c(person(given = "Arya", family = "Poddar", role = c("aut", "cre"), email = "aryapoddar290990@gmail.com"),
             person(given = "Aiana", family = "Goyal", role = "ctb", email = "aianagoel002@gmail.com"),
             person(given = "Kanishk", family = "Dogar", role = "ctb", email = "kanishkd4@gmail.com"))
c(person("Ruida", "Zhong", email = "zrd2017@163.com", role = c("aut", "cre")),
    person("Xiaohong", "Chen", email = "eescxh@mail.sysu.edu.cn", role = c("aut", "ctb")),
    person("Zhaoli", "Wang", email = "wangzhl@scut.edu.cn", role = c("aut", "ctb")),
    person("Chengguang", "Lai", email = "laichg@scut.edu.cn", role = c("aut", "ctb")),
    person("Steve", "Goddard", role = c("ctb")),
    person("Nathan", "Wells", role = c("ctb")),
    person("Mike", "Hayes", role = c("ctb")))
person("Sagie", "Gur-Ari", email = "sagiegurari@gmail.com", role = c("aut", "cre"))
c(
    person("Thomas", "Sibley", email = "trsibley@uw.edu", role = c("aut", "cre")),
    person("University of Washington", role = c("cph")) )
c(
    person('Paul', 'Staab', , 'develop@paulstaab.de', role=c('aut', 'cre', 'cph')),
    person('Zhu', 'Sha', role=c('aut', 'cph')),
    person('Dirk', 'Metzler', role=c('aut', 'cph', 'ths')),
    person('Gerton', 'Lunter', role=c('aut', 'cph', 'ths'))
    )
c( person("Florian", "Schwendinger", role = c("ctb", "cre"), email = "FlorianSchwendinger@gmx.at"),
              person("Brendan", "O'Donoghue", role = c("aut", "cph")),
              person("Timothy A.", "Davis", role = "cph"),
              person("Patrick R.", "Amestory", role = "cph"),
              person("Iain S.", "Duff", role = "cph") )
c(person("Adrian", "Baddeley", role = c("aut", "cre"),
		     email = "Adrian.Baddeley@curtin.edu.au"),
	      person("Vittorio", "Broglio", role = c("ctb","dtc")),
	      person(c("Pedro", "Antonio"), "Neves", role = c("ctb", "dtc")),
	      person("Andrew", "Bassom", role = "ctb"),
	      person("Peter", "Buzzacott", role = "ctb"))
c(
    person("Olatunji", "Johnson", email = "olatunjijohnson21111@gmail.com", role = c("aut", "cre")),
    person("Emanuele", "Giorgi", email = "e.giorgi@lancaster.ac.uk", role = "aut"),
    person("Peter", "Diggle", email = "p.diggle@lancaster.ac.uk", role = "aut"))
c(
  person("Matthias", "Templ", email="matthias.templ@gmail.com", role = c("aut", "cre")),
  person("Bernhard", "Meindl", email = "Bernhard.Meindl@statistik.gv.at", role = c("aut")),
  person("Alexander", "Kowarik", email="Alexander.Kowarik@statistik.gv.at", role = "aut"))
c(person("Angel M.", "Garcia", email = "amgv0009@red.ujaen.es", role = c("aut", "cre")),
    person("Pedro", "Gonzalez", email = "pglez@ujaen.es", role = c("aut", "cph")),
    person("Cristobal J.", "Carmona", email = "ccarmona@ujaen.es", role = c("aut", "cph")),
    person("Francisco", "Charte", email = "francisco@fcharte.com", role = "ctb"),
    person("Maria J.", "del Jesus", email = "mjjesus@ujaen.es", role = c("aut", "cph"))
    )
c(
    person("Guillaumot", "Charlene", email ="charleneguillaumot21@gmail.com",role=c("aut","cre")),
    person("Martin","Alexis",email = "alexis.martin@mnhn.fr",role = "aut"),
    person("Eleaume","Marc",email = "marc.eleaume@mnhn.fr",role = "aut"),
    person("Saucede","Thomas",email = "thomas.saucede@u-bourgogne.fr",role = "aut"))
c(
    person("Samuel", "Bosch", email = "mail@samuelbosch.com", role = c("aut", "cre")),
    person("Lennert", "Tyberghein", email = "lennert.tyberghein@vliz.be", role = c("ctb")),
    person("Olivier", "De Clerck", email = "olivier.declerck@ugent.be", role = c("ctb")))
c(
             person(given="Xiaoquan", family="Kong", email="u1mail2me@gmail.com", role=c("aut", "cre", "cph")),
             person(given="Renyan", family="Duan", role=c("ths")),
             person(given="Minyi", family="Huang", role=c("ths"))
             )
c(person(given = "Nadja", family = "Klein", role = c("aut", "cre"), email = "nadja.klein@hu-berlin.de"))
c(person(given  = 'Benjamin',
                    family = 'Baumgartner',
                    email  = 'benjamin@baumgrt.com',
                    role   = c('aut', 'cre')),
             person(given  = 'Riccardo',
                    family = 'Gatto',
                    email  = 'gatto@stat.unibe.ch',
                    role   = c('ctb', 'ths')),
             person(given  = 'Sebastian',
                    family = 'Szugat',
                    email  = 'szugat@statistik.tu-dortmund.de',
                    role   = c('ctb')))
c(person("P. Joser", "Atauchi", role = c("aut", "cre"),
                     email = "patauchi@gmail.com"))
person("Ali", "Uenlue", email = "ali.uenlue@icloud.com",
    role = c("aut", "cre"))
c(
    person("Scott", "Chamberlain", role = c("aut", "cre"),
      email = "myrmecocystus+r@gmail.com"),
    person("Robert Scott", "Reis", email = "reis.robert.s@gmail.com", role="aut")
  )
c(person("Jean-Pierre", "Gattuso", role = c("aut", "cre", "cph"), email = "gattuso@obs-vlfr.fr"),
  person("Jean-Marie", "Epitalon", role = "aut", email = "Jean-Marie.Epitalon@orange.fr"),
  person("Heloise", "Lavigne", role = "aut", email = "lavigne@obs-vlfr.fr"),
  person("James", "Orr", role = "aut"),
  person("Bernard", "Gentili", role = "ctb"),
  person("Mathilde", "Hagens", role = "ctb"),
  person("Andreas", "Hofmann", role = "ctb"),
  person("Aurélien", "Proye", role = "ctb"),
  person("Karline", "Soetaert", role = "ctb"),
  person("James", "Rae", role = "ctb")
  )
c(person("Mark", "Edmondson",email = "r@sunholo.com",
                  role = c("aut", "cre")),
           person("Jennifer", "Bryan",email="jenny@stat.ubc.ca", role = "ctb"),
           person("Parzakonis", "Manos",email="parzakonis,m@gmail.com", role = "ctb"))
c(person("James", "Balamuta",
                    email = "balamut2@illinois.edu", role = c("aut", "cre"),
                    comment = c(ORCID = "0000-0003-2826-8458")))
person("Mike", "Toews", role = c("aut", "cre"),
                  email = "mwtoews@gmail.com")
c(person("Anne Corinne", "Huggins-Manley", role = c("aut", "cre"), email = "ahuggins@coe.ufl.edu"), person("Douglas", "Whitaker", role = "aut"))
c(person("Gábor", "Csárdi", role=c("aut", "cre"), 
             email="csardi.gabor@gmail.com"),
    person("Andrie", "de Vries", role=c("aut"), 
            email="apdevries@gmail.com"))
c(
    person("Bob", "Rudis", email = "bob@rud.is", role = c("aut", "cre"), 
           comment = c(ORCID = "0000-0001-5670-2640")),
    person("SEOmoz", "Inc", role = c("aut", "cph"),
           comment = "url-cpp library, <https://github.com/seomoz/url-cpp>")
  )
c(person("Justin", "de Benedictis-Kessner", email = "justindbk@gmail.com", role = c("aut", "cre")),
  person("Christian", "Lemp", email = "christianlemp@gmail.com", role = c("ctb")))
c(person("Remi", "Patin", role = c("aut", "cre"), email = "remi.patin@normale.fr"),
             person("Marie-Pierre", "Etienne", role = "aut"), 
             person("Emilie", "Lebarbier", role = "aut"), 
             person("Simon", "Benhamou", role = "aut"))
c(person("Frank","Papenmeier", role=c("aut","cre"),
    email="frank.papenmeier@uni-tuebingen.de"),
    person("Konstantin","Sering", role="ctb"))
c(person(given = c("Vito","M.","R."), family = "Muggeo", role =
        c("aut", "cre"), email = "vito.muggeo@unipa.it"))
person("Benjamin", "Elbers", email = "be2239@columbia.edu", 
  role = c("aut", "cre"), comment = c(ORCID = "0000-0001-5392-3448"))
c(
    person("Jonathan", "Callahan", email="jonathan@mazamascience.com", role=c("aut")),
    person("Rob", "Casey", email="rob@iris.washington.edu", role=c("aut")),
    person("Mary", "Templeton", email="met@iris.washington.edu", role=c("aut")),
    person("Gillian","Sharer", email="gillian@iris.washington.edu", role=c("aut","cre"))
    )
c(
    person("Daniel", "Laughlin", email = "danielclaughlin@gmail.com", role = c("cre","aut")),
    person("Loic", "Chalmandrier", email = "lchalman@uwyo.edu", role = "aut"))
person("Jason", "Richardson", email = "jcricha3@mail.usf.edu", role = c("aut", "cre"))
c(person("Simon", "Potter",
                    role = c("aut", "trl", "cre"),
                    email = "simon@sjp.co.nz"),
             person("Simon", "Sapin", role = "aut"),
             person("Ian", "Bicking", role = "aut"))
c(
    person("John", "Harrison", , "johndharrison0@gmail.com", role = c("aut", "cre")))
c(
        person("Ruichu","Cai",email="cairuichu@gmail.com",role=c("ths","aut")),
        person("Jie","Qiao",email="qiaojie2004@vip.qq.com",role=c("aut","cre")),
        person("Zhenjie","Zhang",email="zhenjie@adsc.com.sg",role=c("ths","aut")),
        person("Zhifeng","Hao",email="zfhao@gdut.edu.cn",role=c("ths","aut"))
        )
person(given = "Frank",family = "Technow",		
           email = "Frank.Technow@pioneer.com",
           comment = "Pioneer Hi-Bred International, Inc., Johnston, Iowa", role = c("aut","cre"))
c(person("John", "Fox", role = c("aut", "cre"), email = "jfox@mcmaster.ca"),
	person("Zhenghua", "Nie", role = "aut", email="zhenghua.nie@gmail.com"),
    person("Jarrett", "Byrnes", role = "aut", email="byrnes@nceas.ucsb.edu"),
    person("Michael", "Culbertson", role = "ctb"),
    person("Saikat", "DebRoy", role = "ctb"),
    person("Michael", "Friendly", role = "ctb"),
    person("Benjamin", "Goodrich", role = "ctb"),
    person(given="Richard H.", family="Jones", role = "ctb"),
    person("Adam", "Kramer", role = "ctb"),
    person("Georges", "Monette", role = "ctb"),
    person("R-Core", role = "ctb")
	)
c(person("Michal", "Maj", email = "michal.maj@appsilondatascience.com",
  role = "aut"),
  person("Dominik", "Krzeminski", email = "dominik@appsilondatascience.com",
  role = "cre"),
  person(family = "Appsilon Data Science", role = "cph"))
c(person("Patrick", "Mair", role = c("aut", "cre"), email = "mair@fas.harvard.edu"), person("Jose Fernando", "Vera", role = "aut", email = "jfvera@ugr.es"))
c(person("Rina", "Foygel Barber", role=c("aut"), 
                   email="rina@uchicago.edu"), 
            person("Mathias", "Drton", role=c("aut"), 
                   email="md5@uw.edu"), 
            person("Luca", "Weihs", role=c("cre","aut"), 
                   email="lucaw@uw.edu"))
c(person("Soumya", "Ray",
              email = "soumya.ray@gmail.com", role = c("aut", "ths")),
             person("Nicholas", "Danks",
              email = "nicholasdanks@hotmail.com", role = c("aut","cre")),
             person("Juan Manuel Velasquez", "Estrada", role = "aut"))
c(person("Armin", "Monecke", role=c("aut", "cph","cre"),
        email="armin.monecke@stat.uni-muenchen.de"),
        person("Friedrich", "Leisch", role=c("aut", "ths")))
person("Remi", "Bacha", email = "hello@remibacha.com", role = c("aut", "cre"))
c(person("Andreas M. Brandmaier", email="andy@brandmaier.de", role=c("aut","cre")), person("John J. Prindle", email="jprindle@usc.edu",role=c("aut")))
c(
    person("John", "Harrison", , "johndharrison0@gmail.com",
      role = c("aut", "cre"), comment = "R package semantic"),
    person("Marko", "Živanović", role = c("aut", "cph"),
      comment = "semver cpp library, https://github.com/zmarko/semver"))
c(person("Olaf", "Mersmann", role=c("aut", "cre"),
        email="olafm@p-value.net"), person("Quinn", "Weber",
        role="ctb"))
c(
    person("Gustavo","Paterno", email = "paternogbc@gmail.com", role = c("cre","aut")),
    person("Gijsbert", "Werner", email = "gijsbert.werner@zoo.ox.ac.uk", role = "aut"),
    person("Caterina", "Penone", email = "caterina.penone@gmail.com", role = "aut"),
    person("Pablo", "Martinez", email = "pablo_sc82@hotmail.com", role = "ctb"))
c(person("Alexandra", "Kuznetsova", role = c("aut"),
                          email = "alku@dtu.dk"),
                   person("Per", "Bruun Brockhoff", role = c("aut", "ths", "cre"),
                   email = "perbb@dtu.dk"),
                   person("Rune", "Haubo Bojesen Christensen", 
                   role = c("aut"),
                   email = "Rune@ChristensenStatistics.dk"))
c(person(given="Rune Haubo Bojesen", family="Christensen",
	   email="Rune@ChristensenStatistics.dk", role=c("aut")),
	   person(given="Per Bruun", family="Brockhoff",
	   email="perbb@dtu.dk", role=c("aut", "cre")),
	   person(given="Alexandra", family="Kuznetsova", role = "ctb"),
	   person(given="Sophie", family="Birot", role = "ctb"),
	   person(given="Karolina Amelia", family="Stachlewska", role = "ctb"))
c(person("Stefan", "Feuerriegel", email="sentiment@sfeuerriegel.com",
                    role=c("aut", "cre")),
             person("Nicolas", "Proellochs", email="nicolas.proellochs@is.uni-freiburg.de",
                    role=c("aut")))
c(person("Tyler", "Rinker", email = "tyler.rinker@gmail.com", role =
         c("aut", "cre")))
c(person("David", "Ardia", email = "david.ardia@unine.ch", role = c("aut")),
  person("Keven", "Bluteau", email = "keven.bluteau@unine.ch", role = c("aut")),
  person("Samuel", "Borms", email = "samuel.borms@unine.ch", role = c("aut", "cre")),
  person("Kris", "Boudt", email = "kris.boudt@vub.be", role = c("aut")))
c(
    person("Ameur", "Manceur", role = "aut",
    email = "ameur.manceur@mail.mcgill.ca"),
    person("Timothy", "Schwinghamer", role = c("aut", "cre"),
    email = "schwinghamer@hotmail.com"),
    person("Pierre", "Dutilleul", role = c("aut", "cph"),
    email = "pierre.dutilleul@mcgill.ca", ))
c(person("Delphine", "Charif", role = "aut"),
             person("Olivier", "Clerc", role = "ctb"),
             person("Carolin", "Frank", role = "ctb"),
             person(c("Jean","R."), "Lobry", role = c("aut", "cph")),
             person("Anamaria", "Necşulea", role = "ctb"),
             person("Leonor", "Palmeira", role = "ctb"),
             person("Simon", "Penel", role = "cre", email = "simon.penel@univ-lyon1.fr"),
             person("Guy", "Perrière", role = "ctb"))
c(person("Xiaowei", "Zhan", email = "zhanxw@gmail.com", role = c("aut", "cre")),
           person("Dajiang", "Liu", email = "dajiang.liu@gmail.com", role = "aut"),
           person("Attractive Chaos", email = "attractor@live.co.uk", role = "cph", comment = "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."),
           person("Broad Institute / Massachusetts Institute of Technology", role = "cph"),
           person("Genome Research Ltd (GRL)", role = "cph"))
c(person(given = "Laurent", family = "Gatto",
	            email = "lg390@cam.ac.uk",
 		    role = c("aut", "cre")),
             person(given = "Robert", family ="Stojnic",
  	            email = "rs550@cam.ac.uk",
		    role = "aut"))
c(person("Judith", "Maro", role=c("aut", "cre"), email = "judy_maro@harvardpilgrim.org"), person("Laura", "Hou", role="aut", email="laura_hou@harvardpilgrim.org"))
person("Jisca", "Huisman", email = "jisca.huisman@gmail.com",
    role = c("aut", "cre"))
c(person("Bob", "Rudis", email = "bob@rud.is", role = c("aut", "cre")),
             person("Edward", "Visel", email = "edward.visel@gmail.com", role = "ctb"))
c(
	person("Michael", "Hahsler", role = c("aut", "cre", "cph"),
	    email = "mhahsler@lyle.smu.edu"),
	person("Christian", "Buchta", role = c("aut", "cph")),
	person("Kurt", "Hornik", role = c("aut", "cph")),
	person("Fionn", "Murtagh", role = c("ctb", "cph")),
	person("Michael", "Brusco", role = c("ctb", "cph")),
	person("Stephanie", "Stahl", role = c("ctb", "cph")),
	person("Hans-Friedrich", "Koehn", role = c("ctb", "cph")))
c(
  person(given = "Peter", family = "Teunis", email = "p.teunis@emory.edu", role = c("aut"), comment = "Author of the method and original code."),
  person(given = "Jan", family = "van Eijkeren", role = c("aut"), comment = "Author of the method and original code."), 
  person(given = "Daniel", family = "Lewandowski", email = "daniel@nextpagesoft.net", role = c("com", "ctb"), comment = "Creator of the R package."), 
  person(given = "Chantal", family = "Quinten", email = "seroincidence@ecdc.europa.eu", role = c("cre", "ctb"), comment = "Project manager and the package maintainer."))
c(
    person("Matt", "Moores", role = c("aut", "cre"), email = "mmoores@gmail.com", comment = c(ORCID = "0000-0003-4531-3572")),
    person("Jake", "Carson", role="aut", email="Jake.Carson@warwick.ac.uk"),
    person("Mark", "Girolami", role="aut", email="M.Girolami@warwick.ac.uk"),
    person("Engineering and Physical Sciences Research Council", role="fnd", comment="EPSRC programme grant ref: EP/L014165/1"),
    person("University of Warwick", role="cph"))
c(
  person("Yihui", "Xie", role = c("aut", "cre"), email = "xie@yihui.name", comment = c(ORCID = "0000-0003-0645-5666")),
  person("Carson", "Sievert", role = "ctb"),
  person("Jesse", "Anderson", role = "ctb"),
  person("Ramnath", "Vaidyanathan", role = "ctb"),
  person()
  )
c(person("Eric", "Lamb", role = c("aut", "cre"),email =
    "eric.lamb@usask.ca"),person("Kerrie", "Mengersen", role =
    "aut"),person("Katherine", "Stewart", role = "aut"),person("Udayanga",
    "Attanayake", role = "aut"),person("Steven", "Siciliano", role = "aut"))
c(person(given = "David", family = "Meyer", role = c("aut", "cre"), email = "David.Meyer@R-project.org"),
             person(given = "Kurt", family = "Hornik", role = c("aut")),
             person(given = "Christian", family = "Buchta", role = "ctb"))
person("Richard", "Cotton", email = "richierocks@gmail.com", role = c("aut", "cre"))
c(
  person(given = 'Rahul', family = 'Satija', email = 'rsatija@nygenome.org', role = 'aut', comment = c(ORCID = '0000-0001-9448-8833')),
  person(given = 'Andrew', family = 'Butler', email = 'abutler@nygenome.org', role = 'aut', comment = c(ORCID = '0000-0003-3608-0463')),
  person(given = 'Paul', family = 'Hoffman', email = 'seuratpackage@gmail.com', role = c('aut', 'cre'), comment = c(ORCID = '0000-0002-7693-8957')),
  person(given = 'Jeff', family = 'Farrell', email = 'jfarrell@g.harvard.edu', role = 'ctb'),
  person(given = 'Shiwei', family = 'Zheng', email = 'szheng@nygenome.org', role = 'ctb', comment = c(ORCID = '0000-0001-6682-6743')),
  person(given = 'Christoph', family = 'Hafemeister', email = 'chafemeister@nygenome.org', role = 'ctb', comment = c(ORCID = '0000-0001-6365-8254')),
  person(given = 'Patrick', family = 'Roelli', email = 'proelli@nygenome.org', role = 'ctb')
  )
c(person(given = "Edzer",
             family = "Pebesma",
             role = c("aut", "cre"),
             email = "edzer.pebesma@uni-muenster.de",
             comment = c(ORCID = "0000-0001-8049-7069")),
      person(given = "Roger",
             family = "Bivand",
             role = "ctb",
             comment = c(ORCID = "0000-0003-2392-6140")),
      person(given = "Etienne",
             family = "Racine",
             role = "ctb"),
      person(given = "Michael",
             family = "Sumner",
             role = "ctb"),
      person(given = "Ian",
             family = "Cook",
             role = "ctb"),
      person(given = "Tim",
             family = "Keitt",
             role = "ctb"),
      person(given = "Robin",
             family = "Lovelace",
             role = "ctb"),
      person(given = "Hadley",
             family = "Wickham",
             role = "ctb"),
      person(given = "Jeroen",
             family = "Ooms",
             role = "ctb",
             comment = c(ORCID = "0000-0002-4035-0289")),
      person(given = "Kirill",
             family = "Müller",
             role = "ctb"))
c(person("Yann", "Desjeux", , "yann.desjeux@inra.fr", c("aut", "cre")),
  person("Laure", "Latruffe", role = "aut"), person("Alain", "Carpentier", role = "ctb"))
person("Hu", "Sheng", email = "shenghu@nju.edu.cn",
    role = c("aut", "cre"))
c( person("Kevin", "Titeca", email = "sfinxinteractomics@gmail.com", role = c("aut", "cre")),
              person("Jan", "Tavernier", role = "ths"),
              person("Sven", "Eyckerman", role = "ths"))
c(
	   person("Matteo", "Seminaroti",
	          role = c("aut", "cph"),
		  email = "matteo.seminaroti@gmail.com"),
	   person("Utz-Uwe", "Haus", role = c("aut", "cre", "cph"),
	          email = "uhaus@cray.com"),
	   person("Monique", "Laurent", role = c("ctb"),
	          email = "monique.laurent@cwi.nl")
	)
c(person("Martin","Maechler", role=c("aut","cre"), email="maechler@stat.math.ethz.ch")
	   , person("Werner", "Stahel", role = "ctb",
	   	   comment = "Functions: compresid2way(), f.robftest(), last(), p.scales(), p.dnorm()")
	   , person("Andreas", "Ruckstuhl", role = "ctb",
	   	   comment = "Functions: p.arrows(), p.profileTraces(), p.res.2x()")
	   , person("Christian", "Keller", role = "ctb",
	   	   comment = "Functions: histBxp(), p.tachoPlot()")
	   , person("Kjetil", "Halvorsen", role = "ctb",
	   	   comment = "Functions: KSd(), ecdf.ksCI()")
	   , person("Alain", "Hauser", role = "ctb",
	   	   comment = "Functions: cairoSwd(), is.whole(), toLatex.numeric()*")
	   , person("Christoph", "Buser", role = "ctb",
	   	   comment = "to function Duplicated()")
	   , person("Lorenz", "Gygax", role = "ctb",
	   	   comment = "to function p.res.2fact()")
	   , person("Bill", "Venables", role = "ctb",
	   	   comment = "Functions: empty.dimnames(), primes()")
	   , person("Tony", "Plate", role = "ctb", comment = "to inv.seq()")
	   # minor contributors:
	   , person("Isabelle", "Flückiger", role = "ctb")
	   , person("Marcel", "Wolbers", role = "ctb")
	   , person("Markus", "Keller", role = "ctb")
	   , person("Sandrine", "Dudoit", role = "ctb")
	   , person("Jane", "Fridlyand", role = "ctb")
	   , person("Greg", "Snow", role = "ctb", comment = "to loessDemo()")
	   , person("Henrik Aa.", "Nielsen", role = "ctb", comment = "to loessDemo()")
	   , person("Vincent", "Carey", role = "ctb")
	   , person("Ben", "Bolker", role = "ctb")
	   , person("Philippe", "Grosjean", role = "ctb")
	   , person("Frédéric", "Ibanez", role = "ctb")
	   , person("Caterina", "Savi", role = "ctb")
	   , person("Charles", "Geyer", role = "ctb")
	   , person("Jens", "Oehlschlägel", role = "ctb")
 )
c(
    person("Balasubramanian", "Narasimhan",
    email = "naras@stat.stanford.edu", role = c("aut", "cre")),
    person("Mei-Chiung", "Shih",
    email = "meichiun@stanford.edu", role = "aut"))
c(
    person("Jack", "Baker", email = "j.baker1@lancaster.ac.uk", role = c("aut", "cre", "cph")),
    person( "Christopher", "Nemeth", role = c("aut", "cph") ), 
    person( "Paul", "Fearnhead", role = c( "aut", "cph" ) ),
    person( "Emily B.", "Fox", role = c("aut", "cph") ), 
    person( "STOR-i", role = c( "cph" ) ))
c(person(given=c("Damian", "W."), family="Betebenner", email="dbetebenner@nciea.org", role=c("aut", "cre")),
            person(given=c("Adam", "R."), family="Van Iwaarden", email="avaniwaarden@gmail.com", role="aut"),
            person(given="Ben", family="Domingue", email="ben.domingue@gmail.com", role="aut"))
c(
    person("Michael", "Dorman", email = "dorman@post.bgu.ac.il", role = c("aut", "cre")),
    person("Evyatar", "Erell", email = "erell@bgu.ac.il", role = c("ctb")),
    person("Itai", "Kloog", email = "ikloog@gmail.com", role = c("ctb")),
    person("Adi", "Vulkan", email = "vulkan@post.bgu.ac.il", role = c("ctb")))
c(person("Lisa Anne", "Libungan", role = c("aut", "cre"), 
  email = "lisa.libungan@gmail.com"),
  person("Snaebjorn", "Palsson", role = c("aut", "ths"),
  email = "snaebj@hi.is"))
c(person("Mohamed", "Uduman", role=c("aut"), 
                    email="mohamed.uduman@yale.edu"),
             person("Gur", "Yaari", role=c("aut"), 
                    email="gur.yaari@biu.ac.il"),
             person("Namita", "Gupta", role=c("aut"), 
                    email="namita.gupta@yale.edu"),
             person("Jason", "Vander Heiden", role=c("aut", "cre"), 
                    email="jason.vanderheiden@yale.edu"),
             person("Ang", "Cui", role=c("ctb"), 
                    email="angcui@mit.edu"),
             person("Susanna", "Marquez", role=c("ctb"), 
                    email="susanna.marquez@yale.edu"),
             person("Julian", "Zhou", role=c("ctb"), 
                    email="julian.zhou@yale.edu"),
             person("Nima", "Nouri", role=c("ctb"), 
                    email="nima.nouri@yale.edu"),
             person("Steven", "Kleinstein", role=c("aut", "cph"), 
                    email="steven.kleinstein@yale.edu"))
c(person("Andrey A", "Shabalin",
                    role = c("aut", "cre"),
                    email = "andrey.shabalin@gmail.com",
                    comment = c(ORCID = "0000-0003-0309-6821")),
            person("Edwin J C G", "van den Oord",
                    email = "ejvandenoord@vcu.edu",
                    role = "aut"))
c(person("Dominik", "Krzemiński",
                    email = "dominik@appsilon.com",
                    role = c("cre", "aut"))
            )
person("Filip", "Stachura", email = "filip@appsilondatascience.com",
  role = c("aut", "cre"))
c(
  person("Vincent", "Nijs", role = c("aut", "cre"), email = "radiant@rady.ucsd.edu"),
  person("Forest", "Fang", role = "aut", email = "forest.fang@outlook.com"),
  person(family = "Trestle Technology, LLC", role = "aut", email = "cran@trestletechnology.net"),
  person("Jeff", "Allen", role = "aut", email = "cran@trestletechnology.net"),
  person(family = "Institut de Radioprotection et de Surete Nucleaire", role = c("cph"), email = "yann.richet@irsn.fr"),
  person(family = "Ajax.org B.V.", role = c("ctb", "cph"), comment = "Ace"))
person("William", "Murphy", email = "william.murphy.rd@gmail.com",
                  role = c("cre", "aut"))
c(
    person("Dean", "Attali", email = "daattali@gmail.com",
      role = c("aut", "cre"), comment = "R interface"),
    person("Tristan", "Edwards", role = c("aut"),
       comment = "sweetalert library")
    )
c(
    person("Winston", "Chang", role = c("aut", "cre"), email = "winston@rstudio.com"),
    person(family = "RStudio", role = "cph"),
    person("Mark", "Otto", role = "ctb",
    comment = "Bootstrap library"),
    person("Jacob", "Thornton", role = "ctb",
    comment = "Bootstrap library"),
    person(family = "Bootstrap contributors", role = "ctb",
    comment = "Bootstrap library; authors listed at https://github.com/twbs/bootstrap/graphs/contributors"),
    person(family = "Twitter, Inc", role = "cph",
    comment = "Bootstrap library"),
    person("Brian", "Reavis", role = c("ctb", "cph"),
    comment = "selectize.js library"),
    person("Egor", "Khmelev", role = c("ctb", "cph"),
    comment = "jslider library"),
    person(family = "SpryMedia Limited", role = c("ctb", "cph"),
    comment = "DataTables library")
    )
c(person("Andras","Sali",email="andras.sali@alphacruncher.hu",role=c("aut","cre"),comment="Creator of Shiny-wrapper code"),person("Luke","Hass",role=c("ctb","cph"),comment="Author of included CSS loader code"))
c(
    person("Winston", "Chang", role = c("aut", "cre"), email = "winston@rstudio.com"),
    person("Barbara", "Borges Ribeiro", role = "aut", email = "barbara@rstudio.com"),
    person(family = "RStudio", role = "cph"),
    person(family = "Almasaeed Studio", role = c("ctb", "cph"), comment = "AdminLTE theme for Bootstrap"),
    person(family = "Adobe Systems Incorporated", role = c("ctb", "cph"), comment = "Source Sans Pro font")
    )
c(
  person("David", "Granjon", email = "dgranjon@ymail.com", role = c("aut", "cre")),
  person(family = "Almasaeed Studio", role = c("ctb", "cph"), comment = "AdminLTE2 theme for Bootstrap 3"),
  person("Winston", "Chang", role = c("ctb", "cph"), comment = "Functions from shinydashboard"),
  person("Petr", "Tichy", role = c("ctb", "cph"), comment = "CSS preloader"),
  person("Victor", "Perrier", email = "victor.perrier@dreamrs.fr", role = "ctb",
          comment = "improved the shinydashboardPlusGallery"))
person("Andy", "Merlino", email = "merlinoa88@gmail.com", role =
    c("aut", "cre"))
c(person(given = "Thomas Lin",
             family = "Pedersen",
             role = c("cre", "aut"),
             email = "thomasp85@gmail.com"),
      person(given = "Vincent",
             family = "Nijs",
             role = "aut"),
      person(given = "Eric",
             family = "Nantz",
             role = "aut"))
c(
              person(given = 'Jonathan',family = 'Sidi',email = 'yonicd@gmail.com',role = c('aut','cre')),
              person(given = 'Tal',family = 'Galili',email = 'tal.galili@metrumrg.com',role = c('aut'))
              )
person("Chris", "Mason-Thom", email = "christopher.w.thom@outlook.com", role=c("aut","cre"))
c(person("Alexander", "Fu", email = "aafu@ucdavis.edu",
        role = c("aut")),
     person("Ariel", "Shin", email = "arishin@ucdavis.edu",
        role = c("aut")),
     person("Norm", "Matloff", email = "nsmatloff@ucdavis.edu",
        role = c("aut", "cre")))
c(person("Patricia", "Martinkova", role = c("aut", "cre"), email = "martinkova@cs.cas.cz"), person("Adela", "Drabinova", role = c("aut")), person("Ondrej", "Leder", role = "aut"), person("Jakub", "Houdek", role = "aut"), person("Lubomir", "Stepanek", role = "ctb"))
person("Yang", "Tang", email = "tang_yang@outlook.com",
  role = c("aut", "cre"))
person("Jasmine", "Dumas", email = "jasmine.dumas@gmail.com", role = c("aut", "cre"))
c(
    person("Eric", "Anderson", role = c("aut", "cre"), email = "eric.ray.anderson@gmail.com"),
    person("Alvin", "Wang", role = c("ctb", "cph"), comment = "Materialize CSS library"),
    person("Alan", "Chang", role = c("ctb", "cph"), comment = "Materialize CSS library"),
    person("Alex", "Mark", role = c("ctb", "cph"), comment = "Materialize CSS library"),
    person("Kevin", "Louie", role = c("ctb", "cph"), comment = "Materialize CSS library")
    )
person("Ewan", "Keith", email = "ewan.keith100@gmail.com", role = c("aut", "cre"))
c(person("Jonah", "Gabry", role = c("aut", "cre"),
    email = "jsg2201@columbia.edu"),
      person(family = "Stan Development Team", role = "ctb"),
      person("Michael", "Andreae", role = "ctb"),
      person("Michael", "Betancourt", role = "ctb"),
      person("Bob", "Carpenter", role = "ctb"),
      person("Yuanjun", "Gao", role = "ctb"),
      person("Andrew", "Gelman", role = "ctb"),
      person("Ben", "Goodrich", role = "ctb"),
      person("Daniel", "Lee", role = "ctb"),
      person("Dongying", "Song", role = "ctb"),
      person("Rob", "Trangucci", role = "ctb"))
c(
    person("Winston", "Chang", role = c("aut", "cre"), email = "winston@rstudio.com"),
    person("Gábor", "Csárdi", role = "aut", email = "gabor@rstudio.com")
    )
c(
    person("Winston", "Chang", role = c("aut", "cre"), email = "winston@rstudio.com"),
    person(family = "RStudio", role = "cph"),
    person("Thomas", "Park", role = c("ctb", "cph"), comment = "Bootswatch themes"),
    person("Lukasz", "Dziedzic", role = c("ctb", "cph"), comment = "Lato font"),
    person("Nathan", "Willis", role = c("ctb", "cph"), comment = "News Cycle font"),
    person(family = "Google Corporation", role = c("ctb", "cph"), comment = "Open Sans and Roboto fonts"),
    person("Matt", "McInerney", role = c("ctb", "cph"), comment = "Raleway font"),
    person(family = "Adobe Systems Incorporated", role = c("ctb", "cph"), comment = "Source Sans Pro font"),
    person(family = "Canonical Ltd", role = c("ctb", "cph"), comment = "Ubuntu font")
    )
c(
  person(family="Trestle Technology, LLC", role="aut", email="cran@trestletechnology.net"),
  person("Jeff", "Allen", role="aut", email="cran@trestletechnology.net"),
  person(family="Institut de Radioprotection et de Sûreté Nucléaire", role=c("cph"), email = "yann.richet@irsn.fr"),
  person("Ivan", "Bozhanov", role=c("ctb", "cph"), comment = "jsTree"),
  person(family="The Dojo Foundation", role=c("ctb", "cph"), comment="require.js"),
  person(family="jQuery Foundation, Inc.", role=c("ctb", "cph")),
  person("Mike", "Schaffer", role=c("cre"), email="mschaff@gmail.com"),
  person("Timm", "Danker", role=c("ctb"), email="tidafr@carina.uberspace.de"),
  person("Michael", "Bell", role=c("ctb"), email="bell_michael_a@lilly.com"))
c(
  person("Victor", "Perrier", email = "victor.perrier@dreamrs.fr", role = c("aut", "cre")),
  person("Fanny", "Meyer", email = "fanny.meyer@dreamrs.fr", role = c("aut")),
  person("David", "Granjon", email = "david.granjon@uzh.ch", role = "aut",
         comment = "jQuery Knob shiny interface & sliders appearance"),
  person("SnapAppointments", "", role = "cph", comment = "bootstrap-select"),
  person("Mattia", "Larentis", role = c("ctb", "cph"), comment = "Bootstrap Switch"),
  person("Emanuele", "Marchi", role = c("ctb", "cph"), comment = "Bootstrap Switch"),
  person("Mark", "Otto", role = "ctb", comment = "Bootstrap library"),
  person("Jacob", "Thornton", role = "ctb", comment = "Bootstrap library"),
  person(family = "Bootstrap contributors", role = "ctb", comment = "Bootstrap library"),
  person(family = "Twitter, Inc", role = "cph", comment = "Bootstrap library"),
  person(family = "Flatlogic", role = "cph", comment = "Awesome Bootstrap Checkbox"),
  person("mouse0270", role = c("ctb", "cph"), comment = "Material Design Switch"),
  person("Tristan", "Edwards", role = c("ctb", "cph"), comment = "SweetAlert"),
  person("Fabian", "Lindfors", role = c("ctb", "cph"), comment = "multi.js"),
  person("Anthony", "Terrien", role = c("ctb", "cph"), comment = "jQuery Knob"),
  person("Daniel", "Eden", role = c("ctb", "cph"), comment = "animate.css"),
  person("Ganapati", "V S", role = c("ctb", "cph"), comment = "bttn.css"),
  person("Brian", "Grinstead", role = c("ctb", "cph"), comment = "Spectrum"),
  person("Lokesh", "Rajendran", role = c("ctb", "cph"), comment = "pretty-checkbox"),
  person("Leon", "Gersen", role = c("ctb", "cph"), comment = "wnumb & noUiSlider"),
  person("Timofey", "Marochkin", role = c("ctb", "cph"), comment = "air-datepicker"),
  person("Tobias", "Ahlin", role = c("ctb", "cph"), comment = "CSS spin")
  )
c(person("Anthony", "Raborn", email = "anthony.w.raborn@gmail.com", role = c("aut", "cre")), person("Walter", "Leite", email = "Walter.Leite@coe.ufl.edu", role = "aut"))
c(person("Daniela", "Dunkler", email = "daniela.dunkler@meduniwien.ac.at", role = c("aut", "cre")),
    person("Georg", "Heinze", role = "aut"))
c( person("M. Umut", "Caglar", role = c("aut", "cre"), email =
    "umut.caglar@gmail.com"), person("Claus O.", "Wilke", role = c("aut"), email =
    "wilke@austin.utexas.edu") )
c(person("Liran","Lerman",role =
        c("cre","aut"),email="llerman@ulb.ac.be"),person("Gianluca","Bontempi",role
        =
        c("aut"),email="gbonte@ulb.ac.be"),person("Olivier","Markowitch",role
        = c("aut"),email="olivier.markowitch@ulb.ac.be"))
person("John", "Coene", email = "jcoenep@gmail.com", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-6637-4107"))
person("Ian", "Kloo", email = "iankloo@gmail.com", role = c("aut", "cre"))
c(person("Uwe", "Ligges", role = c("aut", "cre"), email="ligges@statistik.tu-dortmund.de", comment = "new maintainer"),
 person("Tom", "Short", role = "aut", email = "tshort@eprisolutions.com", comment="port to R"),
 person("Paul", "Kienzle", role = "aut", comment="majority of the original sources"),
 person("Sarah", "Schnackenberg", role = "ctb", comment="various test cases and bug fixes"),
 person("David", "Billinghurst", role = "ctb"),
 person("Hans-Werner", "Borchers", role = "ctb"),
 person("Andre", "Carezia", role = "ctb"),
 person("Pascal", "Dupuis", role = "ctb"),
 person("John W.", "Eaton", role = "ctb"),
 person("E.", "Farhi", role = "ctb"),
 person("Kai", "Habel", role = "ctb"),
 person("Kurt", "Hornik", role = "ctb"),
 person("Sebastian", "Krey", role = "ctb"),
 person("Bill", "Lash", role = "ctb"),
 person("Friedrich", "Leisch", role = "ctb"),
 person("Olaf", "Mersmann", role = "ctb"),
 person("Paulo", "Neis", role = "ctb"),
 person("Jaakko", "Ruohio", role = "ctb"),
 person("Julius O.", "Smith III", role = "ctb"),                   	   
 person("Doug", "Stewart", role = "ctb"),
 person("Andreas", "Weingessel", role = "ctb"))
c(
    person("Michal", "Burdukiewicz",
    email = "michalburdukiewicz@gmail.com", role = c("cre", "aut")),
    person("Piotr", "Sobczyk",
    role = "aut"))
c(person(given = "Andrew", family = "Dhawan", email="andrew.dhawan@oncology.ox.ac.uk", role = c("aut")), person(given = "Alessandro", family = "Barberis", email="alessandro.barberis@oncology.ox.ac.uk", role = c("aut")), person(given = "Wei-Chen", family = "Cheng", email="wei-chen.cheng@oncology.ox.ac.uk", role = c("aut")), person(given = "Francesca", family = "Buffa", email="francesca.buffa@oncology.ox.ac.uk", role = c("aut","cre")))
c(
    person("Arsalane Chouaib", "Guidoum",
    email = "acguidoum@usthb.dz",
    role = c("cre", "aut")),
    person("Kamal", "Boukhetala",
    role = "aut",email = "kboukhetala@usthb.dz"))
c(person(c("Berwin", "A."), "Turlach",
                    role=c("aut", "cre"),
                    email="Berwin.Turlach@gmail.com"),
             person("Nader", "Tajvidi", role="ctb",
                    email="Nader.Tajvidi@matstat.lu.se"))
person("Anestis", "Touloumis", role = c("aut", "cre"),
                      email = "A.Touloumis@brighton.ac.uk",
                      comment = c(ORCHID = "0000-0002-5965-1639"))
c(person("Phil", "Chalmers", email = "rphilip.chalmers@gmail.com", role = c("aut", "cre")),
             person("Matthew", "Sigal", role = c("ctb")),
             person("Ogreden", family="Oguzhan", role = c("ctb")))
c(person("Bernardo","Sousa-Pinto",role=c("aut","cre"),email="bernardo@med.up.pt"),
             person("Joao Julio","Cerqueira",role="ctb"),
             person("Cristina","Costa-Santos",role="ctb"),
             person ("John B","Carlisle",role="ctb"),
             person ("John A","Loadsman",role="ctb"),
             person("Armando","Teixeira-Pinto",role="aut"),
             person("Hernani","Goncalves",role="aut"))
person("Martin","Becker", role = c("aut","cre"),
    email = "martin.becker@mx.uni-saarland.de",
    comment=c(ORCID="0000-0003-2336-9751"))
person("Andreas", "Alfons", email = "alfons@ese.eur.nl",
                  role = c("aut", "cre"))
person("Brandon", "LeBeau", email = "lebebr01+simglm@gmail.com",
     role = c("aut", "cre"))
c(
    person("Maciej", "Bartoszuk", role = c("aut", "cre"),
    email = "bartoszuk@rexamine.com"),
    person("Marek", "Gagolewski", role = "aut",
    email = "gagolews@rexamine.com")
    )
c(person("Stefan", "Widgren", role = c("aut", "cre"),
                    email = "stefan.widgren@gmail.com",
                    comment = c(ORCID = "0000-0001-5745-2284")),
             person("Robin", "Eriksson", role = "aut",
                    comment = c(ORCID = "0000-0002-4291-712X")),
             person("Stefan", "Engblom", role = "aut",
                    comment = c(ORCID = "0000-0002-3614-1732")),
             person("Pavol", "Bauer", role = "aut",
                    comment = c(ORCID = "0000-0003-4328-7171")),
             person("Attractive Chaos", role = "cph",
                    comment = "Author of 'kvec.h', a generic dynamic array"))
c(person("Julián", "Urbano", email = "urbano.julian@gmail.com", role = c("aut", "cre")),
             person("Thomas", "Nagler", email = "thomas.nagler@tum.de", role = c("ctb")))
c(person("Markus Baaske", role = c("aut", "cre"),
			 email = "markus.baaske@uni-jena.de"),
			 person("Felix", "Ballani", role = c("aut","ctb"),
			 email = "ballani@math.tu-freiberg.de"))
c(
    person("Iñaki", "Ucar", email="iucar@fedoraproject.org",
      role=c("aut", "cph", "cre"), comment=c(ORCID="0000-0001-6403-5550")),
    person("Bart", "Smeets", email="bartsmeets86@gmail.com", role=c("aut", "cph")))
c(
    person("Iñaki", "Ucar", email="i.ucar86@gmail.com", 
      role=c("aut", "cph", "cre"), comment=c(ORCID="0000-0001-6403-5550")),
    person("Bart", "Smeets", email="bartsmeets86@gmail.com", role=c("aut", "cph")))
c(
    person("Itai", "Dattner", email = "idattner@stat.haifa.ac.il", role = "aut"),
    person("Rami", "Yaari", email = "ramiyaari@gmail.com", role = c("aut", "cre")))
c(person("Elisa", "Salviato", email = "elisa.salviato.88@gmail.com", role = c("aut", "cre")),
  person("Vera", "Djordjilovic", email = "djordjilovic@stat.unipd.it", role = "aut"),
  person("Chiara","Romualdi",email="chiara.romualdi@unipd.it",role="aut"),
  person("Monica","Chiogna",email="monica@stat.unipd.it",role="aut"))
c(person("VP", "Nagraj", email = "vpnagraj@virginia.edu", role =
    c("aut")), person("Nathan", "Sheffield", email = "nathan@code.databio.org",
    role = c("aut", "cre")))
person(given="David",family="Dernoncourt",email="me@daviddernoncourt.com",role=c("aut","cre"),comment="LUNAM Universite, Universite Angers, Laboratoire d'ergonomie et d'epidemiologie en sante au travail (LEEST), w/ support from the French National Research Program for Environmental and Occupational Health of Anses (2012/18)")
c( person("Mark", "van der Loo",
        role=c("aut","cre"),email="mark.vanderloo@gmail.com"))
c(
    person("Green", "Peter", role=c("aut", "cre"), email="simr.peter@gmail.com",
        comment=c(ORCID="0000-0002-0238-9852")),
    person("MacLeod", "Catriona", role="aut"),
    person("Alday", "Phillip", role="ctb"))
c(person("Olivier", "Lepais", role=c("aut", "cre"), email = "olepais@st-pee.inra.fr"), person("Jason", "Weir", role=c("aut")))
c(person("Lorenzo", "Quaglietta", role = c("aut"), email = "lontrenzo@gmail.com")
		,person("Miguel", "Porto", role = c("aut", "cre"), email = "mpbertolo@gmail.com")
		,person("Erida", "Gjini", role = c("ctb"), email = "egjini@igc.gulbenkian.pt")
		)
c(person("Christina", "Nieuwoudt", 
                    role = c("aut", "cre"),
                    email = "cnieuwou@sfu.ca"),
             person("Jinko", "Graham", role = "aut"))
c(person(given = c("Terrence","D."), family = "Jorgensen", role = c("aut", "cre"), email="TJorgensen314@gmail.com"),
        person(given = "Sunthud", family = "Pornprasertmanit", role = "aut", email = "psunthud@gmail.com"), 
		person(given = "Patrick", family = "Miller", role = "aut", email="pmille13@nd.edu"), 
		person(given = "Alexander", family = "Schoemann", role = "aut", email="schoemanna@ecu.edu"), 
		person(given = "Corbin", family = "Quick", role = "ctb", email="corbinq@umich.edu"))
person("W. Joel", "Schneider", email = "w.joel.schneider@gmail.com", role = c("aut", "cre"))
c(person("Sam", "Brilleman", email = "sam.brilleman@monash.edu", 
                    role = c("cre", "aut", "cph")),
             person("Alessandro", "Gasparini", email = "ag475@leicester.ac.uk", 
                    role = "ctb"))
person("Marsel", "Scheer", role = c("aut", "cre"), email = "scheer@freescience.de")
c(
  person("MAKHGAL", "Ganbold", role = c("aut", "cre"), email = "makhgal@seas.num.edu.mn"),
  person("BAYARBAATAR", "Amgalan", role = "aut", email = "bayarbaatar@seas.num.edu.mn"))
person("Jacob", "Bien", email = "jbien@cornell.edu", role = c("aut", "cre"))
c(person("Nikos", "Sidiropoulos", role = c("aut", "cre"),
    email = "nikos.sidiro@gmail.com"), person("Sina Hadi", "Sohi", role = "aut",
    email = "sinasohi@gmail.com"), person("Nicolas", "Rapin", role = "aut",
    email = "nicolas.rapin@bric.ku.dk"), person("Frederik Otzen", "Bagger",
    role = "aut", email = "frederik@binf.ku.dk"))
c(person(given = "Jonathan",
                    family = "Sidi", 
                    email = "yonis@metrumrg.com", 
                    role = c("aut","cre"),
                    comment = c(ORCID = "0000-0002-4222-1819")), 
             person(given = "Anton",
                    family = "Grishin",
                    role = "ctb"),
             person(given = "Lorenzo",
                    family = "Busetto",
                    role = "ctb")
             )
c(
  person(c("James","E."), "Pustejovsky", email = "jepusto@gmail.com", role = c("aut", "cre")),
  person(c("Daniel","M."), "Swan", email = "dswan@utexas.edu", role = "aut")
  )
person("Gianmarco", "Vacca", email = "gianmarco.vacca@unicatt.it", role = c("aut","cre"))
person("Mikko", "Korpela", role = c("aut", "cre"),
        email = "mvkorpel@iki.fi")
c(
  person("Victor", "Picheny", , "victor.picheny@inra.fr", c("aut")),
  person("Remi", "Servien", , "remi.servien@inra.fr", c("aut")),
  person("Nathalie", "Villa-Vialaneix", , "nathalie.villa-vialaneix@inra.fr", c("aut", "cre")))
person(given = c("Erik", "Barry"), family = "Erhardt", email = "erike@stat.unm.edu", role = c("aut", "cre"))
c(person("James", "Balamuta", email = "balamut2@illinois.edu", role = c("aut", "cre", "cph")),
             person("Thijs","van den Berg", email = "thijs@sitmo.com", role = c("aut", "cph")),
             person("Ralf", "Stubner", email = "ralf.stubner@daqana.com", role = c("ctb")))
c(person("Clara", "Anton Fernandez", role = c("aut", "cre"),
		     email = "caf@nibio.no"),
	      person("Nikolas", "von Lupke", role = "ctb"))
c(person("Clara", "Anton Fernandez", role = c("aut", "cre"),
		     email = "caf@nibio.no"))
c(person("Josymar", "Torrejon-Magallanes", email = "ejosymart@gmail.com", role = c("cre", "aut")))
c(
    person("Daniel", "Lüdecke", email = "d.luedecke@uke.de", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-8895-3206")),
    person("Carsten", "Schwemmer", email = "carsten.schwemmer@uni-bamberg.de", role = "ctb")
          )
c(
  EA = person("Eric", "Archer", email = "eric.archer@noaa.gov", role = c("aut")),
  MDL = person("Michelle", "DePrenger-Levin", email = "michelledpl@gmail.com", role = c("aut")),
  SH = person("Sean", "Hoban", email = "shoban@alumni.nd.edu", role = c("aut")),
  LL = person("Libby", "Liggins", email = "L.Liggins@massey.ac.nz", role = c("aut")),
  CP = person("Christian", "Parobek", email = "christian.parobek@gmail.com", role = c("aut")),
  AS = person("Allan", "Strand", email = "stranda@gmail.com", role = c("aut", "cre")))
c(
  person("Amelia", "McNamara", email="amcnamara@smith.edu", role = "aut"),
  person("Eduardo", "Arino de la Rubia", email="earino@gmail.com", role = "aut"),
  person("Hao", "Zhu", email="haozhu233@gmail.com", role = "aut"),
  person("Julia", "Lowndes", email="lowndes@nceas.ucsb.edu", role = 'ctb'),
  person("Shannon", "Ellis", email="sellis18@jhmi.edu", role = "aut"),
  person("Elin", "Waring", email="elin.waring@gmail.com", role = "cre"),
  person("Michael", "Quinn", email="msquinn@google.com", role = "aut"),
  person("Hope", "McLeod", email="hmgit2@gmail.com", role = 'ctb'),
  person("Hadley", "Wickham", email="hadley@rstudio.com", role = 'ctb'),
  person("Kirill", "Müller", email="krlmlr+r@mailbox.org", role = 'ctb'),
  person(family = "RStudio, Inc.", role = "cph", comment = "Spark functions"),
  person("Connor", "Kirkpatrick", email="hello@connorkirkpatrick.com", role = 'ctb'),
  person("Scott", "Brenstuhl", email="brenstsr@miamioh.edu", role = 'ctb'),
  person("Patrick", "Schratz", email="patrick.schratz@gmail.com", role = 'ctb'),
  person("lbusett", "", email="lbusett@gmail.com", role='ctb'),
  person("Mikko","Korpela", email="mvkorpel@iki.fi", role='ctb'),
  person("Jennifer","Thompson", email="thompson.jennifer@gmail.com", role='ctb'),
  person("Harris","McGehee", email="mcgehee.harris@gmail.com", role='ctb')
  )
c(person("Kurt", "Hornik", role = c("aut", "cre"),
                    email = "Kurt.Hornik@R-project.org"),
             person("Ingo", "Feinerer", role = "aut"),
             person("Martin", "Kober", role = "aut"))
c(person("Tyler", "Morgan-Wall", email = "tylermw@gmail.com", role = c("aut", "cre")),
             person("George", "Khoury", email = "george.m.khoury@gmail.com", role = c("aut")))
person("Filipe", "Teixeira", email = "Filipe.MarquesTeixeira@Ugent.be", role = c("aut", "cre"))
c(person("Kurt", "Hornik", role = c("aut", "cre"),
                     email = "Kurt.Hornik@R-project.org",
		     comment = c(ORCID = "0000-0003-4198-9911")),
             person("David", "Meyer", role = "aut"),
             person("Christian", "Buchta", role = "aut"))
c(person("Gourab", "Mukherjee", email = "gourab@usc.edu", role = c("aut")), person("Arjun", "Panda", email = "arjunpanda@gwu.edu", role = c("aut","cre")), person("Ann", "Arvin", email = "aarvin@stanford.edu", role = c("aut")), person("Adrish", "Sen", email = "adrishs@stanford.edu", role = c("aut")), person("Nandini", "Sen", email = "nandinis@stanford.edu", role = c("aut")))
person("Daniel", "Farewell",
    email = "farewelld@cf.ac.uk", role = c("aut", "cre"))
person("Srikanth", "KS", email = "sri.teach@gmail.com", role = c("aut", "cre"))
c(person("Bj\u00F8rn Tore", "Kopperud", role =c("aut", "cre"), email = "b.t.kopperud@nhm.uio.no"),
             person("Jason", "Pienaar", role = c("aut")),
             person("Kjetil Lysne", "Voje", role = c("aut")),
             person("Steven Hecht", "Orzack", role = c("aut")),
             person("Thomas F.", "Hansen", role = c("aut")),
             person("Mark", "Grabowski", role = c("ctb")))
person("Christopher", "Baker", email = "chriscrewbaker@gmail.com", role = c("aut", "cre"))
c(person("Patrick", "Mair", role = c("aut", "cre"), email = "mair@fas.harvard.edu"), person("Jan", "De Leeuw", role = "aut"), person("Ingwer", "Borg", role = "ctb"), person("Patrick J. F.", "Groenen", role = "ctb"))
c(
    person("Jun", "Yan", email = "jun.yan@uconn.edu", role = "aut"),
    person("Vladimir", "Pozdnyakov", email = "vladimir.pozdnyakov@uconn.edu", role = "aut"),
    person("Chaoran", "Hu", email = "chaoran.hu@uconn.edu", role = c("aut", "cre")))
c(
    person("Maxwell", "Joseph", email = "maxwell.b.joseph@colorado.edu",
            role = c("aut", "cre")),
    person("Matthew", "Oakley", email = "matthew.oakley@colorado.edu",
            role = "aut"),
    person("Zachary", "Schira", email = "zasc3143@colorado.edu",
            role = "aut")
    )
c(
   person("Ignacio", "Cordón", email = "nacho.cordon.castillo@gmail.com", role = c("aut", "cre")),
   person("Francisco", "Charte", email = "francisco@fcharte.com", role = "aut"),
   person("Julián", "Luengo", email = "julianlm@decsai.ugr.es", role = "aut"),
   person("Salvador", "García", email = "salvagl@decsai.ugr.es", role = "aut"),
   person("Francisco", "Herrera", email = "herrera@decsai.ugr.es", role = "aut")
 )
person("William", "Artman", email = "William_Artman@URMC.Rochester.edu", role = c("aut", "cre"))
c(person("Jonathan", "Bartlett", email = "j.w.bartlett@bath.ac.uk",role = c("aut", "cre")),
    person("Ruth", "Keogh", email = "ruth.keogh@lshtm.ac.uk",role = c("aut")))
c(person("Jakob", "Bossek", email = "j.bossek@gmail.com", role =
    c("aut", "cre")), person("Pascal", "Kerschke", email = "kerschke@uni-muenster.de",
    role = "ctb"))
person("Matthew", "Strimas-Mackey", email = "mstrimas@gmail.com", role = c("aut", "cre"), comment = c(ORCID = "0000-0001-8929-7776"))
c(person("Jeffrey B.", "Arnold", role = c("aut", "cre"),
    email = "jeffrey.arnold@gmail.com"),
    person("Alan", "Agresti", role = c("cph")),
    person("Barbara", "Finlay", role = c("cph")))
person(given=c("Carter", "T."), family="Butts", role=c("aut","cre","cph"), email = "buttsc@uci.edu")
c(
    person("Malte", "Grosser", , "malte.grosser@gmail.com", role = c("aut", "cre")))
c(
    person("Huw", "Campbell", email = "huw.campbell@gmail.com", role = c("aut", "cre"))
  , person("Jeff", "Dean", role = "ctb")
  , person("Sanjay", "Ghemawat", role = "ctb")
  , person("Steinar", "Gunderson", role = "ctb")
  , person("Martin", "Gieseking", email = "martin.gieseking@uos.de", role = "ctb")
  , person("Google Inc.", role = "cph")
  )
c(person("Josh", "Browning", role = c("aut", "cre"),
		     email = "rockclimber112358@gmail.com"),
	         person("Carina", "Schneider", role = "aut",
             email = "carinamariaschneider@gmail.com"))
person("Milan", "Bouchet-Valat", email="nalimilan@club.fr",
                  role=c("aut", "cre"))
c(person("Leticia", "Ramirez-Ramirez", role = "aut"), person("Kusha", "Nezafati", role = "aut"), person("Yuzhou", "Chen", role = "aut"), person("Vyacheslav", "Lyubchich", role = c("aut", "cre"), email = "lyubchich@umces.edu"), person("Yulia R.", "Gel", role = "aut"))
c(person("Mickael", "Canouil", role = c("aut", "cre"), email = "mickael.canouil@cnrs.fr"),
    person("Loic", "Yengo", role = "ctb", email = "loic.yengo@good.ibl.fr"))
c(
    person("Italo", "Granato", email="italo.granato@gmail.com", role = c("aut", "cre")),
    person("Roberto", "Fritsche-Neto", email="roberto.neto@usp.br", role =c("aut", "ths"))
    )
c(person("Frances", "Kuo", role = "aut"),
             person("Stephen", "Joe", role = "aut"),
             person("Makoto", "Matsumoto", role = "ctb",
                    email="mmat@hiroshima-u.ac.jp"),
             person("Shinsuke", "Mori", role = "ctb",
                    email="shinsukemori@hiroshima-u.ac.jp"),
             person("Mutsuo", "Saito", role = "cre",
                    email="sai10@hiroshima-u.ac.jp"))
person("Sebastian", "Funk", email = "sebastian.funk@lshtm.ac.uk", role = c("aut", "cre"))
c(person(given = "Brian", family = "Connelly", email = "bdc@bconnelly.net",
                    role = c("aut", "cre")))
c(person('Ullrich', 'Dettmann', email = 'ullrich.dettmann@thuenen.de',
                  role = c('aut', 'cre')), 
          person('Felix', 'Andrews', role = 'ctb', comment = 'For the code copied from the hydromad::SCEoptim function (Version: 0.9-15) which is not on r-cran (https://github.com/floybix/hydromad). The SCEoptim function is adapted, and substantially revised from Brecht Donckels MATLAB code which is in turn adopted from Qingyun Duans MATLAB code'),
          person('Brecht', 'Donckels', role = 'ctb', comment = 'For the Matlab code which was adapted and reviesed in the hydromad::SCEoptim function.'),
          person('Qingyun', 'Duan', role = 'ctb', comment = 'For the MATLAB code adapted from Brecht Donkels.'))
