and vulnerability to climate change. Processing of large files and parallel methods are supported. Ecological-niche factor analysis is described in Hirzel et al. (2002) <doi:10.2307/3071784> and Basille et al. (2008) <doi:10.1016/j.ecolmodel.2007.09.006>.
Implementation of Tobit type I and type II families for censored regression using the 'mgcv' package, based on methods detailed in Woods (2016) <doi:10.1080/01621459.2016.1180986>.
Implementations of threshold regression approaches for linear
	     regression models with a covariate subject to random censoring,
	     including deletion threshold regression and completion threshold regression.
	     Reverse survival regression, which flip the role of response variable and the
	     covariate, is also considered.
Fit censored linear regression models where the random errors follow a finite mixture of Scale Mixture Normal distributions.
             Fit censored linear models of finite mixture multivariate Student-t and Normal distributions.
             Fit censored mixture regression models based on scale mixture of normal distributions.
Implement an interval censor method 
 to break ties when using data with ties to fitting a 
 bivariate copula.
Maximum Likelihood estimation of censored regression (Tobit) models
   with cross-sectional and panel data.
Fits univariate censored linear regression model under Normal or Student-t distribution
Fits linear regression models for censored spatial data. Provides different estimation methods as the SAEM (Stochastic Approximation of Expectation Maximization) algorithm and seminaive that uses Kriging prediction to estimate the response at censored locations and predict new values at unknown locations. Also offers graphical tools for assessing the fitted model.
A scraper to collect US Census data from the American Community Survey (ACS) data
  and metadata. Available datasets include population, income, education and employment levels by 
  age, sex and race. See <http://mcdc.missouri.edu/websas/caps10acsb.html> for more information.  
  Unlike other census related packages, this package does not require a U.S. Census Bureau API.
Contains selected variables from the time series profiles for statistical areas level 2 from the 2006, 2011, and 2016 censuses of population and housing, Australia. Also provides methods for viewing the questions asked for convenience during analysis.
A wrapper for the U.S. Census Bureau APIs that returns data frames of 
	Census data and metadata. Available datasets include the 
	Decennial Census, American Community Survey, Small Area Health Insurance Estimates,
	Small Area Income and Poverty Estimates, and Population Estimates and Projections.
	See <https://www.census.gov/data/developers/data-sets.html> for more information.
Converts the United States Census geographic code for city, state (FIP and ICP),
    region, and birthplace, into the name of the region. e.g. takes an input of
    Census city code 5330 to it's actual city, Philadelphia. Will return NA for code
    that doesn't correspond to real location.
Use the US Census API to collect summary data tables
    for SF1 and ACS datasets at arbitrary geographies.
The 'Censys' public search engine enables researchers to quickly ask 
    questions about the hosts and networks that compose the Internet. Details on how 
    'Censys' was designed and how it is operated are available at <https://www.censys.io/about>. 
    Both basic and extended research access queries are made available. More information 
    on the SQL dialect used by the 'Censys' engine can be found at 
    <https://cloud.google.com/bigquery/docs/reference/legacy-sql>.
Calculates centrality indices additional to the 'igraph' package centrality functions.
Optimization solver based on the Cross-Entropy method.
Use pathway topology information to assign weight to
        pathway nodes.
Performs cepstral based discriminant analysis of groups of time series 
    when there exists Variability in power spectra from time series within the same group 
    as described in R.T. Krafty (2016) "Discriminant Analysis of Time Series in the 
    Presence of Within-Group Spectral Variability" Journal of Time Series Analysis.
Functions and Data to support Context Driven Exploratory Projection Pursuit.
Retorna detalhes de dados de CEPs brasileiros, bairros, logradouros e tal. (Returns info of Brazilian postal codes, city names, addresses and so on.)
Read Condensed Cornell Ecology Program ('CEP') and legacy
    'CANOCO' files into R data frames.
Implements the iterated RMCD method of Cerioli (2010)
	for multivariate outlier detection via robust Mahalanobis distances. Also
	provides the finite-sample RMCD method discussed in the paper, as well as 
	the methods provided in Hardin and Rocke (2005) <doi:10.1198/106186005X77685> 
	and Green and Martin (2017).
An implementation of the covariance estimation method
   proposed in Chi and Lange (2014), "Stable estimation of a covariance matrix guided by nuclear norm penalties,"
   Computational Statistics and Data Analysis 80:117-128.
Collection of perceptually uniform colour maps made by Peter Kovesi (2015) "Good Colour Maps: How to Design Them" <arXiv:1509.03700> 
    at the Centre for Exploration Targeting (CET).
Analysis of configuration frequencies for simple and repeated measures, multiple-samples CFA, hierarchical CFA, bootstrap CFA, functional CFA, Kieser-Victor CFA, and Lindner's test using a conventional and an accelerated algorithm.
Numerical integration of cause-specific survival curves to arrive at cause-specific cumulative incidence functions,
  with three usage modes: 1) Convenient API for parametric survival regression followed by competing-risk analysis, 2) API for
  CFC, accepting user-specified survival functions in R, and 3) Same as 2, but accepting survival functions in C++.
Estimate quantiles using formula (18) from
        http://www.jaschke-net.de/papers/CoFi.pdf (Yaschke; 2001)
This project provides a group of new functions to calculate the
    outputs of the two main components of the Canadian Forest Fire Danger Rating
    System (CFFDRS) at various time scales: the Fire Weather Index (FWI) System and
    the Fire Behaviour Prediction (FBP) System. Some functions have two versions,
    table and raster based.
Comprehensive data analysis software, and the name "cg" stands for "compare groups." Its genesis and evolution are driven by common needs to compare administrations, conditions, etc. in medicine research and 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.
A constrained generalized additive model is fitted by the cgam routine. Given a set of predictors, each of which may have a 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 ShapeSelect routine chooses a subset of predictor variables and describes the component relationships with the response. For each predictor, the user needs only specify a set of possible shape or order restrictions. A model selection method chooses the shapes and orderings of the relationships as well as the variables. The cone information criterion (CIC) is used to select the best combination of variables and shapes. A genetic algorithm may be used when the set of possible models is large. In addition, the cgam routine implements a two-dimensional isotonic regression using warped-plane splines without additivity assumptions.  It can also fit a convex or concave regression surface with triangle splines without additivity assumptions. See Mary C. Meyer (2013)<doi:10.1080/10485252.2013.797577> for more details.
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.
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) at <www.cbioportal.org>.
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
Provides functions to import data from Agilent CGH arrays and process them according to the cghRA workflow. Implements several algorithms such as WACA, STEPS and cnvScore and an interactive graphical interface.
This code provides several different functions for cleaning and analyzing continuous glucose monitor data. Currently it works with 'Dexcom' (<https://www.dexcom.com>) or 'iPro 2' (<http://professional.medtronicdiabetes.com/ipro2-professional-cgm>) data. The makecsv() function can convert files to CSV if they have been saved as 'Excel' documents. The cleaniprodata() and cleandexcomdata() functions take a directory of CGM data files and clean them for analysis. Finally, cgmvariables() iterates through a directory of cleaned CGM data files and produces a single spreadsheet with data for each file. This spreadsheet is compatible with REDCap data upload ("--1" is added to each subject ID automatically for double data entry).
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.
Miscellaneous functions useful for teaching statistics as well as actually practicing the art. They typically are not “new” methods but rather wrappers around either base R or other packages. Currently contains: 'Plot2WayANOVA' which as the name implies conducts a 2 way ANOVA and plots the results using 'ggplot2'. 'neweta' which is a helper function that appends the results of a Type II eta squared calculation onto a classic ANOVA table. Mode which finds the modal value in a vector of data. 'SeeDist' which wraps around 'ggplot2' to provide visualizations of univariate data.
Various statistical methods and models which are
    typically used for the estimation of outstanding claims reserves
    in general insurance, including those to estimate the claims
    development result as required under Solvency II.
Performs adjustments of a user-supplied independence loglikelihood 
    function using a robust sandwich estimator of the parameter covariance 
    matrix, based on the methodology in Chandler and Bate (2007) 
    <doi:10.1093/biomet/asm015>.  This can be used for cluster correlated data 
    when interest lies in the parameters of the marginal distributions or for 
    performing inferences that are robust to certain types of model 
    misspecification.  Functions for profiling the adjusted loglikelihoods are 
    also provided, as are functions for calculating and plotting confidence 
    intervals, for single model parameters, and confidence regions, for pairs 
    of model parameters.
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.
Implements the multiple changepoint algorithm PELT with a
    nonparametric cost function based on the empirical distribution of the data. The cpt.np() function should be your first point of call.
    This package is an extension to the \code{changepoint} package which uses parametric changepoint methods.  For further information on the methods see the
    documentation for \code{changepoint}.
This implements the methods developed in, L. Bybee and Y. Atchade. (2017) <arXiv:1707.04306>.  Contains a series of methods for estimating change-points given user specified black-box models.  The methods include binary segmentation for multiple change-point estimation. For estimating each individual change-point the package includes simulated annealing, brute force, and, for Gaussian graphical models, an application specific rank-one update implementation.  Additionally, code for estimating Gaussian graphical models is included.  The goal of this package is to allow for the efficient estimation of change-points in complicated models with high dimensional data.
Detection of change-points for variance of heteroscedastic Gaussian variables with piecewise constant variance function. Adelfio, G. (2012), Change-point detection for variance piecewise constant models, Communications in Statistics, Simulation and Computation, 41:4, 437-448, <doi:10.1080/03610918.2011.592248>.
A multiple testing procedure for clustered alternative hypotheses. It is assumed that the p-values under the null hypotheses follow U(0,1) and that the distributions of p-values from the alternative hypotheses are stochastically smaller than U(0,1). By aggregating information, this method is more sensitive to detecting signals of low magnitude than standard methods. Additionally, sporadic small p-values appearing within a null hypotheses sequence are avoided by averaging on the neighboring p-values.
Advertisers use a variety of online marketing channels to reach consumers and they want to know the degree each channel contributes to their marketing success. This is called the online multi-channel attribution problem. This package contains a probabilistic algorithm for the attribution problem. The model uses a k-order Markov representation to identify structural correlations in the customer journey data. The package also contains three heuristic algorithms (first-touch, last-touch and linear-touch approach) for the same problem. The algorithms are implemented in C++.
Shiny Web Application for the Multichannel Attribution Problem. It is basically a user-friendly graphical interface for running and comparing all the attribution models in package 'ChannelAttribution'. For customizations or interest in other statistical methodologies for web data analysis please contact <davide.altomare@gmail.com>.
Computes and visualize the results of the 0-1 test for chaos proposed
    by Gottwald and Melbourne (2004) <DOI:10.1137/080718851>. The algorithm is
    available in parallel for the independent values of parameter c. Additionally,
    fast RQA is added to distinguish chaos from noise.
The main objective of the package is to enter a word of at least two letters based on which an Iterated Function System with Probabilities is constructed, and a two-dimensional fractal containing the chosen word infinitely often is generated via the Chaos Game. Additionally, the package allows to project the two-dimensional fractal on several three-dimensional surfaces and to transform the fractal into another fractal with uniform marginals.
The Characteristic Functions Toolbox (CharFun) consists of a set of algorithms for evaluating selected characteristic functions and algorithms for numerical inversion of the (combined and/or compound) characteristic functions, used to evaluate the probability density function (PDF) and the cumulative distribution function (CDF).
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).
Make fake data, supporting addresses, person names, dates,
    times, colors, coordinates, currencies, digital object identifiers
    ('DOIs'), jobs, phone numbers, 'DNA' sequences, doubles and integers
    from distributions and within a range.
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.
Contains methods for creating multivariate Chebyshev
  approximation of functions on a hypercube. Some methods for
  non-Chebyshev grids are also provided.
Utility functions that allow checking the basic validity of a function argument or any other value, 
             including generating an error and assigning a default in a single line of code. The main purpose of
             the package is to provide simple and easily readable argument checking to improve code robustness.
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.
The goal of checkpoint is to solve the problem of package
    reproducibility in R. Specifically, checkpoint allows you to install packages
    as they existed on CRAN on a specific snapshot date as if you had a CRAN time
    machine. To achieve reproducibility, the checkpoint() function installs the
    packages required or called by your project and scripts to a local library
    exactly as they existed at the specified point in time. Only those packages
    are available to your project, thereby avoiding any package updates that came
    later and may have altered your results. In this way, anyone using checkpoint's
    checkpoint() can ensure the reproducibility of your scripts or projects at any
    time. To create the snapshot archives, once a day (at midnight UTC) Microsoft
    refreshes the Austria CRAN mirror on the "Microsoft R Archived Network"
    server (<https://mran.microsoft.com/>). Immediately after completion
    of the rsync mirror process, the process takes a snapshot, thus creating the
    archive. Snapshot archives exist starting from 2014-09-17.
Expressive assertive pipe-friendly, dependency-free functions 
  to check the properties of common R objects.
  In the case of failure the functions issue informative error messages that 
  (hopefully!) allow users to quickly identify and fix any problems.
  The functions are designed to be used in scripts and packages.
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. Allows inter-web comparisons such as examining changes in community structure over environmental, temporal or spatial gradients.
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).
Contains a set of methods for fitting models and methods for
    validating the resulting models. The statistical methodologies comprise
    a comprehensive collection of approaches whose validity and utility have
    been accepted by experts in the Cheminformatics field. As promising new
    methodologies emerge from the statistical and data-mining communities, they
    will be incorporated into the laboratory. These methods are aimed at discovering
    quantitative structure-activity relationships (QSARs). However, the user can
    directly input their own choices of descriptors and responses, so the capability
    for comparing models is effectively unlimited.
R companion to the book "Introduction to Multivariate Statistical Analysis in Chemometrics" written by K. Varmuza and P. Filzmoser (2009).
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). Data used in the package are available from github.
A collection of functions for top-down exploratory data analysis
    of spectral data obtained via nuclear magnetic resonance (NMR), infrared (IR) or
    Raman spectroscopy. Includes functions for plotting and inspecting spectra, peak
    alignment, hierarchical cluster analysis (HCA), principal components analysis
    (PCA) and model-based clustering. Robust methods appropriate for this type of
    high-dimensional data are available. ChemoSpec is designed with metabolomics
    data sets in mind, where the samples fall into groups such as treatment and
    control. Graphical output is formatted consistently for publication quality
    plots. ChemoSpec is intended to be very user friendly and help you get usable
    results quickly. A vignette covering typical operations is available.
Provides an alternative approach to multiple testing
        by calculating a simultaneous upper confidence bounds for the
        number of true null hypotheses among any subset of the hypotheses of interest, 
        using the methods of Goeman and Solari (2011) <doi:10.1214/11-STS356>.
The software matches the current history to the closest history in a time series to build a forecast.
Light weight implementation of the standard distribution 
  functions for the chi distribution, wrapping those for the chi-squared 
  distribution in the stats package.
Provides two index partitions for three-way contingency tables:
   partition of the association measure chi-squared and of the predictability index tau 
   under several representative hypotheses about the expected frequencies (hypothesized probabilities).
Calculates childhood (neonatal, postneonatal, infant, child, and under-five) mortality rates using Demographic and Health Survey (DHS) microdata.
    The 'childhoodmortality' package was developed in accordance to the methodology outlined in the "DHS Guide to Statistics" (Rutstein 2006,
    <http://dhsprogram.com/pubs/pdf/DHSG1/Guide_to_DHS_Statistics_29Oct2012_DHSG1.pdf>) Specifically, the package uses a synthetic cohort
    life table approach, combining mortality probabilities for age segments with actual cohort mortality experience into more common age
    segments. Standard errors for mortality estimates are computed using the jackknife repeated replication method outlined in the "Estimates of Sampling Errors"
    appendix of DHS Final Reports (DHS 2004, <http://dhsprogram.com/pubs/pdf/FR175/19AppendixB.pdf>). This methodology controls for sample design.
Calculation of standard deviation scores and percentiles adduced from different
    growth standards (WHO, UK, Germany, Italy, China, etc). Therefore, the calculation of SDS-values
    for different measures like BMI, weight, height, head circumference, different
    ratios, etc. are easy to carry out. Also, references for laboratory values in
    children and adults are available, e.g., serum lipids, iron-related blood parameters, IGF, liver enzymes. In the
    new version, there are also functions combining the lms() function from package 'gamlss' with
    resampling methods for using with repeated measurements and family dependencies. A searchable list
    of items can be found here: <https://github.com/mvogel78/childsds/wiki>.
The phenology of plants (i.e. the timing of their annual life
    phases) depends on climatic cues. For temperate trees and many other plants,
    spring phases, such as leaf emergence and flowering, have been found to result
    from the effects of both cool (chilling) conditions and heat. Fruit tree
    scientists (pomologists) have developed some metrics to quantify chilling
    and heat (e.g. see Luedeling (2012) <doi.org/10.1016/j.scienta.2012.07.011>).
    'chillR' contains functions for processing temperature records into
    chilling (Chilling Hours, Utah Chill Units and Chill Portions) and heat units
    (Growing Degree Hours). Regarding chilling metrics, Chill Portions are often
    considered the most promising, but they are difficult to calculate. This package
    makes it easy. 'chillR' also contains procedures for conducting a PLS analysis
    relating phenological dates (e.g. bloom dates) to either mean temperatures or
    mean chill and heat accumulation rates, based on long-term weather and phenology
    records (Luedeling and Gassner (2012) <doi.org/10.1016/j.agrformet.2011.10.020>).
    As of version 0.65, it also includes functions for generating weather
    scenarios with a weather generator, for conducting climate change analyses
    for temperature-based climatic metrics and for plotting results from such
    analyses. Since version 0.70, 'chillR' contains a function for interpolating
    hourly temperature records.
Efforts are made to make Chinese text mining easier, faster, and robust to errors. 
    Document term matrix can be generated by only one line of code; detecting encoding, 
    segmenting and removing stop words are done automatically. 
	Some convenient tools are also supplied.
A collection of functions to pre-process amplification curve data from polymerase chain reaction (PCR) or isothermal amplification reactions. Contains functions to normalize and baseline amplification curves, to detect both the start and end of an amplification reaction, several smoothers (e.g., LOWESS, moving average, cubic splines, Savitzky-Golay), a function to detect false positive amplification reactions and a function to determine the amplification efficiency. Quantification point (Cq) methods include the first (FDM) and second approximate derivative maximum (SDM) methods (calculated by a 5-point-stencil) and the cycle threshold method. Data sets of experimental nucleic acid amplification systems (VideoScan HCU, capillary convective PCR (ccPCR)) and commercial systems are included. Amplification curves were generated by helicase dependent amplification (HDA), ccPCR or PCR. As detection system intercalating dyes (EvaGreen, SYBR Green) and hydrolysis probes (TaqMan) were used.
Nonparametric Tests to identify the differential enrichment region for two conditions or time-course ChIP-seq data. It includes: data preprocessing function, estimation of a small constant used in hypothesis testing, a kernel-based two sample nonparametric test, two assumption-free  two sample nonparametric test.
An exact and a variational inference for
    coupled Hidden Markov Models applied to the joint detection of copy number variations.
Threshold regression models are also called two-phase regression, broken-stick regression, split-point regression, structural change models, and regression kink models. Methods for both continuous and discontinuous threshold models are included, but the support for the former is much greater. This package is described in Fong, Huang, Gilbert and Permar (2017) chngpt: threshold regression model estimation and inference, BMC Bioinformatics, in press, <DOI:10.1186/s12859-017-1863-x>.
An integrated set of tools for thermodynamic calculations in geochemistry and
  compositional biology. Thermodynamic properties are taken from a database for minerals
  and inorganic and organic aqueous species including biomolecules, or from amino acid
  group additivity for proteins. High-temperature properties are calculated using the
  revised Helgeson-Kirkham-Flowers equations of state for aqueous species, and activity
  coefficients can be calculated for specified ionic strength. Functions are
  provided to define a system using basis species, automatically balance reactions,
  calculate the chemical affinities of formation reactions for selected species, calculate
  equilibrium activities, and plot the results on chemical activity diagrams.
Processes data from Lattes CV 
    (<http://lattes.cnpq.br/>) XML files. Extract, condition, and plot 
    lists of journal and conference papers, book chapters, books, 
    and more.
This package consists of functions to design DCMs and other types of choice 
  studies (including MaxDiff and other tradeoffs)
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.
Amends errors, augments data and aids analysis of John Snow's map
    of the 1854 London cholera outbreak.
Sampling from the Cholesky factorization of a Wishart random 
    variable, sampling from the inverse Wishart distribution, sampling from 
    the Cholesky factorization of an inverse Wishart random variable, 
    computing densities for the Wishart and inverse Wishart distributions, 
    and computing the multivariate gamma and digamma functions.
Choplump Tests are Permutation Tests for Comparing Two Groups with Some Positive but Many Zero Responses
Resampling is a standard step in particle filtering and in
    sequential Monte Carlo. This package implements the chopthin resampler, which
    keeps a bound on the ratio between the largest and the smallest weights after
    resampling.
Learning the structure of graphical models from datasets with thousands of variables.
    More information about the research papers detailing the theory behind Chordalysis is available at
    <http://www.francois-petitjean.com/Research> (KDD 2016, SDM 2015, ICDM 2014, ICDM 2013).
    The R package development site is <https://github.com/HerrmannM/Monash-ChoR>.
Maximum likelihood estimation in respondent driven samples.
Choropleths are thematic maps where geographic regions, such as
    states, are colored according to some metric, such as the number of people
    who live in that state. This package simplifies this process by 1.
    Providing ready-made functions for creating choropleths of common maps. 2.
    Providing data and API connections to interesting data sources for making
    choropleths. 3. Providing a framework for creating choropleths from
    arbitrary shapefiles. 4. Overlaying those maps over reference maps from
    Google Maps.
Contains an administrative-level-1 map of the world.
    Administrative-level-1 is the generic term for the largest sub-national
    subdivision of a country. This package was created for use with the
    choroplethr package.
Contains 3 maps. 1) US States 2) US Counties 3) Countries of the
    world.
Extracts music chords from the 'CifraClub' website <https://www.cifraclub.com.br/>.
	The package also has functions for cleaning the extracted data and 
	feature extraction.
A programmatic interface to the Chromosome Counts Database
    (http://ccdb.tau.ac.il/). This package is part of the rOpenSci suite
    (http://ropensci.org)
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.
Provides chronological objects which can handle dates and times.
Functions for use in perturbing data prior to use of nonparametric smoothers
             and clustering.
Text data can be processed chunkwise using 'dplyr' commands. These
    are recorded and executed per data chunk, so large files can be processed with
    limited memory using the 'LaF' package.
Read tables chunk by chunk using a C++ backend and a simple R interface.
Calculation of consensus values for atomic weights, isotope amount ratios, and isotopic abundances with the associated uncertainties using multivariate meta-regression approach for consensus building.
In many studies across different disciplines, detailed measures of the variables of interest are available. If assumptions can be made regarding the direction of effects between the assessed variables, this has to be considered in the analysis. The functions in this package implement the novel approach CIEE (causal inference using estimating equations; Konigorski et al., 2018, <DOI:10.1002/gepi.22107>) for estimating and testing the direct effect of an exposure variable on a primary outcome, while adjusting for indirect effects of the exposure on the primary outcome through a secondary intermediate outcome and potential factors influencing the secondary outcome. The underlying directed acyclic graph (DAG) of this considered model is described in the vignette. CIEE can be applied to studies in many different fields, and it is implemented here for the analysis of a continuous primary outcome and a time-to-event primary outcome subject to censoring. CIEE uses estimating equations to obtain estimates of the direct effect and robust sandwich standard error estimates. Then, a large-sample Wald-type test statistic is computed for testing the absence of the direct effect. Additionally, standard multiple regression, regression of residuals, and the structural equation modeling approach are implemented for comparison.
Estimate of cumulative incidence function in two samples. Provide weighted summary statistics based on various methods and weights.
Functions for the input/output and visualization of
    medical imaging data in the form of 'CIFTI' files 
    <https://www.nitrc.org/projects/cifti/>.
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.
Functions for computing, comparing and demonstrating top informative centrality measures within a network.
Detecting and visualizing nonlinear interaction effects of single nucleotide polymorphisms or epistatic interactions, especially high-order epistatic interactions, are important topics in bioinformatics because of their significant mathematical and computational challenges. We present CINOEDV (Co-Information based N-Order Epistasis Detector and Visualizer) for detecting, visualizing, and analyzing high-order epistatic interactions by introducing virtual vertices into the construction of a hypergraph. CINOEDV was developed as an alternative to existing software to build a global picture of epistatic interactions and unexpected high-order epistatic interactions, which might provide useful clues for understanding the underlying genetic architecture of complex diseases.
Plot confidence interval from the objects of statistical tests such as
  t.test(), var.test(), cor.test(), prop.test() and fisher.test() ('htest' class),
  Tukey test [TukeyHSD()], Dunnett test [glht() in 'multcomp' package],
  logistic regression [glm()], and Tukey or Games-Howell test [posthocTGH() in
  'userfriendlyscience' package].
  Users are able to set the styles of lines and points.
  This package contains the function to calculate odds ratios and their confidence
  intervals from the result of logistic regression.
Isotonic regression (IR), as well as a great small-sample improvement to IR called
    CIR, interval estimates for both, and additional utilities.
This package contains functions for fitting circumplex
        structural models for correlation matrices (with negative
        correlation) by the method of maximum likelihood.
Perform a Bayesian analysis of a circular outcome General Linear
    Model (GLM), which allows regressing a circular outcome on linear and
    categorical predictors. Posterior samples are obtained by means of an MCMC
    algorithm written in 'C++' through 'Rcpp'. Estimation and credible intervals
    are provided, as well as hypothesis testing through Bayes Factors.
    See Mulder and Klugkist (2017) <doi:10.1016/j.jmp.2017.07.001>.
Circular layout is an efficient way for the visualization of huge 
    amounts of information. Here this package provides an implementation 
    of circular layout generation in R as well as an enhancement of available 
    software. The flexibility of the package is based on the usage of low-level 
    graphics functions such that self-defined high-level graphics can be easily 
    implemented by users for specific purposes. Together with the seamless 
    connection between the powerful computational and visual environment in R, 
    it gives users more convenience and freedom to design figures for 
    better understanding complex patterns behind multiple dimensional data.
A series of wrapper functions to
    implement the 10 maximum likelihood models of animal orientation
    described by Schnute and Groot (1992) <DOI:10.1016/S0003-3472(05)80068-5>. The
    functions also include the ability to use different optimizer
    methods and calculate various model selection metrics (i.e., AIC,
    AICc, BIC).
Includes functions for the analysis of circular data using distributions based on Nonnegative Trigonometric Sums (NNTS). The package includes functions for calculation of densities and distributions, for the estimation of parameters, for plotting and more.
Detection of outliers in circular-circular regression models, modifying its and estimating of models parameters.
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.
Circular drift-diffusion model for continuous reports.
Jointly model the accuracy of cognitive responses and item choices
    within a bayesian hierarchical framework as described by Culpepper and
    Balamuta (2015) <doi:10.1007/s11336-015-9484-7>. In addition, the package
    contains the datasets used within the analysis of the paper.
Specific dimension reduction methods for replicated graphs 
    (multiple undirected graphs repeatedly measured on a common set of 
    nodes). The package contains efficient procedures for estimating 
    a shared baseline propensity matrix and graph-specific low rank 
    matrices. The algorithm uses block coordinate descent algorithm to 
    solve the model, which alternatively performs L2-penalized logistic 
    regression and multiple partial eigenvalue decompositions, as described 
    in the paper Wang et al. (2017) <arXiv:1707.06360>.
A likelihood-based hypothesis testing approach is implemented for assessing causal mediation. For example, it could be used to test for mediation of a known causal association between a DNA variant, the 'instrumental variable', and a clinical outcome or phenotype by gene expression or DNA methylation, the potential mediator. Another example would be testing mediation of the effect of a drug on a clinical outcome by the molecular target. The hypothesis test generates a p-value or permutation-based FDR value with confidence intervals to quantify uncertainty in the causal inference. The outcome can be represented by either a continuous or binary variable, the potential mediator is continuous, and the instrumental variable can be continuous or binary and is not limited to a single variable but may be a design matrix representing multiple variables.
Supports quantitative
    research in scientometrics and bibliometrics. Provides
    various tools for preprocessing bibliographic
    data retrieved, e.g., from Elsevier's SciVerse Scopus,
    computing bibliometric impact of individuals,
    or modeling 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.
Contain an RStudio addin to insert BibTex citation in Rmarkdown documents with a minimal user interface.
Functions to append confidence intervals, prediction intervals,
    and other quantities of interest to data frames. All appended quantities
    are for the response variable, after conditioning on the model and covariates.
    This package has a data frame first syntax that allows for easy piping.
    Currently supported models include (log-) linear, (log-) linear mixed,
    and generalized linear models.
Functions and an 'RStudio' add-in that search a 'BibTeX'-file to create and
  insert formatted Markdown citations into the current document.
Input: a csv-file for each database table and a
        controlfile describing relations between tables. Output: An
        extended ER diagram
Retrieves data and estimates unmeasured flows of water through the 
  urban network. Any city may be modeled with preassembled data, but data for 
  US cities can be gathered via web services using this package and dependencies 
  'geoknife' and 'dataRetrieval'.
Computes a confidence interval for a specified linear combination of the 
  regression parameters in a linear regression model with iid normal errors
  with known variance when there is uncertain prior information that a distinct
  specified linear combination of the regression parameters takes a given 
  value.  This confidence interval, found by numerical constrained 
  optimization, has the required minimum coverage and utilizes this uncertain 
  prior information through desirable expected length properties.
  This confidence interval has the following three practical applications. 
  Firstly, if the error variance has been accurately estimated from previous 
  data then it may be treated as being effectively known. Secondly, for 
  sufficiently large (dimension of the response vector) minus (dimension of 
  regression parameter vector), greater than or equal to 30 (say),
  if we replace the assumed known value of the error variance by its usual 
  estimator in the formula for the confidence interval then the resulting 
  interval has, to a very good approximation, the same coverage probability 
  and expected length properties as when the error variance is known. Thirdly,
  some more complicated models can be approximated by the linear regression 
  model with error variance known when certain unknown parameters are replaced 
  by estimates. This confidence interval is described in Kabaila, P. and 
  Mainzer, R. (2017) <arXiv:1708.09543>, and is a member of the family of 
  confidence intervals proposed by Kabaila, P. and Giri, K. (2009) 
  <doi:10.1016/j.jspi.2009.03.018>.
A set of tools around common workflows and a convenient interface to make
  requests directly to the 'Civis data science API' <https://www.civisanalytics.com/platform/>.
We provide a computationally efficient and robust implementation of the recently proposed C-JAMP (Copula-based Joint Analysis of Multiple Phenotypes) method (Konigorski et al., 2018, submitted). C-JAMP allows estimating and testing the association of one or multiple predictors on multiple outcomes in a joint model, and is implemented here with a focus on large-scale genome-wide association studies with two phenotypes. The use of copula functions allows modeling a wide range of multivariate dependencies between the phenotypes, and previous results are supporting that C-JAMP can increase the power of association studies to identify associated genetic variants in comparison to existing methods (Konigorski, Yilmaz, Pischon, 2016, <DOI:10.1186/s12919-016-0045-6>; Konigorski, Yilmaz, Bull, 2014, <DOI:10.1186/1753-6561-8-S1-S72>). In addition to the C-JAMP functions, functions are available to generate genetic and phenotypic data, to compute the minor allele frequency (MAF) of genetic markers, and to estimate the phenotypic variance explained by genetic markers.
An R implementation of the Average Marginal Component-specific
    Effects (AMCE) estimator presented in Hainmueller, J., Hopkins, D., and Yamamoto
    T. (2014) <DOI:10.1093/pan/mpt024> Causal Inference in Conjoint Analysis: Understanding Multi-Dimensional
    Choices via Stated Preference Experiments. Political Analysis 22(1):1-30.
Toolkit for statistical, machine learning, and targeted learning
  analyses. Functionality includes loading & auto-installing packages,
  standardizing datasets, creating missingness indicators, imputing missing
  values, creating multicore or multinode clusters, automatic SLURM integration,
  enhancing SuperLearner and TMLE with automatic parallelization, and many other
  SuperLearner analysis & plotting enhancements.
Client for 'CKAN' 'API' (http://ckan.org/). Includes interface
    to 'CKAN' 'APIs' for search, list, show for packages, organizations, and
    resources. In addition, provides an interface to the 'datastore' 'API'.
Fast optimal univariate clustering and segementation
 by dynamic programming. Three types of problem including
 univariate k-means, k-median, and k-segments are solved with
 guaranteed optimality and reproducibility. The core algorithm
 minimizes the sum of within-cluster distances using respective
 metrics. Its advantage over heuristic clustering algorithms in
 efficiency and accuracy is increasingly pronounced as the
 number of clusters k increases. Weighted k-means and unweighted
 k-segments algorithms can also optimally segment time series
 and perform peak calling. An auxiliary function generates
 histograms that are adaptive to patterns in data. In contrast to
 heuristic methods, this package provides a powerful set of tools
 for univariate data analysis with guaranteed optimality.
Implements 'Markovitz' Critical Line Algorithm ('CLA') for
  classical mean-variance portfolio optimization.  Care has been taken for
  correctness in light of previous buggy implementations.
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.
Performs 'classical' age-depth modelling of dated sediment deposits - prior to applying more sophisticated techniques such as Bayesian age-depth modelling. Any radiocarbon dated depths are calibrated. Age-depth models are constructed by sampling repeatedly from the dated levels, each time drawing age-depth curves. Model types include linear interpolation, linear or polynomial regression, and a range of splines. See Blaauw (2010). <doi:10.1016/j.quageo.2010.01.002>.
Allows for idealisation of patch clamp recordings by implementing the non-parametric JUmp Local dEconvolution Segmentation (JULES) filter, see F. Pein, I. Tecuapetla-Gómez, O. Schütte, C. Steinem, and A. Munk (2017) <arXiv:1706.03671>.
Implementation of the Wilkinson and Ivany (2002) approach to paleoclimate analysis, applied to isotope data extracted from clams.
Get description of images from Clarifai API. For more information,
    see <http://clarifai.com>. Clarifai uses a large deep learning cloud to come
    up with descriptive labels of the things in an image. It also provides how
    confident it is about each of the labels.
Various functions for classification, including k-nearest
  neighbour, Learning Vector Quantization and Self-Organizing Maps.
Defines the classes used for "class comparison" problems
  in the OOMPA project (<http://oompa.r-forge.r-project.org/>). Class
  comparison includes tests for differential expression; see Simon's
  book for details on typical problem types.
Defines the classes used for "class discovery" problems
  in the OOMPA project (<http://oompa.r-forge.r-project.org/>). Class
  discovery primarily consists of unsupervised clustering methods with
  attempts to assess their statistical significance.
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.
Generates a visualization of binary classifier performance as a grid of
  diagnostic plots with just one function call. Includes ROC curves,
  prediction density, accuracy, precision, recall and calibration plots, all using
  ggplot2 for easy modification.
  Debug your binary classifiers faster and easier!
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.
Efficient implementation of k-nearest neighbor estimation and kernel estimation for functional data classification.
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.
Selected commonly used methods 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.
Bindings to Google's C++ library Compact Language Detector 2
    (see <https://github.com/cld2owners/cld2#readme> for more information). Probabilistically
    detects over 80 languages in plain text or HTML. For mixed-language input it returns the
    top three detected languages and their approximate proportion of the total classified 
    text bytes (e.g. 80% English and 20% French out of 1000 bytes). There is also a 'cld3'
    package on CRAN which uses a neural network model instead.
Google's Compact Language Detector 3 is a neural network model for language 
    identification and the successor of 'cld2' (available from CRAN). The algorithm is still
    experimental and takes a novel approach to language detection with different properties
    and outcomes. It can be useful to combine this with the Bayesian classifier results 
    from 'cld2'. See <https://github.com/google/cld3#readme> for more information.
An electronic health care record (EHR) data cleaning and processing
    platform. It focus on heterogeneous high resolution longitudinal data. It works with 
    Critical Care Health Informatics Collaborative (CCHIC) dataset. It is
    created to address various data reliability and accessibility problems of
    EHRs as such.
Provides a set of utility tools to inspect spatial objects, facilitate
  handling and reporting of topology errors and geometry validity issues.
  Finally, it provides a geometry cleaner that will fix all geometry problems,
  and eliminate (at least reduce) the likelihood of having issues when doing
  spatial data processing.
Provides a set of fast tools for converting a textual corpus into a set of normalized
  tables. Users may make use of the 'udpipe' back end with no external dependencies, a Python back
  end with 'spaCy' <https://spacy.io> or the Java back end 'CoreNLP'
  <http://stanfordnlp.github.io/CoreNLP/>. Exposed annotation tasks include
  tokenization, part of speech tagging, named entity recognition, entity linking, sentiment
  analysis, dependency parsing, coreference resolution, and word embeddings. Summary
  statistics regarding token unigram, part of speech tag, and dependency type frequencies
  are also included to assist with analyses.