c( person( "Julien", "Moeys", role = c("aut", "cre"), email = "jules_m78-soiltexture@yahoo.fr" ), person( "Wei", "Shangguan", role = "ctb" ), person( "Rainer", "Petzold", role = "ctb" ), person( "Budiman", "Minasny", role = "ctb" ), person( "Bogdan", "Rosca", role = "ctb" ), person( "Nic", "Jelinski", role = "ctb" ), person( "Wiktor", "Zelazny", role = "ctb" ), person( "Rodolfo", "Marcondes Silva Souza", role = "ctb" ), person( "Jose Lucas", "Safanelli", role = "ctb" ), person( "Alexandre", "ten Caten", role = "ctb" ) )
c(
    person("Andrey", "Ziyatdinov", email = "andrey.ziyatdinov@upc.edu", role = c("cre", "aut")),
    person("Helena", "Brunel", role = "aut"),
    person("Angel", "Martinez-Perez", role = "aut"),
    person("Alfonso", "Buil", role = "aut"),
    person("Alexandre", "Perera", role = "cph"),
    person("Jose Manuel", "Soria", role = "cph"))
c(person("Nathalie", "Villa-Vialaneix", role = c("aut", "cre"), email="nathalie.villa-vialaneix@inra.fr"),
             person("Jerome", "Mariette", role= "aut", email="jerome.mariette@inra.fr"),
             person("Madalina", "Olteanu", role = "aut", email="madalina.olteanu@univ-paris1.fr"),
             person("Fabrice", "Rossi", role = "aut", email="fabrice.rossi@univ-paris1.fr"),
             person("Laura", "Bendhaiba", role = "ctb", email="laurabendhaiba@gmail.com"),
             person("Julien", "Boelaert", role= "ctb", email="julien.boelaert@gmail.com"))
c(person("Stefan", "Siegert", role = c("aut", "cre"),
                     email = "s.siegert@exeter.ac.uk"),
             person("Robin", "Williams", role = c("aut"), 
                     email="robin.williams@atass-sports.co.uk")
            )
c(person("Lee", "McDaniel", role = c("aut", "cre"),email = "lmcda4@lsuhsc.edu"),
			person("Jonathan", "Schildcrout", role = "aut"))
person("Andrey", "Anikin", email = "rty.anik@rambler.ru", role = c("aut", "cre"))
person("Federico", "Mattiello", role = c("aut", "cre"),
    email = "federico.mattiello@gmail.com")
c(person("Poppy", "Miller", email = "p.miller@lancaster.ac.uk",
  role = c("aut", "cre", "cph")),
  person("Chris", "Jewell", role = "aut"),
  person("Jonathan", "Marshall", role = "ctb"),
  person("Nigel", "French", role = "ctb"))
c(person("Elisa", "Salviato", email = "elisa.salviato.88@gmail.com", role = c("aut", "cre")),
  person("Vera", "Djordjilovic", email = "vera.djordjilovic@medisin.uio.no", role = "aut"),
  person("Chiara","Romualdi",email="chiara.romualdi@unipd.it",role="aut"),
  person("Monica","Chiogna",email="monica@stat.unipd.it",role="aut"))
c(person("Edzer", "Pebesma", role = c("aut", "cre"),
				email = "edzer.pebesma@uni-muenster.de"),
			person("Roger", "Bivand", role = "aut",
            	email = "Roger.Bivand@nhh.no"),
			person("Barry", "Rowlingson", role = "ctb"),
			person("Virgilio", "Gomez-Rubio", role = "ctb"),
			person("Robert", "Hijmans", role = "ctb"),
			person("Michael", "Sumner", role = "ctb"),
			person("Don", "MacQueen", role = "ctb"),
			person("Jim", "Lemon", role = "ctb"),
			person("Josh", "O'Brien", role = "ctb"),
			person("Joseph", "O'Rourke", role = "ctb"))
c(
    person("Balasubramanian", "Narasimhan",
    email = "naras@stat.stanford.edu", role = c("aut", "cre")),
    person("Mei-Chiung", "Shih",
    email = "meichiun@stanford.edu", role = "aut"),
    person("Pei", "He",
    email = "he.pei.hepei@gmail.com", role = "aut"))
c( person("Silvia", "D'Angelo", role = c("aut", "cre"), 
           email = "silvia.dangelo@uniroma1.it"), 
	         person("Michael", "Fop", role = "aut"),
	         person("Marco", "Alfò", role = "ctb"), 
	         person("Thomas Brendan", "Murphy", role = "ctb")
	         )
c(person("Edzer", "Pebesma", role = c("aut", "cre"), email = "edzer.pebesma@uni-muenster.de", comment = c(ORCID = "0000-0001-8049-7069")),
	person("Benedikt", "Graeler", role = "ctb"),
	person("Tom", "Gottfried", role = "ctb"),
	person("Robert J.", "Hijmans", role = "ctb"))
c(person("Till", "Junge", role = c("aut", "cre"),
                    email = "till.junge@altermail.ch"),
             person("Sandra", "Penic", role = "aut",
                    email = "sandra.penic@unil.ch"),
             person("Mathieu", "Cossuta", role = "aut",
                    email = "mcossutta@gmail.com"),
             person("Guy", "Elcheroth", role = "aut",
                    email = "guy.elcheroth@unil.ch"),
             person("Stephanie", "Glaeser", role = "ctb",
                    email = "stephanie.glaeser@unil.ch"),
             person("Davide", "Morselli", role = "ctb",
                    email = "davide.morselli@unil.ch"))
c( person("Kenneth", "Benoit", email = "kbenoit@lse.ac.uk", role =
    c("aut", "cre", "cph")), 
    person("Akitaka", "Matsuo", email = "A.Matsuo@lse.ac.uk", role = "aut") )
person("Vilmantas", "Gegzna",
                    email = "GegznaV@gmail.com",
                    role = c("aut", "cre","cph")
            )
c(
    person("Alex M", "Chubaty", email = "alex.chubaty@gmail.com",
    role = c("aut", "cre")),
    person("Eliot J B", "McIntire", email = "eliot.mcintire@canada.ca",
    role = c("aut")),
    person("Yong", "Luo", email = "yluo1@lakeheadu.ca",
    role = c("ctb")),
    person("Steve", "Cumming", email = "Steve.Cumming@sbf.ulaval.ca",
    role = c("ctb")),
    person("Her Majesty the Queen in Right of Canada, as represented by the Minister of Natural Resources Canada",
    role = "cph")
    )
c(
    person("Alex M", "Chubaty", email = "alex.chubaty@gmail.com",
    role = c("aut", "cre")),
    person("Her Majesty the Queen in Right of Canada, as represented by the Minister of Natural Resources Canada",
    role = "cph")
    )
c(
    person("Alex M", "Chubaty", email = "alex.chubaty@gmail.com",
    role = c("aut", "cre"), comment = c(ORCID = "https://orcid.org/0000-0001-7146-8135")),
    person("Eliot J B", "McIntire", email = "eliot.mcintire@canada.ca",
    role = c("aut"), comment=c(ORCID = "https://orcid.org/0000-0002-6914-8316")),
    person("Yong", "Luo", email = "yluo1@lakeheadu.ca",
    role = c("ctb")),
    person("Steve", "Cumming", email = "Steve.Cumming@sbf.ulaval.ca",
    role = c("ctb")),
    person("Ceres", "Barros", email = "cbarros@mail.ubc.ca",
    role = c("ctb")),
    person("Her Majesty the Queen in Right of Canada, as represented by the Minister of Natural Resources Canada",
    role = "cph")
    )
c(
    person("Alex M", "Chubaty", email = "alex.chubaty@gmail.com",
    role = c("aut", "cre")),
    person("Eliot J B", "McIntire", email = "eliot.mcintire@canada.ca",
    role = c("aut")),
    person("Yong", "Luo", email = "yluo1@lakeheadu.ca",
    role = c("ctb")),
    person("Steve", "Cumming", email = "Steve.Cumming@sbf.ulaval.ca",
    role = c("ctb")),
    person("Jean", "Marchal", email = "jean.d.marchal@gmail.com",
    role = c("ctb")),
    person("Her Majesty the Queen in Right of Canada, as represented by the Minister of Natural Resources Canada",
    role = "cph")
    )
person("Eamon", "O'Dea", email = "odea35@gmail.com",
    role = c("aut", "cre"))
c(person("Reinhard", "Furrer", role = c("aut", "cre"),
                    email = "reinhard.furrer@math.uzh.ch"),
             person("Florian", "Gerber", role = c("ctb"),
                    email = "florian.gerber@math.uzh.ch"),
             person("Roman", "Flury", role = c("ctb"),
                    email = "roman.flury@math.uzh.ch"),
             person("Daniel", "Gerber", role = "ctb",
                    email = "daniel_gerber_2222@hotmail.com"),
             person("Kaspar", "Moesinger", role = "ctb",
                    email = "kaspar.moesinger@gmail.com"),
             person("Youcef", "Saad", role = "ctb",
                    comment = "SPARSEKIT http://www-users.cs.umn.edu/~saad/software/SPARSKIT/"),
             person(c("Esmond", "G."), "Ng", role = "ctb",
                    comment = "Fortran Cholesky routines"),
             person(c("Barry", "W."), "Peyton", role = "ctb",
                    comment = "Fortran Cholesky routines"),
             person(c("Joseph", "W.H."), "Liu", role = "ctb",
                    comment = "Fortran Cholesky routines"),
             person(c("Alan", "D."), "George", role = "ctb",
                    comment = "Fortran Cholesky routines"),
             person(c("Lehoucq", "B."), "Rich", role = "ctb",
                    comment = "ARPACK"),
             person(c("Maschhoff"), "Kristi", role = "ctb",
                    comment = "ARPACK"),
             person(c("Sorensen", "C."), "Danny", role = "ctb",
                    comment = "ARPACK"),
             person(c("Yang"), "Chao", role = "ctb",
                    comment = "ARPACK"))
c(person("François", "Rousset", role = c("aut", "cre", "cph"),
                     email = "francois.rousset@umontpellier.fr",
                     comment = c(ORCID = "0000-0003-4670-0371")),
              person("Jean-Baptiste", "Ferdy", role = c("aut","cph")),
              person("Alexandre", "Courtiol", role = "aut",
                     comment = c(ORCID = "0000-0003-0637-2959")),
              person("GSL authors", role = "ctb", comment = "src/gsl_bessel.*")
              )
person( "Jose Manuel","Vera Oteo", email = "vera.josemanuel@gmail.com",
                role = c("aut","cre"))
c(
  person("Martin", "Wilson", email = "martin@pipegrep.co.uk", role = c("cre","aut")),
  person("John", "Muschelli", role = "ctb"))
c(person("Jan", "Wijffels", role = c("aut", "cre", "cph"), email =
    "jwijffels@bnosac.be"), person("BNOSAC", role = "cph"))
c(person(family = "Mirai Solutions GmbH", role = "aut",
                    email = "info@mirai-solutions.com"),
             person("Martin", "Studer", role = "cre",
                    email = "martin.studer@mirai-solutions.com"),
             person("Nicola", "Lambiase", role = "ctb",
                    email = "nicola.lambiase@mirai-solutions.com"),
             person("Omer", "Demirel", role = "ctb"))
c(
    person("Ramnath", "Vaidyanathan", email = "ramnath.vaidya@gmail.com", role = c("aut", "cre")),
    person("Kent", "Russell", email = "kent.russell@timelyportfolio.com", role = c("aut", "ctb")),
    person("Gareth", "Watts", role = c("aut", "cph"), comment = "jquery.sparkline library in htmlwidgets/lib, https://github.com/gwatts/jquery.sparkline"),
    person(family = "jQuery Foundation", role = "cph", comment = "jQuery library"),
    person(family = "jQuery contributors", role = c("ctb", "cph"),
      comment = "jQuery library; authors listed in inst/htmlwidgets/lib/jquery/jquery-AUTHORS.txt"
    )
  )
c(
    person("Javier", "Luraschi", email = "javier@rstudio.com", role = c("aut", "cre")),
    person("Kevin", "Kuo", role = "aut", email = "kevin.kuo@rstudio.com",
           comment = c(ORCID = "0000-0001-7803-7901")),
    person("Kevin", "Ushey", role = "aut", email = "kevin@rstudio.com"),
    person("JJ", "Allaire", role = "aut", email = "jj@rstudio.com"),
    person("Samuel", "Macedo", role = "ctb", email = "samuelmacedo@recife.ifpe.edu.br"),
    person(family = "RStudio", role = c("cph")),
    person(family = "The Apache Software Foundation", role = c("aut", "cph"))
    )
c(person("Thomas", "Leeper", role = c("aut", "cre"), email =
        "thosjleeper@gmail.com"), person("Andy", "Barbour", role =
        "ctb"))
c(
    person("Javier", "Luraschi", email = "javier@rstudio.com", role = c("aut", "cre"))
    )
c(
  person(c("Tilman", "M."), "Davies", role = c("aut", "cre"), email = "tdavies@maths.otago.ac.nz"),
  person(c("Jonathan", "C."), "Marshall", role = c("aut"), email = "j.c.marshall@massey.ac.nz"))
c(
    person("Bryon", "Aragam", email = "sparsebn@gmail.com", role = c("aut", "cre")),
    person("Jiaying", "Gu", role = c("aut")),
    person("Dacheng", "Zhang", role = c("aut")),
    person("Qing", "Zhou", role = c("aut"))
    )
c(
    person("Bryon", "Aragam", email = "sparsebn@gmail.com", role = c("aut", "cre")),
    person("Jiaying", "Gu", role = c("aut"))
    )
c(
  person("Konstantinos", "Benidis", role = "aut", email = "benidisk@gmail.com"),
  person(c("Daniel", "P."), "Palomar", role = c("cre", "aut"), email = "daniel.p.palomar@gmail.com")
  )
c(
    person("Andrew", "Zammit-Mangion", , "andrewzm@gmail.com", c("aut", "cre")),
    person("Timothy", "Davis", , "davis@tamu.edu", role = "ctb"),
    person("Patrick","Amestoy", ,"Patrick.Amestoy@enseeiht.fr", role = "ctb"),
    person("Iain","Duff", ,"iain.duff@stfc.ac.uk", role = "ctb"),
	person("John K.","Reid", ,"John.Reid@stfc.ac.uk", role = "ctb"))
person(given = "Riccardo", family = "Rastelli", role = c("aut", "cre"), email = "riccardoras@gmail.com")
c(
    person("Martin", "Barron", email = "mbarron2@nd.edu",role = c("aut")),
    person("Jun", "Li", email = "jun.li@nd.edu",role = c("aut","cre"))
    )
c(
  person("Gregory", "Popovitch", role = c("aut", "cph"), email = "greg7mdp@gmail.com"),
  person("Google Inc", role = c("aut", "cph")),
  person("Dmitriy", "Selivanov", role = "cre", email = "selivanov.dmitriy@gmail.com")
  )
c(person("Gertjan", "van den Burg", role=c("aut", "cre"), 
                    email="gertjanvandenburg@gmail.com"), 
              person("Patrick", "Groenen", email="groenen@ese.eur.nl", role="ctb"), 
              person("Andreas", "Alfons", email="alfons@ese.eur.nl", role="ctb"))
c(person("Doug", "Rohde", email="dr+svd@tedlab.mit.edu", role="aut"),
             person("Michael", "Berry", role="aut"),
             person("Theresa", "Do", role="aut"),
             person("Gavin", "O'Brien", role="aut"),
             person("Vijay", "Krishna", role="aut"),
             person("Sowmini", "Varadhan", role="aut"),
             person("University of Tennessee Research Foundation", role = "cph", comment = "files src/las2.c, src/svdlib.[ch], src/svdutil.[ch]"),
             person("Stefan", "Evert", email="stefan.evert@fau.de", role=c("cre", "aut", "cph"), comment="copyright holder for files src/main.c, R/*, man/*, tests/*"))
c(person("Simone", "Vazzoler", role = c("aut", "cre"),
                     email = "svazzole@gmail.com"),
             person("Lorenzo", "Frattarolo", role = "aut"),
             person("Monica", "Billio", role = "aut"))
c(person("Dmitriy", "Selivanov", role = c("aut", "cre"),
    email = "selivanov.dmitriy@gmail.com"), 
    person("Felix", "Riedel", role = c("aut"),
    email = "felix.riedel@gmail.com"))
c(person("Kieran","Alden", email = "kieran.alden@gmail.com", role =c("cre","aut")),
	     person("Mark","Read",role ="aut"),
	     person("Paul","Andrews",role ="aut"),
	     person("Jason","Cosgrove",role ="aut"),
	     person("Mark","Coles",role ="aut"),
	     person("Jon","Timmis",role ="aut"))
c(person("Brian", "Ripley", role = c("aut", "cre", "cph"),
                    email = "ripley@stats.ox.ac.uk"),
	     person("Roger", "Bivand", role = "ctb"),
             person("William", "Venables", role = "cph"))
person("Stamatis", "Kalogirou", email = "stamatis@lctools.science",
                  role = c("aut", "cre"))
person("Derek", "Corcoran", email = "derek.corcoran.barrios@gmail.com",
  role = c("aut", "cre"))
c(person(family="Evans", given="Jeffrey S.", email = "jeffrey_evans@tnc.org", 
             role = c("aut", "cre")), person(family="Ram", given="Karthik", role = "ctb") )
c(
    person("Cici", "Chen", email = "cicichen@u.washington.edu", role = "ctb"),
    person("Albert Y.", "Kim", email = "albert@stat.washington.edu", role = c("aut", "cre")),
    person("Michelle", "Ross", email = "micher3@u.washington.edu", role = "ctb"),
    person("Jon", "Wakefield", email = "jonno@u.washington.edu", role = c("aut"))
    )
person("Paula", "Moraga", email = "p.e.moraga-serrano@lancaster.ac.uk", role = c("aut", "cre"))
c(person("Mathieu", "Ribatet", role = c("aut", "cre"),
	   email = "mathieu.ribatet@umontpellier.fr"),
	   person("Richard", "Singleton", role = "ctb"),
	   person("R Core team", role = "ctb"))
c(
    person("Virgilio", "Gómez-Rubio", , "Virgilio.Gomez@uclm.es", c("aut", "cre")),
    person("Pingping", "Zheng", , , "aut"),
    person("Peter", "Diggle", , "p.diggle@lancaster.ac.uk", c("aut", "cph")),
    person("Richard", "Cotton", , "richierocks@gmail.com", "ctb"),
    person("Alan", "Murta", , , c("ctb", "cph")),
    person("Jonathan Richard", "Shewchuk", , , c("ctb", "cph")), 
    person("Barry", "Rowlingson", , , c("ctb", "cph")),
    person("Wm. Randolph", "Franklin", , , c("ctb", "cph")),
    person("David C.", "Sterratt", role=c("cph","aut"),
    email="david.c.sterratt@ed.ac.uk"),
    person("Elias", "Pipping", role=c("ctb")),
    person("Roger D.", "Peng", , "rpeng@jhsph.edu", c("aut")),
    person("Duncan", "Murdoch", , , c("aut")),
    person("Barry", "Rowlingson", , , c("aut"))
    )
c(person("Felipe", "Osorio", role = c("aut", "cre"), email = "felipe.osorios@usm.cl"),
             person("Ronny", "Vallejos", role = "aut", email = "ronny.vallejos@usm.cl"),
             person("Francisco", "Cuevas", role = "ctb"),
             person("Diego", "Mancilla", role = "ctb"))
c(
      person("Timothée", "Giraud", email = "timothee.giraud@cnrs.fr", role = c("cre","aut")),
      person("Hadrien", "Commenges", email = "hcommenges@parisgeo.cnrs.fr", role = c("aut")), 
      person("Joël", "Boulier", email = "joel.boulier@univ-paris1.fr", role = c("ctb")))
c(
	person("Maria Xose Rodriguez-Alvarez",
		email = "mxrodriguez@bcamath.org", role = c("aut", "cre")),
	person("Martin Boer", role = "aut"),
	person("Paul Eilers", role = "aut"),
	person("Fred van Eeuwijk", role = "ctb"))
c(
    person("Alec L.", "Robitaille", role = c("aut", "cre"), email = "robit.alec@gmail.com"),
    person("Quinn", "Webber", role = c("aut")),
    person("Eric", "Vander Wal", role = c("aut"))
    )
c(person("Adrian", "Baddeley", 
                    role = c("aut", "cre"),
       	            email = "Adrian.Baddeley@curtin.edu.au"),
	     person("Rolf", "Turner", 
                    role = "aut",
 	            email="r.turner@auckland.ac.nz"),
	     person("Ege",   "Rubak", 
                    role = "aut",
		    email = "rubak@math.aau.dk"))
c(person("Adrian", "Baddeley", 
                    role = c("aut", "cre"),
       	            email = "Adrian.Baddeley@curtin.edu.au"))
c(person("Michael D.","Sumner", role = c("aut", "cre"), email = "mdsumner@gmail.com"))
c(person("Andrew", "Finley", role=c("aut", "cre"), email="finleya@msu.edu"), person("Sudipto", "Banerjee", role="aut", email="sudipto@ucla.edu"))
c( person(given = "Dennis", family = "Walvoort", email =
        "dennis.Walvoort@wur.nl", role = c("aut", "cre", "cph")),
        person(given = "Dick", family = "Brus", email =
        "dick.brus@wur.nl", role = c("aut", "cph")), person(given =
        "Jaap", family = "de Gruijter", email =
        "Jaap.degruijter@wur.nl", role = c("aut", "cph")))
person("Samuel I.", "Berchuck", email = "sib2@duke.edu", role = c("aut", "cre"))
c(person("Roger", "Bivand", role = "aut", email="Roger.Bivand@nhh.no", comment = c(ORCID = "0000-0003-2392-6140")),
    person("Jakub", "Nowosad", role = c("aut", "cre"), email="nowosad.jakub@gmail.com", comment = c(ORCID = "0000-0002-1057-3721")),
    person("Robin", "Lovelace", role = "aut", comment = c(ORCID = "0000-0001-5679-6536")),
    person("Mark", "Monmonier", role = "ctb", comment = "author of the state.vbm dataset"), 
    person("Greg", "Snow", role = "ctb", comment = "author of the state.vbm dataset")
    )
c(person("Roger", "Bivand", role = c("cre", "aut"), email = "Roger.Bivand@nhh.no", comment=c(ORCID="0000-0003-2392-6140")),	person("Micah", "Altman", role = "ctb"),
	person("Luc", "Anselin", role = "ctb"),
	person("Renato", "Assunção", role = "ctb"),
	person("Olaf", "Berke", role = "ctb"),
	person("Andrew", "Bernat", role = "ctb"),
	person("Guillaume", "Blanchet", role = "ctb"),
	person("Eric", "Blankmeyer", role = "ctb"),
	person("Marilia", "Carvalho", role = "ctb"),
	person("Bjarke", "Christensen", role = "ctb"),
	person("Yongwan", "Chun", role = "ctb"),
	person("Carsten", "Dormann", role = "ctb"),
	person("Stéphane", "Dray", role = "ctb"),
	person("Virgilio", "Gómez-Rubio", role = "ctb"),
        person("Martin", "Gubri", role = "ctb"),
	person("Rein", "Halbersma", role = "ctb"),
	person("Elias", "Krainski", role = "ctb"),
	person("Pierre", "Legendre", role = "ctb"),
	person("Nicholas", "Lewin-Koh", role = "ctb"),
	person("Hongfei", "Li", role = "ctb"),
	person("Jielai", "Ma", role = "ctb"),
        person("Abhirup", "Mallik", role = c("ctb", "trl")),
	person("Giovanni", "Millo", role = "ctb"),
	person("Werner", "Mueller", role = "ctb"),
	person("Hisaji", "Ono", role = "ctb"),
	person("Pedro", "Peres-Neto", role = "ctb"),
	person("Gianfranco", "Piras", role = "ctb"),
	person("Markus", "Reder", role = "ctb"),
	person("Michael", "Tiefelsdorf", role = "ctb"),
	person("René", "Westerholt", role="ctb"),
	person("Danlin", "Yu", role = "ctb"))
c(person("Michael D.","Sumner", role = c("aut", "cre"), email = "mdsumner@gmail.com"), 
             person("Joscha", "Legewie", role = "ctb"), 
             person("Jussi", "Jousimo", role = "ctb"))
c(
    person("Andreas", "Beger", , "adbeger@gmail.com", c("aut", "cre"), comment = c(ORCID = "0000-0003-1883-3169")),
    person("Daina", "Chiba", , "dchiba@essex.ac.uk", "aut"),
    person("Daniel W.", "Hill, Jr.", , "dwhill@uga.edu", "aut"),
    person("Nils W.", "Metternich", , "n.metternich@ucl.ac.uk", "aut"),
    person("Shahryar", "Minhas", , "hermes829@gmail.com", "aut"),
    person("Michael D.", "Ward", , "michael.don.ward@gmail.com", c("aut", "cph"))
    )
person("Miguel", "de Carvalho", role = c("aut", "cre"),
	   	  email = "miguel.decarvalho@ed.ac.uk")
person("Luis", "Uzai", email = "uzai_ff@hotmail.com", role = c("aut", "cre"))
c(person(given="Stefan", family="Siegert",   role = c("aut", "cre"), 
                                                        email="s.siegert@exeter.ac.uk"),
             person(given="Jonas",  family="Bhend",     role="ctb"),
             person(given="Igor",   family="Kroener",   role="ctb"),
             person(given="Matteo", family="De Felice", role="ctb")
            )
c(person(given = "Pierre", family = "Roudier", email = "roudierp@landcareresearch.co.nz", role = c("aut", "cre")),
    person(given = "Max", family = "Kuhn", role = "ctb"),
    person(given = "Kristian Hovde", family = "Liland", role = "ctb"),
    person(given = "Bjorn-Helge", family = "Mevik", role = "ctb"),
    person(given = "Hadley", family = "Wickham", role = "ctb"),
    person(given = "Raphael", family = "Viscarra Rossel", role = "dtc"))
c( person("Robin", "Van Oirbeek", role = c("aut") ),
              person("Adriaan", "Blommaert", role = c("aut", "cre"),
                     email = "adriaan.blommaert@openanalytics.eu"),
              person("Nicolas", "Sauwen", role = c("aut"),
                     email = "nicolas.sauwen@openanalytics.eu"),
              person("Tor", "Maes", role = "ctb" ),
              person("Jan", "Dijkmans", role = "ctb" ),
              person("Jef", "Cuypers", role = "ctb" ),
              person("Tatsiana", "Khamiakova", role = "ctb" ),
              person("Michel", "Tiel", role = "ctb" ),
              person("Claudia" , "Beleites" , role = "ctb" )
              )
c(person("Jose Eduardo", "Meireles", email = "meireles@umn.edu", role = c("aut", "cre")),
             person("Anna K.", "Schweiger", email =  "anna.k.schweiger@gmail.com", role = "aut"),
             person("Jeannine", "Cavender-Bares", email = "cavender@umn.edu", role = "aut")
             )
c(person("Marco", "Enea",
                      email="emarco76@libero.it",
	              role=c("aut", "cre")),
             person("Ronen", "Meiri", role=c("ctb"), comment="on behalf of DMWay Analytics LTD"),
             person("Tomer", "Kalimi", role=c("ctb"),comment="on behalf of DMWay Analytics LTD"))
c(person("Sy Han (Steven)", "Chiou", email = "schiou@utdallas.edu", role = c("aut", "cre")),
	   person("Xiaojing", "Wang", role = "aut"),
	   person("Jun", "Yan", role = "aut"))
c(
	person('Santiago', 'Beguería', role=c('aut','cre'),
	email='santiago.begueria@csic.es'),
	person(c('Sergio','M.'), 'Vicente-Serrano', role='aut',
	email='svicen@ipe.csic.es'))
c(
    person("Jeroen", "Ooms", , "jeroen@berkeley.edu", role = c("cre", "aut")),
    person("Jim", "Hester", , "james.hester@rstudio.com", role = "aut"))
person("Pierre", "Roudier", email = "pierre.roudier@gmail.com", role = c("aut", "cre"))
c(
      person("Alexander", "Brenning",
        email = "alexander.brenning@uni-jena.de",
        role = c("aut", "cre"),
        comment = c(ORCID = '0000-0001-6640-679X')),
      person("Patrick", "Schratz",
        email = "patrick.schratz@gmail.com", 
        role = c("aut"),
        comment = c(ORCID = '0000-0003-0748-6624')),
      person("Tobias", "Herrmann", 
        email = "herrmann.tobias89@gmail.com",
        role = "aut",
        comment = c(ORCID = '0000-0001-9768-0708'))  
      )
c(
  person("Vural", "Aksakalli", email = "vaksakalli@gmail.com", role = c("aut", "cre")),
  person("Babak", "Abbasi", email = "babak.abbasi@rmit.edu.au", role = c("aut", "ctb")),
  person("Yong Kai", "Wong", email = "yongkai1017@gmail.com", role = c("aut", "ctb")),
  person("Zeren", "D. Yenice", email = "zerendyenice@gmail.com", role = "ctb"))
person("Philipp", "Otto", role = c("cre", "aut"), email = "potto@europa-uni.de", comment = c(ORCID = "https://orcid.org/0000-0002-9796-6682"))
c(
	person("Roger", "Bivand", role = c("cre", "aut"), email = "Roger.Bivand@nhh.no"),
	person("Rainer", "Krug", role = "ctb", email="Rainer@krugs.de"),
	person("Markus", "Neteler", role = "ctb"))
c(person("Andrew", "Hart", role = c("aut", "cre"), email = "ahart@dim.uchile.cl"),
  person("Servet", "Martínez", role = "aut"),
  person("Universidad de Chile", role = "cph"),
  person("INRIA-Chile", role = "cph"))
c(
	person("Roger", "Bivand", role = c("cre", "aut"), email = "Roger.Bivand@nhh.no", comment=c(ORCID="0000-0003-2392-6140")),
	person("Danlin", "Yu", role = "aut"),
	person("Tomoki", "Nakaya", role = "ctb"),
	person("Miquel-Angel", "Garcia-Lopez", role = "ctb"))
c(
    person(given="Edward A.", family="Roualdes",
    email= "eroualdes@csuchico.edu", role = c("cre", "aut")),
    person(given="Simon J.", family="Bonner", role = "aut"))
c(person("Fabian", "Scheipl", role = c("aut", "cre"),
                     email = "fabian.scheipl@stat.uni-muenchen.de"),
              person("Bettina", "Gruen", role = "ctb"))
c(person("Johannes W.", "Dietrich", 
                     email = "johannes.dietrich@ruhr-uni-bochum.de",
                     role = c("aut", "cph", "cre")))
c(
	person("Roger", "Bivand", role = c("cre"), email = "Roger.Bivand@nhh.no"),
	person("Barry", "Rowlingson", role = "aut"),
	person("Peter", "Diggle", role = "aut"),
	person("Giovanni", "Petris", role = "ctb"),
	person("Stephen", "Eglen", role = "ctb"))
c(
    person("Bob", "Rudis", email = "bob@rud.is", role = c("aut", "cre"), 
           comment = c(ORCID = "0000-0001-5670-2640"))
  )
c(
    person(given = "Wenjie", family = "Wang",
           email = "wenjie.2.wang@uconn.edu",
           role = c("aut", "cre")),
    person(given = "Jun", family = "Yan", role = "aut")
    )
c(person("Anna", "Neufeld", email = "aneufeld@uw.edu", role = c("aut", "cre")), person("Brianna", "Heggeseth", email = "bheggese@macalester.edu", role = c("aut","ths")))
c(
    person(given=c("Eric","C."), family="Chi", email="ecchi1105@gmail.com", role=c("aut","cre","cph")),
    person(given="Jason", family="Xu", email="jqxu@ucla.edu", role="ctb"),
    person(given="Meng", family="Yang", email="myang13@ncsu.edu", role="ctb"),
    person(given="Kenneth", family="Lange", email="klange@ucla.edu", role="ctb")
    )
person("Sam", "Parsons", email = "sam.parsons@psy.ox.ac.uk",
  role = c("aut", "cre"))
c(person(given = "Giovanni", family = "Millo", role = c("aut", "cre"), email = "giovanni.millo@generali.com"),
             person(given = "Gianfranco", family = "Piras", role = c("aut"), email = "gpiras@mac.com"))
person("Jin", "Li", email = "jin.li@ga.gov.au", role = c("aut", "cre"))
c(
    person(given = "John",
           family = "Muschelli",
           role = c("cre", "aut"),
           email = "muschellij2@gmail.com"))
c(
    person("Jalal-Edine", "ZAWAM", email = "jalal-edine.zawam@rte-france.com", role = c("aut", "cre")),
    person("Benoit", "Thieurmel", email = "benoit.thieurmel@datastorm.fr", role = "aut"),
    person("RTE", role = "cph")
    )
c(person("Moliere", "Nguile-Makao", role = c("aut", "cre"), email = "moliere.nguile@gmail.com"),
	      person("Alexandre", "Bureau", role = "aut",email = "alexandre.bureau@msp.ulaval.ca"))
c(
    person("Yun","Cai",email="Yun.Cai@dal.ca",role= c("aut","cre")),
    person("Hong","Gu",email="hgu@dal.ca",role="aut"),
    person("Toby","Kenney",email="tkenney@mathstat.dal.ca",role="aut"))
c(person("Andrew", "Finley", role=c("aut", "cre"), email="finleya@msu.edu"), person("Abhirup", "Datta", role="aut", email="abhidatta@jhu.edu"), person("Sudipto", "Banerjee", role="aut", email="sudipto@ucla.edu"), person("Alexander", "Mckim", role="ctb", email="amckim@g.clemson.edu"))
c(
    person("Scott", "Chamberlain", role = c("aut", "cre"),
      email = "myrmecocystus@gmail.com", 
      comment = c(ORCID = "0000-0003-1444-9135")),
    person("Karthik", "Ram", role = "ctb"),
    person("Ted", "Hart", role = "ctb")
    )
c(	
	      person("Thomas", "Bartz-Beielstein", role = "aut"),
				person("Joerg", "Stork", role = "aut"),
				person("Martin", "Zaefferer", role = c("aut", "cre"),email = "martin.zaefferer@gmx.de"),
	      person("Margarita", "Rebolledo", role = "ctb"),
	      person("Christian", "Lasarczyk", role = "ctb"),
				person("Joerg", "Ziegenhirt", role = "ctb"),
				person("Wolfgang","Konen",role = "ctb"),
				person("Oliver","Flasch", role = "ctb"),
				person("Patrick","Koch", role = "ctb"),
				person("Martina","Friese", role = "ctb"),
				person("Lorenzo","Gentile", role = "ctb"),
				person("Frederik","Rehbach", role = "ctb")
				)
c(
        person("Frederik","Rehbach", role = c("aut", "cre"), email = "frederik.rehbach@th-koeln.de"),
        person("Martin", "Zaefferer", role = "aut"),
        person("Thomas", "Bartz-Beielstein", role = "ctb"),
		person("Andreas","Fischbach", role = "ctb"),
		person("Lorenzo","Gentile", role = "ctb"))
person("Charlie", "Thompson", email = "charles.thompson@barcelonagse.eu", role = c("aut", "cre"))
c(
    person("Alessandro", "Samuel-Rosa", role = c("aut", "cre"), email = "alessandrosamuelrosa@gmail.com"),
    person("Lucia Helena Cunha dos", "Anjos", role = "ths", email = "lanjos@ufrrj.br"),
    person("Gustavo de Mattos", "Vasques", role = "ths", email = "gustavo.vasques@embrapa.br"),
    person("Gerard B M", "Heuvelink", role = "ths", email = "gerard.heuvelink@wur.nl"),
    person("Edzer", "Pebesma", role = "ctb",  email = "edzer.pebesma@uni-muenster.de"),
    person("Jon", "Skoien", role = "ctb",  email = "jon.skoien@gmail.com"),
    person("Joshua", "French", role = "ctb",  email = "joshua.french@ucdenver.edu"),
    person("Pierre", "Roudier", role = "ctb", email = "roudierp@landcareresearch.co.nz"),
    person("Dick", "Brus", role = "ctb", email = "dick.brus@wur.nl"),
    person("Murray", "Lark", role = "ctb", email = "mlark@bgs.ac.uk")
    )
c(
    person("Tom", "Kincaid", role=c("aut", "cre"),
           email = "Kincaid.Tom@epa.gov"),
    person("Tony", "Olsen",  role="aut",
           email = "Olsen.Tony@epa.gov"),
    person("Don", "Stevens",  role="ctb"),
    person("Christian", "Platt",  role="ctb"),
    person("Denis", "White",  role="ctb"),
    person("Richard", "Remington",  role="ctb")
    )
person('Zachary', 'Weller', email = 'wellerz@stat.colostate.edu', role = c('aut', 'cre'))
c(person("Youyi", "Fong", role = "cre", email = "youyifong@gmail.com"), person("Krisztian", "Sebestyen", role = "aut", email = "ksebestyen@gmail.com"), person("Martin", "Maechler", role = "ctb", email = "maechler@stat.math.ethz.ch"))
c(person("Kasia", "Sawicka", email = "katwic55@ceh.ac.uk", role = c("aut", "cre")),
             person("Gerard", "Heuvelink", email = "gerard.heuvelink@wur.nl", role = c("aut")),
             person("Dennis", "Walvoort", email = "dennis.walvoort@wur.nl", role = c("aut")),
             person("Stefan", "van Dam", email = "stefan.vandam@wur.nl", role = c("ctb")),
             person("Damiano", "Luzzi", email = "damiano.luzzi@wur.nl", role = c("ctb"))
             )
c(person("Paolo", "Inglese", email = "p.inglese14@imperial.ac.uk", role = c("aut", "cre")),
        person("Goncalo", "Correia", role = c("aut", "ctb")))
c(
    person("Martijn", "Schuemie", , "schuemie@ohdsi.org", role = c("aut", "cre")),
    person("Marc", "Suchard", role = c("aut"))
    )
c(person("William", "Brannon", email="wwbrannon@email.wm.edu", role=c("aut", "cre")))
c(
    person("Hassen"  , "Allegue"        , email = "hassen.allegue@gmail.com"          , role = c("aut","cre")),
    person("Yimen"   , "G. Araya-Ajoy"  , email = "yimencr@gmail.com"                 , role = "aut"),
    person("Niels"   , "J. Dingemanse"  , email = "ndingemanse@orn.mpg.de"            , role = "aut"),
    person("Ned"     , "A. Dochtermann" , email = "ned.dochtermann@gmail.com"         , role = "aut"),
    person("Laszlo"  , "Z. Garamszegi"  , email = "laszlo.garamszegi@ebd.csic.es"     , role = "aut"),
    person("Shinichi", "Nakagawa"       , email = "itchyshin@gmail.com"               , role = "aut"),
    person("Denis"   , "Reale"          , email = "reale.denis@uqam.ca"               , role = "aut"),
    person("Holger"  , "Schielzeth"     , email = "holger.schielzeth@uni-bielefeld.de", role = "aut"),
    person("David"   , "F. Westneat"    , email = "david.westneat@uky.edu"            , role = "aut"))
c(person("Attila", "Csala", role = c("aut", "cre"), email = "a@csala.me"),
			person("Koos", "Zwinderman", role = "ctb", email = "a.h.zwinderman@amc.uva.nl"))
c(person("Hyeyoung", "Maeng", email = "h.maeng@lse.ac.uk", role = c("aut", "cre")), person("Piotr", "Fryzlewicz", email = "p.fryzlewicz@lse.ac.uk", role="aut"))
c(person("Greg", "Freedman Ellis", email = "greg.freedman@gmail.com", role = c("aut", "cre")),
  person("Thomas", "Lumley", role = "ctb")
  )
c(person("Sean", "Anderson", email =
    "sean@seananderson.ca", role = c("aut", "cre")),
    person("Cole", "Monnahan", role = "aut"),
    person("Kelli", "Johnson", role = "aut"),
    person("Kotaro", "Ono", role = "aut"),
    person("Juan", "Valero", role = "aut"),
    person("Curry", "Cunningham", role = "aut"),
    person("Allan", "Hicks", role = "aut"),
    person("Felipe", "Hurtado-Ferro", role = "aut"),
    person("Peter", "Kuriyama", role = "aut"),
    person("Roberto", "Licandeo", role = "aut"),
    person("Carey", "McGilliard", role = "aut"),
    person("Melissa", "Muradian", role = "ctb"),
    person("Merrill", "Rudd", role = "aut"),
    person("Christine", "Stawitz", role = "aut"),
    person("Cody", "Szuwalski", role = "aut"),
    person("Ian", "Taylor", role = "aut"),
    person("Katyana", "Vert-pre", role = "aut"),
    person("Athol", "Whitten", role = "aut"))
person("Dave", "Zhao", email = "sdzhao@illinois.edu", role = c("aut", "cre"))
c(person("Mabel", "González", role = "aut",
                    email = "mabelc@correo.ugr.es",
                    comment = c(ORCID = "0000-0003-0152-444X")),
             person("Osmani", "Rosado-Falcón", role = "aut",
                    email = "osmanir@uclv.cu",
                    comment = c(ORCID = "0000-0002-2639-3354")),
             person("José Daniel", "Rodríguez", role = "aut",
                    email = "josedaniel@uclv.cu",
                    comment = c(ORCID = "0000-0002-8489-4106")),
             person("Christoph", "Bergmeir", role = c("ths", "cre"),
                    email = "c.bergmeir@decsai.ugr.es",
                    comment = c(ORCID = "0000-0002-3665-9021")),
             person("Isaac", "Triguero", role = "ctb",
                    email = "isaac.triguero@nottingham.ac.uk",
                    comment = c(ORCID = "0000-0002-0150-0651")),
             person("José Manuel", "Benítez", role = "ths",
                    email = "j.m.benitez@decsai.ugr.es",
                    comment = c(ORCID = "0000-0002-2346-0793"))
             )
c(
  person("Alexander", "Duerre", role=c("aut","cre"), email="alexander.duerre@tu-dortmund.de"),
  person("Daniel", "Vogel", role=c("aut"), email="daniel.vogel@abdn.ac.uk"))
c(person(given = "Charles", family = "Auerbach", role = c("aut", "cre"), email = "auerbach@yu.edu"),
             person(given = "Wendy", family = "Zeitlin", role = "aut", email = "Wendy.Zeitlin@yu.edu"))
c(
  person("Joe", "Thorley", email = "joe@poissonconsulting.ca", role = c("aut", "cre", "ctr"), 
    comment = c(ORCID = "0000-0002-7683-4592")),
  person("Carl", "Schwarz", role = c("aut", "ctr")),
  person("Ali", "Azizishirazi", email = "Ali.Azizishirazi@gov.bc.ca", role = "ctb"),
  person("Angeline", "Tillmanns", role = "ctb"),
  person("Stephanie", "Hazlitt", role = "ctb"),
  person("Province of British Columbia", role = "cph")
  )
c(
    person("Ran", "Bi", email = "biran@iastate.edu", role = c("aut", "cre")),
    person("Peng", "Liu", email = "pliu@iastate.edu", role = "aut"),
    person("Tim", "Triche", email = "tim.triche@gmail.com", role = "ctb"))
person("Peter","Curtis", email = "peterrobertcurtis@gmail.com",
               role = c("aut", "cre"))
c(person("Jay", "Ver Hoef", role = c("aut", "cre"), email = "ver.hoef@SpatialStreamNetworks.com"),
             person("Erin", "Peterson", role = c("aut"), email = "peterson@SpatialStreamNetworks.com"))
c( person("Mark S.", "Handcock", role=c("aut","cre","cph"),
    email="handcock@stat.ucla.edu"), person("Krista J.", "Gile", role=c("aut","cph"),
    email="gile@math.umass.edu"))
c(person(given="Takuya", family="Yanagida", email="takuya.yanagida@univie.ac.at", role=c("cre","aut")),
  person(given="Keiko", family="Sakai",email="keiko.sakai@@oit.ac.jp", role="aut"))
person("Kush", "Kapur", email = "kush.kapur@childrens.harvard.edu", role = c("aut", "cre"))
person("Andrie", "de Vries", role=c("aut", "cre"),
    email="apdevries@gmail.com")
c(person("Tao", "Jiang", role = c("aut", "cre"),
                     email = "tjiang8@ncsu.edu"))
c(
    person("Loris", "Michel", role = "aut"),
    person("Melvin", "Kianmanesh Rad", role = "aut"),
    person("Adrien", "Lamit", role = "aut"),
    person("Michael", "Schmutz", role = "cre", email = "michael.schmutz@finma.ch"),
    person(family = "Swiss Financial Market Supervisory Authority FINMA", role = "cph")
    )
c(
              person(c("Muhammad", "Yaseen"),  email = "myaseen208@gmail.com", role = c("aut", "cre"))
            , person(c("Kent", "M.", "Eskridge"),  email = "keskridge1@unl.edu", role = c("aut", "ctb"))
            , person(c("Ghulam", "Murtaza"),  email = "gmurtaza208@gmail.com", role = c("aut", "ctb"))
            )
c(
    person("Bruce", "Swihart", email="bruce.swihart@gmail.com", role=c("cre", "aut")),
    person("Jim"  , "Lindsey", email="jlindsey@gen.unimaas.nl", role="aut", comment="Jim created this package, Bruce is maintaining the CRAN version"),
    person("Philippe", "Lambert", email="phlambert@stat.ucl.ac.be", role="aut")
    )
c(person(given = "Michel",  family = "Philipp", role = c("aut", "cre"), email = "michel.philipp.mp@gmail.com"),
             person(given = "Carolin", family = "Strobl",  role = "aut",           email = "Carolin.Strobl@psychologie.uzh.ch"),
             person(given = "Achim",   family = "Zeileis", role = "aut",           email = "Achim.Zeileis@R-project.org", comment = c(ORCID = "0000-0003-0918-3766")),
             person(given = "Thomas",  family = "Rusch",   role = "aut",           email = "Thomas.Rusch@wu.ac.at"),
             person(given = "Kurt",    family = "Hornik",  role = "aut",           email = "Kurt.Hornik@wu.ac.at", comment = c(ORCID = "0000-0003-4198-9911")))
c(
    person("Ridho", "Rahmadi", , "r.rahmadi@cs.ru.nl", c("aut", "cre")),
    person("Perry", "Groot", role = c("aut", "ths")),
    person("Tom", "Heskes", role = c("aut", "ths")),
    person("Christoph", "Stich", role = c("ctb"))
    )
c(person("Benjamin", "Hofner", role = c("aut", "cre"),
	            email = "benjamin.hofner@pei.de"),
       	     person("Torsten", "Hothorn", role = c("aut")))
c(person("Cedric", "Briand", role = c("aut", "cre"), email = "cedric.briand00@gmail.com"),
	      person("Marion", "Legrand", role = "aut", email="tableau-salt-loire@logrami.fr"),
	      person("Timothee", "Besse", role = "aut", email="tableau-ang-loire@logrami.fr"))
c(person("Cedric", "Briand", role = c("aut", "cre"), email = "cedric.briand00@gmail.com"))
c(person("Anna", "Zamojska", role="aut", email="anna.zamojska@ug.edu.pl"),
	   person("Piotr", "Zientara", role="aut", email="zientara@fest.pl"),
	   person("Sebastian", "Susmarski", role="aut", email="sebastian.susmarski@ug.edu.pl"),
	   person("Lech", "Kujawski", role=c("aut", "cre"), email = "lech.kujawski@ug.edu.pl"))
person("Christopher D.", "Eager",
    email = "eagerstats@gmail.com", role = c("aut", "cre"))
c(person("Ben",family="Goodrich", email="benjamin.goodrich@columbia.edu", role=c('cre','aut')),
             person("Joshua", "Pritikin", role = "ctb"),
             person("Andrew", "Gelman", role = "aut"),
             person("Bob", "Carpenter",  role = "aut"),
             person("Matt", "Hoffman", role = "aut"),
             person("Daniel", "Lee", role = "aut"),
             person("Michael", "Betancourt", role = "aut"),
             person("Marcus", "Brubaker", role = "aut"),
             person("Jiqiang", "Guo", role = "aut"),
             person("Peter", "Li", role = "aut"),
             person("Allen", "Riddell", role = "aut"),
             person("Marco", "Inacio", role = "aut"),
             person("Mitzi", "Morris", role = "aut"),
             person("Jeffrey", "Arnold", role = "aut"),
             person("Rob", "Goedman", role = "aut"),
             person("Brian", "Lau", role = "aut"),
             person("Rob", "Trangucci", role = "aut"),
             person("Jonah", "Gabry", role = "aut"),
             person("Alp", "Kucukelbir", role = "aut"),
             person("Robert", "Grant", role = "aut"),
             person("Dustin", "Tran", role = "aut"),
             person("Michael", "Malecki", role = "aut"),
             person("Yuanjun", "Gao", role = "aut"),
             person("Trustees of", "Columbia University", role = "cph"),
             person("Lawrence Livermore", "National Security", role = "cph", comment = "CVODES"),
             person("The Regents of the", "University of California", role = "cph", comment = "CVODES"),
             person("Southern Methodist", "University", role = "cph", comment = "CVODES"))
c(person(given = "John", family = "Muschelli", 
        role = c("aut", "cre"), email = "muschellij2@gmail.com", 
        comment = NULL))
c(
    person("Priyanga Dilini", "Talagala", email="dilini.talagala@monash.edu", role= c("aut","cre")),
    person("Ogan", "Mancarci", email="ogan.mancarci@gmail.com", role='aut'),
    person("Daniel", "Padfield", email= "d.padfield@exeter.ac.uk", role ='aut')
    )
c(person("Matthias", "Pucher", email = "matthias.pucher@wcl.ac.at",
                  role = c("aut", "cre")),
                  person("Daniel", "Graeber", email = "daniel.graeber@ufz.de",
                  role = c("aut", "ctb")),
                  person("Stefan", "Preiner", email = "stefan.preiner@boku.ac.at",
                  role = c("ctb")),
                  person("Renata", "Pinto", email = "renatamspinto@sapo.pt",
                  role = c("ctb")),
                  person("Nora Maria", "Zechmeister",
                  role = c("ctb")),
                  person("Christoph", "Zechmeister",
                  role = c("ctb")))
c(person("Gerry", "Tonkin-Hill", email = "tonkin-hill.g@wehi.edu.au", role = c("aut")),
    person( "Stuart", "Lee", email = "lee.s@wehi.edu.au", role = c("cre", "aut")))
c(person(given = "Edzer",
             family = "Pebesma",
             role = c("aut", "cre"),
             email = "edzer.pebesma@uni-muenster.de",
             comment = c(ORCID = "0000-0001-8049-7069")),
      person(given = "Michael",
             family = "Sumner",
             role = "ctb"),
      person(given = "Etienne",
             family = "Racine",
             role = "ctb"),
      person(given = "Adriano",
             family = "Fantini",
             role = "ctb"))
person("Matthieu","Gomez", email = "mattg@princeton.edu", role =
    c("aut", "cre"))
c(person("Joe", "Swintek", role = c("aut", "cre"), email = "swintek.joe@epa.gov"),
		person("Kevin", "Flynn", role = "ctb", email = "Flynn.Kevin@epa.gov"),
		person("Jon", "Haselman", role = "ctb", email = "Haselman.Jon@epa.gov")
	)
c(person(given = "David", family = "Meyer", role = c("aut", "cre"), email = "David.Meyer@R-project.org"),
             person(given = "Torsten", family = "Hothorn", role = c("aut")),
             person(given = "Friedrich", family = "Leisch", role = "aut"))
person("Andreas", "Beger", role = c("cre", "aut"), email =
    "adbeger@gmail.com", comment = c(ORCID = "0000-0003-1883-3169"))
c(person(given="Suzana S.", family="Santos",
        email="suzana@ime.usp.br", role=c("aut")),
        person(given="Andre", family="Fujita",
        email="fujita@ime.usp.br", role=c("aut", "cre")))
c(person("Paul", "Johnson", role = c("aut", "cre"), email = "pauljohn@ku.edu"),
             person("Brent", "Kaplan", role = c("ctb"), email = "bkaplan4@ku.edu"),
             person("Meghan", "Sullivan", role = c("ctb"), email = "meg.sullivan@ku.edu"),
             person("Charles", "Redmon", role = "ctb", email = "redmon@ku.edu"),
             person("Zack", "Roman", role = "ctb", email = "zroman@ku.edu"),
			 person("Chong", "Xing", role = "ctb", email = "cxing@ku.edu"))
c(person("Paul", "Poncet", , "paulponcet@yahoo.fr", role = c("aut", "cre")), 
    person("The R Core Team", role = c("aut", "cph"), 
           comment = "C function 'BinDist' copied from package 'stats'"), 
    person("The R Foundation", role = "cph", 
           comment = "C function 'BinDist' copied from package 'stats'"), 
    person("Adrian", "Baddeley", role = "ctb", 
           comment = "C function 'BinDist' copied from package 'stats'"))
c(
	     person("Mark S.", "Handcock", role=c("aut"), email="handcock@stat.ucla.edu"),
	     person("David R.", "Hunter", role=c("aut"), email="dhunter@stat.psu.edu"),
	     person("Carter T.", "Butts", role=c("aut"), email="buttsc@uci.edu"),
	     person("Steven M.", "Goodreau", role=c("aut"), email="goodreau@uw.edu"),
	     person("Pavel N.", "Krivitsky", role=c("aut"), email="pavel@uow.edu.au", comment=c(ORCID="0000-0002-9101-3362")),
	     person("Skye", "Bender-deMoll", role=c("aut"), email="skyebend@skyeome.net"),
	     person("Martina", "Morris", role=c("aut","cre"), email="morrism@uw.edu")
	     )
c(
	person("Pavel N.", "Krivitsky", role=c("aut","cre"), email="pavel@uow.edu.au"),
	person("Skye", "Bender-deMoll", role=c("ctb"), email="skyebend@uw.edu"))
c(
  person("Martina", "Morris", email = "morrism@uw.edu", role = c("cre", "aut")),
  person("Emily", "Beylerian", email = "ebeylerian@path.org", role = "aut"),
  person("Kirk", "Li", email = "kirkli@uw.edu", role = "ctb"),
  person("Samuel", "Jenness", email = "sjenness@uw.edu", role = "ctb"))
person("Brett", "Klamer", email = "code@brettklamer.com", role = c("aut", "cre"))
c(person("Michael", "Friendly", email = "friendly@yorku.ca", role = c("aut", "cre")),
             person("Phil", "Chalmers", email = "rphilip.chalmers@gmail.com", role = c("ctb")),
             person("Matthew", "Sigal", email = "matthewsigal@gmail.com", role = c("ctb"))
            )
c(person("Colin", "Rundel", role=c("aut"), email="rundel@gmail.com"),
             person("Mine", "Cetinkaya-Rundel", role=c("aut"), email="mine@stat.duke.edu"),
             person("Merlise", "Clyde", role=c("aut", "cre"), email="clyde@duke.edu",
	        		       comment=c(ORCID=0000-0002-3595-1872)),
             person("David", "Banks", role=c("aut"), email="banks@stat.duke.edu"))
c(person("Matteo", "Dell'Omodarme", role = c("aut", "cre"),
        email = "mattdell@fastmail.fm"), person("Giada", "Valle", role
        = "aut"))
c(person("Thijs","Janzen", email = "thijsjanzen@gmail.com",role=c("aut","cre")), person("Fons","van der Plas",email="",role=c("aut")),person("Thorsten","Hauffe",email="",role=c("ctb")))
c(person("Pein", "Florian", role = c("aut", "cre"), email = "fpein@uni-goettingen.de"),
    person("Thomas", "Hotz", role = "aut", email = "thomas.hotz@tu-ilmenau.de"),
    person("Hannes", "Sieling", role = "aut", email = "hsielin@uni-goettingen.de"),
    person("Timo", "Aspelmeier", role = "ctb", email = "timo.aspelmeier@mathematik.uni-goettingen.de"))
c(
      person("Christopher", "Brown", , "chris.brown@decisionpatterns.com", c("aut", "cre") ),
      person("Decision Patterns", role = "cph")
    )
c(person(given = "Elise", family = "Dusseldorp", role = c("aut", "cre", "cph"), email = "elise.dusseldorp@fsw.leidenuniv.nl"),
             person(given = "Claudio", family = "Conversano", role = c("aut", "cph"), email = "conversa@unica.it"),
	     person("Cor", "Ninaber", role = "ctb"),
	     person("Kristof", "Meers", role = "ctb"),
	     person("Peter", "Neufeglise", role = "trl"),
	     person("Juan", "Claramunt", role = "ctb"))
person("Ryan", "Hafen", email = "rhafen@gmail.com", role = c("aut", "cre"))
c(
    person("Amin", "Bazaz", role = c("aut"), comment = "Polytech'Montpellier"),
    person("Heraldo", "Borges", email = "stmotif@eic.cefet-rj.br", role = c("aut", "cre"), comment = "CEFET/RJ"),
    person("Eduardo", "Ogasawara", role = c("aut"), comment = "CEFET/RJ"))
person("Gregor", "Kastner", role = c("aut", "cre"), email = "gregor.kastner@wu.ac.at")
c(person("Scott D.", "Foster", role = c("aut", "cre"),
                     email = "scott.foster@csiro.au"))
c(person("Kenneth", "Benoit", email = "kbenoit@lse.ac.uk", role = "aut"),
    person("David", "Muhr", email = "muhrdavid+github@gmail.com", role = c("aut", "cre")), 
    person("Kohei", "Watanabe", email = "watanabe.kohei@gmail.com", role = "aut"))
c(person("Brooke", "Anderson", 
                    email = "brooke.anderson@colostate.edu",
                    role = c("aut", "cre")),
             person("Andrea", "Schumacher",
                    email = "andrea.schumacher@colostate.edu",
                    role = c("aut")),
             person("Seth", "Guikema",
                    email = "sguikema@umich.edu",
                    role = c("aut")),
             person("Steven", "Quiring",
                    email = "quiring.10@osu.edu",
                    role = c("aut")),
             person("Joshua", "Ferreri",
                    email = "joshua.m.ferreri@gmail.com",
                    role = c("aut")),
             person("Andrea", "Staid",
                    email = "astaid@gmail.com",
                    role = c("ctb")),
             person("Michael", "Guo",
                    email = "michaelzgao@gmail.com",
                    role = c("ctb")),
             person("Lei", "Ming",
                    email = "lei.meng@wmich.edu",
                    role = c("ctb")),
             person("Laiyin", "Zhu",
                    email = "laiyin.zhu@wmich.edu",
                    role = c("ctb")))
c(person("David", "Silkworth", email = "djsilk@openreliability.org", role = "aut"), person("Jacob", "Ormerod", email = "jake@openreliability.org", role = "cre"), person("OpenReliability.org", role = "cph"))
c(
    person("Robin", "Lovelace", email = "rob00x@gmail.com", role = c("aut", "cre"),
    comment = c(ORCID = "0000-0001-5679-6536")),
    person("Richard", "Ellison", role = c("aut")),
    person("Barry", "Rowlingson", role = c("ctb"), comment = "Author of overline"),
    person("Nick", "Bearman", role = c("ctb"), comment = "Co-author of gclip"),
    person("Malcolm", "Morgan", role = c("ctb"), comment = "Co-author of angle_diff"),
    person("Nikolai", "Berkoff", role = c("ctb"), comment = "Co-author of line2route"),
    person("Scott ", "Chamberlin", role = c("rev"), comment = "Scott reviewed the package for rOpenSci, see https://github.com/ropensci/onboarding/issues/10")
    )
c(person("Edith", "Gabriel", role=c("aut","cre"),email="edith.gabriel@univ-avignon.fr"),
             person("Peter J","Diggle",role="aut",email="p.diggle@lancaster.ac.uk"),
             person("Barry","Rowlingson",role="aut",email="b.rowlingson@lancaster.ac.uk"),
             person("Francisco J","Rodriguez-Cortes",role="aut", email="cortesf@uji.es"))
c(
    person("Eric", "Lecoutre", email = "eric.lecoutre@welovedatascience.com", role = c("aut","cre")),
    person("Sven", "Wauters", email = "sven.wauters@welovedatascience.com", role = "aut"))
c(
  EA = person("Eric", "Archer", email = "eric.archer@noaa.gov", role = c("aut", "cre")),
  PA = person("Paula", "Adams", email = "peadams@crimson.ua.edu", role = c("aut")),
  BS = person("Brita", "Schneiders", email = "britaschneiders@u.northwestern.edu", role = c("aut")),
  SF = person("Sarina", "Fernandez", email = "sarinafernandez5@gmail.com", role = c("aut")),
  WA = person("Warren", "Asfazadour", email = "wasfazad@hotmail.com", role = c("aut")))
c(
    person("Louis-Paul", "Rivest",, "Louis-Paul.Rivest@mat.ulaval.ca", c("aut", "cre")),
    person("Sophie", "Baillargeon",, "Sophie.Baillargeon@mat.ulaval.ca", "aut")
    )
c(
    person("Karuna G.", "Reddy",, "reddy_k@usp.ac.fj", c("aut", "cre")),
    person("M. G. M.", "Khan",, "khan_mg@usp.ac.fj", "aut")
    )
c(person("Michael", "Hahsler", role = c("aut", "cre", "cph"),
                email = "mhahsler@lyle.smu.edu"),
            person("Matthew", "Bolanos", role = c("aut", "cph")),
            person("John", "Forrest", role = c("aut", "cph")),
            person("Matthias", "Carnein", role = c("ctb")),
            person("Dennis", "Assenmacher", role = c("ctb"))
            )
c(person("Michael", "Hahsler", role = c("aut", "cre", "cph"),
	    email = "mhahsler@lyle.smu.edu"),
	person("Matthew", "Bolanos", role = c("aut", "cph")),
	person("John", "Forrest", role = c("aut", "cph")))
person("Rory", "Nolan", email = "rorynoolan@gmail.com", 
                  role = c("aut", "cre"), 
                  comment = c(ORCID = "0000-0002-5239-4043"))
person("Tim", "Keitt", email = "tkeitt@gmail.com", role = c("aut", "cre"))
c(
    person(
      "Peter", "Meissner",
      email = "retep.meissner@gmail.com",
      role = c("aut", "cre")
    )
  )
c( person("Mark", "van der Loo", role=c("aut","cre"),email="mark.vanderloo@gmail.com")
            , person("Jan", "van der Laan", role="ctb")
            , person("R Core Team",""     , role="ctb")
            , person("Nick","Logan"       , role="ctb")
            , person("Chris","Muir"       , role="ctb"))
c(
    person("Hadley", "Wickham", , "hadley@rstudio.com", c("aut", "cre", "cph")),
    person("RStudio", role = c("cph", "fnd"))
    )
person("Bert", "Gunter", email = "bgunter@comcast.net",
                  role = c("aut","cre") )
c(
    person("Kota", "Mori", email = "kmori05@gmail.com", role = c("aut", "cre"))
  )
c(
              person(c("Muhammad", "Yaseen"),  email = "myaseen208@gmail.com", role = c("aut", "cre"))
            , person(c("Adeela", "Munawar"),  email = "adeela.uaf@@gmail.com", role = c("aut"))
            , person(c("Walter", "W.", "Stroup"),  email = "wstroup@unl.edu", role = "ctb")
            , person(c("Kent", "M.", "Eskridge"),  email = "keskridge1@unl.edu ", role = "ctb")
            )
c(person(given = "Achim", family = "Zeileis", role = c("aut", "cre"), email = "Achim.Zeileis@R-project.org"),
             person(given = "Friedrich", family = "Leisch", role = "aut", email = "Friedrich.Leisch@R-project.org"),
             person(given = "Kurt", family = "Hornik", role = "aut", email = "Kurt.Hornik@R-project.org"),
	     person(given = "Christian", family = "Kleiber", role = "aut", email = "Christian.Kleiber@unibas.ch"),
	     person(given = "Bruce", family = "Hansen", role = "ctb"),
	     person(give = c("Edgar", "C."), family = "Merkle", role = "ctb"))
c(person("Nathan", "Morris", role = c("aut", "cre"), email = "nathan.morris@cwru.edu"),
             person("Yeunjoo", "Song", role = c("aut"), email = "yeunjoo.song@cwru.edu"),
             person("Stephen", "Cahn", role = "ctb"))
c(
  person("Sainath", "Adapa", email = "adapasainath@gmail.com", role = c("aut", "cre")),
  person("Nitin", "Madasu", email = "nitin.mdsu@gmail.com", role = c("aut"))
  )
c(
    person("John", "Emerson", email = "john.emerson@yale.edu", role = c("cre", "aut")),
    person("Saksham", "Chandra", role = "aut"),
    person("Lilla", "Orr", role = "aut"),
    person("Efim", "Abrikosov", role = "ctb"),
    person("Shadie", "Khubba", role = "ctb"),
    person("William", "Meyerson", role = "ctb"),
    person("Shylee", "Ezroni", role = "ctb"),
    person("Junchao", "Ma", role = "ctb"))
c(person("Kirill", "Müller", role = c("aut"), email = "krlmlr+r@mailbox.org"),
  person("Lorenz", "Walthert", role = c("cre", "aut"), email = "lorenz.walthert@icloud.com"))
c(
  person("Leslie", "Huang",
         role = c("aut", "cph", "cre"),
         email = "lesliehuang@nyu.edu"),
  person(c("Patrick", "O."), "Perry",
         role = c("aut", "cph")),
  person("Arthur", "Spirling",
         role = c("aut", "cph")))
c(
          person(given = "Jurian", family = "Baas", email = "jurian@jurianbaas.nl", role = c("aut", "cre", "cph")),
          person(given = "Ad", family ="Feelders", email = "A.J.Feelders@uu.nl", role = c("ctb")))
c(
    person("Nicolas", "Ballarini", email = "nicolas.ballarini@meduniwien.ac.at",
    role = c("aut","cre")),
    person("Yi-Da", "Chiu", email = "yi-da.chiu@alumni.ucl.ac.uk", 
    role = c("aut")),
    person("Jake", "Conway", role = "ctb"),
    person("Nils", "Gehlenborg", role = "ctb"),
    person("Michal", "Bojanowski", role = "ctb"),
    person("Robin", "Edwards", role = "ctb"))
c(person(given = "Elizabeth M.", "Sweeney",  
  email = "elizabethmargaretsweeney@gmail.com", role = c("aut", "cre")),
  person(given = "John", "Muschelli",  
  email = "muschellij2@gmail.com", role = c("aut"))
  )
person("Lukasz", "Bartnik", email = "l.bartnik@gmail.com", role = c("aut", "cre"))
c(person("Yiying", "Fan", email = "y.fan67@csuohio.edu", role = c("aut", "cre")),person("Jiayang", "Sun", email = "jsun@case.edu", role = c("aut")),person("Xingye", "Qiao", email = "qiao@math.binghamton.edu", role = c("aut")))
c(person("Shenghai", "Dai", role = c("aut", "cre"), 
                     email = "dais@indiana.edu"),
             person("Xiaolin", "Wang", role = "aut"),
             person("Dubravka", "Svetina", role = "aut"))
c(person("Jorge" ,"Orestes Cerdeira", role="aut", email="jo.cerdeira@fct.unl.pt"), 
             person("Pedro", "Duarte Silva", role="aut", email="psilva@porto.ucp.pt"), 
             person("Jorge", "Cadima", role=c("aut","cre"),  email="jcadima@isa.utl.pt"), 
             person("Manuel",  "Minhoto", role="aut", email="minhoto@uevora.pt"))
c( person("Marwan","Hassani",role=c("aut","cre"),email="rsubspace@cs.rwth-aachen.de"),
             person("Matthias","Hansen",role=c("aut")),
             person("Emmanuel","Müller",role="ctb"),
             person("Ira","Assent",role="ctb"),
             person("Stephan","Günnemann",role="ctb"),
             person("Timm","Jansen",role="ctb"),
             person("Thomas","Seidl",role="ctb"),
             person("University of", "Waikato",role=c("ctb","cph")))
c(person(given = "Earo",
             family = "Wang",
             role = c("aut", "cre"),
             email = "earo.wang@gmail.com",
             comment = c(ORCID = "0000-0001-6448-5260")),
      person(given = "Di",
             family = "Cook",
             role = c("aut", "ths"),
             comment = c(ORCID = "0000-0002-3813-7155")),
      person(given = "Rob",
             family = "Hyndman",
             role = c("aut", "ths"),
             comment = c(ORCID = "0000-0002-2140-5352")))
person("Conor", "Neilson", email="condwanaland@gmail.com", role=
    c("aut", "cre"))
c(
    person(given = "Bryan D", family = "Martin", email= "bmartin6@uw.edu", role = c("cre","aut")), 
    person(given = "Zehang R", family = "Li", email= "lizehang@uw.edu", role = "aut"),
    person(given = "Yuan", family = "Hsiao", email = "yahsiao@uw.edu", role = "aut"),
    person(given = "Jessica", family = "Godwin", email = "jlg0003@uw.edu", role = "aut"),
    person(given = "Jon", family = "Wakefield", email = "jonno@uw.edu", role = "aut"),
    person(given = "Samuel J", family = "Clark", email = "work@samclark.net", role = "aut")
  )
c(
      person(
        "Mike", "Bostock"
        , role = c("aut", "cph")
        , comment = "d3.js library, http://d3js.org"
      ),
      person(
        "Kerry", "Rodden"
        , role = c("aut", "cph")
        , comment = "sequences library in htmlwidgets/lib, https://gist.github.com/kerryrodden/7090426"
      ),
      person(
        "Kevin", "Warne"
        , role = c("aut", "cph")
        , comment = "d2b sunburst library in htmlwidgets/lib, https://github.com/d2bjs/d2b"
      ),
      person(
        "Kent", "Russell"
        , role = c("aut", "cre")
        , comment = "R interface"
        , email = "kent.russell@timelyportfolio.com"
      ),
      person(
        "Florian", "Breitwieser"
        , role = c("ctb")
        , comment = "R interface"
      ),
      person(
        "CJ", "Yetman"
        , role = c("ctb")
        , comment = c("R interface", ORCID = "0000-0001-5099-9500")
        , email = "cj@cjyetman.com"
      )
    )
c(
    person("Vladimir", "Agafonkin", role = c("aut", "cph"),
       comment = "suncalc.js library/lib, https://github.com/mourner"),
    person("Benoit", "Thieurmel", role = c("aut", "cre"),
       comment = "R interface", email = "benoit.thieurmel@datastorm.fr")
    )
c(
    person("Satyaprakash", "Nayak", "", "sn248@cornell.edu", c("aut", "cre")),
    person("Scott D", "Cohen", "", "", c("ctb")),
    person("Alan C", "Hindmarsh", "", "", c("ctb")),
    person("Radu", "Serban", "", "", c("ctb")),
    person("Dan", "Shumaker", "", "", c("ctb")),
    person("Daniel R", "Reynolds", "", "", c("ctb")),
    person("Aaron", "Collier", "", "", c("ctb")),
    person("David", "Gardner", "", "", c("ctb")),
    person("Carol", "Woodward", "", "", c("ctb")),
    person("Slaven", "Peles", "", "", c("ctb")),
    person("Peter", "Brown", "", "", c("ctb")),
    person("Hilari C", "Tiedeman", "", "", c("ctb")),
    person("Ting", "Yan", "", "", c("ctb")),
    person("Lawrence Livermore National Security", role =  c("cph")),
    person("Southern Methodist University", role =  c("cph"))
    )
c(
  person("Wush", "Wu", email = "wush978@gmail.com", role = c("aut", "cre")), 
  person("Shang-Ying", "Shiu", email = "", role = c("aut", "ctb")))
c(person("Yun", "Ling", role = "aut"),
             person("Martin", "Lysy",
	            email = "mlysy@uwaterloo.ca",
		    role = c("aut", "cre")))
c(person("Rebecca", "Barter", email = "rebeccabarter@berkeley.edu",
  role = c("aut", "cre")), person("Bin", "Yu", email = "binyu@berkeley.edu", role = c("aut")))
c(person("Eric", "Polley", email = "polley.eric@mayo.edu", role = c("aut", "cre")), person("Erin", "LeDell", role = c("aut")), person("Chris", "Kennedy", role = c("aut")), person("Sam", "Lendle", role = c("ctb")), person("Mark", "van der Laan", role = c("aut", "ths")))
c(person("Jim", "Stigler", email = "jstigler@gmail.com", role = c("cre", "aut")),
             person("Jeff", "Chrabaszcz", role = "aut"))
c(
  person("Brandon", "Greenwell",
         email = "greenwell.brandon@gmail.com",
         role = c("aut", "cre")),
  person("Andrew", "McCarthy",
         email = "andrew.mccarthy@theperducogroup.com",
         role = c("aut")),
  person("Brad", "Boehmke",
         email = "bradleyboehmke@gmail.com",
         role = c("aut")),
  person("Dungang", "Liu",
         email = "dungang.liu@uc.edu",
         role = c("ctb"))
  )
c(person("John", "Lawrence", email = "john.lawrence@fda.hhs.gov",role = c("aut", "cre")),
    person("Jianjin","Xu",role="ctb"),
    person("Sue Jane","Wang",role="ctb"),
    person("Jim","Hung",role="ctb"))
c(MH = person("Michael", "Höhle",
                  email = "hoehle@math.su.se",
                  role = c("aut", "ths"),
                  comment = c(ORCID = "0000-0002-0423-6702")),
             SM = person("Sebastian", "Meyer",
                  email = "seb.meyer@fau.de",
                  role = c("aut", "cre"),
                  comment = c(ORCID = "0000-0002-1791-9449")),
             MP = person("Michaela", "Paul", role = "aut"),
             LH = person("Leonhard", "Held",
                  email = "Leonhard.Held@uzh.ch",
                  role = c("ctb", "ths")),
             person("Howard", "Burkom", role = "ctb"),
             person("Thais", "Correa", role = "ctb"),
             person("Mathias", "Hofmann", role = "ctb"),
             person("Christian", "Lang", role = "ctb"),
             person("Juliane", "Manitz", role = "ctb"),
             person("Andrea", "Riebler", role = "ctb"),
             person("Daniel", "Sabanés Bové", role = "ctb"),
             MS = person("Maëlle", "Salmon", role = "ctb"),
             DS = person("Dirk", "Schumacher", role = "ctb"),
             person("Stefan", "Steiner", role = "ctb"),
             person("Mikko", "Virtanen", role = "ctb"),
             person("Wei", "Wei", role = "ctb"),
             person("Valentin", "Wimmer", role = "ctb"),
             person("R Core Team", role = "ctb",
                    comment = "A few code segments are modified versions of
                               code from base R"))
c(person(given=c("Dennis", "M."), family="Feehan", 
                    email="feehan@berkeley.edu", 
                    role=c("aut", "cre")),
             person(given=c("Matthew", "J."), family="Salganik", 
                    email="mjs3@princeton.edu",
                    role="ths"))
person("Andrie", "de Vries", role=c("aut", "cre", "cph"),
    email="apdevries@gmail.com")
c(person("Juris", "Breidaks", email = "rcsb@csb.gov.lv",  role = c("aut", "cre")), person("Martins", "Liberts", role = "aut"), person("Janis", "Jukams", role = "aut"))
person(given = "Gianluca",
           family = "Baio",
           role = c("aut", "cre"),
           email = "gianluca@stats.ucl.ac.uk")
c(person(c("Terry", "M"), "Therneau",
                    email="therneau.terry@mayo.edu",
	            role=c("aut", "cre")),
               person("Thomas", "Lumley", role=c("ctb", "trl"),
	       comment="original S->R port and R maintainer until 2009"))
person("Marcel", "Wiesweg", email = "marcel.wiesweg@uk-essen.de", role = c("aut", "cre"))
person("Dominic", "Pearce", email = "dominic.pearce@ed.ac.uk", 
    role = c("aut", "cre"))
c(
    person("Alboukadel", "Kassambara", role = c("aut", "cre"), email = "alboukadel.kassambara@gmail.com"),
    person("Marcin", "Kosinski", role = c("aut"), email = "m.p.kosinski@@gmail.com"),
    person("Przemyslaw", "Biecek", role = c("ctb"), email = "przemyslaw.biecek@gmail.com"),
    person("Scheipl", "Fabian", role = c("ctb"), email = "fabian.scheipl@gmail.com"))
person("Daniel", "Lindholm", email = "daniel.lindholm@me.com", role = c("aut", "cre"))
c(
    person("David", "Benkeser", email = "benkeser@emory.edu",
           role = c("aut", "cre", "cph"),
           comment = c(ORCID = "0000-0002-1019-8343")),
    person("Nima", "Hejazi", email = "nhejazi@berkeley.edu",
           role = "aut",
           comment = c(ORCID = "0000-0002-7127-2789"))
  )
person("Fong Chun", "Chan", , "fongchunchan@gmail.com", role = c("aut", "cre"))
c(
  person("Aleksandra", "Grudziaz", email = "aleksandra.grudziaz@gmail.com", role = c("aut", "cre")),
  person("Alicja", "Gosiewska", email = "alicjagosiewska@gmail.com", role = c("aut")),
  person("Przemyslaw", "Biecek", email = "przemyslaw.biecek@gmail.com", role = c("aut", "ths"))
  )
c(person("Alexander", "Lange", role = c("aut", "cre"), email = "alexander.lange@uni-goettingen.de"), person("Bernhard", "Dalheimer", role = c("aut"), email = "bernhard.dalheimer@uni-goettingen.de"), person("Helmut", "Herwartz", role = c("aut"), email = "hherwartz@uni-goettingen.de"), person("Simone", "Maxand", role = c("aut"), email = "smaxand@uni-goettingen.de"), person("Hannes", "Riebl", role = c("ctb")))
c(person("Philippe", "Grosjean", role = c("aut", "cre"),
  email = "phgrosjean@sciviews.org"))
person("Yingyan", "Zhu", email = "lexi.yy.zhu@gmail.com", role = c("aut", "cre"))
c(
  person("Hadley", "Wickham", , "hadley@rstudio.com", "aut"),
  person("Lionel", "Henry", , "lionel@rstudio.com", c("aut", "cre")),
  person("T Jake", "Luciani", , "jake@apache.org", "aut"),
  person("Matthieu", "Decorde", , "matthieu.decorde@ens-lyon.fr", "aut"),
  person("Vaudor", "Lise", , "lise.vaudor@ens-lyon.fr", "aut"),
  person("Tony", "Plate", role = "ctb", comment = "Early line dashing code"),
  person("David", "Gohel", role = "ctb", comment = "Line dashing code and raster code"),
  person("Yixuan", "Qiu", role = "ctb", comment = "Improved styles; polypath implementation"),
  person("Håkon", "Malmedal", role = "ctb", comment = "Opacity code"),
  person("RStudio", role = "cph")
  )
c(
              person(
                "Anders", "Riutta et. al.", role = c("aut", "cph"),
                comment = "svg-pan-zoom.js BSD-licensed library in htmlwidgets/lib,
                https://github.com/ariutta/svg-pan-zoom"
              ),
              person(
                "Jorik", "Tangelder", role = c("aut", "cph"),
                comment = "hammer.js MIT-licensed touch library in htmlwidgets/lib,
                https://github.com/hammerjs/hammer"
              ),
              person(
                "Kent", "Russell", role = c("aut", "cre"),
                comment = "R interface to svg-pan-zoom.js",
                email = "kent.russell@timelyportfolio.com"
              )
            )
c(person("Philippe", "Grosjean", role = c("aut", "cre"),
  email = "phgrosjean@sciviews.org"),
  person("Romain", "Francois", role = "ctb",
  email = "romain@r-enthusiasts.com"),
  person("Kamil", "Barton", role = "ctb",
  email = "kamil.barton@uni-wuerzburg.de"))
c(person("Do Hyun", "Kim", email = "09dohkim@gmail.com", role = c("aut","cre")),person("Seung Jun", "Shin", email = "sjshin@korea.ac.kr", role = c("aut")))
c(person("Philippe", "Grosjean", role = c("aut", "cre"),
  email = "phgrosjean@sciviews.org"),
  person("Matthew", "Dowle", role = "ctb",
  email = "mdowle@mdowle.plus.com"))
c(person("Philippe", "Grosjean", role = c("aut", "cre"),
  email = "phgrosjean@sciviews.org"),
  person("Romain", "Francois", role = "aut",
  email = "romain@r-enthusiasts.com"))
c(person("Xingye", "Qiao", email = "qiao@math.binghamton.edu", role = c("aut", "cre")),person("Jiayang", "Sun", email = "jsun@case.edu", role = c("aut")),person("Yiying", "Fan", email = "Y.FAN67@csuohio.edu", role = c("aut")))
c(
    person("Javier", "Luraschi", email = "javier@rstudio.com", role = c("aut", "cre")),
    person(family = "RStudio", role = c("cph")),
    person(family = "SmartBear Software", role = c("aut", "cph"))
    )
person("Lee F. Richardson", email = "leerichardson2013@gmail.com", role = c("aut", "cre"))
c(
    person("Sean", "Kross", email = "sean@seankross.com", role = c("aut", "cre")),
    person("Nick", "Carchedi", role = "aut"),
    person("Bill", "Bauer", role = "aut"),
    person("Gina", "Grdina", role = "aut"),
    person("Filip", "Schouwenaars", role = "ctb"),
    person("Wush", "Wu", role = "ctb")
    )
c(
    person("Sean", "Kross", , "sean@seankross.com", c("aut", "cre")),
    person("Nick", "Carchedi", role = "aut"),
    person("Chih-Cheng", "Liang", role = "ctb"),
    person("Wush", "Wu", role = "ctb")
    )
c(
  person("Dominik", "Leutnant", email = "leutnant@fh-muenster.de", role = c("aut", "cre"), comment = c(ORCID = "0000-0003-3293-2315")), 
  person("Anneke", "Doering", email = "doering@hydromech.uni-hannover.de", role = c("aut"))
  )
c(
  person(given = "Marcus W.", family = "Beck",
    role = c("aut","cre"),
    email = "marcusb@sccwrp.org"),
  person(given = "Kimberly", family = "Cressman", 
    role = c("ctb"),
    email = "kimberly.cressman@dmr.ms.gov")
    )
c(
  person(given = "Julie", family = "Padilla",
    role = c("aut", "cre"),
	email = "jpadilla@limno.com"),
  person(given = "Marcus", family = "Beck",
    role = c("ctb"),
	email = "marcus@sccwrp.org"),
  person(given = "Kimberly", family = "Cressman",
    role = c("ctb"),
	email = "kimberly.cressman@dmr.ms.gov"),
  person(given = "Bob", family = "Rudis",
    role = c("ctb"),
	email = "bob@rud.is")
	)
c(
         person("Mayo", "Roettger", role = c("cre"), email = "mayo.roettger@hhu.de"),
	     person("Gabriel", "Gelius-Dietrich", role = "aut"),
             person(c("C.", "Jonathan"), "Fritzemeier", role = "ctb", email = "clausjonathan.fritzemeier@uni-duesseldorf.de"),
             person("Rajen", "Piernikarczyk", role = "ctb"),
             person(c("Marc", "Andre"), "Daxer", role = "ctb"),
             person("Benjamin", "Braasch", role = "ctb"),
             person("Abdelmoneim", "Desouki", role = "ctb"),
             person(c("Martin", "J."), "Lercher", role = "ctb"))
c(person("Abdelmoneim", "Amer Desouki", role = c("aut","cre"), email = "abdelmoneim.amer@uni-duesseldorf.de"))
c(person("Martin", "Rimmler", role = c("aut", "cre"),
	   email = "martin.rimmler@gmail.com"),
	person("Thomas", "Mueller", role = "aut"))
c(person("Jeroen", "Ooms", role = c("aut", "cre"), 
      email = "jeroen@berkeley.edu", comment = c(ORCID = "0000-0002-4035-0289")),
    person("Gábor", "Csárdi", , "csardi.gabor@gmail.com", role = "ctb"))
person("Matthew", "Jockers", email = "mjockers@gmail.com",
  role = c("aut", "cre"))
person("Seth", "Wenchel", email = "seth@wenchel.com", role = c("aut", "cre", "cph"))
c(
    person("Theo", "Boutaris", email = "teoboot2007@hotmail.com", role = c("aut", "cre", "cph")),
    person("Clemens", "Zauchner", email = "cst.zauchner@gmail.com", role = "aut"))
c(person("Petr", "Lenhard", email = "petr.lenhard@inferencetech.com", role = c("aut", "cre")),	      
	      person("Inference Technologies", email = "mail@inferencetech.com", role = "cph"),
              person("Petra", "Hudeckova", email = "hudeckovapetra@gmail.com", role = "ctb"))
c(person(given = "Ernest", family = "Kwan", role="aut"),
        person(given = "Michael", family = "Friendly", role=c("aut",
        "cre"), email="friendly@yorku.ca") )
person("Jimmy", "Oh", role = c("aut", "cre"), email = "joh024@aucklanduni.ac.nz")
c(person("Thomas J.", "Leeper",
                    role = "aut", 
                    email = "thosjleeper@gmail.com",
                    comment = c(ORCID = "0000-0003-4097-6326")),
             person("Tom", "Paskhalis",
                    role = "cre",
                    email = "tpaskhalis@gmail.com",
                    comment = c(ORCID = "0000-0001-9298-8850")),
             person("David", "Gohel",
                    role = "ctb",
                    comment = "rOpenSci reviewer"),
             person("Lincoln", "Mullen",
                    role = "ctb",
                    comment = "rOpenSci reviewer"))
c(person("Thomas J.", "Leeper",
                    role = "aut", 
                    email = "thosjleeper@gmail.com",
                    comment = c(ORCID = "0000-0003-4097-6326")),
             person("Tom", "Paskhalis",
                    role = "cre",
                    email = "tpaskhalis@gmail.com",
                    comment = c(ORCID = "0000-0001-9298-8850")),
             person("Manuel", "Aristarán", role = c("ctb", "cph"), 
                    comment = "tabula java library"))
c(person("Johan", "Larsson", email = "johanlarsson@outlook.com",
                    role = c("aut", "cre"),
                    comment = c(ORCID = "0000-0002-4029-5945")),
             person("Deepayan", "Sarkar", role = c("ctb", "cph"),
                    comment = "lattice"),
             person("Brian", "Ripley", role = c("ctb"),
                    comment = "stats::plot.acf"))
c(person("Lukas", "Burk", email = "lukas@quantenbrot.de", role = c("aut", "cre")),
             person("Tobias", "Anton", email = "unsichtbarer@quantenbrot.de", role = "aut"),
             person("Daniel", "Lüdecke", email = "d.luedecke@uke.de", role = "ctb"))
c(person(given = "Isabella", family = "Gollini", role = c("aut","cre"), email = "igollini.stats@gmail.com"), person("Jonathan", "Rougier", role = "ctb"))
person("Thomas", "Mailund", , "mailund@birc.au.dk", c("aut", "cre"))
c(person("Stephanie", "Kobakian", email = "stephanie.kobakian@gmail.com", role = c("aut", "cre")),
             person("Mitchell", "O'Hara-Wild", email = "mail@mitchelloharawild.com", role = c("aut")))
c(person("Shawn",    "Garbett",  email = "Shawn.Garbett@vumc.org",        role = c("aut", "cre")),
             person("Thomas",   "Stewart",  email = "Thomas.Stewart@Vanderbilt.edu", role = c("ctb")),
             person("Jennifer", "Thompson", email = "Jennifer.L.Thompson@vumc.org",  role = c("ctb")),
             person("Frank",    "Harrell",  email = "F.Harrell@Vanderbilt.edu",      role = c("ctb"))
             )
c(person(given = "Edgar", family = "Kublin", role = "aut"),
             person(given = "Johannes", family = "Breidenbach", role = c("ctb","cre"),
					email = "job@nibio.no"))
c(person("Jan", "Wijffels", role = c("aut", "cre", "cph"), email = "jwijffels@bnosac.be"), person("BNOSAC", role = "cph"), person("Oliver", "Belmans", role = c("cph", "aut")))
c(person("Christian", "Buchta", role = "aut"),
             person("Kurt", "Hornik", role = c("aut", "cre"),
                    email = "Kurt.Hornik@R-project.org"),
             person("Ingo", "Feinerer", role = "aut"),
             person("David", "Meyer", role = "aut"))
c(person("Luca", "Weihs", email="lucaw@uw.edu", role=c("aut", "cre")),
            person("Emin", "Martinian", role=c("ctb"), comment="Created the red-black tree library included in package."))
person("Lukas Burk", email = "lukas@quantenbrot.de", role = c("aut", "cre"))
c(
    person("Scott", "Chamberlain", role = "aut", 
       email = "myrmecocystus+r@gmail.com", comment = c(ORCID = "0000-0003-1444-9135")),
    person("Zachary", "Foster", role = c("aut", "cre"), 
       email = "zacharyfoster1989@gmail.com", comment = c(ORCID = "0000-0002-5075-0948"))
    )
c(person(given = "Scott",
             family = "Chamberlain",
             role = c("aut", "cre"),
             email = "myrmecocystus@gmail.com",
             comment = c(ORCID = "0000-0003-1444-9135")),
      person(given = "Eduard",
             family = "Szoecs",
             role = "aut"),
      person(given = "Zachary",
             family = "Foster",
             role = "aut"),
      person(given = "Zebulun",
             family = "Arendsee",
             role = "aut"),
      person(given = "Carl",
             family = "Boettiger",
             role = "ctb"),
      person(given = "Karthik",
             family = "Ram",
             role = "ctb"),
      person(given = "Ignasi",
             family = "Bartomeus",
             role = "ctb"),
      person(given = "John",
             family = "Baumgartner",
             role = "ctb"),
      person(given = "James",
             family = "O'Donnell",
             role = "ctb"),
      person(given = "Jari",
             family = "Oksanen",
             role = "ctb"),
      person(given = "Bastian Greshake",
             family = "Tzovaras",
             role = "ctb"),
      person(given = "Philippe",
             family = "Marchand",
             role = "ctb"),
      person(given = "Vinh",
             family = "Tran",
             role = "ctb"),
      person(given = "Maëlle",
             family = "Salmon",
             role = "ctb"),
      person(given = "Gaopeng", 
             family = "Li",
             role = "ctb"),
      person(given = "rOpenSci",
             role = "fnd",
             comment = "https://ropensci.org/"))
person("Scott", "Chamberlain", email = "myrmecocystus+r@gmail.com",
    role = c("aut", "cre"))
person("Miguel", "Alvarez", email="kamapu78@gmail.com", role=c("aut", "cre"))
c(person("Scott", "Sherrill-Mix", role = c("aut", "cre"),
                     email = "shescott@upenn.edu"))
c(
    person("Vijay", "Barve", ,"vijay.barve@gmail.com", role = c("aut", "cre"), 
      comment = c(ORCID = "0000-0002-4852-2567")))
person(given = "Michael W.",
           family = "Kearney",
           role = c("aut", "cre"),
           email = "kearneymw@missouri.edu",
           comment = c(ORCID = "0000-0002-0730-4694"))
c(
    person("Michael", "Schramm", role=c("aut", "cre"),
      email = "michael.schramm@ag.tamu.edu",
      comment = c(ORCID = "0000-0003-1876-6592")),
    person("Frank", "Harrell", role=c("ctb"))
    )
c(person("Boris P.", "Hejblum", role = c("aut", "cre"),
		     email = "boris.hejblum@u-bordeaux.fr"), 
		     person("Damien", "Chimits", role = c("aut")), 
		     person("Anthony", "Devaux", role = c("aut"))
		    )
c(person("Denis", "Agniel", role = c("aut"),
                    email = "denis.agniel@gmail.com"),
	           person("Boris P.", "Hejblum", role = c("aut", "cre"),
	                  email = "boris.hejblum@u-bordeaux.fr"))
c(person("Dayne L", "Filer", role = c("aut", "cre"),
    email = "dayne.filer@gmail.com"), person("Parth", "Kothiya", role = "ctb"),
    person("Woodrow R", "Setzer", role = "ctb"), person("Richard S", "Judson",
    role = "ths"), person("Matthew T", "Martin", role = c("ctb", "ths"),
    email = "martin.matthew@epa.gov"))
c(person("Paul", "Pearson", email = "pearsonp@hope.edu", 
    role = c("aut", "cre","trl")), person("Daniel", "Muellner", 
    role = c("aut","ctb")), person("Gurjeet","Singh", role = c("aut","ctb")))
c(
    person("Raoul", "Wadhwa", email = "raoulwadhwa@gmail.com", role = c("aut", "cre")),
    person("Andrew", "Dhawan", role = "aut"),
    person("Drew", "Williamson", role = "aut"),
    person("Jacob", "Scott", role = "aut"))
person("Oscar", "Perpinan Lamigueiro", email="oscar.perpinan@gmail.com", role=c('cre', 'aut'))
person("Ed", "Baker", email = "ed@ebaker.me.uk", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-5887-9543"))
c(person("Xiongtao", "Dai", email="xdai@iastate.edu", 
                   role=c("aut", "cre", "cph")),
            person("Hao", "Wang", role="aut"), 
            person("Jan", "Dvorak", role="ctb"), 
            person("Jeffrey", "Bennetzen", role="ctb"), 
            person("Hans-Georg", "Mueller", role="ctb"))
c(
    person("Jason", "Freels", email = "auburngrads@live.com", role = c("aut","cre")),
    person("Bradley", "Boehmke", email = "bradleyboehmke@gmail.com", role = c("ctb"))
    )
c(
    person("Ben", "Baumer", email = "ben.baumer@gmail.com", role = c("aut", "cre")), 
    person("Gregory", "Matthews", email = "gjm112@gmail.com", role = c("aut")))
c(
      person("David", "Ciar", email = "dciar86@ceh.ac.uk", role = c("cre","aut")),
      person("James", "Wright", email = "j.s.wright@liverpool.ac.uk", role = c("aut"))
    )
person("Ernest", "Benedito", email = "ebeneditos@gmail.com", role = c("aut", "cre"))
person("George", "Wang", email = "biogeek@gmail.com", role = c("aut", "cre"))
c(
    person("Christoph", "Sax", role = c("aut", "cre"), email = "christoph.sax@gmail.com"),
    person("Peter", "Steiner", role = c("aut")),
    person("Tommaso", "Di Fonzo", role = "ctb"))
person("Etienne", "Low-Decarie", email = "etienne.decarie@gmail.com",
  role = c("aut", "cre"))
c(
  person("Brantly", "Callaway", email = "brantly.callaway@temple.edu", role = c("aut","cre")), person("Weige", "Huang", email = "weige.huang@temple.edu", role=c("aut")))
person("Zachary", "McCaw", email = "zmccaw@g.harvard.edu", role = c("aut", "cre"))
c(person("Suleiman A", "Khan", role = c("aut","cre"),
		     email = "khan.suleiman@gmail.com"),
		person("Muhammad", "Ammad-ud-din", role = "aut",
		     email = "muhammad.ammad-ud-din@aalto.fi"))
c(
  person("JJ", "Allaire", role = c("aut", "cre"), email = "jj@rstudio.com"),
  person(family = "RStudio", role = c("cph", "fnd")),
  person("Yuan", "Tang", role = c("aut", "cph"),
         email = "terrytangyuan@gmail.com",
         comment = c(ORCID = "0000-0001-5243-233X")),
  person("Dirk", "Eddelbuettel", role = c("ctb", "cph"),
         email = "edd@debian.org"),
  person("Nick", "Golding", role = c("ctb", "cph"),
         email = "nick.golding.research@gmail.com"),
  person("Tomasz", "Kalinowski", role = c("ctb", "cph")),
  person(family = "Google Inc.", role = c("ctb", "cph"),
         comment = "Examples and Tutorials")
  )
person("Robert","Zamora", email = "zamora.rr@gmail.com", role = c("aut", "cre"))
c(person("David", "Gerard", email = "gerard.1787@gmail.com", role = c("aut", "cre"), comment = c(ORCID = "0000-0001-9450-5023")),
             person("Peter", "Hoff", email = "peter.hoff@duke.edu", role = "aut"))
c(
	person("Pavel N.", "Krivitsky", role=c("aut","cre"), email="pavel@uow.edu.au", comment=c(ORCID="0000-0002-9101-3362")),
        person("Mark S.", "Handcock", role=c("aut","ths"), email="handcock@stat.ucla.edu"),
        person("David R.", "Hunter", role=c("ctb"), email="dhunter@stat.psu.edu"),
        person("Steven M.", "Goodreau", role=c("ctb","ths"), email="goodreau@u.washington.edu"),
        person("Martina", "Morris", role=c("ctb","ths"), email="morrism@u.washington.edu"),
	person("Nicole Bohme", "Carnegie", role=c("ctb"), email="nicole.carnegie@nyu.edu"),
        person("Carter T.", "Butts", role=c("ctb"), email="buttsc@uci.edu"),
	person("Ayn", "Leslie-Cook", role=c("ctb"), email="aynlc3@uw.edu"),
	person("Skye", "Bender-deMoll", role=c("ctb"), email="skyebend@skyeome.net"),
  person("Li","Wang",role=c("ctb"),email="lxwang@gmail.com"),
  person("Kirk","Li",role=c("ctb"),email="kirkli@uw.edu"))
c(person('Martin R.', 'Smith',
                  email='martin.smith@durham.ac.uk',
                  role=c("aut", "cre", "cph"), 
                  comment=c(ORCID = '0000-0001-5660-1727'))
            )
person("Christopher", "Prener", email = "chris.prener@slu.edu",
                  role = c("aut", "cre"))
person("Yihui", "Xie", role = c("aut", "cre"), email = "xie@yihui.name", comment = c(ORCID = "0000-0003-0645-5666"))
c(
    person("Hadley", "Wickham", , "hadley@rstudio.com", c("aut", "cre")),
    person("RStudio", role = c("cph", "fnd")),
    person("R Core team", role = "ctb",
      comment = "Implementation of utils::recover()")
    )
c(person("Hugh", "Parsonage", role = c("aut", "cre"), email = "hugh.parsonage@gmail.com"))
c(person("Harry", "Southworth", role=c("aut", "cre"),
                    email="harry.southworth@gmail.com"),
             person(c("Janet", "E."), "Heffernan", role="aut"),
             person(c("Paul", "D."), "Metcalfe", role="aut",
                    ,email="paul.metcalfe@gmail.com"),
             person("Yiannis", "Papastathopoulos", role="ctb"),
             person("Alec", "Stephenson", role="ctb"),