Check your R code for some of the most common layout flaws.
    Many tried to teach us how to write code less dreadful, be it implicitly as
    B. W. Kernighan and D. M. Ritchie (1988) <ISBN:0-13-110362-8>
    in 'The C Programming Language' did, be it
    explicitly as R.C. Martin (2008) <ISBN:0-13-235088-2> in
    'Clean Code: A Handbook of Agile Software Craftsmanship' did.
    So we should check our code for files too long or wide, functions with too
    many lines, too wide lines, too many arguments or too many levels of 
    nesting.
    Note: This is not a static code analyzer like pylint or the like. Checkout
    https://github.com/jimhester/lintr instead.
Implements an empirical Bayes approach for simultaneous variable clustering and regression. This version also (re)implements in C++ an R script proposed by Howard Bondell that fits the Pairwise Absolute Clustering and Sparsity (PACS) methodology (see Sharma et al (2013) <DOI:10.1080/15533174.2012.707849>).
Composite likelihood approach is implemented to estimating statistical models for spatial ordinal and proportional data based on Feng et al. (2014) <doi:10.1002/env.2306>. Parameter estimates are identified by maximizing composite log-likelihood functions using the limited memory BFGS optimization algorithm with bounding constraints, while standard errors are obtained by estimating the Godambe information matrix.
Conditioned Latin hypercube sampling, as published by Minasny and McBratney (2006) <DOI:10.1016/j.cageo.2005.12.009>. 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, <DOI:10.1201/b12728-46>).
A suite of tools designed to build attractive command line
    interfaces ('CLIs'). Includes tools for drawing rules, boxes, trees, and
    'Unicode' symbols with 'ASCII' alternatives.
Clustering categorical sequences by means of finite mixtures with Markov model components is the main utility of ClickClust. The package also allows detecting blocks of equivalent states by forward and backward state selection procedures.
Tools for assessing data quality, performing exploratory analysis, 
    fixing data errors in numerical, factor and date variables and creating report 
    tables from models and summaries.
A set of tools to read, analyze and write lists of click sequences
    on websites (i.e., clickstream). A click can be represented by a number,
    character or string. Clickstreams can be modeled as zero- (only computes
    occurrence probabilities), first- or higher-order Markov chains.
CliFlo is a web portal to the New Zealand National Climate
    Database and provides public access (via subscription) to around 6,500
    various climate stations (see <https://cliflo.niwa.co.nz/> for more
    information). Collating and manipulating data from CliFlo
    (hence clifro) and importing into R for further analysis, exploration and
    visualisation is now straightforward and coherent. The user is required to
    have an internet connection, and a current CliFlo subscription (free) if
    data from stations, other than the public Reefton electronic weather
    station, is sought.
A profile likelihood based method of estimation and inference on the correlation coefficient of bivariate data with different types of censoring and missingness.
Functions to homogenize climatological series and to produce climatological summaries and grids from the homogenized results, plus functions to draw wind-roses and Walter&Lieth diagrams.
Calculates first, and second order, average minimal depth of a
    maximal subtree for a forest object produced by the R 'ranger'
    package. This variable importance metric is implemented as described in
    Ishwaran et. al. ("High-Dimensional Variable Selection for Survival Data",
    March 2010, <doi:10.1198/jasa.2009.tm08622>).
Classification of climate according to Koeppen - Geiger, of aridity
    indices, of continentality indices, of water balance after Thornthwaite, of
    viticultural bioclimatic indices. Drawing climographs: Thornthwaite, Peguy,
    Bagnouls-Gaussen.
PCIC's implementation of Climdex routines for computation of
    extreme climate indices.
A suite of routines for downscaling coarse scale global
    climate model (GCM) output to a fine spatial resolution. Includes
    Bias-Corrected Spatial Downscaling (BCDS), Constructed Analogues
    (CA), Climate Imprint (CI), and Bias Correction/Constructed
    Analogues with Quantile mapping reordering (BCCAQ). Developed by
    the the Pacific Climate Impacts Consortium (PCIC), Victoria,
    British Columbia, Canada.
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.
Functions for fitting GEV and POT (via point process fitting)
    models for extremes in climate data, providing return values, return
    probabilities, and return periods for stationary and nonstationary models.
    Also provides differences in return values and differences in log return
    probabilities for contrasts of covariate values. Functions for estimating risk
    ratios for event attribution analyses, including uncertainty. Under the hood,
    many of the functions use functions from 'extRemes', including for fitting the
    statistical models.
Contains functions to detect and visualise periods of climate
    sensitivity (climate windows) for a given biological response.
Bayesian and ML Emax model fitting, graphics and simulation for clinical dose
    response.  The summary data from the dose response meta-analyses in  
    Thomas, Sweeney, and Somayaji (2014) <doi:10.1080/19466315.2014.924876> and
    Thomas and Roy (2016) <doi:10.1080/19466315.2016.1256229> are included 
    in the package.  The prior distributions for the Bayesian analyses default to
    the posterior predictive distributions derived from these references.
Utilities to make your clinical collaborations easier if not
          fun. It contains functions for designing studies such as Simon
          2-stage and group sequential designs and for data analysis such
          as Jonckheere-Terpstra test and estimating survival quantiles.
Provides estimates of the several summary measures for clinical trials including
    the average hazard ratio, the weighted average hazard ratio, the restricted 
    superiority probability ratio, the restricted mean survival difference and 
    the ratio of restricted mean times lost, based on the short-term and 
    long-term hazard ratio model (Yang, 2005 <doi:10.1093/biomet/92.1.1>) 
    which accommodates various non-proportional hazards scenarios. The inference
    procedures and the asymptotic results for the summary measures are discussed 
    in Yang (2017, pre-print).
Calculates equations commonly used in clinical pharmacokinetics and clinical pharmacology, such as equations for dose individualization, compartmental pharmacokinetics, drug exposure, anthropomorphic calculations, clinical chemistry, and conversion of common clinical parameters. Where possible and relevant, it provides multiple published and peer-reviewed equations within the respective R function.
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.
Simple utility functions to read from and write to the Windows,
    OS X, and X11 clipboards.
A small subset of Unicode symbols, that are useful
    when building command line applications. They fall back to
    alternatives on terminals that do not support Unicode.
    Many symbols were taken from the 'figures' 'npm' package
    (see <https://github.com/sindresorhus/figures>).
Estimation and inference for linear models where some or all of the
    fixed-effects coefficients are subject to order restrictions. This package uses
    the robust residual bootstrap methodology for inference, and can handle some
    structure in the residual variance matrix.
A set of functions to fit a boosting conditional logit model.
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.
Fit a sequence of conditional logistic regression models with lasso, for small to large sized samples.
Composite likelihood parameter estimate and asymptotic covariance matrix are calculated for the spatial ordinal data with replications, where spatial ordinal response with covariate and both spatial exponential covariance within subject and independent and identically distributed measurement error.  Parameter estimation can be performed by either solving the gradient function or maximizing composite log-likelihood. Parametric bootstrapping is used to estimate the Godambe information matrix and hence the asymptotic standard error and covariance matrix with parallel processing option.
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.
Central limit theorem experiments presented by data frames or plots. Functions include generating theoretical sample space, corresponding probability, and simulated results as well.
Provides several cluster-robust variance estimators (i.e.,
    sandwich estimators) for ordinary and weighted least squares linear regression
    models, including the bias-reduced linearization estimator introduced by Bell
    and McCaffrey (2002) 
    <http://www.statcan.gc.ca/pub/12-001-x/2002002/article/9058-eng.pdf> and 
    developed further by Pustejovsky and Tipton (2017) 
    <DOI:10.1080/07350015.2016.1247004>. The package includes functions for estimating
    the variance- covariance matrix and for testing single- and multiple-
    contrast hypotheses based on Wald test statistics. Tests of single regression
    coefficients use Satterthwaite or saddle-point corrections. Tests of multiple-
    contrast hypotheses use an approximation to Hotelling's T-squared distribution.
    Methods are provided for a variety of fitted models, including lm() and mlm
    objects, glm(), ivreg (from package 'AER'), plm() (from package 'plm'), gls()
    and lme() (from 'nlme'), robu() (from 'robumeta'), and rma.uni() and rma.mv()
    (from 'metafor').
CLUster Evaluation (CLUE) is a computational method for identifying optimal number of clusters in a given time-course dataset clustered by cmeans or kmeans algorithms and subsequently identify key kinases or pathways from each cluster. Its implementation in R is called ClueR. See Readme on <https://github.com/PengyiYang/ClueR> for more details.
We developed the clues R package to provide 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.
Provides utilities for clustering subjects and variables of mixed data types. Similarities between subjects are measured by Gower's general similarity coefficient with an extension of Podani for ordinal variables. Similarities between variables can be assessed i) by combination of appropriate measures of association for different pairs of data types or ii) based on distance correlation. Alternatively, variables can also be clustered by the 'ClustOfVar' approach. The main feature of the package is the generation of a mixed-data heatmap. For visualizing similarities between either subjects or variables, a heatmap of the corresponding distance matrix can be drawn. Associations between variables can be explored by a 'confounder plot', which allows visual detection of possible confounding, collinear, or surrogate factors for some variables of primary interest. Distance matrices and dendrograms for subjects and variables can be derived and used for further visualizations and applications. This work was supported by BMBF grant 01ZX1609B, Germany.
Non-parametric tests (Wilcoxon rank sum test and Wilcoxon signed rank test) for clustered data.
Tests, utilities, and case studies for analyzing significance in
  clustered binary matched-pair data. The central function clust.bin.pair uses
  one of several tests to calculate a Chi-square statistic. Implemented are the
  tests Eliasziw (1991) <doi:10.1002/sim.4780101211>, Obuchowski (1998)
  <doi:10.1002/(SICI)1097-0258(19980715)17:13%3C1495::AID-SIM863%3E3.0.CO;2-I>,
  Durkalski (2003) <doi:10.1002/sim.1438>, and Yang (2010)
  <doi:10.1002/bimj.201000035> with McNemar (1947) <doi:10.1007/BF02295996>
  included for comparison. The utility functions nested.to.contingency and
  paired.to.contingency convert data between various useful formats. Thyroids
  and psychiatry are the canonical datasets from Obuchowski and Petryshen (1989)
  <doi:10.1016/0165-1781(89)90196-0> respectively.
Clustering method to cluster both effects curves, through quantile regression coefficient modeling, and curves in functional data analysis. Sottile G. and Adelfio G. (2017) <https://iwsm2017.webhosting.rug.nl/IWSM_2017_V2.pdf>.
Methods for Cluster analysis.  Much extended the original from
	Peter Rousseeuw, Anja Struyf and Mia Hubert,
	based on Kaufman and Rousseeuw (1990) "Finding Groups in Data".
Provides functionality for the analysis of clustered data using the cluster bootstrap.
A collection of data sets for teaching cluster analysis.
Estimating causal effects from observational studies assuming 
    clustered (or partial) interference. These inverse probability-weighted
    estimators target new estimands arising from population-level treatment 
    policies. The estimands and estimators are introduced in Barkley et al. 
    (2017) <arXiv:1711.04834>.
Identification and visualization of groups of closely spaced mutations in the DNA sequence of cancer genome. The extremely mutated zones are searched in the symmetric dissimilarity matrix using the anti-Robinson matrix properties. Different data sets are obtained to describe and plot the clustered mutations information.
Visualise clustering algorithms with GGobi.  Contains both
    general code for visualising clustering results and specific
    visualisations for model-based, hierarchical and SOM clustering.
We developed the clusterGeneration package to provide 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.
One haplotype is a combination of SNP
  (Single Nucleotide Polymorphisms) within the QTL (Quantitative Trait Loci).
  clusterhap groups together all individuals of a population with the same haplotype.
  Each group contains individual with the same allele in each SNP,
  whether or not missing data. Thus, clusterhap groups individuals,
  that to be imputed, have a non-zero probability of having the same alleles
  in the entire sequence of SNP's. Moreover, clusterhap calculates such
  probability from relative frequencies.
A dynamic programming algorithm for optimal clustering multidimensional data with sequential constraint. The algorithm minimizes the sum of squares of within-cluster distances. The sequential constraint allows only subsequent items of the input data to form a cluster. The sequential constraint is typically required in clustering data streams or items with time stamps such as video frames, GPS signals of a vehicle, movement data of a person, e-pen data, etc. The algorithm represents an extension of Ckmeans.1d.dp to multiple dimensional spaces. Similarly to the one-dimensional case, the algorithm guarantees optimality and repeatability of clustering. Method clustering.sc.dp can find the optimal clustering if the number of clusters is known. Otherwise, methods findwithinss.sc.dp and backtracking.sc.dp can be used.
Evaluate arbitrary function calls using workers on HPC schedulers
    in single line of code. All processing is done on the network without
    accessing the file system. Remote schedulers are supported via SSH.
Integrative context-dependent clustering for heterogeneous
    biomedical datasets. Identifies local clustering structures in related
    datasets, and a global clusters that exist across the datasets.
Estimate and return the needed parameters for visualisations designed for 'OpenBudgets' <http://openbudgets.eu/> data. Calculate cluster analysis measures in budget data of municipalities across Europe, according to the 'OpenBudgets' data model. It involves a set of techniques and algorithms used to find and divide the data into groups of similar observations. Also, can be used generally to extract visualisation parameters convert them to 'JSON' format and use them as input in a different graphical interface.
Calculate power for cluster randomized trials (CRTs) that compare two means, two proportions, or two counts using closed-form solutions. In addition, calculate power for cluster randomized  crossover trials using Monte Carlo methods. For more information, see Reich et al. (2012) <doi:10.1371/journal.pone.0035564>.
Gaussian mixture models, k-means, mini-batch-kmeans and k-medoids
    clustering with the option to plot, validate, predict (new data) and estimate the
    optimal number of clusters. The package takes advantage of 'RcppArmadillo' to
    speed up the computationally intensive parts of the functions.
Nonparametric rank based tests (rank-sum tests and signed-rank tests) for clustered data, especially useful for clusters having informative cluster size and intra-cluster group size.
A function for validating microarry clusters via
        reproducibility
Calculate p-values and confidence intervals using cluster-adjusted
    t-statistics (based on Ibragimov and Muller (2010) <DOI:10.1198/jbes.2009.08046>, pairs cluster bootstrapped t-statistics, and wild cluster bootstrapped t-statistics (the latter two techniques based on Cameron, Gelbach, and Miller (2008) <DOI:10.1162/rest.90.3.414>. Procedures are included for use with GLM, ivreg, plm (pooling or fixed effects), and mlogit models.
Distance measures (GDM1, GDM2,	Sokal-Michener, Bray-Curtis, for symbolic interval-valued data), cluster quality indices (Calinski-Harabasz, Baker-Hubert, Hubert-Levine, Silhouette, Krzanowski-Lai, Hartigan, Gap,	Davies-Bouldin),	data normalization formulas (metric data, interval-valued symbolic data), data generation (typical and non-typical data), HINoV method,	replication analysis, linear ordering methods, spectral clustering, agreement indices between two partitions, plot functions (for categorical and symbolic interval-valued data). 
 (MILLIGAN, G.W., COOPER, M.C. (1985) <doi:10.1007/BF02294245>, 
 HUBERT, L., ARABIE, P. (1985), <doi:10.1007%2FBF01908075>, 
 RAND, W.M. (1971) <doi:10.1080/01621459.1971.10482356>, 
 JAJUGA, K., WALESIAK, M. (2000) <doi:10.1007/978-3-642-57280-7_11>, 
 MILLIGAN, G.W., COOPER, M.C. (1988) <doi:10.1007/BF01897163>, 
 CORMACK, R.M. (1971) <doi:10.2307/2344237>, 
 JAJUGA, K., WALESIAK, M., BAK, A. (2003) <doi:10.1007/978-3-642-55721-7_12>, 
 CARMONE, F.J., KARA, A., MAXWELL, S. (1999) <doi:10.2307/3152003>, 
 DAVIES, D.L., BOULDIN, D.W. (1979) <doi:10.1109/TPAMI.1979.4766909>, 
 CALINSKI, T., HARABASZ, J. (1974) <doi:10.1080/03610927408827101>,
 HUBERT, L. (1974) <doi:10.1080/01621459.1974.10480191>, 
 TIBSHIRANI, R., WALTHER, G., HASTIE, T. (2001) <doi:10.1111/1467-9868.00293>, 
 KRZANOWSKI, W.J., LAI, Y.T. (1988) <doi:10.2307/2531893>, 
 BRECKENRIDGE, J.N. (2000) <doi:10.1207/S15327906MBR3502_5>, 
 WALESIAK, M., DUDEK, A. (2008) <doi:10.1007/978-3-540-78246-9_11>).
Allows one to assess the stability of individual objects, clusters 
    and whole clustering solutions based on repeated runs of the K-means and K-medoids 
    partitioning algorithms.
Calculate some statistics aiming to help analyzing the clustering tendency of given data. In the first version, Hopkins' statistic is implemented.
An R package that provides a suite of tools to evaluate
        clustering algorithms, clusterings, and individual clusters.
Implements a Ward-like hierarchical clustering
    algorithm including spatial/geographical constraints.
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.
Functions to perform k-prototypes partitioning clustering for
    mixed variable-type data according to Z.Huang (1998): Extensions to the k-Means
    Algorithm for Clustering Large Data Sets with Categorical Variables, Data Mining
    and Knowledge Discovery 2, 283-304, <DOI:10.1023/A:1009769707641>.
An implementation of a variable selection procedure in clustering by mixture models for discrete data (clustMMDD). Genotype data are examples of such data with two unordered observations (alleles) at each locus for diploid individual. The two-fold problem of variable selection and clustering is seen as a model selection problem where competing models are characterized by the number of clusters K, and the subset S of clustering variables. Competing models are compared by penalized maximum likelihood criteria. We considered asymptotic criteria such as Akaike and Bayesian Information criteria, and a family of penalized criteria with penalty function to be data driven calibrated.
Cluster analysis of a set of variables. Variables can be quantitative, qualitative or a mixture of both.
Provides an interface to perform cluster analysis on a corpus of
    text. Interfaces to Quanteda to assemble text corpuses easily. Deviationalizes
    text vectors prior to clustering using technique described by Sherin (Sherin,
    B. [2013]. A computational study of commonsense science: An exploration in the
    automated analysis of clinical interview data. Journal of the Learning Sciences,
    22(4), 600-638. Chicago. <doi:10.1080/10508406.2013.836654>). Uses
    cosine similarity as distance metric for two stage clustering process, involving
    Ward's algorithm hierarchical agglomerative clustering, and k-means clustering.
    Selects optimal number of clusters to maximize "variance explained" by clusters,
    adjusted by the number of clusters. Provides plotted output of clustering
    results as well as printed output. Assesses "model fit" of clustering solution
    to a set of preexisting groups in dataset.
A class of methods that combine dimension reduction and clustering of continuous or categorical data. For continuous data, the package contains implementations of factorial K-means (Vichi and Kiers 2001; <DOI:10.1016/S0167-9473(00)00064-5>) and reduced K-means (De Soete and Carroll 1994; <DOI:10.1007/978-3-642-51175-2_24>); both methods that combine principal component analysis with K-means clustering. For categorical data, the package provides MCA K-means (Hwang, Dillon and Takane 2006; <DOI:10.1007/s11336-004-1173-x>), i-FCB (Iodice D'Enza and Palumbo 2013, <DOI:10.1007/s00180-012-0329-x>) and Cluster Correspondence Analysis (van de Velden, Iodice D'Enza and Palumbo 2017; <DOI:10.1007/s11336-016-9514-0>), which combine multiple correspondence analysis with K-means.
Deciding what resolution to use can be a difficult question when
    approaching a clustering analysis. One way to approach this problem is to
    look at how samples move as the number of clusters increases. This package
    allows you to produce clustering trees, a visualisation for interrogating
    clusterings as resolution increases.
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.
Functions for the clustering of variables around Latent Variables.
    Each cluster of variables, which may be defined as a local or directional
    cluster, 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 "noise" cluster or sparse latent variables can also de
    defined.
Variable selection for Gaussian model-based clustering as implemented in the 'mclust' package. The methodology allows to find the (locally) optimal subset of variables in a data set 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 parallelisation is also available.
Gaussian-Based Visualization of Gaussian and Non-Gaussian Model-Based Clustering done on any type of data. Visualization is based on the probabilities of classification.
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.
Pure R implementation of the Covariance Matrix Adaptation -
    Evolution Strategy (CMA-ES) with optional restarts (IPOP-CMA-ES).
Provides functions to perform matching algorithms for causal inference with clustered data, as described in B. Arpino and M. Cannas (2016) <doi:10.1002/sim.6880>. Pure within-cluster and preferential within-cluster matching are implemented. Both algorithms provide causal estimates with cluster-adjusted estimates of standard errors.
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 maximum likelihood estimation and
    weighted least squares estimation of categorical marginal models (CMMs;
    e.g., Bergsma and Rudas, 2002, <http://www.jstor.org/stable/2700006?;
    Bergsma, Croon and Hagenaars, 2009, <DOI:10.1007/b12532>.
Provides the source and examples for James P. Howard, II, 
             "Computational Methods for Numerical Analysis with R," 
			 <https://jameshoward.us/cmna>, a forthcoming book on
			 numerical methods in R.
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.
Manhattan plot, a type of scatter plot, was widely used to display the association results. However, it is usually time-consuming and laborious for a
 non-specialist user to write scripts and adjust parameters of an elaborate plot. Moreover, the ever-growing traits measured have necessitated the 
 integration of results from different Genome-wide association study researches. Circle Manhattan Plot is the first open R package that can lay out 
 Genome-wide association study P-value results in both traditional rectangular patterns, QQ-plot and novel circular ones. United in only one bull's eye style 
 plot, association results from multiple traits can be compared interactively, thereby to reveal both similarities and differences between signals.
A toolkit for flexible modeling of count processes where data (over- or under-) dispersion exists.
    Estimations can be obtained under two data constructs where one has:
    (1) data on number of events in an s-unit time interval, or (2) only wait-time data.
    This package is supplementary to the work set forth in Zhu et al. (2016) <doi:10.1080/00031305.2016.1234976>.
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.
Estimation, testing and regression modeling of
 subdistribution functions in competing risks using quantile regressions,
 as described in Peng and Fine (2009) <DOI:10.1198/jasa.2009.tm08228>.
A collection of useful helper routines developed by
  students of the Center for the Mathematical Research, Stankin,
  Moscow.
The Satellite Application Facility on Climate Monitoring (CM SAF) 
  is a ground segment of the European Organization for the Exploitation of 
  Meteorological Satellites (EUMETSAT) and one of EUMETSATs Satellite Application 
  Facilities. The CM SAF contributes to the sustainable observing of the climate 
  system by providing essential climate variables related to the energy and water 
  cycle of the atmosphere (<http://www.cmsaf.eu>). It is a joint cooperation of seven 
  National Meteorological and Hydrological Services.
  The 'cmsaf' R-package provides a collection of R-operators for the analysis and 
  manipulation of CM SAF NetCDF formatted data. The 'cmsaf' R-package is tested 
  for CM SAF NetCDF data, which are structured in three-dimensional arrays 
  (longitude, latitude, time) on a rectangular grid. Layered CM SAF data have to 
  be converted with the provided levbox_mergetime() function. The 'cmsaf' R-package 
  operators have only minor checks for deviations from the recommended data structure, 
  and give only few specific error messages.
  Scripts for an easy application of the 'cmsaf' package operators and the preparation
  and visualization of CM SAF NetCDF data are provided at the CM SAF webpage 
  (<http://www.cmsaf.eu/EN/Products/Tools/Tools_node.html>).
Various utilities for the complex multivariate Gaussian distribution.
Provides comprehensive functionalities for causal modeling with Coincidence Analysis (CNA), which is a configurational comparative method of causal data analysis that was first introduced in Baumgartner (2009) <doi:10.1177/0049124109339369>. CNA is related to Qualitative Comparative Analysis (QCA), but contrary to the latter, it is custom-built for uncovering causal structures with multiple outcomes. While previous versions have only been capable of processing dichotomous variables, the current version generalizes CNA for multi-value and continuous variables whose values are interpreted as membership scores in fuzzy sets.
Provided R functions for working with the Conditional Negative Binomial distribution.
A GUI with which users can construct and interact
        with Canonical Correspondence Analysis and Canonical Non-Symmetrical Correspondence Analysis and provides inferential results by using Bootstrap Methods.
Implementations of recent complex-valued wavelet shrinkage procedures for smoothing irregularly sampled signals.
Implementations of recent complex-valued wavelet spectral procedures for analysis of irregularly sampled signals.
Contains functions for computing the nonparametric maximum likelihood estimate of a log-concave density function from univariate observations. This log-density estimate is always a piecewise linear function.
Methods for assigning copy number states and breakpoints in resequencing experiments of prokaryotic organisms.
This package evaluates DNA copy number data, using both their initial 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.
Efficient computations for null models that require shuffling columns on big matrix data.
             This package provides functions for faster computation of diversity measure statistics
             when independent random shuffling is applied to the columns of a given matrix. 
             Given a diversity measure f and a matrix M, the provided functions can generate random samples 
             (shuffled matrix rows of M), the mean and variance of f, and the p-values of this measure 
             for two different null models that involve independent random shuffling of the columns of M.
             The package supports computations of alpha and beta diversity measures.
This package contains example data sets with Copy Number Variants and imputed SNPs to be used by CNVassoc package.
Coalescent simulators can rapidly simulate biological sequences
    evolving according to a given model of evolution.
    You can use this package to specify such models, to conduct the simulations
    and to calculate additional statistics from the results.
    It relies on existing simulators for doing the simulation, and currently
    supports the programs 'ms', 'msms' and 'scrm'. It also supports finite-sites
    mutation models by combining the simulators with the program 'seq-gen'.
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.
An implementation of a MCMC method to calculate
    probabilities for a coalition majority based on survey results,
    see Bender and Bauer (2018) <doi:10.21105/joss.00606>.
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.
Generate balance tables and plots for covariates of groups preprocessed through matching, weighting or subclassification, for example, using propensity scores. Includes integration with 'MatchIt', 'twang', 'Matching', 'optmatch', 'CBPS', 'ebal', and 'WeightIt' for assessing balance on the output of their preprocessing functions. Users can also specify data for balance assessment not generated through the above packages. Also included are methods for assessing balance in clustered or multiply imputed data sets.
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 Splines via
        Linear Programming and Sparse Matrices.
A copula based clustering algorithm that finds clusters according to the complex multivariate dependence structure of the data generating process. The updated version of the algorithm is described in Di Lascio, F.M.L. and Giannerini, S. (2016). "Clustering dependent observations with copula functions". Statistical Papers, p.1-17. <doi:10.1007/s00362-016-0822-3>.
Allows for pooled analysis of microarray data by batch-correcting control samples, and then applying the derived correction parameters to non-control samples to obtain bias-free, inter-dataset corrected data.
Statistical tests for the comparison between two correlations
    based on either independent or dependent groups. Dependent correlations can
    either be overlapping or nonoverlapping. A web interface is available on the
    website http://comparingcorrelations.org. A plugin for the R GUI and IDE RKWard
    is included. Please install RKWard from https://rkward.kde.org to use this
    feature. The respective R package 'rkward' cannot be installed directly from a
    repository, as it is a part of RKWard.
The algorithm extracts shared variation from a collection of data sets using regression models.
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 communities.
Statistical tests for the comparison between two or more alpha
    coefficients based on either dependent or independent groups of individuals.
    A web interface is available at http://comparingcronbachalphas.org. A plugin
    for the R GUI and IDE RKWard is included. Please install RKWard from https://
    rkward.kde.org to use this feature. The respective R package 'rkward' cannot be
    installed directly from a repository, as it is a part of RKWard.
Provides functions for summarizing and plotting the
	output from Markov Chain Monte Carlo (MCMC) simulations, as
	well as diagnostic tests of convergence to the equilibrium
	distribution of the Markov chain.
A minimum set of functions to perform compositional data analysis
    using the log-ratio approach introduced by John Aitchison (1982) <http://www.jstor.org/stable/2345821>. Main functions
    have been implemented in c++ for better performance.
Markov chain Monte Carlo burn-in based on "bridge" statistics, in the way of coda::heidel.diag, but including non asymptotic tabulated statistics.
Generates all necessary C functions allowing the user to work with
    the compiled-code interface of ode() and bvptwp(). The implementation supports
    "forcings" and "events". Also provides functions to symbolically compute
    Jacobians, sensitivity equations and adjoint sensitivities being the basis for
    sensitivity analysis. Alternatively to 'deSolve', the Sundials 'CVODES' solver is
    implemented for computation of model sensitivities.
Easily automate the following tasks to describe data frames: 
  computing reliabilities (internal consistencies, retest, multilevel) for psychological scales, 
  summarise the distributions of scales and items graphically and using descriptive statistics,
  combine this information with metadata (such as item labels and labelled values) that is derived from R attributes.
  To do so, the package relies on 'rmarkdown' partials, so you can generate HTML, PDF, and Word documents. Codebooks
  are also available as tables (CSV, Excel, etc.).
Tools for analyzing R expressions
  or blocks of code and determining the dependencies between them.
  It focuses on R scripts, but can be used on the bodies of functions.
  There are many facilities including the ability to summarize  or get a high-level
  view of code, determining dependencies between variables,  code improvement
  suggestions.
The 'Codemeta' Project defines a 'JSON-LD' format for describing
  software metadata, as detailed at <https://codemeta.github.io>. This package
  provides utilities to generate, parse, and modify 'codemeta.json' files 
  automatically for R packages, as well as tools and examples for working with
  'codemeta.json' 'JSON-LD' more generally.
Computation of Multiscale Codependence Analysis and spatial eigenvector maps, as an additional feature.
Categorize links from multiple networks in 3 categories: Common links (alpha) specific links (gamma), and different links (beta). Also categorizes the links into sub-categories and groups. The package includes a visualization tool for the networks. More information about the methodology can be found at: Gysi et. al., 2018 <arXiv:1802.00828>.
A collection of coding functions as alternatives to the standard
    functions in the stats package, which have names starting with 'contr.'.  Their
    main advantage is that they provide a consistent method for defining marginal
    effects in factorial models.  In a simple one-way ANOVA model the
    intercept term is always the simple average of the class means.
A toolbox of ecological community dynamics metrics that are
    explicitly temporal. Functions fall into two categories: temporal diversity
    indices and community stability metrics. The diversity indices are temporal
    analogs to traditional diversity indices such as richness and rank-abundance
    curves. Specifically, functions are provided to calculate species turnover, mean
    rank shifts, and lags in community similarity between time points. The community
    stability metrics calculate overall stability and patterns of species covariance
    and synchrony over time.
Cronbach's alpha and McDonald's omega are 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 coefficient alpha and coefficient omega with missing data and non-normal data. Robust standard errors and confidence intervals are also provided. A test is also available to test the tau-equivalent and homogeneous assumptions. Version 0.5 added the bootstrap confidence intervals.
Plots the coefficients from model objects.  This very quickly shows the user the point estimates and confidence intervals for fitted models.
Simulate species occurrence and abundances (counts) along
    gradients.
Simulates the composition of samples of vegetation
        according to gradient-based vegetation theory.  Features a
        flexible algorithm incorporating competition and complex
        multi-gradient interaction.
species coexistence modeling under asymmetric dispersal
        and fluctuating source-sink dynamics;testing the proportion of
        coexistence scenarios driven by neutral and niche processes
Generate cofeature (feature by sample) matrices. The package 
  utilizes ggplot2::geom_tile() to generate the matrix allowing for easy
  additions from the base matrix.
Calculates complete functional regulation analysis and visualize
    the results in a single heatmap. The provided example data is for biological
    data but the methodology can be used for large data sets to compare quantitative
    entities that can be grouped. For example, a store might divide entities into
    cloth, food, car products etc and want to see how sales changes in the groups
    after some event. The theoretical background for the calculations are provided
    in New insights into functional regulation in MS-based drug profiling, Ana Sofia
    Carvalho, Henrik Molina & Rune Matthiesen, Scientific Reports <doi:10.1038/srep18826>.
Convolution of gamma distributions in R. The convolution of 
    gamma distributions is the sum of series of gamma 
    distributions and all gamma distributions here can have different 
    parameters. This package can calculate density, distribution function 
    and do simulation work.
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.
Extract real-time Bitcoin price details by accessing 'CoinDesk' Bitcoin price Index API <https://www.coindesk.com/api/>.
To extract and monitor price and market cap of 'Crypto currencies' from 'Coin Market Cap' <https://coinmarketcap.com/api/>.
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.
We propose a consistent monitoring procedure to detect a
    structural change from a cointegrating relationship to a spurious
    relationship. The procedure is based on residuals from modified least
    squares estimation, using either Fully Modified, Dynamic or Integrated
    Modified OLS. It is inspired by Chu et al. (1996) <DOI:10.2307/2171955> in
    that it is based on parameter estimation on a pre-break "calibration" period
    only, rather than being based on sequential estimation over the full sample.
    See the discussion paper <DOI:10.2139/ssrn.2624657> for further information.
    This package provides the monitoring procedures for both the cointegration
    and the stationarity case (while the latter is just a special case of the
    former one) as well as printing and plotting methods for a clear
    presentation of the results.
Cointegration methods are widely used in empirical macroeconomics
    and empirical finance. It is well known that in a cointegrating
    regression the ordinary least squares (OLS) estimator of the
    parameters is super-consistent, i.e. converges at rate equal to the
    sample size T. When the regressors are endogenous, the limiting
    distribution of the OLS estimator is contaminated by so-called second
    order bias terms, see e.g. Phillips and Hansen (1990) <DOI:10.2307/2297545>.
    The presence of these bias terms renders inference difficult. Consequently,
    several modifications to OLS that lead to zero mean Gaussian mixture
    limiting distributions have been proposed, which in turn make
    standard asymptotic inference feasible. These methods include
    the fully modified OLS (FM-OLS) approach of Phillips and Hansen
    (1990) <DOI:10.2307/2297545>, the dynamic OLS (D-OLS) approach of Phillips
    and Loretan (1991) <DOI:10.2307/2298004>, Saikkonen (1991)
    <DOI:10.1017/S0266466600004217> and Stock and Watson (1993)
    <DOI:10.2307/2951763> and the new estimation approach called integrated
    modified OLS (IM-OLS) of Vogelsang and Wagner (2014)
    <DOI:10.1016/j.jeconom.2013.10.015>. The latter is based on an augmented
    partial sum (integration) transformation of the regression model. IM-OLS is
    similar in spirit to the FM- and D-OLS approaches, with the key difference
    that it does not require estimation of long run variance matrices and avoids
    the need to choose tuning parameters (kernels, bandwidths, lags). However,
    inference does require that a long run variance be scaled out.
    This package provides functions for the parameter estimation and inference
    with all three modified OLS approaches. That includes the automatic
    bandwidth selection approaches of Andrews (1991) <DOI:10.2307/2938229> and
    of Newey and West (1994) <DOI:10.2307/2297912> as well as the calculation of
    the long run variance.
Performs least squares constrained optimization on a linear objective function. It contains
    a number of algorithms to choose from and offers a formula syntax similar to lm().
Implements a generalized version of the CDH test (<DOI:10.1371/journal.pone.0028145> and <DOI:10.1186/s12859-016-1006-9>)
     for detecting compound heterozygosity on a
    genome-wide level, due to usage of generalized linear models it allows flexible
    analysis of binary and continuous traits with covariates.
An update to the previous version ( 0.1.0 ) of the package.
    Functions in the previous version required the binary outcome variable of the data set to be strictly a factor.In version 0.2.0  the binary outcome variable may be both a factor (with two levels) or an integer (or numeric ).
    All functions of the previous version have been retained.No new functions have been added.
    Like the previous version,provides functions to collapse the levels of an attribute based on response rates.
    It also provides functions to compute and display information value, and weight of evidence (WoE) for the attributes, and to convert numeric variables to categorical ones by binning.
    These functions only work for binary classification problems.
Interactive Reingold-Tilford tree diagrams created using 'D3.js', where every node can be expanded and collapsed by clicking on it.
    Tooltips and color gradients can be mapped to nodes using a numeric column in the source data frame.
    See 'collapsibleTree' website for more information and examples.
We often want to take all (or most) of the objects in one environment (such as the parameter values of a function) and pass them to another. This might be calling a second function, or iterating over a list, calling the same function. These functions wrap often repeated code.
    Current stable version (committed on October 14, 2017).
Computation of Colless-Like, Sackin and cophenetic balance indices of a phylogenetic tree and study of the distribution of these balance indices under the alpha-gamma model. For more details see A. Mir, F. Rossello, L. Rotger (2013) <doi:10.1016/j.mbs.2012.10.005>, M. J. Sackin (1972) <doi:10.1093/sysbio/21.2.225>, D. H. Colless (1982) <doi:10.2307/2413420>.