             person("Stuart", "Coles", role="ctb"))
c(
              person(given = 'Jonathan',family = 'Sidi',email = 'yonis@metrumrg.com',role = c('aut','cre')),
              person(given = 'Daniel',family = 'Polhamus',email = 'danp@metrumrg.com',role = c('aut'))
              )
c(person("Dmitriy", "Selivanov", role = c("aut", "cre", "cph"),
    email = "selivanov.dmitriy@gmail.com"), 
    person("Qing", "Wang", role = c("aut", "cph"),
    email = "selivanov.dmitriy@gmail.com", 
    comment = "Author of the WaprLDA C++ code"))
c(person("Kurt", "Hornik", role = c("aut", "cre"),
                    email = "Kurt.Hornik@R-project.org"),
             person("Johannes", "Rauch", role = "aut"),
             person("Christian", "Buchta", role = "aut",
                    email = "christian.buchta@wu.ac.at"),
             person("Ingo", "Feinerer", role = "aut",
                    email = "feinerer@logic.at"))
c( 
        person("Tyler", "Rinker", email = "tyler.rinker@gmail.com", role = c("aut", "cre")), 
        person("ctwheels", "StackOverflow", role = "ctb") 
    )
person("Michael Wayne", "Kearney", , 
    "kearneymw@missouri.edu", role = c("aut", "cre"), 
    comment = c(ORCID = "0000-0002-0730-4694"))
person("Patrick", "Reidy", email = "patrick.francis.reidy@gmail.com", 
    role = c("aut", "cre"))
c(
    person("Thomas", "Jones", role = c("aut", "cre"), email = "jones.thos.w@gmail.com"),
    person("William", "Doane", email = "wil@drdoane.com", role = "ctb")
    )
c(
    person("Jan", "Idziak", email = "JanIdziak@gmail.com", role = "cre"),
    person("Maciej", "Eder", email = "maciejeder@gmail.com", role = "aut"),
    person("Tomasz", "Melcer", email = "liori@exroot.org", role = "aut"),
    person("Andrew", "Goldstone", email = "andrew.goldstone@rutgers.edu", role = "cph"))
c(person('Jan', 'Wijffels', role = c('aut', 'cre', 'cph'), email = 'jwijffels@bnosac.be'), person('BNOSAC', role = 'cph'))
c(person("Tyler", "Rinker", email = "tyler.rinker@gmail.com", role = c("aut", "cre")),
           person("Bryan", "Goodrich", role = "ctb"), person("Dason", "Kurkiewicz", role = "ctb"))
c(person("Lincoln", "Mullen", role = c("aut", "cre"),
    email = "lincoln@lincolnmullen.com"))
c(
        person("Tyler", "Rinker", email = "tyler.rinker@gmail.com", role = c("aut", "cre")), 
        person("Joran", "Elias", role = "ctb"), 
        person("Matthew", "Flickinger", role = "ctb"), 
        person('Paul', 'Foster', role = "ctb")
    )
c(person("Tyler", "Rinker", email = "tyler.rinker@gmail.com", role = c("aut",
         "cre")))
c(
  person("JJ", "Allaire", role = c("aut", "cre"),
         email = "jj@rstudio.com"),
  person("Yuan", "Tang", role = c("aut"),
         email = "terrytangyuan@gmail.com",
         comment = c(ORCID = "0000-0001-5243-233X")),
  person("Kevin", "Ushey", role = c("aut"),
         email = "kevin@rstudio.com"),
  person("RStudio", role = c("cph", "fnd")),  
  person(family = "Google Inc.", role = c("cph"))
  )
c(
  person("JJ", "Allaire", role = c("aut"),
         email = "jj@rstudio.com"),
  person("Yuan", "Tang", role = c("aut"),
         email = "terrytangyuan@gmail.com",
         comment = c(ORCID = "0000-0001-5243-233X")),
  person("Kevin", "Ushey", role = c("aut"),
         email = "kevin@rstudio.com"),
  person("Kevin", "Kuo", role = c("aut", "cre"),
         email = "kevin.kuo@rstudio.com",
         comment = c(ORCID = "0000-0001-7803-7901")),
  person("Daniel", "Falbel", role = c("ctb", "cph")),
  person("RStudio", role = c("cph", "fnd")),  
  person(family = "Google Inc.", role = c("cph"))
  )
c(
  person("JJ", "Allaire", role = c("aut", "cre"), email = "jj@rstudio.com"),
  person(family = "RStudio", role = c("cph", "fnd")),
  person("Mike", "Bostock", role = c("cph"),
         comment = "D3 library - https://d3js.org/"),
  person("Masayuki", "Tanaka", role = c("cph"),
         comment = "C3 library - http://c3js.org/"),
  person(family = "jQuery Foundation", role = "cph",
         comment = "jQuery library"),
  person(family = "jQuery contributors", role = c("cph"),
         comment = "jQuery library; authors: inst/views/components/jquery-AUTHORS.txt"),
  person("Shaun", "Bowe", role = c("cph"),
         comment= "jQuery visibilityChanged plugin"),
  person(family = "Materialize", role = c("cph"),
         comment = "Materizlize library - https://materializecss.com/"),
  person("Yuxi", "You", role = c("cph"),
         comment = "Vue.js library - https://vuejs.org/"),
  person("Kevin", "Decker", role = c("cph"),
         comment = "jsdiff library - https://github.com/kpdecker/jsdiff/"),
  person("Rodrigo", "Fernandes", role = c("cph"),
         comment = "diff2html library - https://diff2html.xyz/"),
  person("Ivan", "Sagalaev", role = c("cph"),
         comment = "highlight.js library - https://highlightjs.org/"),
  person("Yauheni", "Pakala", role = c("cph"),
         comment = "highlightjs-line-numbers library")
  )
person("Michael W.", "Kearney", ,
      "kearneymw@missouri.edu", role = c("aut", "cre"),
      comment = c(ORCID = "0000-0002-0730-4694"))
c(
    person("Jun", "Peng", role = c("aut","cre"), email="pengjun10@mails.ucas.ac.cn"),
    person("Jorge", "More", role = "ctb"), 
    person("Burton", "Garbow", role = "ctb"), 
    person("Kenneth", "Hillstrom", role = "ctb"),
    person("John", "Burkardt", role = "ctb"),
    person("Linda R.", "Petzold", role = "ctb"),
    person("Alan C.", "Hindmarsh", role = "ctb"),
    person("R. Woodrow", "Setzer", role = "ctb"),
    person("Andrew", "Horchler", role = "ctb")
    )
c(person("Torsten", "Hothorn", role = c("aut", "cre"),
                    email = "Torsten.Hothorn@R-project.org"))
c(person("Dirk", "Schumacher", email = "mail@dirk-schumacher.net", role = c("aut", "cre")))
c(person(given = "Naoto", family = "Koshimizu", email = "orehacks@gmail.com", role  = c("aut", "cre")))
c(
    person("Stephen", "Woloszynek", email = "sw424@drexel.edu", role = c("aut", "cre"))
    )
c(
    person("Jacob", "Price", email = "jrp355@drexel.edu", role = c("aut")),
    person("Stephen", "Woloszynek", email = "sw424@drexel.edu", role = c("cre","aut"))
    )
c(
  person("Rob", "Hyndman", role=c("aut", "cre", "cph"), email="Rob.Hyndman@monash.edu"),
  person("Nikolaos", "Kourentzes", role=c("aut","cph")))
c(person("Rich", "FitzJohn", role = c("aut", "cre"),
    email = "rich.fitzjohn@gmail.com"),
    person("Howard", "Chu", role = "cph"),
    person("Symas Corporation", role = "cph"),
    person("Martin", "Hedenfalk", role = "cph"),
    person("The OpenLDAP Foundation", role = "cph"))
c(person("Tobias", "Muetze", role=c("aut","cre"),
                    email="tobias.muetze@outlook.com"),
             person("Tim", "Friede", role="ctb",
                    email="tim.friede@med.uni-goettingen.de"))
c(
    person("B. W.", "Lewis", role=c("aut","cre"), email="blewis@illposed.net"),
    person(given="Three.js authors", role="cph", comment="three.js JavaScript library"),
    person("Alexey", "Stukalov", role=("ctb"), email="astukalov@gmail.com"),
    person("Yihui","Xie", role=("ctb"), email="xie@yihui.name"),
    person("Andreas", "Briese", role=("ctb"), email="ab@edutoolbox.de"),
    person("B.",  "Thieurmel", role=("ctb"), email="bthieurmel@gmail.com")
    )
c(
    person(c("Paul", "J."), "Northrop", email = "p.northrop@ucl.ac.uk", 
      role = c("aut", "cre", "cph")),
    person("Nicolas", "Attalides", role = "aut")
    )
c(person("Marek", "Jagielski",
                    role = c("aut", "cre", "cph"),
                    email = "marek.jagielski@gmail.com"),
             person("Lixin", "Yu",
                    role = c("aut", "cph")))
c(
    person("Kirill", "Müller", , "krlmlr+r@mailbox.org", c("aut", "cre")),
    person("Hadley", "Wickham", , "hadley@rstudio.com", "aut"),
    person("Romain", "Francois", , "romain@r-enthusiasts.com", "ctb"),
    person("RStudio", role = "cph")
    )
c(
    person("Moritz","Mueller-Navarra", email="muellernavarra@gmail.com",role=c("aut","cre")),
    person("Sylvin","Mueller-Navarra", email="4@sylvin.de",role="aut"),
    person("Andreas", "Boesch", role="ctb"))
c(
        person("Matthew", "Kay", role = c("aut", "cre"), email = "mjskay@umich.edu"),
        person("Timothy", "Mastny", role = "ctb", email = "tim.mastny@gmail.com")
    )
c(
    person("Mika", "Braginsky", email = "mika.br@gmail.com", role = c("aut", "cre")),
    person("Daniel", "Yurovsky", email = "dyurovsky@gmail.com", role = c("aut"))
    )
c(
    person(given = "Kyle", family = "Walker", email="kyle.walker@tcu.edu", role=c("aut", "cre")), 
    person(given = "Kris", family = "Eberwein", email = "eberwein@knights.ucf.edu", role = "ctb"))
c(person("Constantin", "Ahlmann-Eltze", email = "artjom31415@googlemail.com", role = c("aut", "cre")),
    person("Stan Developers", role="cph", 
           comment="Code from the Stan Math library is reused in 'cluster_interval.cpp'"),
    person("David", "Robinson", role="cph", 
           comment="Code from the fuzzyjoin package is reused"))
c(person("Sam", "Albers", email = "sam.albers@gov.bc.ca", role = c("aut", "cre"),
    comment = c(ORCID = "0000-0002-9270-7884")),
    person("David", "Hutchinson", email = "david.hutchinson@canada.ca", role = "ctb"),
    person("Dewey", "Dunnington", email = "dewey@fishandwhistle.net", role = "ctb"),
    person("Ryan", "Whaley", email = "rdgwhaley@gmail.com", role = "ctb"),
    person("Province of British Columbia", role = "cph"),
    person("Luke", "Winslow", role = "rev", 
            comment = "Reviewed for rOpenSci"),
    person("Laura", "DeCicco", role = "rev", 
            comment = "Reviewed for rOpenSci")
    )
c(
    person(c("Joshua", "M"), "Rosenberg", role = c("aut", "cre"), email = "jmichaelrosenberg@gmail.com"),
    person(c("Jennifer", "A"), "Schmidt", role = "ctb", email = "jaschmid@msu.edu"),
    person(c("Patrick", "N"), "Beymer", role = "ctb", email = "beymerpa@msu.edu"),
    person(c("Daniel"), "Anderson", role = "ctb", email = "daniela@uoregon.edu"),
    person(c("Caspar"), "van Lissa", role = "ctb", email = "c.j.vanlissa@uu.nl")
    )
c(
    person("Kuhn", "Max", , "max@rstudio.com", role = c("aut", "cre")),
    person("Hadley", "Wickham", , "hadley@rstudio.com", role = c("aut")),
    person("RStudio", role = c("cph", "fnd"))
  )
c(
    person("Hadley", "Wickham", , "hadley@rstudio.com", c("aut", "cre")),
    person("Lionel", "Henry", , "lionel@rstudio.com", "aut"),
    person("RStudio", role = "cph")
    )
person("Willem", "Sleegers", email = "w.sleegers@me.com", role = c("aut", "cre"))
c(person("Gabriela", "De Queiroz", role = c("ctb"),
             email = "gabidequeiroz@gmail.com"),
             person("Emil", "Hvitfeldt", role = c("ctb"),
             email = "emilhhvitfeldt@gmail.com"),
             person("Os", "Keyes", role = c("ctb"),
             email = "ironholds@gmail.com",
             comment = c(ORCID = "0000-0001-5196-609X")),
             person("Kanishka", "Misra", role = c("ctb"),
             email = "kmisra@purdue.edu"),
             person("Jeff", "Erickson", role = c("ctb"),
             email = "jeff@erick.so"),
             person("David", "Robinson", role = c("aut"),
             email = "admiral.david@gmail.com"),
             person("Julia", "Silge", role = c("aut", "cre"), 
             email = "julia.silge@gmail.com", 
             comment = c(ORCID = "0000-0002-3671-836X")))
c(
    person("Tom", "Buckley", role = c("aut", "cre"), email = "tom@tbuckl.com"),
    person("Danton", "Noriega-Goodwin", role = c("aut"), email = "danton.noriega@gmail.com"),
    person("Flavio", "Poletti", role = c("aut"), email = "flavio.poletti@hotmail.ch"),
    person("Angela", "Li", role = c("aut"), email = "angela@rladies.org"),
    person("Mark", "Padgham", role = c("ctb"), email = "mark.padgham@email.com"),
    person("Elaine", "McVey", role = c("ctb"), email = "elaine.mcvey@transloc.com"),
    person("Charles Hans", "Thompson", role = c("ctb"), email = "hans.thompson1@gmail.com"),
    person("Michael", "Sumner", role = c("ctb")),
    person("Patrick", "Hausmann", role = c("ctb"), email = "patrick.hausmann@covimo.de"),
    person("Bob", "Rudis", role = c("ctb"), email = "bob@rud.is"),
    person("Kearey", "Smith", role = c("ctb")),
    person("Dave", "Vautin", role = c("ctb")),
    person("Kyle", "Walker", role = c("ctb"))
  )
c(
           person("Guangchuang", "Yu",   email = "guangchuangyu@gmail.com", role = c("aut", "cre", "cph"), comment = c(ORCID = "0000-0002-6485-8781")),
           person("Bradley", "Jones",    email = "brj1@sfu.ca",             role = "ctb"),
           person("Zebulun", "Arendsee", email = "zbwrnz@gmail.com",        role = "ctb")
           )
c(
    person("Duncan", "Garmonsway", email = "nacnudus@gmail.com", role = c("aut", "cre")),
    person("Hadley", "Wickham", role = c("ctb"), comment = "Author of included readxl fragments"),
    person("Jenny", "Bryan", role = c("ctb"), comment = "Author of included readxl fragments"),
    person("RStudio", role = "cph", comment = "Copyright holder of included readxl fragments"),
    person("Marcin", "Kalicinski", role = c("ctb", "cph"), comment = "Author of included RapidXML code")
    )
c(person("Daniel", "Gadala-Maria", role=c("aut"), 
                    email="daniel.gadala-maria@yale.edu"),
             person("Susanna", "Marquez", role=c("aut"), 
                    email="susanna.marquez@yale.edu"),                    
             person("Moriah", "Cohen", role=c("aut"), 
                    email="moriah.cohen@biu.ac.il"),   
             person("Gur", "Yaari", role=c("aut"), 
                    email="gur.yaari@biu.ac.il"),
             person("Jason", "Vander Heiden", role=c("ctb", "cre"), 
                    email="jason.vanderheiden@yale.edu"),
             person("Steven", "Kleinstein", role=c("aut", "cph"), 
                    email="steven.kleinstein@yale.edu"))
c(person("Antti", "Honkela", role = c("aut", "cre"),
                     email = "antti.honkela@helsinki.fi"),
              person("Miika-Petteri", "Matikainen", role = "aut"))
c(
      person(given="Kyle", family="Walker", email="kyle.walker@tcu.edu", role=c("aut", "cre")),
      person(given="Bob", family="Rudis", email="bob@rudis.net", role="ctb")
    )
c( person("Charlie", "Sharpsteen", role = "aut"),
        person("Cameron", "Bracken", role = "aut"), person("Kirill",
        "Müller", role = c("ctb"), email =
        "krlmlr+r@mailbox.org"), person("Yihui", "Xie", role = "ctb"),
        person("Ralf", "Stubner", email = "ralf.stubner@daqana.com", 
        role = "cre"))
c(person("Matthew", "Leonawicz", email = "mfleonawicz@alaska.edu", role = c("aut", "cre")),
    person("Scenarios Network for Alaska and Arctic Planning", role = c("cph", "fnd"))
    )
c(person("Haeran", "Cho", role = c("aut", "cre"),
                     email = "haeran.cho@bristol.ac.uk"),
              person("Piotr", "Fryzlewicz", role = "aut"))
c(person("Vitalie", "Spinu", email = "spinuvit@gmail.com", role = c("aut", "cre")),
             person("Google Inc.", role = c("ctb", "cph")))
c(
 person("Mohsin", "Vindhani", email = "mohsin@systeminsights.com", role = c("aut", "cre")),
 person("Alex", "Joseph", email = "alex@systeminsights.com", role = c("aut")),
 person("Ananthapadmanabhan", email = "ananthu@systeminsights.com", role = c("aut")),
 person("Subramanyam", "Ravishankar", email = "subramanyam@systeminsights.com", role = c("aut"))
 )
c(
    person("Kleanthis", "Koupidis", email = "koupidis@okfn.gr", role = c("aut", "cre")),
    person("Charalampos", "Bratsas",  email = "charalampos.bratsas@okfn.org", role = "aut")
    )
c(
    person("Dean", "Attali", email = "daattali@gmail.com",
      role = c("aut", "cre"), comment = "R interface"),
    person(family = "Almende B.V.", role = c("aut", "cph"),
       comment = "vis.js library, http://visjs.org, http://www.almende.com/home")
      )
c(
    person("Sudeshna", "Adak", role = "aut"),
    person("Ronghui", "Xu", role = "aut"),
    person("Euyhyun", "Lee", role = c("trl", "cre"),email = "e4lee@ucsd.edu"),
    person("Steven", "Edland", role = "ctb"),
    person("Lon", "White", role = "ctb"))
person("Nathan", "Teetor", email = "nathanteetor@gmail.com", role = c("aut", "cre"))
c(
  person("Yihui", "Xie", role = c("aut", "cre", "cph"), email = "xie@yihui.name", comment = c(ORCID = "0000-0003-0645-5666")),
  person(family = "RStudio, Inc.", role = "cph"),
  person("Fernando", "Cagua", role = "ctb"),
  person()
  )
c(person("Stefano", "Costa", role = c("aut", "cre"), email = "steko@iosa.it"),
             person("Luca", "Bianconi", role = "aut", email = "luca@iosa.it"),
             person("Elisabetta", "Starnini", role = "ctb"))
c(person("Matthew E.", "Baker", email = "mbaker@umbc.edu", role = c("aut", "cph")),
  person("Ryan S.", "King",email = "Ryan_S_King@baylor.edu", role = c("aut", "cph")),
  person("David", "Kahle", email = "david.kahle@gmail.com", role = c("aut", "cph", "cre")))
c(person("Lee", "McDaniel", role = c("aut", "cre"),email = "lmcda4@lsuhsc.edu"))
person("David","Harvey", email = "harvey@depauw.edu", role = c("aut", "cre"))
c(
    person(given = "Yan", family = "Li",
           email = "yan.4.li@uconn.edu",
           role = c("aut", "cre")),
    person(given = "Kun", family = "Chen", role = "aut"),
    person(given = "Jun", family = "Yan", role = "aut"))
c(person("Ingo", "Feinerer", role = c("aut", "cre"),
                    email = "feinerer@logic.at",
                    comment = c(ORCID = "0000-0001-7656-8338")),
             person("Kurt", "Hornik", role = "aut",
                    comment = c(ORCID = "0000-0003-4198-9911")),
             person("Artifex Software, Inc.", role = c("ctb", "cph"),
                    comment = "pdf_info.ps taken from GPL Ghostscript"))
c(person("Milan", "Bouchet-Valat", email="nalimilan@club.fr",
                    role=c("aut", "cre")),
             person("Grigorij", "Ljubownikow",
                    email="g.ljubownikow@auckland.ac.nz",
                    role="ctb"))
c(person("Ingo", "Feinerer", role = c("aut"),
                    email = "feinerer@logic.at",
		    comment = c(ORCID = "0000-0001-7656-8338")),
             person("Wolfgang", "Mauerer", role = c("aut")),
	     person("Kurt", "Hornik", role = c("aut", "cre"),
	            email = "Kurt.Hornik@R-project.org",
                    comment = c(ORCID = "0000-0003-4198-9911")))
c(person("Mario", "Annau", role = c("aut", "cre"),
    email = "mario.annau@gmail.com"))
c(
	person("Martijn", "Tennekes", email = "mtennekes@gmail.com", role = c("aut", "cre")),
	person("Joel", "Gombin", email = "joel.gombin@gmail.com", role = "ctb"),
	person("Sebastian", "Jeworutzki", email = "Sebastian.Jeworutzki@rub.de", role = "ctb"),
	person("Kent", "Russell", email = "kent.russell@timelyportfolio.com", role = "ctb"),
	person("Richard", "Zijdeman", email = "richard.zijdeman@iisg.nl", role = "ctb"),
	person("John", "Clouse", email = "johnaclouse@gmail.com", role = "ctb"),
	person("Robin", "Lovelace", email = "rob00x@gmail.com", role = "ctb"),
	person("Jakub", "Nowosad", email = "nowosad.jakub@gmail.com", role = "ctb"),
	person("Jannes", "Muenchow", email = "jannes.muenchow@uni-jena.de", role = "ctb")
	)
person("Martijn", "Tennekes", email = "mtennekes@gmail.com", role = c("aut", "cre"))
c(person("Kasper","Kristensen",role=c("aut","cre","cph"),email="kaskr@dtu.dk"),
             person("Brad","Bell",role="cph"),
             person("Hans","Skaug",role="ctb"),
             person("Arni","Magnusson",role="ctb"),
             person("Casper","Berg",role="ctb"),
             person("Anders","Nielsen",role="ctb"),
             person("Martin","Maechler",role="ctb"),
             person("Theo","Michelot",role="ctb"),
             person("Mollie","Brooks",role="ctb"),
             person("Alex","Forrence",role="ctb"),
             person("Christoffer","Moesgaard Albertsen",role="ctb"),
             person("Cole","Monnahan",role="ctb") )
person("Stephen", "Turner", email = "vustephen@gmail.com", role = c("aut", "cre"), comment = c(ORCID = "0000-0001-9140-9028"))
c(person("Susan", "Gruber", role = c("aut", "cre"), 
				email = "sgruber@cal.berkeley.edu"),
                    person("Mark", "van der Laan", role = c("aut"),
                                email = "laan@berkeley.edu"))
c(person("Ting Fung (Ralph)", "Ma", email = "tingfung.ma@wisc.edu", role = c("cre", "aut")),
    person("Sujit K.", "Ghosh", email = "sujit_ghosh@ncsu.edu", role = "aut"),
    person("Yifang", "Li", role = "aut"))
c(person(given = "Philip", family = "Leifeld", email = "philip.leifeld@glasgow.ac.uk", role = c("aut", "cre")), person(given = c("Skyler", "J."), family = "Cranmer", email = "cranmer.12@osu.edu", role = "ctb"))
person("Moritz", "Hanke", email = "hanke@leibniz-bips.de",
  role = c("aut", "cre"))
c(person("Vincent", "Guyader", email = "vincent@thinkr.fr", role = c("aut", "cre")),person("ThinkR", role = c("cph", "fnd")))
c(person("Lincoln", "Mullen", role = c("aut", "cre"),
        email = "lincoln@lincolnmullen.com",
        comment = c(ORCID = "0000-0001-5103-6917")),
        person("Os", "Keyes", role = c("ctb"),
        email = "ironholds@gmail.com",
        comment = c(ORCID = "0000-0001-5196-609X")),
        person("Dmitriy", "Selivanov", role = c("ctb"),
        email = "selivanov.dmitriy@gmail.com"),
        person("Jeffrey", "Arnold", role = c("ctb"),
        email = "jeffrey.arnold@gmail.com",
        comment = c(ORCID = "0000-0001-9953-3904")),
        person("Kenneth", "Benoit", role = c("ctb"),
        email = "kbenoit@lse.ac.uk",
        comment = c(ORCID = "0000-0002-0797-564X")))
c(person("Jeremy", "Hadler", role = c("aut", "cre"),
                      email = "hadler13@yahoo.com"),
                      person("Max", "Morris", role = "ths"), person("Heike",
                      "Hofmann", role = "ctb"))
c(person(given=c("Damian", "W."), family="Betebenner", email="dbetebenner@nciea.org", role=c("aut", "cre")),
                person(given="Andrew", family="Martin", role="ctb"),
		person(given="Jeff", family="Erickson", role="ctb"))
c(person("Bettina", "Grün", role = c("aut", "cre"),
                    email = "Bettina.Gruen@jku.at"),
             person("Kurt", "Hornik", role = "aut"))
c(person("Lars", "Koppers", email="koppers@statistik.tu-dortmund.de", role=c("aut", "cre"), comment = c(ORCID = "0000-0002-1642-9616")),
	     person("Jonas", "Rieger", email="riegerjonas@gmx.de", role=c("aut")),
	     person("Karin", "Boczek", email="karin.boczek@tu-dortmund.de", role=c("ctb"), comment = c(ORCID = "0000-0003-1516-4094")),
	     person("Gerret", "von Nordheim", email="gerret.vonnordheim@tu-dortmund.de", role=c("ctb"), comment = c(ORCID = "0000-0001-7553-3838")))
c(
    person(given = "Wenjie", family = "Wang", role = c("aut", "cre"),
           email = "wenjie.2.wang@uconn.edu",
           comment = c(ORCID = "0000-0003-0363-3180")),
    person(given = "Yan", family = "Li", role = "aut"),
    person(given = "Jun", family = "Yan", role = "aut")
    )
person(c("Eric", "D."), "Watt",
                  email = "eric@ericdwatt.com",
                  role = c("aut", "cre"))
person("ZHANG_Chen", "Developer", email = "447974102@qq.com", role = c("aut", "com", "cre"))
person("Gabor", "Csardi",, "csardi.gabor@gmail.com", c("aut", "cre"))
c(person(given = "Ioannis", family = "Kosmidis",
	            role = c("aut", "cre"), email = "ioannis.kosmidis@warwick.ac.uk",
		    comment = c(ORCID = "0000-0003-1556-0302")),
             person(given = "Hannah", family = "Frick",
	   	    role = "aut", email = "hannah.frick@gmail.com",
	            comment = c(ORCID = "0000-0002-6049-5258")),
	     person(given = "Robin", family = "Hornak",
	            role = "aut", email = "robinhornak@gmail.com"))
c(
    person("Gabriel", "Becker", role = c("aut", "cre"),
        email = "becker.gabriel@gene.com"),
    person("Sara", "Moore", role = c("aut"),
        email = "moore.sara@gene.com"))
c(person("Aikaterini", "Chatzopoulou", role = c("aut", "cre"),
                     email = "kchatzopoul@okfn.gr"),
              person("Kleanthis", "Koupidis", role = "aut",
                     email ="koupidis@okfn.gr"),
              person("Charalampos","Bratsas", role = "aut",
                      email = "charalampos.bratsas@okfn.org"),
              person("Panagiotis", "Tzenos", role = "dtc",
                     email = "ptzenos@certh.gr"),
              person("Josep Maria", "Salanova", role = "dtc",
                     email = "jose@certh.gr"))
c(person("Lily", "Medina", email = "lilymiru@gmail.com", role = "aut"),
    person("Piedad", "Castro", role="aut", email = "p25castro@gmail.com"),
    person("Ann-Kristin", "Kreutzmann", role=c("aut", "cre"), email="ann-kristin.kreutzmann@fu-berlin.de"),
    person("Natalia", "Rojas-Perilla", role="aut", email = "natalia.rojas@fu-berlin.de"))
c(
    person("Scott", "Chamberlain", role = c("aut", "cre"), email = "myrmecocystus@gmail.com"),
    person("Zachary", "Foster", role = "aut", email = "zacharyfoster1989@gmail.com"),
    person("Ignasi", "Bartomeus", role = "aut", email = "nacho.bartomeus@gmail.com"),
    person("David", "LeBauer", role = "aut", email = "dlebauer@gmail.com"),
    person("David", "Harris", role = "aut", email = "davharris@ucdavis.edu")
  )
c(person("Diego","Monteiro",role=c("aut","cre"),email="dvm1607@gmail.com"),
  person("Karine", "Reis", role = "ctb",email="karine.ferreira@inpe.br"),
  person("Rafael", "Santos", role = "ctb", email="rafael.santos@inpe.br"),
  person("Carlos", "Alberto", role = "ctb",email="carlos.noronha@inpe.br"),
  person("Felipe", "Carvalho", role = "ctb",email="lipecaso@gmail.com"))
c(person("Edzer", "Pebesma", role = c("aut", "cre"), email = "edzer.pebesma@uni-muenster.de", comment = c(ORCID = "0000-0001-8049-7069")),
	person("Benedikt", "Klus", role = "aut"),
	person("Benedikt", "Graeler", role = "ctb"),
	person("Nikolai", "Gorte", role = "ctb"),
	person("Mehdi", "Moradi", role = "aut"))
person("Jim", "McLean", email = "jim_mclean@optusnet.com.au",
    role = c("aut", "cre"))
c(
  person("Alexis", "Gabadinho", role = c("aut", "cph")),
  person("Matthias", "Studer", role = c("aut", "cph")),
  person("Nicolas", "Müller", role = "aut"),
  person("Reto", "Bürgin", role = "aut"),
  person("Pierre-Alexandre", "Fonta", role = "aut"),
  person("Gilbert", "Ritschard", role = c("aut", "cre", "cph"),
  email = "gilbert.ritschard@unige.ch"))
c(
  person("Gilbert", "Ritschard", role = c("aut", "cre", "ths", "cph"),
  email = "gilbert.ritschard@unige.ch"),
  person("Matthias", "Studer", role = "aut"),
  person("Reto", "Buergin", role = "aut"),
  person("Alexis", "Gabadinho", role = "ctb"),
  person("Pierre-Alexandre", "Fonta", role = "ctb"),
  person("Nicolas", "Muller", role = "ctb"),
  person("Patrick", "Rousset", role = "ctb"))
c(person(given="Chel Hee", family="Lee", email = "chl948@mail.usask.ca", role = c("aut", "cre")), person(given="Edward", family="Kang", role="ctb", email="ewkang@hotmail.com"), person(given="Sunyoung", family="Kim", role="ctb", email="skim1970@hanmail.net"), person(given="Heather", family="Kim", role="ctb", email="heatherkimca@gmail.com"))
c(person("Dominic", "Schuhmacher", email = "dominic.schuhmacher@mathematik.uni-goettingen.de", role = c("aut","cre")),
             person("Björn", "Bähre", role = "aut", comment = "aha and power diagrams"),
             person("Carsten", "Gottschlich", role = "aut", comment = "simplex and shortlist"),
             person("Florian", "Heinemann", role = "aut", comment = "transport_track"),
             person("Bernhard", "Schmitzer", role = "aut", comment = "shielding"),
             person("Timo", "Wilm", role = "ctb", comment = "wpp"))
person(given = "Jeremy Thoms", family = "Hetzel", email =
        "jthetzel@gmail.com", role = c("aut", "cre"))
c(person("Nicole Bohme", "Carnegie", role = c("aut", "cre"), email = "nicole.carnegie@montana.edu"),
             person("Masataka",       "Harada", role = "aut",           email = "masatakaharada@gmail.com"),
             person("Vincent",         "Dorie", role = "aut",           email = "vjd4@nyu.edu"),
             person("Jennifer",         "Hill", role = "ctb",           email = "jennifer.hill@nyu.edu"))
person("Brian", "Ripley", role = c("aut", "cre"),
                  email = "ripley@stats.ox.ac.uk")
person("Piro", "Polo", role = c("aut", "cre"),
	            email = "piropolo98@gmail.com")
c(person("Carl", "Boettiger", role = c("aut", "cre"),
    email = "cboettig@gmail.com"),
    person("Duncan", "Temple Lang", role = "aut"))
c(person("Daniel W.", "Heck", email="heck@uni-mannheim.de", role=c("aut","cre")),
             person("Nina R.", "Arnold", email="arnold@uni-mannheim.de", role = c("aut","dtc")),
             person("Denis", "Arnold", email="denis.arnold@uni-tuebingen.de", role = c("aut")),
             person("Alexander", "Ly", email="a.ly@uva.nl", role = c("ctb")))
c(person("Christian", "Zang", role = c("aut", "cre", "cph", "trl"),
    email = "christian.zang@wzw.tum.de"), person("Franco", "Biondi", role =
    c("ctb", "cph")))
person("Julia", "Fukuyama", email = "julia.fukuyama@gmail.com",
    role = c("aut", "cre"))
c(person("Erik", "Volz", role = c("aut", "cre"),
                     email = "erik.volz@gmail.com") )
c(
	person("Martijn", "Tennekes", email="mtennekes@gmail.com", role=c("aut", "cre")),
	person("Peter", "Ellis", email="peter.ellis2013nz@gmail.com", role="ctb"))
c(person("Martin R.", 'Smith',
                  email="martin.smith@durham.ac.uk", 
                  role=c("aut", "cre", "cph"), 
                  comment=c(ORCID = "0000-0001-5660-1727")),
               person(given = "Emmanuel", family="Paradis",
               role = c("cph")),
               person(given = "Martin", family="Brazeau",
               role = c("cph"))
            )
c(TJ = person("Thibaut", "Jombart", email="thibautjombart@gmail.com", role = "aut"),
 	     MK = person("Michelle", "Kendall", email="michelle.kendall@bdi.ox.ac.uk", role = c("aut", "cre")),
	     JAG = person("Jacob", "Almagro-Garcia", role = "aut"),
	     CC = person("Caroline", "Colijn", role = "aut"))
c(
      person("Ryan", "Hafen", email = "rhafen@gmail.com", role = c("aut", "cre")),
      person("Barret", "Schloerke", email = "schloerke@gmail.com", role = "aut")
    )
c(
    person("Jakub", "Chromec", , "chromec.jakub@gmail.com", role = c("aut", "cre")),
    person("Irina", "Matusevich", role = "ctb")
    )
person("Min", "Shi", email = "shi2@niehs.nih.gov", role = c("aut", "cre"))
c(person("Kristian", "Brock", email = "kristian.brock@gmail.com", role = c("aut", "cre")),
             person("Trustees of", "Columbia University", role = "cph"))
person("Mathias", "Milfeldt", email = "mathias@milfeldt.dk", role = c("aut", "cre"))
c(person("Paul", "Poncet", , "paulponcet@yahoo.fr", role = c("aut", "cre")), 
    person("Stefan Milton", "Bache", , "stefan@stefanbache.dk", role = "aut", 
           comment = "for functions copied or modified from the 'magrittr' package"), 
    person("Hadley", "Wickham", , "h.wickham@gmail.com", role = "aut", 
           comment = "for functions copied or modified from the 'magrittr' package"))
person(given = "Phil Mike",
           family = "Jones",
           role = c("aut", "cre"),
           email = "philmikejones@gmail.com",
           comment = c(ORCID = "0000-0001-5173-3245"))
person("James", "Grange", ,"grange.jim@gmail.com",  c("aut", "cre"))
c(person("Samuel", "Noll", email = "uncle.sam@gmx.net", role = c("aut", "cre")), person("Reinhard", "Furrer", role = c("ctb"), email = "reinhard.furrer@math.uzh.ch"), person("Benjamin", "Reiser", role = c("ctb")), person("Christos T.", "Nakas", role = c("ctb"), email = "cnakas@uth.gr"))
c(person("Michael D.","Sumner", role = c("aut", "cre"),
    email = "mdsumner@gmail.com"),
    person("Sebastian", "Luque", role = "ctb",
    email = "spluque@gmail.com"), 
    person("Anthony", "Fischbach", role = "ctb",
    email = "afischbach@usgs.gov"))
c(person("Michael D.","Sumner", role = c("aut", "cre"),
    email = "mdsumner@gmail.com"),
    person("Simon", "Wotherspoon", role = "ctb",
    email = "Simon.Wotherspoon@utas.edu.au"))
person("Robin", "Lamarche-Perrin", email = "Robin.Lamarche-Perrin@lip6.fr", role = c("aut", "cre"))
c(
    person("Muhammad Kashif", "Hanif",  role = c("cre","aut"),
    email = "mkashifhanif@gcuf.edu.pk"),
    person("Rehman", "Ahmad", role = "aut", email = "rehman.ahmad777@gmail.com"),
    person("Karl-Heinz", "Zimmermann", email = "k.zimmermann@tuhh.de", role = "aut"),
    person("Zia ul", "Qayyum" , role = "aut", email = "ziaqayyum@uog.edu.pk")
    )
c(person("Hamza", "Farooq", role = c("aut"),
                     email = "hamza_faroooq@yahoo.com"),
             person("Hafiz Zain Ul", "Haq", role = c("aut", "cre"),
                     email = "zainalibajwa4u@yahoo.com"),
             person("Muhammad Kashif", "Hanif", role = c("aut"),
                     email = "mkashifhanif@gcuf.edu.pk"),
             person("Shamsa", "Javaid", role = c("aut"),
                     email = "sjrjse426@gmail.com"),
             person("Karl-Heinz", "Zimmermann", role = c("aut"),
                     email = "k.Zimmermann@tuhh.de"))
c(
    person("Tom", "Buckley", role = c("aut", "cre"), email = "tom@tbuckl.com"),
    person("Danton", "Noriega-Goodwin", role = c("aut"), email = "danton.noriega@gmail.com"),
    person("Elaine", "McVey", role = c("aut"), email = "elaine.mcvey@transloc.com"),
    person("Charles Hans", "Thompson", role = c("ctb"), email = "hans.thompson1@gmail.com"),
    person("Michael", "Sumner", role = c("ctb"))
  )
c(person("Olaf", "Mersmann", role=c("aut", "cre"), 
                    email="olafm@p-value.net"),
             person("Heike", "Trautmann", role=c("aut")),
             person("Detlef", "Steuer", role=c("aut")),
             person("Björn", "Bornkamp", role=c("aut")))
c(person("Yves", "Croissant", role = c("aut", "cre"), email = "yves.croissant@univ-reunion.fr"),
             person("Achim", "Zeileis", role = "aut", email = "Achim.Zeileis@R-project.org"))
person("Jürgen", "Altfeld", email = "jaltfeld@altfeld-im.de", role = c("aut", "cre", "cph"))
person("Christoph", "Sax", email = "christoph.sax@gmail.com", role = c("aut", "cre"))
c(
  person("Tobias", "Liboschik", role=c("aut", "cre"), email="liboschik@statistik.tu-dortmund.de"),
  person("Roland", "Fried", role=c("aut"), email="fried@statistik.tu-dortmund.de"),
  person("Konstantinos", "Fokianos", role=c("aut"), email="fokianos@ucy.ac.cy"),
  person("Philipp", "Probst", role=c("aut"), email="philipp.probst@tu-dortmund.de"),
  person("Jonathan", "Rathjens", role=c("ctb"), email="jonathan.rathjens@tu-dortmund.de"))
person("Tianjian", "Yang", email = "yangtj5@mail2.sysu.edu.cn",
    role = c("aut", "cre"))
c(person("Antonio Fabio", "Di Narzo", role = "aut"),
             person("Jose Luis", "Aznarte", role = "ctb"),
             person("Matthieu", "Stigler", role = c("aut","cre"), email="Matthieu.Stigler@gmail.com"))
c(person("Vitor", "Cerqueira", email = "cerqueira.vitormanuel@gmail.com", role = c("aut", "cre")),
			 person("Luis", "Torgo", role = "ctb"),
             person("Carlos", "Soares", role = "ctb"))
person("Jiri", "Tomcala", email = "jiri.tomcala@vsb.cz", role = c("aut", "cre"))
c(person("Adrian", "Trapletti", role = "aut",
                    email = "adrian@trapletti.org"),
             person("Kurt", "Hornik", role = c("aut", "cre"),
                    email = "Kurt.Hornik@R-project.org"),
             person("Blake", "LeBaron", role = "ctb",
                    comment = "BDS test code"))
c(person("Vishakh", "Padmakumar", email = "vishakhpadmakumar@gmail.com", role = c("aut", "cre")), person("Rishab", "Ranga", email = "rishab.ranga1996@gmail.com", role = c("aut")), person("Amogha", "Udupa", email = "amogha15@gmail.com", role = c("aut")), person("Param", "Hanji", email = "param.catchchaos@gmail.com", role = c("aut")))
person("Francisco", "Martinez", email = "fmartin@ujaen.es",
    role = c("aut", "cre"))
c(
    person("Earo", "Wang", email = "earo.wang@gmail.com", role = c("aut", "cre"), comment = c(ORCID = "0000-0001-6448-5260")),
    person("Di", "Cook", role = c("aut", "ths"), comment = c(ORCID = "0000-0002-3813-7155")),
    person("Rob", "Hyndman", role = c("aut", "ths"), comment = c(ORCID = "0000-0002-2140-5352")),
    person("Mitchell", "O'Hara-Wild", role = c("ctb"))
    )
c(person("Alexander D.","Becker",
		role=c("aut","cre"),email="adbecker@princeton.edu"),
	  person("Sinead E.","Morris",role=c("ctb")))
c(
    person("Francisco", "Bischoff", email = "fbischoff@med.up.pt", role = c("aut", "cre"),
    comment = c(ORCID = "https://orcid.org/0000-0002-5301-8672")),
    person("Michael", "Yeh", email = "myeh003@ucr.edu", role = c("res", "ccp", "ctb"),
    comment = c(ORCID = "https://orcid.org/0000-0002-9807-2963")),
    person("Diego", "Silva", email = "diegofs@ufscar.br", role = c("res", "ccp", "ctb"),
    comment = c(ORCID = "https://orcid.org/0000-0002-5184-9413")),
    person("Yan", "Zhu", email = "yzhu015@ucr.edu", role = c("res", "ccp", "ctb"),
    comment = c(ORCID = "https://orcid.org/0000-0002-5952-2108")),
    person("Hoang", "Dau", email = "hdau001@ucr.edu", role = c("res", "ccp", "ctb"),
    comment = c(ORCID = "https://orcid.org/0000-0003-2439-5185"))
  )
c(
           person("Skye", "Bender-deMoll", role=c("aut","cre"), email="skyebend@uw.edu"),
           person("Martina", "Morris", role=c("aut"), email="morrism@u.washington.edu"),
           person("James", "Moody", role=c("ctb"), email="jmoody77@soc.duke.edu")
           )
c(
	person("Michael", "Hahsler", role = c("aut", "cre", "cph"),
	    email = "mhahsler@lyle.smu.edu"),
	person("Kurt", "Hornik", role = c("aut", "cph")))
c(person(c("Rebecca", "Pontes"),"Salles", role = c("aut", "cre","cph"),email = "rebeccapsalles@acm.org", comment = "CEFET/RJ"),
			 person("Eduardo","Ogasawara", role = c("ths"),email = "eogasawara@ieee.org", comment = "CEFET/RJ"))
person("Peter", "Laurinec", email = "tsreprpackage@gmail.com",
  role = c("aut", "cre"), 
  comment = c(ORCID = "0000-0002-3501-8783"))
person("Arden", "Burrell", email = "arden.burrell@unsw.edu.au", role = c("aut", "cre"))
c(person(given="David", family="DEVREKER", role = c("aut", "cre"), email =
        "David.Devreker@ifremer.fr" ), person(given="Alain", family="LEFEBVRE",
        role = c("aut"), email = "Alain.Lefebvre@ifremer.fr" ) )
c(person(c("Ian", "H."), "Hutchinson", role = "aut",
	       comment = "author of tth/ttm C sources"),
	     person("Friedrich", "Leisch", role = c("aut", "cre"),
	       email = "Friedrich.Leisch@R-project.org", 
               comment = "author of R wrappers to tth/ttm"),
	     person("Achim", "Zeileis", role = "aut",
	       email = "Achim.Zeileis@R-project.org", 
               comment = "author of R wrappers to tth/ttm"))
c(person("Gaurav", "Sood", email = "gsood07@gmail.com", role = c("aut", "cre")),
             person("Kate", "Lyons", email = "k.lyons7@gmail.com", role = "ctb"),
             person("John", "Muschelli", email = "muschellij2@gmail.com", role = "ctb"))
c(
      person("Marta","Marticorena",role = "aut"),
      person("Gustavo","Gimenez",email = "gustavo.gimenez@faea.uncoma.edu.ar",role="cre"),
      person("Cecilia","Gonzalez",role = "ctb"),
      person("Sergio","Bramardi",role = "aut"))
c(
    person("Yihui", "Xie", role = c("aut", "cre"), email = "xie@yihui.name", comment = c(ORCID = "0000-0003-0645-5666")),
    person("JJ", "Allaire", role = "aut"),
    person("Andrzej", "Oles", role = "ctb"),
    person("Dave", "Liepmann", role = "ctb", comment = "Tufte CSS in inst/rmarkdown/templates/tufte_html/resources"),
    person(family = "RStudio, Inc.", role = "cph")
    )
c(person("C.B.", "Barber", role = c("aut", "cph"), 
    comment = "Qhull library"),
    person("The Geometry Center", "University of Minnesota", role = c("cph"), 
    comment = "Qhull library"), 
    person("Pavlo", "Mozharovskyi", role = c("aut", "cre"), 
    email = "pavlo.mozharovskyi@ensai.fr"))
c(person("Frank","Schaarschmidt", role=c("aut","cre"), email="schaarschmidt@biostat.uni-hannover.de"),
 person("Christian", "Ritz", role="aut"),
 person("Ludwig", "Hothorn", role="ctb"))
c(
    person("Philipp", "Probst", email = "philipp_probst@gmx.de", role = c("aut", "cre")),
    person("Simon", "Klau", email = "simonklau@ibe.med.uni-muenchen.de", role = "ctb"))
c( person(given = c("Jennifer", "F."), family = "Bobb", role =
        "aut", email = "jbobb@hsph.harvard.edu"), person(given =
        "Ravi", family = "Varadhan", role = c("aut", "cre"), email =
        "ravi.varadhan@jhu.edu"), person(given = "Hui", family =
        "Zhao", role = "ctb"))
person("Jack", "Horne", role = c("aut", "cre"), email = "jack@jackhorne.net")
c(
    person("Anna", "Cena", role = c("aut")),
    person("Marek", "Gagolewski", role = c("aut")),
    person("Barbara", "Zogala-Siudem", role = c("aut", "cre"),
    email = "zogala@rexamine.com"),
    person("Marcin", "Kosinski", role = c("aut")),
    person("Natalia", "Potocka", role = c("aut"))
    )
c(
	person("Peter", "Franke", email = "peter.franke@ucdconnect.ie", role = c("aut","cre")),
	person("Andrew", "Parnell", role = c("aut")))
c(person("Alberto", "Viglione", role=c("aut", "cre"), email="viglione@hydro.tuwien.ac.at"),
             person("Juraj", "Parajka", role=c("aut"), email="parajka@hydro.tuwien.ac.at"))
c(person(given = "Mark", family = "Pinese", role = c("aut", "cre",
    "cph"), email = "m.pinese@garvan.org.au"))
person("Peter K.", "Dunn", email="pdunn2@usc.edu.au", role=c("cre", "aut") )
person("Will", "Lowe", email = "wlowe@princeton.edu", role = c("aut", "cre"))
person("Sean", "Kross", email = "sean@seankross.com", role = c("aut", "cre"))
c(
  person("Marta", "Cousido Rocha", email = "martacousido@uvigo.es", role = c("aut", "cre")),
  person("José Carlos", "Soage González", email = "jsoage@uvigo.es", role = "ctr"),
  person("Jacobo", "de Uña Álvarez", email = "jacobo@uvigo.es", role = c("aut", "ths")),
  person("Jeffrey", "D. Hart", email = "hart@stat.tamu.edu", role = "aut"))
c(person(given = "Michael", family = "Friendly", role=c("aut", "cre"), email="friendly@yorku.ca"),
            person(given = c("Richard", "M."), family = "Heiberger", role="aut", email="rmh@temple.edu"),
            person(given = "John", family = "Fox", role="ctb", email="jfox@mcmaster.ca")
            )
c(
  person(
    family = "Landau",
    given = c("William", "Michael"),
    email = "will.landau@gmail.com",
    role = c("aut", "cre"),
    comment = c(ORCID = "0000-0003-1878-3253")
  ),
  person(
    family = "Eli Lilly and Company",
    role = "cph"
  ))
c(
    person("Bob", "Rudis", , "bob@rud.is", c("aut", "cre")),
    person("Lindsey", "Simon", role = "aut", comment = "uap-core"),
    person("Tobie", "Langel", role = "aut", comment = "uap-core"))
c(person("Chris", "Reudenbach", email = "reudenbach@uni-marburg.de", role = c("cre", "aut")),
             person("Sebastian", "Richter", role = c("ctb")),
             person("Lars", "Opgenoorth",  role = c("ctb")),
             person("Florian", "Detsch", role = c("ctb")),
             person("Hanna", "Meyer", role = c("ctb"))
             )
person("Chanyub", "Park", , "mrchypark@gmail.com", c("aut", "cre"))
c(
  person("Andrew", "Collier", email = "andrew@exegetic.biz", role = c("aut", "cre")),
  person("Arthur", "Wu", email = "arthurwu1@gmail.com", role = c("aut")),
  person("Jonathan","Carroll", email = "rpkg@jcarroll.com.au", role = c("ctb"))