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.
Markov chain Monte Carlo based inference routines for collapsed latent position cluster models or social networks, which includes searches over the model space (number of clusters in the latent position cluster model). The label switching algorithm used is that of Nobile and Fearnside (2007) <doi:10.1007/s11222-006-9014-7> which relies on the algorithm of Carpaneto and Toth (1980) <doi:10.1145/355873.355883>.
Provides some low level functions for processing PLINK input and output files.
Weekly notified dengue cases and climate variables in Colombo district Sri Lanka from 2008/ week-52 to 2014/ week-21.
Performs the colocalisation tests described in
    Plagnol et al (2009) <doi:10.1093/biostatistics/kxn039>,
    Wallace et al (2013) <doi:10.1002/gepi.21765> and
    Giambartolomei et al (2013) <doi:10.1371/journal.pgen.1004383>.
Loads and displays images, selectively masks specified background
  colors, bins pixels by color using either data-dependent or automatically
  generated color bins, quantitatively measures color similarity among images
  using one of several distance metrics for comparing pixel color clusters, and 
  clusters images by object color similarity. Originally written for use with
  organism coloration (reef fish color diversity, butterfly mimicry, etc), but
  easily applicable for any image set.
It implements colored Independent Component Analysis (Lee et al., 2011) and spatial colored Independent Component Analysis (Shen et al., 2014). They are two algorithms to perform ICA when sources are assumed to be temporal or spatial stochastic processes, respectively.
Temporally autocorrelated populations are correlated in their vital rates (growth, death, etc.) from year to year. It is very common for populations, whether they be bacteria, plants, or humans, to be temporally autocorrelated. This poses a challenge for stochastic population modeling, because a temporally correlated population will behave differently from an uncorrelated one.
    This package provides tools for simulating populations with white noise (no temporal autocorrelation), red noise (positive temporal autocorrelation), and blue noise (negative temporal autocorrelation).  The algebraic formulation for autocorrelated noise comes from Ruokolainen et al. (2009) <doi:10.1016/j.tree.2009.04.009>. The simulations are based on an assumption of an asexually reproducing population, but it can also be used to simulate females of a sexually reproducing species.
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.
Build dendrograms with sample groups highlighted by different colors. Visualize results of hierarchical clustering analyses as dendrograms whose leaves and labels are colored according to sample grouping. Assess whether data point grouping aligns to naturally occurring clusters.
Allows to generate colors from palettes defined in the colormap module of 'Node.js'. (see <https://github.com/bpostlethwaite/colormap> for more information). In total it provides 44 distinct palettes made from sequential and/or diverging colors. In addition to the pre defined palettes you can also specify your own set of colors. There are also scale functions that can be used with 'ggplot2'.
Different methods to generate a color palette based on a specified base color and a number of colors that should be created.
Shows color patches for encoding fold changes (e.g. log ratios) together with confidence values 
    within a single diagram. This is especially useful for rendering gene expression data as well as
    other types of differential experiments. In addition to different rendering methods (ggplot extensions)
    functionality for perceptually optimizing color palettes are provided.
    Furthermore the package provides extension methods of the colorspace color-class in order to
    simplify the work with palettes (a.o. length, as.list, and append are supported).
A 'ggplot2' extension to visualize two
    variables through one color aesthetic via mapping to a color space
    projection. With this technique for 2-D color mapping, one can create a
    bivariate choropleth in R as well as other visualizations with multivariate
    color scales. Includes two new scales and a new guide for 'ggplot2'.
Color palettes for EPL, MLB, NBA, NHL, and NFL teams.
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
	     along with an interactive palette picker (with either a Tcl/Tk
	     or a shiny GUI).
Calculate with spectral properties of light sources, materials, cameras, eyes, and scanners.
    Build complex systems from simpler parts using a spectral product algebra. For light sources,
    compute CCT and CRI.  For object colors, compute optimal colors and Logvinenko coordinates.
    Work with the standard CIE illuminants and color matching functions, and read spectra from 
    text files, including CGATS files.  A user guide and 7 vignettes are included.
R package with handy functions to help users select and play with
    color schemes in an HSV color model
Provides access to the COLOURlovers <http://www.colourlovers.com/>
    API, which offers color inspiration and color palettes.
A colour picker that can be used as an input in Shiny apps
    or Rmarkdown documents. The colour picker supports alpha opacity, custom
    colour palettes, and many more options. A Plot Colour Helper tool is
    available as an RStudio Addin, which helps you pick colours to use in your
    plots. A more generic Colour Picker RStudio Addin is also provided to let 
    you select colours to use in your R code.
Colour vision models, colour spaces and colour thresholds. Includes Vorobyev & Osorio (1998) Receptor Noise Limited models, Chittka (1992) colour hexagon, and Endler & Mielke (2005) model. Models have been extended to accept any number of photoreceptor types.
Powerful functions to select and rename columns in dataframes, lists and numeric types 
  by 'Perl' regular expression. Regular expression ('regex') are a very powerful grammar to match 
  strings, such as column names.
A collection of command-line color styles based on the 'crayon'
  package. 'Colt' styles are defined in themes that can easily be switched, to
  ensure command line output looks nice on dark as well as light consoles.
Provides methods for the import/export and automated analysis of concept maps and concept landscapes (sets of concept maps).
Genome-wide association studies (GWAS) have been widely used for identifying common variants associated with complex diseases. Due to the small effect sizes of common variants, the power to detect individual risk variants is generally low. Complementary to SNP-level analysis, a variety of gene-based association tests have been proposed. However, the power of existing gene-based tests is often dependent on the underlying genetic models, and it is not known a priori which test is optimal.  Here we proposed COMBined Association Test (COMBAT) to incorporate strengths from multiple existing gene-based tests, including VEGAS, GATES and simpleM. Compared to individual tests, COMBAT shows higher overall performance and robustness across a wide range of genetic models. The algorithm behind this method is described in Wang et al (2017) <doi:10.1534/genetics.117.300257>.
A comprehensive synergy/antagonism analyses of drug combinations with 
		quality graphics and data. The analyses can be performed by Bliss 
		independence and Loewe additivity models. 'COMBIA' provides improved 
		statistical analysis and makes only very weak assumption of data 
		variability while calculating bootstrap intervals (BIs). It is based
		on heteroscedasticity controlled resampling (bootstrapping) and includes
		a global (omnibus) test. Finally, package saves analyzed data, 2D and 3D
	 	plots ready to use in research publications. 'COMBIA' does not require manual
		data entry. Data can be directly input from wetlab experimental platforms for
	 	example fluostar, automated robots etc. One only needs to call a single function, 
		analyzeCOMBO(), to perform all analysis (examples are provided with sample data).
Suite of R functions for combination of probabilities using a game-theoretic method.
Estimation of optimal portfolio weights as combination of simple portfolio strategies, like the tangency, global minimum variance (GMV) or naive (1/N) portfolio. It is based on a utility maximizing 8-fund rule. Popular special cases like the Kan-Zhou(2007) 2-fund and 3-fund rule or the Tu-Zhou(2011) estimator are nested.
We offer two statistical tests to combine p-values: selfcontained.test vs competitive.test. The goal is to test whether a vector of pvalues are jointly significant when we combine them together.
Series of partially balanced incomplete block designs (PBIB) based on the combinatory method (S) introduced in (Imane Rezgui et al, 2014) <doi:10.3844/jmssp.2014.45.48>; and it gives their associated U-type design.
Provides iterators for combinations, permutations, subsets, and
    Cartesian product, which allow one to go through all elements without creating a
    huge set of all possible values.
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 algorithm for identifying combinations of mutually exclusive alterations in cancer genomes. CoMEt represents the mutations in a set M of k genes with a 2^k dimensional contingency table, and then computes the tail probability of observing T(M) exclusive alterations using an exact statistical test.
Functions to calculate various common and less common thermal comfort indices, convert physical variables, and evaluate the performance of thermal comfort indices.
Provided are Computational methods for Immune Cell-type Subsets, including:(1) DCQ (Digital Cell Quantifier) to infer global dynamic changes in immune cell quantities within a complex tissue; and (2) VoCAL (Variation of Cell-type Abundance Loci) a deconvolution-based method that utilizes transcriptome data to infer the quantities of immune-cell types, and then uses these quantitative traits to uncover the underlying DNA loci.
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.
Autosimilarity curves, standardization of spatial extent, dissimilarity indexes that overweight rare species, phylogenetic and functional (pairwise and multisample) dissimilarity indexes and nestedness for phylogenetic, functional and other diversity metrics. This should be a complement to available packages, particularly 'vegan'.
Functions to
    produce nicely formatted comments to use in R-scripts (or
    Latex/HTML/markdown etc). A comment with formatting is printed to the
    console and can then be copied to a script.
Useful libraries for building a Java based GUI under R
The CommonMark specification defines a rationalized version of markdown
    syntax. This package uses the 'cmark' reference implementation for converting
    markdown text into various formats including html, latex and groff man. In
    addition it exposes the markdown parse tree in xml format. The latest version of
    this package also adds support for Github extensions including tables, autolinks
    and strikethrough text.
Java JAR files for the Apache Commons Mathematics Library for use by users and other packages.
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.
Provides functions to measure the difference between constrained and unconstrained gene tree distributions using various tree distance metrics. Constraints are enforced prior to this analysis via the estimation of a tree under the community tree model.
The CommunityCorrelogram package is designed for the geostatistical analysis of ecological community datasets with either a spatial or temporal distance component.
Computing comorbidity scores such as the weighted Charlson score
  (Charlson, 1987 <doi:10.1016/0021-9681(87)90171-8>) and the Elixhauser
  comorbidity score (Elixhauser, 1998 <doi:10.1097/00005650-199801000-00004>)
  using ICD-9-CM or ICD-10 codes (Quan, 2005 <doi:10.1097/01.mlr.0000182534.19832.83>).
Comparison of two ROC curves through the methodology proposed 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 to compare a model object to a comparison object.
  If the objects are not identical, the functions can be instructed to
  explore various modifications of the objects (e.g., sorting rows,
  dropping names) to see if the modified versions are identical.
Proposed by Harrell, the C index or concordance C, is considered an overall measure of discrimination in survival analysis between a survival outcome that is possibly right censored and a predictive-score variable, which can represent a measured biomarker or a composite-score output from an algorithm that combines multiple biomarkers. This package aims to statistically compare two C indices with right-censored survival outcome, which commonly arise from a paired design and thus resulting two correlated C indices.
Unified interface for the estimation of causal networks, including
    the methods 'backShift' (from package 'backShift'), 'bivariateANM' (bivariate
    additive noise model), 'bivariateCAM' (bivariate causal additive model),
    'CAM' (causal additive model) (from package 'CAM'), 'hiddenICP' (invariant
    causal prediction with hidden variables), 'ICP' (invariant causal prediction)
    (from package 'InvariantCausalPrediction'), 'GES' (greedy equivalence
    search), 'GIES' (greedy interventional equivalence search), 'LINGAM', 'PC' (PC
    Algorithm), 'FCI' (fast causal inference), 
    'RFCI' (really fast causal inference) (all from package 'pcalg') and
    regression.
Compares two dataframes which have the same column
    structure to show the rows that have changed. Also gives a git style diff format
    to quickly see what has changes in addition to summary statistics.
Create data summaries for quality control, extensive reports for exploring data, as well as publication-ready univariate or bivariate tables in several formats (plain text, HTML,LaTeX, PDF, Word or Excel. Create figures to quickly visualise the distribution of your data (boxplots, barplots, normality-plots, etc.). Display statistics (mean, median, frequencies, incidences, etc.). Perform the appropriate tests (t-test, Analysis of variance, Kruskal-Wallis, Fisher, log-rank, ...) depending on the nature of the described variable (normal, non-normal or qualitative). Summarize genetic data (Single Nucleotide Polymorphisms) data displaying Allele Frequencies and performing Hardy-Weinberg Equilibrium tests among other typical statistics and tests for these kind of data.
Input: 2 ODM files (ODM version 1.3) Output: list of
        identical, matching, similar and differing data items
Makes comparisons quickly for different functions or code
    blocks performing the same task with the function mbc(). 
    Can be used to compare model fits to the same data or
    see which function runs faster.
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.  Verification Bias is this situation when the specimens for doing the second (sampled) test is not under investigator control.  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 also predictive values) and agreement statistics (percent agreement, percent agreement by category, Kappa (unweighted), Kappa (quadratic weighted) and symmetry tests (reduces to McNemar's test for binary tests)).  See: Katki HA, Li Y, Edelstein DW, Castle PE.  Estimating the agreement and diagnostic accuracy of two diagnostic tests when one test is conducted on only a subsample of specimens. Stat Med. 2012 Feb 28; 31(5) <doi:10.1002/sim.4422>.
Functions for calculating and evaluating likelihood ratios from uni/multivariate continuous observations
Computes density function, cumulative distribution function, quantile function and random numbers for a multisection composite distribution specified by the user.  Also fits the user specified distribution to a given data set.  More details of the package can be found in the following paper submitted to the R journal Wiegand M and Nadarajah S (2017)  CompDist: Multisection composite distributions.
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 retrieval and storage of
    functional genomics data via a MySQL database.
Organizing and Analyzing Social Dominance
    Hierarchy Data.
Computing the point estimator and pointwise confidence interval of the cumulative incidence function from the cause-specific hazards model.
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.
Contains functions to enhance approaches to the Composite Indicators methods, focusing, in particular, on the normalisation and weighting-aggregation steps.
Allows for the easy computation of complexity: the proportion of the parameter space in line with the hypothesis by chance. The package comes with a Shiny application in which the calculations can be conducted as well.
Extension of several functions to the complex domain, including the matrix exponential and logarithm, and the determinant.
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 Conway-Maxwell Poisson (COM-Poisson or CMP) regression models
    to count data (Sellers & Shmueli, 2010) <doi:10.1214/09-AOAS306>. The
    package provides functions for model estimation, dispersion testing, and
    diagnostics. Zero-inflated CMP regression (Sellers & Raim, 2016)
    <doi:10.1016/j.csda.2016.01.007> is also supported.
Regression, classification, contour plots, hypothesis testing and fitting of distributions for compositional data are some of the functions included. The standard textbook for such data is John Aitchison's (1986). "The statistical analysis of compositional data". Chapman & Hall.
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.
Univariate feature selection and compound covariate methods under the Cox model with high-dimensional features (gene expressions).
 Available are survival data for non-small-cell lung cancer patients with gene expressions (Chen et al 2007 New Engl J Med) <DOI:10.1056/NEJMoa060096>,
 statistical methods in Emura et al (2012 PLoS ONE) <DOI:10.1371/journal.pone.0047627>,
 Emura & Chen (2016 Stat Methods Med Res) <DOI:10.1177/0962280214533378>, and Emura et al. (2018-)<submitted>.
 Algorithms for generating correlated gene expressions are also available.
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.
Different tools for describing and analysing paired comparison data are presented. Main methods are estimation of products scores according Bradley Terry Luce model. A segmentation of the individual could be conducted on the basis of a mixture distribution approach. The number of classes can be tested by the use of Monte Carlo simulations. This package deals also with multi-criteria paired comparison data.
A set of procedures for the analysis of Random Fields using likelihood and non-standard likelihood methods is provided. Spatial analysis often involves dealing with large dataset. Therefore even simple studies may be too computationally demanding. Composite likelihood inference is emerging as a useful tool for mitigating such computational problems. This methodology shows satisfactory results when compared with other techniques such as the tapering method. Moreover, composite likelihood (and related quantities) have some useful 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).
Interface with and extract data from the United Nations Comtrade API <https://comtrade.un.org/data/>. Comtrade provides country level shipping data for a variety of commodities, these functions allow for easy API query and data returned as a tidy data frame.
Continuous smoothing of probability density function on a compact or semi-infinite support is performed using four continuous associated kernels: extended beta, gamma, lognormal and reciprocal inverse Gaussian. The cross-validation technique is also implemented for bandwidth selection.
Simple functions for joining strings. Construct human-friendly messages whose elements aren't known in advance, like in stop, warning, or message, from clean code.
The concaveman function ports the 'concaveman' (<https://github.com/mapbox/concaveman>) library from 'mapbox'. It computes the concave polygon(s) for one or several set of points.
There are 4 main functions in this package: ckmeans(), lcvqe(), mpckm() and ccls(). They take an unlabeled dataset and two lists of must-link and cannot-link constraints as input and produce a clustering as output.
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.
A set of utilities for matching products in different classification codes used in international trade research. It supports concordance between HS (Combined), ISIC Rev. 2,3, and SITC1,2,3,4 product classification codes, as well as BEC, NAICS, and SIC classifications. It also provides code nomenclature / descriptions look-up, Rauch classification look-up (via concordance to SITC2) and trade elasticity look-up (via concordance to SITC2/3 or HS3.ss).
Implements concordance regression which can be used to estimate generalized odds of concordance.
	Can be used for non- and semi-parametric survival analysis with non-proportional hazards, for binary and 
    for continuous outcome data.
Higher order likelihood-based inference for logistic and 
  loglinear models.
Apply and visualize conditional formatting to data frames in R.
    It renders a data frame with cells formatted according to
    criteria defined by rules, using a tidy evaluation syntax. The table is
    printed either opening a web browser or within the 'RStudio' viewer if
    available. The conditional formatting rules allow to highlight cells
    matching a condition or add a gradient background to a given column. This
    package supports both 'HTML' and 'LaTeX' outputs in 'knitr' reports, and
    exporting to an 'xlsx' file.
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.
Code for a variety of nonlinear conditional independence tests: 
  Kernel conditional independence test (Zhang et al., UAI 2011, <arXiv:1202.3775>),
  Residual Prediction test (based on Shah and Buehlmann, <arXiv:1511.03334>),
  Invariant environment prediction,
  Invariant target prediction,
  Invariant residual distribution test,
  Invariant conditional quantile prediction (all from Heinze-Deml et al., <arXiv:1706.08576>).
Set of functions for the easy analyses of conditioning data.
Implements specialized conditions, i.e., typed errors,
    warnings and messages. Offers a set of standardized conditions (value error,
    deprecated warning, io message, ...) in the fashion of Python's built-in
    exceptions.
Conditional density estimation with mixtures for
        heavy-tailed distributions
Computes conditional multivariate normal probabilities, random deviates and densities.
An implementation of the computational strategy for the
    comprehensive analysis of condition-dependent operon maps in prokaryotes
    proposed by Fortino et al. (2014) <DOI:10.1186/1471-2105-15-145>. 
    It uses RNA-seq transcriptome profiles to improve prokaryotic operon map inference.
Method to implement some newly developed methods for the
    estimation of the conditional survival function.
Runs a function iteratively over each row of either a dataframe 
    or the results of a query.  Use the 'BigQuery' and 'DBI' wrappers to 
    iteratively pass each row of query results to a function.  If a field 
    contains a 'JSON' string, it will be converted to an object.  This is 
    helpful for queries that return 'JSON' strings that represent objects.  
    These fields can then be treated as objects by the pipeline.
Exploring fitted models by interactively taking 2-D and 3-D
  sections in data space.
Routines doing cone projection and quadratic programming, as well as doing estimation and inference for constrained parametric regression and shape-restricted regression problems. See Mary C. Meyer (2013)<doi:10.1080/03610918.2012.659820> for more details.
Plots the two-dimensional confidence region for probability distribution (Weibull or
  inverse Gaussian) parameters corresponding to a user given dataset and level of significance. 
  The crplot() algorithm plots more points in areas of greater curvature to ensure a smooth
  appearance throughout the confidence region boundary.  An alternative heuristic plots a specified
  number of points at roughly uniform intervals along its boundary.  Both heuristics build upon the 
  radial profile log-likelihood ratio technique for plotting two-dimensional confidence regions given 
  by Jaeger (2016) <doi:10.1080/00031305.2016.1182946>.
This small library contains a series of simple tools for
        constructing and manipulating confounded and fractional
        factorial designs.
Functions for estimating and reporting multi-year 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.
Manage configuration values across multiple environments (e.g.
  development, test, production). Read values using a function that determines
  the current environment and returns the appropriate value.
Enhances the 'ini' package by adding the ability to interpolate variables.
	     The INI configuration file is read into an R6 ConfigParser object (loosely inspired by Pythons ConfigParser module)
	     and the keys can be read, where '%(....)s' instances are interpolated by other included options or outside variables.
Implements the JSON, INI, YAML and TOML parser for R setting and writing of configuration file. The functionality of this package is similar to that of package 'config'.
Produces descriptive interpretations of confidence intervals.
    Includes (extensible) support for various test types, specified as sets
    of interpretations dependent on where the lower and upper confidence limits
    sit. Provides plotting functions for graphical display of interpretations.
Implementation of transductive conformal prediction (see Vovk, 2013, <doi:10.1007/978-3-642-41142-7_36>) and inductive conformal prediction (see Balasubramanian et al., 2014, ISBN:9780124017153) for classification problems.
Conducts sensitivity analyses for unmeasured confounding in
    random-effects meta-analysis per Mathur & VanderWeele (in preparation).
    Given output from a random-effects meta-analysis with a relative risk
    outcome, computes point estimates and inference for: (1) the proportion
    of studies with true causal effect sizes more extreme than a specified threshold
    of scientific significance; and (2) the minimum bias factor and confounding
    strength required to reduce to less than a specified threshold the proportion
    of studies with true effect sizes of scientifically significant size.
    Creates plots and tables for visualizing these metrics across a range of bias values.
    Provides tools to easily scrape study-level data from a published forest plot or 
    summary table to obtain the needed estimates when these are not reported.
Offers several functions for Configural Frequencies
    Analysis (CFA), which is a useful statistical tool for the analysis of
    multiway contingency tables. CFA was introduced by G. A. Lienert as
    'Konfigurations Frequenz Analyse - KFA'. Lienert, G. A. (1971). 
    Die Konfigurationsfrequenzanalyse: I. Ein neuer Weg zu Typen und Syndromen. 
    Zeitschrift für Klinische Psychologie und Psychotherapie, 19(2), 99–115.
For multiple testing.
    Computes estimates and confidence bounds for the
    False Discovery Proportion (FDP), the fraction of false positives among
    all rejected hypotheses.
    The methods in the package use permutations of the data. Doing so, they
    take into account the dependence structure in the data.
Downloads and tidies data from the Brazilian Federal Senate and Chamber of Deputies Application Programming Interfaces available at <http://legis.senado.gov.br/dadosabertos/> and <http://www.camara.leg.br/SitCamaraWS/> respectively.
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 ellipse LMG (geometric parameters) and conic LMA (algebraic parameters). Algebraic ellipse fitting with Fitzgibbon-Pilu-Fisher and Taubin.
plot conics (ellipses, hyperbolas, parabolas)
Provides a set of tools that can be used across 'data.frame' and
    'imputationList' objects.
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.
Collects several different methods for analyzing and
    working with connectivity data in R.  Though primarily oriented towards
    marine larval dispersal, many of the methods are general and useful for
    terrestrial systems as well.
Computes the Conover-Iman test (1979) 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. conover.test makes k(k-1)/2 multiple pairwise comparisons based on Conover-Iman t-test-statistic of the rank differences. 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, Conover-Iman test may be understood as a test for median difference. conover.test accounts for tied ranks. The Conover-Iman test is strictly valid if and only if the corresponding Kruskal-Wallis null hypothesis is rejected.
Multi-species estimation of geographical range parameters
	for preliminary assessment of conservation status following Criterion B of the 
	International Union for Conservation of Nature (IUCN, 
	see <http://www.iucnredlist.org>).
Consonance Analysis is a useful numerical and graphical approach
    for evaluating the consistency of the measurements and the panel of people
    involved in sensory evaluation. It makes use of several uni and multivariate
    techniques either graphical or analytical. It shows the implementation of this
    procedure in a graphical interface.
Given response y, continuous predictor x, and covariate matrix, the relationship between E(y) and x is estimated with a shape constrained regression spline.  Function outputs fits and various types of inference.
Compute the median ranking according to the Kemeny's axiomatic approach. 
    Rankings can or cannot contain ties, rankings can be both complete or incomplete. 
    The package contains both branch-and-bound algorithms and heuristic solutions recently proposed.
    The package also provide some useful utilities for deal with preference rankings.
    Essential references:
    Emond, E.J., and Mason, D.W. (2002) <doi:10.1002/mcda.313>; 
    D'Ambrosio, A., Amodio, S., and Iorio, C. (2015) <doi:10.1285/i20705948v8n2p198>; 
    Amodio, S., D'Ambrosio, A., and Siciliano R. (2016) <doi:10.1016/j.ejor.2015.08.048>; 
    D'Ambrosio, A., Mazzeo, G., Iorio, C., and Siciliano, R. (2017) <doi:10.1016/j.cor.2017.01.017>.
CODATA internationally recommended values of the fundamental physical 
    constants, provided as symbols for direct use within the R language. Optionally, 
    the values with errors and/or the values with units are also provided if the 
    'errors' and/or the 'units' packages are installed. The Committee on Data
    for Science and Technology (CODATA) is an interdisciplinary committee of the
    International Council for Science which periodically provides the internationally 
    accepted set of values of the fundamental physical constants. This package 
    contains the "2014 CODATA" version, published on 25 June 2015:
    Mohr, P. J., Newell, D. B. and Taylor, B. N. (2016)
    <DOI:10.1103/RevModPhys.88.035009>, <DOI:10.1063/1.4954402>.
Examine any number of time series data frames to identify 
    instances in which various criteria are met within specified time
    frames. In clinical medicine, these types of events are often
    called "constellations of signs and symptoms", because a single 
    condition depends on a series of events occurring within a certain 
    amount of time of each other. This package was written to work with
    any number of time series data frames and is optimized for speed 
    to work well with data frames with millions of rows.
Provides functions for
        efficient computations of nonlinear spatial predictions with
        local change of support. This 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. This 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.
Fits mixtures of multivariate contaminated normal distributions
        (with eigen-decomposed scale matrices) via the expectation conditional-
	maximization algorithm under a clustering or classification paradigm.
Various utilities for evaluating continued fractions.
Bayesian analysis of complete and incomplete contingency tables.
Create contour lines for a non regular series of points, potentially from a non-regular canvas.
Provides functions for making contour plots.
  The contour plot can be created from grid data, a function,
  or a data set. If non-grid data is given, then a Gaussian
  process is fit to the data and used to create the contour plot.
One degree of freedom contrasts for lm, glm, gls, and geese objects.
Solves control systems problems relating to time/frequency response, LTI systems design and analysis, transfer function manipulations, and system conversion.
Nonparametric two-sample procedure for comparing survival quantiles.
Converts between the Date class and d/m/y for several
        calendars, including Persian, Islamic, and Hebrew
Functions for clustering regions that form convergence clubs, according to the definition of Phillips and Sul (2009) <doi:10.1002/jae.1080>.
This is a pedagogical package, designed to help students understanding convergence of
             random variables. It provides a way to investigate interactively various modes of
	     convergence (in probability, almost surely, in law and in mean) of a sequence of i.i.d.
	     random variables. Visualisation of simulated sample paths is possible through interactive
	     plots. The approach is illustrated by examples and exercises through the function
	     'investigate', as described in
	     Lafaye de Micheaux and Liquet (2009) <http://dx.doi.org/10.1198/tas.2009.0032>.
	     The user can study his/her own sequences of random variables.
Converts graphical file formats (SVG,
    PNG, JPEG, BMP, GIF, PDF, etc) to one another. The exceptions are the
    SVG file format that can only be converted to other formats and in contrast,
    PDF format, which can only be created from others graphical formats.
    The main purpose of the package was to provide a solution for converting SVG
    file format to PNG which is often needed for exporting graphical files
    produced by R widgets.
Provides conversion functionality between a broad range of
    scientific, historical, and industrial unit types.
Quantifies and assesses the significance of convergent evolution using two different methods (and 5 different measures) as described in Stayton (2015) <DOI:10.1111/evo.12729>.  Also displays results in a phylomorphospace framework.
Provides a simple high-level wrapper for
    'Julia' package 'Convex.jl' (see <https://github.com/JuliaOpt/Convex.jl> for
    more information),
    which makes it easy to describe and solve convex optimization problems in R.
    The problems can be dealt with include:
    linear programs,
    second-order cone programs,
    semidefinite programs,
    exponential cone programs.
Variance estimation on indicators of income concentration and
    poverty using complex sample survey designs. Wrapper around the
    survey package.
Fits convolution-based nonstationary
    Gaussian process models to point-referenced spatial data. The nonstationary
    covariance function allows the user to specify the underlying correlation
    structure and which spatial dependence parameters should be allowed to
    vary over space: the anisotropy, nugget variance, and process variance.
    The parameters are estimated via maximum likelihood, using a local
    likelihood approach. Also provided are functions to fit stationary spatial
    models for comparison, calculate the Kriging predictor and standard errors,
    and create various plots to visualize nonstationarity.
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.
Read and preprocess fasta format data, and construct the co-occurrence network for downstream analyses. This R package is to construct the co-occurrence network with the algorithm developed by Du (2008) <DOI:10.1101/gr.6969007>. It could be used to transform the data with high-dimension, such as DNA or protein sequence, into co-occurrence networks. Co-occurrence network could not only capture the co-variation pattern between variables, such as the positions in DNA or protein sequences, but also reflect the relationship between samples. Although it is originally used in DNA and protein sequences, it could be also used to other kinds of data, such as RNA, SNP, etc.
Fast implementations of the co-operations: covariance,
    correlation, and cosine similarity.  The implementations are
    fast and memory-efficient and their use is resolved
    automatically based on the input data, handled by R's S3
    methods.  Full descriptions of the algorithms and benchmarks
    are available in the package vignettes.
Computes several cooperative games and allocation rules associated with minimum cost spanning tree problems and minimum cost arborescence problems.
Automated cleaning of geographic species occurrence records by automated flagging of problems common to biodiversity data from biological collections. Includes automated tests to easily flag (and exclude) records assigned to country or province centroid, the open ocean, the headquarters of the Global Biodiversity Information Facility, urban areas or the location of biodiversity institutions (museums, zoos, botanical gardens, universities). Furthermore identifies per species outlier coordinates, zero coordinates, identical latitude/longitude and invalid coordinates. Also implements an algorithm to identify data sets with a significant proportion of rounded coordinates. Especially suited for large data sets. See <https://github.com/azizka/CoordinateCleaner/wiki> for more details and tutorials.
Extensive functions for bivariate copula (bicopula) computations and related
 operations concerning oft cited bicopula theory described by Nelsen (2006), Joe (2014), and
 other selected works. The lower, upper, product, and select other bicopula are implemented.
 Arbitrary bicopula expressions include the diagonal, survival copula, the dual of a copula,
 co-copula, numerical bicopula density, and maximum likelihood estimation. Level
 curves (sets), horizontal and vertical sections also are supported. Numerical derivatives and
 inverses of a bicopula are provided; simulation by the conditional distribution method thus is
 supported. Bicopula composition, convex combination, and products are provided. Support
 extends to Kendall Function as well as the Lmoments thereof, Kendall Tau, Spearman Rho and
 Footrule, Gini Gamma, Blomqvist Beta, Hoeffding Phi, Schweizer-Wolff Sigma, tail dependency
 (including pseudo-polar representation) and tail order, skewness, and bivariate Lmoments.
 Evaluators of positively/negatively quadrant dependency, left increasing and right
 decreasing are available. Kullback-Leibler divergence, Vuong's procedure, Spectral Measure,
 and Lcomoments for copula inference are available. Quantile and median regressions for
 V with respect to U and U with respect to V are available. Empirical copulas (EC) are supported.
Provides tools for fitting the copCAR (Hughes, 2015)
    <DOI:10.1080/10618600.2014.948178> regression model for discrete
	areal data. Three types of estimation are supported (continuous
	extension, composite marginal likelihood, and distributional transform),
	for three types of outcomes (Bernoulli, negative binomial, and Poisson).
Provides functions to  compute and plot Coverage
    Probability Excursion (CoPE) sets
    for real valued functions on a 2-dimensional domain. CoPE sets are obtained
    from repeated noisy observations of the function on the entire domain.
    They are designed to bound the excursion
    set of the target function at a given level from above and below with
    a predefined probability. The target
    function can be a parameter in spatially-indexed linear regression.
    Support by NIH grant R01 CA157528 is gratefully acknowledged.
The program implements the COPPE-Cosenza Fuzzy Hierarchy Model. 
    The model was based on the evaluation of local alternatives, representing 
    regional potentialities, so as to fulfill demands of economic projects. 
    After defining demand profiles in terms of their technological coefficients, 
    the degree of importance of factors is defined so as to represent  
    the productive activity. The method can detect a surplus of supply without 
    the restriction of the distance of classical algebra, defining a hierarchy 
    of location alternatives. In COPPE-Cosenza Model, the distance between 
    factors is measured in terms of the difference between grades of memberships
    of the same factors belonging to two or more  sets under comparison. The 
    required factors are classified under the following linguistic variables: 
    Critical (CR); Conditioning (C); Little Conditioning (LC); and Irrelevant 
    (I). And the alternatives can assume the following linguistic variables: 
    Excellent (Ex), Good (G), Regular (R), Weak (W), Empty (Em), Zero (Z) and 
    Inexistent (In). The model also provides flexibility, allowing different 
    aggregation rules to be performed and defined by the Decision Maker. Such 
    feature is considered in this package, allowing the user to define other 
    aggregation matrices, since it considers the same linguistic variables 
    mentioned.
Computes the required number of patients for two time-to-event end-points as primary endpoint in phase III clinical trial.
Classes (S4) of commonly used elliptical, Archimedean,
 extreme-value and other copula families, as well as their rotations,
 mixtures and asymmetrizations. Nested Archimedean copulas, related
 tools and special functions. Methods for density, distribution, random
 number generation, bivariate dependence measures, Rosenblatt transform,
 Kendall distribution function, perspective and contour plots. Fitting of
 copula models with potentially partly fixed parameters, including
 standard errors. Serial independence tests, copula specification tests
 (independence, exchangeability, radial symmetry, extreme-value
 dependence, goodness-of-fit) and model selection based on
 cross-validation. Empirical copula, smoothed versions, and
 non-parametric estimators of the Pickands dependence function.
Data sets used for copula modeling in addition to those in
 the package 'copula'. These include a random subsample from the US National
 Education Longitudinal Study (NELS) of 1988 and nursing home data from
 Wisconsin.
Modelling of sensitivity and specificity on their natural scale
    using copula based bivariate beta-binomial distribution to yield marginal
    mean sensitivity and specificity. The intrinsic negative correlation between
    sensitivity and specificity is modelled using a copula function. A forest plot
    can be obtained for categorical covariates or for the model with intercept only.
    Nyaga VN, Arbyn M, Aerts M (2017) <doi:10.18637/jss.v082.c01>.
Provides a platform where EDAs (estimation of
    distribution algorithms) based on copulas can be implemented and
    studied. The package offers complete implementations of various
    EDAs based on copulas and vines, a group of well-known
    optimization problems, and utility functions to study the
    performance of the algorithms. Newly developed EDAs can be easily
    integrated into the package by extending an S4 class with generic
    functions for their main components.
Estimation and statistical process control are performed under copula-based time-series models.
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.
It has functions to implement the copula mixed models for bivariate and trivariate meta-analysis of diagnostic test accuracy studies.
Contains several IRT and non-IRT based statistical indices proposed in the literature for detecting answer copying on multiple-choice examinations. Includes the indices that have been shown as effective and reliable based on the simulation studies. Provides results for the Omega index, Generalized Binomial Test, K index, K1 and K2 indices, and S1 and S2 indices.
Calculates the co-ranking matrix to assess the
    quality of a dimensionality reduction.
Provides a bundle of basic and fundamental bioinformatics tools,
    such as network querying and alignment, subnetwork extraction and search,
    network biomarker identification.
Perform a correlational class analysis of the data, resulting in a partition of the data into separate modules.
Partition data points (variables) into communities/clusters, similar to clustering algorithms, such as k-means and hierarchical clustering.  This package implements a clustering algorithm based on a new metric CORD, defined for high dimensional parametric or semi-parametric distributions.  Read http://arxiv.org/abs/1508.01939 for more details.
Functions for calculating the OPTICS Cordillera. The OPTICS Cordillera measures the amount of 'clusteredness' in a numeric data matrix within a distance-density based framework for a given minimum number of points comprising a cluster, as described in Rusch, Hornik, Mair (2017) <doi:10.1080/10618600.2017.1349664>. There is an R native version and a version that uses 'ELKI', with methods for printing, summarizing, and plotting the result. There also is an interface to the reference implementation of OPTICS in 'ELKI'.
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.
Computed tomography (CT) imaging is a powerful tool for understanding the composition of sediment cores. This package streamlines and accelerates the analysis of CT data generated in the context of environmental science. Included are tools for processing raw DICOM images to characterize sediment composition (sand, peat, etc.). Root analyses are also enabled, including measures of external surface area and volumes for user-defined root size classes. For a detailed description of the application of computed tomography imaging for sediment characterization, see: Davey, E., C. Wigand, R. Johnson, K. Sundberg, J. Morris, and C. Roman. (2011) <DOI: 10.1890/10-2037.1>.
Core Hunter is a tool to sample diverse, representative subsets from large germplasm
    collections, with minimum redundancy. Such so-called core collections have applications in plant
    breeding and genetic resource management in general. Core Hunter can construct cores based on
    genetic marker data, phenotypic traits or precomputed distance matrices, optimizing one of many
    provided evaluation measures depending on the precise purpose of the core (e.g. high diversity,
    representativeness, or allelic richness). In addition, multiple measures can be simultaneously
    optimized as part of a weighted index to bring the different perspectives closer together.
    The Core Hunter library is implemented in Java 8 as an open source project (see
    <http://www.corehunter.org>).
A suite of machine learning algorithms written in C++ with the R 
 interface contains several learning techniques for classification and regression.
 Predictive models include e.g., classification and regression trees with
 optional constructive induction and models in the leaves, random forests, kNN, 
 naive Bayes, and locally weighted regression. All predictions obtained with these
 models can be explained and visualized with the 'ExplainPrediction' package.  
 This package 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, and DKM. These methods can be used for feature selection 
 or discretization of numeric attributes.
 The OrdEval algorithm and its visualization is used for evaluation
 of data sets with ordinal features and class, enabling analysis according to the 
 Kano model of customer satisfaction. 
 Several algorithms support parallel multithreaded execution via OpenMP.  
 The top-level documentation is reachable through ?CORElearn.
Provides a minimal interface for applying
    annotators from the 'Stanford CoreNLP' java library. Methods
    are provided for tasks such as tokenisation, part of speech
    tagging, lemmatisation, named entity recognition, coreference
    detection and sentiment analysis.
Core functions for simulating quantities of interest
    from generalised linear models (GLM). This package will form the backbone of
    a series of other packages that improve the interpretation of GLM estimates.
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.
Includes binning categorical variables into lesser number of categories based on t-test, converting categorical variables into continuous features 
	using the mean of the response variable for the respective categories, understanding the relationship between the response variable and predictor variables 
	using data transformations.
A matrix of agreement patterns and counts for record pairs is the input for the procedure.  An EM algorithm is used to impute plausible values for missing record pairs.  A second EM algorithm, incorporating possible correlations between per-field agreement, is used to estimate posterior probabilities that each pair is a true match - i.e. constitutes the same individual.
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.
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 Schafer and Strimmer (2005) 
  <DOI:10.2202/1544-6115.1175> and Opgen-Rhein and Strimmer (2007) 
  <DOI:10.2202/1544-6115.1252>.  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.
A set of functions used to compare co-occurrence between two corpora.
Text corpus data analysis, with full support for international text (Unicode).  Functions for reading data from newline-delimited 'JSON' files, for normalizing and tokenizing text, for searching for term occurrences, and for computing term occurrence frequencies, including n-grams.
Provides text analysis in R, focusing on the use of a tokenized text format. In this format, the positions of tokens are maintained, and each token can be annotated (e.g., part-of-speech tags, dependency relations).
    Prominent features include advanced Lucene-like querying for specific tokens or contexts (e.g., documents, sentences),
    similarity statistics for words and documents, exporting to DTM for compatibility with many text analysis packages,
    and the possibility to reconstruct original text from tokens to facilitate interpretation.
Implementation of two-dimensional (2D) correlation analysis based
    on the Fourier-transformation approach described by Isao Noda (I. Noda
    (1993) <DOI:10.1366/0003702934067694>). Additionally there are two plot
    functions for the resulting correlation matrix: The first one creates
    colored 2D plots, while the second one generates 3D plots.
This package implements non-parametric analyses for clustered
    binary and multinomial 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.
Can be useful for finding associations among different positions in a position-wise aligned sequence dataset. The approach adopted for finding associations among positions is based on the latent multivariate normal distribution.
Correcting area under ROC (AUC) for measurement error based on probit-shift model.
There are many estimators of false discovery rate. In this package we compute the Nonlocal False
   Discovery Rate (NFDR) and the estimators of local false discovery rate: Corrected False discovery
   Rate (CFDR), Re-ranked False Discovery rate (RFDR) and the blended estimator. 
   Bickel, D. R. (2016) <http://hdl.handle.net/10393/34277>.
Analyzes and modifies metabolomics raw data (generated using GC-APCI-MS, Gas Chromatography-Atmospheric Pressure Chemical Ionization-Mass Spectrometry) to correct overloaded signals, i.e. ion intensities exceeding detector saturation leading to a cut-off peak. Data in xcmsRaw format are accepted as input and mzXML files can be processed alternatively. Overloaded signals are detected automatically and modified using an Gaussian or Isotopic-Ratio approach, QC plots are generated and corrected data are stored within the original xcmsRaw or mzXML respectively to allow further processing.
Linear regression based on a recursive structural equation model
    (explicit multiples correlations) found by a M.C.M.C. algorithm. It permits to face
    highly correlated variables. Variable selection is included (by lasso,
    elastic net, etc.). It also provides some graphical tools for basic
    statistics.
A collection of tools for correspondence regression, i.e. the
    correspondence analysis of the crosstabulation of a categorical variable Y
    in function of another one X, where X can in turn be made up of the
    combination of various categorical variables. Consequently, correspondence
    regression can be used to analyze the effects for a polytomous or
    multinomial outcome variable.
Calculates the probabilities of k successes given n trials of a binomial random variable with non-negative correlation across trials. The function takes as inputs the scalar values the level of correlation or association between trials, the success probability, the number of trials, an optional input specifying the number of bits of precision used in the calculation, and an optional input specifying whether the calculation approach to be used is from Witt (2014) <doi:10.1080/03610926.2012.725148> or from Kuk (2004) <doi:10.1046/j.1467-9876.2003.05369.x>. The output is a (trials+1)-dimensional vector containing the likelihoods of 0, 1, ..., trials successes.
Correlplot contains diverse routines for the construction of different plots for representing correlation matrices.
Calculates correlation of variables and displays the results
    graphically. Included panel functions can display points, shading, ellipses, and
    correlation values with confidence intervals.
In clinical practice and research settings in medicine and the behavioral sciences, it is often of interest to quantify the correlation of a continuous endpoint that was repeatedly measured (e.g., test-retest correlations, ICC, etc.). This package allows for estimating these correlations based on mixed-effects models. Part of this software has been developed using funding provided from the European Union's 7th Framework Programme for research, technological development and demonstration under Grant Agreement no 602552.
A graphical display of a correlation matrix or general matrix.
    It also contains some algorithms to do matrix reordering. In addition,
    corrplot is good at details, including choosing color, text labels,
    color labels, layout, etc.
A tool for exploring correlations.
    It makes it possible to easily perform routine tasks when
    exploring correlation matrices such as ignoring the diagonal,
    focusing on the correlations of certain variables against others,
    or rearranging and visualising the matrix in terms of the
    strength of the correlations.
Statistical summary of Structure output.
Modeling the correlation transitions under specified distributional assumptions within the realm of discretization in the context of the latency and threshold concepts.
Set of methods to constrain numerical series and time series within
             arbitrary boundaries.
Performs adaptive shrinkage of correlation and covariance
    matrices using a mixture model prior over the Fisher
    z-transformation of the correlations. A separate shrinkage
    intensity may be specified for each cell of the correlation or the
    covariance table.
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.
Implements generalized constrained optimal sample allocation framework for two-group multilevel regression discontinuity studies and multilevel randomized trials with continuous outcomes. Bulus, M. (2017). Design Considerations in Three-level Regression Discontinuity Studies (Doctoral dissertation). University of Missouri, Columbia, MO.
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.
Statistical procedures for calculating population–mean cosinor, non–stationary cosinor, estimation of best–fitting period, tests of population rhythm differences and more. See Cornélissen, G. (2014). <doi:10.1186/1742-4682-11-16>.
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.
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.
Density, distribution function, quantile function, random
    generation and survival function for the Cosine Weibull Distribution as defined
    by SOUZA, L. New Trigonometric Class of Probabilistic Distributions. 219 p.
    Thesis (Doctorate in Biometry and Applied Statistics) - Department of Statistics
    and Information, Federal Rural University of Pernambuco, Recife, Pernambuco,
    2015 (available at <http://www.openthesis.org/documents/New-trigonometric-classes-probabilistic-distributions-602633.html>) and BRITO, C. C. R. Method Distributions generator and
    Probability Distributions Classes. 241 p. Thesis (Doctorate in Biometry and
    Applied Statistics) - Department of Statistics and Information, Federal Rural
    University of Pernambuco, Recife, Pernambuco, 2014 (available upon request).
Implements cointegration/cotrending rank selection
        algorithm in Guo and Shintani (2013) "Consistant
        cotrending rank selection when both stochastic and nonlinear
        deterministic trends are present". 
        The Econometrics Journal 16: 473-483 
        <doi:10.1111/j.1368-423X.2012.00392.x>.
        Numbered examples correspond to Feb 2011 preprint 
        <http://www.fas.nus.edu.sg/ecs/events/seminar/seminar-papers/05Apr11.pdf>.
Interface to the couchDB document database <http://couchdb.apache.org>.
Functions, data and code for Hilbe, J.M. 2011. Negative Binomial Regression, 2nd Edition (Cambridge University Press) and Hilbe, J.M. 2014. Modeling Count Data (Cambridge University Press).
Implements the estimation and inference methods for counterfactual analysis described in Chernozhukov, Fernandez-Val and Melly (2013) <DOI:10.3982/ECTA10582> "Inference on Counterfactual Distributions," Econometrica, 81(6). The counterfactual distributions considered are the result of changing either the marginal distribution of covariates related to the outcome variable of interest, or the conditional distribution of the outcome given the covariates. They can be applied to estimate quantile treatment effects and wage decompositions.
Flexible univariate count models based on renewal
    processes. The models may include covariates and can be specified
    with familiar formula syntax as in glm().
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).
Quantifies United States flood impacts at the county level using
    United States Geological Service (USGS) River Discharge data for the USGS
    API. This package builds on R packages from the USGS, with the goal of
    creating county-level time series of flood status that can be more easily
    joined with county-level impact measurements, including health outcomes.
    This work was supported in part by grants from the National Institute of
    Environmental Health Sciences (R00ES022631), the Colorado Water Center,
    and the National Science Foundation, Integrative Graduate Education and
    Research Traineeship (IGERT) Grant No. DGE-0966346 "I-WATER: Integrated
    Water, Atmosphere, Ecosystems Education and Research Program" at
    Colorado State University.
Inputs date-times in Coordinated Universal Time (UTC)
    and converts to a local date-time and local date for US counties, based on
    each county's Federal Information Processing Standard (FIPS) code.
    This work was supported in part by grants from the National Institute of
    Environmental Health Sciences (R00ES022631) and the National Science
    Foundation (1331399).
Interacts with NOAA data sources (including the NCDC API at
  <http://www.ncdc.noaa.gov/cdo-web/webservices/v2> and ISD data) using
  functions from the 'rnoaa' package to obtain and compile weather time
  series for U.S. counties. This work was supported in part by grants from the
  National Institute of Environmental Health Sciences (R00ES022631) and the
  Colorado State University Water Center.
Contact prediction using shrinked covariance (COUSCOus). COUSCOus is a residue-residue contact detecting method approaching the contact inference using the glassofast implementation of Matyas and Sustik (2012, The University of Texas at Austin UTCS Technical Report 2012:1-3. TR-12-29.) that solves the L_1 regularised Gaussian maximum likelihood estimation of the inverse of a covariance matrix. Prior to the inverse covariance matrix estimation we utilise a covariance matrix shrinkage approach, the empirical Bayes covariance estimator, which has been shown by Haff (1980) <DOI:10.1214/aos/1176345010> to be the best estimator in a Bayesian framework, especially dominating estimators of the form aS, such as the smoothed covariance estimator applied in a related contact inference technique PSICOV.
Facilitates local polynomial regression for state dependent covariates in state-space models. The functionality can also be used from 'C++' based model builder tools such as 'Rcpp'/'inline', 'TMB', or 'JAGS'.
Tests on properties of space-time covariance functions.
    Tests on symmetry, separability and for assessing 
    different forms of non-separability are available. Moreover tests on 
    some classes of covariance functions, such that the classes of 
    product-sum models, Gneiting models and integrated product models have 
    been provided.
Allows Brownian motion, fractional Brownian motion,
    and integrated Ornstein-Uhlenbeck process components to
    be added to linear and non-linear mixed effects models
    using the structures and methods of the 'nlme' package.
Computes p-values using the largest root test using 
    an approximation to the null distribution by Johnstone (2008) <DOI:10.1214/08-AOS605>.
Converts any word, sentence or speech into Trump's infamous
  "covfefe" format. Reference: <https://www.nytimes.com/2017/05/31/us/politics/covfefe-trump-twitter.html>.
  Inspiration thanks to: <https://codegolf.stackexchange.com/questions/123685/covfefify-a-string>.
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.
We implement a collection of techniques for estimating covariance matrices. 
              Covariance matrices can be built using missing data. Stambaugh Estimation and 
              FMMC methods can be used to construct such matrices. Covariance matrices can 
              be built by denoising or shrinking the eigenvalues of a sample covariance 
              matrix. Such techniques work by exploiting the tools in Random Matrix Theory 
              to analyse the distribution of eigenvalues. Covariance matrices can also 
              be built assuming that data has many underlying regimes. Each regime is 
              allowed to follow a Dynamic Conditional Correlation model. Robust covariance 
              matrices can be constructed by multivariate cleaning and smoothing of noisy data.
Track and report code coverage for your package and (optionally)
    upload the results to a coverage service like 'Codecov' <http://codecov.io> or
    'Coveralls' <http://coveralls.io>. Code coverage is a measure of the amount of
    code being exercised by a set of tests. It is an indirect measure of test
    quality and completeness. This package is compatible with any testing
    methodology or framework and tracks coverage of both R code and compiled
    C/C++/FORTRAN code.
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 implements robust covariance estimation
        by the nearest neighbor variance estimation (NNVE) method of
        Wang and Raftery (2002) <DOI:10.1198/016214502388618780>.
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.
Model-free selection of covariates in high dimensions 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) <DOI:10.1093/biomet/asr041> and VanderWeele and Shpitser (2011) <DOI:10.1111/j.1541-0420.2011.01619.x>. Confounder selection can be performed via either Markov/Bayesian networks, random forests or LASSO.
Functions for testing if the covariance structure of 2-dimensional data
    (e.g. samples of surfaces X_i = X_i(s,t)) is separable, i.e. if covariance(X) = C_1 x C_2.
    A complete descriptions of the implemented tests can be found in the paper
    arXiv:1505.02023.
This package computes covariance test for the lasso.
Testing functions for Covariance Matrices. These tests include high-dimension homogeneity of covariance
  matrix testing described by Schott (2007) <doi:10.1016/j.csda.2007.03.004> and high-dimensional one-sample tests of 
  covariance matrix structure described by Fisher, et al. (2010) <doi:10.1016/j.jmva.2010.07.004>. Covariance matrix
  tests use C++ to speed performance and allow larger data sets.
Covariance is of universal prevalence across various disciplines within statistics.
    We provide a rich collection of geometric and inferential tools for convenient analysis of
    covariance structures, topics including distance measures, mean covariance estimator,
    covariance hypothesis test for one-sample and two-sample cases, and covariance estimation.
    For an introduction to covariance in multivariate statistical analysis,
    see Schervish (1987) <doi:10.1214/ss/1177013111>.
Implements a specific form of segmented linear regression
    with two independent variables. The visualization of that function looks 
    like a quarter segment of a cowbell giving the package its name. 
    The package has been specifically constructed for the case where minimum 
    and maximum value of the dependent and two independent variables 
    are known a prior, which is usually the case
    when those values are derived from Likert scales.
Some helpful extensions and modifications to the 'ggplot2'
    package. In particular, this package makes it easy to combine multiple
    'ggplot2' plots into one and label them with letters, e.g. A, B, C, etc.,
    as is often required for scientific publications. The package also provides
    a streamlined and clean theme that is used in the Wilke lab, hence the
    package name, which stands for Claus O. Wilke's plot package.
Allows printing of character strings as messages/warnings/etc.
    with ASCII animals, including cats, cows, frogs, chickens, ghosts,
    and more.
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.
Implements Firth's penalized maximum likelihood bias reduction method  for Cox regression
  which has been shown to provide a solution in case of monotone likelihood (nonconvergence of likelihood function).
  The program fits profile penalized likelihood confidence intervals which were proved to outperform
  Wald confidence intervals.
Performs analysis of right-censored length-biased data using Cox model. It contains model fitting and checking, and the stationarity assumption test. The model fitting and checking methods are described in Qin and Shen (2010) <doi:10.1111/j.1541-0420.2009.01287.x> and Lee, Ning, and Shen (2018) <doi:10.1007/s10985-018-9422-y>.
Sparse estimation for Cox PH models is done via
    Minimum approximated Information Criterion (MIC) by Su, Wijayasinghe, 
    Fan, and Zhang (2016) <DOI:10.1111/biom.12484>. MIC mimics the best 
    subset selection using a penalized likelihood approach yet with no need 
    of a tuning parameter. The problem is further reformulated with a 
    re-parameterization step so that it reduces to one unconstrained non-convex
    yet smooth programming problem, which can be solved efficiently. Furthermore,
    the re-parameterization tactic yields an additional advantage in terms of
    circumventing post-selection inference.
Estimate coefficients of Cox proportional hazards model using stochastic gradient descent algorithm for batch data.
Implements weighted estimation in Cox regression as proposed by
             Schemper, Wakounig and Heinze (Statistics in Medicine, 2009, 
             <doi:10.1002/sim.3623>) and as described in Dunkler, Ploner, Schemper and 
             Heinze (Journal of Statistical Software, 2018, <doi:10.18637/jss.v084.i02>). 
             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.
             The package provides options to estimate time-dependent effects conveniently by
             including interactions of covariates with arbitrary functions of time, with or without
             making use of the weighting option.
A high performance package estimating Cox Model when an even has more than one causes. It also supports random and fixed effects, tied events, and time-varying variables.
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
It fits a CoxSEI (Cox type Self-Exciting Intensity) model to right-censored counting process data.
Functions for calculating the conditional power
             for different models in survival time analysis
             within randomized clinical trials
             with two different treatments to be compared
             and survival as an endpoint.
Functions to check whether a vector of p-values respects the assumptions of FDR (false discovery rate) control procedures and to compute adjusted p-values.
The package includes functions to test and compare causal models.
A Bayesian meta-analysis method for studying cross-phenotype
    genetic associations. It uses summary-level data across multiple phenotypes to
    simultaneously measure the evidence of aggregate-level pleiotropic association
    and estimate an optimal subset of traits associated with the risk locus. CPBayes
    is based on a spike and slab prior and is implemented by Markov chain Monte
    Carlo technique Gibbs sampling.
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
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. Also includes tools to apply quality control filters, perform permutation tests, and create QQ plots, manhattan plots, and scatterplots for individual CpG sites.
Frequently used methods in genomic applications with emphasis on parallel computing (OpenMP).
 At its core, the package has a Gibbs Sampler that allows running univariate linear
 mixed models that have both, sparse and dense design matrices. The parallel sampling method
 in case of dense design matrices (e.g. Genotypes) allows running Ridge Regression or BayesA for
 a very large number of individuals. The Gibbs Sampler is capable of running Single Step Genomic Prediction models.
 In addition, the package offers parallelized functions for common tasks like genome-wide
 association studies and cross validation in a memory efficient way.
Filter CpGs based on Intra-class Correlation Coefficients (ICCs) when replicates are available. ICCs are calculated by fitting linear mixed effects models to all samples including the un-replicated samples. Including the large number of un-replicated samples improves ICC estimates dramatically. The method accommodates any replicate design.
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.
This is the R Interface to the C API of IBM ILOG CPLEX. It necessarily depends on IBM ILOG CPLEX (>= 12.1).
Likelihood-based and Bayesian methods for various compound Poisson linear models.
Sequential and batch change detection for univariate data streams, using the change point model framework. Functions are provided to allow nonparametric distribution-free change detection in the mean, variance, or general distribution of a given sequence of observations. Parametric change detection methods are also provided for Gaussian, Bernoulli and Exponential sequences. Both the batch (Phase I) and sequential (Phase II) settings are supported, and the sequences may contain either a single or multiple change points.
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 developed 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 developed by B.Liquet & D.Commenges in 2005. The naive method with no correction is also available.
Implementation of the Control Polygon Reduction and Control Net
    Reduction methods for finding parsimonious B-spline regression models.
Permits to estimate the conditional probability function of a competing event, and to fit, using the temporal process regression or the pseudo-value approach, a proportional-odds model to the conditional probability function (or other models by specifying another link function). See <doi:10.1111/j.1467-9876.2010.00729.x>.
Calculate date of birth, age, and gender, and generate anonymous
    sequence numbers from CPR numbers.
    <https://en.wikipedia.org/wiki/Personal_identification_number_(Denmark)>.
Nonparametric change point estimation for survival data based on p-values of exact binomial tests.
Non-parametric test for equality of multivariate distributions.  Trains a classifier to classify (multivariate) observations as coming from one of several distributions.  If the classifier is able to classify the observations better than would be expected by chance (using permutation inference), then the null hypothesis that the distributions are equal is rejected.
Incorporates colour gradients from the 'cpt-city' web archive available at <http://soliton.vm.bytemark.co.uk/pub/cpt-city/>.
Allows to retrieve data from the
    'CPTEC/INPE' weather forecast API. 'CPTEC' stands for 'Centro de Previsão
    de Tempo e Estudos Climáticos' and 'INPE' for 'Instituto Nacional de
    Pesquisas Espaciais'. 'CPTEC' is the most advanced numerical weather and
    climate forecasting center in Latin America, with high-precision short
    and medium-term weather forecasting since the beginning of 1995. See
    <http://www.cptec.inpe.br/> for more information.
Estimate quantile regression(QR) and composite quantile regression (cqr) and with adaptive lasso penalty using interior point (IP), majorize and minimize(MM), coordinate descent (CD), and alternating direction method of multipliers algorithms(ADMM).
Estimation, based on conditional maximum likelihood, of the quadratic exponential model proposed by Bartolucci, F. & Nigro, V. (2010, Econometrica) <DOI:10.3982/ECTA7531> and of a simplified and a modified version of this model. The quadratic exponential model is suitable for the analysis of binary longitudinal data when state dependence (further to the effect of the covariates and a time-fixed individual intercept) has to be taken into account. Therefore, this is an alternative to the dynamic logit model having the advantage of easily allowing conditional inference in order to eliminate the individual intercepts and then getting consistent estimates of the parameters of main interest (for the covariates and the lagged response). The simplified version of this model does not distinguish, as the original model does, between the last time occasion and the previous occasions. The modified version formulates in a different way the interaction terms and it may be used to test in a easy way state dependence as shown in Bartolucci, F., Nigro, V. & Pigini, C. (2013, Econometric Reviews) <DOI:10.1080/07474938.2015.1060039>. The package also includes estimation of the dynamic logit model by a pseudo conditional estimator based on the quadratic exponential model, as proposed by Bartolucci, F. & Nigro, V. (2012, Journal of Econometrics) <DOI:10.1016/j.jeconom.2012.03.004>.
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.
Tool for analyzing competing risks models. The main point of interest is testing differences between groups (as described in R.J Gray (1988) <doi:10.1214/aos/1176350951> and J.P. Fine, R.J Gray (1999) <doi:10.2307/2670170>) and visualizations of survival and cumulative incidence curves.
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.
A set of functions to manage 'CRAN'-like repositories
    efficiently.
'API' to the database of 'CRAN' package downloads from the
    'RStudio' 'CRAN mirror'. The database itself is at <http://cranlogs.r-pkg.org>,
    see <https://github.com/metacran/cranlogs.app> for the raw 'API'.
Provides core visualisations and summaries for the CRAN package database. The package provides comprehensive methods for cleaning up and organising the information in the CRAN package database, for building package directives networks (depends, imports, suggests, enhances) and collaboration networks, and for computing summaries and producing interactive visualisations from the resulting networks. Network visualisation is through the 'visNetwork' <https://CRAN.R-project.org/package=visNetwork> R package. The package also provides functions to coerce the networks to 'igraph' <https://CRAN.R-project.org/package=igraph> objects for further analyses and modelling.
One of the strengths of R is its vast package ecosystem. Indeed, R packages extend from visualization to Bayesian inference and from spatial analyses to pharmacokinetics (<https://cran.r-project.org/web/views/>). There is probably not an area of quantitative research that isn't represented by at least one R package. At the time of this writing, there are more than 10,000 active CRAN packages. Because of this massive ecosystem, it is important to have tools to search and learn about packages related to your personal R needs. For this reason, we developed an RStudio addin capable of searching available CRAN packages directly within RStudio.
Various R tools for http://crantastic.org/
Fit continuous-time
    correlated random walk models with time indexed
    covariates to animal telemetry data. The model is fit using the Kalman-filter on
    a state space version of the continuous-time stochastic
    movement process.
Colored terminal output on terminals that support 'ANSI'
    color and highlight codes. It also works in 'Emacs' 'ESS'. '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 or truncated regression with 
  conditional heteroscedasticity are provided. First, continuous 
  distributions can be used for the (right and/or left censored or truncated)
  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.
Provides a new method for identification of clusters of genomic
 regions within chromosomes. Primarily, it is used for calling clusters of 
 cis-regulatory elements (COREs). 'CREAM' uses genome-wide maps of genomic regions
 in the tissue or cell type of interest, such as those generated from chromatin-based 
 assays including DNaseI, ATAC or ChIP-Seq. 'CREAM' considers proximity of the elements 
 within chromosomes of a given sample to identify COREs in the following steps:
 1) It identifies window size or the maximum allowed distance between the elements 
 within each CORE, 2) It identifies number of elements which should be clustered 
 as a CORE, 3) It calls COREs, 4) It filters the COREs with lowest order which 
 does not pass the threshold considered in the approach.
A set of functions for computing the CreditMetrics risk model
Provides tools for pricing credit default swaps using
             C code for the International Swaps and Derivatives
             Association (ISDA) CDS Standard Model. See
             <http://www.cdsmodel.com/cdsmodel/documentation.html>
             for more information about the model and 
             <http://www.cdsmodel.com/cdsmodel/cds-disclaimer.html>
             for license details for the C code.
Evaluation of default probability of sovereign and corporate entities based on structural or intensity based models and calibration on market Credit Default Swap quotes. Damiano Brigo, Massimo Morini, Andrea Pallavicini (2013): "Counterparty Credit Risk, Collateral and Funding. With Pricing Cases for All Asset Classes".
Functions for constructing simultaneous credible bands and identifying subsets via the "credible subsets" (also called "credible subgroups") method.
It provides functions to bootstrap Credit Curves from market quotes (Credit Default Swap - CDS - spreads) and price Credit Default Swaps - CDS.
Builds a 'SQLite' database file of pre-calculated transcription 
    factor/microRNA-gene correlations (co-expression) in cancer from the 
    Cistrome Cancer Liu et al. (2011) <doi:10.1186/gb-2011-12-8-r83> and 
    'miRCancerdb' databases (in press). Provides custom classes and functions 
    to query, tidy and plot the correlation data.
Implements modeling and computational tools for conditional
    random fields (CRF) model as well as other probabilistic undirected
    graphical models of discrete data with pairwise and unary potentials.
Tools for analyzing performances of cricketers based on stats in
    ESPN Cricinfo Statsguru. The toolset can  be used for analysis of Tests,ODIs 
    and Twenty20 matches of both batsmen and bowlers.
A finite mixture of Zero-Inflated Poisson (ZIP) models for analyzing criminal trajectories.
Statistical Methods for Crime Series Linkage. This package provides 
      code for criminal case linkage, crime series identification, crime series 
      clustering, and suspect identification.
Implements convex regression with interpretable sharp partitions
    (CRISP), which considers the problem of predicting an outcome variable on the basis of two covariates, using an interpretable yet non-additive model. CRISP partitions the covariate space into blocks in a data-adaptive way, and fits a mean model within each block. Unlike other partitioning methods, CRISP is fit using a non-greedy approach by solving a convex optimization problem, resulting in low-variance fits. More details are provided in Petersen, A., Simon, N., and Witten, D. (2016). Convex Regression with Interpretable Sharp Partitions. Journal of Machine Learning Research, 17(94): 1-31 <http://jmlr.org/papers/volume17/15-344/15-344.pdf>.
CRM simulator for Phase I Clinical Trials
Text mining client for 'Crossref' (<https://crossref.org>). Includes
    functions for getting getting links to full text of articles, fetching full
    text articles from those links or Digital Object Identifiers ('DOIs'),
    and text extraction from 'PDFs'.
Implements the Cross-contribution Compensating Multiple
    standard Normalization (CCMN) method and other normalization
    algorithms.
Implements a wide range of model-based dose
    escalation designs, ranging from classical and modern continual
    reassessment methods (CRMs) based on dose-limiting toxicity endpoints to
    dual-endpoint designs taking into account a biomarker/efficacy outcome. The
    focus is on Bayesian inference, making it very easy to setup a new design
    with its own JAGS code. However, it is also possible to implement 3+3
    designs for comparison or models with non-Bayesian estimation. The whole
    package is written in a modular form in the S4 class system, making it very
    flexible for adaptation to new models, escalation or stopping rules.
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.
Functions to help implement the extraction / subsetting / indexing
    function [ and replacement function [<- of custom matrix-like types (based
    on S3, S4, etc.), modeled as closely to the base matrix class as possible
    (with tests to prove it).
Create, edit, and remove 'cron' jobs on your unix-alike system. The package provides a set of easy-to-use wrappers
    to 'crontab'. It also provides an RStudio add-in to easily launch and schedule your scripts.
A device closing function which is able to crop graphics (e.g.,
  PDF, PNG files) on Unix-like operating systems with the required underlying
  command-line tools installed.
Provides peruvian agricultural production data from the Agriculture Minestry of Peru (MINAGRI). The first version includes
             6 crops: rice, quinoa, potato, sweet potato, tomato and wheat; all of them across 24 departments. Initially,  in excel files which has been transformed
             and assembled using tidy data principles, i.e. each variable is in a column, each observation is a row and each value is in a cell.
             The variables variables are sowing and harvest area per crop, yield, production and price per plot, every one year, from 2004 to 2014.
Computes a partial clustering algorithm that combines
    the Ward's minimum variance and Complete Linkage algorithms, providing
    automatic estimation of a suitable number of clusters and identification of
    outlier elements.
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.
Cross-screening is a new method that uses both random halves of the sample to screen and test many hypotheses. It generally improves statistical power in observational studies when many hypotheses are tested simultaneously. References: 1. Qingyuan Zhao, Dylan S Small, and Paul R Rosenbaum. Cross-screening in observational studies that test many hypotheses. <arXiv:1703.02078>. 2. Qingyuan Zhao. On sensitivity value of pair-matched observational studies. <arXiv:1702.03442>.
Provides building blocks for allowing HTML widgets to communicate
    with each other, with Shiny or without (i.e. static .html files). Currently
    supports linked brushing and filtering.
Enables transformation of Verbal Autopsy data collected with the WHO 2016 questionnaire (Revision 1.4.1) for automated coding of Cause of Death using different computer algorithms. Currently supports user-supplied mappings, and provides unvalidated, experimental-stage mapping definitions to transform to InterVA4, Tariff 2, and InSilicoVA.
  This package is made available by WHO, in collaboration with Swiss Tropical and Public Health Institute.
Contains generic functions for performing 
  cross validation and for computing diagnostic errors.
Defines classes and methods to cross-validate various
  binary classification algorithms used for "class prediction"
  problems.
A pair of functions for renaming and encoding data frames
	     using external crosswalk files. It is especially useful when
	     constructing master data sets from multiple smaller data
	     sets that do not name or encode variables consistently
	     across files. Based on similar commands in 'Stata'.
Generate crosswords from a list of words.
Chinese restaurant process [Pitman (1995) <doi:10.1007/BF01213386>] is used in order to compose Dirichlet process [Ferguson (1973) <doi:10.1214/aos/1176342360>]. The clustering which uses Chinese restaurant process does not need to decide the number of clusters in advance. This algorithm automatically adjusts it. And this package calculates the ambiguity of clusters as entropy [Yngvason (1999) <doi:10.1016/S0370-1573(98)00082-9>].
Modelling credit risks based on the concept of "CreditRisk+", First Boston Financial Products, 1997 and "CreditRisk+ in the Banking Industry", Gundlach & Lehrbass, Springer, 2003.
In order to facilitate analysis of datasets hosted on the Crunch
    data platform <http://crunch.io/>, the 'crplyr' package implements 'dplyr'
    methods on top of the Crunch backend. The usual methods "select", "filter",
    "mutate", "group_by", and "summarize" are implemented in such a way as to
    perform as much computation on the server and pull as little data locally
    as possible.
Cross-recurrence quantification analysis 
    of 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.
Cross-recurrence quantification analysis 
    for word series, from text, known as categorical
    recurrence analysis. Uses the 'crqa' R package
    by Coco and Dale (2014) <doi:10.3389/fpsyg.2014.00510>. 
    Functions are wrappers to facilitate exploration 
    of the sequential properties of text.
In competing risks regression, the proportional subdistribution hazards(PSH) model is popular for its direct assessment of covariate effects on the cumulative incidence function. This package allows for penalized variable selection for the PSH model. Penalties include LASSO, SCAD, MCP, and their group versions.
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.
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, <http://www.nserc-crsng.gc.ca>), the Social Sciences and Humanities Research Council of Canada (SSHRC, <http://www.sshrc-crsh.gc.ca>), and the Shared Hierarchical Academic Research Computing Network (SHARCNET, <https://www.sharcnet.ca>).
Provides the implementation of analytical and graphical approaches for checking the assumptions of the Fine and Gray model.
Functions for nonlinear regression parameters estimation by algorithms based on Controlled Random Search algorithm.
  Both functions (crs4hc(), crs4hce()) adapt current search strategy by four heuristics competition. In addition, crs4hce() improves adaptability by adaptive stopping condition.
Provides wrapper functions for running classification and
    regression tests using different machine learning techniques, such as Random
    Forests and decision trees. The package provides standardized methods for
    preparing data to suit the algorithm's needs, training a model, making
    predictions, and evaluating results. Also, some functions are provided to run
    multiple instances of a test.
Implements a semi-parametric GEE estimator accounting for missing data with Inverse-probability weighting (IPW) and for imbalance in covariates with augmentation (AUG). The estimator IPW-AUG-GEE is Doubly robust (DR).
Sample size estimation in cluster (group) randomized trials.  Contains traditional power-based methods, empirical smoothing (Rotondi and Donner, 2009), and updated meta-analysis techniques (Rotondi and Donner, 2012).
A simple HTTP client, with tools for making HTTP requests,
    and mocking HTTP requests. The package is built on R6, and takes
    inspiration from Ruby's 'faraday' gem (<https://rubygems.org/gems/faraday>).
    The package name is a play on curl, the widely used command line tool
    for HTTP, and this package is built on top of the R package 'curl', an
    interface to 'libcurl' (<https://curl.haxx.se/libcurl>).
The Crunch.io service <http://crunch.io/> provides a cloud-based
    data store and analytic engine, as well as an intuitive web interface.
    Using this package, analysts can interact with and manipulate Crunch
    datasets from within R. Importantly, this allows technical researchers to
    collaborate naturally with team members, managers, and clients who prefer a
    point-and-click interface.
To facilitate building custom dashboards on the Crunch data
    platform <http://crunch.io/>, the 'crunchy' package provides tools for
    working with 'shiny'. These tools include utilities to manage authentication
    and authorization automatically and custom stylesheets to help match the
    look and feel of the Crunch web application.
Functions for reading in and manipulating CRU TS3.21: Climatic
    Research Unit (CRU) Time-Series (TS) Version 3.21 data.
Exploratory tools to identify closely related subjects using autosomal genetic marker data.
Retrieves crypto currency current and historical information as well as information on the exchanges they are listed on. For current and historical it will retrieve the daily open, high, low and close values for all crypto currencies. This retrieves the historical market data by web scraping tables provided by 'Cryptocurrency Market Capitalizations' <https://coinmarketcap.com>.
Performs cryptographic randomness tests on a sequence of random
    integers or bits. Included tests are greatest common divisor, birthday spacings,
    book stack, adaptive chi-square, topological binary, and three random walk
    tests. Tests except greatest common divisor and birthday spacings are not
    covered by standard test suites. In addition to the chi-square goodness-of-fit
    test, results of Anderson-Darling, Kolmogorov-Smirnov, and Jarque-Bera tests are
    also generated by some of the cryptographic randomness tests.