  )
c(person("Paula", "Branco", email = "paobranco@gmail.com", role = c("aut", "cre")), person("Rita", "Ribeiro", email = "rpribeiro@dcc.fc.up.pt", role = c("aut", "ctb")), person("Luis", "Torgo", email = "ltorgo@dcc.fc.up.pt", role = c("aut", "ctb")))
person("Jacob", "Kaplan", email = "jacobkap@sas.upenn.edu", role = c       ("aut", "cre"))
c(person("Shixiang", "Wang", email = "w_shixiang@163.com", role = c("aut", "cre"), 
            comment = c(ORCID = "0000-0001-9855-7357")),
            person("Martin", "Morgan", role="aut"))
c(person('Jan', 'Wijffels', role = c('aut', 'cre', 'cph'), email = 'jwijffels@bnosac.be'), 
    person('BNOSAC', role = 'cph'), 
    person("Institute of Formal and Applied Linguistics, Faculty of Mathematics and Physics, Charles University in Prague, Czech Republic", role = 'cph'),
    person('Milan Straka', role = c('ctb', 'cph'), email = 'straka@ufal.mff.cuni.cz'), 
    person('Jana Straková', role = c('ctb', 'cph'), email = 'strakova@ufal.mff.cuni.cz'))
c(person("Gjalt-Jorn", "Peters",
                    email = "gjalt-jorn@userfriendlyscience.com",
                    role = c("aut", "cre", "ctb")))
c(person(given="Emilie", family="POISSON-CAILLAULT", role = c("aut"), email =
        "emilie.caillault@lisic.univ-littoral.fr" ), person(given="Paul", family="TERNYNCK",
        role = c("aut", "cre"), email = "ternynck@lisic.univ-littoral.fr" ) )
c(
  person("James", "Balamuta", 
         email = "balamut2@illinois.edu", role = c("aut", "cre"),
         comment = c(ORCID = "0000-0003-2826-8458")),
  person("Steven Andrew", "Culpepper" ,
         email = "sculpepp@illinois.edu", role = "ctb",
         comment = "Provided the Minimal Orange Beamer Theme"),
  person("David", "Dalpiaz" ,
         email = "dalpiaz2@illinois.edu", role = "ctb",
         comment = "Collaborated on the LaTeX Journal Theme"),
  person("Jose Luis", "Rodriguez",
         email = "jrodriguezorjuela@luc.edu", role = "ctb",
         comment = "Provided the Market Information Lab (MIL) Beamer Theme")
  )
c(
    person("Thomas J.", "Leeper", , "thosjleeper@gmail.com", c("aut", "cre"))
    )
person("Ken", "Hanscombe", email = "ken.hanscombe@gmail.com", role = c("aut", "cre"))
person("Timothy", "Wong", email = "timothy.wong@hotmail.co.uk", role = c("aut", "cre"))
c(person("Timothy C", "Bates", role = c("aut", "cre"),
    email = "timothy.c.bates@gmail.com", comment = c(ORCID = "0000-0002-1153-9007")))
c(person("Hans", "Landsheer", role = c("aut", "cre"),
                     email = "j.a.landsheer@uu.nl"))
c(person("Jay", "Lee", email = "joongsup@gmail.com", role = c("aut", "cre")))
c(person("Thomas", "Leeper", role = c("aut","cre"),
                    email = "thosjleeper@gmail.com"),
             person("Micah", "Altman", role = c("aut")))
c(person("Markus Baaske", role = c("aut", "cre"),
			 email = "markus.baaske@uni-jena.de"),
			 person("Felix", "Ballani", role = "ctb",
			 email = "ballani@math.tu-freiberg.de")
			)
c(
  person("Marius D.", "Pascariu", role = c("aut", "cre"), email = "rpascariu@outlook.com", comment = c(ORCID = "0000-0002-2568-6489")),
  person("Silvia", "Rizzi", role = "aut", email = "srizzi@health.sdu.dk"),
  person("Jonas", "Schoeley", role = "aut", comment = c(ORCID = "0000-0002-3340-8518")),
  person("Maciej J.", "Danko", role = "aut", email = "Danko@demogr.mpg.de", comment = c(ORCID = "0000-0002-7924-9022"))
  )
c(person("Markus","Kalisch",
                    email="kalisch@stat.math.ethz.ch", role=c("aut","cre")),
                    person("Manuel", "Schuerch", role="ctb"))
person("Shaun", "Jackman", , "sjackman@gmail.com", c("cre"))
c(
    person(
    "Brodie", "Gaslam", email="brodie.gaslam@yahoo.com",
    role=c("aut", "cre"))
    )
c(person("Edzer", "Pebesma", role = c("aut", "cre"), email = "edzer.pebesma@uni-muenster.de", comment = c(ORCID = "0000-0001-8049-7069")),
		person("Thomas", "Mailund", role = "aut", email = "mailund@birc.au.dk"),
		person("Tomasz", "Kalinowski", role = "aut"),
		person("James", "Hiebert", role = "ctb"),
		person("Iñaki", "Ucar", role = "ctb", email = "iucar@fedoraproject.org", comment = c(ORCID = "0000-0001-6403-5550"))
		)
c(person("Michael D.","Sumner", role = c("aut", "cre"), email = "mdsumner@gmail.com"), 
             person("Simon", "Wotherspoon", role = "ctb"),
             person("Hadley", "Wickham", role = "ctb", comment = "named the concept, provided excellent 
             guidance via tidyr source code"))
c(
    person("Duncan", "Garmonsway", email = "nacnudus@gmail.com", role = c("aut", "cre"))
    )
c(
    person("Jeroen", "Ooms", ,"jeroen@berkeley.edu", role = c("aut", "cre")),
    person("Free Software Foundation, Inc", role = "cph"))
person("Haeran", "Cho", email = "haeran.cho@bristol.ac.uk", role=c("aut", "cre"))
person("David", "Gerard", email = "gerard.1787@gmail.com", role = c("aut", "cre"), comment = c(ORCID = "0000-0001-9450-5023"))
person("Avery", "McIntosh", email = "aimcinto@bu.edu", role = c("aut", "cre"))
c(
    person("Jake", "Conway", email = "jake_conway@hms.harvard.edu", role = "cre"),
    person("Nils", "Gehlenborg", email = "nils@hms.harvard.edu", role = "aut"))
person("Roberto", "Sichera", email = "roberto.sichera@unipa.it", role = c("aut", "cre") )
c(
    person("James", "Lamb", email = "james.lamb@uptake.com", role = c("aut", "cre")),
    person("Nick", "Paras", email = "nick.paras@uptake.com", role = c("aut")),
    person("Austin", "Dickey", email = "austin.dickey@uptake.com", role = c("aut")),
    person("Michael", "Frasco", email = "mfrasco6@gmail.com", role = c("ctb")),
    person("Weiwen", "Gu", email = "weiwen.gu@uptake.com", role = c("ctb")),
    person("Will", "Dearden", email = "william.dearden@uptake.com", role = c("ctb")),
    person("Uptake Technologies Inc.", role = c("cph")))
c(person("Bernhard", "Pfaff", email = "bernhard@pfaffikus.de", role = c("aut", "cre")), person("Eric", "Zivot",email = "ezivot@u.washington.edu", role = "ctb"), person("Matthieu", "Stigler", role = "ctb"))
c(
    person("John", "Malc", email = "cincenko@outlook.com", role = c("aut", "cre"),  comment = "@dmpe")
    )
c(person("Lincoln", "Mullen",
    email = "lincoln@lincolnmullen.com",
    role = c("aut", "cre"),
    comment = c(ORCID = "0000-0001-5103-6917")),
    person("Jordan", "Bratt",
    email = "jfbratt@gmail.com",
    role = c("aut"),
    comment = c(ORCID = "0000-0001-9051-7203")),
    person("United States Census Bureau", role = "cph"))
c(
    person(c("Jared", "P."), "Lander", email = "packages@jaredlander.com", role = c("cre", "aut")),
    person("Nicholas", "Galasinao", email = "nicholas.galasinao@gmail.com", role = "ctb"),
    person("Joshua", "Kraut", email = "joshua.kraut@gmail.com", role = "ctb"),
    person("Daniel", "Chen", email = "dan dcheny@gmail.com", role = "ctb"))
c(person("Gjalt-Jorn", "Peters",
                    email = "gjalt-jorn@userfriendlyscience.com",
                    role = c("aut", "cre", "ctb")),
             person("Peter", "Verboon",
                    role = c("ctb")),
             person("James", "Green",
                    role = c("ctb")))
c(
    person("Hadley", "Wickham", , "hadley@rstudio.com", role = c("aut", "cre"), 
      comment = c(ORCID = "0000-0003-4757-117X")
    ),
    person("Jennifer", "Bryan", , "jenny@rstudio.com", role = "aut",
      comment = c(ORCID = "0000-0002-6983-2759")
    ),
    person("RStudio", role = c("cph", "fnd"))
  )
c(
    person("Wenlong", "Liu", role = c("aut","cre"),
        email = 'wliu14@ncsu.edu')
    )
c(person(given="Neil J.", family="Gunther", role
    = "aut"), person(given="Stefan", family="Moeding", role
    = c("aut", "cre"), email = "stm@moeding.net"))
person("Paolo", "Di Lorenzo", email = "paolo@dilorenzo.pl", role = c("aut", "cre"))
c(
  person(c("Patrick", "O."), "Perry",
         role = c("aut", "cph", "cre"),
         email = "patperry@gmail.com"),
  person("Unicode, Inc.",
         role = c("cph", "dtc"),
         comment = "Unicode Character Database"))
c(
    person("Gustavo","Coelho", role=c("aut","cre"), email="gustavo.coelho@ipea.gov.br"),
    person("Lucas","Mation", role=c("aut"), email="lucas.mation@ipea.gov.br"),
    person("Daniel","Lima", role=c("ctb"), email="daniel.viegas@ipea.gov.br"),
    person("Igor","Noberto", role=c("ctb"), email="igor.noberto@ipea.gov.br"),
    person("João Victor","Machado", role=c("ctb"), email="joao.machado@ipea.gov.br")
    )
person("Adriano", "Rivolli", email = "rivolli@utfpr.edu.br",
                  role = c("aut", "cre"))
c(person(given = "Scott S.", family = "Emerson", role = "aut"),
	     person(given = "Andrew J.", family = "Spieker", role = "aut"),
	     person(given = "Brian D.", family = "Williamson", role= "aut"),
	     person(given = "Travis Y.", family = "Hee Wai", role = c("aut","cre"),email="theewai@uw.edu"),
	     person(given = "Solomon", family = "Lim", role = "aut"),
	     person("R Core Team", role = "ctb"),
	     person(given = "Terry M", family = "Therneu", role = "ctb"),
             person("Thomas", "Lumley", role = "ctb"))
person("Eugene", "Ha", email = "eha@posteo.de", role = c("aut", "cre"))
c(
  person("Chris", "Cooper", role = "aut"),
  person("Dorota", "H. Sendorek", role = "ctb"),
  person("Paul", "C. Boutros", role = c("cre", "cph"), email = "Paul.Boutros@oicr.on.ca")
  )
person("Greer B.", "Humphrey",
                  email = "greer.humphrey@student.adelaide.edu.au",
                  role = c("aut", "cre"))
c(person("Mark", "van der Loo",role=c("cre","aut"),email="mark.vanderloo@gmail.com")
    , person("Edwin", "de Jonge", role="aut")
    , person("Paul","Hsieh",role="ctb"))
c(
  person("Leif", "Jonsson", email = "leif.jonsson@gmail.com", role = c("aut", "cre")),
  person("Milo", "Yip", email = "miloyip@gmail.com", role = "aut"),
  person("Tencent", role = "cph"),
  person("THL A29 Limited", role = "cph"),
  person("Alexander", "Chemeris", role = "aut")
  )
c( person("Edwin", "de Jonge", email = "edwindjonge@gmail.com", role = c("aut", "cre"))
            , person("Mark", "van der Loo", email = "mark.vanderloo@gmail.com",role = c("aut"))
            , person("Jacco", "Daalmans", role = c("ctb"))
            )
c(person("Victor", "Trevino", role = c("aut", "cre"),
    email = "vtrevino@itesm.mx"))
person("Clay", "Ford", , "joe.clayton.ford@gmail.com", role = c("cre", "aut"))
c(
    person('Jay', 'Hesselberth', role = c('aut', 'cre'),
      email = 'jay.hesselberth@gmail.com',
      comment = c(ORCID = '0000-0002-6299-179X')),
    person('Kent', 'Riemondy', role = c('aut'),
      email = 'kent.riemondy@ucdenver.edu',
      comment = c(ORCID = '0000-0003-0750-1273'))
    )
person("Ivan", "Zoccolan", email = "ivan.zoccolan@gmail.com", role = c("aut", "cre"))
c(
    person("Hengxin", "Li", email = "h343li@uwaterloo.ca", 
    role = c("aut", "cph")),
    person("Ben", "Feng", email = "ben.feng@uwaterloo.ca", 
    role = c("aut", "cph", "cre")),
    person("GuoJun", "Gan", email = "Guojun.Gan@uconn.edu", 
    role = c("ctb")))
person("Emilio Xavier", "Esposito", email = "emilio@exeResearch.com", role = c("aut", "cre"))
c(person("Michael", "Sumner", email = "mdsumner@gmail.com", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-2471-7511")), 
             person("Simon", "Wotherspoon", role = "ctb", comment = "figured out the mechanism for the resampling algorithm"), 
             person("Mark", "Padgham", role = "ctb", comment = "helped get started :)"), 
             person("Edzer", "Pebesma", role = "ctb", comment = "wrote allocate_attribute, copied here from sf"),
             person("Roger", "Bivand", role = "ctb", comment = "wrote configure.ac, copied here from rgdal"),
             person("Jim", "Hester", role = "ctb", comment = "wrote CollectorList.h, copied here from fs package"))
c(person("Juris", "Breidaks", email = "Juris.Breidaks@csb.gov.lv", role = c("aut", "cre")),
             person("Martins", "Liberts", role = "aut"),
             person("Santa", "Ivanova", role = "aut"))
c(person("Mehrad", "Mahmoudian", role = c("aut", "cre"),
		     email = "m.mahmoudian@gmail.com"))
c(
    person("Runze","Li", email = "runzelipsu@gmail.com", role = "aut"),
    person("Liying","Huang", email = "lxh37@PSU.EDU", role = c("aut","cre")),
    person("John","Dziak", email = "jjd264@psu.edu", role ="aut"))
c(
    person("Philipp", "Probst", email = "philipp_probst@gmx.de", role = c("aut", "cre")),
    person("Silke","Janitza", email = "silke.janitza@gmail.com", role = "ctb"))
c(person("Naimin", "Jing", email = "naimin.jing@temple.edu",
    role = c("aut", "cre")),
    person("Hexin", "Bai", role = "aut"),
    person("Tong", "Wang", role = "aut"),
    person("Cheng Yong", "Tang", role = "aut"))
c(person("Kristy","Robledo", email = "robledo.kristy@gmail.com", role = c("aut", "cre")),person("Ian", "Marschner", role = c("ths")))
c(person("Bernhard", "Pfaff", email = "bernhard@pfaffikus.de", role = c("aut", "cre")), person("Matthieu", "Stigler", role = "ctb"))
c(person("Christian", "Danne", email = "dannec@tcd.ie", role = c("aut", "cre")))
c(person(given = "David", family = "Meyer", role = c("aut", "cre"), email = "David.Meyer@R-project.org"),
             person(given = "Achim", family = "Zeileis", role = "aut",
	            email = "Achim.Zeileis@R-project.org",
                    comment = c(ORCID = "0000-0003-0918-3766")),
             person(given = "Kurt", family = "Hornik", role = "aut"),
             person(given = "Florian", family = "Gerber", role = "ctb"),
             person(given = "Michael", family = "Friendly", role = "ctb"))
c(person(given = "Michael", family = "Friendly", role=c("aut", "cre"), email="friendly@yorku.ca"),
            person(given = "Heather", family = "Turner", role="ctb"),
            person(given = "Achim", family = "Zeileis", role="ctb"),
            person(given = "Duncan", family = "Murdoch", role="ctb"),
            person(given = "David", family = "Firth", role="ctb")
            )
c(
    person(c("Brian", "J."), "Knaus", role = c("cre", "aut"),
    email = "briank.lists@gmail.com", comment = c(ORCID = "0000-0003-1665-4343")),
    person(c("Niklaus", "J."), "Grunwald", role = "aut",
    email = "grunwaln@science.oregonstate.edu", comment = c(ORCID = "0000-0003-1656-7602")),
    person(c("Eric", "C."), "Anderson", role = "ctb",
    email = "eric.anderson@noaa.gov"),
    person(c("David", "J."), "Winter", role = "ctb",
    email = "david.winter@gmail.com"),
    person(c("Zhian", "N."), "Kamvar", role = "ctb",
    email = "zkamvar@gmail.com", comment = c(ORCID = "0000-0003-1458-7108")),
    person(c("Javier", "F."), "Tabima", role = "ctb",
    email = "caifaz01@gmail.com", comment = c(ORCID = "0000-0002-3603-2691"))
    )
c(person("Scott", "Chamberlain", role = c("aut", "cre"),
    email = "sckott@protonmail.com", comment = c(ORCID="0000-0003-1444-9135")))
c(
  person("Reto", "Buergin", role = c("aut", "cre", "cph"), email = "rbuergin@gmx.ch"),
  person("Gilbert", "Ritschard", role = c("ctb", "ths"),
         email = "gilbert.ritschard@unige.ch"))
c(person("Pieter", "Schoonees", email = "schoonees@gmail.com",
    role = c("aut", "cre", "cph")), person("Niel", "le Roux", role = "ctb"))
c(
    person("Lionel", "Henry", , "lionel@rstudio.com", c("cre", "aut")),
    person("RStudio", role = "cph"),
    person("Carl", "Sutherland", role = "aut", comment = "jg-imagediff library"),
    person("Humble Software", role = "cph", comment = "jg-imagediff library"),
    person("David", "Hong", role = "aut", comment = "TwoFace library"),
    person(family = "jQuery Foundation", role = "cph", comment = "jQuery library"),
    person(family = "jQuery contributors", role = c("ctb", "cph"),
           comment = "jQuery library; authors listed in inst/htmlwidgets/lib/jquery-authors.txt")
    )
c(
    person("Jacob Gerard", "Levernier", email = "jlevern@upenn.edu", role = c("aut", "cre"), comment = "Designed and authored the package source code and documentation. Roles: author, creator, designer, engineer, programmer"), # Additional MARC Relator roles: "dsr", "eng", "prg"
    person("Heather Gaile", "Wacha", email = "wacha2@wisc.edu", role = c("aut"), comment = "Provided intellectual overview and consultation during development for use with medieval cartographic datasets. Roles: conceptor, consultant, data contributor")) # Additional MARC Relator roles: "ccp", "csp", "dtc"
c(
      person("Bob", "Rudis", email = "bob@rudis.net", role = c("aut", "cre")),
      person("Kanit", "Wongsuphasawat,", comment = "Vega-Lite library", role = c("aut")),
      person("Jeffrey", "Heer", comment = "Vega-Lite library", role = c("aut")),
      person("Arvind", "Satyanarayan", comment = "Vega-Lite library", role = c("aut")),
      person("Mike", "Bostock", comment = "D3 library", role = c("aut"))
    )
c(person("Jari", "Oksanen", role=c("aut","cre"),
	     email="jhoksane@gmail.com"),
             person("Roeland", "Kindt", role="aut"),
             person(given=c("Gavin", "L."), family="Simpson", role="aut",
             email="ucfagls@gmail.com"))
c(
	person('Miquel', 'De Cáceres', role=c('aut','cre'),
	email='miquelcaceres@gmail.com'))
c(
  person(given = "Sergio", family = "Ibarra-Espinosa",
         role = c("aut", "cre"),
         email = "sergio.ibarra@usp.br",
         comment = c(ORCID = "0000-0002-3162-1905")),
  person(given = "Daniel",
         family = "Schuch",
         role = c("aut"),
         email = "schuch@usp.br",
         comment = c(ORCID = "0000-0001-5977-4519")))
c(
    person("Ian", "Lyttle", email = "ian.lyttle@schneider-electric.com", role = c("aut", "cre", "cph")),
    person(family = "Schneider Electric", role = c("cph"))
  )
person("Adrian", "Dusa", role = c("aut", "cre", "cph"),
                  email = "dusa.adrian@unibuc.ro")
c(
    person("Jared", "Huling", , "jaredhuling@gmail.com", c("aut", "cre"), comment = c(ORCID = "0000-0003-0670-4845")),
    person("Muxuan", "Liang", , , c("ctb")),
    person("Yixuan", "Qiu", , , c("cph")),
    person("Gael", "Guennebaud", , , c("cph")),
    person("Ray", "Gardner", , , c("cph")),
    person("Jitse", "Niesen", , , c("cph"))
    )
c(person("Zehao", "Xu", email = "z267xu@uwaterloo.ca", role = c("aut", "cre")), person("R. Wayne", "Oldford", role = "aut"), person("Martin", "Lysy", role = "ctb"))
person(given = "Jordan D.", family = "Dworkin",  
  email = "jdwor@mail.med.upenn.edu", role = c("aut", "cre"))
c(person("Andrew", "Sajo-Castelli", email="asajo@usb.ve", role = c("aut", "cre")), person("Desiree", "Villalta", role = "ctb"),person("Lelys", "Bravo", role = "ctb"))
c(
    person("Brodie", "Gaslam", email="brodie.gaslam@yahoo.com",
    role=c("aut", "cre")),
    person(family="Paxdiablo", role="cph", comment="Hash
    table implementation in src/pfhash.h"),
    person(family="R Core Team",
    email="R-core@r-project.org", role="cph",
    comment="Used/adapted several code snippets from R sources, see src/misc-alike.c and src/valname.c"
    ))
c(
              person(c("Muhammad", "Yaseen"),  email = "myaseen208@gmail.com", role = c("aut", "cre"))
            , person(c("Luc",  "Duchateau"),   email = "Luc.Duchateau@ugent.be", role = "ctb")
            , person(c("Paul",  "Janssen"),    email = "paul.janssen@uhasselt.be", role = "ctb")
            , person(c("John", "Rowlands"),    email = " ", role = "ctb")
            )
c(
		    person(given="Andre", family="Schuetzenmeister", email="andre.schuetzenmeister@roche.com", role = c("cre", "aut")),
			person(given="Florian", family="Dufey", email="florian.dufey@roche.com", role = c("aut")),
			person(given="Andrea", family="Geistanger", email="andrea.geistanger@roche.com", role = c("ctb"))
		    )
c(person("Sarah", "Goslee", role = c("aut", "cre"),
                     email = "Sarah.Goslee@ars.usda.gov"),
              person("Heather", "Gall", role = "aut"),
			  person("Tamie", "Veith", role = "aut"))
c(person("Thomas", "Yee", role = c("aut", "cre", "cph"),
                    email = "t.yee@auckland.ac.nz"),
             person("James", "Gray", role = "dtc"))
c(person("Victor", "Miranda", role = c("aut", "cre", 
           "cph"),
            email = "v.miranda@auckland.ac.nz"),
            person("Thomas", "Yee", role = c( "ctb", "ths", 
            "cph"),
            email = "t.yee@auckland.ac.nz"))
c(person("Ruida", "Zhong", email = "zrd2017@163.com", role = c("aut", "cre")),
    person("Xiaohong", "Chen", email = "eescxh@mail.sysu.edu.cn", role = c("aut", "ctb")),
    person("Zhaoli", "Wang", email = "wangzhl@scut.edu.cn", role = c("aut", "ctb")),
    person("Chengguang", "Lai", email = "laichg@scut.edu.cn", role = c("aut", "ctb")),
    person("Joseph J", "Hamman", role = c("ctb"), comment = "Contributor and maintainer of VIC source code"),
    person("Keith", "Cherkauer", role = c("ctb"))
    )
person("Anh", "Le", email = "anh.le@duke.edu", role = c("aut", "cre"))
c(person("Brian D.", "Williamson", email = "brianw26@uw.edu", role = c("aut", "cre")),
            person("Noah", "Simon", role = c("aut")), person("Marco", "Carone", role = c("aut")))
c(
    person("Ulf", "Schepsmeier",, "ulf.schepsmeier@tum.de", role = "aut"),
    person("Jakob", "Stoeber",, role = "aut"),
    person("Eike Christian", "Brechmann",, role = "aut"),
    person("Benedikt", "Graeler",, role = "aut"),
    person("Thomas", "Nagler",, "thomas.nagler@tum.de", role = c("aut", "cre")),
    person("Tobias", "Erhardt",, "tobias.erhardt@tum.de", role = "aut"),
    person("Carlos", "Almeida",, role = "ctb"),
    person("Aleksey", "Min",, role = c("ctb", "ths")),
    person("Claudia", "Czado",, role = c("ctb", "ths")),
    person("Mathias", "Hofmann",, role = "ctb"),
    person("Matthias", "Killiches",, role = "ctb"),
    person("Harry", "Joe",, role = "ctb"),
    person("Thibault", "Vatter",, role = "ctb")
    )
c(
        person("Thomas", "Nagler",, "mail@tnagler.com", role = c("aut", "cre")),
        person("Dani", "Kraus",,, role = c("ctb"))
        )
c(person("Yasser", "Gonzalez-Fernandez",
                    role = c("aut", "cre"),
                    email = "ygonzalezfernandez@gmail.com"),
             person("Marta", "Soto",
                    role = "aut",
                    email = "mrosa@icimaf.cu"),
             person("Joris", "Meys",
                    role = "ctb",
                    email = "Joris.Meys@UGent.be"))
c(
  person("Brandon", "Greenwell", 
          email = "greenwell.brandon@gmail.com",
          role = c("aut", "cre"), 
          comment = c(ORCID = "0000-0002-8120-0084")),
  person("Brad", "Boehmke",
         email = "bradleyboehmke@gmail.com",
         role = c("aut"),
         comment = c(ORCID = "0000-0002-3611-8516")),
  person("Bernie", "Gray",
         email = "bfgray3@gmail.com",
         role = c("aut"))
  )
c(
      person("Simon", "Garnier", email = "garnier@njit.edu", role = c("aut", "cre")),
      person("Noam", "Ross", email = "noam.ross@gmail.com", role = c("ctb", "cph")),
      person("Bob", "Rudis", email = "bob@rud.is", role = c("ctb", "cph")),
      person("Marco", "Sciaini", email = "sciaini.marco@gmail.com", role = c("ctb", "cph")),
      person("Cédric", "Scherer", email = "scherer@izw-berlin.de", role = c("ctb", "cph"))
  )
c(
  person("Nicholas", "Tierney", role = c("aut", "cre"),
      email = "nicholas.tierney@gmail.com", 
      comment = c(ORCID = "https://orcid.org/0000-0003-1460-8722")),
  person("Sean", "Hughes", role = "rev", 
      comment =c(ORCID = "https://orcid.org/0000-0002-9409-9405",
                 "Sean Hughes reviewed the package for rOpenSci, 
                  see https://github.com/ropensci/onboarding/issues/87")),
  person("Mara", "Averick", role = "rev", 
      comment = "Mara Averick reviewed the package for rOpenSci, see 
      https://github.com/ropensci/onboarding/issues/87"),
  person("Stuart", "Lee", role = c("ctb")),
  person("Earo", "Wang", role = c("ctb"))
      )
c(
    person("Nastasia", "Fouret", , "nastasia.fouret@gmail.com", c("aut", "cph")),
    person( "Mederic", "Descoins", , "mederic.descoins@chu-reunion.fr", role=, c("aut", "cph")),
    person("Elodie", "Garnier", , "elodie.garnier@chu-reunion.fr", c("aut", "cre", "cph")),
    person( "CEPOI - EA 7388", role="cph" )
    )
c(
    person(family = "Almende B.V.", role = c("aut", "cph"),
       comment = "vis.js library in htmlwidgets/lib, http://visjs.org, http://www.almende.com/home"),
    person("Benoit", "Thieurmel", role = c("aut", "cre"),
       comment = "R interface", email = "benoit.thieurmel@datastorm.fr"),
    person(given = "Titouan", family = "Robert", 
    email = "titouan.robert@datastorm.fr", role = c("aut", "ctb"))
    )
person(
            "Sandro", "Raabe"
            , role = c("aut", "cre")
            , email = "shosaco_nospam@hotmail.com"
            )
c(person("Ashwini", "Venkatasubramaniam", role = c("aut", "cre"), email = "ashwinikv01@gmail.com"), person("Julian", "Wolfson", role = c("aut", "ctb"), email = "julianw@umn.edu"))
c(person("James", "Balamuta", role = c("aut", "cph", "cre"), 
	                  email = "james.balamuta@gmail.com"))
c(person("Michael", "Sieger", role=c("cre","aut"),
        email="sieger-michael@web.de"), person("Georg",
        "Ohmayer", role=c("aut", "ths"),
        email="georg.ohmayer@hswt.de"), person("Hochschule
        Weihenstephan-Triesdorf", role=("cph")))
person("Ender", "Celik", email = "celik.p.ender@gmail.com",
                  role = c("aut", "cre"))
c(person(given=c("Drew", "Griffin"), family="Levy",
    role = "aut",
    email = "levy.drew@gene.com"),
    person(given=c("David", "C."), family="Norris",
    role = c("aut","cre"),
    email = "david@dnc-llc.com"))
c(
    person("Dmitriy", "Sorokin", role = c("aut", "cre"),
           email = "dementiy@yandex.ru"),
    person("Anton", "Antonov", role = "ctb",
           email = "tonytonov@gmail.com"))
c(
            person("Philipp", "Wittenberg", 
            email = "pwitten@hsu-hh.de", 
            role = c("aut", "cre"),
            comment = c(ORCID = "0000-0001-7151-8243")),
            person("Sven", "Knoth", 
            email = "<Sven.Knoth@gmx.de>", 
            role = "aut",
            comment = c(ORCID = "0000-0002-9666-5554")))
c(person("Nicholas","Hamilton", ,"n.hamilton@unsw.edu.au", role = c("aut", "cre")),
    person("Michael","Ferry",,"m.ferry@unsw.edu.au",role = "aut"))
person("Saturnino", "Luz", email = "luzs@acm.org", role = c("aut", "cre"))
c(
    person("Carlo", "Pacioni", email="C.Pacioni@Murdoch.edu.au", role=c("aut", "cre")),
    person(c("Florian", "W."), "Mayer", email="Florian.Mayer@dpaw.wa.gov.au", role="aut"))
c(
    person("Bob", "Rudis", email = "bob@rud.is", role = c("aut", "cre"), 
           comment = c(ORCID = "0000-0001-5670-2640")),
      person("ProPublica", comment = "Cartogram SVGs used to make the plot data.frames", role = c("cph")),
      person("GovTrack", comment = "Cartogram SVG used to make the plot data.frame", role = c("cph"))
    )
c(person("Angel", "Garcia de la Garza", email = "agarciadlg@gmail.com", role = c("aut", "cre")),
             person("Simon", "Vandekar", email= "simonv@mail.med.upenn.edu", role="aut"),
             person("David", "Roalf", email= "roalf@mail.med.upenn.edu", role="aut"),
             person("Kosha", "Ruparel", email= "kruparel@mail.med.upenn.edu", role="aut"),
             person("Ruben", "Gur", email= "gur@mail.med.upenn.edu", role="aut"),
             person("Raquel", "Gur", email= "raquel@mail.med.upenn.edu", role="aut"),
             person("Theodore", "Satterthwaite", email= "sattertt@mail.med.upenn.edu", role="aut"),
             person("R. Taki", "Shinohara", email= "rshi@mail.med.upenn.edu", role="aut")
             )
person('Ashley', 'Baldry', email = 'arbaldry91@gmail.com', role = c('aut', 'cre'))
c(person("Robin", "Genuer", email = "Robin.Genuer@u-bordeaux.fr", role = c("aut", "cre")),
             person("Jean-Michel", "Poggi", role = "aut"),
             person("Christine", "Tuleau-Malot", role = "aut"))
c(person("Vincent", "Porretta", role = c("aut", "cre"),
		     email = "vincentporretta@gmail.com"),
	      person("Aki-Juhani", "Kyröläinen", role = "aut"),
	      person("Jacolien", "van Rij", role = "ctb"),
		  person("Juhani", "Järvikivi", role = "ctb"))
person("Robert Myles", "McDonnell", email = "robertmylesmcdonnell@gmail.com", role = c("aut", "cre"))
c(
      person("Bob", "Rudis", email = "bob@rud.is", role = c("aut", "cre")),
      person("Dave", "Gandy", role = "aut", comment = "Font Awesome")
    )
c(
        person("Tyler", "Rinker", email = "tyler.rinker@gmail.com", role = c("aut", "cre")),
        person("Josh", "O'Brien", role = "ctb"), 
        person("Ananda", "Mahto", role = "ctb"), 
        person("Matthew", "Sigal", role = "ctb"),
        person("Jonathan", "Carroll", role = "ctb"),
        person("Scott", "Westenberger", role = "ctb")  
        )
c(
    person("Andy", "Yao", , "andy.yao17@gmail.com", c("aut", "cre")),
    person("David", "Kane", , "dave.kane@gmail.com", "aut")
    )
c(person("Jamie M.", "Kass", email = "jkass@gradcenter.cuny.edu", role = c("aut", "cre")),
  person("Gonzalo E.", "Pinilla-Buitrago", email = "gpinillabuitrago@gradcenter.cuny.edu", role = "aut"),
	person("Bruno", "Vilela", email = "bvilela@wustl.edu", role = "aut"),
	person("Matthew E.", "Aiello-Lammens", email = "matt.lammens@gmail.com", role = "aut"),
	person("Robert", "Muscarella", email = "bob.muscarella@gmail.com", role = "aut"),
	person("Cory", "Merow", email = "corymerow@gmail.com", role = "aut"),
	person("Robert P.", "Anderson", email = "randerson@ccny.cuny.edu", role = "aut"))
c(person("Marcelo", "Araya-Salas", role = c("aut", "cre"), email = "araya-salas@cornell.edu"), person("Grace", "Smith-Vidaurre", role = c("aut"), email = "gsmithvi@gmail.com"))
c(
    person("Ernest", "Guevarra", email = "ernestgmd@gmail.com", role =  c("aut", "cre")),
    person("Valid International", "office@validinternational.org", email = "", role = "cph"),
    person("Water and Sanitation for the Urban Poor", "", email = "", role = "cph")
  )
person(given = "James P.", family = "Howard, II",
                          email = "jh@jameshoward.us", role = c("aut", "cre"))
c(person("Will", "Beasley", role = c("aut", "cre"), email =
    "wibeasley@hotmail.com"), person("Joe", "Rodgers", role = "aut", email =
    "joseph.l.rodgers@vanderbilt.edu"), person("Matthew", "Schuelke", role =
    "ctb", email = "schuelkem@slu.edu"), person("Ronnie",
    "Coleman", role = "ctb"), person("Mark Joseph", "Lachowicz", role =
    "ctb", email = "mark.j.lachowicz@vanderbilt.edu"))
c(person("Stuart", "Barber", role=c("aut"),
	email="stuart@maths.leeds.ac.uk"),
        person("Guy", "Nason", role=c("cre", "ctb"),
        email="G.P.Nason@bristol.ac.uk"))
person("Javier", "Fernandez-Macho", email = "javier.fernandezmacho@ehu.es", role = c("aut", "cre"))
c(person('Philippe', 'Marchand', email = 'marchand.philippe@gmail.com',
                  role = c('aut', 'cre')),
             person('David', 'Gill', role = 'aut'))
c(person("Guy", "Nason", role=c("aut", "cre"),
        email="G.P.Nason@bristol.ac.uk"))
c(
  person("Jesse", "Piburn", email = "piburnjo@ornl.gov", role = c("aut", "cre")),
  person("UT-Battelle, LLC", role = "cph")
  )
c(
     person("Steffi", "LaZerte", email = "steffi@steffi.ca", role = c("aut","cre"), comment = c(ORCID = "0000-0002-7690-8360")),
     person("Sam", "Albers", email = "sam.albers@gmail.com", role = c("ctb"), comment = c(ORCID = "0000-0002-9270-7884")))
c(person("Brooke", "Anderson",
    email = "brooke.anderson@colostate.edu", role = c("aut", "cre")),
    person("Roger", "Peng", email = "rdpeng@gmail.com", role = c("aut")),
    person("Joshua", "Ferreri", email = "joshua.m.ferreri@gmail.com",
    role = c("aut")))
c(person("Eduard", "Szöcs", role = c("aut", "cre"),
    email = "eduardszoecs@gmail.com"),
    person("Daniel", "Muench", role = "ctb"),
    person("Johannes", "Ranke", role = "ctb"),
    person("Eric", "Scott", role = "ctb"),
    person("Jan", "Stanstrup", role = "ctb"),
    person("Robert", "Allaway", role = "ctb"))
c(person("Richard", "Barnes", role = c("aut", "cre"), email = "rbarnes@umn.edu"))
c(
    person("Scott", "Chamberlain", role = c("aut", "cre"), email =
    "myrmecocystus+r@gmail.com", comment = c(ORCID="0000-0003-1444-9135")),
    person("rOpenSci", role = "fnd", comment = "https://ropensci.org")
    )
c(person("Zhiyong", "Zhang", role = c("aut", "cre"),
                     email = "johnnyzhz@gmail.com"),
              person("Yujiao", "Mai", role = "aut"),
              person("Miao", "Yang", role = "ctb",
                     email = "Miao.Yang.100@nd.edu"))
c(person("Keon-Woong", "Moon", role = c("aut", "cre"),
                     email = "cardiomoon@gmail.com"),
           person("Tommaso","Martino",role=c("ctb"),
           email="todoslogos@gmail.com"))
c(
	person("Florian S.", "Schaffner", email = "florian.schaffner@outlook.com", role = c("aut", "cre"), comment = c(ORCID = "0000-0003-3352-6191")),
	person("Charles", "Crabtree", email = "ccrabtr@umich.edu", role = c("ctb")))
c(
    person("Winston", "Chang", email = "winston@rstudio.com", role = c("aut", "cre")),
    person("Yihui", "Xie", role = "ctb"),
    person("Francois", "Guillem", role = "ctb"),
    person("Barret", "Schloerke", role = "ctb"),
    person("Nicolas", "Perriault", role = "ctb", comment = "The CasperJS library")
    )
person("Will", "Bowditch", , "will.bowditch@dft.gov.uk", role = c("aut", "cre"))
c(person("Thomas J.", "Leeper",
                    email = "thosjleeper@gmail.com",
                    role = c("aut"), 
                    comment = c(ORCID = "0000-0003-4097-6326")),
             person("Jodi", "Beggs",
                    email = "jodi@alum.mit.edu",
                    role = c("cre", "aut"))      
             )
person("Chanseok","Park", email="statpnu@gmail.com", 
                  role = c("aut", "cre"))
c(person("David", "Silkworth", email = "djsilk@openreliability.org", role = "aut"), person("Jurgen", "Symynck", role = "aut"), person("Jacob", "Ormerod", email = "jake@openreliability.org", role = "cre"), person("OpenReliability.org", role = "cph"))
person("Hensel", "Tim-Gunnar", 
           email = "tim-gunnar.hensel@tu-berlin.de", 
           role = c("aut", "cre"))
person("Matthias", "Studer", email = "matthias.studer@unige.ch", role = c("aut", "cre"))
c(
  person(c("Kathleen", "M."), "Coburn", email = "kcoburn@ucmerced.edu", role = c("aut", "cre")),
  person(c("Jack", "L."), "Vevea", email = "jvevea@ucmerced.edu", role = c("aut")))
c(person("Karthik", "Ram", role = c("aut", "cre"),
    email = "karthik.ram@gmail.com", comment = c(ORCID = "0000-0002-0233-1757")),
    person("Hadley", "Wickham", role = "aut",
    email = "h.wickham@gmail.com"),
    person("Clark", "Richards", role = "ctb",
    email = "crichards@whoi.edu"),
    person("Aaron", "Baggett", role = "ctb",
    email = "aaronbaggett@gmail.com"))
c(person("Paul", "McKeigue",
                    email="paul.mckeigue@ed.ac.uk", role=c("aut"),
                    comment=c(ORCID="0000-0002-5217-1034")),
             person("Marco", "Colombo",
                    email="m.colombo@ed.ac.uk", role=c("ctb", "cre"),
                    comment=c(ORCID="0000-0001-6672-0623")))
c(
  person("In Song", "Kim", email = "insong@mit.edu", role = c("aut", "cre")),
  person("Kosuke", "Imai", email = "kimai@Princeton.edu", role = c("aut")),
  person("Erik", "Wang", email = "haixiaow@princeton.edu", role = c("aut"))
  )
person("Iaroslav", "Domin", email = "ya.domin@gmail.com", role = c("aut", "cre"))
c(
    person("Christopher", "Gandrud", email = "zelig.zee@gmail.com",
    role = c("aut", "cre")),
    person("Gary", "King", role = "aut"),
    person("Ben", "Sabath", role = "ctb"),
    person("Heather", "Stoll", role = "aut"),
    person("Langche", "Zeng", role = "aut")
    )
person("Giulio Valentino", "Dalla Riva", email = "me@gvdallariva.net", role = c("aut", "cre"))
person("Mark", "Ewing", email = "b.mark@ewingsonline.com", role = c("aut", "cre"))
c(person(given = "Gábor",
                    family = "Csárdi", 
                    email = "csardi.gabor@gmail.com", 
                    role = c("aut", "cre")),
            person(given = "Maëlle",
                   family = "Salmon",
                    role = "ctb"))
c(	person("Ulrich", "Wittelsbuerger", role = c("aut", "cre"),email = "ulrich.wittelsbuerger@uni-duesseldorf.de"),
				person("Heng", "Li", role = "ctb"),
				person("Bob", "Handsaker", role = "ctb")			)
c(
    person("Bhaskar", "Karambelkar", email = "bhaskarvk@gmail.com", role = c("aut", "cre")),
    person("Yihui", "Xie", role = c("ctb")),
    person("NPR Visual Team", role = c("ctb", "cph"), comment = "pym.js JavaScript library"),
    person("Bjørn Klinggaard", role = c("ctb", "cph"), comment = "bLazy.js JavaScript library")
    )
c(person("David", "Robinson", email = "admiral.david@gmail.com", role = c("aut", "cre")),
             person("Kanishka", "Misra", role = "ctb"))
c(
    person("Mikhail", "Popov", email = "mikhail@wikimedia.org",
           role = c("aut", "cre"), comment = "@bearloga on Twitter"),
    person("Wikimedia Foundation", role = "cph")
    )
c(
    person("Joseph", "Stachelek", email = "stachel2@msu.edu", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-5924-2464"))
    )
c(person("Avner", "Bar-Hen", role = c("aut", "cre"),
    email = "Avner.Bar-Hen@mi.parisdescartes.fr"),
    person("Louise", "Baschet", role = "ctb"),
    person("Francois-Xavier", "Jollois", role = "ctb"),
    person("Jeremie", "Riou", role = "ctb")
    )
c(
    person("Scott", "Chamberlain", role = c("aut", "cre"),
      email = "myrmecocystus+r@gmail.com"),
    person("Ethan", "Welty", role = "aut")
  )
person(
  given = c("William", "Michael"),
  family = "Landau",  
  email = "will.landau@gmail.com",
  role = c("aut", "cre"))
c(
    person("Hendrik", "Schultheis", email = "hendrik.schultheis@mpi-bn.mpg.de", role = c("aut", "cre")),
    person("Jens", "Preussner", email = "jens.preussner@mpi-bn.mpg.de", role = "aut"),
    person("Looso", "Mario", email = "mario.looso@mpi-bn.mpg.de", role = "aut"))
c(
    person("Jim", "Hester", , "james.f.hester@gmail.com", role = c("aut", "cre")),
    person("Kirill", "Müller", , "krlmlr+r@mailbox.org", role = "aut"),
    person("Kevin", "Ushey", email = "kevinushey@gmail.com", role = c("aut")),
    person("Hadley", "Wickham", , "hadley@rstudio.com", role = "aut"),
    person("Winston", "Chang", role = "aut"),
    person("Richard", "Cotton", role = c("ctb")),
    person("RStudio", role = "cph"))
person("You", "Li", email = "You.Li2@ed.ac.uk",
  role = c("aut", "cre"))
c(
    person(given="Xiaodong", family="Luo", email = "Xiaodong.Luo@sanofi.com", role =c("aut", "cre")),
    person("Sanofi", role = "cph"))
c(
    person("Arne C. Bathke", role = "aut"),
    person("Edgar Brunner", role = "aut"),
    person("Martin Happ", role = c("aut", "cre"),
                      email = "martin.happ@aon.at",
                      comment = c(ORCID = "0000-0003-0009-2665")),
    person("Frank Konietschke", role = "aut")
  )
c(
    person("Justin M.", "Shea", role = c("aut", "cre"), email = "jshea@roosevelt.edu"),
    person("Kennth H.", "Brown", role = c("ctb"), email = "KennethBrown@MissouriState.edu"))
c(
    person("Mika", "Braginsky", email = "mika.br@gmail.com", role = c("aut", "cre")),
    person("Daniel", "Yurovsky", email = "yurovsky@stanford.edu", role = "ctb"),
    person("Michael", "Frank", email = "mcfrank@stanford.edu", role = "ctb"),
    person("Danielle", "Kellier", email = "dkellier@stanford.edu", role = "ctb")
    )
c(person("Ingo", "Feinerer", role = "aut"),
             person("Kurt", "Hornik", role = c("aut", "cre"),
                    email = "Kurt.Hornik@R-project.org",
		    comment = c(ORCID = "0000-0003-4198-9911")),
             person("Mike", "Wallace", role = c("ctb", "cph"),
                    comment = "Jawbone Java WordNet API library"))
person(given = "Michael", family = "Friendly", role=c("aut",
        "cre"), email="friendly@yorku.ca")
c(
    person("John", "Blischak", role = c("aut", "cre"),
           email = "jdblischak@uchicago.edu"),
    person("Peter", "Carbonetto", role = c("aut")),
    person("Matthew", "Stephens", role = c("aut")))
c(person("David", "Carslaw", role = c("aut", "cre"), email =
    "david.carslaw@york.ac.uk"))
c( person("Jan", "Holstein" ,  
              email ="janmholstein@gmail.com",  
              role=c("aut","cre", "cph"))     
              )
person("Christopher", "Baker", email = "chriscrewbaker@gmail.com",
  role = c("aut", "cre"))
c(
  person("Alan", "Jassby", role = c("aut")),
  person("James", "Cloern", role = c("ctb")),
  person("Joseph", "Stachelek", role = c("ctb", "cre"), email = "stachel2@msu.edu")
  )
c(
    person("Raphael", "Winkelmann", ,"raphael@phonetik.uni-muenchen.de", role = c("aut", "cre")),
    person("Lasse", "Bombien", , "lasse@phonetik.uni-muenchen.de", role = c("aut")),
    person("Michel", "Scheffers", role = c("aut")))
c(
    person("Jeroen", "Ooms", ,"jeroen@berkeley.edu", role = c("aut", "cre"),
      comment = c(ORCID = "0000-0002-4035-0289")),
    person("John McNamara", role = "cph",
      comment = "Author of libxlsxwriter (see AUTHORS and COPYRIGHT files for details)"))
c(person(given="Patrick", family="Mair", email="mair@fas.harvard.edu", role=c("cre","aut")),
             person(given="Rand", family="Wilcox", email="rwilcox@usc.edu", role=c("aut")))
c(person("Graham", "Williams", email="graham.williams@togaware.com", role="aut"),
	person("Joshua Z", "Huang", email="zx.huang@szu.edu.cn", role="aut"),
	person("Xiaojun", "Chen", email="xjchen.hitsz@gmail.com", role="aut"),
	person("Qiang", "Wang", role="aut"),
	person("Longfei", "Xiao", role="aut"),
	person("He", "Zhao", email="Simon.Yansen.Zhao@gmail.com", role="cre"))
c(person(given = "Qinghan",
             family = "Meng",
             email = "qinghan.meng@gmail.com",
             role="aut"),
      person(given = "He",
             family = "Zhao",
             email = "Simon.Yansen.Zhao@gmail.com",
             role = c("aut", "cre")),
      person(given = c("Graham", "J."),
             family = "Williams",
             email = "graham.williams@togaware.com",
             role = "aut"),
      person(given = "Junchao",
             family = "Lv",
             role = "aut"),
      person(given = "Baoxun",
             family = "Xu",
             role = "aut"),
      person(given = c("Joshua", "Zhexue"),
             family = "Huang",
             email = "zx.huang@szu.edu.cn",
             role = "aut"))
c(person("Victor", "Maus", role = c("aut"), email ="vwmaus1@gmail.com"), 
             person("Pedro", "Andrade", role = c("aut"), email ="pedro.andrade@inpe.br"), 
             person("Alber", "Sanchez", role = c("aut"),email ="alber.ipia@inpe.br"), 
             person("Luiz Fernando", "Assis", role = c("aut"), email ="luizffga@dpi.inpe.br"),  
             person("Gilberto", "Ribeiro", role = c("aut"), email ="gilberto.queiroz@inpe.br"),
             person("Gilberto", "Camara", role = c("aut", "cre"), email = "gilberto.camara@inpe.br"))
c(
    person("John", "Malc", email = "cincenko@outlook.com", role = c("aut", "cre"), comment = "@dmpe"),
    person("Maksim", "Pecherskiy", role = c("ctb"), comment = "@MrMaksimize")
    )
person("Chandler", "Armstrong", email = "omni.armstrong@gmail.com",
                  role = c("aut", "cre"))
c(person("Thomas", "Mendlik", role = c("aut", "cre"),
	             email = "thomas.mendlik@uni-graz.at"),