Functions to calculate the relative crystallinity of starch by X-ray Diffraction (XRD) and Infrared Spectroscopy (FTIR). Starch is biosynthesized by plants in the form of granules semicrystalline. For XRD, the relative crystallinity is obtained by separating the crystalline peaks from the amorphous scattering region. For FTIR, the relative crystallinity is achieved by setting of a Gaussian holocrystalline-peak in the 800-1300 cm-1 region of FTIR spectrum of starch which is divided into amorphous region and crystalline region. The relative crystallinity of native starch granules varies from 14 of 45 percent. This package was supported by FONDECYT 3150630 and CIPA Conicyt-Regional R08C1002 is gratefully acknowledged.
The joint distribution of potential outcomes is not typically identified under standard identifying assumptions such as selection on observables or even when individuals are randomly assigned to being treated. This package contains methods for obtaining tight bounds on distributional treatment effect parameters when panel data is available and under a Copula Stability Assumption as in Callaway (2017) <https://ssrn.com/abstract=3028251>.
Monte Carlo conditional inference for the parameters of a
  linear nonnormal regression model.
Calculate the confidence interval and p value for change in C-statistic. The adjusted C-statistic is calculated by using formula as "Somers' Dxy rank correlation"/2+0.5. The confidence interval was calculated by using the bootstrap method. The p value was calculated by using the Z testing method. Please refer to the article of Peter Ganz et al. (2016) <doi:10.1001/jama.2016.5951>.
Germline and somatic locus data which contain the total read depth and B allele 
    read depth using Bayesian model (Dirichlet Process) to cluster. Meanwhile, the cluster 
    model can deal with the SNVs mutation and the CNAs mutation.
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-today). Includes functions for data extraction and the computation of distance matrices and -lists.
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.
    See Gonzalez-Farias, G., Dominguez-Molina, J., and Gupta, A. (2004).
    The closed skew normal distribution, 
    Skew-elliptical distributions and their applications: a journey beyond normality,
    Chapman and Hall/CRC, Boca Raton, FL, pp. 25-42.
Provides the Correlates of State Policy data set for easy use in R.
Includes two functions to evaluate the hypothesis of complete spatial randomness (csr) in point processes.  The function 'mwin' calculates quadrat counts to estimate the intensity of a spatial point process through the moving window approach proposed by Bailey and Gatrell (1995).  Event counts are computed within a window of a set size over a fine lattice of points within the region of observation.  The function 'pielou' uses the nearest neighbor test statistic and asymptotic distribution proposed by Pielou (1959) to compare the observed point process to one generated under csr.  The value can be compared to that given by the more widely used test proposed by Clark and Evans (1954).
Cell-type specific differential expression of a microarray
        experiment of heterogeneous tissue samples, using SAM.
A collection of tools for estimating a network from a random sample of cognitive social structure (CSS) slices. Also contains functions for evaluating a CSS in terms of various error types observed in each slice.
Selection of the number of clusters in cluster analysis using
    stability methods.
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).
Reads and writes CSV with selected conventions.
 Uses the same generic function for reading and writing to promote consistent formats.
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.
Support for import from and export to the CSVY file format. CSVY is a file format that combines the simplicity of CSV (comma-separated values) with the metadata of other plain text and binary formats (JSON, XML, Stata, etc.) by placing a YAML header on top of a regular CSV.
Estimation and inference methods for the continuous threshold expectile regression.
    It can fit the continuous threshold expectile regression and test the existence of change point,
    for the paper, "Feipeng Zhang and Qunhua Li (2016). A continuous threshold expectile regression, submitted."
Analysis of genetical genomic data to identify genetic loci associated with correlation changes in quantitative traits (CTL).
The CTM package is designed to solve problems of text mining and is specific for Chinese document.
Functions for estimating Markov generator matrices from discrete-time observations. The implemented approaches comprise diagonal adjustment, weighted adjustment and quasi-optimization of matrix logarithm based candidate solutions, an expectation-maximization algorithm as well as a Gibbs sampler.
Software to facilitates taking movement data in xyt format and pairing it with raster covariates within a continuous time Markov chain (CTMC) framework.  As described in Hanks et al. (2015) <DOI:10.1214/14-AOAS803> , this allows flexible modeling of movement in response to covariates (or covariate gradients) with model fitting possible within a Poisson GLM framework.
Implements the general template for collaborative targeted maximum likelihood estimation. It also provides several commonly used C-TMLE instantiation, like the vanilla/scalable variable-selection C-TMLE (Ju et al. (2017) <doi:10.1177/0962280217729845>) and the glmnet-C-TMLE algorithm (Ju et al. (2017) <arXiv:1706.10029>).
Functions for identifying, fitting, and applying continuous-space, continuous-time stochastic movement models to animal tracking data.
  The package is described in Calabrese & Fleming (2016) <doi:10.1111/2041-210X.12559> and its methods are based on those introduced in
  Fleming et al (2014) <doi:10.1086/675504>,
  Fleming et al (2014) <doi:10.1111/2041-210X.12176>,
  Fleming et al (2015) <doi:10.1890/14-2010.1>,
  Fleming et al (2016) <doi:10.1890/15-1607>,
  Péron et al (2016) <doi:10.1186/s40462-016-0084-7>,
  Fleming & Calabrese (2016) <doi:10.1111/2041-210X.12673>,
  Péron et al (2017) <doi:10.1002/ecm.1260>,
  and
  Fleming et al (2017) <doi:10.1016/j.ecoinf.2017.04.008>.
Estimation of quantile regression models for survival data.
Functions to fit continuous time autoregressive models with the Kalman filter.
A multivariate continuous (and discrete) time dynamic modelling
    package for panel and time series data, using linear stochastic differential
    equations. Contains a faster frequentist set of functions using OpenMx for
    single subject and mixed-effects (random intercepts only) structural
    equation models, or a hierarchical Bayesian implementation using Stan that
    allows for random effects over all model parameters. Allows for modelling of
    multiple noisy measurements of multiple stochastic processes, time varying
    input / event covariates, and time invariant covariates used to predict the
    parameters.
A collection of common test and item analyses from a classical test theory (CTT) framework. Analyses can be applied to both dichotomous and polytomous data. Functions provide reliability analyses (alpha), item statistics, disctractor analyses, disattenuated correlations, scoring routines, and empirical ICCs.
Interactive shiny application for running classical test theory (item analysis).
Infrastructure for task views to CRAN-style repositories: Querying task views and installing the associated
             packages (client-side tools), generating HTML pages and storing task view information in the repository
	     (server-side tools).
For ordinal rating data, estimate and test models within the family of
    CUB models and their extensions (where CUB stands for Combination of a 
	discrete Uniform and a shifted Binomial distributions). Simulation routines, plotting facilities 
	and fitting measures are also provided.
R wrapper around the cubature C library of
    Steven G. Johnson for adaptive multivariate integration over hypercubes.
    This version provides both hcubature and pcubature routines in addition
    to a vector interface that results in substantial speed gains.
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, et al. (2009)
       <doi:10.1073/pnas.0812841106>.
Functions for visualizing, animating, solving and 
        analyzing the Rubik's cube. Includes data structures for
		solvable and unsolvable cubes, random moves and random 
		state scrambles and cubes, 3D displays and animations 
		using 'OpenGL', patterned cube generation, and lightweight 
		solvers. See Rokicki, T. (2008) <arXiv:0803.3435> for the
		Kociemba solver.
Regression modeling using rules with added instance-based corrections.
Functions for MultiDimensional Feature Selection (MDFS):
    * calculating multidimensional information gains,
    * finding interesting tuples for chosen variables,
    * scoring variables,
	* finding important variables,
	* plotting selection results.
	CuCubes is also known as CUDA Cubes and it is a library that allows fast
	CUDA-accelerated computation of information gains in binary classification
	problems.
	This package wraps CuCubes and provides an alternative CPU version as well
	as helper functions for building MultiDimensional Feature Selectors.
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.
Utility functions that provides wrapper to descriptive base functions
  like cor, mean and table.  It makes use of the formula interface to pass
  variables to functions.  It also provides operators to concatenate (%+%), to
  repeat (%n%) and manage character vectors for nice display.
Provides tools for measuring the compositionality of signalling systems (in particular the information-theoretic measure due to Spike (2016) <http://hdl.handle.net/1842/25930> and the Mantel test for distance matrix correlation (after Dietz 1983) <doi:10.1093/sysbio/32.1.21>), functions for computing string and meaning distance matrices as well as an implementation of the Page test for monotonicity of ranks (Page 1963) <doi:10.1080/01621459.1963.10500843> with exact p-values up to k = 22.
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.
Cumulative descriptive statistics for (arithmetic, geometric, harmonic) mean, median, mode, variance, skewness and kurtosis.
The curl() and curl_download() functions provide highly
    configurable drop-in replacements for base url() and download.file() with
    better performance, support for encryption (https, ftps), gzip compression,
    authentication, and other 'libcurl' goodies. The core of the package implements a
    framework for performing fully customized requests where data can be processed
    either in memory, on disk, or streaming via the callback or connection
    interfaces. Some knowledge of 'libcurl' is recommended; for a more-user-friendly
    web client see the 'httr' package which builds on this package with http
    specific tools and logic.
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.
Partial application is the process of reducing the arity of a
    function by fixing one or more arguments, thus creating a new function
    lacking the fixed arguments. The curry package provides three different ways
    of performing partial function application by fixing arguments from either
    end of the argument list (currying and tail currying) or by fixing multiple
    named arguments (partial application). This package provides this
    functionality through the %<%, %-<%, and %><% operators which allows for
    a programming style comparable to modern functional languages. Compared
    to other implementations such a purrr::partial() the operators in curry
    composes functions with named arguments, aiding in autocomplete etc.
Computes the maximum likelihood estimator, the smoothed maximum likelihood
    estimator and pointwise bootstrap confidence intervals for the distribution 
    function under current status data. 
    Groeneboom and Hendrickx (2017) <doi:10.1214/17-EJS1345>.
Performs multiple comparison procedures on curve observations among different treatment groups. The methods are applicable in a variety of situations (such as independent groups with equal or unequal sample sizes, or repeated measures) by using parametric bootstrap. References to these procedures can be found at Konietschke, Gel, and Brunner (2014) <doi:10.1090/conm/622/12431> and Westfall (2011) <doi:10.1080/10543406.2011.607751>.
Test Equality of Curves with Homoscedastic or
        Heteroscedastic Errors.
Filtering, also known as gating, of flow cytometry samples using 
             the curvHDR method, which is described in Naumann, U., Luta, G. and 
             Wand, M.P. (2010) <DOI:10.1186/1471-2105-11-44>.
Cobb's maximum likelihood method for cusp-catastrophe modeling
        (Grasman, van der Maas, & Wagenmakers, 2009, JSS, 32:8;
        Cobb, L, 1981, Behavioral Science, 26:1, 75--78).
        Includes a cusp() function for model fitting, and several
        utility functions for plotting, and for comparing the
        model to linear regression and logistic curve models.
Functions for evaluating and visualizing predictive model performance (specifically: binary classifiers) in the field of customer scoring. These metrics include lift, lift index, gain percentage, top-decile lift, F1-score, expected misclassification cost and absolute misclassification cost. See Berry & Linoff (2004, ISBN:0-471-47064-3), Witten and Frank (2005, 0-12-088407-0) and Blattberg, Kim & Neslin (2008, ISBN:978–0–387–72578–9) for details. Visualization functions are included for lift charts and gain percentage charts. All metrics that require class predictions offer the possibility to dynamically determine cutoff values for transforming real-valued probability predictions into class predictions.
Customized training is a simple technique for transductive
    learning, when the test covariates are known at the time of training. The
    method identifies a subset of the training set to serve as the training set
    for each of a few identified subsets in the training set. This package
    implements customized training for the glmnet() and cv.glmnet() functions.
Computation of decision intervals (H) and average run lengths (ARL) for CUSUM charts.
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.
Estimate cutpoints that optimize a specified metric in binary classification tasks
    and validate performance using bootstrapping. Some methods for more robust cutpoint
    estimation and various plotting functions are included.
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
Compute expected shortfall (ES) and Value at Risk (VaR) from a
    quantile function, distribution function, random number generator or
    probability density function.  ES is also known as Conditional Value at
    Risk (CVaR). Virtually any continuous distribution can be specified.
    The functions are vectorized over the arguments. The computations are
    done directly from the definitions, see e.g. Acerbi and Tasche (2002)
    <doi:10.1111/1468-0300.00091>.
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 functions, AUC and cvAUC, are simple wrappers for functions from the ROCR package.
Statistical inferences for estimating the calibration equation with error-in observations
Constrained randomization by Raab and Butcher (2001) <doi:10.1002/1097-0258(20010215)20:3%3C351::AID-SIM797%3E3.0.CO;2-C> 
            is suitable for cluster randomized trials (CRTs) with a
            small number of clusters (e.g., 20 or fewer). The procedure of
            constrained randomization is based on the baseline values of some
            cluster-level covariates specified. The intervention effect on
            the individual outcome can then be analyzed through
            clustered permutation test introduced by Gail, et al. (1996) <doi:10.1002/(SICI)1097-0258(19960615)15:11%3C1069::AID-SIM220%3E3.0.CO;2-Q>. 
            Motivated from Li, et al. (2016) <doi:10.1002/sim.7410>, the package performs constrained randomization on the baseline
            values of cluster-level covariates and cluster permutation test on the individual-level outcome for cluster randomized trials.
Methods for color vision deficiencies (CVD), to help understanding and mitigating issues with CVDs and to generate tests for diagnosis and interpretation.
Contains functions for testing for significant differences between multiple coefficients of variation. Includes Feltz and Miller's (1996) <DOI:10.1002/(SICI)1097-0258(19960330)15:6%3C647::AID-SIM184%3E3.0.CO;2-P> asymptotic test and Krishnamoorthy and Lee's (2014) <DOI:10.1007/s00180-013-0445-2> modified signed-likelihood ratio test. See the vignette for more, including full details of citations.
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.
Perform canonical variate regression (CVR) for two sets of covariates and a univariate
            response, with regularization and weight parameters tuned 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.
An iterative algorithm for solving a convex
    formulation of the biclustering problem.
Alternating Minimization Algorithm (AMA) and Alternating Direction
    Method of Multipliers (ADMM) splitting methods for convex clustering.
An object-oriented modeling language for disciplined convex
    programming (DCP). It allows the user to formulate convex optimization problems
    in a natural way following mathematical convention and DCP rules. The system
    analyzes the problem, verifies its convexity, converts it into a canonical form,
    and hands it off to an appropriate solver to obtain the solution.
Miscellaneous useful or interesting functions.
This package estimates gaussian cluster weighted linear regressions by EM algorithm.
extend cxxfunction by saving the dynamic shared objects
        for reusing across R sessions
A suite of functions for analysing cycling data.
Cyclomatic complexity is a software metric (measurement),
    used to indicate the complexity of a program. It is a quantitative
    measure of the number of linearly independent paths through a program's
    source code. It was developed by Thomas J. McCabe, Sr. in 1976.
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 model fitting tool incorporates cyclic coordinate descent and
    majorization-minimization approaches to fit a variety of regression models
    found in large-scale observational healthcare data.  Implementations focus
    on computational optimization and fine-scale parallelization to yield
    efficient inference in massive datasets.
A toolkit for querying 'Team Cymru' <http://team-cymru.org> 'IP'
    address, Autonomous System Number ('ASN'), Border Gateway Protocol ('BGP'), Bogon
    and 'Malware' Hash Data Services.
This library contains a primary function that divides
        chewing sequences in cycles and cycles into phases.  See
        get.all.breaks for an example.
The cystiSim package provides an agent-based model for Taenia solium transmission and control. cystiSim was developed within the framework of CYSTINET, the European Network on taeniosis/cysticercosis, COST ACTION TD1302.
Tools to interface with Cytobank's API via R, organized by various
    endpoints that represent various areas of Cytobank functionality. Learn more
    about Cytobank at <https://www.cytobank.org>.
Tools to process cytometry data from Cytobank into easily usable form for analysis of populations, markers, and signaling using the 'CytobankAPI' package. Learn more about Cytobank at <https://www.cytobank.org>. For more information about types of cytometry data that can be analyzed, please see: Bendall, S. C., Simonds, E. F., Qiu, P., Amir, E. D., Krutzik, P. O., Finck, R.,... Nolan, G. P. (2011) <doi:10.1126/science.1198704> and Adan, A., Alizada, G., Kiraz, Y., Baran, Y., Nalbant, A. (2017). <doi:10.3109/07388551.2015.1128876>.
A collection of tools that leverage the CytobankAPI R package <https://cran.r-project.org/web/packages/CytobankAPI/vignettes/cytobank-quickstart.html> to complete more complex workflows, and add/extend various Cytobank features.
Calculates ecological diversity indices for a microbial
        community
Given the hypothesis of a bimodal distribution of cells for
    each marker, the algorithm constructs a binary tree, the nodes of which are
    subpopulations of cells. At each node, observed cells and markers are modeled
    by both a family of normal distributions and a family of bimodal normal mixture
    distributions. Splitting is done according to a normalized difference of AIC
    between the two families.
Typical morphological profiling datasets have millions of cells
    and hundreds of features per cell. When working with this data, you must
    clean the data, normalize the features to make them comparable across
    experiments, transform the features, select features based on their
    quality, and aggregate the single-cell data, if needed. 'cytominer' makes
    these steps fast and easy. Methods used in practice in the field are
    discussed in Caicedo (2017) <doi:10.1038/nmeth.4397>. An overview of the
    field is presented in Caicedo (2016) <doi:10.1016/j.copbio.2016.04.003>.
The relationship between statistical dependency and causality lies
    at the heart of all statistical approaches to causal inference. The D2C
    package implements a supervised machine learning approach to infer the
    existence of a directed causal link between two variables in multivariate
    settings with n>2 variables. The approach relies on the asymmetry of some
    conditional (in)dependence relations between the members of the Markov
    blankets of two variables causally connected. The D2C algorithm predicts
    the existence of a direct causal link between two variables in a
    multivariate setting by (i) creating a set of of features of the
    relationship based on asymmetric descriptors of the multivariate dependency
    and (ii) using a classifier to learn a mapping between the features and the
    presence of a causal link
Creates interactive genome browser with 'R'. It joins the data analysis power of R and the visualization libraries of JavaScript in one package.
Create interactive heat maps that are usable from the R console, in
    the 'RStudio' viewer pane, in 'R Markdown' documents, and in 'Shiny' apps. Hover
    the mouse pointer over a cell to show details, drag a rectangle to zoom, and
    click row/column labels to highlight.
This packages is intended to make it easy to create D3 JavaScript
    network, tree, dendrogram, and Sankey graphs from R using data frames.
    !!! NOTE: Active development has moved to the networkD3 package. !!!
Builds interactive 'd3.js' hierarchical visualisation easily. D3partitionR makes it easy to build and customize sunburst, circle treemap, treemap, partition chart, ...
Provides functions that offer seamless 'D3Plus' integration. The examples provided here are taken from the official 'D3Plus' website <http://d3plus.org>.
Helper functions for using 'd3.js' in R.
Create and customize interactive collapsible 'D3' trees using the 'D3'
    JavaScript library and the 'htmlwidgets' package. These trees can be used
    directly from the R console, from 'RStudio', in Shiny apps and R Markdown documents.
    When in Shiny the tree layout is observed by the server and can be used as a reactive filter
    of structured data.
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 gene 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.
Allows to calculate Data Agreement Criterion (DAC) scores. This can be done to determine prior-data conflict or to evaluate and compare multiple priors, which can be experts' predictions. Bousquet (2008) <doi.org/10.1080/02664760802192981>.
An implementation of data analytic methods in R for analyses for data with ceiling/floor effects. The package currently includes functions for mean/variance estimation and mean comparison tests. Implemented methods are from Aitkin (1964) <doi:10.1007/BF02289723> and Liu & Wang (in prep).
The three-way data consists of a set of variables measured on several groups of individuals. To each group is associated an estimated probability density function. The package provides tools to create or manage such data and functional methods (principal component analysis, multidimensional scaling, cluster analysis, discriminant analysis...) for such probability densities.
The content falls into the following groupings: (i) Data, (ii)
    Factor manipulation functions, (iii) Design functions, (iv) ANOVA functions, (v)
    Matrix functions, (vi) Projector and canonical efficiency functions, and (vii)
    Miscellaneous functions. There is a vignette describing how to use the 
    Design functions for randomizing and assessing designs available in the file 
    'daeDesignNotes.pdf'. The ANOVA functions facilitate the extraction of 
    information when the 'Error' function has been used in the call to 'aov'.
Contains Data frames and functions used in the book "Design and Analysis of Experiments with R".
Diff, patch and merge for data frames. Document changes in data
    sets and use them to apply patches. Changes to data can be made visible by using
    render_diff. The V8 package is used to wrap the 'daff.js' JavaScript library
    which is included in the package.
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
Integrates the information from multiple linkage maps to
        create a consensus directed graph, which is then linearized to
        produce a consensus map.
A port of the web-based software 'DAGitty', available at 
    <http://dagitty.net>, for analyzing structural causal models 
    (also known as directed acyclic graphs or DAGs).
    This package computes covariate adjustment sets for estimating causal
    effects, enumerates instrumental variables, derives testable
    implications (d-separation and vanishing tetrads), generates equivalent
    models, and includes a simple facility for data simulation.
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.
Simulates and computes the (maximum) likelihood of a dynamical model of island biota assembly through speciation, immigration and extinction. See Valente et al. 2015. Ecology Letters 18: 844-852, <DOI:10.1111/ele.12461>.
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.  See package?DAKS for an overview.
Machine Learning (ML) models are widely used and have various applications in classification 
  or regression. Models created with boosting, bagging, stacking or similar techniques are often
  used due to their high performance, but such black-box models usually lack of interpretability.
  'DALEX' package contains various explainers that help to understand the link between input variables and model output.
  The single_variable() explainer extracts conditional response of a model as a function of a single selected variable.
  It is a wrapper over packages 'pdp' and 'ALEPlot'.
  The single_prediction() explainer attributes arts of model prediction to articular variables used in the model.
  It is a wrapper over 'breakDown' package.
  The variable_dropout() explainer assess variable importance based on consecutive permutations.
  All these explainers can be plotted with generic plot() function and compared across different models.
Automates fitting of double GLM in 'JAGS'. Includes automatic
    generation of 'JAGS' scripts, running 'JAGS' via 'rjags', and summarizing the
    resulting output.
The DALY Calculator is a free, open-source Graphical User
             Interface (GUI) for stochastic disability-adjusted life year
             (DALY) calculation.
A collection of functions which aim to assist common computational workflow for analysis of matabolomic data..
Interactively train neural networks on Numerai, <https://numer.ai/>, data. Generate tournament predictions and write them to a CSV.
Miscellaneous set of functions I use in my teaching either at the University of Wisconsin-Milwaukee or the Inter-university Consortium for Political and Social Research Summer Program in Quantitative Methods.  Broadly, the functions help with presentation and interpretation of GLMs.
Simulates and computes (maximum) likelihood of a dynamical model of community assembly that takes into account phylogenetic history.
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.
Contains the function used to create the Dandelion Plot. Dandelion Plot is a visualization method for R-mode Exploratory Factor Analysis.
An implementation of Discriminant Analysis via Projections (DAP) method for high-dimensional binary classification in the case of unequal covariance matrices. See Irina Gaynanova and Tianying Wang (2018) <arXiv:1711.04817v2>.
The recovery of visual sensitivity in a dark environment is known
    as dark adaptation. In a clinical or research setting the recovery is typically
    measured after a dazzling flash of light and can be described by the Mahroo,
    Lamb and Pugh (MLP) model of dark adaptation. The functions in this package take
    dark adaptation data and use nonlinear regression to find the parameters of the
    model that 'best' describe the data. They do this by firstly, generating rapid
    initial objective estimates of data adaptation parameters, then a multi-start
    algorithm is used to reduce the possibility of a local minimum. There is also a
    bootstrap method to calculate parameter confidence intervals. The functions rely
    upon a 'dark' list or object. This object is created as the first step in the
    workflow and parts of the object are updated as it is processed.
Provides programmatic access to the 'Dark Sky' 'API' 
    <https://darksky.net/dev/docs>, which provides current or historical global 
    weather conditions.
Functions are provided that facilitate the import and analysis of
    snp and silicodart (presence/absence) data. The main focus is on data generated
    by DarT (Diversity Arrays Technology). However, once SNP or related fragment
    presence/absence data from any source is imported into a genlight object many
    of the functions can be used. Functions are available for input and output of
    SNP and silicodart data, for reporting on and filtering on various criteria
    (e.g. CallRate, Heterozygosity, Reproducibility, maximum allele frequency).
    Advanced filtering is based on Linkage Disequilibrium and HWE. Other functions
    are available for visualization after PCoA, or to facilitate transfer of data
    between genlight/genind objects and newhybrids, related, phylip packages etc.
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".
The dashboard package allows users to create web pages which display 
	interactive data visualizations working in a standard modern browser. It displays them locally 
	using the Rook server. Nor knowledge about web technologies nor Internet connection are 
	required. D3.js is a JavaScript library for manipulating documents based on data. 
	D3 helps the dashboard package bring data to life using HTML, SVG and CSS.
Provides methods for reading, displaying,
    processing and writing files originally arranged for
    the 'DSSAT-CSM' fixed width format. The 'DSSAT-CSM' 
    cropping system model is described at J.W. Jones,
    G. Hoogenboomb, C.H. Porter, K.J. Boote, W.D. Batchelor,
    L.A. Hunt, P.W. Wilkens, U. Singh, A.J. Gijsman, 
    J.T. Ritchie (2003) <doi:10.1016/S1161-0301(02)00107-7>.
An implementation of common higher order functions with syntactic
    sugar for anonymous function. Provides also a link to 'dplyr' for common
    transformations on data frames to work around non standard evaluation by
    default.
Package 'Data2LD' was developed to support functional data 
 analysis using the functions in package 'fda'. 
 The functions in this package are
 designed for the use of differential equations as modelling objects as
 described in J. Ramsay G. and Hooker (2017,ISBN 978-1-4939-7188-6) 
 Dynamic Data Analysis, New York: Springer.  The package includes data sets 
 and script files for analyzing many of the examples in this book. 
 'Matlab' versions of the code and sample analyses are available by ftp from
 <http://www.psych.mcgill.ca/misc/fda/downloads/Data2LD/>.  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.
Makes it easy to engage with the Application Program Interface (API)
    of the TCdata360 and Govdata360 platforms at <https://tcdata360.worldbank.org/>
    and <https://govdata360.worldbank.org/>, respectively.
    These APIs provide access to over 5000 trade, competitiveness, and governance
    indicator data, metadata, and related information from sources
    both inside and outside the World Bank Group.
    Package functions include easier download of data sets, metadata, and
    related information, as well as searching based on user-inputted query.
Provides an interface to the C++ FILEVECTOR library
    facilitating analysis using large (giga- to tera-bytes) matrices.
    Matrix storage is organized in a way that either columns or rows
    are quickly accessible. DatABEL is primarily aimed to support
    genome-wide association analyses e.g. using GenABEL, MixABEL and
    ProbABEL.
Algorithms implementing populations of agents that interact with one another and sense their environment may exhibit emergent behavior such as self-organization and swarm intelligence. Here, a swarm system called databionic swarm (DBS) is introduced. DBS is able to adapt itself to structures of high-dimensional data such as natural clusters characterized by distance and/or density based structures in the data space. The first module is the parameter-free projection method called Pswarm (Pswarm()), which exploits the concepts of self-organization and emergence, game theory, swarm intelligence and symmetry considerations. The second module is the parameter-free high-dimensional data visualization technique, which generates projected points on the topographic map with hypsometric tints defined by the generalized U-matrix (GeneratePswarmVisualization()). The third module is the clustering method itself with non-critical parameters (DBSclustering()). Clustering can be verified by the visualization and vice versa. The term DBS refers to the method as a whole. It enables even a non-professional in the field of data mining to apply its algorithms for visualization and/or clustering to data sets with completely different structures drawn from diverse research fields. The package is based on the book of Thrun, M.C.: "Projection Based Clustering through Self-Organization and Swarm Intelligence" (2018) <DOI:10.1007/978-3-658-20540-9>.
Functions to check variables against a
    set of data quality rules. A rule file can be accompanied by look-up tables. In
    addition, there are some convenience functions that may
    serve as an example for defining clearer 'data rules'. An
    HTML based user interface facilitates initial exploration of the
    functionality.
Checks column names, classes, values, keys and joins in data frames.
    Also checks length, class and values of vectors and scalars.
    Returns an informative error message if user-defined conditions are not met.
Includes functions that researchers or practitioners may use to clean
    raw data, transferring html, xlsx, txt data file into other formats. And it
    also can be used to manipulate text variables, extract numeric variables from
    text variables and other variable cleaning processes. It is originated from a
    author's project which focuses on creative performance in online education
    environment. The resulting paper of that study will be published soon.
Tools for combining and cleaning data sets, particularly
    with grouped and time series data.
Easy comparison of two tabular data
    objects in R. Specifically designed to show differences between two sets of
    data in a useful way that should make it easier to understand the differences,
    and if necessary, help you work out how to remedy them. Aims
    to offer a more useful output than all.equal() when your two data sets do not
    match, but isn't intended to replace all.equal() as a way to test for equality.
Query for metrics from 'Datadog' (<https://www.datadoghq.com/>) via its API.
Methods for dividing data into subsets, applying analytical
    methods to the subsets, and recombining the results.  Comes with a generic
    MapReduce interface as well.  Works with key-value pairs stored in memory,
    on local disk, or on HDFS, in the latter case using the R and Hadoop
    Integrated Programming Environment (RHIPE).
This is a GUI application for defining
  attributes and setting valid values of variables, and then,
  entering questionnaire data in a data.frame.
Data exploration process for data analysis and model building, so
    that users could focus on understanding data and extracting insights. The
    package automatically scans through each variable and does data profiling.
    Typical graphical techniques will be performed for both discrete and
    continuous features.
Writes data frames to xls files. It supports 
        multiple sheets and basic formatting.
Our method automatically generates models of dynamic decision-
    making that both have strong predictive power and are interpretable in human
    terms. We use an efficient model representation and a genetic algorithm-based
    estimation process to generate simple deterministic approximations that explain
    most of the structure of complex stochastic processes. We have applied the
    software to empirical data, and demonstrated it's ability to recover known data-
    generating processes by simulating data with agent-based models and correctly
    deriving the underlying decision models for multiple agent models and degrees of
    stochasticity.
Functions to save either '.dtable' or '.dtbin' files that can be read by DataGraph, a graphing and analysis application for macOS. Can save a data frame, collection of data frames and sequences of data frames and individual vectors. For more information see <https://www.visualdatatools.com/DataGraph/Help/DataFiles/R/index.html>.
Functions to import multiple files of multiple data file types ('.xlsx', '.xls', '.csv', '.txt')
             from a given directory into R data frames.
Data screening is an important first step of any statistical
    analysis. dataMaid autogenerates a customizable data report with a thorough
    summary of the checks and the results that a human can use to identify possible
    errors. It provides an extendable suite of test for common potential
    errors in a dataset.
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.
Easily create interactive choropleth maps then add bubbles and arcs by coordinates or region name. 
    These maps can be used directly from the console, from 'RStudio', in 'Shiny' apps and 'R Markdown' documents. 'Shiny' 
    proxies allow to interactively add arcs and bubbles, change choropleth values, or change labels.
Provides an S4 infrastructure for unified handling
    of internal datasets and web based data sources. The package is
    currently in beta; things may break, change or go away without
    warning.
Designed to create a basic data dictionary and append to the original dataset's attributes list. The package makes use of a tidy dataset and creates a data frame that will serve as a linker that will aid in building the dictionary. The dictionary is then appended to the list of the original dataset's attributes. The user will have the option of entering variable and item descriptions by writing code or use alternate functions that will prompt the user to add these.
Post Global Financial Crisis derivatives reforms have lifted the 
  veil off over-the-counter (OTC) derivative markets. Swap Execution Facilities
  (SEFs) and Swap Data Repositories (SDRs) now publish data on swaps that are 
  traded on or reported to those facilities (respectively). This package provides
  you the ability to get this data from supported sources.
Provides read and write access to data and metadata from
    the DataONE network <https://www.dataone.org> of data repositories.  
    Each DataONE repository implements a consistent repository application 
    programming interface. Users call methods in R to access these remote 
    repository functions, such as methods to query the metadata catalog, get 
    access to metadata for particular data packages, and read the data objects 
    from the data repository. Users can also insert and update data objects on 
    repositories that support these methods.
Provides a flexible container to transport and
    manipulate complex sets of data. These data may consist of multiple data files and
    associated meta data and ancillary files. Individual data objects have
    associated system level meta data, and data files are linked together using
    the OAI-ORE standard resource map which describes the relationships between the files. 
    The OAI-ORE standard is described at <https://www.openarchives.org/ore>. Data packages 
    can be serialized and transported as structured files that have been created following 
    the BagIt specification. The BagIt specification is described at 
    <https://tools.ietf.org/html/draft-kunze-bagit-08>.
RStudio addins and R functions that make copy-pasting vectors and tables to text painless.
Do most of the painful data preparation for a data science project with a minimum amount of code; Take advantages of data.table efficiency and use some algorithmic trick in order to perform data preparation in a time and RAM efficient way.
The package performs variable level data quality checks including
    missing values, unique values, frequency tables, and generates summary
    statistics
Collection of functions to help retrieve U.S. Geological Survey
    (USGS) and U.S. Environmental Protection Agency (EPA) water quality and
    hydrology data from web services. USGS web services are discovered from 
    National Water Information System (NWIS) <https://waterservices.usgs.gov/> and <https://waterdata.usgs.gov/nwis>. 
    Both EPA and USGS water quality data are obtained from the Water Quality Portal <https://www.waterqualitydata.us/>.
For working with the 'DataRobot' predictive modeling platform's API <https://www.datarobot.com/>.
The Datasaurus Dozen is a set of datasets with the same summary statistics. They 
             retain the same summary statistics despite having radically different distributions.
             The datasets represent a larger and quirkier object lesson that is typically taught
             via Anscombe's Quartet (available in the 'datasets' package). Anscombe's Quartet
             contains four very different distributions with the same summary statistics and as 
             such highlights the value of visualisation in understanding data, over and above
             summary statistics. As well as being an engaging variant on the Quartet, the data
             is generated in a novel way. The simulated annealing process used to derive datasets 
             from the original Datasaurus is detailed in "Same Stats, Different Graphs: Generating 
             Datasets with Varied Appearance and Identical Statistics through Simulated Annealing" 
             <http://dx.doi.org/10.1145/3025453.3025912>.
Download and import time series from <http://www.dataseries.org>, a comprehensive and up-to-date collection of open data from Switzerland.
Visual interface for loading datasets in RStudio from all installed (unloaded) packages.
Implements several algorithms for Euclidean distance matrix completion,
  Sensor Network Localization, and sparse Euclidean distance matrix completion using
  the minimum spanning tree.
For estimation of a variable of interest using Kalman filter by incorporating results from previous assessments, i.e. through development weighted estimates where weights are assigned inversely proportional to the variance of existing and new estimates. For reference see Ehlers et al. (2017) <doi:10.20944/preprints201710.0098.v1>.
Based on a SAS data step. This allows for row-wise dynamic building
    of data, iteratively importing slices of existing dataframes, conducting
    analyses, and exporting to a results frame. This is particularly useful for
    differential or time-series analyses, which are often not well suited to vector-
    based operations.
Implementation of advanced data structures such as hashmaps,
    heaps, or queues. Advanced data structures are essential
    in many computer science and statistics problems, for example graph
    algorithms or string analysis. The package uses 'Boost' and 'STL' data
    types and extends these to R with 'Rcpp' modules.
It allows the user to retrieve the data from the systems of 
    DATASUS (SUS IT department related to the Brazilian Ministry of Health, 
    see <http://datasus.saude.gov.br/informacoes-de-saude/tabnet/> for more
    information) much in the same way that is done in the online portal.
    For now the package allows access to the SINASC and SIM's (ICD-10) 
    systems, that is, the 'Estatísticas Vitais'.
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, a fast friendly file reader and parallel file writer. Offers a natural and flexible syntax, for faster development.
Create tree structures from hierarchical data, and traverse the
    tree in various orders. Aggregate, cumulate, print, plot, convert to and from
    data.frame and more. Useful for decision trees, machine learning, finance,
    conversion from and to JSON, and many other applications.
Contains facilities such as getting the current timestamp in decimal seconds, computing interval w.r.t. a reference timestamp, and custom plotting with error bars.