             person("Georg", "Heinrich", role = c("aut"),
                    email = "g.heinrich@uni-graz.at"),
             person("Armin", "Leuprecht", role = c("aut"),
                    email = "armin.leuprecht@uni-graz.at"),
             person("Andreas", "Gobiet", role = c("ths"),
                    email = "andreas.gobiet@zamg.ac.at"))
c(
    person(given="Xiaodong", family="Luo", email = "Xiaodong.Luo@sanofi.com", role =c("aut", "cre")),
    person(given="Junshan", family="Qiu",  role = "ctb"),
    person(given="Steven", family="Bai",  role = "ctb"),
    person(given="Hong", family="Tian",  role = "ctb"),
    person(given="Mike", family="Mikailov",  role = "ctb"),
    person("Sanofi", role = "cph"))
c(person("Nicolas", "Turenne", role = "aut", email = "nturenne@u-pem.fr"),
    person("Tien T.","Phan", role = c("aut","cre"),  email = "phantien84@gmail.com"))
c(person("Heike", "Hofmann", role = c("aut", "cre"),
                      email = "hofmann@iastate.edu"), 
             person("Ganesh", "Krishnan", role = c("aut"),
                      email = "ganeshk@iastate.edu"),
             person("Eric", "Hare", email = "eric@oaiti.org", role = "aut")
             )
c(
    person("Yihui", "Xie", role = c("aut", "cre"), email = "xie@yihui.name", comment = c(ORCID = "0000-0003-0645-5666")),
    person("Claus Thorn", "Ekstrøm", role = "ctb"),
    person("Daniel", "Anderson", role = "ctb"),
    person("Dawei", "Lang", role = "ctb"),
    person("Garrick", "Aden-Buie", role = "ctb"),
    person("John", "Little", role = "ctb"),
    person("Joseph", "Casillas", role = "ctb"),
    person("Michael Wayne", "Kearney", role = "ctb"),
    person("Nan-Hung", "Hsieh", role = "ctb"),
    person("Ole Petter", "Bang", role = "ctb", comment = "CSS in rmarkdown/templates/xaringan/resources/default.css"),
    person("Patrick", "Schratz", role = "ctb"),
    person("Sean", "Lopp", role = "ctb"),
    person("Lucy", "D'Agostino McGowan", role = "ctb", comment = c(ORCID = "0000-0001-7297-9359")),
    person("Emi", "Tanaka", role = "ctb"),
    person("Yongfu", "Liao", role = "ctb"),
    person("Malcolm", "Barrett", role = "ctb", comment = c(ORCID = "0000-0003-0299-5825"))
    )
c(person("Hadi", "Esfandyari", email = "esfandyari.hadi@gmail.com", role = c("aut", "cre")),person("Anders", "Christian Sørensen", email = "achristian.sørensen@mbg.au.dk",role = c("aut")))
c(
            person("Kei","Nakagawa",email="kei.nak.0315@gmail.com",role=c("aut","cre"),comment = c(ORCID = "0000-0001-5046-8128")),
            person("Mitsuyoshi","Imamura",email="ic140tg528@gmail.com",role=c("aut"))
          )
c(
  person(given = "Philip", family = "Leifeld", email = "philip.leifeld@glasgow.ac.uk", role = c("aut", "cre")), 
  person(given = c("Skyler", "J."), family = "Cranmer", email = "cranmer.12@osu.edu", role = "aut"), 
  person(given = c("Bruce", "A."), family = "Desmarais", email = "bdesmarais@psu.edu", role = "aut"))
c(person("Gert","Janssenswillen",email = "gert.janssenswillen@uhasselt.be", role = c("aut","cre")),
 person("Benoît","Depaire",email = "benoit.depaire@uhasselt.be", role = "ctb"))
c(
  person("Yihui", "Xie", role = c("aut", "cre", "cph"), email = "xie@yihui.name", comment = c(ORCID = "0000-0003-0645-5666")),
  person("Daijiang", "Li", role = "ctb"),
  person()
  )
c(
  person("Tianqi", "Chen", role = c("aut"),
         email = "tianqi.tchen@gmail.com"),
  person("Tong", "He", role = c("aut", "cre"),
         email = "hetong007@gmail.com"),
  person("Michael", "Benesty", role = c("aut"),
         email = "michael@benesty.fr"),
  person("Vadim", "Khotilovich", role = c("aut"),
         email = "khotilovich@gmail.com"),
  person("Yuan", "Tang", role = c("aut"),
         email = "terrytangyuan@gmail.com",
         comment = c(ORCID = "0000-0001-5243-233X")),
  person("Hyunsu", "Cho", role = c("aut"),
         email = "chohyu01@cs.washington.edu"),
  person("Kailong", "Chen", role = c("aut")),
  person("Rory", "Mitchell", role = c("aut")),
  person("Ignacio", "Cano", role = c("aut")),
  person("Tianyi", "Zhou", role = c("aut")),
  person("Mu", "Li", role = c("aut")),
  person("Junyuan", "Xie", role = c("aut")),
  person("Min", "Lin", role = c("aut")),
  person("Yifeng", "Geng", role = c("aut")),
  person("Yutian", "Li", role = c("aut")),
  person("XGBoost contributors", role = c("cph"),
         comment = "base XGBoost implementation")
  )
c(person(given="Meik", family="Michalke", email="meik.michalke@hhu.de", role=c("aut", "cre")))
c(person("Robert Myles ", "McDonnell", email = "robertmylesmcdonnell@gmail.com", role = c("aut", "cre")), 
           person("Colin", "Fay", email = "contact@colinfay.me", role = c("ctb")),
           person("Randall", "Munroe", role = "cph"))
c(person("Mirai Solutions GmbH", role = "aut",
                    email = "xlconnect@mirai-solutions.com"),
             person("Martin", "Studer", role = "cre",
                    email = "martin.studer@mirai-solutions.com"),
             person("The Apache Software Foundation", role = c("ctb", "cph"),
                    comment = "Apache POI"),
             person("Graph Builder", role = c("ctb", "cph"),
                   comment = "Curvesapi Java library"))
c(person("Mirai Solutions GmbH", role = "aut",
                    email = "xlconnect@mirai-solutions.com"),
             person("Martin", "Studer", role = "cre",
                    email = "martin.studer@mirai-solutions.com"),
             person("The Apache Software Foundation", role = c("ctb", "cph"),
                    comment = "Apache POI, Apache XMLBeans, Apache Commons Codec, Apache Commons Collections"),
             person("Stephen", "Colebourne", role = c("ctb", "cph"),
                    comment = "Joda-Time Java library"))
c(person(c("Adrian", "A."), "Dragulescu", 
                    role = c("aut", "cre"),
                    email = "adrian.dragulescu@gmail.com"), 
             person("The Apache Software Foundation", 
                    role = c("ctb", "cph"), 
                    comment = "Apache POI, Apache XMLBeans", "Apache
                    Commons Codec"), 
             person("MetaStuff, Ltd.", 
                    role = c("ctb", "cph"),
                    comment = "Dom4j"))
c(
    person("Robin", "Linacre", , "robinlinacre@hotmail.com",
    role = c("aut", "cre")),
    person(
    "Karik", "Isichei", email="karik.isichei@gmail.com",
    role=c("aut"))
    )
c(person("Aurele", "Morvan", role = c("aut", "cre"),
		email = "aurele@saagie.com"))
c(person("Yong", "Chen", role=c("aut", "cre"),
                     email ="ychen123@upenn.edu"),
		     person("Chuan", "Hong", role="aut"),
			 person("Haitao", "Chu", role="ctb"),
			 person("Yulun", "Liu", role="ctb"))
c(
    person("Xiaobei", "Zhao", email="xiaobei@binf.ku.dk",
    role=c("aut", "cre", "cph"))
    )
c(
    person("Hadley", "Wickham", , "hadley@rstudio.com", "aut"),
    person("James", "Hester", , "james.hester@rstudio.com", c("aut", "cre")),
    person("Jeroen", "Ooms", role = "aut"),
    person("RStudio", role = "cph"),
    person("R Foundation", role = "ctb",
      comment = "Copy of R-project homepage cached as example")
    )
person("Alex", "Zanidean", email = "AZanidean@mhc.ab.ca",
                  role = c("aut","cre"))
c(
    person("Gábor", "Csárdi",, "csardi.gabor@gmail.com", c("aut", "cre")),
    person("Fathi", "Boudra", role = "aut"),
    person("Rex", "Dieter", role = "aut"),
    person("Kevin", "Krammer", role = "aut"),
    person("Jeremy", "White", role = "aut"))
c(
      person('Benjamin', 'Guiastrennec',
             role  = c('aut', 'cre', 'cph'),
             email = 'guiastrennec@gmail.com'),
      person('Andrew C.', 'Hooker', 
             role  = c('aut', 'cph'),
             email = 'andrew.hooker@farmbio.uu.se'),
      person('Sebastian', 'Ueckert',
             role  = c('aut', 'cph'),
             email = 'sebastian.ueckert@farmbio.uu.se'),
      person('Mike K.', 'Smith', 
             role  = 'ctb',
             email = 'mike.k.smith@pfizer.com'),
      person('Mats O.', 'Karlsson',
             role  = c('aut', 'cph'),
             email = 'mats.karlsson@farmbio.uu.se'))
c(person("Andrew C.","Hooker",role=c("aut","cre","cph"),email="andrew.hooker@farmbio.uu.se"),
             person("Mats O.","Karlsson",role=c("aut","cph")),
             person("Justin J.","Wilkins",role="aut"),
             person("E. Niclas","Jonsson",role=c("aut","trl","cph")),
             person("Ron","Keizer",role="ctb",comment="functionality for bootstrap of GAM and SCM"))
person("Pablo", "Seibelt", email = "pabloseibelt@sicarul.com",
  role = c("aut", "cre"))
c(
  person("Yuri", "Zhukov", email = "zhukov@umich.edu", role = c("cre")),
  person("Christian", "Davenport", email = "christiandavenport@mac.com", role = c("aut")),
  person("Nadiya", "Kostyuk", email = "nadiya@umich.edu", role = c("aut")))
c(person("David B.", "Dahl", role="aut"),
             person("David", "Scott", role=c("aut","cre"),
               email="d.scott@auckland.ac.nz"),
             person("Charles", "Roosen", role="aut"),
             person("Arni", "Magnusson", role="aut"),
             person("Jonathan", "Swinton", role="aut"),
             person("Ajay", "Shah", role="ctb"),
             person("Arne", "Henningsen", role="ctb"),
             person("Benno", "Puetz", role="ctb"),
             person("Bernhard", "Pfaff", role="ctb"),
             person("Claudio", "Agostinelli", role="ctb"),
             person("Claudius", "Loehnert", role="ctb"),
             person("David", "Mitchell", role="ctb"),
             person("David", "Whiting", role="ctb"),
             person("Fernando da", "Rosa", role="ctb"),
             person("Guido", "Gay", role="ctb"),
             person("Guido", "Schulz", role="ctb"),
             person("Ian", "Fellows", role="ctb"),
             person("Jeff", "Laake", role="ctb"),
             person("John", "Walker", role="ctb"),
             person("Jun", "Yan", role="ctb"),
             person("Liviu", "Andronic", role="ctb"),
             person("Markus", "Loecher", role="ctb"),
             person("Martin", "Gubri", role="ctb"),
             person("Matthieu", "Stigler", role="ctb"),
             person("Robert", "Castelo", role="ctb"),
             person("Seth", "Falcon", role="ctb"),
             person("Stefan", "Edwards", role="ctb"),
             person("Sven", "Garbade", role="ctb"),
             person("Uwe", "Ligges", role="ctb"))
c(
    person("Roy", "Mendelssohn", email = "roy.mendelssohn@noaa.gov", role = c("aut","cre")),
    person("Cindy", "Bessey",  role = "ctb"),
    person("Dave", "Foley", role = "ctb"))
c(
  person(given=c("Jeffrey","A."), family="Ryan", role=c("aut","cph")),
  person(given=c("Joshua","M."), family="Ulrich", role=c("cre","aut"), email="josh.m.ulrich@gmail.com"),
  person(given="Ross", family="Bennett", role="ctb"),
  person(given="Corwin", family="Joy", role="ctb")
  )
person("Willem", "van den Boom", role = c("aut", "cre"), email = "willem@wvdboom.nl")
c(
    person("Max", "Kuhn", , "max@rstudio.com", c("aut", "cre")),
    person("Davis", "Vaughan", , "davis@rstudio.com", c("aut")),
    person("RStudio", role = "cph"))
person("Nathaniel", "Phillips", email = "yarrr.book@gmail.com",
                  role = c("aut", "cre"))
person("Tomasz", "Kalinowski", email = "tkalinow@asu.edu",
                  role = c("aut", "cre"))
person("Brian", "O'Meara", email = "omeara.brian@gmail.com", role = c("aut", "cre"))
c(
    person("Joe", "Thorley", email = "joe@poissonconsulting.ca", role = c("aut", "cre")),
    person("Hadley", "Wickham", email = "hadley@rstudio.com", role = "aut"))
person("Antoine", "Soetewey", email = "antoine.soetewey@uclouvain.be",
                  role = c("aut", "cre"))
c(person("Scott", "Chamberlain", role = c("aut", "cre"),
    email = "sckott@protonmail.com", comment = c(ORCID = "0000-0003-1444-9135")))
c(
    person(given = "Nathan", family = "Teetor", email = "nathanteetor@gmail.com", role = c("aut", "cre")),
    person(given = "Paul", family = "Teetor", role = "ctb"))
c(
    person("Olivier M. F.", "Martin", email = "oliviermfmartin@gmail.com", role = c("aut", "cre")),
    person("Michel", "Ducher", email = "michel.ducher@chu-lyon.fr", role = "aut"))
c(
    person("Quentin", "Geissmann", role = c("aut", "cre"), email = "qgeissmann@gmail.com"),
    person("Luis", "Garcia", role = c("aut"), email = "luis.garcia@uni-muenster.de")
    )
c(
    person("Christine", "Choirat", role = "aut"),
    person("Christopher", "Gandrud", email = "zelig.zee@gmail.com",
    role = c("aut", "cre")),
    person("James", "Honaker", role = "aut"),
    person("Kosuke", "Imai", role = "aut"),
    person("Gary", "King", role = "aut"),
    person("Olivia", "Lau", role = "aut"),
    person("IQSS", "Harvard University", role = "cph")
    )
c(
    person("Christine.", "Choirat", role = "aut"),
    person("Christopher", "Gandrud", email = "zelig.zee@gmail.com",
    role = c("aut", "cre")),
    person("James", "Honaker", role = "aut"),
    person("Kosuke", "Imai", role = "aut"),
    person("Gary", "King", role = "aut"),
    person("Olivia", "Lau", role = "aut")
    )
c(
    person("Christopher", "Gandrud", email = "zelig.zee@gmail.com",
    role = c("cre")),
    person("James", "Honaker", role = "aut")
    )
c(
    person("Christopher", "Gandrud", email = "zelig.zee@gmail.com",
    role = c("aut", "cre"))
    )
c(person(given = "Marius", family = "Hofert", role = c("aut", "cre"),
                    email = "marius.hofert@uwaterloo.ca"),
             person(given = "Wayne", family = "Oldford", role = "aut",
                    email = "rwoldford@uwaterloo.ca"))
c(
             person("Dan", "Richard", 
                    email = "djrichar@ualberta.ca", 
                    role = c("aut", "cre")),
             person("Karen", "Buro",
                    email = "BuroK@macewan.ca",
                    role = "ctb"),
             person("Wanhua", "Su",
                    email = "SuW3@macewan.ca",
                    role = "ctb")
            )
c(
    person("Ming", "Yang", email = "mingyang@biostatstudio.com", role = c("aut", "cre")),
    person("Gideon", "Zamba", role = "aut"),
    person("Joseph", "Cavanaugh", role = "aut")
    )
c(person("Ariel", "Duarte-López", role = c("aut", "cre"),
                    email = "aduarte@ac.upc.edu",
		    comment = c(ORCID = "0000-0002-7432-0344")),
  person("Marta", "Pérez-Casany", role = c("aut"),
                    email = "marta.perez@upc.edu",
		    comment = c(ORCID = "0000-0003-3675-6902")))
person("Juan Camilo", "Diaz Zapata", email = "jucdiazza@unal.edu.co", role = c("aut", "cre"))
c(
	person("Joona" , "Lehtomaki", role = c("aut", "cre"), email = "joona.lehtomaki@gmail.com"))
c(person(given = "Achim", family = "Zeileis", role = c("aut", "cre"), email = "Achim.Zeileis@R-project.org",
                    comment = c(ORCID = "0000-0003-0918-3766")),
             person(given = "Gabor", family = "Grothendieck", role = "aut", email = "ggrothendieck@gmail.com"),
             person(given = c("Jeffrey", "A."), family = "Ryan", role = "aut", email = "jeff.a.ryan@gmail.com"),
             person(given = c("Joshua", "M."), family = "Ulrich", role = "ctb", email = "josh.m.ulrich@gmail.com"),
             person(given = "Felix", family = "Andrews", role = "ctb", email = "felix@nfrac.org"))
person("Ran-Ran", "He", email = "heranran2006@163.com",
  role = c("aut", "cre"))
c(person("Philippe", "Grosjean", role = c("aut", "cre"),
  email = "phgrosjean@sciviews.org"),
  person("Kevin", "Denis", role = "aut",
  email = "kevin.denis@umons.ac.be"),
  person("Guillaume", "Wacquet", role = "aut",
  email = "guillaume.wacquet@umons.ac.be"))
c(person(given="Corentin M", family="Barbu",
    email="corentin.barbu@gmail.com", role=c("aut", "cre")),
    person(given="Sebastian", family="Gibb", role="ctb",
    email="mail@sebastiangibb.de"))
c(person("Tom", "August", role = c("aut", "cre"),
                    email = "tomaug@ceh.ac.uk"),
             person("Tim", "Lucas", role = "aut", comment = c(ORCID = "0000-0003-4694-8107")),
             person("Nick", "Golding", role = "aut"),
             person("Emiel", "van Loon", role = "ctb"),
             person("Greg", "McInerny", role = "aut"))
c(
    person("Mark", "Myatt", email = "mark@brixtonhealth.com", role = "aut"),
    person("Ernest", "Guevarra", email = "ernestgmd@gmail.com", role = c("aut", "cre")))
c(
        person("Konstantin", "Sorokin", email = "kvs@sigterm.ru", role = c("aut", "cre")),
        person("Facebook, Inc", role = c("aut","cph"), comment = "Fast real-time compression library")
    )
c(person("Angelo", "Canty", role = "aut",
email = "cantya@mcmaster.ca"),
person("Brian", "Ripley", role = c("aut", "trl", "cre"),
email = "ripley@stats.ox.ac.uk",
comment = "author of parallel support"))
c(person("Brian", "Ripley", role = c("aut", "cre", "cph"),
email = "ripley@stats.ox.ac.uk"),
person("William", "Venables", role = "cph"))
c(person("Martin","Maechler", role=c("aut","cre"), email="maechler@stat.math.ethz.ch")
,person("Peter", "Rousseeuw", role="aut", email="peter.rousseeuw@kuleuven.be",
comment = "Fortran original")
,person("Anja", "Struyf", role="aut", comment= "S original")
,person("Mia", "Hubert", role="aut", email= "Mia.Hubert@uia.ua.ac.be", comment= "S original")
,person("Kurt", "Hornik", role=c("trl", "ctb"),
email="Kurt.Hornik@R-project.org", comment="port to R; maintenance(1999-2000)")
,person("Matthias", "Studer", role="ctb")
,person("Pierre", "Roudier", role="ctb")
,person("Juan",   "Gonzalez", role="ctb")
,person("Kamil",  "Kozlowski", role="ctb")
)
c(
person("R Core Team",
email = "R-core@R-project.org", role = c("aut", "cph", "cre")),
person("Roger", "Bivand", role = c("ctb", "cph")),
person(c("Vincent", "J."), "Carey", role = c("ctb", "cph")),
person("Saikat", "DebRoy", role = c("ctb", "cph")),
person("Stephen", "Eglen", role = c("ctb", "cph")),
person("Rajarshi", "Guha", role = c("ctb", "cph")),
person("Swetlana", "Herbrandt", role = "ctb"),
person("Nicholas", "Lewin-Koh", role = c("ctb", "cph")),
person("Mark", "Myatt", role = c("ctb", "cph")),
person("Ben", "Pfaff", role = "ctb"),
person("Brian", "Quistorff", role = "ctb"),
person("Frank", "Warmerdam", role = c("ctb", "cph")),
person("Stephen", "Weigand", role = c("ctb", "cph")),
person("Free Software Foundation, Inc.", role = "cph"))
c(person("Matt", "Wand", role = "aut",
email = "Matt.Wand@uts.edu.au"),
person("Brian", "Ripley", role = c("trl", "cre", "ctb"),
email = "ripley@stats.ox.ac.uk",
comment = "R port and updates"))
c(person("Brian", "Ripley", role = c("aut", "cre", "cph"),
email = "ripley@stats.ox.ac.uk"),
person("Bill", "Venables", role = "ctb"),
person(c("Douglas", "M."), "Bates", role = "ctb"),
person("Kurt", "Hornik", role = "trl",
comment = "partial port ca 1998"),
person("Albrecht", "Gebhardt", role = "trl",
comment = "partial port ca 1998"),
person("David", "Firth", role = "ctb"))
c(person("Douglas","Bates", role="aut")
, person("Martin","Maechler", email="mmaechler+Matrix@gmail.com",
role = c("aut","cre"))
, person("Timothy A.", "Davis", role="ctb",
comment = c("SuiteSparse and 'cs' C libraries, notably CHOLMOD, AMD;
collaborators listed in
dir(pattern = '^[A-Z]+[.]txt$', full.names=TRUE,
system.file('doc', 'SuiteSparse', package='Matrix'))"))
, person("Jens", "Oehlschlägel", role="ctb", comment="initial nearPD()")
, person("Jason", "Riedy", role="ctb",
comment = c("condest() and onenormest() for octave",
"Copyright: Regents of the University of California"))
, person("R Core Team", role = "ctb", comment="base R matrix implementation")
)
c(person("José", "Pinheiro", role = "aut", comment = "S version"),
person("Douglas", "Bates", role = "aut", comment = "up to 2007"),
person("Saikat", "DebRoy", role = "ctb", comment = "up to 2002"),
person("Deepayan", "Sarkar", role = "ctb", comment = "up to 2005"),
person("EISPACK authors", role = "ctb", comment = "src/rs.f"),
person("Siem", "Heisterkamp", role = "ctb", comment = "Author fixed sigma"),
person("Bert", "Van Willigen",role = "ctb", comment = "Programmer fixed sigma"),
person("R-core", email = "R-core@R-project.org",
role = c("aut", "cre")))
c(person("Terry", "Therneau", role = "aut",
email = "therneau@mayo.edu"),
person("Beth", "Atkinson", role = c("aut", "cre"),
email = "atkinson@mayo.edu"),
person("Brian", "Ripley", role = "trl",
email = "ripley@stats.ox.ac.uk",
comment = "producer of the initial R port, maintainer 1999-2017"))
c(person("Brian", "Ripley", role = c("aut", "cre", "cph"),
email = "ripley@stats.ox.ac.uk"),
person("Roger", "Bivand", role = "ctb"),
person("William", "Venables", role = "cph"))
c(person(c("Terry", "M"), "Therneau",
email="therneau.terry@mayo.edu",
role=c("aut", "cre")),
person("Thomas", "Lumley", role=c("ctb", "trl"),
comment="original S->R port and R maintainer until 2009"))
Send an email for <jafioruci@gmail.com> with title
'bayesDccGarch Bug'
Wim Van der Elst <Wim.vanderelst@gmail.com>
Send an email for <jafiorucci@gmail.com> with title
'forecTheta Bug'
https://github.com/selva86/InformationValue/issues License:
GPL (>= 2)
Social and professional topics~Medical
records, Applied computing~Health care information systems,
Applied computing~Health informatics, Applied
computing~Bioinformatics
Computing methodologies ~ Classification and
regression trees, Computing methodologies ~ Supervised learning
by classification, Computing methodologies ~ Massively parallel
and high-performance simulations, Computing methodologies ~
Distributed simulation
'adaptive-gmrf-2covar.R' 'adaptive-gmrf.R'
'plot-weights-real.R' 'plot-weights-sim.R'
'sim-adaptive-gmrf-2covar.R' 'sim-adaptive-gmrf.R'
'sim-fmri-2covar.R' 'sim-fmri.R'
adegenet.package.R datasets.R dist.genlight.R orthobasis.R
classes.R constructors.R accessors.R basicMethods.R handling.R
auxil.R minorAllele.R setAs.R SNPbin.R strataMethods.R
hierarchyMethods.R glHandle.R glFunctions.R glSim.R
find.clust.R hybridize.R scale.R fstat.R import.R seqTrack.R
chooseCN.R genind2genpop.R loadingplot.R sequences.R
gstat.randtest.R makefreq.R colorplot.R monmonier.R spca.R
coords.monmonier.R haploGen.R old2new.R global_local_tests.R
dapc.R compoplot.R xvalDapc.R haploPop.R PCtest.R dist.genpop.R
Hs.R propShared.R export.R HWE.R propTyped.R inbreeding.R
glPlot.R gengraph.R simOutbreak.R mutations.R snpposi.R
snpzip.R pairDist.R snapclust.R AIC.snapclust.R
AICc.snapclust.R BIC.snapclust.R KIC.snapclust.R
snapclust.choose.k.R servers.R showmekittens.R spca_randtest.R
export_to_mvmapper.R doc_C_routines.R zzz.R
adeGsenv.R parameter.R utils.R utilstriangle.R genericMethod.R
utilsclass.R panelfunctions.R ADEg.R ADEgS.R utilsADEgS.R
ADEg.C1.R C1.barchart.R C1.curve.R C1.curves.R C1.density.R
C1.gauss.R C1.dotplot.R C1.hist.R C1.interval.R ADEg.S1.R
S1.boxplot.R S1.class.R S1.distri.R S1.label.R S1.match.R
ADEg.S2.R S2.arrow.R S2.class.R S2.corcircle.R S2.density.R
S2.distri.R S2.image.R S2.label.R S2.logo.R S2.match.R
S2.traject.R S2.value.R ADEg.T.R T.image.R T.value.R T.cont.R
ADEg.Tr.R Tr.class.R Tr.label.R Tr.match.R Tr.traject.R
addhist.R addline.R addpoint.R addsegment.R addtext.R
ade4-kplot.R ade4-scatter.R ade4-score.R ade4-plot.R
multiplot.R s.Spatial.R utilskey.R
'as_Membership.R' 'biplot.R' 'c_fuzzycluster.R'
'class_membership.R' 'class_co_fuzzycluster.R'
'class_fuzzycluster.R' 'class_fuzzycluster_list.R'
'class_validation.R' 'co.vote.R' 'cpair_fuzzy.R' 'fuzzy.CM.R'
'fuzzy.GG.R' 'fuzzy.GK.R' 'is_Membership.R' 'membership.R'
'method_co_fuzzycluster.R' 'method_fuzzycluster.R'
'method_fuzzycluster_list.R' 'method_membership.R'
'method_validation.R' 'minWeightBipartiteMatching.R'
'print_Membership.R' 'print_co_fuzzycluster.R'
'print_fuzzycluster.R' 'print_validation.R' 'radarplot.R'
'validation.R' 'zzz.R'
'AFM3DPrinter.R' 'AFMFractalDimensionAnalyser.R' 'AFMImage.R'
'AFMNetworksAnalyser.R' 'AFMPSDAnalyser.R'
'AFMVariogramAnalyser.R' 'AFMImageAnalyser.R'
'AFMReportMaker.R' 'pkgname.R' 'runAFMApp.R'
'ahp_mat.R' 'ahp_aggjudge.R' 'ahp_aggpref.R' 'ahp_bweight.R'
'ahp_cr.R' 'ahp_indpref.R' 'ahp_error.R' 'ahp_harker.R'
'ahp_missing.R' 'ahp_pwerror.R' 'ahpsurvey.R' 'data.R'
'Alakazam.R' 'AminoAcids.R' 'Classes.R' 'Core.R' 'Data.R'
'Diversity.R' 'Gene.R' 'Lineage.R' 'RcppExports.R' 'Sequence.R'
'Topology.R'
'Acl.r' 'Client.r' 'Algorithm.r' 'DataDirectory.r'
'DataFile.r' 'data_utilities.r'
'AMModels-package.R' 'amData.R' 'amModel.R' 'amModelLib.R'
'catwrap.R' 'classes.R' 'getters.R' 'grepAMModelLib.R'
'insertAMModelLib.R' 'ls.R' 'metaSummary.R' 'methods-amModel.R'
'methods-amModelLib.R' 'modelMgr.R' 'rmModelsData.R'
'simCovariate.R'
'anchoredDistr.R' 'MADproject.R' 'calcLikelihood.R'
'calcPosterior.R' 'data.R' 'plot.R' 'print.R' 'readMAD.R'
'reduceData.R' 'test_convergence.R'
'MembershipFunction.R' 'MembershipFunction-show.R' 'BellMF.R'
'GaussianMF.R' 'NormalizedGaussianMF.R'
'MembershipFunction-evaluateMF.R' 'Anfis.R'
'Anfis-initialize.R' 'Anfis-getters.R' 'Anfis-printshow.R'
'Anfis-metrics.R' 'Anfis-package.R' 'Anfis-plotMF.R'
'Anfis-plot.R' 'Anfis-predict.R' 'Anfis-training.R'
'Anfis-trainSet.R' 'Anfis3-example.R'
'MembershipFunction-derivateMF.R' 'MembershipFunction-getset.R'
'MembershipFunction-print.R'
Antitrust.R Bertrand.R logit.R linear.R loglin.R logitALM.R
logitNests.R logitNestsALM.R logitCap.R ces.R cesNests.R aids.R
pcaids.R pcaidsNests.R cmcr.R upp.R hhi.R sim.R auction2ndcap.R
auction2ndlogit.R Cournot.R Stackelberg.R auction2ndlogitALM.R
antitrust_shiny.R cesALM.R logitCapALM.R bert.R
'DC-Binary.R' 'DC-Show.R' 'DC-aoos.R' 'DC-defineClass.R'
'DC-public-interfaces.R' 'NAMESPACE.R' 'RL-Infix.R'
'RL-envHelper.R' 'RL-retList.R' 'S4-expressions.R'
'S4-generics.R' 'S4-generics-test.R' 'S4-roxygen-parser.R'
'S4-types.R' 'S4RC-Accessor.R' 'S4RC-Private.R'
'S4RC-defineRefClass.R'
'aopCytoscape_Class.R' 'aop_cytoscape_methods.R'
'aop_graph_analysis.R' 'bmr_Class.R' 'pod_analysis.R'
AllClasses.R AllGenerics.R access-methods.R coerce-methods.R
show-methods.R labels-methods.R length-methods.R revDend.R
heatmap-methods.R plot-methods.R cutree-methods.R
sort-methods.R aggExCluster-methods.R apcluster-methods.R
apclusterL-methods.R apclusterK-methods.R apclusterDemo.R
preferenceRange-methods.R similarity.R simpleDist.R
conversions.R
doc.R internal.R multidna.class.R multiphyDat.class.R
add.gaps.R rm.gaps.R show.R multidna.constructor.R
multiphyDat.constructor.R accessors.R subset.R concatenate.R
plot.R readfiles.R datasets.R dist.R getTree.R exports.R
'editAPSIM.R' 'helperFns.R' 'runAPSIM.R' 'sensitivityAPSIM.R'
'visualize.R' 'emulateAPSIM.R'
'archetypes-barplot.R' 'generics.R' 'archetypes-class.R'
'archetypes-kit-blocks.R' 'archetypes-kit.R' 'archetypes-map.R'
'archetypes-movie.R' 'archetypes-panorama.R' 'pcplot.R'
'archetypes-pcplot.R' 'archetypes-robust.R'
'archetypes-screeplot.R' 'archetypes-step.R'
'archetypes-weighted.R' 'archetypes-xyplot.R' 'memento.R'
'simplex-pot.R' 'skeletonplot.R'
'unittestdata.R' 'arrayhelpers.R' 'apply.R' 'array2df.R'
'array2vec.R' 'colSums.R' 'countrows.R' 'delold.R'
'dropdimnames.R' 'ensuredim.R' 'groupsum.R' 'init.R' 'makeNd.R'
'ndim.R' 'numericindices.R' 'restoredim.R' 'rowsum.R' 'slice.R'
'stack.R' 'ta.R' 'unittests.R'
'asciiAnova.r' 'asciiDataFrame.r' 'asciiDefault.r'
'asciiDensity.r' 'asciiDescr.r' 'asciiEpi.r' 'asciiGlm.r'
'asciiHmisc.r' 'asciiHtest.r' 'asciiList.r' 'asciiLm.r'
'asciiMatrix.r' 'asciiMemisc.r' 'asciiPrcomp.r'
'asciiSmoothSpline.r' 'asciiSummaryTable.r' 'asciiSurvival.r'
'asciiTable.r' 'asciiTs.r' 'asciiVector.r' 'cacheSweaveAscii.r'
'cbind.r' 'export.r' 'generic.r' 'groups.r' 'interleave.r'
'paste.matrix.r' 'plim.r' 'print.character.matrix.r'
'RweaveAscii.r' 'show.asciidoc.r' 'show.org.r' 'show.pandoc.r'
'show.r' 'show.rest.r' 'show.t2t.r' 'show.textile.r'
'SweaveAscii.r' 'tocharac.r' 'weaverAscii.r' 'zzz.r' 'print.r'
'assert-is-date-string.R' 'imports.R' 'assert-is-time.R'
'is-date-string.R' 'workaround.R' 'is-time.R'
'imports.R' 'assert-is-connection.R' 'assert-is-file-size.R'
'assert-is-file.R' 'internal-connection.R' 'is-connection.R'
'is-file-size.R' 'is-file.R'
'imports.R' 'assert-has-terms.R' 'assert-is-empty-model.R'
'has-terms.R' 'is-empty-model.R'
'assert-is-divisible-by.R' 'assert-is-equal-to.R' 'imports.R'
'assert-is-in-range.R' 'assert-is-infinity-nan.R'
'assert-is-real-imaginary.R' 'assert-is-whole-number.R'
'is-divisible-by.R' 'is-equal-to.R' 'is-in-range.R'
'is-infinity-nan.R' 'is-real-imaginary.R' 'is-whole-number.R'
'are-same-size.R' 'assert-are-same-size.R' 'imports.R'
'assert-has-attributes.R' 'assert-has-dims.R'
'assert-has-dupes.R' 'assert-has-names.R' 'assert-has-slot.R'
'assert-is-atomic-recursive-vector.R'
'assert-is-empty-scalar.R' 'assert-is-monotonic.R'
'assert-is-null.R' 'assert-is-unsorted.R' 'has-attributes.R'
'has-dims.R' 'has-dupes.R' 'has-names.R' 'has-slot.R'
'is-atomic-recursive-vector.R' 'is-empty-scalar.R'
'is-monotonic.R' 'is-null.R' 'is-unsorted.R' 'utils.R'
'imports.R' 'assert-is-32-64-bit.R' 'assert-is-current.R'
'assert-is-decimal-point.R' 'assert-is-ide.R'
'assert-is-on-os-path.R' 'assert-is-os.R' 'assert-is-r-mode.R'
'assert-is-r-version.R' 'assert-r-has-capability.R'
'is-32-64-bit.R' 'is-current.R' 'is-decimal-point.R' 'is-ide.R'
'is-on-os-path.R' 'is-os.R' 'is-r-mode.R' 'is-r-version.R'
'locale.R' 'r-has-capability.R'
'imports.R' 'assert-is-a-type.R' 'assert-is-condition.R'
'assert-is-date.R' 'assert-is-formula.R' 'assert-is-function.R'
'assert-is-type-base.R' 'assert-is-type-data.table.R'
'assert-is-type-dplyr.R' 'assert-is-type-grDevices.R'
'assert-is-type-methods.R' 'assert-is-type-stats.R'
'assert-is-type-utils.R' 'is-a-type.R' 'is-condition.R'
'is-date.R' 'is-formula.R' 'is-function.R' 'is-type-base.R'
'is-type-data.table.R' 'is-type-dplyr.R' 'is-type-grDevices.R'
'is-type-methods.R' 'is-type-stats.R' 'is-type-utils.R'
'assert-that.r' 'on-failure.r' 'assertions-file.r'
'assertions-scalar.R' 'assertions.r' 'base.r'
'base-comparison.r' 'base-is.r' 'base-logical.r' 'base-misc.r'
'utils.r' 'validate-that.R'
'AssociationTestWithCorrectPS.R' 'CalculateGapK.R'
'CalculateWAll.R' 'CalExpect.R' 'ChangeX.R' 'CorrMatNRTest.R'
'DR_main.R' 'EigenStrat_main.R' 'FindCNumRandom.R'
'MAX3_main.R' 'MAX3Sign.R' 'MDS.R' 'PCoC_main.R'
'ModifyNormalization.R' 'NMAX3_main.R' 'NPT_main.R'
'RhombusFormula.R' 'ScoreTest.R' 'SimilarityMatrix.R'
'SMT_main.R' 'Str2Num.R' 'TrendTest.R' 'TW_main.R'
'UniformSample.R' 'UniformTest.R' 'WaldTest.R'
'ASCORE.Ord.R' 'ASCORE.R' 'ASSU.Ord.R' 'ASSU.R' 'ASSUW.Ord.R'
'ASSUW.R' 'ASUM.Ord.R' 'ASUM.R' 'AssotesteR-internal.R' 'BST.R'
'CALPHA.R' 'CARV.R' 'CAST.R' 'CMAT.R' 'CMC.R' 'GDBR.R'
'MULTI.R' 'ORWSS.R' 'RARECOVER.R' 'RBT.R' 'RVT1.R' 'RVT2.R'
'RWAS.R' 'SCORE.R' 'SEQSUM.R' 'SKAT.R' 'SSU.R' 'SSUW.R' 'SUM.R'
'TTEST.R' 'UMINP.R' 'VT.R' 'WSS.R' 'WST.R' 'gdbr_IBS.R'
'gdbr_wIBS.R' 'kernel_IBS.R' 'kernel_twowayx.R' 'kernel_wIBS.R'
'my_check.R' 'print.assoctest.R'
'arima.R' 'aurelius.R' 'avro.R' 'avro_from_df.R'
'avro_fullname.R' 'avro_type.R' 'avro_typemap.R' 'converters.R'
'ets.R' 'forecast.R' 'gbm.R' 'gen_unique_name.R' 'glm.R'
'glmnet.R' 'holtwinters.R' 'jsonobj.R' 'kcca.R' 'kmeans.R'
'knn.R' 'lda.R' 'lm.R' 'naiveBayes.R' 'pfa.R' 'pfa_cellpool.R'
'pfa_document.R' 'pfa_engine.R' 'pfa_expr.R' 'pfa_utils.R'
'randomForest.R' 'read_pfa.R' 'rpart.R' 'write_pfa.R' 'zzz.R'
'autocog.R' 'known_cog_groups.R' 'add_cog_group.R'
'field_info.R' 'add_cog_group_.R' 'known_layer_cogs.R'
'add_layer_cogs.R' 'add_layer_cogs_.R' 'cog_desc.R'
'cog_spec.R' 'layer_count.R' 'layer_info.R' 'of_type.R'
'plot_class.R' 'plot_cogs.R'
'aVirtualTwins.R' 'data.R' 'object.R' 'difft.R' 'setClass.R'
'predict.R' 'forest.R' 'forest.double.R' 'forest.fold.R'
'forest.one.R' 'forest.wrapper.R' 'formatRCTDataset.R'
'incidences.R' 'object.wrapper.R' 'tools.R' 'tree.R'
'tree.class.R' 'tree.reg.R' 'tree.wrapper.R'
'zzz.R' 'LossDevModelInput.R' 'AnnualAggLossDevModelInput.R'
'LossDevModelOutput.R' 'NodeOutput.R'
'AnnualAggLossDevModelOutput.R'
'BreakAnnualAggLossDevModelOutput.R'
'StandardAnnualAggLossDevModelOutput.R'
'AnnualAggLossDevModelOutputWithZeros.R' 'BALD.R'
'BreakAnnualAggLossDevModelInput.R' 'DocumentationOfData.R'
'StandardAnnualAggLossDevModelInput.R' 'util.R'
'diagnostics.R' 'CP_model.R' 'diagonal.R' 'Tucker_model.R'
'exp_var.R' 'get_data_params.R' 'get_influence.R'
'get_model_params.R' 'im_2_mat.R' 'im_mat.R' 'input_data.R'
'kernelize.R' 'lower_bnd_Tucker.R' 'lower_bnd_CP.R'
'mk_model.R' 'mk_toy.R' 'mult_3d.R' 'nrmse.R' 'plot_preds.R'
'plot_roc.R' 'plot_test_RMSE.R' 'plot_test_cor.R'
'plot_test_exp_var.R' 'rmse.R' 'rot.R' 'safe_log.R'
'safe_prod.R' 'show_mat.R' 'test.R' 'test_CP.R' 'test_Tucker.R'
'test_results.R' 'train.R' 'train_CP.R' 'train_Tucker.R'
'update_core_Tucker.R' 'update_mode1_Tucker.R'
'update_mode2_Tucker.R' 'update_mode3_Tucker.R'
'update_mode1_CP.R' 'update_mode2_CP.R' 'update_mode3_CP.R'
'mle2-class.R' 'mle2-methods.R' 'mle.R' 'confint.R'
'predict.R' 'profile.R' 'update.R' 'dists.R' 'IC.R' 'slice.R'
BoundedDensity.R KernelDensity.R Chen99Kernel.R
MicroBetaChen99Kernel.R MacroBetaChen99Kernel.R
BoundaryKernel.R NoBoundaryKernel.R NormalizedBoundaryKernel.R
Muller91BoundaryKernel.R
JonesCorrectionMuller91BoundaryKernel.R
Muller94BoundaryKernel.R
JonesCorrectionMuller94BoundaryKernel.R BernsteinPolynomials.R
Vitale.R BrVitale.R KakizawaB1.R KakizawaB2.R KakizawaB3.R
HirukawaJLNKernel.R HirukawaTSKernel.R
MacroBetaHirukawaJLNKernel.R MacroBetaHirukawaTSKernel.R
utils.R bde.R
bdsmatrix.R gchol.R gchol.bdsmatrix.R as.matrix.bdsmatrix.R
bdsBlock.R bdsI.R bdsmatrix.ibd.R bdsmatrix.reconcile.R
diag.bdsmatrix.R listbdsmatrix.R multiply.bdsmatrix.R
solve.bdsmatrix.R solve.gchol.R solve.gchol.bdsmatrix.R
backsolve.R
'RcppExports.R' 'order.R' 'utils.R' 'benchmark.R'
'benchr-package.R' 'boxplot.R' 'mean.R' 'plot.R' 'units.R'
'summary.R' 'print.R' 'zzz.R'
'bfa-package.R' 'main.R' 'bfa_copula.R' 'bfa_gauss.R'
'bfa_mixed.R' 'rng.R' 'postproc.R' 'class.R' 'get_coda.R'
'plots.R' 'ratings08.R' 'wrappers.R'
'auxil.R' 'bigGP.R' 'collectDistribute.R'
'distributedComputation.R' 'krigeProblem.R'
'bigml-internal.R' 'formEncodeURL.R' 'bigml-package.R'
'createDataset.R' 'createModel.R' 'createPrediction.R'
'createSource.R' 'getDataset.R' 'getModel.R' 'getPrediction.R'
'getSource.R' 'listDatasets.R' 'listModels.R' 'listSources.R'
'quickDataset.R' 'quickModel.R' 'quickPrediction.R'
'quickSource.R' 'setCredentials.R' 'deleteResource.R'
'RcppExports.R' 'data_frame.r' 'data_matrix.r' 'family.r'
'map_reg.r'
'RcppExports.R' 'auth.R' 'bigrquery.R' 'bq-dataset.R'
'bq-download.R' 'bq-field.R' 'bq-job.R' 'bq-param.R'
'bq-parse.R' 'bq-perform.R' 'bq-project.R' 'bq-projects.R'
'bq-query.R' 'bq-refs.R' 'bq-request.R' 'bq-table.R'
'bq-test.R' 'camelCase.R' 'dbi-driver.R' 'dbi-connection.R'
'dbi-result.R' 'dplyr.R' 'gs-object.R' 'old-dataset.R'
'old-id.R' 'old-job-extract.R' 'old-job-query.R'
'old-job-upload.R' 'old-job.R' 'old-project.R' 'old-projects.R'
'old-query.R' 'old-table.R' 'old-tabledata.R' 'secret.R'
'utils.R' 'zzz.R'
'AUC.R' 'FBM-attach.R' 'crochet.R' 'FBM.R' 'FBM-code256.R'
'FBM-copy.R' 'RcppExports.R' 'SVD.R' 'apply-parallelize.R'
'biglasso.R' 'bigstatsr.R' 'colstats.R' 'counts.R'
'crossprodSelf.R' 'mult.R' 'plot.R' 'predict.R' 'randomSVD.R'
'read.R' 'scaling.R' 'tcrossprodSelf.R' 'transpose.R'
'univLinReg.R' 'univLogReg.R' 'utils-assert.R' 'utils.R'
'zzz.R'
'RcppExports.R' 'utils.R' 'all-generics.R'
'biofiles-package.R' 'filter.R' 'flanking.R' 'gbHeader-class.R'
'gbLocation-class.R' 'gbFeature-class.R'
'gbFeatureTable-class.R' 'gbRecord-class.R'
'gbRecordList-class.R' 'genome-record-from-ncbi.R'
'make-ranges.R' 'misc-functions.R' 'parser-general.R'
'parser-embl.R' 'parser-gbk.R' 'select.R' 'shift.R'
'write-feature-table.R' 'write-genbank.R' 'zzz.R'
'R-bizdays-package.r' 'datasets.R' 'calendar.R'
'adjust.date.R' 'bizdays.options.R' 'bizseq.R' 'is.bizday.R'
'offset.R' 'bizdiff.R' 'bizdays.R' 'create-calendars.R'
'calendar-export.R' 'getdate.R'
'blandr.data.preparation.r' 'blandr.dataset.fibre.r'
'blandr.dataset.sbp.r' 'blandr.dataset.o2sats.r'
'blandr.dataset.pefr.r' 'blandr.dataset.load.r'
'blandr.plot.ggplot.r' 'blandr.plot.rplot.r'
'blandr.plot.limits.r' 'blandr.statistics.r' 'blandr.draw.r'
'blandr.output.text.r' 'blandr.display.and.draw.r'
'blandr.display.and.plot.r' 'blandr.method.comparison.r'
'blandr.output.report.r' 'blandr.plot.normality.r'
'blandr.plot.qq.r' 'blandr.plot.r' 'jamovibaanalysis.b.R'
'jamovibaanalysis.h.R' 'jamovibaplothistogram.b.R'
'jamovibaplothistogram.h.R' 'jamovibaplotqq.b.R'
'jamovibaplotqq.h.R' 'jamovibastats.b.R' 'jamovibastats.h.R'
'coclusterStrategy.R' 'RCocluster.R' 'optionclasses.R'
'cocluster.R' 'coclusterBinary.R' 'coclusterCategorical.R'
'coclusterContingency.R' 'coclusterContinuous.R' 'onattach.R'
'as.blockmatrix.matrix.R' 'as.matrix.blockmatrix.R'
'blockmatrix.R' 'dim.blockmatrix.R' 'is.zero.blockmatrix.R'
'length.blockmatrix.R' 'matmult.blockmatrix.R'
'methods.blockmatrix.R' 'names.blockmatrix.R'
'ncol.blockmatrix.R' 'ncol_elements.blockmatrix.R'
'nrow.blockmatrix.R' 'nrow_elements.blockmatrix.R'
'solve.blockmatrix.R' 't.blockmatrix.R' 'value.blockmatrix.R'
'value.replacement.blockmatrix.R' 'zbracket[.blockmatrix.R'
'zbracket[.replacement.blockmatrix.R'
'BMAmevt-package.R' 'Laplace.evt.r' 'MCpriorIntFun.nl.r'
'MCpriorIntFun.pb.r' 'MCpriorIntFun.r' 'add.frame.r'
'transf.to.equi.r' 'cons.angular.dat.r' 'ddirimix.grid.r'
'ddirimix.grid1D.r' 'ddirimix.r' 'dgridplot.R' 'diagnose.r'
'discretize.R' 'dnestlog.grid.r' 'dnestlog.r'
'dpairbeta.grid.r' 'dpairbeta.r' 'emp_density.r'
'excessProb.condit.dm.r' 'excessProb.nl.r' 'excessProb.pb.r'
'lAccept.ratio.r' 'marginal.lkl.nl.r' 'marginal.lkl.pb.r'
'marginal.lkl.r' 'maxLikelihood.r' 'posterior.predictive.nl.r'
'posterior.predictive.pb.r' 'posterior.predictive3D.r'
'posteriorDistr.bma.r' 'posteriorMCMC.nl.r'
'posteriorMCMC.pb.r' 'posteriorMCMC.r' 'posteriorMean.r'
'posteriorWeights.r' 'prior.nl.r' 'prior.pb.r' 'proposal.nl.r'
'proposal.pb.r' 'rdirimix.r' 'rect.integrate.r' 'rnestlog.r'
'rpairbeta.r' 'rstable.posit.R' 'scores3D.r' 'transf.to.rect.r'
'wrapper.R' 'zzz.r'
'bmkconverge.R' 'bmk.R' 'bmksensitive.R' 'bmksummary.R'
'HBconverg1.R' 'HDistNoSize.R' 'HDistSize.R' 'HWconverg1.R'
'beq.lin.dimensionless.R' 'beq.lin.R'
'beq.song.dimensionless.R' 'beq.song.R' 'boussinesq-package.R'
'coefficient.song.solution.R'
'0briskaR.R' 'Class-Individuals.R' 'Class-Landscape.R'
'Individuals-Methods.R' 'simulPrecip.R' 'toxicIntensity.R'
'Landscape-Methods.R' 'Landscape-Individuals-Methods.R'
'briskaR.R' 'pollen-data.R' 'ecoToxic.R' 'demo.R'
'bgbb.R' 'bgnbd.R' 'pnbd.R' 'dc.R' 'spend.R'
'0-onLoad.R' '1-setGeneric.R' 'BuyseRes-object.R'
'BuyseRes-summary.R' 'BuyseRes-confint.R' 'BuyseRes_get.R'
'BuyseRes_show.R' 'BuyseSim-object.R' 'BuyseSim-summary.R'
'BuyseSim_show.R' 'BuyseTest-check.R' 'BuyseTest-inference.R'
'BuyseTest-initialization.R' 'BuyseTest-package.R'
'BuyseTest-print.R' 'BuyseTest.R' 'BuyseTest.options-object.R'
'BuyseTest.options.R' 'PairScore.R' 'RcppExports.R'
'constStrata.R' 'discreteRoot.R' 'powerBuyseTest.R'
'seBuyseTest.R' 'simBuyseTest.R' 'valid.R'
'functions.R' 'models.R' 'forecast.R' 'prior.R'
'RcppExports.R' 'CaliCo-package.R' 'Kernel.R' 'calibration.R'
'sequentialDesign.R'
catnet.class.R catnet.def.R graph2catnet.R catnet.dags.R
catnet.probs.R catnet.joint.prob.R catnet.marginal.prob.R
catnet.samples.R catnet.loglik.R catnet.entropy.R
catnet.categor.R catnet.dist.R catnet.plot.R catnet.find.R
catnet.search.R catnet.predict.R catnet.chisq.R catnet.histo.R
catnet.cluster.R catnet.bif.R catnet.quant.R catnet.pathway.R
zzz.R
'cdcov.R' 'cdcor.R' 'cdcor.ada.R' 'bw.R' 'cdcsis.R'
'category.R' 'ceg.R' 'event_tree.R' 'staged_tree.R'
'ceg_model.R' 'cont_table.R' 'data_documentation.R'
'model_search_algorithm.R'
'exhaustive_model_search_algorithm.R' 'dinamic_programming.R'
'distribution_of_probability.R' 'dirchlet_distribution.R'
'dirchlet_mpnl_distribution.R'
'heuristic_model_search_algorithm.R' 'lib_funtions.R'
'multinomial_distribution.R' 'oahc.R'
'posterior_distribution.R' 'prior_distribution.R'
'stratified_event_tree.R' 'stratified_staged_tree.R'
'stratified_ceg_model.R' 'variable.R'
'CENFA.R' 'GLcenfa-class.R' 'GLcenfa.R' 'GLdeparture-class.R'
'GLdeparture.R' 'RcppExports.R' 'brStick.R' 'calc.R'
'climdat.fut.R' 'climdat.hist.R' 'cnfa-class.R' 'cnfa.R'
'covij.R' 'departure-class.R' 'departure.R' 'enfa-class.R'
'enfa.R' 'exposure_map.R' 'names.R' 'parCov.R' 'parScale.R'
'scatter.R' 'sensitivity_map.R' 'slots.R' 'stretchPlot.R'
'tree-data.R' 'vulnerability-class.R' 'vulnerability.R'
'vulnerability_map.R'
'AssertCollection.R' 'allMissing.R' 'anyInfinite.R'
'anyMissing.R' 'anyNaN.R' 'asInteger.R' 'assert.R' 'helper.R'
'makeExpectation.R' 'makeTest.R' 'makeAssertion.R'
'checkAccess.R' 'checkArray.R' 'checkAtomic.R'
'checkAtomicVector.R' 'checkBit.R' 'checkCharacter.R'
'checkChoice.R' 'checkClass.R' 'checkComplex.R' 'checkCount.R'
'checkDataFrame.R' 'checkDataTable.R' 'checkDate.R'
'checkDirectoryExists.R' 'checkEnvironment.R' 'checkFALSE.R'
'checkFactor.R' 'checkFileExists.R' 'checkFlag.R'
'checkFunction.R' 'checkInt.R' 'checkInteger.R'
'checkIntegerish.R' 'checkList.R' 'checkLogical.R'
'checkMatrix.R' 'checkNamed.R' 'checkNames.R' 'checkNull.R'
'checkNumber.R' 'checkNumeric.R' 'checkOS.R' 'checkPOSIXct.R'
'checkPathForOutput.R' 'checkR6.R' 'checkScalar.R'
'checkScalarNA.R' 'checkSetEqual.R' 'checkString.R'
'checkSubset.R' 'checkTRUE.R' 'checkTibble.R' 'checkVector.R'
'coalesce.R' 'isIntegerish.R' 'matchArg.R' 'qassert.R'
'qassertr.R' 'vname.R' 'wfwl.R' 'zzz.R'
closed.R shortcuts.R Meinshausen.R regionmethod.R DAGmethod.R
DAGstructure.R structuredHolm.R
'acs.R' 'choropleth.R' 'admin1.R' 'admin1_region.R'
'choroplethr_animate.R' 'choroplethr_wdi.R' 'country.R' 'usa.R'
'county.R' 'county_zoom.R' 'data.R' 'deprecated.R'
'get_county_demographics.R' 'get_state_demograhpics.R'
'get_tract_demographics.R' 'startup_messages.R' 'state.R'
'tracts.R' 'utils.R'
'ZZZ.R' 'auxiliary.R' 'classes.R' 'constructors.R'
'deprecated.R' 'methods.R' 'RcppExports.R'
'CIplot.R' 'CIplot.default.R' 'CIplot.htest.R'
'CIplot.TukeyHSD.R' 'CIplot.glht.R' 'CIplot.glm.R'
'CIplot.ORci.R' 'CIplot.posthocTGH.R' 'ORci.R' 'print.ORci.R'
'await.R' 'civis_future.R' 'civis_ml.R' 'civis_ml_plot.R'
'civis_ml_utils.R' 'civis_ml_workflows.R' 'client_base.R'
'dbi-driver.R' 'dbi-connection.R' 'dbi-result.R' 'dplyr.R'
'generate_client.R' 'generated_client.R' 'io.R' 'logs.R'
'pagination_helpers.R' 'reports.R' 'rstudio.R' 'tables.R'
'utils.R' 'zzz.R'
'bugs.R' 'classify.R' 'gpcm.rc.R' 'prob_functions.R'
'scores.R' 'w_lord.R'
'RcppExports.R' 'ccRecord.R' 'ccTable.R' 'cchic_xml.R'
'data.R' 'data.quality.report.R' 'deltaTime.R' 'demographics.R'
'filter.categorical.R' 'filter.missingness.R' 'filter.range.R'
'imputation.R' 'stdid.R' 'utilities.R' 'zzz.R'
'utils.R' 'internals.R' 'checks.R' 'cleanr-package.R'
'options.R' 'wrappers.R' 'zzz.R'
'clhs-internal.R' 'clhs-data.frame.R' 'clhs-package.R'
'clhs-raster.R' 'utils.R' 'clhs.R' 'clhs-sp.R' 'plot.R'
'similarity.R'
'dataFrame.R' 'cfStation.R' 'cfData.R' 'cfDataList.R'
'cfData-plotMethods.R' 'cfDatatype.R' 'cfQuery.R' 'cfUser.R'
'findStations.R'
generics.R clp_ptrClass.R clp.R clpAPI.R zzz.R
'similarity.r' 'sim_normal.r' 'sim_unif.r' 'sim_student.r'
'clustomit.r' 'sim_data.r' 'helper-boot.r' 'clusteval.r'
'random_clustering.r' 'comembership.r'
'clusvis.R' 'estimation.R' 'smartinit.R' 'RcppExports.R'
'plot.R' 'clusvismixmod.R' 'clusvisvarsellcm.R' 'modessearch.R'
'RcppExports.R' 'cache.R' 'coala.R' 'model.R' 'feature.R'
'feature_growth.R' 'feature_ignore_singletons.R'
'feature_migration.R' 'feature_mutation.R' 'feature_outgroup.R'
'feature_pop_merge.R' 'feature_recombination.R'
'feature_sample.R' 'feature_selection.R'
'feature_size_change.R' 'feature_sumstats.R'
'feature_unphased.R' 'import_popgenome.R' 'interface_abc.R'
'locus.R' 'model_build.R' 'model_check.R' 'model_examples.R'
'model_getters.R' 'model_print.R' 'model_scale.R'
'model_simulate.R' 'onLoad.R' 'parameter.R' 'parameter_prior.R'
'parameter_variation.R' 'parameter_zero_inflation.R'
'search_executable.R' 'segsites.R' 'simulation_tasks.R'
'simulator_class.R' 'simulator_ms.R' 'simulator_msms.R'
'simulator_scrm.R' 'simulator_seqgen.R' 'sumstat.R'
'sumstat_dna.R' 'sumstat_file.R' 'sumstat_four_gamete.R'
'sumstat_ihh.R' 'sumstat_jsfs.R' 'sumstat_mcmf.R'
'sumstat_nucleotide_div.R' 'sumstat_omega.R'
'sumstat_seg_sites.R' 'sumstat_sfs.R' 'sumstat_tajimas_d.R'
'sumstat_trees.R' 'tools.R'
'CFR_estimation.R' 'dic.fit.mcmc.R' 'dic.fit.R'
'get.obs.type.R' 'S4stuff.R' 'sampleSizeSimulation.R'
'coarseDataTools-package.R'
'0helper.r' 'as.htest.r' 'cocor-package.R'
'cocor.dep.groups.nonoverlap.r' 'cocor.dep.groups.overlap.r'
'cocor.indep.groups.r' 'cocor.r' 'get.cocor.input.r'
'get.cocor.results.r'
classes.R librarySymbols.R functionHandlers.R codeDepends.R
sectionDepends.R sweave.R xml.R jss.R frags.R codeTypes.R gc.R
graph.R parallel.R deps.R separateBlocks.R callGraph.R isPlot.R
isOutput.R refScript.R sideEffects.R highlightCode.R
freeVariables.R convenienceFuns.R
'colorpatch.R' 'colorpatch_methods.R' 'colorpatch_ggplot.R'
'colorpatch_impl.R' 'data.R'
'adjacent.R' 'anagrams.R' 'analogous.R' 'col2HSV.R'
'complementary.R' 'opposite.R' 'pals.R' 'pizza.R'
'sequential.R' 'setColors.R' 'splitComp.R' 'square.R'
'tetradic.R' 'triadic.R' 'wheel.R'
'eplot.R' 'addxaxis.R' 'compactr-package.R' 'mm.R' 'aplot.R'
'ConnMatTools.R' 'eigs.R' 'Jacobi_EPT_reserve_selection.R'
'utils.R' 'connectivity_estimation.R'
'connectivity_estimation.distributions.R'
'connectivity_estimation.finite_settlement.R'
'connectivity_estimation.multinomial.R'
'connectivity_estimation.multiple.R' 'datasets.R' 'dpr_model.R'
'estimating_marked_egg_fraction.R' 'retentionStats.R'
'jacobi_etal_2012.R'
'contoureR-package.R' 'RcppExports.R' 'RcppExports-Doc.R'
'convexHull.R' 'delaunayMesh.R' 'contourLines.R'
'orderPoints.R'
'aggregation-matrix.R' 'aggregation-matrix-default.R'
'aggregation-matrix-membership-difference.R' 'factor.R'
'factors-of-interest.R' 'option-factor-availability.R'
'option-resources.R' 'option.R' 'option-portfolio.R'
'project-criterion.R' 'project-criteria.R' 'project.R'
'project-portfolio.R' 'coppe-cosenza.R' 'coppe-cosenzaR.R'
'datacheck.R' 'nbevent.R' 'probanofix.R' 'probafix.R'
'ncoprimary.R' 'nsurvival.R'
AllClass.R Classes.R AllGeneric.R Auxiliaries.R aux-acopula.R
asymCopula.R mixCopula.R rotCopula.R Copula.R special-func.R
amhCopula.R claytonCopula.R frankCopula.R cop_objects.R
nacopula.R dC-dc.R amhExpr.R An.R archmCopula.R cCopula.R
claytonExpr.R ellipCopula.R empcop.R empPsi.R acR.R
estimation.R evCopula.R evTests.R exchTests.R fgmCopula.R
fitCopula.R fitLambda.R fitMvdc.R fixedPar.R frankExpr.R
galambosCopula.R galambosExpr-math.R galambosExpr.R
ggraph-tools.R pairsRosenblatt.R prob.R gofTrafos.R
gofEVTests.R gofCopula.R graphics.R gumbelCopula.R gumbelExpr.R
huslerReissCopula.R huslerReissExpr.R indepCopula.R
indepTests.R joeCopula.R K.R logseries.R mvdc.R margCopula.R
matrix_tools.R normalCopula.R opower.R plackettCopula.R
plackettExpr.R rstable1.R safeUroot.R schlatherCopula.R
stable.R timing.R tCopula.R tawnCopula.R tawnExpr.R tevCopula.R
varianceReduction.R wrapper.R xvCopula.R zzz.R
EDA.R edaSeed.R edaSelect.R edaOptimize.R edaReport.R
edaReplace.R edaTerminate.R edaRun.R margins.R problems.R
edaIndepRuns.R edaCriticalPopSize.R CEDA.R VEDA.R
'coranking-package.R' 'coranking.R' 'coranking_internals.R'
'lcmc.R' 'criteria.R' 'image.R' 'rankmatrix.R' 'submatrix.R'
'Countr-package.R' 'RcppExports.R' 'anc.R' 'coefnames.R'
'convCount_loglik.R' 'convCount_moments.R' 'convCount_probs.R'
'dWeibull.R' 'dWeibullgamma.R' 'data.R' 'renewal_IV.R'
'renewal_tools.R' 'renewal_cstr.R' 'tools.R'
'renewal_methods.R'
'sepindex.R' 'couples.R' 'blocks.R' 'covablocks.R'
'covastat.R' 'covaprop.R' 'covastatM.R' 'dataprep.R' 'vv_13.R'
'add_sub.R' 'axis_canvas.R' 'cowplot.R' 'draw.R'
'get_legend.R' 'get_panel.R' 'gtable.R' 'plot_grid.R'
'plot_to_gtable.R' 'save.R' 'setup.R' 'switch_axis.R'
'themes.R' 'utils_ggplot2.R'
generics.R cplexConst.R cplexErrorClass.R cplexPtrClass.R
cplex.R cplexAPI.R cplex_checkAPI.R cplex_longparamAPI.R zzz.R
'CPsurv-package.R' 'bootbiascorrect.R' 'cpest.R'
'cpsurv-data.R' 'cpsurv.R' 'km.sim.survtimes.R' 'methods.R'
'neg.loglik.WeibExp.R' 'sim.survdata.R'
'ansi-256.r' 'combine.r' 'string.r' 'utils.r'
'crayon-package.r' 'disposable.r' 'has_ansi.r' 'has_color.r'
'styles.r' 'machinery.r' 'parts.r' 'print.r' 'style-var.r'
'show.r' 'string_operations.r'
'classes.R' 'crmn-package.R' 'norm.R' 'misc.R' 'generic.R'
'helpers.R' 'Data-class.R' 'Data-methods.R' 'Rules-class.R'
'Model-class.R' 'Design-class.R' 'writeModel.R'
'McmcOptions-methods.R' 'McmcOptions-class.R' 'Samples-class.R'
'mcmc.R' 'Simulations-class.R' 'Model-methods.R'
'Rules-methods.R' 'Design-methods.R' 'fromQuantiles.R'
'Samples-methods.R' 'Simulations-methods.R' 'crmPack-package.R'
'cw_matrix_to_df.R' 'Crossword.R' 'cw_greplv.R'
'cw_normalize_words.R' 'cw_to_json.R' 'cw_wordlist_animal_en.R'
'ami.R' 'CTcrqa.R' 'calcphi.R' 'checkts.R' 'crqa.R'
'drpdfromts.R' 'lorenzattractor.R' 'optimizeParam.R' 'plotRP.R'
'runcrqa.R' 'simts.R' 'spdiags.R' 'takephi.R' 'theiler.R'
'tt.R' 'wincrqa.R' 'windowdrp.R'
'AllClasses.R' 'AllGenerics.R' 'variable-definition.R'
'R-to-variable.R' 'SO-survey.R' 'abstract-categories.R'
'abstract-category.R' 'add-subvariable.R' 'add-variable.R'
'api.R' 'append-dataset.R' 'as-data-frame.R' 'auth.R'
'batches.R' 'case-variables.R' 'categories.R' 'category.R'
'change-category-id.R' 'combine-categories.R'
'compare-categories.R' 'compare-datasets.R'
'compare-subvariables.R' 'compare-variables.R' 'compare.R'
'conditional-transform.R' 'consent.R' 'context-manager.R'
'copy.R' 'crunch-data-frame.R' 'crunch.R' 'crunchbox.R'
'cube-collapse-dimensions.R' 'cube-dims.R' 'cube-index-table.R'
'cube-query.R' 'cube-residuals.R' 'cube-result.R'
'cube-subset.R' 'cube-transforms.R' 'cube-variables.R' 'cut.R'
'dataset-catalog.R' 'dataset-extract.R' 'dataset-order.R'
'dataset-stream.R' 'dataset-update.R' 'dataset.R'
'dichotomize.R' 'drop-rows.R' 'edit-lock.R' 'export-dataset.R'
'expressions.R' 'filters.R' 'folders-projects.R' 'folders.R'
'fork-and-merge.R' 'formula.R' 'gadgets.R' 'geo.R'
'get-datasets.R' 'github.R' 'hide-variables.R' 'insertions.R'
'interact.R' 'join-dataset.R' 'json.R' 'make-array.R'
'members.R' 'merge-crunch-data-frame.R' 'misc.R'
'multitables.R' 'new-dataset.R' 'ordering.R'
'permission-catalog.R' 'pretty-print.R' 'progress.R'
'project-folder.R' 'projects.R' 'search.R' 'session.R'
'share.R' 'shoji-catalog.R' 'shoji-entity.R' 'shoji-folder.R'
'shoji-order-slots.R' 'shoji-order.R' 'shoji.R' 'show.R'
'subtotals-and-headings.R' 'subvariables.R'
'summary-insertions.R' 'tab-book.R' 'teams.R'
'transform-calculate.R' 'transform.R' 'tuple.R' 'univariate.R'
'user-info.R' 'user.R' 'variable-as-methods.R'
'variable-catalog.R' 'variable-delete.R'
'variable-derivation.R' 'variable-folder.R'
'variable-metadata.R' 'variable-order.R' 'variable-summary.R'
'variable-to-R.R' 'variable-type.R' 'variable-update.R'
'variable.R' 'variables.R' 'versions.R' 'weight.R' 'zcl.R'
'csfit.R' 'csSAM-package.R' 'csSAM.R' 'csSamWrapper.R'
'fdrCsSAM.R' 'fdrSAM.R' 'findSigGene.R' 'make.monotone.R'
'plotCsSAM.R' 'runSAM.R' 'ttest.func.R' 'varr.R'
'utils.R' 'curry.R' 'partial.R' 'scaffold.R' 'tail_curry.R'
'accessors.R' 'aggregate.R' 'bwplot.R' 'cost.R' 'cvFit.R'
'cvFolds.R' 'cvReshape.R' 'cvSelect.R' 'cvTool.R' 'cvTuning.R'
'densityplot.R' 'dotplot.R' 'plot.R' 'print.R' 'repCV.R'
'selectBest.R' 'subset.R' 'summary.R' 'utils.R' 'xyplot.R'
'CVXR.R' 'data.R' 'globals.R' 'generics.R' 'utilities.R'
'interface.R' 'expressions.R' 'constant.R' 'variable.R'
'lin_ops.R' 'atoms.R' 'affine.R' 'problem.R' 'constraints.R'
'elementwise.R' 'solver.R' 'mosek-solver.R' 'gurobi-solver.R'
'lpsolve_solver.R' 'glpk_solver.R' 'solver_utilities.R'
'problem_data.R' 'transforms.R' 'exports.R' 'rcppUtils.R'
'R6List.R' 'ProblemData-R6.R' 'LinOp-R6.R' 'LinOpVector-R6.R'
'RcppExports.R' 'CVXcanon-R6.R' 'Deque.R' 'canonInterface.R'
'Class-Dasst.R' 'Dasst-methods.R' 'Dasst.R' 'dasstOptions.R'
'extractData.R' 'extractHeader.R' 'buildContents.R' 'data.R'
'gatherTables.R' 'read.dssat.R' 'stackTables.R' 'write.dssat.R'
'NAMESPACE.R' 'FormulaList.R' 'helper.R' 'DataFrame.R'
'as.function.R' 'bindRows.R' 'deparse.R' 'extract.R' 'map.R'
'mutar.R' 'pipeExport.R' 'replace.R' 'verboseApply.R'
'aggregateForBarplot.R' 'aggregateForHistogram.R'
'allCheckFunctions.R' 'allClasses.R' 'allSummaryFunctions.R'
'allVisualFunctions.R' 'allXFunctions.R' 'makeXFunction.R'
'visualFunction.R' 'basicVisual.R' 'summaryFunction.R'
'centralValue.R' 'check.R' 'checkResult.R' 'messageGenerator.R'
'checkFunction.R' 'identifyMissing.R' 'minMax.R' 'classes.R'
'clean.R' 'countMissing.R' 'dataMaid-package.R'
'dataMaid_as_factor.R' 'description.R' 'identifyCaseIssues.R'
'identifyLoners.R' 'identifyNums.R' 'identifyOutliers.R'
'identifyOutliersTBStyle.R' 'identifyWhitespace.R' 'isCPR.R'
'isSingular.R' 'isEmpty.R' 'isKey.R' 'isSupported.R'
'makeCodebook.R' 'makeDataReport.R' 'misc.R' 'quartiles.R'
'refCat.R' 'render.R' 'setChecks.R' 'setSummaries.R'
'setVisuals.R' 'smartNum.R' 'standardVisual.R' 'summarize.R'
'summaryResult.R' 'tableVisual.R' 'uniqueValues.R'
'unpackLabelled.R' 'variableType.R' 'visualize.R'
'AbstractTableDescriber.R' 'auth_request.R' 'D1Node.R'
'AuthenticationManager.R' 'CNode.R' 'CertificateManager.R'
'D1Object.R' 'MNode.R' 'D1Client.R' 'EMLParser.R'
'dataone-package.R' 'yyy.R' 'zzz.R'
'Constants.R' 'SystemMetadata.R' 'dmsg.R' 'DataObject.R'
'DataPackage.R' 'ResourceMap.R' 'datapack-package.r' 'zzz.R'
'checks.R' 'datastructures-package.R' 'methods_clear.R'
'methods_insert.R' 'methods_size.R' 'methods_pop.R'
'methods_peek.R' 'ds_deque.R' 'ds_deque_queue.R'
'ds_deque_stack.R' 'methods_values.R' 'methods_decrease.R'
'methods_handle.R' 'ds_heap.R' 'ds_heap_binomial.R'
'ds_heap_fibonacci.R' 'methods_remove.R' 'methods_head.R'
'ds_map.R' 'methods_keys.R' 'methods_get.R' 'ds_map_bimap.R'
'ds_map_unordered.R' 'ds_map_hashmap.R' 'ds_map_multimap.R'
'zzz.R'
avelinkdis.R centdis.R distable.R randdis.R valuep.R
functionall.R
'hidden.R' 'DBObject.R' 'DBDriver.R' 'table.R'
'DBConnection.R' 'ANSI.R' 'DBI-package.R' 'DBResult.R'
'data-types.R' 'data.R' 'deprecated.R' 'interpolate.R'
'list-pairs.R' 'quote.R' 'rd.R' 'rownames.R' 'table-create.R'
'table-insert.R' 'transactions.R'
'DBItest.R' 'context.R' 'expectations.R' 'import-dbi.R'
'import-testthat.R' 'run.R' 's4.R' 'spec.R'
'spec-getting-started.R' 'spec-compliance-methods.R'
'spec-driver-constructor.R' 'spec-driver-class.R'
'spec-driver-data-type.R' 'spec-connection-data-type.R'
'spec-result-create-table-with-data-type.R'
'spec-driver-connect.R' 'spec-connection-disconnect.R'
'spec-result-send-query.R' 'spec-result-fetch.R'
'spec-result-roundtrip.R' 'spec-result-clear-result.R'
'spec-result-get-query.R' 'spec-result-send-statement.R'
'spec-result-execute.R' 'spec-sql-quote-string.R'
'spec-sql-quote-identifier.R' 'spec-sql-read-table.R'
'spec-sql-write-table.R' 'spec-sql-list-tables.R'
'spec-sql-exists-table.R' 'spec-sql-remove-table.R'
'spec-meta-bind-runner.R' 'spec-meta-bind-tester-extra.R'
'spec-meta-bind.R' 'spec-meta-bind-.R' 'spec-meta-is-valid.R'
'spec-meta-has-completed.R' 'spec-meta-get-statement.R'
'spec-meta-get-row-count.R' 'spec-meta-get-rows-affected.R'
'spec-transaction-begin-commit-rollback.R'
'spec-transaction-with-transaction.R' 'spec-driver-get-info.R'
'spec-connection-get-info.R' 'spec-sql-list-fields.R'
'spec-meta-column-info.R' 'spec-meta-get-info-result.R'
'spec-driver.R' 'spec-connection.R' 'spec-result.R'
'spec-sql.R' 'spec-meta.R' 'spec-transaction.R'
'spec-compliance.R' 'spec-stress-connection.R' 'spec-stress.R'
'spec-all.R' 'spec-.R' 'test-all.R' 'test-getting-started.R'
'test-driver.R' 'test-connection.R' 'test-result.R'
'test-sql.R' 'test-meta.R' 'test-transaction.R'
'test-compliance.R' 'test-stress.R' 'tweaks.R' 'utf8.R'
'utils.R'
'cache.r' 'compat-purrr.R' 'data-lahman.r'
'data-nycflights13.r' 'db-compute.R' 'db-mysql.r'
'db-odbc-access.R' 'db-odbc-hive.R' 'db-odbc-impala.R'
'db-odbc-mssql.R' 'db-odbc-oracle.R' 'db-odbc-redshift.R'
'db-odbc-teradata.R' 'db-postgres.r' 'db-roracle.R'
'db-sqlite.r' 'dbi-s3.r' 'dbplyr.R' 'do.r' 'explain.r'
'utils.r' 'ident.R' 'lazy-ops.R' 'memdb.R' 'partial-eval.r'
'query.r' 'remote.R' 'schema.R' 'simulate.r' 'sql-build.R'
'sql-escape.r' 'sql-expr.R' 'sql-generic.R' 'sql-optimise.R'
'sql-query.R' 'sql-render.R' 'sql.R' 'src-sql.r' 'src_dbi.R'
'tbl-lazy.R' 'tbl-sql.r' 'test-frame.R' 'testthat.r'
'translate-sql-helpers.r' 'translate-sql-window.r'
'translate-sql-base.r' 'translate-sql-clause.r'
'translate-sql-odbc.R' 'translate-sql-paste.R'
'translate-sql.r' 'utils-format.r' 'window.R' 'zzz.R'
'ClassMethod-dcGOR.r' 'dcDAGannotate.r' 'dcRDataLoader.r'
'dcEnrichment.r' 'visEnrichment.r' 'dcDAGdomainSim.r'
'dcRWRpipeline.r' 'dcConverter.r' 'dcBuildInfoDataFrame.r'
'dcBuildAnno.r' 'dcBuildOnto.r' 'dcAlgoPropagate.r'
'dcAlgoPredict.r' 'dcAlgoPredictMain.r' 'dcAlgoPredictGenome.r'
'dcAncestralML.r' 'dcAncestralMP.r' 'dcSubtreeClade.r'
'dcSubtreeTips.r' 'dcTreeConnectivity.r' 'dcDuplicated.r'
'dcSplitArch.r' 'dcAlgo.r' 'dcFunArgs.r' 'dcSparseMatrix.r'
'dcList2Matrix.r' 'dcAlgoPredictPR.r' 'dcSupraBetter.r'
'dcRWRpredict.r' 'dcNaivePredict.r'
'dcminfo.R' 'amps.R' 'lcdmobsp2.R' 'lcdmxpd.R'
'sim_CRUM_N1000.R' 'sim_DINA_N1000.R' 'utils.R'
addarrows.R autosearch.R conditional.R cycletest.R
drawnetwork.R findex.R generic.R genlatex.R heuristic.R
inspectprob.R jointcont.R jointdisc.R jointprior.R learning.R
makesimprob.R fullsimprob.R maketrylist.R master.R network.R
networkfamily.R node.R numbermixed.R perturb.R postc.R postc0.R
postdist.R readnet.R rnetwork.R savenet.R unique.R
'chance_event.R' 'paramtnormci_fit.R' 'paramtnormci_numeric.R'
'rtnorm90ci.R' 'rdistq_fit.R' 'rdist90ci_exact.R'
'estimate1d.R' 'random.R' 'rmvnorm90ci_exact.R' 'estimate.R'
'mcSimulation.R' 'welfareDecisionAnalysis.R' 'eviSimulation.R'
'individualEvpiSimulation.R' 'estimate_read_csv_old.R'
'decisionSupport.R' 'decisionSupport-package.R' 'discount.R'
'gompertz_yield.R' 'make_CPT.R' 'plainNames2data.frameNames.R'
'plsr.mcSimulation.R' 'random_state.R' 'sample_CPT.R'
'sample_simple_CPT.R' 'temp_situations.R' 'vv.R'
'AllClasses.R' 'AllGenerics.R' 'Constructors.R'
'DEMICel-methods.R' 'DEMIClust-methods.R' 'DEMIDiff-methods.R'
'DEMIExperiment-methods.R' 'DEMIGroup-methods.R'
'DEMIResult-methods.R' 'cluster-methods.R' 'custom-methods.R'
'cytoband-methods.R' 'diffexp-methods.R' 'messages.R'
'normalization-methods.R' 'other-methods.R' 'zzz.R'
'assertions.R' 'authors-at-r.R' 'built.R' 'classes.R'
'collate.R' 'constants.R' 'deps.R' 'description.R' 'encoding.R'
'latex.R' 'non-oo-api.R' 'package-archives.R' 'read.R'
'remotes.R' 'str.R' 'syntax_checks.R' 'urls.R' 'utils.R'
'validate.R' 'version.R'
'aggregate_item_responses.r' 'assertions.r' 'class-control.r'
'class-dgo_fit.r' 'class-dgirt_fit.r' 'constants.r'
'class-dgirtin.r' 'class-dgmrp_fit.r' 'dgirt.r'
'dichotomize_item_responses.r' 'methods-control.r'
'methods-dgirtfit-plot.r' 'methods-dgirtfit-poststratify.r'
'methods-dgirtfit.r' 'methods-dgirtin.r' 'name_helpers.r'
'package.R' 'rake_partial.r' 'restrict_input_data.r'
'reweight_item_responses.r' 'shape.r' 'shape_hierarchical.r'
'toy_dgirt_in.r' 'toy_dgirtfit.r' 'validate_dgirtIn.r'
'validate_input_data.r'
'DGVM3D.R' 'classes.R' 'flame.R' 'geometry.R' 'input.R'
'misc.R' 'stand.R' 'vegetation.R' 'visualize.R'
'conversions.R' 'dielectric.r' 'plot.r' 'zzz.r' 'drude.r'
'metals.r'
'capt.R' 'pager.R' 'check.R' 'finalizer.R' 'misc.R' 'html.R'
'styles.R' 's4.R' 'core.R' 'diff.R' 'get.R' 'guides.R'
'hunks.R' 'layout.R' 'myerssimple.R' 'rdiff.R' 'rds.R' 'set.R'
'subset.R' 'summmary.R' 'system.R' 'text.R' 'tochar.R' 'trim.R'
'word.R'
'utils.R' 'bernstein_polynomials.R' 'privacy_params.R'
'mechanisms.R' 'bernstein_mechanism.R' 'diffpriv.R'
'exponential_mechanism.R' 'numeric_mechanism.R'
'gaussian_mechanism.R' 'laplace_mechanism.R'
'sensitivity_sampler.R'
'GetSampleFromDataFrame.R' 'RelationStrengthSimilarity.R'
'ScalablePCA.R' 'GetSampleFromFile.R' 'GetSampleFromDb.R'
'RssCell.R' 'RssThisRadius.R' 'EdgelistFill.R'
'EdgelistFromAdjacency.R' 'EdgelistFromIgraph.R'
'GenerateDilsNetwork.R' 'MergeEdgelists.R'
'AdjacencyFromEdgelist.R' 'IgraphFromEdgelist.R'
'MeasureNetworkInformation.R' 'RssSuggestedNetwork.R'
'RelativeNetworkInformation.R'
'misc.R' 'dimRedData-class.R' 'dataSets.R'
'dimRedMethod-class.R' 'dimRedResult-class.R' 'diffmap.R'
'dimRed.R' 'drr.R' 'embed.R' 'fastica.R' 'get_info.R'
'graph_embed.R' 'hlle.R' 'isomap.R' 'kpca.R' 'leim.R' 'lle.R'
'loe.R' 'mds.R' 'mixColorSpaces.R' 'nmds.R' 'pca.R' 'plot.R'
'quality.R' 'rotate.R' 'soe.R' 'tsne.R'
utility.function.R compare.R dotplot.R lineplot.R
densityplot.R ggplot2.R positioning.functions.R doc.R lattice.R
scatterplot.R contourplot.R
'DiscriMiner-package.R' 'FRatio.R' 'betweenCov.R'
'betweenSS.R' 'binarize.R' 'classify.R' 'corRatio.R' 'desDA.R'
'discPower.R' 'disqual.R' 'easyMCA.R' 'geoDA.R' 'getWithin.R'
'groupMeans.R' 'groupMedians.R' 'groupQuants.R' 'groupStds.R'
'groupVars.R' 'linDA.R' 'my_betweenCov.R' 'my_catDA.R'
'my_discFunctions.R' 'my_geoDA.R' 'my_groupMeans.R'
'my_linDA.R' 'my_mca.R' 'my_plsDA.R' 'my_quaDA.R' 'my_tdc.R'
'my_verify.R' 'my_withinCov.R' 'plot.plsda.R' 'print.desda.R'
'print.disqual.R' 'print.geoda.R' 'print.linda.R'
'print.plsda.R' 'print.quada.R' 'print.qualmca.R' 'quaDA.R'
'totalCov.R' 'totalSS.R' 'withinCov.R' 'withinSS.R'
'my_plsDA_old.R' 'plsDA_old.R' 'plsDA.R'
"distrr.R" "invented_data.R" "gibutils.R" "jointfuns.R"
"Fhat_conditional.R" "distrr_funs.R" "dcc_new.R" "wq_df.R"
AllClass.R AllGenerics.R AllMethod.R austFilter.R bouts.R
calibrate.R detDive.R detPhase.R distSpeed.R diveStats.R
oneDiveStats.R plotTDR.R plotZOC.R readLocs.R readTDR.R
speedStats.R stampDive.R zoc.R zzz.R
'calc.genoprob2.R' 'calcpos.R' 'cintern.R' 'dlcross.cross.R'
'dlcross.dlmap.R' 'dlcross.other.R' 'dlcross.R' 'dldetect.R'
'dllocalize.R' 'dlmap.R' 'dlmapdet.R' 'dlmaploc.R' 'dltest.R'
'ngen.dlcross.R' 'ngen.dlmap.R' 'ngen.R' 'nmrk.dlcross.R'
'nmrk.dlmap.R' 'nmrk.R' 'nphen.dlcross.R' 'nphen.dlmap.R'
'nphen.R' 'plot.dlcross.R' 'plot.dlmap.R' 'print.dlcross.R'
'print.dlmap.R' 'profileplot.dlmap.R' 'profileplot.R' 'pvfx.R'
'qchibar.R' 'summary.dlcross.R' 'summary.dlmap.R'
'waldtest.asreml.R' 'waldtest.R'
AllClasses.R AllGenerics.R DependencyModel-accessors.R
dependency.score.R fit.dependency.model.R toydata.R internals.R
show-methods.R utilities.R set.M.R set.beta.R get.W.R
firstlib.R drcca-functions.R optimize.parameters.R update.phi.R
update.W.R z.expectation.R
'dGSEA.r' 'dGSEAview.r' 'dGSEAwrite.r' 'visGSEA.r'
'dPvalAggregate.r' 'dNetInduce.r' 'dBUMfit.r' 'dBUMscore.r'
'dNetFind.r' 'dNetPipeline.r' 'dNetConfidence.r' 'visNet.r'
'visNetMul.r' 'visNetAnimate.r' 'visNetReorder.r'
'dNetReorder.r' 'visNetArc.r' 'visNetCircle.r' 'dRWR.r'
'dRWRcontact.r' 'dRWRpipeline.r' 'dContrast.r' 'dCommSignif.r'
'dSVDsignif.r' 'dFDRscore.r' 'dDAGinduce.r' 'dDAGreverse.r'
'dDAGroot.r' 'dDAGtip.r' 'dDAGlevel.r' 'dDAGannotate.r'
'dDAGancestor.r' 'dDAGtermSim.r' 'dDAGgeneSim.r' 'visDAG.r'
'dEnricher.r' 'dEnricherView.r' 'visBoxplotAdv.r'
'dRDataLoader.r' 'dCheckParallel.r' 'dFunArgs.r'
'T.R' 'capply.R' 'collect.R' 'compose.R' 'consecutive.R'
'dostats.R' 'wargs.R' 'onarg.R' 'pval.R' 'utils.R'
'DataInput.R' 'downscale.R' 'OptimiseParameters.R'
'OptimiseParametersGNB.R' 'OptimiseParametersFNB.R'
'OptimiseParametersLogis.R' 'OptimiseParametersThomas.R'
'PredictFunctions.R' 'ResidFunctions.R' 'StartingParams.R'
'ThomasFunctions.R' 'plot.predict.downscale.R'
'predict.downscale.R' 'ensemble.downscale.R' 'upgrain.R'
'hui.downscale.R' 'HuiFunctions.R' 'upgrain.threshold.R'
'bartlett.r' 'chisquare.r' 'dosefactor.r' 'DIdata.R'
'DRdata.R' 'dunnettst3.r' 'jonckheere.r' 'lbcd.r' 'nlaad.r'
'nlbcd.r' 'outlier.r' 'pkg_prep.r' 'prelimstats.r' 'segment.r'
'shapiro.r' 'firstDeriv.R' 'noel.r' 'dunnetts.R'
'dunnetts.format.r' 'dunns.R' 'dunns.format.r'
'drsmooth.print.r' 'segment.plot.r' 'segment.print.r'
'spline.plot.r' 'drsmooth.R' 'drsmooth-package.R'
'dichot_smooth.R' 'expand.R' 'fishers.exact.R' 'smooth.R'
'dsproto.R' 'axeslabel.R' 'dsarrows.R' 'dscurve.R' 'dsdots.R'
'dsmodel.R' 'dspoint.R' 'dsrange.R' 'dsregion.R'
'dssimulation.R' 'features.R' 'modelConstruction.R'
'DDF.Data.R' 'generic.functions.R' 'DDF.Analysis.R'
'DS.Analysis.R' 'Survey.Design.R' 'PT.Design.R'
'PT.Nested.Design.R' 'PT.Systematic.Design.R'
'PT.Random.Design.R' 'LT.Design.R' 'LT.User.Specified.Design.R'
'LT.EqSpace.ZZ.Design.R' 'LT.EqAngle.ZZ.Design.R'
'LT.Random.Design.R' 'LT.Systematic.Design.R' 'Density.R'
'Population.Description.R' 'Region.R' 'Region.Table.R'
'Sample.Table.R' 'Obs.Table.R' 'Single.Obs.DDF.Data.R'
'Transect.R' 'Line.Transect.R' 'Detectability.R' 'Population.R'
'LT.Survey.Results.R' 'Survey.Results.R' 'DSM.Analysis.R'
'Simulation.R' 'Class.Constructors.R' 'Design.Summary.R'
'LT.SegmentedGrid.Design.R' 'LT.SegmentedTrack.Design.R'
'Survey.R' 'LT.Survey.R' 'Point.Transect.R' 'PT.Survey.R'
'Population.Summary.R' 'Simulation.Summary.R'
'Single.Obs.LT.Survey.R' 'Single.Obs.PT.Survey.R'
'accumulate.PP.results.R' 'accumulate.warnings.R'
'add.covariate.values.R' 'add.dist.error.R' 'add.miss.dists.R'
'add.summary.results.R' 'calc.area.R'
'calc.poss.detect.dists.lines.R'
'calc.poss.detect.dists.lines.largeN.R'
'calc.poss.detect.dists.points.R' 'calculate.fitted.R'
'calculate.scale.param.R' 'check.LinkID.order.R'
'check.covariates.R' 'check.intersection.R' 'check.shapefile.R'
'check.sim.setup.R' 'coords.from.shapefile.R' 'create.bins.R'
'data.for.distance.R' 'description.summary.R'
'extract.spat.poly.coords.R' 'generate.pop.D.R'
'generate.pop.N.R' 'get.ave.density.R' 'get.bound.box.R'
'get.line.sampler.info.R' 'get.point.sampler.info.R'
'get.shapefile.names.R' 'get.surface.constant.R'
'get.surface.gam.R' 'hn.detect.R' 'hr.detect.R' 'in.polygons.R'
'is.gap.R' 'message.handler.R' 'modify.strata.for.analysis.R'
'rename.duplicates.R' 'rtpois.R' 'save.sim.results.R'
'setcov.R' 'simulate.detections.R' 'single.simulation.loop.R'
'store.ddf.results.R' 'store.dht.results.R'
'addTobca.R' 'bca.R' 'bcaRel.R' 'belplau.R' 'decode.R'
'dotprod.R' 'doubles.R' 'dsrwon.R' 'dst.R' 'elim.R' 'encode.R'
'extmin.R' 'inters.R' 'nameRows.R' 'nzdsr.R' 'plautrans.R'
'productSpace.R' 'reduction.R' 'shape.R' 'tabresul.R'
'CENTROIDS-dba.R' 'CENTROIDS-pam.R' 'CENTROIDS-sdtw-cent.R'
'CENTROIDS-shape-extraction.R' 'CLUSTERING-all-cent2.R'
'CLUSTERING-compare-clusterings.R'
'CLUSTERING-cvi-evaluators.R' 'CLUSTERING-ddist2.R'
'CLUSTERING-partitional-fuzzy.R'
'CLUSTERING-repeat-clustering.R' 'CLUSTERING-tadpole.R'
'CLUSTERING-tsclust-controls.R' 'CLUSTERING-tsclust.R'
'DISTANCES-dtw-basic.R' 'DISTANCES-dtw-lb.R' 'DISTANCES-dtw2.R'
'DISTANCES-gak.R' 'DISTANCES-lb-improved.R'
'DISTANCES-lb-keogh.R' 'DISTANCES-sbd.R' 'DISTANCES-sdtw.R'
'GENERICS-cvi.R' 'S4-Distmat.R' 'S4-PairTracker.R'
'S4-SparseDistmat.R' 'S4-tsclustFamily.R'
'S4-TSClusters-classes.R' 'S4-TSClusters-methods.R'
'SHINY-interactive-clustering.R' 'SHINY-ssdtwclust.R'
'SHINY-utils.R' 'UTILS-as-methods.R' 'UTILS-compute-envelope.R'
'UTILS-data.R' 'UTILS-expressions.R' 'UTILS-globals-internal.R'
'UTILS-nccc.R' 'UTILS-reinterpolate.R' 'UTILS-rng.R'
'UTILS-tslist.R' 'UTILS-utils.R' 'UTILS-zscore.R' 'pkg.R'
'class-crossValidation.R' 'class-twdtwRaster.R'
'class-twdtwAssessment.R' 'class-twdtwTimeSeries.R'
'class-twdtwMatches.R' 'createPatterns.R' 'data.R' 'dtw.R'
'dwtSat.R' 'getInternals.R' 'getTimeSeries.R' 'linearWeight.R'
'logisticWeight.R' 'methods.R' 'miscellaneous.R' 'plot.R'
'plotAccuracy.R' 'plotAdjustedArea.R' 'plotAlignments.R'
'plotArea.R' 'plotChanges.R' 'plotClassification.R'
'plotCostMatrix.R' 'plotDistance.R' 'plotMapSamples.R'
'plotMaps.R' 'plotMatches.R' 'plotPaths.R' 'plotPatterns.R'
'plotTimeSeries.R' 'resampleTimeSeries.R' 'subset.R' 'twdtw.R'
'twdtwApply.R' 'twdtwApplyParallel.R' 'twdtwAssess.R'
'twdtwClassify.R' 'twdtwCrossValidate.R' 'twdtwDist.R'
'twdtwXtable.R' 'zzz.R'
'util.R' 'assert.R' 'tree.R' 'names.R' 'assign.R'
'RcppExports.R' 'dynfrail_fit.R' 'dynfrail_aux.R' 'dynfrail.R'
'dynfrail_arguments.R' 'dynfrail_prep.R' 'print.dynfrail.R'
'dynrData.R' 'dynrRecipe.R' 'dynrModelInternal.R'
'dynrModel.R' 'dynrCook.R' 'dynrPlot.R' 'dynrFuncAddress.R'
'dynrMi.R' 'dynrVersion.R' 'dataDoc.R'
'RcppExports.R' 'add_class.R' 'inherit_default_params.R'
'calculate_distance.R' 'check_packages.R' 'expand_matrix.R'
'extend_with.R' 'install_packages.R' 'mapdf.R' 'package.R'
'pritt.R' 'random_time_string.R' 'scaling.R' 'tibble.R'
'POP.R' 'INMB_DIRECT.R' 'VAR_INMB_DIRECT.R' 'EVPI.R'
'internal.R' 'INMB.R' 'Lambda.R' 'VAR_INMB.R' 'VAR_INMB_DIFF.R'
'fonctions_sujets.R'
'ZZZ.R' 'generics.R' 'auxiliar.R' 'int.genind.R'
'ecogen.1OF6.definition.R' 'ecogen.2OF6.constructor.R'
'ecogen.3OF6.basic.methods.R' 'ecogen.4OF6.brackets.R'
'ecogen.5OF6.get&set.R' 'ecogen.6OF6.converters.R'
'ecopop.1OF6.definition.R' 'ecopop.2OF6.constructor.R'
'ecopop.3OF6.basic.methods.R' 'ecopop.4OF6.brackets.R'
'ecopop.5OF6.get&set.R' 'ecopop.6OF6.converters.R'
'accessors.R' 'classes.R' 'control.R' 'deprecated.R'
'eco.formula.R' 'eco.NDVI.R' 'eco.NDVI.post.R' 'eco.alfreq.R'
'eco.association.R' 'eco.bearing.R' 'eco.cbind.R' 'eco.clear.R'
'eco.convert.R' 'eco.cormantel.R' 'eco.correlog.R'
'eco.detrend.R' 'eco.forestplot.R' 'eco.format.R' 'eco.gsa.R'
'eco.kin.loiselle.R' 'eco.lagweight.R' 'eco.lmtree.R'
'eco.lsa.R' 'eco.malecot.R' 'eco.mantel.R' 'eco.merge.R'
'eco.pairtest.R' 'eco.plotCorrelog.R' 'eco.plotGlobal.R'
'eco.plotLocal.R' 'eco.plotWeight.R' 'eco.post.geneland.R'
'eco.rankplot.R' 'eco.rasterplot.R' 'eco.rbind.R'
'eco.remove.R' 'eco.slide.con.R' 'eco.slide.matrix.R'
'eco.split.R' 'eco.subset.R' 'eco.theilsen.R' 'eco.variogram.R'
'eco.weight.R' 'int.break.R' 'int.convert.R' 'int.crosscor.R'
'int.geary.R' 'int.jackknife.R' 'int.joincount.R'
'int.kin.loiselle.R' 'int.mantel.R' 'int.moran.R'
'int.multitable.R' 'int.order.R' 'int.random.test.R'
'miscellaneous.R' 'fill_ecogen_with_pop.R' 'plot.generic.R'
'plot.methods.R' 'roxygen.auxiliar.R' 'show_summary.methods.R'
'eco.dom_af.R' 'eco.kin.hardy.R' 'eco.nei_dist.R'
'adjacency.R' 'as.igraph.R' 'editset.R' 'editarray.R'
'editmatrix.R' 'as.matrix.R' 'backtracker.R' 'blocks.R' 'c.R'
'cateditmatrix.R' 'checkDatamodel.R' 'checkRows.R' 'contains.R'
'disjunct.R' 'duplicated.R' 'echelon.R' 'editAttr.R'
'editarrayAttr.R' 'editfile.R' 'editmatrixAttr.R'
'editrules-data.R' 'eliminate.R' 'errorLocalizer.R'
'errorLocalizer_mip.R' 'errorLocation.R' 'expandEdits.R'
'generateEdits.R' 'getH.R' 'getUpperBounds.R' 'getVars.R'
'is.R' 'isFeasible.R' 'isObviouslyInfeasible.R'
'isObviouslyRedundant.R' 'isSubset.R' 'list2env.R'
'localizeErrors.R' 'mip.R' 'parseCat.R' 'parseEdits.R'
'parseMix.R' 'parseNum.R' 'perturbWeights.R' 'pkg.R' 'plot.R'
'plot_errorLocation.R' 'print.R' 'reduce.R' 'removeRedundant.R'
'softEdits.R' 'str.R' 'subsetting.R' 'substValue.R' 'summary.R'
'violatedEdits.R' 'writeELAsMip.R' 'zzz.R'
'EDMeasure-package.R' 'cmdm_functions.R' 'pmdd.R' 'mdd.R'
'cmdm_test.R' 'mdc.R' 'mddm.R' 'mdm.R' 'mdm_ica_functions.R'
'mdm_ica.R' 'mdm_test.R' 'pmdc.R'
'harrival.R' 'hspec.R' 'hmoment.R' 'hllf.R' 'hfit.R'
'utilities.R' 'hreal.R' 'hsim.R' 'script.R'
'classes.R' 'classes-stmml.R' 'coercions.R' 'eml_find.R'
'get_unitList.R' 'get_attributes.R' 'eml_get.R'
'eml_validate.R' 'eml_view.R' 'get_TextType.R' 'get_coverage.R'
'is_standardUnit.R' 'literature_coercions.R' 'methods.R'
'read_eml.R' 's4_to_xml.R' 'set_TextType.R' 'set_attributes.R'
'set_coverage.R' 'set_methods.R' 'set_physical.R'
'set_unitList.R' 'show_methods.R' 'write_eml.R' 'xml_to_s4.R'
'zzz.R'
"check.data.R" "convert.units.R" "create.EM.R"
"define.limits.R" "EMMAgeo-internal.R" "EMMAgeo-package.R"
"EMMA.R" "get.limits.R" "get.l.opt.R" "get.l.R" "get.q.R"
"GUI.R" "interpolate.classes.R" "mix.EM.R" "model.em.R"
"Mqs.uncertainty.R" "residual.EM.R" "robust.EM.R"
"test.factors.R" "test.l.R" "test.l.max.R" "test.parameters.R"
"test.robustness.R"
'cec2009.r' 'control.R' 'crowding_distance.r' 'dominance.r'
'emoa.r' 'front_edge.R' 'hypervolume.r' 'indicators.r'
'logger.R' 'poly_mutation.r' 'sb_crossover.r' 'selection.r'
'sympart.r' 'utilities.r'
'Contacts.r' 'ContactTrace.r' 'EpiContactTrace-package.r'
'in-degree.r' 'ingoing-contact-chain.r' 'network-structure.r'
'network-summary.r' 'out-degree.r' 'outgoing-contact-chain.r'
'plot.r' 'report.r' 'shortest-paths.r' 'show.r' 'trace.r'
'tree.r'
'RcppExports.R' 'betabinom.R' 'data.R' 'utils.R'
'mle-factory.R' 'epiphy.R' 'intensity-classes.R'
'distr-fitting.R' 'indices.R' 'mapcomp.R' 'power-law.R'
'sadie.R' 'spatial-hier.R'
'constants.R' 'utils.R' 'units.R' 'assertions.R' 'impl.R'
'parse.R' 'epw.R' 'idd.R' 'idd_object.R' 'idf.R' 'idf_object.R'
'validate.R' 'format.R' 'run.R' 'sql.R' 'job.R' 'param.R'
'install.R' 'eplusr.R' 'zzz.R'
'eqs2lavaan.R' 'eqsCorr.R' 'eqsCov.R' 'eqsDesc.R'
'out2lavaan.R' 'plotCov.R'
'VerifyTree.R' 'ClusterNodeSelection.R' 'EliminateCluster.R'
'GetLeaves.R' 'GetPairs.R' 'hclust2tree.R' 'tree2plot.R'
'addnoise.R' 'categorical.R' 'conditional.R' 'mip.R'
'errorlocalizer.R' 'errorlocate-package.r' 'errorlocation.R'
'expr_manip.R' 'expr_simplify.R' 'linear.r' 'locate-errors.R'
'mip_lpsolve.R' 'mip_rule.R' 'replace-errors.R' 'soft-rule.R'
'values.R'
calEdgeCorScore.R ESEA.Main.R PlotGlobEdgeCorProfile.R
PlotPathwayGraph.R PlotRunEnrichment.R SavePathway2File.R
getEnvironmentData.R GetExampleData.R GetEdgesBackgrandData.R
GetPathwayEdgeData.R
'excerptr-package.R' 'internals.R' 'get_excerpts.R' 'utils.R'
'options.R' 'excerptr.R' 'zzz.R'
'AAA.R' 'eppMatrix.R' 'SpatialPointsBreeding.R'
'DirichletPolygons.R' 'epp.R' 'eppSimDat.R' 'expp-package.R'
'neighborsDataFrame.R'
'magrittr.R' 'generic-methods.R'
'FacebookGenericCollection-class.R'
'FacebookPagesCollection-class.R'
'FacebookUsersCollection-class.R'
'FacebookAlbumsCollection-class.R'
'FacebookGenericCollection.R' 'FacebookUsersCollection.R'
'FacebookPagesCollection.R' 'FacebookAlbumsCollection.R'
'FacebookPostsCollection-class.R'
'FacebookCommentsCollection-class.R'
'FacebookPostsCollection.R' 'FacebookCommentsCollection.R'
'FacebookConversationsCollection-class.R'
'FacebookConversationsCollection.R'
'FacebookEventsCollection-class.R' 'FacebookEventsCollection.R'
'FacebookGenericCollection-methods.R'
'FacebookGroupsCollection-class.R' 'FacebookGroupsCollection.R'
'FacebookLikesCollection-class.R' 'FacebookLikesCollection.R'
'FacebookMessagesCollection-class.R'
'FacebookMessagesCollection.R'
'FacebookMixedCollection-class.R'
'FacebookPhotosCollection-class.R' 'FacebookPhotosCollection.R'
'FacebookReactionsCollection-class.R'
'FacebookReactionsCollection.R'
'FacebookVideosCollection-class.R' 'FacebookVideosCollection.R'
'facebook.S4.R' 'facebook.current.accounts.R' 'utils.R'
'facebook.image.formats.R' 'facebook.object.likes.R'
'facebook.participants.R' 'facebook.permissions.R'
'facebook.search.R' 'facebook.senders.R'
'facebook.video.formats.R' 'fbOAuth.R' 'formatFbDate.R'
'onAttach.R' 'onLoad.R'
'cluster_utilities.R' 'decathlon2.R' 'print.factoextra.R'
'get_hmfa.R' 'fviz_hmfa.R' 'get_mfa.R' 'fviz_mfa.R'
'deprecated.R' 'fviz_add.R' 'eigenvalue.R' 'utilities.R'
'dist.R' 'fviz_dend.R' 'hcut.R' 'get_pca.R' 'fviz_cluster.R'
'eclust.R' 'facto_summarize.R' 'fviz.R' 'fviz_ca.R'
'fviz_contrib.R' 'fviz_cos2.R' 'fviz_ellipses.R' 'fviz_famd.R'
'get_mca.R' 'fviz_mca.R' 'fviz_mclust.R' 'fviz_nbclust.R'
'fviz_pca.R' 'fviz_silhouette.R' 'get_ca.R'
'get_clust_tendency.R' 'get_famd.R' 'hkmeans.R' 'housetasks.R'
'multishapes.R' 'poison.R' 'zzz.R'
'constants.R' 'fansi-package.R' 'has.R' 'internal.R' 'load.R'
'misc.R' 'nchar.R' 'strip.R' 'strwrap.R' 'strtrim.R'
'strsplit.R' 'substr2.R' 'tohtml.R' 'unhandled.R'
'FastImputation.R' 'TrainFastImputation.R' 'UnfactorColumns.R'
'BoundNormalizedVariable.R' 'NormalizeBoundedVariable.R'
'CovarianceWithMissing.R' 'FI_train.R' 'FI_test.R' 'FI_true.R'
'utilities.R' 'fastqc.R' 'fastqc_install.R' 'qc_aggregate.R'
'qc_plot.R' 'qc_problems.R' 'qc_read.R' 'qc_report.R'
'qc_unzip.R'
'a_FatTailsR-package.R' 'b_data.R' 'c_trigohp.R' 'd_logishp.R'
'e_conversion.R' 'f_kiener1.R' 'g_kiener2.R' 'h_kiener3.R'
'i_kiener4.R' 'j_kiener7.R' 'k_kienerES.R' 'l_moments.R'
'm_laplaceroll.R' 'n_estimation.R' 'o_estimation2.R'
'p_regression.R'
'aaa.R' 'FDboost-package.R' 'FDboost.R' 'baselearners.R'
'baselearnersX.R' 'bootstrapCIs.R' 'constrainedX.R'
'crossvalidation.R' 'FDboostLSS.R' 'hmatrix.R' 'methods.R'
'stabsel.R' 'utilityFunctions.R'
'loggers.R' 'aaa.R' 'HandlerStack.R' 'Fire.R' 'FutureStack.R'
'delay_doc.R' 'event_doc.R' 'fake_request.R' 'fiery-package.R'
'plugin_doc.R'
filehash.R filehash-DB1.R filehash-RDS.R coerce.R dump.R
hash.R queue.R stack.R zzz.R
'font-getters.R' 'fontset.R' 'fontset-bitstream-vera.R'
'fontset-dejavu.R' 'fontset-liberation.R' 'fontset-symbola.R'
'html-dependency.R' 'utils.R'
'AbstractClasses.R' 'DataContainers.R' 'Forecasts.R'
'SimulatedIncidenceMatrix.R' 'IncidenceForecast.R'
'IncidenceMatrix.R' 'Models.R' 'SimpleForecast.R'
'MoveAheadModel.R' 'ObservationList.R'
'RcppExports.R' 'acfARp.R' 'f.R' 'forecastSNSTS-package.R'
'measure-of-accuracy.R' 'models.R'
classes_definitions.R classes_constructors.R accessors.R
simufreqD.R simupopD.R AuxFunc.R changepop.R PE.R likelihood.R
likestim.R mincontri.R A2.simu.R A3.simu.R A4.simu.R
mastermix.R N2Exact.R N2error.R simMixSNP.R wrapdataL.R
simPCR2.R simPCR2TK.R PV.R Hbsimu.R LR.R LRmixTK.R
'as.character.formula.R' 'env.R' 'get.vars.R' 'invert.R'
'is.one.sided.R' 'is.two.sided.R' 'lhs.vars.R' 'op.type.R'
'parts.R' 'parts.lhs.get.R' 'parts.lhs.set.R' 'parts.op.get.R'
'parts.op.set.R' 'parts.rhs.get.R' 'parts.rhs.set.R'
'rhs.vars.R' 'split.terms.R' 'terms.R' 'toggle.sign.R' 'zzz.R'
fra_chaos.R fra_class.R fra_detm.R fra_dim.R fra_fdp.R
fra_filt.R fra_hurst.R fra_kde.R fra_model.R fra_neig.R
fra_stny.R fra_surr.R fra_pkg.R
'RcppExports.R' 'autoplot.emfrail.R' 'ca_test_cph.R'
'ca_test_fit.R' 'em_fit.R' 'emfrail_aux.R' 'emfrail.R'
'emfrail_arguments.R' 'emfrail_methods.R' 'fast_Estep.R'
'plot.emfrail.R' 'predict.emfrail.R' 'print.emfrail.R'
'print.emfrail_summary.R' 'summary.emfrail.R'
'distances.R' 'expressions.R' 'franc.R' 'ngrams.R'
'normalize.R' 'script.R' 'speakers.R' 'trigrams.R'
'multiple_function_on_function.R'
'nonlinear_function_on_function.R'
'high_dimensional_function_on_function.R' 'ff.interaction.R'
'RcppExports.R'
alphafrontier.2d.r alphafrontier.3d.r alphascore.r
ordermscore.r ordermscore.boot.r ordermfrontier.2d.r ROCscore.r
'fun-package.R' 'htmlspecialchars.R' 'shutdown.R'
'alzheimer_test.R' 'lights_out.R' 'mine_sweeper.R'
'random_password.R' 'sliding_puzzle.R' 'tag_cloud.R'
'tower_of_hanoi.R' 'gomoku.R'
'cpp_data.R' 'funclust.r' 'input.R' 'output.R' 'plot.R'
'mfpca.R' 'harmCut.r' 'mfpcaPlot.R'
'funDataClass.R' 'coerce.R' 'funDataMethods.R' 'get_set.R'
'names.R' 'plotMethods.R' 'simulation.R' 'str.R' 'subset.R'
'summary.R' 'zzz.R'
'options.R' 'appender.R' 'constants.R' 'layout.R' 'logger.R'
'scat.R' 'futile.logger-package.R'
'class-FuzzyData.R' 'class-PairwiseComparisonMatrix.R'
'class-FuzzyPairwiseComparisonMatrix.R' 'class-FuzzyWeights.R'
'class-Weights.R' 'function-bindFuzzyData.R'
'function-buildFuzzyPairwiseComparisonMatrix.R'
'function-buildPairwiseComparisonMatrix.R'
'function-calculateAHP.R' 'function-calculateWeights.R'
'function-compare.R' 'function-consistencyIndex.R'
'function-consistencyRatio.R' 'function-defuzzify.R'
'function-getFuzzyNumbers.R' 'function-getFuzzyScale.R'
'function-insertInto.R' 'function-internal-optimization.R'
'function-internal-weights.R' 'function-print.R'
'function-strictConsistency.R' 'function-textRepresentation.R'
'function-weakConsistency.R' 'package-FuzzyAHP.R'
'Bool-generics.R' 'utils.R' 'Segment-generics.R'
'GaApiRequest.R' 'meta.R' 'globaldata.R' 'Expr-generics.R'
'operand-classes.R' 'comparator-classes.R' 'var-classes.R'
'expr-classes.R' 'segment-classes.R' 'Bool-methods.R'
'Comparator-generics.R' 'comparator-coerce.R'
'Comparator-methods.R' 'TableFilter-generics.R'
'table-filter-classes.R' 'var-list-classes.R' 'query-classes.R'
'Query-generics.R' 'Creds-methods.R' 'Date-generics.R'
'date-coerce.R' 'ga-api-classes.R' 'management-api-classes.R'
'Date-methods.R' 'operand-coerce.R' 'Operand-generics.R'
'Operand-methods.R' 'Var-list-generics.R' 'Var-generics.R'
'var-list-coerce.R' 'var-coerce.R' 'Var-methods.R'
'expr-coerce.R' 'Expr-methods.R' 'GaGetCoreReport.R'
'view-coerce.R' 'GaView-methods.R' 'ga-api-coerce.R'
'GetGa-methods.R' 'Query-methods.R' 'segment-coerce.R'
'Sequence-generics.R' 'Segment-methods.R' 'Sequence-methods.R'
'table-filter-coerce.R' 'TableFilter-methods.R'
'Var-list-methods.R' 'deprecated.R' 'ganalytics-package.R'
'googleAnalyticsR-coerce.R' 'gtm-api-classes.R' 'sequence.R'
'zzz.R'
'Evaluator.R' 'GenAlgControl.R' 'formatSegmentation.R'
'evaluate.R' 'fitness.R' 'genAlg.R' 'getEvalFun.R' 'subsets.R'
'toCControlList.R' 'validData.R'
parse_text.R sim_numbers.R isNA.R mintersect.R pad.R args.R
history.R cdf2qf.R S4utils.R pseudoInverse.R
'CA.R' 'CR.R' 'DTM.R' 'Est.R' 'ExportedFuncs.R' 'GDINA.R'
'GDI.R' 'GDINA-package.R' 'HO.R' 'Louis.R' 'M2.R' 'Mstep.R'
'Mstep_DTM.R' 'RcppExports.R' 'anova.GDINA.R' 'att.struc.R'
'autoGDINA.R' 'bootSE.R' 'coef.R' 'designmatrix.R' 'dif.R'
'ecpe.R' 'simGDINA.R' 'itemfit.R' 'modelcomp.R' 'extract.R'
'frac20.R' 'initials.R' 'itemparm.GDINA.R' 'monocheck.R'
'personparm.GDINA.R' 'plotIRF.GDINA.R' 's3GDINA.R'
'print.GDINA.R' 'score.R' 'sim10GDINA.R' 'sim20seqGDINA.R'
'sim21seqDINA.R' 'sim30DINA.R' 'sim30GDINA.R' 'sim30pGDINA.R'
'startGDINA.R' 'structuralparm.R' 'summary.GDINA.R' 'utils.R'
'zzz.R'
'anohe.R' 'arrayize.R' 'blobbogram.R' 'template.R' 'code.R'
'data.R' 'deviance.R' 'forest.R' 'solveLP.R' 'inits.R'
'likelihoods.R' 'll-helper.counts.R' 'll.binom.cloglog.R'
'll.binom.log.R' 'll.binom.logit.R' 'll.call.R'
'll.normal.identity.R' 'll.poisson.log.R'
'minimum.diameter.spanning.tree.R' 'mtc.data.studyrow.R'
'mtc.hy.prior.R' 'mtc.model.R' 'mtc.model.consistency.R'
'mtc.model.nodesplit.R' 'mtc.model.regression.R'
'mtc.model.ume.R' 'mtc.model.use.R' 'mtc.network.R'
'mtc.network.xml.R' 'stopIfNotConsistent.R' 'mtc.result.R'
'mtc.run.R' 'nodesplit.R' 'plotCovariateEffect.R' 'priors.R'
'rank.probability.R' 'regression.R' 'relative.effect.R'
'relative.effect.table.R'
'AccData.r' 'Recalibrate.R' 'convert.time.R' 'epoch.R'
'get.intervals.r' 'GRtime.r' 'header.info.R' 'parse.time.R'
'read.bin.R' 'stftmod.R' 'svm.R' 'util.R'
geoSpectral.R SpcHeader-Class.R SpcHeader-Methods.R
Spectra-Class.R Spectra-Methods.R SpcList-Class.R
SpcList-Methods.R Spectra-ImportFuns.R
'gestalt.R' 'utils.R' 'compose.R' 'constant.R' 'partial.R'
'fn.R' 'context.R' 'posure.R'
'annotate_textp.r' 'coord_proj.r' 'formatters.r' 'fortify.r'
'geom2plotly.r' 'geom_ash.r' 'geom_bkde.r' 'geom_bkde2d.r'
'geom_dumbbell.R' 'geom_cartogram.r' 'geom_encircle.r'
'geom_lollipop.r' 'geom_table.r' 'geom_twoway_bar.r'
'geom_xspline.r' 'geom_xspline2.r' 'stat-stepribbon.r'
'ggalt-package.r' 'grob_absolute.r' 'guide_axis.r'
'stateface.r' 'utils.r' 'zzz.r'
'RcppExports.R' 'aaa.R' 'arc_bar.R' 'arc.R' 'bezier.R'
'bspline.R' 'circle.R' 'facet_grid_paginate.R'
'facet_wrap_paginate.R' 'facet_zoom.R' 'ggforce_package.R'
'ggproto-classes.R' 'interpolate.R' 'link.R' 'scale-unit.R'
'sina.R' 'themes.R' 'trans.R'
'logos.r' 'text.r' 'thin.r' 'data.r' 'alphabet.r' 'position.r'
'utilities.R' 'aes.remap.R' 'utilities.eval2.R'
'utilities.eval.R' 'aes.wrangle.R' 'aes.loop.R' 'aes.map.R'
'aes.rename.R' 'ggloop.R' 'lplus.main.R' 'lplus.utilities.R'
'ggmcmc.R' 'ggs.R' 'ggs_autocorrelation.R'
'ggs_compare_partial.R' 'ggs_crosscorrelation.R'
'ggs_density.R' 'ggs_histogram.R' 'ggs_running.R'
'ggs_traceplot.R' 'ggs_pairs.R' 'data.R' 'help.R' 'functions.R'
'ggs_Rhat.R' 'ggs_geweke.R' 'ggs_caterpillar.R'
'ggs_separation.R' 'globals.R' 'ggs_ppmean.R' 'ggs_ppsd.R'
'ggs_rocplot.R'
'ggparliament_package.R' 'data.R' 'draw_majoritythreshold.R'
'helper_funcs.R' 'parliament_data.R' 'draw_partylabels.R'
'draw_totalseats.R' 'geom_parliament_seats.R'
'geom_emphasize_parliamentarians.R' 'geom_parliament_bar.R'
'geom_highlight_government.R' 'geom_overhang_seats.R'
'theme_ggparliament.R'
'facet_share.R' 'geom_arcbar.R' 'geom_bartext.R'
'geom_boxjitter.R' 'geom_circle.R' 'geom_parliament.R'
'geom_tshighlight.R' 'ggproto-classes.R' 'stat-arcbar.R'
'stat-boxjitter.R' 'stat-circle.R' 'stat-parliament.R'
'utilities.R'
'utilities_color.R' 'utilities_base.R' 'desc_statby.R'
'utilities.R' 'add_summary.R' 'annotate_figure.R' 'as_ggplot.R'
'axis_scale.R' 'background_image.R' 'bgcolor.R' 'border.R'
'compare_means.R' 'diff_express.R' 'facet.R' 'font.R'
'gene_citation.R' 'geom_exec.R' 'get_legend.R' 'get_palette.R'
'ggadd.R' 'ggarrange.R' 'ggballoonplot.R' 'ggpar.R'
'ggbarplot.R' 'ggboxplot.R' 'ggdensity.R' 'ggpie.R'
'ggdonutchart.R' 'stat_conf_ellipse.R' 'stat_chull.R'
'ggdotchart.R' 'ggdotplot.R' 'ggecdf.R' 'ggerrorplot.R'
'ggexport.R' 'gghistogram.R' 'ggline.R' 'ggmaplot.R'
'ggpaired.R' 'ggparagraph.R' 'ggpubr_args.R' 'ggqqplot.R'
'utilities_label.R' 'stat_cor.R' 'stat_stars.R' 'ggscatter.R'
'ggscatterhist.R' 'ggstripchart.R' 'ggtext.R' 'ggtexttable.R'
'ggviolin.R' 'gradient_color.R' 'grids.R' 'reexports.R'
'rotate.R' 'rotate_axis_text.R' 'rremove.R' 'set_palette.R'
'show_line_types.R' 'show_point_shapes.R'
'stat_compare_means.R' 'stat_mean.R' 'stat_pvalue_manual.R'
'stat_regline_equation.R' 'text_grob.R' 'theme_pubr.R'
'theme_transparent.R'
'data.R' 'ggridges.R' 'geoms.R' 'geomsv.R' 'geoms-gradient.R'
'geom-density-line.R' 'position.R' 'scale-.R'
'scale-cyclical.R' 'scale-point.R' 'scale-vline.R' 'stats.R'
'theme.R' 'utils_ggplot2.R' 'utils.R'
'utils.R' 'aes_som.R' 'ggsom.R' 'ggsom_line.R' 'ggsom_rect.R'
'ggsom_ribbon.R' 'zzz.R'
'flip-aes.R' 'geom-barh.R' 'legend-draw.R' 'geom-boxploth.R'
'geom-colh.R' 'geom-crossbarh.R' 'geom-errorbarh.R'
'geom-histogramh.R' 'geom-linerangeh.R' 'geom-pointrangeh.R'
'geom-violinh.R' 'ggstance.R' 'position-dodgev.R'
'position-dodge2v.R' 'position-jitterdodgev.R'
'position-stackv.R' 'position.R' 'stat-binh.R'
'stat-boxploth.R' 'stat-counth.R' 'stat-summaryh.R'
'stat-xdensity.R'
'ggtern-package.R' 'aes.R' 'coord-tern.R' 'calc-tern-tlr2xy.R'
'calc-mahalanobis-distance.R' 'calc-kde2d-weighted.R'
'doc-data.R' 'doc-theme-convenience.R' 'depreciated.R'
'labels-new.R' 'labels-percent.R' 'legend-draw-tern.R'
'ggtern-constructor.R' 'gg-internal.R'
'modifications-gridExtra.R' 'onLoad.R' 'plot.R' 'plot-build.R'
'plot-construction.R' 'position-.R' 'position-nudge-tern.R'
'position-jitter-tern.R' 'save.R' 'scales-tern.R'
'strip-unapproved.R' 'tern-limits.R' 'theme.R'
'theme-arrowlength.R' 'theme-clockwise.R' 'theme-defaults.R'
'theme-elements.R' 'theme-gridsontop.R' 'theme-bordersontop.R'
'theme-legend-position.R' 'theme-latex.R' 'theme-mesh.R'
'theme-noarrows.R' 'theme-nomask.R' 'theme-novar-tern.R'
'theme-rotate.R' 'theme-showgrid.R' 'theme-showlabels.R'
'theme-ticks.R' 'theme-showtitles.R' 'theme-ticksoutside.R'
'theme-zoom.R' 'utilities.R' 'utilities-help.R'
'geom-density-tern.R' 'stat-density-tern.R' 'geom-mask.R'