Provides access to Dataverse version 4 APIs <https://dataverse.org/>, 
    enabling data search, retrieval, and deposit. For Dataverse versions <= 4.0, 
    use the deprecated 'dvn' package <https://cran.r-project.org/package=dvn>.
Tools for deciphering the contents of
	unknown objects or environments from within the terminal,
	a problem often encountered when working with unfamiliar packages or debugging complex functions.
	If working in xterm256 or ANSI terminals the output is coloured by default
	to improve readability (e.g. the standard Ubuntu terminal).
Various visualizations of high-dimensional data such as heat map and silhouette plot for grouped data, visualizations of the distribution of distances, the scatter-density plot for two variables, the Shepard density plot and many more are presented here. Additionally, 'DataVisualizations' makes it possible to inspect the distribution of each feature of a dataset visually through the combination of four methods. More detailed explanations can be found in the book of Thrun, M.C.:"Projection-Based Clustering through Self-Organization and Swarm Intelligence" (2018) <DOI:10.1007/978-3-658-20540-9>.
High-level tools for working with 'data.world' data sets. 'data.world' is a platform 
    where you can find interesting data, store and showcase your own data and data projects, 
    and find and collaborate with other members. In addition to exploring, querying and 
    charting data on the data.world site, you can access data via 'API' endpoints and 
    integrations. Use this package to access, query and explore data sets, and to 
    publish your insights. Visit <https://data.world>, for additional information.
Provides methods for working with nominal dates, times, and 
 durations. Base R has sophisticated facilities for handling time, but these 
 can give unexpected results if, for example, timezone is not handled properly. 
 This package provides a more casual approach to support cases which 
 do not require rigorous treatment.  It systematically deconstructs the 
 concepts origin and timezone, and de-emphasizes the display of seconds. It 
 also converts among nominal durations such as seconds, hours, days, and weeks.
 See '?datetime' and '?duration' for examples. Adapted from 'metrumrg' 
 <http://r-forge.r-project.org/R/?group_id=1215>.
Utilities for handling dates and times, such
   as selecting particular days of the week or month,
   formatting timestamps as required by RSS feeds, or
   converting timestamp representations of other software
   (such as 'MATLAB' and 'Excel') to R. The package is
   lightweight (no dependencies, pure R implementations) and
   relies only on R's standard classes to represent dates
   and times ('Date' and 'POSIXt'); it aims to provide
   efficient implementations, through vectorisation and the
   use of R's native numeric representations of timestamps
   where possible.
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 thin wrapper around the 'Datorama' API.
    Ideal for analyzing marketing data from <https://datorama.com>.
Interface with the 'Dat' p2p network protocol <https://datproject.org>. Clone archives from the network, share your own files, and install packages from the network.
A collection of functions accompanying the book "Data Analysis in Vegetation Ecology". 3rd ed. CABI, Oxfordshire, Boston.
Various utilities for the Davies distribution.
In applications it is usual that some additional information is available. This package dawai (an acronym for Discriminant Analysis With Additional Information) performs linear and quadratic discriminant analysis with additional information expressed as inequality restrictions among the populations means. It also computes several estimations of the true error rate.
Programmatic interface to the 'Daymet' web services
    (<http://daymet.ornl.gov>). Allows for easy downloads of
    Daymet climate data directly to your R workspace or your computer.
    Routines for both single pixel data downloads and
    gridded (netCDF) data are provided.
Fits Bayesian additive regression trees (BART; Chipman, George, and McCulloch (2010) <doi:10.1214/09-AOAS285>) 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
A program for analyzing vegetation time series, with two algorithms: 1) change detection algorithm that detects trend changes, determines their type (abrupt or non-abrupt), and estimates their timing, magnitude, number, and direction; 2) generalization algorithm that simplifies the temporal trend into main features. The user can set the number of major breakpoints or magnitude of greatest changes of interest for detection, and can control the generalization process by setting an additional parameter of generalization-percentage.
A tool to ensure the validity of database writes.
  It provides a set of utilities to analyze and type check the properties
  of data frames that are to be written to databases with SQL support.
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.
Provides an implementation of a mixture of hidden Markov models 
    (HMMs) for discrete sequence data in the Discrete Bayesian HMM Clustering 
    (DBHC) algorithm. The DBHC algorithm is an HMM Clustering 
    algorithm that finds a mixture of discrete-output HMMs while using 
    heuristics based on Bayesian Information Criterion (BIC) to search for the 
    optimal number of HMM states and the optimal number of clusters.
Client for programmatic access to the South Florida Water
  Management District's 'DBHYDRO' database at 
  <https://www.sfwmd.gov/science-data/dbhydro>, with functions
  for accessing hydrologic and water quality data.
A database interface 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.
A helper that tests 'DBI' back ends for conformity
    to the interface.
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).
Trains logistic regression model by discretizing continuous variables via gradient boosting approach. The proposed method tries to achieve a tradeoff between interpretation and prediction accuracy for logistic regression by discretizing the continuous variables. The variable binning is accomplished in a supervised fashion. The model trained by this package is still a single 
  logistic regression model, but not a sequence of logistic regression models. The fitted model
  object returned from the model training consists of two tables. One table is used to give the
  boundaries of bins for each continuous variable as well as the corresponding coefficients,
  and the other one is used for discrete variables. This package can also be used for binning
  continuous variables for other statistical analysis.
Simple computation of spatial statistic functions of distance to characterize the spatial structures of mapped objects, following Marcon, Traissac, Puech, and Lang (2015) <doi:10.18637/jss.v067.c03>.
             Includes classical functions (Ripley's K and others) and more recent ones used by spatial economists (Duranton and Overman's Kd, Marcon and Puech's M). 
             Relies on 'spatstat' for some core calculation.
Leverages 'dplyr' to process the calculations of a plot inside a database. 
    This package provides helper functions that abstract the work at three levels:
    outputs a 'ggplot', outputs the calculations, outputs the formula
    needed to calculate bins.
A 'dplyr' back end for databases that allows you to work with 
    remote database tables as if they are in-memory data frames. Basic features
    works with any database that has a 'DBI' back end; more advanced features 
    require 'SQL' translation to be provided by the package author.
A fast reimplementation of several density-based algorithms of
    the DBSCAN family for spatial data. Includes the DBSCAN (density-based spatial
    clustering of applications with noise) and OPTICS (ordering points to identify
    the clustering structure) clustering algorithms HDBSCAN (hierarchical DBSCAN) and the LOF (local outlier
    factor) algorithm. The implementations uses the kd-tree data structure (from
    library ANN) for faster k-nearest neighbor search. An R interface to fast kNN
    and fixed-radius NN search is also provided.
Prediction methods where explanatory information is coded as a matrix of distances between individuals. Distances can either be directly input as a distances matrix, a squared distances matrix, an inner-products matrix or computed from observed predictors.
Performs differential network analysis to infer disease specific gene networks.
Finding dominant latent signals that regulate dynamic correlation between many pairs of variables.
Functions for analyzing dichotomous choice contingent valuation (CV) data. It provides 
   functions for estimating parametric and nonparametric models for single-, one-and-one-half-, 
   and double-bounded CV data.
A differential community detection (DCD) based approach to effectively locate differential sub-networks in paired scale-free biological networks, e.g. case vs control - Raghvendra Mall et al (2017) <doi:10.1145/3107411.3107418>.
A collection of routines and documentation that allows one to
  perform voxel-wise quantitative analysis of dynamic contrast-enhanced MRI 
  (DEC-MRI) and diffusion-weighted imaging (DWI) data, with emphasis on 
  oncology applications.
Data cloud geometry (DCG) applies random walks in finding community structures for social networks.
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.
There lacks a package for analysing 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. The new functionalities are: 1) to create domain-centric ontologies; 2) to predict ontology terms for input protein sequences (precisely domain content in the form of architectures) plus to assess the predictions; 3) to reconstruct ancestral discrete characters using maximum likelihood/parsimony.
Functions for reading DCP and CDF.bin files generated by the dChip software.
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
    as described in Solymos 2010 (R Journal 2(2):29--37).
    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.
Model-based methods for the detection of disease clusters
  using GLMs, GLMMs and zero-inflated models.
Data Converter Module (DCM) converts the dataset format from split into stack and to the reverse.
A set of asymptotic methods that can be used to directly estimate the expected (Fisher) information matrix by Liu, Tian, and Xin (2016) <doi:10.3102/1076998615621293> in diagnostic classification models or cognitive diagnostic models are provided when marginal maximum likelihood estimation is used. For these methods, both the item and structural model parameters are considered simultaneously. Specifically, the observed information matrix, the empirical cross-product information matrix and the sandwich-type co-variance matrix that can be used to estimate the asymptotic co-variance matrix (or the model parameter standard errors) within the context of diagnostic classification models are provided.
S4 classes around infrastructure provided by the
    'coda' and 'dclone' packages to make package development easy as a breeze
    with data cloning for hierarchical models.
Data cleaning scripts typically contain a lot of 'if this change that'
    type of statements. Such statements are typically condensed expert knowledge.
    With this package, such 'data modifying rules' are taken out of the code and
    become in stead parameters to the work flow. This allows one to maintain, document,
    and reason about data modification rules separately from the workflow.
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.
Traversal graph algorithm for listing linear n-peptide constraints for overlapping protein regions. (Lebre and Gascuel, The combinatorics of overlapping genes, freely available from arXiv at : http://arxiv.org/abs/1602.04971).
Computing and plotting the distance covariance and correlation
             function of a univariate or a multivariate time series. Both versions
             of biased and unbiased estimators of distance covariance and correlation are provided.
             Test statistics for testing pairwise independence are also implemented. 
             Some data sets are also included.
A Davidian curve defines a seminonparametric density, whose shape and flexibility can be tuned by
  easy to estimate parameters. Since a special case of a Davidian curve is the standard normal density,
  Davidian curves can be used for relaxing normality assumption in statistical applications (Zhang & Davidian, 2001)
  <doi:10.1111/j.0006-341X.2001.00795.x>. This package provides the density function, the gradient of
  the loglikelihood and a random generator for Davidian curves.
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 procedures for depth-based supervised learning, which are entirely non-parametric, in particular the DDalpha-procedure (Lange, Mosler and Mozharovskyi, 2014 <doi:10.1007/s00362-012-0488-4>). The training data sample is transformed by a statistical depth function to a compact low-dimensional space, where the final classification is done. It also offers an extension to functional data and routines for calculating certain notions of statistical depth functions. 50 multivariate and 5 functional classification problems are included.
Implements maximum likelihood and bootstrap methods based on the diversity-dependent birth-death process to test whether speciation or extinction are diversity-dependent, under various models including various types of key innovations.
   See Etienne et al. 2012, Proc. Roy. Soc. B 279: 1300-1309, <DOI:10.1098/rspb.2011.1439>, Etienne & Haegeman 2012, Am. Nat. 180: E75-E89, <DOI:10.1086/667574> and Etienne et al. 2016. Meth. Ecol. Evol. 7: 1092-1099, <DOI:10.1111/2041-210X.12565>.
   Also contains functions to simulate the diversity-dependent process.
A wrapper for the Duke Analytics model deployment API. See <http://www.dukeanalytics.com/products/dukedeployR.php> for more details.
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. Contains cDNA and shipping
	credit flow data.
This package provides useful functions for various DDI (Data Documentation Initiative) related outputs.
A set of three two-census methods to the estimate the degree of death registration coverage for a population. Implemented methods include the Generalized Growth Balance method (GGB), the Synthetic Extinct Generation method (SEG), and a hybrid of the two, GGB-SEG. Each method offers automatic estimation, but users may also specify exact parameters or use a graphical interface to guess parameters in the traditional way if desired.
An interface to explore, analyze, and visualize droplet digital PCR
    (ddPCR) data in R. This is the first non-proprietary software for analyzing
    two-channel ddPCR data. An interactive tool was also created and is available
    online to facilitate this analysis for anyone who is not comfortable with
    using R.
Provides an implementation of the framework of reversed graph embedding (RGE) which projects data into a reduced dimensional space while constructs a principal tree which passes through the middle of the data simultaneously. DDRTree shows superiority to alternatives (Wishbone, DPT) for inferring the ordering as well as the intrinsic structure of the single cell genomics data. In general, it could be used to reconstruct the temporal progression as well as bifurcation structure of any datatype.
Smooth testing of goodness of fit. These tests are data
    driven (alternative hypothesis is dynamically selected based on data). In this
    package you will find various tests for exponent, Gaussian, Gumbel and uniform
    distribution.
Statistical deadband algorithms are based on the Send-On-Delta concept as in Miskowicz(2006,<doi:10.3390/s6010049>). A collection of functions compare effectiveness and fidelity of sampled signals using statistical deadband algorithms.
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.
A Bayesian framework for parameter inference in differential equations.
    This approach offers a rigorous methodology for parameter inference as well as
    modeling the link between unobservable model states and parameters, and
    observable quantities. Provides templates for the DE model, the
    observation model and data likelihood, and the model parameters and their prior
    distributions. A Markov chain Monte Carlo (MCMC) procedure processes these inputs
    to estimate the posterior distributions of the parameters and any derived
    quantities, including the model trajectories. Further functionality is provided
    to facilitate MCMC diagnostics and the visualisation of the posterior distributions
    of model parameters and trajectories.
Debugger for R functions, with code display, graceful
        error recovery, line-numbered conditional breakpoints, access
        to exit code, flow control, and full keyboard input.
Specify debug messages as special string constants, and
    control debugging of packages via environment variables.
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.
Provides constrained triangulation of polygons. Ear cutting (or 
 ear clipping) applies constrained triangulation by successively 'cutting'
 triangles from a polygon defined by path/s. Holes are supported by introducing
 a bridge segment between polygon paths. This package wraps the 'header-only' 
 library 'earcut.hpp' <https://github.com/mapbox/earcut.hpp.git> which includes
 a reference to the method used by Held, M. (2001) <doi:10.1007/s00453-001-0028-4>.
Contains a function called dmur() which accepts four parameters like possible values, probabilities of the values, selling cost and preparation cost. The dmur() function generates various numeric decision parameters like MEMV (Maximum (optimum) expected monitory value), best choice, EPPI (Expected profit with perfect information), EVPI (Expected value of the perfect information), EOL (Expected opportunity loss), which facilitate effective decision-making.
Supporting the quantitative analysis of binary welfare based
    decision making processes using Monte Carlo simulations. Decision support
    is given on two levels: (i) The actual decision level is to choose between
    two alternatives under probabilistic uncertainty. This package calculates
    the optimal decision based on maximizing expected welfare. (ii) The meta
    decision level is to allocate resources to reduce the uncertainty in the
    underlying decision problem, i.e to increase the current information to
    improve the actual decision making process. This problem is dealt with
    using the Value of Information Analysis. The Expected Value of
    Information for arbitrary prospective estimates can be calculated as
    well as Individual Expected Value of Perfect Information.
    The probabilistic calculations are done via Monte Carlo
    simulations. This Monte Carlo functionality can be used on its own.
Integrated differential expression (DE) and differential co-expression (DC) analysis on gene expression data based on DECODE (DifferEntial CO-expression and Differential Expression) algorithm.
Main function "decode" is used to decode coded key values to plain
    text. Function "code" can be used to code plain text to code if there is a
    1:1 relation between the two. The concept relies on 'keyvalue' objects used
    for translation. There are several 'keyvalue' objects included in the areas
    of geographical regional codes, administrative health care unit codes,
    diagnosis codes and more. It is also easy to extend the use by arbitrary 
    code sets.
Predict future values with hybrid combinations of Pattern Sequence based
        Forecasting (PSF), Autoregressive Integrated Moving Average (ARIMA), Empirical Mode
        Decomposition (EMD) and Ensemble Empirical Mode Decomposition (EEMD) methods based
        hybrid methods.
Two global-value-chain decompositions are implemented. Firstly, the
    Wang-Wei-Zhu (Wang, Wei, and Zhu, 2013) algorithm splits bilateral gross exports
    into 16 value-added components. Secondly, the Leontief decomposition (default)
    derives the value added origin of exports by country and industry, which is also
    based on Wang, Wei, and Zhu (Wang, Z., S.-J. Wei, and K. Zhu. 2013. "Quantifying
    International Production Sharing at the Bilateral and Sector Levels.").
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.
Takes sample information in the form of the fraction of mutations
    in each of 96 trinucleotide contexts and identifies the weighted combination
    of published signatures that, when summed, most closely reconstructs the
    mutational profile.
Empirical Bayes methods for learning prior distributions from data.
    An unknown prior distribution (g) has yielded (unobservable) parameters, each of
    which produces a data point from a parametric exponential family (f). The goal
    is to estimate the unknown prior ("g-modeling") by deconvolution and Empirical
    Bayes methods.
DECORATE (Diverse Ensemble Creation by Oppositional Relabeling
    of Artificial Training Examples) builds an ensemble of J48 trees by recursively
    adding artificial samples of the training data ("Melville, P., & Mooney, R. J. (2005). Creating diversity in ensembles using artificial data. Information Fusion, 6(1), 99-111. <doi:10.1016/j.inffus.2004.04.001>").
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.
Attempt to repair inconsistencies and missing values in data 
    records by using information from valid values and validation rules 
    restricting the data.
Provides deep boosting models training, evaluation, predicting and
 hyper parameter optimising using grid search and cross validation.
 Based on Google's Deep Boosting algorithm, and Google's C++ implementation.
 Cortes, C., Mohri, M., & Syed, U. (2014) <http://machinelearning.wustl.edu/mlpapers/papers/icml2014c2_cortesb14>.
Implement some deep learning architectures and neural network
    algorithms, including BP,RBM,DBN,Deep autoencoder and so on.
Tests for, and describe differences in event count profiles in groups of reconstructed cophylogenies
A simple syntax to change the default values for function
  arguments, whether they are in packages or defined locally.
Creates a directory of archived files with a descriptive 'PDF'
 document at the root level (i.e. 'define.pdf') containing tables
 of definitions of data items and relative-path hyperlinks to 
 the documented files. Converts file extensions to 'txt' per FDA
 expectations and converts 'CSV' files to 'SAS' Transport format.
 Relies on data item descriptors stored as per R package 'spec'.
 See 'package?define'. See also '?define'. 
 Requires a compatible installation of 'pdflatex',
 e.g. <https://miktex.org/>.
Numerical quadrature of functions of one variable over a finite or infinite interval
	with double exponential formulas.
Detection of differentially expressed genes between two
        distinct groups of samples.
Efficient Bayesian parameter inference for systems of ordinary 
    differential equations. The inference is based on adaptive gradient matching 
    (AGM, Dondelinger et al. 2013 <http://proceedings.mlr.press/v31/dondelinger13a.pdf>, 
    Macdonald 2017 <http://theses.gla.ac.uk/7987/1/2017macdonaldphd.pdf>), 
    which offers orders-of-magnitude improvements in computational 
    efficiency over standard methods that require solving the differential 
    equation system. Features of the package include flexible specification 
    of custom ODE systems as R functions, support for missing variables,
    Bayesian inference via population MCMC.
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 a low-level interface for a deisotoper container 
  implemented in the 'Java' programming language and means of S3 helper 
  functions for plotting and debugging isotopes of mass spectrometric data. 
  The deisotoper algorithm detects and aggregates peaks which belong to the 
  same isotopic cluster of a given mass spectrum.
Performs reference based multiple imputation of recurrent event data
    based on a negative binomial regression model, as described
    by Keene et al (2014) <doi:10.1002/pst.1624>.
Provides probability mass, distribution, quantile, random-variate generation, and method-of-moments parameter-estimation functions for the Delaporte distribution. The Delaporte is a discrete probability distribution which can be considered the convolution of a negative binomial distribution with a Poisson distribution. Alternatively, it can be considered a counting distribution with both Poisson and negative binomial components. It has been studied in actuarial science as a frequency distribution which has more variability than the Poisson, but less than the negative binomial.
Provides sample size and power calculations when the treatment time-lag effect is present and the lag duration is homogeneous across the individual subject. The methods used are described in Xu, Z., Zhen, B., Park, Y., & Zhu, B. (2017) <doi:10.1002/sim.7157>.
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.  Summarises information about the
	tiles of the tessellation.
Implements the DeLorean model (Reid & Wernisch (2016)
    <doi:10.1093/bioinformatics/btw372>) to estimate pseudotimes for
    single cell expression data. The DeLorean model uses a Gaussian process
    latent variable model to model uncertainty in the capture time of
    cross-sectional data.
We implement methods for estimating multivariate densities.
  We include a discretized kernel estimator,
  an adaptive histogram (a greedy histogram and a CART-histogram), 
  stagewise minimization, and bootstrap aggregation.
Measure of agreement Delta was originally by Martín & Femia (2004) <DOI:10.1348/000711004849268>. 
    Since then has been considered as agreement measure for different 
	fields, since their behavior is usually better than the usual Kappa index
	by Cohen (1960) <DOI:10.1177/001316446002000104>. The main issue with Delta 
	is that can not be computed by hand contrary to Kappa.
The deltaPlotR package implements 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 (Magis and Facon (2014) <doi:10.18637/jss.v059.c01>).
Computes the regional correction factor of 14C age offset in marine-derived samples (Delta R). This is possible with the recently established "marine13" calibration curve and the 'BchronCalibrate' function from the 'Bchron' package. The algorithm of Delta R computation includes four steps: measure radiocarbon age of a marine sample; identify its true age; compute its modeled radiocarbon age corresponding to the true age; and finally calculate the difference between its measured and modeled radiocarbon ages. This package has functions that compute Delta R with three methods: by using marine samples with known collection dates (before 1950s), usually molluscan shells from museum collections, (function dr_shell()); by measuring other radioactive isotopes, mainly uranium-thorium (230Th/234U), ratio (dr_pair() function and the "pair" method in the dr_df() function); and finally by using a pair of coeval samples, one being marine and the other terrestrial (dr_pair() function and the "pair" method for the dr_df() function as well). Usually such samples originate from archaeological sites where the context, in which paired samples were found, is taken as a guarantee of their synchronicity.  
 References: 
 Oeschger H, Siegenthaler U, Schotterer U, Gugelmann A (1975) <doi:10.3402/tellusa.v27i2.9900>
 Reimer PJ, Bard E, Bayliss A, Beck JW, Blackwell PG, Bronk Ramsey C, Buck CE, Cheng H, Edwards RL, Friedrich M, Grootes PM, Guilderson TP, Haflidason H, Hajdas I, Hatté C, Heaton TJ, Hoffmann DL, Hogg AG, Hughen KA, Kaiser KF, Kromer B, Manning SW, Niu M, Reimer RW, Richards DA, Scott EM, Southon JR, Staff RA, Turney CSM, van der Plicht J (2013) <doi:10.2458/azu_js_rc.55.16947>
 Soulet G (2015) <doi:10.1016/j.quageo.2015.05.023>
 Stuiver M, Braziunas TF (1993) <doi:10.1017/S0033822200013874> .
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.
Implementation of the DEMI method for the analysis of high-density microarray data.
Generalized Deming regression, Theil-Sen regression and Passing-Bablock regression functions.
Construction and analysis of matrix population models in R.
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.
Tool for the development of multi-linear QSPR/QSAR models (Quantitative structure-property/activity relationship). Theses models are used in chemistry, biology and pharmacy to find a relationship between the structure of a molecule and its property (such as activity, toxicology but also physical properties). The various functions of this package allows: selection of descriptors based of variances, intercorrelation and user expertise; selection of the best multi-linear regression in terms of correlation and robustness; methods of internal validation (Leave-One-Out, Leave-Many-Out, Y-scrambling) and external using test sets.
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 tree's graphical parameters
    - the color, size, type, etc of its branches, nodes and labels. (2)
    Visually and statistically compare different 'dendrograms' to one another.
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.
Various functions to import, verify, process and plot high-resolution dendrometer using daily and stem-cycle approaches.
Provides functions for the calculation and plotting of synchrony in 
      tree growth from tree-ring width chronologies (TRW index). It combines
      variance-covariance (VCOV) mixed modelling with functions that quantify 
      the degree to which the TRW chronologies contain a common temporal 
      signal. It also implements temporal trends in spatial synchrony using a 
      moving window. These methods can also be used with other kind of ecological
      variables that have temporal autocorrelation corrected.
Provides novel dendroclimatological methods, primarily used by the
    Tree-ring research community. There are two core functions. The first one is 
    daily_response(), which finds the optimal sequence of days that are related 
    to one or more tree-ring proxy records. The second one is compare_methods(), 
    which effectively compares several linear and nonlinear regression algorithms.
Re-arranges a dendrogram to optimize visualisation-based cost functions
An implementation of 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.
Provides functions for parameter estimation and real-time predictions  of a single dengue 
 outbreak taking into account model uncertainty using  model averaging.
Given count data from two conditions, it determines which transcripts are differentially expressed across the two conditions using Bayesian inference of the parameters of  a bottom-up model for PCR amplification. This model is  developed in Ndifon Wilfred, Hilah Gal, Eric Shifrut, Rina Aharoni, Nissan Yissachar, Nir Waysbort, Shlomit Reich Zeliger, Ruth Arnon, and Nir Friedman (2012), <http://www.pnas.org/content/109/39/15865.full>, and results in a distribution for the counts that is a superposition of the binomial and negative binomial distribution.
Estimate a low rank matrix from noisy data using singular values
    thresholding and shrinking functions. Impute missing values with matrix completion.
An integrated toolset for the analysis of de novo (sporadic)
    genetic sequence variants. denovolyzeR implements a mutational model that
    estimates the probability of a de novo genetic variant arising in each human
    gene, from which one can infer the expected number of de novo variants in a
    given population size. Observed variant frequencies can then be compared against
    expectation in a Poisson framework. denovolyzeR provides a suite of functions
    to implement these analyses for the interpretation of de novo variation in human
    disease.
We provide 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  also be applied for visualizing other density estimates.
Estimation of functional linear mixed models for densely sampled data based on functional principal component analysis.
An improved implementation (based on k-nearest neighbors) of the 
    density peak clustering algorithm, originally described by Alex Rodriguez 
    and Alessandro Laio (Science, 2014 vol. 344). It can handle large datasets
    (> 100, 000 samples) very efficiently. It was initially implemented by 
    Thomas Lin Pedersen, with inputs from Sean Hughes and later improved by 
    Xiaojie Qiu to handle large datasets with kNNs.
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).
Provide a Dens-based method for estimating functional connection in large scale brain networks using partial correlation.
Density ratio estimation.
    The estimated density ratio function can be used in many applications such as
    the inlier-based outlier detection, covariate shift adaptation and etc.
Graphical methods for compactly illustrating probability distributions, including density strips, density regions, sectioned density plots and varying width strips.
Implements the differential evolution algorithm for global optimization of a real-valued function of a real-valued parameter vector.
Differential Evolution (DE) stochastic algorithms for global
  optimization of problems with and without constraints.
  The aim is to curate a collection of its state-of-the-art variants that
  (1) do not sacrifice simplicity of design,
  (2) are essentially tuning-free, and
  (3) can be efficiently implemented directly in the R language.
  Currently, it only provides an implementation of the 'jDE' algorithm by
  Brest et al. (2006) <doi:10.1109/TEVC.2006.872133>.
Estimation and testing methods for dependently truncated data.
 Semi-parametric methods are based on Emura et al. (2011)<Stat Sinica 21:349-67>, Emura & Wang (2012)<doi:10.1016/j.jmva.2012.03.012>,
 and Emura & Murotani (2015)<doi:10.1007/s11749-015-0432-8>.
 Parametric approaches are based on Emura & Konno (2012)<doi:10.1007/s00362-014-0626-2> and Emura & Pan (2017)<doi:10.1007/s00362-017-0947-z>.
 A regression approach is based on Emura & Wang (2016)<doi:10.1007/s10463-015-0526-9>. Quasi-independence tests are based on Emura & Wang (2010)<doi:10.1016/j.jmva.2009.07.006>.
 Right-truncated data for Japanese male centenarians are given by Emura & Murotani (2015)<doi:10.1007/s11749-015-0432-8>.
Traditional phasing programs are limited to diploid organisms.
 Our method modifies Li and Stephens algorithm with Markov chain Monte Carlo
 (MCMC) approaches, and builds a generic framework that allows haplotype searches
 in a multiple infection setting. This package is primarily developed as part of
 the Pf3k project, which is a global collaboration using the latest
 sequencing technologies to provide a high-resolution view of natural variation
 in the malaria parasite Plasmodium falciparum. Parasite DNA are extracted from
 patient blood sample, which often contains more than one parasite strain, with
 unknown proportions. This package is used for deconvoluting mixed haplotypes,
 and reporting the mixture proportions from each sample.
Fits (multigroup) mixtures of latent or hidden 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.
Fits latent (hidden) Markov models on mixed categorical and continuous (time series) data, otherwise known as dependent mixture models.
Tools for depth functions methodology applied 
        to multivariate analysis. Besides allowing calculation 
        of depth values and depth-based location estimators, the package
        includes functions or drawing contour plots and perspective plots
        of depth functions. Euclidian and spherical depths are supported.
Could be used to obtain spatial depths, spatial ranks and outliers of multivariate random variables. Could also be used to visualize DD-plots (a multivariate generalization of QQ-plots).
Data depth concept offers a variety of powerful and user friendly
    tools for robust exploration and inference for multivariate data. The offered
    techniques may be successfully used in cases of lack of our knowledge on
    parametric models generating data due to their nature. The
    package consist of among others implementations of several data depth techniques
    involving multivariate quantile-quantile plots, multivariate scatter estimators,
    multivariate Wilcoxon tests and robust regressions.
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.
Queues, stacks, and 'deques' are list-like, abstract data types. 
    These are meant to be very cheap to "grow", or insert new objects into.
    A typical use case involves storing data in a list in a streaming fashion,
    when you do not necessarily know how may elements need to be stored.
    Unlike R's lists, the new data structures provided here are not
    necessarily stored contiguously, making insertions and deletions at the
    front/end of the structure much faster.  The underlying implementation
    is new and uses a head/tail doubly linked list; thus, we do not rely on R's
    environments or hashing.  To avoid unnecessary data copying, most operations
    on these data structures are performed via side-effects.
R-based solution for symbolic differentiation. It admits
    user-defined function as well as function substitution
    in arguments of functions to be differentiated. Some symbolic
    simplification is part of the work.
A set of pricing and expository functions that should
    be useful in teaching a course on financial derivatives.
Discrete event simulation (DES) involves modeling of systems
   having discrete, i.e. abrupt, state changes. For instance, when a job
   arrives to a queue, the queue length abruptly increases by 1.  This
   package is an R implementation of the event-oriented approach to DES;
   see the tutorial in Matloff (2008) 
   <http://heather.cs.ucdavis.edu/~matloff/156/PLN/DESimIntro.pdf>.
Tools to read, write, create, and manipulate DESCRIPTION files.
    It is intended for packages that create or manipulate other packages.
Decompose a time series into seasonal, trend and irregular components using transformations to amplitude-frequency domain.
Weighted frequency and contingency tables of categorical
       variables and of the comparison of the mean value of a numerical
       variable by the levels of a factor, and methods to produce xtable
       objects of the tables and to plot them. There are also functions to
       facilitate the character encoding conversion of objects, to quickly
       convert fixed width files into csv ones, and to export a data.frame to
       a text file with the necessary R and SPSS codes to reread the data.
Produce publication quality graphics from output of GGobi's
    describe display plugin.
Allows users to quickly and easily describe data using
    common descriptive statistics.
Estimate and return the needed parameters for visualizations designed for OpenBudgets.eu <http://openbudgets.eu/> datasets. Calculate descriptive statistical measures in Budget data of municipalities across Europe, according to the OpenBudgets.eu data model. There are functions for measuring central tendency and dispersion of amount variables along with their distributions and correlations and the frequencies of categorical variables for a given dataset of the input OpenBudgets.eu fiscal datasets. Also, can be used generally to extract visualization parameters convert them to JSON format and use them as input in a different graphical interface.
Generate descriptive statistics such as measures of location,
    dispersion, frequency tables, cross tables, group summaries and multiple
    one/two way tables. Visualize and compute percentiles/probabilities of 
    normal, t, f, chi square and binomial distributions.
Easily create descriptive and comparative tables.
    It makes use and integrates directly with the tidyverse family of packages, and pipes.
    Tables are produced as data frames/lists of data frames for easy manipulation after creation,
    and ready to be saved as csv, or piped to DT::datatable() or pander::pander() to integrate into reports.
A collection of miscellaneous basic statistic functions and convenience wrappers for efficiently describing data. The author's intention was to create 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. The package contains furthermore functions to produce documents using MS Word (or PowerPoint) and functions to import data from Excel. 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.
'RStudio' as of recently offers the option to define addins and assign shortcuts to them. This package contains addins for a few most frequently used functions in a data scientist's (at least mine) daily work (like str(), example(), plot(), head(), view(), Desc()). Most of these functions will use the current selection in the editor window and send the specific command to the console while instantly executing it. Assigning shortcuts to these addins will save you quite a few keystrokes.
Deseasonalize daily or monthly time series.
The package provides R scripts for designing genetical
        genomics experiments.
Use simulated annealing to find optimal designs for Poisson regression models with blocks.
Includes functions for the construction of matched samples that are balanced and representative by design.  Among others, these functions can be used for matching in observational studies with treated and control units, with cases and controls, in related settings with instrumental variables, and in discontinuity designs.  Also, they can be used for the design of randomized experiments, for example, for matching before randomization.  By default, 'designmatch' uses the 'GLPK' optimization solver, but its performance is greatly enhanced by the 'Gurobi' optimization solver and its associated R interface.  For their installation, please follow the instructions at <http://user.gurobi.com/download/gurobi-optimizer> and <http://www.gurobi.com/documentation/7.0/refman/r_api_overview.html>.  We have also included directions in the gurobi_installation file in the inst folder.
Functions for (1) ranking, selecting, and prioritising  genes,
    proteins, and metabolites from high dimensional biology experiments, (2)
    multivariate hit calling in high content screens, and (3) combining data
    from diverse sources.
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', 'zvode' 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.
A function for plotting maps of agricultural field experiments that
    are laid out in grids.
Models for analyzing site occupancy and count data models
  with detection error, including single-visit based models,
  conditional distance sampling and time-removal models.
  Package development was supported by the
  Alberta Biodiversity Monitoring Institute (www.abmi.ca)
  and the Boreal Avian Modelling Project (borealbirds.ca).
Allows users to quickly and easily detect data containing
    Personally Identifiable Information (PII) through convenience functions.
Detection of runs of homozygosity and of heterozygosity
    in diploid genomes using two methods: sliding windows (Purcell et al (2007)
    <doi:10.1086/519795>) and consecutive runs (Marras et al (2015)
    <doi:10.1111/age.12259>).
Solvers and test set for stiff and non-stiff differential equations, and 
  differential algebraic equations.
Implementation of DetMCD, a new algorithm for robust and deterministic estimation of location and scatter. The benefits of robust and deterministic estimation are explained in Hubert, Rousseeuw and Verdonck (2012) <doi:10.1080/10618600.2012.672100>.
Density estimation for possibly large data sets and conditional/unconditional random number generation or bootstrapping with distribution element trees. The function 'det.construct' translates a dataset into a distribution element tree. To evaluate the probability density based on a previously computed tree at arbitrary query points, the function 'det.query' is available. The functions 'det1' and 'det2' provide density estimation and plotting for one- and two-dimensional datasets. Conditional/unconditional smooth bootstrapping from an available distribution element tree can be performed by 'det.rnd'. For more details on distribution element trees, see: Meyer, D.W. (2016) <arXiv:1610.00345> or Meyer, D.W., Statistics and Computing (2017) <doi:10.1007/s11222-017-9751-9> and Meyer, D.W. (2017) <arXiv:1711.04632>.
DetLTS, DetMM (and DetS) Algorithms for Deterministic, Robust
    Linear Regression.
Simple GUI to perform some standard tree-ring analyses.
Image series affected by bleaching must be corrected by 
    'detrending' prior to the performance of quantitative analysis. 
    'detrendr' is for correctly detrending images. It uses Nolan's algorithm
    (Nolan et al., 2017 <doi:10.1093/bioinformatics/btx434>).
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.
Compare detrital zircon suites by uploading univariate,
      U-Pb age, or bivariate, U-Pb age and Lu-Hf data, in a 'shiny'-based
      user-interface. Outputs publication quality figures using 'ggplot2', and
      tables of statistics currently in use in the detrital zircon geochronology
      community.
A concise check of the format of one or multiple input arguments (data type, length or value) is provided. Since multiple input arguments can be tested simultaneously, a lengthly list of checks at the beginning of your function can be avoided, hereby enhancing the readability and maintainability of your code.
Data sets and sample analyses from Jay L. Devore (2008),
        "Probability and Statistics for Engineering and the Sciences
        (7th ed)", Thomson.
A set of functions to quantify the relationship between development
    rate and temperature. The package comprises a set of models and estimated parameters
    borrowed from a literature review in ectotherms (mostly arthropods).
Collection of package development tools.
A system for the management, assessment, and psychometric analysis of data from educational and psychological tests. Developed at Cito, The Netherlands, with subsidy from the Dutch Ministry of Education, Culture, and Science.
It handles numerics, characters, factors, and logicals.
Phase I/II adaptive dose-finding design for combination
   studies. Several methods are proposed depending on the type of
   combinations: (1) the combination of two cytotoxic agents, and (2)
   combination of a molecularly targeted agent with a cytotoxic agent.
Compares two dataframes with a common key
            and returns the delta records. The package will return
            three dataframes that contain the added, changed,
            and deleted records.
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, doi:10.1177/014662169501900405) Differential Functioning of Items
    and Tests (DFIT) analyses. It includes functions to use the Monte Carlo Item
    Parameter Replication approach (Oshima, Raju, & Nanda, 2006, doi:10.1111/j.
    1745-3984.2006.00001.x) for obtaining the associated statistical significance
    tests cut-off points. They may also be used for a priori and post-hoc power
    calculations (Cervantes, 2017, doi:10.18637/jss.v076.i05).
Meta-analysis approaches for Phase I dose finding early phases clinical trials in order to better suit requirements in terms of maximum tolerated dose (MTD) and maximal dose regimen (MDR). This package has currently three different approaches: (a) an approach proposed by Zohar et al, 2011, <doi:10.1002/sim.4121> (denoted as ZKO), (b) the Variance Weighted pooling analysis (called VarWT) and (c) the Random Effects Model Based (REMB) algorithm, where user can input his/her own model based approach or use the existing random effect logistic regression model (named as glimem) through the 'dfmeta' package.
Phase I/II adaptive dose-finding design for single-agent
   Molecularly Targeted Agent (MTA), according to the paper "Phase
   I/II Dose-Finding Design for Molecularly Targeted Agent: Plateau
   Determination using Adaptive Randomization", Riviere Marie-Karelle et
   al. (2016) <doi:10.1177/0962280216631763>.
Derivative-Free optimization algorithms. These algorithms do not require gradient information. More importantly, they can be used to solve non-smooth optimization problems.
Provides R-implementation of Decision forest algorithm, which combines the predictions of
    multiple independent decision tree models for a consensus decision. In particular, Decision Forest is a novel 
    pattern-recognition method which can be used to analyze: (1) DNA microarray data; 
    (2) Surface-Enhanced Laser Desorption/Ionization Time-of-Flight Mass Spectrometry  (SELDI-TOF-MS) data; and 
    (3) Structure-Activity Relation (SAR) data.
    In this package, three fundamental functions are provided, as (1)DF_train, (2)DF_pred, and (3)DF_CV.  
    run Dforest() to see more instructions.
    Weida Tong (2003) <doi:10.1021/ci020058s>.
A unified method for designing and analysing dose-finding trials in paediatrics, while bridging information from adults, is proposed in the 'dfped' package. The dose range can be calculated under three extrapolation methods: linear, allometry and maturation adjustment, using pharmacokinetic (PK) data. To do this, it is assumed that target exposures are the same in both populations. The working model and prior distribution parameters of the dose-toxicity and dose-efficacy relationships can be obtained using early phase adult toxicity and efficacy data at several dose levels through 'dfped' package. Priors are used into the dose finding process through a Bayesian model selection or adaptive priors, to facilitate adjusting the amount of prior information to differences between adults and children. This calibrates the model to adjust for misspecification if the adult and paediatric data are very different. User can use his/her own Bayesian model written in Stan code through the 'dfped' package. A template of this model is proposed in the examples of the corresponding R functions in the package. Finally, in this package you can find a simulation function for one trial or for more than one trial. These methods are proposed by Petit et al, (2016) <doi:10.1177/0962280216671348>.
Statistical methods for retrospectively detecting changes in location and/or dispersion of univariate and multivariate variables. Data values are assumed to be independent, can be individual (one observation at each instant of time) or subgrouped (more than one observation at each instant of time). Control limits are computed, often using a permutation approach, so that a prescribed false alarm probability is guaranteed  without making any parametric assumptions on the stable (in-control) distribution.
Statistical methods involving PK measures are provided, in the dose allocation process during a Phase I clinical trials. These methods, proposed by Ursino et al, (2017) <doi:10.1002/bimj.201600084>, enter pharmacokinetics (PK) in the dose finding designs in different ways, including covariates models, dependent variable or hierarchical models. This package provides functions to generate data from several scenarios and functions to run simulations which their objective is to determine the maximum tolerated dose (MTD).
Performs Bayesian model averaging for capture-recapture. This includes code to stratify records, check the strata for suitable overlap to be used for capture-recapture, and some functions to plot the estimated population size.
Feature selection from high dimensional data using a diploid
    genetic algorithm with Incomplete Dominance for genotype to phenotype mapping
    and Random Assortment of chromosomes approach to recombination.
Performs differential correlation analysis on input
    matrices, with multiple conditions specified by a design matrix. Contains
    functions to filter, process, save, visualize, and interpret differential
    correlations of identifier-pairs across the entire identifier space, or with
    respect to a particular set of identifiers (e.g., one). Also contains several
    functions to perform differential correlation analysis on clusters (i.e., modules)
    or genes. Finally, it contains functions to generate empirical p-values for the
    hypothesis tests and adjust them for multiple comparisons. Although the package
    was built with gene expression data in mind, it is applicable to other types of
    genomics data as well, in addition to being potentially applicable to data from
    other fields entirely. It is described more fully in the manuscript
    introducing it, freely available at <doi:10.1186/s12918-016-0349-1>.
Spatial analyses involving binning require that every bin have the same area, but this is impossible using a rectangular grid laid over the Earth or over any projection of the Earth. Discrete global grids use hexagons, triangles, and diamonds to overcome this issue, overlaying the Earth with equally-sized bins. This package provides utilities for working with discrete global grids, along with utilities to aid in plotting such data.
Differential geometric least angle regression method for fitting 
	sparse generalized linear models. In this version of the package, the user can fit
	models specifying Gaussian, Poisson, Binomial, Gamma and Inverse Gaussian family. 
	Furthermore, several link functions can be used to model the relationship between the 
	conditional expected value of the response variable and the linear predictor. The 
	solution curve can be computed using an efficient predictor-corrector or a cyclic 
	coordinate descent algorithm, as described in the paper linked to via the URL below.
Model fitting and evaluation tools for double generalized linear
    models (DGLMs). This class of models uses one generalized linear model (GLM)
    to fit the specified response and a second GLM to fit the deviance of the first
    model.
Dynamic graphical models for multivariate time series data to estimate directed
    dynamic networks in functional magnetic resonance imaging (fMRI), see Schwab et al. (2017) <doi:10.1101/198887>.
A set of functions have been implemented to generate random data to perform Monte Carlo simulations on structural models with formative constructs and interaction and nonlinear effects (Two-Step PLS Mode B structural models). The setup of the true model considers a simple structure with three formative exogenous constructs related to one formative endogenous construct. The routines take into account the interaction and nonlinear effects of the exogenous constructs on the endogenous construct.
Fit dynamic group-level item response theory (IRT) and multilevel
    regression and poststratification (MRP) models from item response data. dgo
    models latent traits at the level of demographic and geographic groups,
    rather than individuals, in a Bayesian group-level IRT approach developed by
    Caughey and Warshaw (2015) <doi:10.1093/pan/mpu021>. The package also
    estimates subpopulations' average responses to single survey items with a
    dynamic MRP model proposed by Park, Gelman, and Bafumi (2004)
    <doi:10.11126/stanford/9780804753005.003.0011>.
Provides data used by package 'dgo' in examples and vignettes.
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.
This is a visualization tool for vegetation structure/succession
    in space and/or time mainly for forest gap models. However, it could also be used to
    visualize observed forest stands. If used for models, they should contain either
    individual trees or cohorts (e.g. LPJ-GUESS by Smith et al. (2014) <doi:10.5194/bg-11-2027-2014>).
    For a list of required and additional data fields see the vignette.
The 'DHARMa' package uses a simulation-based approach to create
    readily interpretable scaled (quantile) residuals for fitted generalized linear mixed
    models. Currently supported are generalized linear mixed models from 'lme4' 
    (classes 'lmerMod', 'glmerMod'), generalized additive models ('gam' from 'mgcv'), 
    'glm' (including 'negbin' from 'MASS', but excluding quasi-distributions) and 'lm' model
    classes. Alternatively, externally created simulations, e.g. posterior predictive simulations 
    from Bayesian software such as 'JAGS', 'STAN', or 'BUGS' can be processed as well. 
    The resulting residuals are standardized to values between 0 and 1 and can be interpreted 
    as intuitively as residuals from a linear regression. The package also provides a number of 
    plot and test functions for typical model misspecification problems, such as 
    over/underdispersion, zero-inflation, and residual spatial and temporal autocorrelation.
Identification of hub genes in a gene co-expression network from gene expression data. The differential network analysis for two contrasting conditions leads to the identification of various types of hubs like Housekeeping, Unique to stress (Disease) and Unique to control (Normal) hub genes.
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.
Contains an implementation of the
	     d-variable Hilbert Schmidt independence criterion
	     and several hypothesis tests based on it, as described
	     in Pfister et al. (2017) <doi:10.1111/rssb.12235>.
Calculates key indicators such as fertility rates (Total Fertility Rate (TFR), General Fertility Rate (GFR), 
  and Age Specific Fertility Rate (ASFR)) using Demographic and Health Survey (DHS) women/individual data, 
  and childhood mortality rates such as Neonatal Mortality Rate (NNMR), Post-neonatal Mortality Rate (PNNMR), 
  Infant Mortality Rate (IMR), Child Mortality Rate (CMR), and Under-5 Mortality Rate (U5MR). 
  In addition to the indicators, the 'DHS.rates' package estimates precision indicators such as Standard Error (SE), 
  Design Effect (DEFT), Relative Standard Error (RSE) and Confidence Interval (CI). 
  The package is developed according to the DHS methodology of calculating the fertility indicators and 
  the childhood mortality rates  outlined in the 
  "DHS Guide to Statistics" (Rutstein and Rojas 2006, <http://dhsprogram.com/pubs/pdf/DHSG1/Guide_to_DHS_Statistics_29Oct2012_DHSG1.pdf>) 
  and the DHS methodology of estimating the precision indicators outlined in 
  the "DHS Sampling and Household Listing Manual" (ICF International 2012, <https://dhsprogram.com/pubs/pdf/DHSM4/DHS6_Sampling_Manual_Sept2012_DHSM4.pdf>).
A set of utilities for calculating the Deficit (frailty) Index (DI) in gerontological studies. 
             The deficit index was first proposed by Arnold Mitnitski and Kenneth Rockwood 
             and represents a proxy measure of aging and also can be served as
             a sensitive predictor of survival. For more information, see 
             (i)"Accumulation of Deficits as a Proxy Measure of Aging" 
             by Arnold B. Mitnitski et al. (2001), 
             The Scientific World Journal 1, <DOI:10.1100/tsw.2001.58>;
             (ii) "Frailty, fitness and late-life mortality in relation to chronological and biological age" 
             by Arnold B Mitnitski et al. (2001), 
             BMC Geriatrics2002 2(1), <DOI:10.1186/1471-2318-2-1>.
Fast functions for effective sample size, weighted multivariate mean and variance computation, 
    and weight diagnostic plot for generic importance sampling type results.
Provides methods by Steinhauser et al. (2016) <DOI:10.1186/s12874-016-0196-1> for meta-analysis of diagnostic accuracy studies with several cutpoints.
Several tools for handling block-matrix diagonals and similar constructs are implemented. Block-diagonal matrices can be extracted or removed using two small functions implemented here. In addition, non-square matrices are supported. Block diagonal matrices occur when two dimensions of a data set are combined along one edge of a matrix. For example, trade-flow data in the 'decompr' and 'gvc' packages have each country-industry combination occur along both edges of the matrix.
Visualises simple graphs (networks) based on a transition matrix, utilities to plot flow diagrams, 
     visualising webs, electrical networks, etc.
     Support for the book "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 (2012), Springer.
     Includes demo(flowchart), demo(plotmat), demo(plotweb).
Build graph/network structures using functions for stepwise addition and
    deletion of nodes and edges. Work with data available in tables for bulk
    addition of nodes, edges, and associated metadata. Use graph selections
    and traversals to apply changes to specific nodes or edges. A wide
    selection of graph algorithms allow for the analysis of graphs. Visualize
    the graphs and take advantage of any aesthetic properties assigned to
    nodes and edges.
Allows for export of DiagrammeR Graphviz objects to SVG.
Performs Diallel Analysis with R using Griffing's and Hayman's approaches. Four different methods (1: Method-I (Parents + F1's + reciprocals); 2: Method-II (Parents and one set of F1's); 3: Method-III (One set of F1's and reciprocals); 4: Method-IV (One set of F1's only)) and two methods (1: Fixed Effects Model; 2: Random Effects Model) can be applied using Griffing's approach.
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 sum 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 models of different types : linear models, additive models, MARS,PolyMARS and Kriging.
Estimation, validation and prediction of kriging models.
        Important functions : km, print.km, plot.km, predict.km.
Efficient Global Optimization (EGO) algorithm and adaptations for
  parallel infill (multipoint EI), problems with noise, and problems with
  constraints.
Performs cluster analysis using an ensemble clustering framework.
    Results from a diverse set of algorithms are pooled together using methods
    such as majority voting, K-Modes, LinkCluE, and CSPA. There are options to
    compare cluster assignments across algorithms using internal and external
    indices, visualizations such as heatmaps, and significance testing for the
    existence of clusters.
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.
The standard Difference-in-Differences (DID) setup involves two periods and two groups -- a treated group and untreated group.  Many applications of DID methods involve more than two periods and have individuals that are treated at different points in time.  This package contains tools for computing average treatment effect parameters in Difference in Differences models with more than two periods and with variation in treatment timing using the methods developed in Callaway and Sant'Anna (2018) <https://ssrn.com/abstract=3148250>.  The main parameters are group-time average treatment effects which are the average treatment effect for a particular group at a a particular time.  These can be aggregated into a fewer number of treatment effect parameters, and the package deals with the cases where there is selective treatment timing, dynamic treatment effects, calendar time effects, or combinations of these.  There are also functions for testing the Difference in Differences assumption, and plotting group-time average treatment effects.
A basic, clear implementation of tree-based gradient boosting
    designed to illustrate the core operation of boosting models. Tuning
    parameters (such as stochastic subsampling, modified learning rate, or
    regularization) are not implemented. The only adjustable parameter is the
    number of training rounds. If you are looking for a high performance boosting
    implementation with tuning parameters, consider the 'xgboost' package.
Enables the user to build a citation network/graph from bibliographic data and, based on modularity and heterocitation metrics, assess the degree of awareness/cross-fertilization between two corpora/communities. This toolset is optimized for Scopus data.
This hosts the findRFM function which generates RFM scores on a 1-5 point scale for
             customer transaction data. The function consumes a data frame with Transaction Number,
             Customer ID, Date of Purchase (in date format) and Amount of Purchase as the attributes.
             The function returns a data frame with RFM data for the sales information.
An implementation of the metrics dief@t and dief@k to measure the diefficiency (or continuous efficiency) of incremental approaches, see Acosta, M., Vidal, M. E., & Sure-Vetter, Y. (2017) <doi:10.1007/978-3-319-68204-4_1>. The metrics dief@t and dief@k allow for measuring the diefficiency during an elapsed time period t or while k answers are produced, respectively. dief@t and dief@k rely on the computation of the area under the curve of answer traces, and thus capturing the answer rate concentration over a time interval.
Physical constants. Gold, silver and glass permittivities,
    together with spline interpolation functions.
A wrapper for the ZEIT ONLINE Content API, available at <http://developer.zeit.de>. 'diezeit' gives access to articles and corresponding metadata from the ZEIT archive and from ZEIT ONLINE. A personal API key is required for usage.
Performs detection of Differential Item Functioning using the method DIFboost as proposed in Schauberger and Tutz (2015): Detection of Differential item functioning in Rasch models by boosting techniques, British Journal of Mathematical and Statistical Psychology.
Estimation of DIFferential COexpressed NETworks using diverse and user metrics.
			 This package is basically used for three functions related to the estimation
			 of differential coexpression. 
			 First, to estimate differential coexpression where
			 the coexpression is estimated, by default, by Spearman correlation. For this,
			 a metric to compare two correlation distributions is needed. The package includes
			 6 metrics. Some of them needs a threshold. A new metric can also be specified as
			 a user function with specific parameters (see difconet.run). The significance is
			 be estimated by permutations.
			 Second, to generate datasets with controlled differential correlation data. This 
			 is done by either adding noise, or adding specific correlation structure.
			 Third, to show the results of differential correlation analyses. Please see
			 <http://bioinformatica.mty.itesm.mx/difconet> for further information.
A method for identifying pattern changes between 2 experimental
         conditions in correlation networks (e.g., gene co-expression networks),
         which builds on a commonly used association measure, such as Pearson's
         correlation coefficient. This package includes functions to calculate
         correlation matrices for high-dimensional dataset and to test
         differential correlation, which means the changes in the correlation
         relationship among variables (e.g., genes and metabolites) between 2
         experimental conditions.
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.
This is an R implementation of Fast and Scalable Learning of Sparse Changes in High-Dimensional Gaussian Graphical Model Structure (DIFFEE). The DIFFEE algorithm can be used to fast estimate the differential network between two related datasets. For instance, it can identify differential gene network from datasets of case and control. By performing data-driven network inference from two high-dimensional data sets, this tool can help users effectively translate two aggregated data blocks into knowledge of the changes among entities between two Gaussian Graphical Model. Please run demo(diffeeDemo) to learn the basic functions provided by this package. For further details, please read the original paper: Beilun Wang, Arshdeep Sekhon, Yanjun Qi (2018) <arXiv:1710.11223>.
Functions and examples from the book Solving Differential 
   Equations in R by Karline Soetaert, Jeff R Cash and Francesca Mazzia.
   Springer, 2012.
An interface to 'DifferentialEquations.jl' from the R programming language. 
  It has unique high performance methods for solving ordinary differential equations (ODE), stochastic differential equations (SDE), 
  delay differential equations (DDE), differential-algebraic equations (DAE), and more. Much of the functionality, 
  including features like adaptive time stepping in SDEs, are unique and allow for multiple orders of magnitude speedup over more common methods. 
  'diffeqr' attaches an R interface onto the package, allowing seamless use of this tooling by R users.
Metrics of difference for comparing pairs of maps representing real or categorical variables at original and multiple resolutions.
Package to fit diffusion-based IRT models to response and 
	response time data. Models are fit 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.
A collection of functions to detect gene probes with different means or variances between two groups in (epi)-genomic data analysis, including classical F test and Levene's test for equal variance, likelihood ratio test for equal mean and equal variance, the wrappers for functions in R Bioconductor package 'missMethyl' (Phipson and Oshlack (2014) <doi:10.1186/s13059-014-0465-4>), the implementation of the methods proposed by Ahn and Wang (2013) <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3621641/>, and the improved Ahn and Wang's methods that we proposed recently (Qiu and Li et al. (2017) <http://oprscience.com/wp-content/uploads/2016/07/new-score-tests-for-equality-of-variances-in-the-application-of-dna-methylation-data-analysis.pdf>).
This R function implements the nonstationary Kriging model proposed by Tuo, Wu and Yu (2014) <DOI:10.1080/00401706.2013.842935> for analyzing multi-fidelity computer outputs. This function computes the maximum likelihood estimates for the model parameters as well as the predictive means and variances of the exact solution (i.e., the conceptually highest fidelity).
Provides an implementation of statistically significant 
    differential sub-network analysis for paired biological networks.
Generate a colorized diff of two R objects for an intuitive
    visualization of their differences.
An implementation of major general-purpose mechanisms for privatizing
    statistics, models, and machine learners, within the framework of differential
    privacy of Dwork et al. (2006) <doi:10.1007/11681878_14>. Example mechanisms
    include the Laplace mechanism for releasing numeric aggregates, and the 
    exponential mechanism for releasing set elements. A sensitivity sampler 
    (Rubinstein & Alda, 2017) <arXiv:1706.02562> permits sampling target 
    non-private function sensitivity; combined with the generic mechanisms, it 
    permits turn-key privatization of arbitrary programs.
An R interface to the 'codediff' JavaScript library (a copy of which is included in the package,
  see <https://github.com/danvk/codediff.js> for information).
  Allows for visualization of the difference between 2 files, usually text files or R scripts, in a browser.
Residual-based baseline identification and peak decomposition for x-ray diffractograms as introduced in Davies/Gather/Mergel/Meise/Mildenberger (2008) <doi:10.1214/08-AOAS181>.
Provides data structures and methods for measuring, coding, 
    and analysing text within text corpora. The package allows for manual as 
    well computer aided coding on character, token and text pair level.
Interactive visualizations and tabulations for diffrprojects. 
    All presentations are based on the htmlwidgets framework allowing for 
    interactivity via HTML and Javascript, Rstudio viewer integration, 
    RMarkdown integration, as well as Shiny compatibility.
Various diffusion models to forecast new product growth. Currently
    the package contains Bass, Gompertz and Gamma/Shifted Gompertz curves. See
    Meade and Islam (2006) <doi:10.1016/j.ijforecast.2006.01.005>.
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.
Tools for performing inference and analysis on a class of quadratic diffusion processes for both scalar and bivariate diffusion systems. For scalar diffusions, a module is provided for solving first passage time problems for both time-homogeneous and time-inhomogeneous GQDs.
Tools for performing inference and analysis using a data-imputation scheme and the method of lines.
Tools for performing inference and analysis on a class of quadratic jump diffusion processes.
Implementation of network diffusion algorithms such as
  heat diffusion or Markov random walks. Network diffusion algorithms generally
  spread information in the form of node weights along the edges of a graph to other nodes.
  These weights can for example be interpreted as temperature, an initial amount
  of water, the activation of neurons in the brain, or the location of a random
  surfer in the internet. The information (node weights) is iteratively propagated
  to other nodes until a equilibrium state or stop criterion occurs.
Performs DIFlasso, a method to detect DIF (Differential Item Functioning) in Rasch Models. It can handle settings with many variables and also metric variables.
Detection of differential item functioning (DIF) among dichotomously scored items and differential distractor functioning (DDF) among unscored items with non-linear regression procedures based on generalized logistic regression models (Drabinova and Martinkova, 2017, <doi:10.1111/jedm.12158>).
Provides a collection of standard methods to detect differential item functioning among dichotomously scored items. Methods for uniform and non-uniform DIF, based on test-score or IRT methods, for comparing two or more than two groups of respondents, are available (Magis, Beland, Tuerlinckx and De Boeck,A General Framework and an R Package for the Detection of Dichotomous Differential Item Functioning, Behavior Research Methods, 42, 2010, 847-862 <doi:10.3758/BRM.42.3.847>).
Item focused recursive partitioning for simultaneous selection of
    items and variables that induce Differential Item Functioning (DIF) based on the
    Rasch Model or the Logistic Regression Approach for DIF detection.
Implementation of a function 'digest()' for the creation 
 of hash digests of arbitrary R objects (using the 'md5', 'sha-1', 'sha-256', 
 'crc32', 'xxhash' and 'murmurhash' algorithms) permitting easy comparison of R
 language objects, as well as a function 'hmac()' to create hash-based
 message authentication code. 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.
The assay sensitivity is the minimum number of copies that the digital PCR assay can detect. Users provide serial dilution results in the format of counts of positive and total reaction wells. The output is the estimated assay sensitivity and the copy number per well in the initial dilute.
Import data from a digital image; it requires user input for
    calibration and to locate the data points. The end result is similar to
    'DataThief' and other other programs that 'digitize' published plots or
    graphs.
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.
A collection of dimensionality reduction
    techniques from R packages and provides a common
    interface for calling the methods.
Estimate the Deterministic Input, Noisy "And" Gate (DINA)
    cognitive diagnostic model parameters using the Gibbs sampler described
    by Culpepper (2015) <doi:10.3102/1076998615595403>.
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 /
 multimodality and provide a test with simulation based p-values,  where
 the original public code has been corrected.
A Bayesian clustering method for replicated time series or replicated measurements from multiple experimental conditions, e.g., time-course gene expression data.  It estimates the number of clusters directly from the data using a Dirichlet-process prior.  See Fu, A. Q., Russell, S., Bray, S. and Tavare, S. (2013) Bayesian clustering of replicated time-course gene expression data with weak signals. The Annals of Applied Statistics. 7(3) 1334-1361. <doi:10.1214/13-AOAS650>.
Allows the computation of clustering coefficients for directed and weighted networks by using different approaches. 
    It allows to compute clustering coefficients that are not present in 'igraph' package. 
    A description of clustering coefficients can be found in "Directed clustering in weighted networks: a new perspective", Clemente, G.P., Grassi, R. (2017),  	
    <doi:10.1016/j.chaos.2017.12.007>.
A set of functions to estimate the controlled direct effect of treatment fixing a potential mediator to a specific value. Implements the sequential g-estimation estimator described in Vansteelandt (2009) <doi:10.1097/EDE.0b013e3181b6f4c9> and Acharya, Blackwell, and Sen (2016) <doi:10.1017/S0003055416000216>.
A collection of functions for directional data analysis. Hypothesis testing, discriminant and regression analysis, MLE of distributions and more are included. The standard textbook for such data is the "Directional Statistics" by Mardia, K. V. and Jupp, P. E. (2000).
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.
Direction analysis is a set of tools designed to identify
    combinatorial effects of multiple treatments and/or perturbations on pathways
    and kinases profiled by microarray, RNA-seq, proteomics, or phosphoproteomics
    data.
Calculate adjusted means and proportions of a variable by groups defined by another variable by direct standardisation, standardised to the structure of the dataset.
Perform nonparametric Bayesian analysis using Dirichlet 
    processes without the need to program the inference algorithms. 
    Utilise included pre-built models or specify custom 
    models and allow the dirichletprocess package to handle the 
    Markov chain Monte Carlo sampling. 
    Our Dirichlet process objects can act as building blocks for a variety 
    of statistical models including and not limited to: density estimation, 
    clustering and prior distributions in hierarchical models.
    See Teh, Y. W. (2011) 
    <https://www.stats.ox.ac.uk/~teh/research/npbayes/Teh2010a.pdf>, 
    among many other sources.
Implements Dirichlet regression models in R.
Implementation of Directional Metropolis Hastings Algorithm for
    MCMC.
Estimate parameters in Dirichlet-Multinomial and compute
        profile log-likelihoods.
Discrete smoothing of probability mass function is performed using three discrete associated kernels: Dirac Discrete Uniform (DiracDU), Binomial and Discrete Triangular. Two automatic bandwidth selection procedures are implemented: the cross-validation method for the three kernels and the local Bayesian approach for Binomial kernel. Note that DiracDU is used for categorical data, Binomial kernel is appropriate for count data with small or moderate sample sizes, and Discrete Triangular kernel is recommended for count data with large sample sizes.
Client for the Discourse API. Discourse is a open source
    discussion forum platform (<https://www.discourse.org/>). It comes with 'RESTful'
    API access to an installation. This client requires that you are authorized
    to access a Discourse installation, either yours or another.
Discrete Laplace exponential family for models such as a generalized linear model
Make 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).
Functions for discordant kinship modeling (and other sibling-based quasi-experimental designs). Currently, the package contains data restructuring functions; functions for generating genetically- and environmentally-informed data for kin pairs.
Structure learning of Bayesian network using coordinate-descent
    algorithm. This algorithm is designed for discrete network assuming a multinomial data set,
    and we use a multi-logit model to do the regression.
    The algorithm is described in Gu, Fu and Zhou (2016) <arXiv:1403.2310>.
Probability mass function, distribution function, quantile function, random generation and parameter estimation for the discrete inverse Weibull distribution.
Probability mass function, distribution function, quantile function, random generation and estimation for the skew discrete Laplace distributions.
Multiple testing procedures for discrete test statistics,
        that use the known discrete null distribution of the p-values
        for simultaneous inference.
Create, manipulate, transform, and simulate from discrete random
    variables. The syntax is modeled after that which is used in mathematical
    statistics and probability courses, but with powerful support for more
    advanced probability calculations. This includes the creation of joint
    random variables, and the derivation and manipulation of their conditional
    and marginal distributions.
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
Provides data transformations, estimation utilities,
    predictive evaluation measures and simulation functions for discrete time
    survival analysis.
Formatting of population and case data, calculation of Standardized
    Incidence Ratios, and fitting the BYM model using INLA.
Model cell type heterogeneity of bulk renal cell carcinoma. The observed gene expression in bulk tumor sample is modeled by a log-normal distribution with the location parameter structured as a linear combination of the component-specific gene expressions.
Implement the methods proposed by Ahmad & Dey (2007) <doi:10.1016/j.datak.2007.03.016> in calculating the dissimilarity matrix at the presence of mixed attributes. This Package includes functions to discretize quantitative variables, calculate conditional probability for each pair of attribute values, distance between every pair of attribute values, significance of attributes, calculate dissimilarity between each pair of objects.
A pipeline to analyze photographs of disk diffusion plates. This removes the need to analyze the plates themselves, and thus analysis can be done separate from the assay. Furthermore, diskImageR removes potential researcher bias, by quantitative assessment of drug resistance as the zone diameter at multiple cutoff values of growth inhibition. This method also extends the disk diffusion assay by measuring drug tolerance (in addition to drug resistance) as the fraction of the subpopulation that is able to grow above the resistance point ("FoG"), and drug sensitivity as the rate of change from no growth to full growth ("slope").
Functions for species distribution modeling, that is, predicting entire geographic distributions form occurrences at a number of sites and the environment at these sites.
An implementation of two exact algorithms for testing the
        Hausdorff and simplex dispersion orderings
The disparity filter algorithm is a network reduction technique to
    identify the 'backbone' structure of a weighted network without destroying
    its multi-scale nature. The algorithm is documented in M. Angeles Serrano,
    Marian Boguna and Alessandro Vespignani in "Extracting the multiscale
    backbone of complex weighted networks", Proceedings of the National Academy
    of Sciences 106 (16), 2009. This implementation of the algorithm supports
    both directed and undirected networks.
A package containing R functions for displaying data and
        results from high-throughput screening experiments.
Functions for estimating Gaussian dispersion regression models (Aitkin, 1987 <doi:10.2307/2347792>), overdispersed binomial logit models (Williams, 1987 <doi:10.2307/2347977>), and overdispersed Poisson log-linear models (Breslow, 1984 <doi:10.2307/2347661>), using a quasi-likelihood approach.
Create disposable R packages for testing.
    You can create, install and load multiple R packages with a single
    function call, and then unload, uninstall and destroy them with another
    function call. This is handy when testing how some R code or an R package
    behaves with respect to other packages.
A modular package for measuring disparity from multidimensional matrices. Disparity can be calculated from any matrix defining a multidimensional space. The package provides a set of implemented metrics to measure properties of the space and allows users to provide and test their own metrics. The package also provides functions for looking at disparity in a serial way (e.g. disparity through time) or per groups as well as visualising the results. Finally, this package provides several basic statistical tests for disparity analysis.
Spatial downscaling of coarse grid mapping to fine grid
    mapping using predictive covariates and a model fitted using the 'caret'
    package. The original dissever algorithm was published by Malone et al. 
    (2012) <doi:10.1016/j.cageo.2011.08.021>, and extended by Roudier et al.
    (2017) <doi:10.1016/j.compag.2017.08.021>.
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 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 (via a
    Horvitz-Thompson-like estimator) if survey area information is provided.
Provides tools for constructing, manipulating and using distance metrics.
Calculates the study size (either number of detections, or
    proportion of region that should be covered) to achieve a target precision for
    the estimated abundance. The calculation allows for the penalty due to unknown
    detection function, and for overdispersion. The user must specify a guess at the
    true detection function.
Implement DiSTATIS and CovSTATIS (three-way multidimensional scaling). For the  analysis of  multiple distance/covariance matrices collected on the same set of observations
Implementing algorithms and fitting models when sites (possibly remote) share
  computation summaries rather than actual data over HTTP with a master R process (using
  'opencpu', for example). A stratified Cox model and a singular value decomposition are
  provided. The former makes direct use of code from the R 'survival' package. (That is,
  the underlying Cox model code is derived from that in the R 'survival' package.)
  Sites may provide data via several means: CSV files, Redcap API, etc. An extensible
  design allows for new methods to be added in the future. Web applications are provided
  (via 'shiny') for the implemented methods to help in designing and deploying the
  computations.
Creates discretised versions of continuous 
      distribution functions by mapping continuous values 
      to an underlying discrete grid, based on a (uniform) 
      frequency of discretisation, a valid discretisation 
      point, and an integration range. For a review of 
      discretisation methods, see 
      Chakraborty (2015) <doi:10.1186/s40488-015-0028-6>.
Download data from the FlorKart database of the floristic field mapping in Germany in a convenient way. The database incorporates distribution data for plants in Germany on the basis of quadrants on a topographical map with a resolution of 1 : 25000 (TK 25). The data is owned and provided by the German Federal Agency for Nature Conservation (BfN) and the Network Phytodiversity in Germany (NetPhyD). For further information please visit <http://www.floraweb.de/pflanzenarten/hintergrundtexte_florkart_organisation.html>. The author of this package is in no way associated with the BfN or NetPhyD.
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, bootstrap resampling, 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').
Extends package 'distr' by functionals, distances, and conditional distributions.
A different way for calculating pdf/pmf, cdf, quantile and random data such that the user is able to consider the name of related distribution as an argument and so easily can changed by a changing argument by user. It must be mentioned that the core and computation base of package 'DISTRIB' is package 'stats'. Although similar functions are introduced previously in package 'stats', but the package 'DISTRIB' has some special applications in some special computational programs.
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.
Implements S4 classes for probability models based on packages 'distr' and
        'distrEx'.
Fast distributed/parallel estimation for multinomial logistic regression via Poisson factorization and the 'gamlr' package.  For details see: Taddy (2015, AoAS), Distributed Multinomial Regression, <arXiv:1311.6139>.
Tools to estimate and manage empirical distributions,
    which should work with survey data. One of the main features is the 
    possibility to create data cubes of estimated statistics, that include
    all the combinations of the variables of interest (see for example functions
    dcc5() and dcc6()).
S4-distribution classes based on package distr for distributions from packages
        'fBasics' and 'fGarch'.
S4-classes for setting up a coherent framework for simulation within the distr
        family of packages.
Provides flexible examples of LLN and CLT for teaching purposes in secondary
        school.
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.
Computes the most common diversity measures used in social and other sciences, and includes new measures from interdisciplinary research.
Contains a number of comparative 'phylogenetic' methods,
  mostly focusing on analysing diversification and character
  evolution.  Contains implementations of 'BiSSE' (Binary State
  'Speciation' and Extinction) and its unresolved tree extensions,
  'MuSSE' (Multiple State 'Speciation' and Extinction), 'QuaSSE',
  'GeoSSE', and 'BiSSE-ness' Other included methods include Markov
  models of discrete and continuous trait evolution and constant rate
  'speciation' and extinction.
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. 
  Parallel computing capabilities and pairwise results without 
  bootstrapping are provided. 
  Also calculates F-statistics from Weir and Cockerham (1984). 
  Various plotting features are provided, as well as Chi-square tests of 
  genetic heterogeneity. 
  Functionality for the calculation of various diversity parameters is 
  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.
Predictions of alpha diversity are fitted from presence data, first abundance is modeled from occupancy models and then, several diversity indices are calculated and finally GLM models are used to predict diversity in different environments and select priority areas.
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.
Provides tools to convert DICOM-format files to NIfTI-1 format.
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/.
A set of tools for empirical analysis of diversity (a number and frequency of different types in population) and similarity (a number and frequency of shared types in two populations) in biological or ecological systems.
Function to test spatial segregation and association based in contingency table analysis of nearest neighbour counts following Dixon (2002) <doi:10.1080/11956860.2002.11682700>. Some 'Fortran' code has been included to the original dixon2002() function of the 'ecespa' package to improve speed.
Implements various decision support tools related to the new product development.
             Subroutines include correlation reliability test, Mahalanobis distance measure for outlier detection, combinatorial search (all possible subset regression), non-parametric efficiency analysis measures: DDF (directional distance function), DEA (data envelopment analysis), HDF (hyperbolic distance function), SBM (slack-based measure), and SF (shortage function), benchmarking, risk analysis, technology adoption model, new product target setting, etc.
Provides functions to facilitate access to the 'DKAN' API (<https://dkan.readthedocs.io/en/latest/apis/index.html>), including the 'DKAN' REST API (metadata), and the 'DKAN' datastore API (data). Includes functions to list, create, retrieve, update, and delete datasets and resources nodes. It also includes functions to search and retrieve data from the 'DKAN' datastore.
Compute diffusion kernels on DNA polymorphisms, including SNP and bi-allelic genotypes.
Provides time series regression models with one predictor using finite distributed lag models, polynomial (Almon) distributed lag models, geometric distributed lag models with Koyck transformation, and autoregressive distributed lag models. It also consists of functions for computation of h-step ahead forecasts from these models. See Baltagi (2011) <doi:10.1007/978-3-642-20059-5> for more information.
An implementation of the differentiable lasso (dlasso) and SCAD (dSCAD) using iterative ridge algorithm. This package allows selecting the tuning parameter by AIC, BIC, GIC and GIC.
Interface for 'Rcpp' users to 'dlib' <http://dlib.net> which is a
    'C++' toolkit containing machine learning algorithms and computer vision tools.
    It is used in a wide range of domains including robotics, embedded devices,
    mobile phones, and large high performance computing environments. This package
    allows R users to use 'dlib' through 'Rcpp'.
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.
Development, simulation testing, and implementation of management
    procedures for data-limited fisheries 
    (see Carruthers et al (2014) <doi:10.1016/j.fishres.2013.12.014>).
Collection of functions for distributed lag linear and non-linear models.
Inference functionalities for distributed-lag linear structural equation models (DLSEMs). DLSEMs are Markovian structural causal models (Pearl, 2000, Chapter 5) <ISBN:978-0-521-89560-6>, where each factor of the joint probability distribution is a distributed-lag linear regression model (Judge et al., 1985, Chapters 9-10) <ISBN: 978-0-471-89530-5>. DLSEMs account for temporal delays in the dependence relationships among the variables and allow to perform dynamic causal inference by assessing causal effects at different time lags. Endpoint-constrained quadratic, quadratic decreasing and gamma lag shapes are available.
Monthly download stats of 'CRAN' and 'Bioconductor' packages.
	     Download stats of 'CRAN' packages is from the 'RStudio' 'CRAN mirror', see <http://cranlogs.r-pkg.org>.
	     'Bioconductor' package download stats is at <https://bioconductor.org/packages/stats/>.
Dynamic model averaging for binary and continuous
        outcomes.
The state-of-the-art algorithms for distance metric learning, including global and local methods such as Relevant Component Analysis, Discriminative Component Analysis, Local Fisher Discriminant Analysis, etc. These distance metric learning methods are widely applied in feature extraction, dimensionality reduction, clustering, classification, information retrieval, and computer vision problems.
Dyadic mixed model analysis with multi-trait responses and
  pedigree-based partitioning of individual variation into a range of
  environmental and genetic variance components for individual and 
  maternal effects.
Implements the daily based Morgan-Morgan-Finney (DMMF) soil erosion model (Choi et al., 2017 <doi:10.3390/w9040278>) for estimating surface runoff and sediment budgets from a field or a catchment on a daily basis.
The framework provides functions to generate ODEs of reaction
    networks, parameter transformations, observation functions, residual functions,
    etc. The framework follows the paradigm that derivative information should be
    used for optimization whenever possible. Therefore, all major functions produce
    and can handle expressions for symbolic derivatives.
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.
Perform differential analysis for
        methylation array data. Detect differentially
        methylated regions (DMRs) from array M-values. 
        The core is a Non-homogeneous Hidden Markov Model
        for estimating spatial correlation and a novel Constrained 
        Gaussian Mixture Model for modeling the M-value pairs of each individual locus.
Model selection algorithms for regression and classification, where the predictors can be numerical and categorical and the number of regressors exceeds the number of observations. The selected model consists of a subset of numerical regressors and partitions of levels of factors. Aleksandra Maj-Kańska, Piotr Pokarowski and Agnieszka Prochenka (2015) <doi:10.1214/15-EJS1050>. Piotr Pokarowski and Jan Mielniczuk (2015) <http://www.jmlr.org/papers/volume16/pokarowski15a/pokarowski15a.pdf>.
Work within the 'dplyr' workflow to add random variates to your data frame. 
  Variates can be added at any level of an existing column.  Also, bounds can be specified 
  for simulated variates.
This package includes functions and data accompanying the book 
	     "Data Mining with R, learning with case studies" by Luis Torgo, CRC Press 2010.
Functions and data accompanying the second edition of the book  "Data Mining with R, learning with case studies" by Luis Torgo, published by CRC Press.
Package for conducting differential network analysis from
        microarray data.
Generates DNA sequences based on Markov model techniques for matched sequences. This can be generalized to several sequences. The sequences (taxa) are then arranged in an evolutionary tree (phylogenetic tree) depicting how taxa diverge from their common ancestors. This gives the tests and estimation methods for the parameters of different models. Standard phylogenetic methods assume stationarity, homogeneity and reversibility for the Markov processes, and  often impose further restrictions on the parameters.
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 DnE package involves functions to analyse the distribution of a set of given data. The basic idea of the analysis is chi-squared test. Functions which have the form as "is.xxdistribution" are used to analyse whether the data obeys the xxdistrbution. If you do not know which distribution to judge, use function is.dt().
The focus of the dnet by Fang and Gough (2014) <doi:10.1186/s13073-014-0064-8> is to make sense of omics data (such as gene expression and mutations) from different angles including: integration with molecular networks, enrichments using ontologies, and relevance to gene evolutionary ages. Integration is achieved to identify a gene subnetwork from the whole gene network whose nodes/genes are labelled with informative data (such as the significant levels of differential expression or survival risks). To help make sense of identified gene 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 calculating semantic similarity between ontology terms (and between genes) and for calculating network affinity based on random walk; both can be done via high-performance parallel computing.
Provides a modified hierarchical test (Liu (2017) <doi:10.1214/17-AOS1539>) for detecting the structural difference between two Semiparametric Gaussian graphical models. The multiple testing procedure asymptotically controls the false discovery rate (FDR) at a user-specified level. To construct the test statistic, a truncated estimator is used to approximate the transformation functions and two R functions including lassoGGM() and lassoNPN() are provided to compute the lasso estimates of the regression coefficients.
Provides density, distribution function, quantile function and random generation for the split-t distribution, and computes the mean, variance, skewness and kurtosis for the split-t distribution (Li, F, Villani, M. and Kohn, R. (2010) <doi:10.1016/j.jspi.2010.04.031>).
Using Local Moran's I for detection of differential network local consistency.
Discriminant Non-Negative Matrix Factorization aims to extend the Non-negative Matrix Factorization algorithm in order to extract features that enforce not only the spatial locality, but also the separability between classes in a discriminant manner. It refers to three article, Zafeiriou, Stefanos, et al. "Exploiting discriminant information in nonnegative matrix factorization with application to frontal face verification." Neural Networks, IEEE Transactions on 17.3 (2006): 683-695. Kim, Bo-Kyeong, and Soo-Young Lee. "Spectral Feature Extraction Using dNMF for Emotion Recognition in Vowel Sounds." Neural Information Processing. Springer Berlin Heidelberg, 2013. and Lee, Soo-Young, Hyun-Ah Song, and Shun-ichi Amari. "A new discriminant NMF algorithm and its application to the extraction of subtle emotional differences in speech." Cognitive neurodynamics 6.6 (2012): 525-535.
Functions are provided to fit temporal lag models to dynamic
    networks. The models are build on top of exponential random graph models (ERGM) framework. There are
    functions for simulating or forecasting networks for future time points.
    Stable Multiple Time Step Simulation/Prediction from Lagged Dynamic Network Regression Models. Mallik, Almquist (2017, under review).
Provides Frequentist (EM) and Bayesian  (MCMC) Methods for Inference of  Birth-Death-Immigration Markov Chains.
Example datasets from the book "An Introduction to Generalised Linear Models" (Year: 2008, ISBN:9781584889502) by Dobson and Barnett. The data were read into R from the 'Excel' files available here: <https://www.crcpress.com/downloads/C9500/GLM_data.zip>.
Contains:
    1) Facilities for working with grouped data: 'do' something to data
    stratified 'by' some variables.
    2) LSmeans (least-squares means), general linear contrasts. 
    3) Miscellaneous other utilities.
Allows accessing 'Docker' 'SDK' from 'R' via the 'Docker' 'Python' 'SDK' using the 'reticulate' package.
  This is a very thin wrapper that tries to do very little and get out of the way.
  The user is expected to know how to use the 'reticulate' package to access 'Python' modules, and how the 'Docker' 'Python' 'SDK' works.
Build a Dockerfile straight from your R session. 'dockerfiler' allows 
    you to create step by step a Dockerfile, and provide convenient tools to wrap 
    R code inside this Dockerfile.
Define a command-line interface by just giving it
    a description in the specific format.
A direct approach to optimal designs for copula models based on
    the Fisher information. Provides flexible functions for building joint PDFs,
    evaluating the Fisher information and finding optimal designs. It includes an
    extensible solution to summation and integration called 'nint', functions for
    transforming, plotting and comparing designs, as well as a set of tools for
    common low-level tasks.
Provides the ability to display something analogous to
    Python's docstrings within R.  By allowing the user to document
    their functions as comments at the beginning of their function
    without requiring putting the function into a package we allow
    more users to easily provide documentation for their functions.
    The documentation can be viewed just like any other help files
    for functions provided by packages as well.
Production of R packages from tagged comments introduced within the code 
             and a minimum of additional documentation files.
Have you ever been tempted to create 'roxygen2'-style documentation
    comments for one of your functions that was not part of one of your
    packages (yet)?
    This is exactly what this package is about: running 'roxygen2' on
    (chunks of) a single code file.
Connect to the 'DocuSign' Rest API <https://www.docusign.com/p/RESTAPIGuide/RESTAPIGuide.htm>, 
  which supports embedded signing, and sending of documents.
A set of helper functions for using R Markdown to create documents
    in docx format, especially documents for use in a classroom or workshop
    setting.
Microsoft Word docx files provide an XML structure that is fairly
    straightforward to navigate, especially when it applies to Word tables and
    comments. Tools are provided to determine table count/structure, comment count
    and also to extract/clean tables and comments from Microsoft Word docx documents.
Various sampling plans are able to be compared using evaluations of their OC, AOQ, ATI etc.
Distances on dual-weighted directed graphs using priority-queue
    shortest paths. Weighted directed graphs have weights from A to B which may
    differ from those from B to A. Dual-weighted directed graphs have two sets
    of such weights. A canonical example is a street network to be used for
    routing in which routes are calculated by weighting distances according to
    the type of way and mode of transport, yet lengths of routes must be
    calculated from direct distances.
Detect Differences in rhythmic time series. Using linear
    least squares and the robust semi-parametric rfit() method. Differences in
    harmonic fitting could be detected as well as differences in scale of the
    noise distribution.
Creates full factorial experimental designs and designs based on orthogonal arrays for (industrial) experiments. Provides diverse quality criteria. Provides utility functions for the class design, which is also used by other packages for designed experiments.
'CRAN' package 'DoE.base' and non-'CRAN' packages 'gurobi' and 'Rmosek' are enhanced with functionality for the creation of optimized arrays for experimentation, where optimization is in terms of generalized minimum aberration. It is also possible to optimally extend existing arrays to larger run size. Optimization requires the availability of at least one of the commercial products 'Gurobi' or 'Mosek' (free academic licenses available for both). For installing 'Gurobi' and its R package 'gurobi', follow instructions at <http://www.gurobi.com/downloads/gurobi-optimizer> and <http://www.gurobi.com/documentation/7.5/refman/r_api_overview.html>. For installing 'Mosek' and its R package 'Rmosek', follow instructions at <https://www.mosek.com/downloads/> and <http://docs.mosek.com/8.1/rmosek/install-interface.html>.
Rare variant association test integrating variant position information. It aims to identify the presence of clusters of disease-risk variants in specific gene regions. For more details, please read the publication from Persyn et al. (2017)  <doi:10.1371/journal.pone.0179364>.
Various kinds of designs for
 (industrial) experiments can be created. The package 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 '%dopar%' adaptor such that any type of futures can
    be used as backends for the 'foreach' framework.
Provides a parallel backend for the %dopar% function using
        the multicore functionality of the parallel package.
Functions to calculate ADI (Average Dominance Index) and FDI (Frequency-Based Dominance Index). Functions to visualize the Data with Social Network Graphs with Dual
    Directions and Music Notation Graph.
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. 'Domino' is a service
    that makes it easy to run your code on scalable hardware, with integrated
    version control and collaboration features designed for analytical workflows
    (see <http://www.dominodatalab.com> for more information).
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
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), <DOI:10.1287/opre.47.1.159>].
    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
        the snow package of Tierney, Rossini, Li, and Sevcikova.
Estimates dose-response relations from summarized dose-response
    data and to combines them according to principles of (multivariate)
    random-effects models.
A small package containing helper utilities for creating function
    for computing statistics.
Renders DOT diagram markup language in R and also provides the possibility to
    export the graphs in PostScript and SVG (Scalable Vector Graphics) formats.
    In addition, it supports literate programming packages such as 'knitr' and
    'rmarkdown'.
Calculation of cluster typicality coefficients as being generated by fuzzy k-means clustering.
Provides .C64(), which is an enhanced version of .C()
    and .Fortran() from the foreign function interface. .C64() supports long
    vectors, arguments of type 64-bit integer, and provides a mechanism to
    avoid unnecessary copies of read-only and write-only arguments. This
    makes it a convenient and fast interface to C/C++ and Fortran code.
Load configuration from a '.env' file, that is
    in the current working directory, into environment variables.
Quick and easy dot-and-whisker plots of regression results.
Contains the discrete nonparametric survivor function estimation algorithm of De Gruttola and Lagakos for doubly interval-censored failure time data and the discrete nonparametric survivor function estimation algorithm of Sun for doubly interval-censored left-truncated failure time data [Victor De Gruttola  & Stephen W. Lagakos (1989) <doi:10.2307/2532030>] [Jianguo Sun (1995) <doi:10.2307/2533008>].
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. See Bodhisattva Sen and Mary C Meyer (2016) <doi:10.1111/rssb.12178> for more details.
Differential exon usage test for RNA-Seq data via an empirical Bayes shrinkage method for the dispersion parameter the utilizes inclusion-exclusion data to analyze the propensity to skip an exon across groups. The input data consists of two matrices where each row represents an exon and the columns represent the biological samples. The first matrix is the count of the number of reads expressing the exon for each sample. The second matrix is the count of the number of reads that either express the exon or explicitly skip the exon across the samples, a.k.a. the total count matrix. Dividing the two matrices yields proportions representing the propensity to express the exon versus skipping the exon for each sample.
Local linear hazard estimator and its multiplicatively bias correction, including three bandwidth selection methods: best one-sided cross-validation, double one-sided cross-validation, and standard cross-validation.
'Kevin Dowd's' book Measuring Market Risk is a widely read book 
          in the area of risk measurement by students and 
          practitioners alike. As he claims, 'MATLAB' indeed might have been the most 
          suitable language when he originally wrote the functions, but,
          with growing popularity of R it is not entirely 
	  valid. As 'Dowd's' code was not intended to be error free and were mainly 
	  for reference, some functions in this package have inherited those 
	  errors. An attempt will be made in future releases to identify and correct 
	  them. 'Dowd's' original code can be downloaded from www.kevindowd.org/measuring-market-risk/. 
          It should be noted that 'Dowd' offers both
          'MMR2' and 'MMR1' toolboxes. Only 'MMR2' was ported to R. 'MMR2' is more 
          recent version of 'MMR1' toolbox and they both have mostly similar 
          function. The toolbox mainly contains different parametric and non 
	  parametric methods for measurement of market risk as well as 
	  backtesting risk measurement methods.
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 set of functions that downscales species occupancy at
    coarse grain sizes to predict species occupancy at fine grain sizes.
Toggles the test and production versions of a large workflow.
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.
A Scannerless GLR parser/parser generator.  Note that GLR standing for "generalized LR", where L stands for "left-to-right" and
   R stands for "rightmost (derivation)".  For more information see <https://en.wikipedia.org/wiki/GLR_parser>. This parser is based on the Tomita
   (1987) algorithm. (Paper can be found at <http://acl-arc.comp.nus.edu.sg/archives/acl-arc-090501d3/data/pdf/anthology-PDF/J/J87/J87-1004.pdf>).
   The original 'dparser' package documentation can be found at <http://dparser.sourceforge.net/>.  This allows you to add mini-languages to R (like
   RxODE's ODE mini-language Wang, Hallow, and James 2015 <DOI:10.1002/psp4.12052>) or to parse other languages like 'NONMEM' to automatically translate
   them to R code.   To use this in your code, add a LinkingTo dparser in your DESCRIPTION file and instead of using #include <dparse.h> use
   #include <dparser.h>.  This also provides a R-based port of the make_dparser <http://dparser.sourceforge.net/d/make_dparser.cat> command called
   mkdparser().  Additionally you can parse an arbitrary grammar within R using the dparse() function, which works on most OSes and is mainly for grammar
   testing.  The fastest parsing, of course, occurs at the C level, and is suggested.
Beta-binomial Mixture Model is used to infer the pattern from count data.
		It can be used for clustering of RNA methylation sequencing data.
Analysis, visualisation and simulation of digital polymerase chain
    reaction (dPCR). Supports data formats of commerical systems (Bio-Rad QX100 and
    QX200; Fluidigm BioMark) and other systems.
fits the primal graphical lasso, via one-at-a-time
        block-coordinate descent.
Compares distributions with one another in terms of their fit to each sample in a dataset that contains multiple samples, as described in Joo, Aguinis, and Bradley (in press). Users can examine the fit of seven distributions per sample: pure power law, lognormal, exponential, power law with an exponential cutoff, normal, Poisson, and Weibull. Automation features allow the user to compare all distributions for all samples with a single command line, which creates a separate row containing results for each sample until the entire dataset has been analyzed.
Perform tree-ring analyses such as detrending, chronology
        building, and cross dating.  Read and write standard file formats
        used in dendrochronology.
The concordance method is a non-parametric method based on bootstrapping that is used to test the hypothesis that two subsets of time series are similar in terms of mean, variance or both. This method was developed to address a concern within dendroclimatology that young trees may produce a differing climate response to older more established trees. Details of this method are available in  Pirie, M. (2013). The Climate of New Zealand reconstructed from kauri tree rings: Enhancement through the use of novel statistical methodology. PhD. Dissertation, School of Environment and Department of Statistics, University of Auckland, New Zealand. This package also produces a figure with 3 panels, each panel is for a different climate variable. An example of this figure in included in "On the influence of tree size on the climate - growth relationship of New Zealand kauri (Agathis australis): insights from annual, monthly and daily growth patterns. J Wunder, AM Fowler, ER Cook, M Pirie, SPJ McCloskey. Trees 27 (4), 937-948". For further R functions for loading your own dendroclimatology datasets and performing dendrochronology analysis refer to the R package "dplR: Dendrochronology Program Library in R". The concordance procedure is intended to add to the standard dendrochronology techniques provided in "dplR".
A fast, consistent tool for working with data frame like objects,
    both in memory and out of memory.
An RStudio addin for teaching and learning data manipulation using the 'dplyr' package.
    You can learn each steps of data manipulation by clicking your mouse without coding.
    You can get resultant data (as a 'tibble') and the code for data manipulation.
A 'Teradata' backend for 'dplyr'. It makes it possible to operate
  'Teradata' database <https://www.teradata.com/products-and-services/teradata-database/>
  in the same way as manipulating data frames with 'dplyr'.
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.
Create, install, and summarise data packages that follow
    the Open Knowledge Foundation's Data Package Protocol.
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. 
        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. 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.
Data preprocessing techniques for classification. Functions for normalization, handling of missing values,discretization, outlier detection, feature selection, and data visualization are included.
Provides a generalized method for printing tabular data within 
	the R environment in order to make the process of presenting high quality 
	tabular output seamless for the user.  Output is directed to the R graphics 
	device so that tables can be exported to any file format supported by the 
	graphics device. Utilizes a formula interface to specify the contents of 
	tables often found in manuscripts or business reports.  In addition, formula 
	interface provides inline formatting of the numeric cells of a table and 
	renaming column labels.
Use Dirichlet process Weibull mixture model and dependent Dirichlet process Weibull mixture model for survival data with and without competing risks. Dirichlet process Weibull mixture model is used for data without covariates and dependent Dirichlet process model is used for regression data. The package is designed to handle exact/right-censored/ interval-censored observations without competing risks and exact/right-censored observations for data with competing risks. Inside each cluster of Dirichlet process, we assume a multiplicative effect of covariates as in Cox model and Fine and Gray model. In addition, we provide a wrapper for DPdensity() function from the R package 'DPpackage'. This wrapper automatically uses Low Information Omnibus prior and can model one and two dimensional data with Dirichlet mixture of Gaussian distributions.
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.
Models the relationship between dose levels and responses in a pharmacological experiment using the 4 Parameter Logistic model. Traditional packages on dose-response modelling such as 'drc' and 'nplr' often draw errors due to convergence failure especially when data have outliers or non-logistic shapes. This package provides robust estimation methods that are less affected by outliers and other initialization methods that work well for data lacking logistic shapes. We provide the bounds on the parameters of the 4PL model that prevent parameter estimates from diverging or converging to zero and base their justification in a statistical principle. These methods are used as remedies to convergence failure problems.  Gadagkar, S. R. and Call, G. B. (2015) <doi:10.1016/j.vascn.2014.08.006> Ritz, C. and Baty, F. and Streibig, J. C. and Gerhard, D. (2015) <doi:10.1371/journal.pone.0146021>.
An R-focused pipeline toolkit
  for reproducible code and high-performance computing.
Creation and use of R Repositories via helper functions 
 to insert packages into a repository, and to add repository information 
 to the current R session. Two primary types of repositories are support:
 gh-pages at GitHub, as well as local repositories on either the same machine
 or a local network. Drat is a recursive acronym: Drat R Archive Template.
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.
We present DRaWR, a network-based method for ranking genes or
    properties related to a given gene set. Such related genes or properties are
    identified from among the nodes of a large, heterogeneous network of biological
    information. Our method involves a random walk with restarts, performed on
    an initial network with multiple node and edge types, preserving more of the
    original, specific property information than current methods that operate
    on homogeneous networks. In this first stage of our algorithm, we find the
    properties that are the most relevant to the given gene set and extract a
    subnetwork of the original network, comprising only the relevant properties. We
    then rerank genes by their similarity to the given gene set, based on a second
    random walk with restarts, performed on the above subnetwork.
Feed longitudinal data into a Bayesian Latent Factor Model to obtain a low-rank representation. Parameters are estimated using a Hamiltonian Monte Carlo algorithm with STAN. See G. Weinrott, B. Fontez, N. Hilgert and S. Holmes, "Bayesian Latent Factor Model for Functional Data Analysis", Actes des JdS 2016.
Analysis of dose-response data is made available through a suite of flexible and versatile model fitting and after-fitting functions.
A penalized/non-penalized implementation for dynamic regression in the presence of autocorrelated residuals (DREGAR) using iterative penalized/ordinary least squares. It applies Mallows CP, AIC, BIC and GCV to select the tuning parameters.
A somewhat outdated package of basic and easy-to-use functions for
  fitting dose-response curves to continuous dose-response data, calculating some
  toxicological parameters and plotting the results. Please consider using
  the more powerful and actively developed 'drc' package.  Functions that are
  fitted are the cumulative density function of the log-normal 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.
Fit restricted mean models for the conditional association
  between an exposure and an outcome, given covariates. Three methods
  are implemented: O-estimation, where a nuisance model for the
  association between the covariates and the outcome is used;
  E-estimation where a nuisance model for the association
  between the covariates and the exposure is used, and doubly robust (DR)
  estimation where both nuisance models are used. In DR-estimation,
  the estimates will be consistent when at least one of the nuisance
  models is correctly specified, not necessarily both.
A tidy implementation of equations that correct for instrumental drift in 
    continuous water quality monitoring data.  There are many sources of water quality data 
    including private (ex: YSI instruments) and open source (ex: USGS and NDBC), each of 
    which are susceptible to errors/inaccuracies due to drift. This package allows the 
    user to correct their data using one or two standard reference values in a uniform, 
    reproducible way. The equations implemented are from Hasenmueller (2011)
    <doi:10.7936/K7N014KS>.
Provides a R driver for Apache Drill<https://drill.apache.org>, which could connect to the Apache Drill cluster<https://drill.apache.org/docs/installing-drill-on-the-cluster> or drillbit<https://drill.apache.org/docs/embedded-mode-prerequisites> and get result(in data frame) from the SQL query and check the current configuration status. This link <https://drill.apache.org/docs> contains more information about Apache Drill.
R codes for imputing dropout events. Many statistical methods in cell type identification, visualization and lineage reconstruction do not account for dropout events ('PCAreduce', 'SC3', 'PCA', 't-SNE', 'Monocle', 'TSCAN', etc). 'DrImpute' can improve the performance of such software by imputing dropout events.
This is a connectivity mapping-based drug repurposing tool that identifies drugs that can potentially reverse query disease phenotype or have similar functions with query drugs.
This is a collection of functions for discontinuous regression
	     analysis and image processing.
Contains quality control routines for multiplex immunoassay data, 
    including several approaches for: treating the background noise of the assay,  
    fitting the dose-response curves and estimating the limits of quantification.
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.
Drop out analysis for psychologists in a R based web application.
    Shiny is used to visualize and analyze drop outs tailored to the methods of
    online survey methodology. Concept and app presented at the SCIP Conference
    in Long Beach, California.
An Implementation of Dimensionality Reduction
    via Regression using Kernel Ridge Regression.
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').
Targeted minimum loss-based estimators of counterfactual means and
    causal effects that are doubly-robust with respect both to consistency and
    asymptotic normality (Benkeser et al (2017), <doi:10.1093/biomet/asx053>; MJ
    van der Laan (2014), <doi:10.1515/ijb-2012-0038>).
An implementation of a Machine Learning Framework for prediction of new drugs Side Effects.
    Firstly drugs are clustered with respect to their features description and secondly predictions are made, according to Bayesian scores.
    Moreover it can perform protein enrichment considering the proteins clustered together in the first step of the algorithm.
    This last tool is of extreme interest for biologist and drug discovery purposes, given the fact that it can be used either as a validation of the clusters obtained, as well as for the possible discovery of new interactions between certain side effects and non targeted pathways.
    Clustering of the drugs in the feature space can be done using K-Means, PAM or K-Seeds (a novel clustering algorithm proposed by the author).
The package performs various analyzes of descriptive statistics, including correlations
A collection of Shiny apps that allow for the simulation and
    exploration of various infectious disease transmission dynamics scenarios.
    The purpose of the package is to help individuals learn 
    about infectious disease epidemiology from a dynamical systems perspective.
    All apps include explanations of the underlying models and instructions on
    what to do with the models.
Two discretization-based Monte Carlo algorithms, namely the Fu-Wang algorithm and the Wang-Lee algorithm, are provided 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.
Tools for multivariate, linear, time-invariant,
	time series models. This 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.
An abstract DList class helps storing large list-type objects in a distributed manner. Corresponding high-level functions and methods for handling distributed storage (DStorage) and lists allows for processing such DLists on distributed systems efficiently. In doing so it uses a well defined storage backend implemented based on the DStorage class.
Datasets and functions that facilitate data analysis labs in data science courses and workshops.
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.
Density surface modelling of line transect data. A Generalized
    Additive Model-based approach is used to calculate spatially-explicit estimates
    of animal abundance from distance sampling (also presence/absence and strip
    transect) data. Several utility functions are provided for model checking,
    plotting and variance estimation.
An expressive language to facilitate simulation and visualization
    of two-dimensional dynamical systems. The basic elements of the language are
    a model wrapping around a function(x,y) which outputs a list(x
    = xprime, y = yprime), and a range. The language supports three
    types of visual objects: visualizations, features, and backgrounds. Visualizations, including dots and arrows,
    depict the behavior of the dynamical system over the entire range.
    Features display
    user-defined curves and points, and their images under the system.
    Backgrounds define and color regions of interest, such as basins of attraction.
    The language
    can also approximate attractors and their basins through simulation.
Fits inhomogeneous Poisson process spatial models
    to line transect sampling data and provides estimate of
    abundance within a region.
Perform a test of a simple null hypothesis about a 
    directly standardized rate and obtain the matching confidence 
    interval using a choice of methods.
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 generated using
    Distance software in advance <http://distancesampling.org/>) 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.
Using Dempster-Shafer Theory of Evidence, also called "Theory of Belief Functions". Basic probability assignments, or mass functions, can be defined on the subsets of a set of possible values. Two mass functions can be combined using Dempster's rule of combination. A mass function can be extended to a larger frame. Marginalization, i.e. reduction to a smaller frame can also be done. These features can be combined to analyze small belief networks and take into account situations where information cannot be satisfactorily described by probability distributions.
A collection of functions to estimate parameters of a diffusion model via a D*M analysis. Build in models are: the Ratcliff diffusion model, the RWiener diffusion model, and Linear Ballistic Accumulator models. Custom models functions can be specified as long as they have a density function.
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.
Functions for direct surrogate variable analysis, which can identify hidden factors in high-dimensional biomedical data.
Elucidation of human disease similarities has emerged as an active research area, which is highly relevant to etiology, disease classification, and drug repositioning. This package was designed and implemented for identifying disease similarities. It contains five functions which are 'DCEA', 'DCpathway', 'DS', 'comDCGL' and 'comDCGLplot'. In 'DCEA' function, differentially co-expressed genes and differentially co-expressed links are extracted from disease vs. health samples. Then 'DCpathway' function assigns differential co-expression values of pathways to be the average differential co-expression value of their component genes. Then 'DS' employs partial correlation coefficient of pathways as the disease similarity for each disease pairs. And 'DS' contains a permutation process for evaluating the statistical significant of observed disease partial correlation coefficients. At last, 'comDCGL' and 'comDCGLplot' sort out shared differentially co-expressed genes and differentially co-expressed links with regulation information and visualize them.
Data objects in R can be rendered as HTML tables using the
    JavaScript library 'DataTables' (typically via R Markdown or Shiny). The
    'DataTables' library has been included in this R package. The package name
    'DT' is an abbreviation of 'DataTables'.
Towards automation of descriptive frequencies and statistics tables.
Deconvolving cell types from high-throughput gene profiling data.
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.
Non-iterative estimator for the cumulative distribution of a doubly truncated variable. de Uña-Álvarez J. (2018) <doi:10.1007/978-3-319-73848-2_37>.
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.
This implements the data table back-end for 'dplyr' so that you
    can seamlessly use data table and 'dplyr' together.
Estimation and comparison of survival distributions of dynamic treatment regimes (DTRs) from sequentially randomized clinical trials.
Combines various decision tree algorithms, plus both
             linear regression and ensemble methods into one package.
             Allows for the use of both continuous and categorical outcomes.
             An optional feature is to quantify the (in)stability to the
             decision tree methods, indicating when results can be trusted
             and when ensemble methods may be preferential.
Dynamic treatment regimens (DTRs) are sequential decision rules tailored at each stage by time-varying subject-specific features and intermediate outcomes observed in previous stages. This package implements three methods: O-learning (Zhao et. al. 2012,2014), Q-learning (Murphy et. al. 2007; Zhao et.al. 2009) and P-learning (Liu et. al. 2014, 2015) to estimate the optimal DTRs.
Dynamic treatment regime estimation and inference via G-estimation,
    dynamic weighted ordinary least squares (dWOLS) and Q-learning. Inference
    via bootstrap and (for G-estimation) recursive sandwich estimation.
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.
Time series clustering along with optimized techniques related
    to the Dynamic Time Warping distance and its corresponding lower bounds.
    Implementations of partitional, hierarchical, fuzzy, k-Shape and TADPole
    clustering are available. Functionality can be easily extended with
    custom distance measures and centroid definitions. Implementations of
    DTW barycenter averaging and a distance based on global alignment kernels
    are also provided. All included distance functions have custom loops
    optimized for the calculation of cross-distance matrices, including
    parallelization support. Several cluster validity indices are included.
Provides an implementation of the Time-Weighted Dynamic Time
    Warping (TWDTW) method for land cover mapping using satellite image time series.
    TWDTW is based on the Dynamic Time Warping technique and has achieved high
    accuracy for land cover classification using satellite data. The method is
    based on comparing unclassified satellite image time series with a set of known
    temporal patterns (e.g. phenological cycles associated with the vegetation).
    Using 'dtwSat' the user can build temporal patterns for land cover types, apply
    the TWDTW analysis for satellite datasets, visualize the results of the time
    series analysis, produce land cover maps, create temporal plots for land cover
    change, and compute accuracy assessment metrics.
Functions to analyze multiple choice data using Dual Scaling
Provides an operator for assigning nested components of a list to
  names via a concise, Haskell-like pattern matching syntax. This is especially
  convenient for assigning individual names to the multiple values that a
  function may return in the form of a list, and for extracting deeply nested
  list components.
Programmatic access to the DuckDuckGo Instant Answer API <https://api.duckduckgo.com/api>.
Expands factors, characters and other eligible classes
        into dummy/indicator variables.
Efficiently create dummies of all factors and character vectors in a data frame. Support is included for learning the categories on one data set (e.g., a training set) and deploying them on another (e.g., a test set).
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.
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
Functionality for analyzing dose-volume histograms (DVH)
        in radiation oncology: Read DVH text files, calculate DVH
        metrics as well as generalized equivalent uniform dose (gEUD),
        biologically effective dose (BED), equivalent dose in 2 Gy
        fractions (EQD2), normal tissue complication probability
        (NTCP), and tumor control probability (TCP). Show DVH
        diagrams, check and visualize quality assurance constraints
        for the DVH. Includes web-based graphical user interface.
Faster versions of base R functions (e.g. mean, standard deviation, covariance, weighted mean), mostly written in C++, along with miscellaneous functions for various purposes (e.g. create histogram with fitted probability density function or probability mass function curve, create body mass index groups, assess linearity assumption in logistic regression).
Provides access to Dataverse version 3 APIs, enabling access to archived data (and metadata), and the ability to create and manipulate studies in a user's dataverse(s). For Dataverse server versions >= 4.0, please use the dataverse package <https://github.com/IQSS/dataverse-client-r> instead.
A set of wrapper functions for 'data.world' REST API endpoints <https://apidocs.data.world>.
Solving large scale distance weighted discrimination.
  The main algorithm is a symmetric Gauss-Seidel based alternating direction method of multipliers (ADMM) method. See Lam, X.Y., Marron, J.S., Sun, D.F., and Toh, K.C. (2018) <arXiv:1604.05473> for more details.
Infers networks with hubs using degree weighted Lasso method.
Regression for a discrete response, where the conditional distribution is modelled via a discrete Weibull distribution.
Contains functions for dyadic network analysis.
An R interface to the 'dygraphs' JavaScript charting library
    (a copy of which is included in the package). Provides rich facilities
    for charting time-series data in R, including highly configurable
    series- and axis-display and interactive features like zoom/pan and
    series/point highlighting.
Solves quadratic programming problems using Richard L. Dykstra's cyclic projection algorithm. Routine allows for a combination of equality and inequality constraints. See Dykstra (1983) <doi:10.1080/01621459.1983.10477029> for details.
Add a "Did You Mean" feature to the R interactive. With this
    package, error messages for misspelled input of variable names or package names
    suggest what you really want to do in addition to notification of the mistake.
Time series regression.  The dyn class interfaces ts,
        irts(), 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.
Fits localized GP model for dynamic computer experiments via singular value decomposition of the response matrix Y for large N (the number of observations) using the algorithm proposed by Zhang et al. (2018) <arXiv:1611.09488>. The current version only supports 64-bit version of R.
Interactive graphical tool for manipulating graphs
Contains functions that lets you fit dynamic hazard models using 
  state space models. The first implemented model is described in Fahrmeir 
  (1992) <doi:10.1080/01621459.1992.10475232> and Fahrmeir (1994) 
  <doi:10.1093/biomet/81.2.317>. Extensions hereof are available where the  
  Extended Kalman filter is replaced by an unscented Kalman filter and other 
  options including particle filters.
Contains methods for detection of clusters in hierarchical clustering dendrograms.
An implementation of the method proposed in Lund and Hansen (2018) for fitting 3-dimensional dynamical array models.  The implementation is based on the glamlasso package, see Lund et al. (2017) <doi:10.1080/10618600.2017.1279548>, for efficient design matrix free lasso regularized estimation in a generalized linear array model. The implementation uses a block relaxation scheme to fit each individual component in the model using functions from the glamlasso package.
A native R implementation and enhancement of the Dynamic TOPMODEL
    semi-distributed hydrological model. Includes some preprocessing, utility and
    routines for displaying outputs.
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 and classical biplot. Try matrices
    of 2-way and 3-way. The GUI can be run in multiple languages.
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.
While there are many well-established measures for identifying critical fluctuations and phase transitions, these measures only work with many points of measurement and thus are unreliable when studying short and coarse-grained time series. This package provides a measure for complexity in a time series that does not rely on long time series (Kaiser (2017), <doi:10.17605/OSF.IO/GWTKX>).