'geom-Xline.R' 'geom-Xisoprop.R' 'geom-confidence-tern.R'
'stat-confidence-tern.R' 'geom-errorbarX.R'
'geom-smooth-tern.R' 'stat-smooth-tern.R' 'geom-mean-ellipse.R'
'stat-mean-ellipse.R' 'geom-interpolate-tern.R'
'stat-interpolate-tern.R' 'stat-interpolate-methods.R'
'geom-crosshair-tern.R' 'geom-point-swap.R' 'geom-hex-tern.R'
'stat-hex-tern.R' 'annotation-tern.R'
'annotation-raster-tern.R' 'geom-text-viewport.R'
'geom-label-viewport.R' 'geom-polygon-closed.R'
'geom-tri-tern.R' 'stat-tri-tern.R'
'blame.R' 'blob.R' 'branch.R' 'bundle_r_package.R'
'checkout.R' 'commit.R' 'config.R' 'contributions.R'
'credential.R' 'diff.R' 'fetch.R' 'git2r.R' 'index.R'
'libgit2.R' 'merge.R' 'note.R' 'odb.R' 'plot.R' 'pull.R'
'punch_card.R' 'push.R' 'reference.R' 'reflog.R' 'refspec.R'
'remote.R' 'repository.R' 'reset.R' 'revparse.R' 'sha.R'
'signature.R' 'stash.R' 'status.R' 'tag.R' 'time.R' 'tree.R'
'when.R'
'GPrior-classes.R' 'posteriors.R' 'GlmBayesMfp-methods.R'
'GlmBayesMfpSamples-class.R' 'GlmBayesMfpSamples-methods.R'
'McmcOptions-class.R' 'McmcOptions-methods.R' 'RcppExports.R'
'helpers.R' 'computeModels.R' 'constructNewdataMatrix.R'
'getFpTransforms.R' 'getDesignMatrix.R' 'coxTBF.R'
'evalZdensity.R' 'formula.R' 'fpScale.R' 'fpTrans.R'
'getFamily.R' 'getLogGPrior.R' 'getLogMargLikEstimate.R'
'getMarginalZ.R' 'sampleGlm.R' 'sampleBma.R' 'getModelCoefs.R'
'glmBayesMfp.R' 'glmBfp-package.R' 'hpds.R' 'inclusionProbs.R'
'optimize.R' 'plotCurveEstimate.R' 'predictCoxTBF.R'
'testCox.R' 'uncenteredDesignMatrix.R' 'writeFormula.R'
'glmnetUtils.r' 'cvGlmnetFormula.r' 'cvaGlmnetFormula.r'
'glmnetFormula.r' 'makeModelComponents.r'
generics.R glpk_ptrClass.R glpk.R glpkAPI.R zzz.R
'analysis.R' 'calcPower.R' 'graphMCP.R' 'ci.R' 'closure.R'
'convertFromOldClassDefinition.R' 'doRUnitTests.R'
'exampleGraphs.R' 'gACT-internal.R' 'gMCP.R' 'gMCP.extended.R'
'gPAD.R' 'generateBounds.R' 'generatePvals.R' 'generateTest.R'
'generateWeights.R' 'graph2latex.R' 'graphTest.R' 'helperGUI.R'
'matrix2graph.R' 'misc.R' 'modifyGraphs.R' 'onLoad.R'
'plotCI.R' 'powerPlot.R' 'rqmvnorm.R' 'sampSize.R'
'startGUIs.R' 'subVariables.R'
'confint.R' 'cumres.coxph.R' 'cumres.default.R' 'cumres.glm.R'
'cumres.lvmfit.R' 'gof-package.R' 'onload.R' 'plot.R' 'print.R'
'vcov.R'
'api.R' 'customization.R' 'lists.R' 'chk_covr.R'
'chk_cyclocomp.R' 'chk_description.R' 'chk_lintr.R'
'chk_namespace.R' 'chk_rcmdcheck.R' 'chk_tnf.R' 'gp.R'
'my_linters.R' 'package.R' 'prep_covr.R' 'prep_cyclocomp.R'
'prep_description.R' 'prep_expressions.R' 'prep_lintr.R'
'prep_namespace.R' 'prep_rcmdcheck.R' 'print.R'
'rstudio_markers.R' 'utils.R'
'check_form_works.R' 'get_form_id.R' 'make_url.R' 'get_form.R'
'get_form_entry_ids.R' 'gformr.R' 'comments_gformr.R'
'get_form_questions.R' 'googleformr-package.R'
'AAA-utils.R' 'R6--aaa-utils.R' 'S3--aaa-setup.R'
'R6--definition.R' 'R6-3.1.1-types-scalars.R'
'R6-6.1-executing-requests.R' 'R6-Schema.R' 'R6z-from-json.R'
'graphql_json.R' 'R6-3.2-directives.R' 'gqlr_schema.R'
'R6-4-introspection.R' 'R6-6.2-executing-operations.R'
'R6-6.3-executing-selection-sets.R' 'R6-6.4-executing-fields.R'
'R6-7-response.R' 'R6-ErrorList.R' 'R6-ObjectHelpers.R'
'R6-Result.R' 'R6-VariableValdationHelper.R'
'S3-3.1.2.3-validation-object-type.R' 'S3-str.R'
'gqlr-package.R' 'server_jug.R'
'upgrade_query_remove_fragments.R' 'validation-arguments.R'
'validation-input-coercion.R'
'validation-selection-set-can-merge.R' 'validation-query.R'
'granovaGG-package.R' 'granovagg.1w-helpers.R'
'theme-defaults.R' 'shared-functions.R' 'granovagg.1w.R'
'granovagg.contr.R' 'granovagg.ds.R'
'package.R' 'utils.R' 'tf_functions.R' 'overloaded.R'
'node_class.R' 'node_types.R' 'variable.R'
'probability_distributions.R' 'mixture.R' 'joint.R'
'unknowns_class.R' 'greta_array_class.R' 'as_data.R'
'distribution.R' 'operators.R' 'functions.R' 'transforms.R'
'structures.R' 'extract_replace_combine.R' 'dag_class.R'
'greta_model_class.R' 'progress_bar.R' 'inference_class.R'
'samplers.R' 'optimisers.R' 'inference.R'
'install_tensorflow.R' 'internals.R' 'calculate.R'
'callbacks.R'
'create.R' 'grnn-package.r' 'guess.r' 'kernel.R' 'learn.R'
'smooth.R'
'getmf.R' 'XZcov.R' 'datbrghtmodterms.R' 'datbrghtterms.R'
'dateduc.R' 'datsim.R' 'datsimcov.R' 'datsimmult.R'
'ddpMCMCplots.R' 'ddp_quantiles.R' 'ddpeffectsplot.R'
'summary_quantiles.R' 'mcmcPlots.R' 'relabel.R' 'growthCurve.R'
'dpgrowmm.R' 'ddpgrow.R' 'dpgrow.R' 'dpgrowmult.R'
'effectsplot.R' 'growplot.R' 'help.R' 'trtplot.R'
'MSPE.R' 'cps.R' 'gen_informative_sample.R' 'gpdpgrow.R'
'gmrfdpgrow.R' 'gp_car_fit_compare_facet.R' 'gp_cluster_plot.R'
'gp_informative_compare_plot.R' 'help.R' 'plot_cluster.R'
'predict_plot.R'
'negativeExponential.R' 'monomolecular.R' 'mitcherlich.R'
'gompertz.R' 'logistic.R' 'chapmanRichards.R' 'richard.R'
'vonBertalanffy.R' 'weibull.R' 'loglogistic.R' 'mmf.R'
'schnute.R' 'stannard.R' 'brody.R' 'growthmodels.R'
'blumberg.R'
'aaa_growthmodel-class.R' 'aab_growthmodel-constructor.R'
'aac_classes.R' 'aad_set_generics.R' 'all_easylinear.R'
'all_growthmodels.R' 'all_splines.R' 'antibiotic.R'
'bactgrowth.R' 'cost.R' 'extcoef_logistic.R' 'extract.R'
'fit_easylinear.R' 'fit_growthmodel.R' 'fit_spline.R'
'grow_baranyi.R' 'grow_exponential.R' 'grow_genlogistic.R'
'grow_gompertz.R' 'grow_huang.R' 'grow_logistic.R'
'grow_richards.R' 'grow_twostep.R' 'growthrates-package.R'
'lm_window.R' 'methods.R' 'multisplit.R' 'names.R'
'parse_formula.R' 'parse_formula_nonlin.R' 'plot.R' 'predict.R'
'utilities.R'
'add-grob.r' 'add-rows-cols.r' 'add-space.r' 'grid.r'
'gtable-layouts.r' 'gtable.r' 'rbind-cbind.r' 'utils.r'
'trim.r' 'filter.r' 'align.r' 'padding.r' 'z.r'
'data.R' 'utils.R' 'define_variance_wrapper.R'
'variance_function.R' 'define_statistic_wrapper.R'
'standard_statistic_wrapper.R' 'qvar.R'
aaaGenerics.R common.R toolkitClasses.R guiWidget.R
guiContainer.R guiComponent.R guiComponentWithItems.R
handlers.R ggroup.R gframe.R gexpandgroup.R gspinbutton.R
gslider.R gsvg.R ggenericwidget.R gtable.R ggraphics.R gtext.R
dialogs.R gtoolbar.R gaction.R ghelp.R gtree.R gbutton.R
ghtml.R gcalendar.R gimage.R gcheckbox.R glabel.R
gcheckboxgroup.R glayout.R gcombobox.R gmenu.R gvarbrowser.R
gcommandline.R gnotebook.R gvariables.R gdf.R gpanedgroup.R
gwindow.R gdfnotebook.R gradio.R gedit.R gseparator.R icons.R
gfile.R gformlayout.R gstatusbar.R bbbGenericsANY.R zzz.R
'misc.R' 'guiToolkit.R' 'BasicInterface.R' 'S4-methods.R'
'S3-methods.R' 'command-stack.R' 'methods.R' 'dialogs.R'
'gWidgets2-package.R' 'gaction.R' 'gbutton.R' 'gcalendar.R'
'gcheckbox.R' 'gcheckboxgroup.R' 'gcombobox.R' 'gdf.R'
'gdfnotebook.R' 'gedit.R' 'ggroup.R' 'gframe.R'
'gexpandgroup.R' 'gfile.R' 'gfilter.R' 'gformlayout.R'
'ggraphics.R' 'ggraphicsnotebook.R' 'ghtml.R' 'gimage.R'
'glabel.R' 'glayout.R' 'gmenu.R' 'gnotebook.R' 'gpanedgroup.R'
'gprogressbar.R' 'gradio.R' 'gseparator.R' 'gslider.R'
'gspinbutton.R' 'gstackwidget.R' 'gstatusbar.R' 'gtable.R'
'gtext.R' 'gtimer.R' 'gtoolbar.R' 'gtree.R' 'ws-model.R'
'gvarbrowser.R' 'gwindow.R' 'handler-methods.R' 'icons.R'
'gWidgets2RGtk2-package.R' 'misc.R' 'gtk-misc.R'
'GComponent.R' 'GContainer.R' 'GWidget.R' 'aaa.R' 'dialogs.R'
'gaction.R' 'gbutton.R' 'gcalendar.R' 'gcheckbox.R'
'gcheckboxgroup.R' 'gcombobox.R' 'gtable.R' 'gmenu.R' 'gdf.R'
'gedit.R' 'ggroup.R' 'gframe.R' 'gexpandgroup.R' 'gfile.R'
'gformlayout.R' 'ggraphics.R' 'gimage.R' 'glabel.R' 'glayout.R'
'gnotebook.R' 'gpanedgroup.R' 'gprogressbar.R' 'gradio.R'
'gseparator.R' 'gslider.R' 'gspinbutton.R' 'gstackwidget.R'
'gstatusbar.R' 'gtext.R' 'gtimer.R' 'gtoolbar.R' 'gtree.R'
'gvarbrowser.R' 'gwindow.R' 'icons.R' 'startup.R'
'List.R' 'gWidgets2tcltk-package.R' 'misc.R' 'tcltk-misc.R'
'dnd.R' 'GComponent.R' 'GContainer.R' 'GWidget.R' 'dialogs.R'
'gmenu.R' 'gaction.R' 'gbutton.R' 'gcalendar.R' 'gcheckbox.R'
'gtable.R' 'gcheckboxgroup.R' 'gcombobox.R' 'tablelist.R'
'gdf.R' 'gedit.R' 'gexpandgroup.R' 'gfile.R' 'gformlayout.R'
'ggroup.R' 'gframe.R' 'gimage.R' 'glabel.R' 'glayout.R'
'gnotebook.R' 'gpanedgroup.R' 'gprogressbar.R' 'gradio.R'
'gseparator.R' 'gslider.R' 'gspinbutton.R' 'gstackwidget.R'
'gstatusbar.R' 'gtext.R' 'gtimer.R' 'gtoolbar.R' 'gtree.R'
'gvarbrowser.R' 'gwindow.R' 'icons.R' 'tk2tip.R' 'zzz.R'
'aggregator.R' 'astfun.R' 'automl.R' 'classes.R' 'config.R'
'connection.R' 'constants.R' 'datasets.R' 'logging.R'
'communication.R' 'kvstore.R' 'frame.R' 'import.R' 'parse.R'
'export.R' 'edicts.R' 'models.R' 'coxph.R' 'coxphutils.R'
'kmeans.R' 'gbm.R' 'glm.R' 'glrm.R' 'pca.R' 'svd.R'
'deeplearning.R' 'stackedensemble.R' 'deepwater.R' 'xgboost.R'
'randomforest.R' 'naivebayes.R' 'word2vec.R' 'w2vutils.R'
'locate.R' 'grid.R' 'predict.R' 'zzz.R'
'H5Location-Attribute.R' 'Attribute.R' 'CommonFG.R'
'CommonFG-DataSet.R' 'CommonFG-Group.R' 'DataSet.R'
'DataSpace.R' 'Dataset-Extend.R' 'Dataset-Subset.R' 'H5File.R'
'H5Group.R' 'Helpers.R' 'RcppExports.R' 'h5-package.R'
'Interactive.R' 'attributes.R' 'withseed.R' 'gather.R'
'harvestr-package.R' 'option.R' 'utils.R'
'hashmap.R' 'classes.R' 'Hashmap-class.R' 'RcppExports.R'
'clone.R' 'load_hashmap.R' 'merge.R' 'plugin.R'
'save_hashmap.R' 'zzz.R'
'AllClass.R' 'AllGeneric.R' 'AllMethod.R' 'clustControl.R'
'trainControl.R' 'hmmvbClust.R' 'hmmvbTrain.R'
'vbSearchControl.R' 'package-HDclust.R' 'RcppExports.R'
'sim3.R' 'sim2.R' 'hmmvbBIC.R'
'Common_functions.R' 'Compound.R' 'H5constants.R'
'Helper_functions.R' 'Misc.R' 'R6Classes.R' 'R6Classes_H5A.R'
'R6Classes_H5D.R' 'R6Classes_H5File.R' 'R6Classes_H5Group.R'
'R6Classes_H5P.R' 'R6Classes_H5R.R' 'R6Classes_H5S.R'
'R6Classes_H5T.R' 'adapt_during_onLoad.R' 'convert.R'
'factor_ext.R' 'globalVariables.R' 'h5errorHandling.R'
'h5wrapper.R' 'hdf5r.R' 'high_level_UI.R' 'open_objs.R' 'zzz.R'
lattice.R BTC.R BTY.R grid.hexagons.R grid.hexlegend.R hbox.R
hdiffplot.R hexbinList.R hexbinplot.R hexbin.s4.R hexpanel.R
hexplom.R hexPlotMA.R hexutil.R hexViewport.R HO.R LINGRAY.R
LOCS.R MAG.R RB.R smoothHexbin.R
'RcppExports.R' 'hSet.R' 'hierarchicalSet.R'
'hierarchicalSets-package.R' 'twitter.R' 'visuals.R'
'For_Rccp_int.R' 'All_classes.R' 'RcppExports.R' 'Utility.R'
'Met_HTS.R' 'Met_MatH.R' 'Met_distributionH.R' 'Fuzzy_cmeans.R'
'H_time_series.R' 'HistDAWass-package.R' 'Kohonen_maps.R'
'principal_components.R' 'regression.R'
'unsuperv_classification.R' 'Plotting_with_ggplot.R'
'00first.R' 'allClasses.R' 'history.R' 'tracking.R' 'zzz.R'
'saveload.R' 'prov.R'
'diagnostic_functions.R' 'group_level_residual_functions.R'
'identification.R' 'plot_functions.R' 'quantile_functions.R'
'adjust_formula_lmList.R' 'case_delete.R' 'LSresids.R'
'HLMresid.R' 'help.R' 'influence_functions.R'
'utility_functions.R' 'rotate_ranefs.R' 'autism.R' 'ahd.R'
'radon.R'
'age2int.R' 'HFDutils.R' 'HMDutils.R' 'readHFD.R' 'readHMD.R'
'userInput.R'
'validate.R' 'hyperspec-class.R' 'unittest.R' 'paste.row.R'
'Arith.R' 'Compare.R' 'DollarNames.R' 'Math.R' 'Summary.R'
'aggregate.R' 'all.equal.R' 'apply.R' 'as.data.frame.R'
'barbiturates.R' 'bind.R' 'call.list.R' 'chk.hy.R' 'chondro.R'
'colMeans.R' 'collapse.R' 'cov.R' 'decomposition.R'
'deprecated.R' 'dim.R' 'dimnames.R' 'droplevels.R' 'empty.R'
'wl2i.R' 'extract.R' 'factor2num.R' 'fileio.optional.R' 'flu.R'
'getbynames.R' 'options.R' 'regexps.R' 'guesswavelength.R'
'paracetamol.R' 'laser.R' 'hyperspec-package.R' 'initialize.R'
'labels.R' 'plotmap.R' 'levelplot.R' 'makeraster.R'
'map.identify.R' 'map.sel.poly.R' 'mark.dendrogram.R'
'mark.peak.R' 'matlab.palette.R' 'mean_sd.R' 'merge.R'
'mvtnorm.R' 'normalize01.R' 'orderwl.R' 'pearson.dist.R'
'plot.R' 'plotc.R' 'plotmat.R' 'plotspc.R' 'plotvoronoi.R'
'qplot.R' 'qplotmixmap.R' 'quantile.R' 'rbind.fill.R'
'read.ENVI.Nicolet.R' 'read.ENVI.R' 'read.txt.Witec.R'
'read.asc.Andor.R' 'read.ini.R' 'read.jdx.R'
'read.mat.Cytospec.R' 'read.mat.Witec.R' 'read.spc.Kaiser.R'
'read.spc.R' 'read.spe.R' 'read.txt.Horiba.R'
'read.txt.Renishaw.R' 'read.txt.Shimadzu.R' 'read.txt.long.R'
'read.txt.wide.R' 'replace.R' 'sample.R' 'scale.R' 'seq.R'
'show.R' 'spc.NA.approx.R' 'spc.bin.R' 'spc.fit.poly.R'
'spc.identify.R' 'spc.loess.R' 'spc.rubberband.R'
'spc.spline.R' 'split.R' 'split.string.R' 'splitdots.R'
'subset.R' 'sweep.R' 'trellis.factor.key.R' 'units.R'
'vandermonde.R' 'wc.R' 'wl.R' 'wleval.R' 'write.txt.long.R'
'write.txt.wide.R' 'y-pastenames.R' 'zzz.R'
CrossValidation.R IBCF.R IBCF.MTME.R IBCFYear.R methods.R
Td2M.R Title.R Wheat_IBCF.R Year_IBCF.R
it_mutils.R it_plot.R it_util.R it_calls.R package.R
'adjacency.R' 'auto.R' 'assortativity.R' 'attributes.R'
'basic.R' 'bipartite.R' 'centrality.R' 'centralization.R'
'cliques.R' 'cocitation.R' 'cohesive.blocks.R' 'community.R'
'components.R' 'console.R' 'conversion.R' 'data_frame.R'
'decomposition.R' 'degseq.R' 'demo.R' 'embedding.R' 'epi.R'
'fit.R' 'flow.R' 'foreign.R' 'games.R' 'glet.R' 'hrg.R'
'igraph-package.R' 'incidence.R' 'indexing.R' 'interface.R'
'iterators.R' 'layout.R' 'layout_drl.R' 'lazyeval.R' 'make.R'
'minimum.spanning.tree.R' 'motifs.R' 'nexus.R' 'operators.R'
'other.R' 'package.R' 'palette.R' 'par.R' 'paths.R' 'plot.R'
'plot.common.R' 'plot.shapes.R' 'pp.R' 'print.R' 'printr.R'
'random_walk.R' 'rewire.R' 'scan.R' 'scg.R' 'sgm.R'
'similarity.R' 'simple.R' 'sir.R' 'socnet.R' 'sparsedf.R'
'structural.properties.R' 'structure.info.R' 'test.R'
'tkplot.R' 'topology.R' 'triangles.R' 'utils.R' 'uuid.R'
'versions.R' 'weakref.R' 'zzz-deprecate.R'
'AllClasses.R' 'AllGenerics.R' 'annotations.R' 'axes.R'
'axis_labels.R' 'axis_titles.R' 'barplot.R' 'clustering.R'
'clusters.R' 'colorbars.R' 'colors.R' 'components.R'
'dendogram.R' 'generic_subplots.R' 'groups.R' 'main_heatmap.R'
'iheatmap.R' 'iheatmapr.R' 'layout.R' 'shiny.R' 'shiny_test.R'
'signal.R' 'subplot.R' 'summary.R' 'to_widget.R' 'utils.R'
ImageClass.R MethodDefinitions.R ComplexImageClass.R
OrthogonalImageClass.R MultipleImagesClass.R OrthMoments.R
MultiCmplxWrapper.R gpzmWrapper.R frWrapper.R fmWrapper.R
fcWrapper.R
'MainFunction.R' 'Diagnostics.R' 'FullConditionals.R'
'Gibbs_FA.R' 'Gibbs_IFA.R' 'Gibbs_IMFA.R' 'Gibbs_IMIFA.R'
'Gibbs_MFA.R' 'Gibbs_MIFA.R' 'Gibbs_OMFA.R' 'Gibbs_OMIFA.R'
'IMIFA.R' 'PlottingFunctions.R' 'SimulateData.R' 'data.R'
'RcppExports.R' 'class_imputeMulti.R' 'data-tract2221.R'
'data_dep_prior_multi.R' 'int-count_levels.R'
'int-impute_multinomial.R' 'int-search_z_Os_y.R'
'int-splitRows.R' 'merge_imputed.R' 'methods_imputeMulti.R'
'multinomial_data_aug.R' 'multinomial_em.R'
'multinomial_impute.R' 'multinomial_stats.R'
'getFollowers.R' 'getFollows.R' 'getUser.R' 'getUserMedia.R'
'instaOAuth.R' 'searchInstagram.R' 'utils.R' 'getTagCount.R'
'getComments.R' 'getLikes.R' 'getPopular.R' 'getLocation.R'
read.emeld.R read.lift.R interlineaR-package.R read.toolbox.R
read.pangloss.R dataset.R lift.specifications.R common.R
'F_bias.R' 'F_daily_mean.R' 'F_date_time.R' 'F_Mo.Th.Ra.R'
'F_par_calibration.R' 'F_plot_meas_sim.R'
'F_shape_calibration.R' 'F_Th_int_series.R' 'F_Th_interp.R'
'InterpolT-package.R'
'parallelization.R' 'check.stopping.R' 'clustering.mode.R'
'clustering.R' 'data.R' 'export.groups.R' 'get.node.info.R'
'ipcaps-package.R' 'process.each.node.R' 'output.template.R'
'save.html.R' 'save.eigenplots.html.R'
'save.plots.label.html.R' 'save.plots.cluster.html.R'
'save.plots.R' 'postprocess.R' 'preprocess.R' 'ipcaps.R'
'top.discriminator.R'
Class-Metric.R BSSUtils.R ISPAQUtils.R basicStatsMetric.R
correlationMetric.R crossCorrelationMetric.R
dailyDCOffsetMetric.R DCOffsetTimesMetric.R gapsMetric.R
PSDMetric.R SNRMetric.R spikesMetric.R STALTAMetric.R
stateOfHealthMetric.R upDownTimesMetric.R
transferFunctionMetric.R
Class-Trace.R Class-Stream.R Class-IrisClient.R
mseedWrappers.R Utils.R spectralUtils.R
'ability.R' 'estimate.R' 'itemfit.R' 'nonpar.R' 'readin.R'
'scale.R' 'tracelines.R' 'utils.R' 'irtoys-package.R'
'interaction.R'
'IsotopeRModelsGroups.R' 'IsotopeRModelsNoGroups.R'
'IsotopeRgui.R' 'Plot_jags.R' 'IsotopeRModelsNoGroupsNoInd.R'
'ISOweek.R' 'ISOweekday.R' 'date2ISOweek.R' 'ISOweek2date.R'
'ISOweek-internal.R'
'ISQG.R' 'Mating.R' 'R6Classes.R' 'Functions.R' 'Trait.R'
'RcppExports.R' 'Hooks.R'
'zzz.R' 'hello.R' 'JASPAR-package.R' 'JASPAR-internal.R'
'make_template.R'
'daily2fraction-method.R' 'fraction2daily-method.R'
'jubilee-adj-fault-line-method.R' 'jubilee-calc-cape-method.R'
'jubilee-package.R' 'jubilee-class.R' 'jubilee-constructor.R'
'jubilee-eqty-ols-method.R' 'jubilee-forward-rtn-method.R'
'jubilee-fred-data-method.R' 'jubilee-locate-file.R'
'jubilee-mcsapply-method.R' 'jubilee-ols-method.R'
'jubilee-predict-method.R' 'jubilee-read-fred-file.R'
'jubilee-repo-class.R' 'jubilee-repo-config.R'
'jubilee-repo-constructor.R' 'jubilee-std-fault-line-method.R'
'tri-wave-class.R' 'tri-wave-constructor.R' 'tri-wave-model.R'
'booter.R' 'comm_close.R' 'comm_info_request.R' 'comm_msg.R'
'comm_open.R' 'complete_request.R' 'execute_request.R'
'history_request.R' 'inspect_request.R' 'is_complete_request.R'
'juniper.R' 'kernel_info_request.R' 'kernelspec.R'
'graphics_device.R' 'RcppExports.R' 'request.R'
'shutdown_request.R' 'zzz.R'
'pathway.r' 'GWASdata.r' 'data.R' 'kernel.r' 'lkmt.r'
'CovFormulas.R' 'allGenerics.R' 'checkGrad.R' 'covComp.R'
'covMan.R' 'covQual.R' 'q1CompSymm.R' 'q1Symm.R'
'covQualNested.R' 'covRadial.R' 'covTS.R' 'covZZAll.R' 'gp.R'
'kFuns.R' 'kernelNorm.R' 'kernels1d_Call.R' 'logLikFuns.R'
'methodGLS.R' 'methodMLE.R' 'miscUtils.R' 'prinKrige.R'
'q1Diag.R' 'simulPar.R' 'simulate_gp.R'
'api.R' 'assertions.R' 'backend-class.R' 'backend-env.R'
'backend-file.R' 'backend-macos.R' 'backend-secret-service.R'
'backend-wincred.R' 'default_backend.R' 'package.R' 'pass.R'
'utils.R'
'00_class_01_klausuR.test.R' '00_class_02_klausuR.R'
'00_class_03_klausuR.answ.R' '00_class_04_klausuR.mult.R'
'01_method_plot.klausuR.R' '01_method_show.klausuR.R'
'01_method_sort.klausuR.R' 'compare.R' 'grand.table.R'
'klausuR-internal.R' 'klausuR-internal.roxy.all.R'
'klausuR-package.R' 'klausur.R' 'klausur.data.R'
'klausur.gen.R' 'klausur.gen.corr.R' 'klausur.gen.marks.R'
'klausur.mufo.R' 'klausur.report.R' 'nret.rescale.R'
'nret.translator.R'
'saddle.R' 'pchisqsum2.R' 'CoxKM.R' 'Data_CoxKM_data.R'
'Data_FKM_charID.R' 'Data_FKM_numID.R' 'Data_FbKM_charID.R'
'Data_FbKM_numID.R' 'Data_LKM_charID.R' 'Data_LKM_numID.R'
'Data_LKM_numID_int.R' 'Data_MFKM_charID.R' 'Data_MFKM_numID.R'
'Data_MKM_charID.R' 'Data_MKM_numID.R' 'FKM.R'
'SKAT_Optimal_Integrate_Func_Davies.R' 'Get_Liu_Params_Mod.R'
'FKMO.R' 'FKMO_Null_Model.R' 'FKM_Null_Model.R' 'FbKM.R'
'FbKMO.R' 'glmmPQL.R' 'FbKMO_Null_Model.R' 'FbKM_Null_Model.R'
'LKM.R' 'LKMO.R' 'LKMO_Null_Model.R' 'LKM_Int.R'
'LKM_Null_Model.R' 'MFKM.R' 'MFKMO.R' 'MFKMO_Null_Model.R'
'MFKM_Null_Model.R' 'MKM.R' 'MKMO.R' 'MKMO_Null_Model.R'
'MKM_Null_Model.R' 'c.KMgene.R' 'coxlr.fit.R' 'ginv_s.R'
'prepCoxKM.R'
global.R clusterLongData.R parKml.R parChoice.R kml.R
'functions4glmClust.R' 'GlmCluster.R' 'glmclust-internal.R'
'glmClust.R' 'kmlcov-package.R' 'kmlCov.R'
global.R plot.R clds.R reduceTraj.R distanceFrechet.R
meanFrechet.R parKmlShape.R kmlShape.R
'block.R' 'cache.R' 'utils.R' 'citation.R' 'hooks-html.R'
'plot.R' 'defaults.R' 'concordance.R' 'engine.R' 'highlight.R'
'themes.R' 'header.R' 'hooks-asciidoc.R' 'hooks-chunk.R'
'hooks-extra.R' 'hooks-latex.R' 'hooks-md.R' 'hooks-rst.R'
'hooks-textile.R' 'hooks.R' 'output.R' 'package.R' 'pandoc.R'
'params.R' 'parser.R' 'pattern.R' 'rocco.R' 'spin.R' 'table.R'
'template.R' 'utils-base64.R' 'utils-conversion.R'
'utils-rd2html.R' 'utils-sweave.R' 'utils-upload.R'
'utils-vignettes.R' 'zzz.R'
'onLoad.R' 'manageDic.R' 'hangulUtils.R' 'koAnalyzerRun.R'
'tagdata.R' 'Concordances.R' 'utils.R'
'00_environment.R' '01_class_01_kRp.tagged.R'
'01_class_02_kRp.TTR.R' '01_class_03_kRp.txt.freq.R'
'01_class_04_kRp.txt.trans.R' '01_class_05_kRp.analysis.R'
'01_class_06_kRp.corp.freq.R' '01_class_09_kRp.lang.R'
'01_class_10_kRp.readability.R' '02_method_cTest.R'
'kRp.filter.wclass.R' 'koRpus-internal.R'
'02_method_clozeDelete.R' '02_method_correct.R'
'02_method_freq.analysis.R' '02_method_hyphen.R'
'02_method_kRp.taggedText.R' '02_method_lex.div.R'
'02_method_plot.kRp.tagged.R' '02_method_query.R'
'02_method_read.corp.custom.R' '02_method_readability.R'
'02_method_show.kRp.lang.R' '02_method_show.kRp.TTR.R'
'02_method_show.kRp.corp.freq.R'
'02_method_show.kRp.readability.R'
'02_method_show.kRp.taggedText.R'
'02_method_summary.kRp.lang.R' '02_method_summary.kRp.TTR.R'
'02_method_summary.kRp.readability.R'
'02_method_summary.kRp.tagged.R'
'02_method_summary.kRp.txt.freq.R' '02_method_text.transform.R'
'02_method_types_tokens.R' 'available.koRpus.lang.R'
'get.kRp.env.R' 'guess.lang.R' 'install.koRpus.lang.R'
'jumbleWords.R' 'kRp.POS.tags.R' 'kRp.cluster.R'
'kRp.text.analysis.R' 'kRp.text.paste.R'
'koRpus-internal.freq.analysis.R' 'koRpus-internal.import.R'
'koRpus-internal.lexdiv.formulae.R'
'koRpus-internal.rdb.formulae.R'
'koRpus-internal.rdb.params.grades.R'
'koRpus-internal.read.corp.custom.R'
'koRpus-internal.roxy.all.R' 'koRpus-package.R' 'lex.div.num.R'
'read.BAWL.R' 'read.corp.LCC.R' 'read.corp.celex.R'
'read.tagged.R' 'readability.num.R' 'segment.optimizer.R'
'set.kRp.env.R' 'set.lang.support.R' 'textFeatures.R'
'tokenize.R' 'treetag.R' 'wrapper_functions_lex.div.R'
'wrapper_functions_readability.R'
'generics.R' 'laf.R' 'laf_column.R' 'meta.R' 'open.R'
'read_dm_blaise.R' 'stats.R' 'textutils.R' 'types.R'
'utility.R'
'any.R' 'lambda.tools-package.R' 'logic.R' 'sequence.R'
'fold.R' 'map.R' 'sample.R' 'transform.R' 'ntry.R'
'angoff.R' 'bin.combs.R' 'cov.lambda4.R' 'impute.cov.R'
'kristof.R' 'lambda1.R' 'lambda2.R' 'lambda3.R' 'lambda5.R'
'lambda6.R' 'omega.tot.R' 'print.Lambda4.pkg.R'
'quant.lambda4.R' 'raju.R' 'user.lambda4.R' 'guttman.R'
'invlogit.R' 'logit.R' 'RcppExports.R' 'graphLand.R'
'periodic_cov.R' 'multiN.R' 'AgriLand.R' 'HLIRdynamics.R'
'demo_landsepi.R' 'landsepi.R' 'plotevolQR.R'
'simul_landsepi.R'
'ldhmm-calc_stats_from_obs.R' 'ldhmm-numericOrNull-class.R'
'ldhmm-package.R' 'ldhmm-class.R' 'ldhmm-conditional_prob.R'
'ldhmm-constructor.R' 'ldhmm-decode_stats_history.R'
'ldhmm-decoding.R' 'ldhmm-forecast_prob.R'
'ldhmm-forecast_state.R' 'ldhmm-forecast_volatility.R'
'ldhmm-fred_data.R' 'ldhmm-gamma_init.R' 'ldhmm-ld_stats.R'
'ldhmm-log_forward.R' 'ldhmm-mle.R' 'ldhmm-mllk.R'
'ldhmm-n2w.R' 'ldhmm-oxford_man_index_list.R'
'ldhmm-oxford_man_plot_obs.R'
'ldhmm-oxford_man_realized_data.R' 'ldhmm-oxford_man_ts.R'
'ldhmm-plot_spx_vix_obs.R' 'ldhmm-pseudo_residuals.R'
'ldhmm-read_sample_object.R' 'ldhmm-simulate_abs_acf.R'
'ldhmm-simulate_state_transition.R' 'ldhmm-sma.R'
'ldhmm-state_ld.R' 'ldhmm-state_pdf.R' 'ldhmm-ts_abs_acf.R'
'ldhmm-ts_log_rtn.R' 'ldhmm-viterbi.R' 'ldhmm-w2n.R'
'sldhmm-class.R'
'data.R' 'misc.R' 'unit.R' 'layer.R' 'network.R' 'leabRa.R'
'ggplot2.r' 'lemon-plot.r' 'axis-annotation.r' 'brackets.R'
'coord-flex.r' 'coord-capped.r' 'dot.r' 'facet-rep-lab.r'
'facet-wrap.r' 'geom-pointline.r' 'lemon_print.r'
'geom-siderange.r' 'grob_utils.r' 'gtable_show-.r'
'guides-axis.r' 'legends.r' 'lemon.r' 'scale-symmetric.r'
'lettercase-package.R' 'make_names.R' 'make_str_replace.R'
'patterns.R' 'str_cap_words.R' 'str_collapse_whitespace.R'
'str_delete.R' 'str_functions.R' 'str_is.R' 'str_lowercase.R'
'str_sentence_case.R' 'str_snake_case.R' 'str_spine_case.R'
'str_title_case.R' 'str_transform.R' 'str_ucfirst.R'
'str_uppercase.R' 'utils.R' 'zzz.R'
'available_data.R' 'common_names.R'
'constraining_loughran_mcdonald.R' 'freq_first_names.R'
'freq_last_names.R' 'function_words.R' 'grady_augmented.R'
'hash_emoticons.R' 'hash_grady_pos.R' 'hash_internet_slang.R'
'hash_lemmas.R' 'hash_sentiment_emojis.R'
'hash_sentiment_huliu.R' 'utils.R' 'hash_sentiment_jockers.R'
'hash_sentiment_jockers_rinker.R'
'hash_sentiment_loughran_mcdonald.R' 'hash_sentiment_nrc.R'
'hash_sentiment_senticnet.R' 'hash_sentiment_sentiword.R'
'hash_sentiment_slangsd.R' 'hash_sentiment_socal_google.R'
'hash_valence_shifters.R' 'key_contractions.R'
'key_corporate_social_responsibility.R' 'key_grade.R'
'key_ratings.R' 'key_regressive_imagery.R' 'lexicon-package.R'
'modal_loughran_mcdonald.R' 'nrc_emotions.R'
'pos_action_verb.R' 'pos_df_irregular_nouns.R'
'pos_df_pronouns.R' 'pos_interjections.R' 'pos_preposition.R'
'profanity_alvarez.R' 'profanity_arr_bad.R'
'profanity_banned.R' 'profanity_racist.R'
'profanity_zac_anger.R' 'sw_dolch.R' 'sw_fry_100.R'
'sw_fry_1000.R' 'sw_fry_200.R' 'sw_fry_25.R' 'sw_jockers.R'
'sw_loughran_mcdonald.R' 'sw_lucene.R' 'sw_mallet.R'
'sw_python.R'
'compute_LICORS_loglik.R' 'compute_mixture_penalty.R'
'compute_NEC.R' 'contCA00.R' 'data2LCs.R' 'estimate_LC_pdfs.R'
'estimate_state_adj_matrix.R' 'estimate_state_probs.R'
'get_LC_config.R' 'image2.R' 'initialize_states.R' 'kmeanspp.R'
'LC-utils.R' 'LICORS-package.R' 'merge_states.R'
'mixed_LICORS.R' 'normalize.R' 'predict_FLC_given_PLC.R'
'rdensity.R' 'relabel_vector.R' 'remove_small_sample_states.R'
'search_knn.R' 'setup_LC_geometry.R' 'sparsify_weights.R'
'states2weight_matrix.R' 'threshold.R' 'weight_matrix2states.R'
'wKDE.R' 'mixed_LICORS-utils.R' 'compute_LC_coordinates.R'
'compute_margin_coordinates.R' 'get_spacetime_grid.R'
'embed2.R'
'RcppExports.R' 'catalog_apply.r' 'catalog_fakerun.r'
'catalog_index.r' 'catalog_laxindex.r' 'catalog_makecluster.r'
'catalog_query.r' 'catalog_retile.r' 'catalog_select.r'
'class-lasheader.r' 'class-las.r' 'class-lascatalog.r'
'class-lascluster.r' 'clusters_apply.r' 'constant.R'
'deprecated.r' 'grid_canopy.r' 'grid_catalog.r'
'grid_density.r' 'grid_hexametrics.r' 'grid_metrics.r'
'grid_metrics3d.r' 'grid_terrain.r' 'grid_tincanopy.r'
'lasaggreagte.r' 'lasclassify.r' 'lasclip.r' 'lascolor.r'
'lasfilter.r' 'lasfilterdecimate.r' 'lasfiltersurfacepoints.r'
'lasground.r' 'lasindentify.r' 'lasmetrics.r' 'lasnormalize.r'
'lasroi.r' 'lassmooth.r' 'lassnags.r' 'lastrees.r'
'lastrees_dalponte.r' 'lastrees_li.r' 'lastrees_li2.r'
'lastrees_silva.r' 'lastrees_watershed.r' 'lasupdateheader.r'
'metrics.r' 'metrics_canopy_roughness.r' 'options.r'
'plot.catalog.r' 'plot.las.r' 'plot.lashexametrics.r'
'plot.lasmetrics.r' 'plot.lasmetrics3d.r' 'plot3d.r'
'readLAS.r' 'stopif.r' 'subcircled.r' 'tree_detection.r'
'tree_hulls.r' 'tree_metrics.r' 'utils_colors.r'
'utils_geometry.r' 'utils_interpolations.r' 'utils_misc.r'
'utils_projection.r' 'utils_spatial.r' 'utils_typecast.r'
'writeLAS.r' 'zzz.r'
'EX.R' 'global_var.R' 'Person.R' 'experiments.R' 'lifelogr.R'
'shinyApp.R' 'viz_daily.R' 'viz_intraday.R' 'viz_sleep.R'
'ColumnLinkedMatrix.R' 'RowLinkedMatrix.R' 'LinkedMatrix.R'
'package.R' 'utils.R'
'linkspotterComplete.R' 'boostedBoxplot.R'
'clusterVariables.R' 'emptyVar.R'
'linkspotterDurationEstimator.R' 'linkspotterGraph.R'
'linkspotterGraphOnMatrix.R' 'linkspotterOnFile.R'
'linkspotterUI.R' 'corCouplesToMatrix.R' 'matrixToCorCouples.R'
'maxNMI.R' 'BeEFdiscretization_numfact.R'
'BeEFdiscretization_numnum.R' 'multiBivariateCorrelation.R'
'NormalizedMI.R' 'uselessVar.R' 'createShinyAppFolder.R'
'anova_contrasts.R' 'contest.R' 'contrast_utils.R'
'data_documentation.R' 'drop1.R' 'estimability.R' 'lmer.R'
'lmerTest.R' 'lmer_anova.R' 'lmer_summary.R' 'ls_means.R'
'ranova.R' 'step.R' 'terms_utils.R' 'utils.R'
'LogisticDx_package.R' 'OR.R' 'ageChd.R' 'bbdm.R' 'dx.R'
'genBinom.R' 'gof.R' 'icu.R' 'lbw.R' 'llbw.R' 'mes.R' 'mlbw.R'
'nhanes3.R' 'pcs.R' 'plotGlm.R' 'printGofGlm.R' 'printSSglm.R'
'printSigGlm.R' 'sig.R' 'ss.R' 'uis.R'
global.r function.r constants.r myMisc3d.r longData.r
longData3d.r distanceFrechet.R imputCross.R imputTraj.R
imputLinearInterpol.R imputCopyMean.R imputation.r partition.r
listPartition.r parLongData.r parWindows.r newPlot.r
'longpower-package.R' 'diggle.linear.power.R'
'edland.linear.power.R' 'liu.liang.linear.power.R' 'lmmpower.R'
'power_mmrm.R' 'print.power.longtest.R'
'LICORS2LSC.R' 'LSC-utils.R' 'plot_LSC_1plus1D.R'
'plot_LSC_2plus1D.R' 'states2LSC.R' 'states2probs.R'
'LSC-package.R' 'plot_LSC_0plus1D.R'
lslxData.R lslxModel.R lslxFitting.R lslx.R
lslx-initialize-method.R lslx-print-method.R
lslx-set-coefficient-method.R lslx-set-block-method.R
lslx-set-directed-method.R lslx-set-heterogeneity-method.R
lslx-set-undirected-method.R lslx-set-data-method.R
RcppExports.R lslx-fit-method.R lslx-get-method.R
lslx-extract-method.R lslx-test-method.R
lslx-summarize-method.R lslx-plot-method.R
lslx-validate-method.R lslx-s3-interface.R polyhedral.R
'ltmle-package.R' 'GeneralUtilities.R' 'ltmle.R' 'ltmle_sg.R'
'BinaryToCensoring.R' 'DeterministicFunctions.R' 'zzz.R'
'Dates.r' 'POSIXt.r' 'RcppExports.R' 'util.r' 'parse.r'
'timespans.r' 'intervals.r' 'difftimes.r' 'durations.r'
'periods.r' 'accessors-date.R' 'accessors-day.r'
'accessors-dst.r' 'accessors-hour.r' 'accessors-minute.r'
'accessors-month.r' 'accessors-quarter.r' 'accessors-second.r'
'accessors-tz.r' 'accessors-week.r' 'accessors-year.r'
'am-pm.r' 'time-zones.r' 'numeric.r' 'coercion.r' 'constants.r'
'data.r' 'decimal-dates.r' 'deprecated.r' 'guess.r' 'hidden.r'
'instants.r' 'leap-years.r' 'ops-addition.r' 'ops-compare.r'
'ops-division.r' 'ops-integer-division.r' 'ops-m+.r'
'ops-modulo.r' 'ops-multiplication.r' 'ops-subtraction.r'
'package.r' 'pretty.r' 'round.r' 'stamp.r' 'update.r' 'zzz.R'
'class-CategoryLabel.R' 'class-ObsLulcRasterStack.R'
'class-PredictiveModelList.R' 'class-ExpVarRasterList.R'
'class-NeighbRasterStack.R' 'class-Model.R'
'class-ThreeMapComparison.R' 'AgreementBudget.R'
'ExpVarRasterList.R' 'FigureOfMerit.R' 'Model.R'
'NeighbRasterStack.R' 'ObsLulcRasterStack.R'
'class-PerformanceList.R' 'PerformanceList.R'
'class-PredictionList.R' 'PredictionList.R'
'ThreeMapComparison.R' 'allocate.R' 'allow.R' 'allowNeighb.R'
'approxExtrapDemand.R' 'as.data.frame.R'
'c.PredictiveModelList.R' 'index.R' 'coerce.R' 'compareAUC.R'
'crossTabulate.R' 'data.R' 'getPredictiveModelInputData.R'
'length.R' 'lulcc-package.R' 'models.R' 'names.R' 'partition.R'
'performance.rocr.R' 'plot.AgreementBudget.R'
'plot.FigureOfMerit.R' 'plot.Performance.R' 'plot.R'
'predict.R' 'resample.R' 'show.R' 'subset.R' 'summary.R'
'total.R'
'Filter-class.R' 'create_Filter.R' 'import_Filter.R'
'export_Filter.R' 'plot_Filter.R' 'combine_Filters.R'
'FilterStack-class.R' 'create_FilterStack.R' 'add_Filter.R'
'remove_Filter.R' 'plot_FilterStack.R' 'default_Filters.R'
'PMT-class.R' 'create_PMT.R' 'import_PMT.R' 'export_PMT.R'
'default_PMT.R' 'plot_PMT.R' 'Stimulation-class.R'
'create_Stimulation.R' 'import_Stimulation.R'
'export_Stimulation.R' 'default_Stimulation.R'
'plot_Stimulation.R' 'Reader-class.R' 'create_Reader.R'
'plot_Reader.R' 'Material-class.R' 'create_Material.R'
'import_Material.R' 'export_Material.R' 'default_Material.R'
'plot_Material.R' 'Experiment-class.R' 'create_Experiment.R'
'plot_Experiment.R' 'LumReader-package.R'
'call_ShinyLumReader.R'
init.R RcppExports.R geohash.R split.R subdivide.R valid.R
transform.R bounding_circle.R geodetic.R bearing.R
snap_to_grid.R twkb.R
'maboost-package.r' 'maboost.R' 'maboost.default.R'
'maboost.formula.R' 'maboost.machine.bin.R'
'maboost.machine.multi.R' 'predict.maboost.R' 'print.maboost.R'
'projsplx.R' 'projsplx_k.R' 'summary.maboost.R'
'update.maboost.R' 'varplot.maboost.R'
'AllClass.r' 'utils.r' 'Ops.r' 'bind.r' 'blockrep.r'
'coerce.r' 'data.r' 'det.r' 'diag.r' 'eigen.r' 'elwise.r'
'madness_pkg.r' 'matwise.r' 'max.r' 'norm.r' 'numderiv.r'
'reshape.r' 'solve.r' 'sum.r' 'sums.r' 'theta.r'
'to_objective.r' 'trace.r' 'twomoments.r' 'vcov.r' 'vec.r'
'accumulate.results.R' 'calculate.dht.R'
'check.bootstrap.options.r' 'check.convergence.R'
'check.fitted.R' 'check.covar.uncertainty.R'
'check.ddf.models.R' 'check.species.code.definitions.R'
'check.species.presence.r' 'create.obs.table.R'
'create.param.arrays.r' 'create.result.arrays.R'
'execute.multi.analysis.R' 'fit.ddf.models.R'
'format.dht.results.R' 'get.datasets.R' 'glossary.R'
'mae.warning.R' 'model.description.R' 'object.description.R'
'process.bootstrap.results.R' 'process.warnings.R'
'prorate.unidentified.R' 'renumber.duplicates.R'
'resample.covariates.R' 'resample.data.R' 'rtpois.R'
'store.param.ests.R' 'summary.ma.allspecies.R'
'summary.ma.allunid.R' 'summary.ma.analysis.R' 'summary.ma.R'
'summary.ma.species.R' 'summary.ma.unid.R'
manifestoR-package.r globals.R pipe_helpers.R cache.R db_api.R
corpus.R manifesto.R codes.R scaling_general.R scaling_rile.R
scaling_functions.R issue_attention.R nicheness.R clarity.R
scaling_bootstrap.R dataset.R codebook.R
'gregt.R' 'varMase.R' 'GREG.R' 'gregElasticNett.R'
'gregElasticNet.R' 'gregTree.R' 'gregtreet.R' 'htt.R'
'horvitzThompson.R' 'logisticGregElasticNett.R'
'logisticGregt.R' 'postStratt.R' 'postStrat.R'
'ratioEstimatort.R' 'ratioEstimator.R' 'treeDesignMatrix.R'
utils.R biom-ext.R client.R graphics.R analysis-support.R
analysis-misc.R distx.R rowstats.R transform.R boxplot.R
princomp.R image.R init.R
'convCheck.R' 'estimator.R' 'innerEstim.R' 'matrixpls.R'
'matrixpls.boot.R' 'matrixpls.crossvalidate.R'
'matrixpls.optim.R' 'matrixpls.plspm.R'
'matrixpls.postestimation.R' 'matrixpls.predict.R'
'matrixpls.sempls.R' 'matrixpls.sim.R' 'matrixpls.util.R'
'outerEstim.R' 'paramEstimator.R' 'reliabilityEstim.R'
'signChange.R' 'weightFun.R' 'weightSign.R'
'decagon_maze.r' 'dodecagon_maze.r' 'eq_triangle_maze.r'
'hexaflake_maze.r' 'hexagon_maze.r' 'holey_line.r'
'holey_path.r' 'iso_trapezoid_maze.r' 'koch_maze.r'
'mazealls.r' 'octagon_maze.r' 'parallelogram_maze.r'
'sierpinski_carpet_maze.r' 'sierpinski_maze.r'
'sierpinski_trapezoid_maze.r' 'utils.r'
AggregateFireFracs.R AggregateVegFracs.R
ReportMeanVegChanges.R SaveFireAreaFracs.R SaveMatrix.R
SaveRatios.R SaveSequence.R SaveVegFracs.R VTnames.R
VegTypeChanges.R SaveVegChangeProbabilityMultipliers.R
SaveFireProbabilityMultipliers.R
matutils.R mc.R mc01zero.R mc02gen.R auto_sim.R classMC.R
auto_classMF.R classmcSpec.R spaces.R classJordan.R jd.R
jdecomp.R classSMC.R smc.R make_chains.R
"mcrMisc.r" "mcLinReg.r" "mcDeming.r" "mcWDeming.r"
"mcPaBaLarge.r" "mcPaBa.r" "mcCalcCI.r" "mcCalcTstar.r"
"mcBootstrap.r" "MCResultMethods.r" "MCResult.r"
"MCResultAnalyticalMethods.r" "MCResultAnalytical.r"
"MCResultJackknifeMethods.r" "MCResultJackknife.r"
"MCResultResamplingMethods.r" "MCResultResampling.r"
"MCResultBCaMethods.r" "MCResultBCa.r" "mcrInterface.r"
"mcrCompareFit.r" "mcrIncludeLegend.r" "zzz.r"
'example-bcg.R' 'methods-for-batch-distribution.R'
'methods-for-data-generation.R' 'methods-for-experiments.R'
'methods-for-inference.R' 'plot-functions.R'
'plot-interval-estimates.R' 'plot-pivotal-distributions.R'
'plot-point-estimates.R' 'zzz.R'
MetaPCA.R functions.R DropDupGenes.R requireAll.R PlotPC2D.R
PreprocessMetaAnalysis.R
MetaQC.R requireAll.R functions.R runQC.R cleanup.R
'aaa.r' 'classes.r' 'generic_methods.r' 'MF-package.r'
'HLBoot.r' 'MFBoot.r' 'MFClus.r' 'MFClusBoot.r' 'MFmp.r'
'MFr.r' 'MFSubj.r'
'applyTraitTradeOffs.R' 'assignNAsToMFGs.R'
'assignStrainTraits.R' 'checkResInfo.R' 'checkSolution.R'
'checkStoichiom.R' 'combineGrowthLimFuncDefault.R'
'combinePathsFuncDefault.R' 'createDF.R' 'data.R'
'derivsDefault.R' 'entryRateFuncDefault.R'
'extraGrowthLimFuncDefault.R' 'getAllResources.R'
'getGroupName.R' 'getKeyRes.R' 'getMolarStoichiom.R'
'getMolarYields.R' 'getNonBoostFrac.R' 'getNumPaths.R'
'getPHcorners.R' 'getStrainPHcorners.R'
'getStrainParamsFromFile.R' 'getValues.R'
'growthLimFuncDefault.R' 'makeInflowFromSoln.R'
'makeParamMatrixG.R' 'makeParamMatrixS.R'
'massBalanceFuncDefault.R' 'microPop-internal.R'
'microPop-package.R' 'microPopModel.R' 'pHFuncDefault.R'
'pHLimFuncDefault.R' 'pHcentreOfMass.R' 'plotTraitChange.R'
'productionFuncDefault.R' 'quickPlot.R'
'removalRateFuncDefault.R' 'uptakeFuncDefault.R'
'rateFuncsDefault.R' 'replaceListItems.R'
'runMicroPopExample.R' 'subsetFunc.R' 'waterUptakeRatio.R'
'RcppExports.R' 'aaaGenerics.R' 'appFunctions.R'
'constraintFunctions.R' 'fastRankSchemes.R' 'internalHelpers.R'
'objectPkModelParent.R' 'objectSetOfSchemes.R'
'objectPkModel.R' 'objectPkModelRange.R'
'objectSetOfTimePoints.R' 'pkFunctions.R' 'schemeStatistics.R'
'rankScheme.R' 'rankTimePoints.R' 'schemeGenerator.R'
'timePointGeneration.R'
'gini.R' 'migration.indices.R' 'crude.R' 'others.R' 'acv.R'
'graph.R'
Corrp2miRfile.R EnrichmentScore.R S2N.R MirSEA.R MsReport.R
GetExampleData.R getEnvironmentData.R GetMiRTargetData.R
GetPathwayData.R PlotCorrelation.R PlotHeatMap.R
PlotRunEnrichment.R
'MissingDataGUI-package.r' 'MissingDataGUI.r'
'WatchMissingValues.r' 'imputation.r' 'utils.r'
'ClusterAlgo.R' 'ClusterAlgoPredict.R' 'ClusterInit.R'
'ClusterStrategy.R' 'IClusterModel.R' 'ClusterModelNames.R'
'global.R' 'ClusterCategorical.R' 'ClusterDiagGaussian.R'
'ClusterGamma.R' 'ClusterMixedData.R' 'ClusterPlot.R'
'ClusterPoisson.R' 'IClusterPredict.R' 'ClusterPredict.R'
'LearnAlgo.R' 'Learners.R' 'MixAll.R' 'kmmNames.R' 'kmm.R'
'kmmMixedData.R' 'missingValues.R'
'mixmapTest.R' 'mixmapPI.R' 'MixMAPclass.R' 'plot.MixMAP.R'
'summary.MixMAP.R'
'MizerParams-class.R' 'MizerSim-class.R' 'selectivity_funcs.R'
'project.R' 'help.R' 'project_methods.R' 'summary_methods.R'
'plots.R' 'wrapper_functions.R' 'data.R' 'RcppExports.R'
'CPOHelp.R' 'fauxCPOConstructor.R' 'auxiliary.R'
'ParamSetSugar.R' 'callInterface.R' 'FormatCheck.R' 'callCPO.R'
'properties.R' 'parameters.R' 'listCPO.R' 'makeCPO.R'
'CPO_applyFun.R' 'CPO_asNumeric.R' 'operators.R' 'NULLCPO.R'
'CPO_meta.R' 'CPO_cbind.R' 'CPO_collapseFact.R'
'CPO_dropConstants.R' 'CPO_encode.R' 'CPO_filterFeatures.R'
'CPO_fixFactors.R' 'CPO_ica.R' 'CPO_impute.R' 'CPO_makeCols.R'
'CPO_missingIndicators.R' 'CPO_modelMatrix.R' 'CPO_pca.R'
'CPO_quantileBinNumerics.R' 'CPO_regrResiduals.R'
'CPO_responseFromSE.R' 'CPO_scale.R' 'CPO_scaleMaxAbs.R'
'CPO_scaleRange.R' 'CPO_select.R' 'CPO_smote.R'
'CPO_spatialSign.R' 'CPO_subsample.R' 'CPO_wrap.R'
'RetrafoState.R' 'attributes.R' 'auxhelp.R'
'composeProperties.R' 'doublecaret.R' 'inverter.R' 'learner.R'
'makeCPOHelp.R' 'print.R' 'zzz.R'
'RcppExports.R' 'extract.R' 'file-dim.R' 'mmapchar.R'
'mmapcharr-package.r' 'utils.R'
'checkinitialvalues.R' 'DeltaMethod.R' 'detfct.R' 'em.R'
'eval.pdf.R' 'fitmix.R' 'flt.gr.R' 'flt.R' 'flt.var.R'
'getpars.R' 'goftests.R' 'integrate.hn.pt.R' 'integrate.hn.R'
'integrate.pdf.R' 'inv.reparam.pi.R' 'keyfct.hn.R' 'keyfct.R'
'mu.calc.R' 'namepars.R' 'plot.ds.mixture.R'
'print.ds.mixture.R' 'print.summary.ds.mixture.R'
'reparam.pi.R' 'scalevalue.R' 'setinitialvalues.R' 'sim.mix.R'
'solvecov.R' 'step.ds.mixture.R' 'summary.ds.mixture.R'
'switchpars.R' 'varNp.R' 'zzz.R' 'mmds-package.R'
addItem.R findItem.R delItem.R addRow.R findRow.R checkArray.R
checkCTechSTu.R getCTechSTM1u.R getCTechSTM2u.R getCharSTM1u.R
getCharSTM2u.R findOptimalBasisM1FromScratch.R
findOptimalBasisM2FromScratch.R compContourM1u.R
compContourM2u.R evalContour.R
'amodule.R' 'NAMESPACE.R' 'getSearchPath.R' 'class.R'
'depend.R' 'export.R' 'expose.R' 'extend.R' 'import.R'
'module-class.R' 'module-coercion.R' 'module-helper.R'
'module.R' 'use.R' 'testModule.R'
AllClasses.R AllGenerics.R alapl.R bms_ortho.R
derivatives_nlps.R distribs.R gam.R msPriorSpec.R modelsearch.R
modelSelection.R normaliwish.R normmix.R nlpMarginal.R dmom.R
mombf.R pmomLM.R pmomPM.R emomLM.R postMode.R pplProbit.R
greedyGLM.R ppmodel.R zellnerLM.R rmom.R cox.R
'normalization.R' 'downsizing.R' 'querries.R' 'filtering.R'
'visualization.R' 'analyses.R' 'data.R' 'mapreduce.R'
mapi.R dbi.R dbapply.R dplyr.R control.R
mapi.R dbi.R dbapply.R dplyr.R control.R embedded.R zzz.R
'utils.R' 'A_NAMESPACE.R' 'computeMu.R' 'multiRun.R'
'optimParams.R' 'plot.R' 'sampleIO.R'
'mortalityTable.R' 'mortalityTable.period.R'
'mortalityTable.ageShift.R' 'ageShift.R'
'mortalityTable.observed.R' 'mortalityTable.joined.R'
'mortalityTable.mixed.R' 'ages.R' 'baseTable.R' 'baseYear.R'
'fillAges.R' 'pensionTable.R' 'commutationNumbers.R'
'mortalityTable.improvementFactors.R'
'mortalityTable.trendProjection.R' 'deathProbabilities.R'
'getCohortTable.R' 'getOmega.R' 'getPeriodTable.R'
'lifeTable.R' 'makeQxDataFrame.R' 'mortalityComparisonTable.R'
'mortalityImprovement.R' 'mortalityTable.MakehamGompertz.R'
'mortalityTable.Weibull.R' 'mortalityTable.deMoivre.R'
'periodDeathProbabilities.R' 'mortalityTable.jointLives.R'
'mortalityTables.list.R' 'mortalityTables.load.R'
'plot.mortalityTable.R' 'plotMortalityTableComparisons.R'
'plotMortalityTables.R' 'plotMortalityTrend.R' 'setLoading.R'
'setModification.R' 'undampenTrend.R'
'whittaker.mortalityTable.R'
'angdiffvec.R' 'areaunder.R' 'getmouseDV.R' 'interpltraj.R'
'makerun.R' 'maxdev.R' 'pathoffset.R' 'sampen.R' 'sampenc.R'
'sampense.R' 'trim0.R' 'trim1.R' 'velaj.R' 'xflip.R'
getPSS.R performpcls.R identifypathway.R GetExampleData.R
getEnvironmentData.R
'export.summary.mpm.R' 'Famin81A.R' 'Golub.R' 'mpm.R'
'plot.mpm.R' 'print.mpm.R' 'print.summary.mpm.R'
'summary.mpm.R' 'zzz.R'
'RcppExports.R' 'utils.R' 'package.R' 'generics.R'
'class_modlist.R' 'class_tgrid.R' 'class_numericlist.R'
'class_matlist.R' 'class_ev.R' 'class_derived.R'
'class_mrgmod.R' 'class_mrgsims.R' 'Aaaa.R' 'annot.R' 'chain.R'
'class_build.R' 'compile.R' 'data_set.R' 'datasets.R' 'env.R'
'events.R' 'funset.R' 'idata_set.R' 'init.R' 'inven.R'
'knobs.R' 'matlist.R' 'matrix.R' 'mcache.R' 'mcode.R'
'model_include.R' 'modlib.R' 'modspec.R' 'mread.R'
'mrgindata.R' 'mrgsims.R' 'mrgsolve.R' 'nmxml.R' 'param.R'
'print.R' 'qsim.R' 'realize_addl.R' 'relabel.R' 'render.R'
'update.R'
'CreateOriginPeriods.R' 'CreateEvaluationDates.R'
'CreateDevelopmentLags.R' 'TriangleAdjustMeasures.R'
'Triangle.R' 'TriangleModel.R' 'TriangleProjection.R'
'CompleteTriangle.R' 'GetTriangleData.R' 'Help.R'
'LatestDiagonal.R' 'PlotModelFactors.R' 'PlotModelGoF.R'
'PlotResiduals.R' 'PlotTriangle.R' 'PlotTriangleModel.R'
'ProjectToDate.R' 'ProjectToDev.R' 'ProjectValues.R'
'SerialCorrelation.R' 'SummaryTriangleModel.R' 'TriangleMeta.R'
'data.R'
MRSP-prespecifier.r MRSP-classes-7.r FISTA-classes-3.r
FISTA-generics-2.r FISTA-27.r MRSP-methods-15.r
MRSP-cfunctions-2.r MRSP-helpers-21.r MRSP-fista-7.r
MRSP-family-23.r MRSP-fit-39.r MRSP-formula-2.r MRSP-3.r zzz.r
mstherm.R classes.R normalization.R modeling.R plot.R
analysis.R export.R
'authentication.R' 'cubes.R' 'datasets.R' 'formjson.R'
'microstrategy.R' 'parsejson.R' 'projects.R' 'reports.R'
common.R MTCDataItem.R merge.R MTCCycle.R MTCDevice.R
deviceXML.R dataExtraction_mtc.R map_gcode_mtc.R parse_gcode.R
simulate_gcode.R zzz.R mtconnectR.R
'mtkAllGenerics.R' 'globalsMtkFuncts.R' 'mtkValue.R'
'mtkFeature.R' 'mtkLevels.R' 'mtkParameter.R' 'mtkDomain.R'
'mtkFactor.R' 'mtkExpFactors.R' 'mtkProcess.R'
'mtkExpWorkflow.R' 'mtkExperiment.R' 'mtkParsor.R'
'mtkResult.R' 'mtkDesignerResult.R' 'mtkDesigner.R'
'mtkMorrisDesigner.R' 'mtkBasicMonteCarloDesigner.R'
'mtkRandLHSDesigner.R' 'mtkNativeDesigner.R'
'mtkSobolDesigner.R' 'mtkFastDesigner.R' 'mtkEvaluatorResult.R'
'mtkEvaluator.R' 'mtkNativeEvaluator.R'
'mtkIshigamiEvaluator.R' 'mtkWWDMEvaluator.R'
'mtkSystemEvaluatorResult.R' 'mtkSystemEvaluator.R'
'mtkAnalyserResult.R' 'mtkAnalyser.R' 'mtkNativeAnalyser.R'
'mtkDefaultAnalyser.R' 'mtkRegressionAnalyser.R'
'mtkMorrisAnalyser.R' 'mtkSobolAnalyser.R' 'mtkPLMMAnalyser.R'
'mtkFastAnalyser.R' 'mtk.addons.R' 'mtkPackage.R'
'betas_plot.R' 'mhplot.R' 'multi_meta.R' 'qqplotter.R'
'Example_file_1.R' 'Example_file_2.R' 'Example_output_file.R'
movG-class.R circularExtension.R entryData.R
entryDataMethods.R extendHorVer.R fittedMethods.R movingGrid.R
movingMean.R residualsMethods.R showMethods.R sketchGrid.R
sketchGridMethods.R summaryMethods.R movingMeanMethods.R
regimes.R treeDists.R grid.R matrixexps.R
matrixparametrizations.R modelparamstransform.R precalcs.R
sdemoments.R sdemomentsphyl.R loglik.R ci.R phylgls.R
modelparamssummary.R modelparams.R estimBM.R estimGLSGC.R
estimMAXLIK.R simulVasicekproc.R simulVasicekprocphyl.R
PhyloSDEestim.R MVslouchSimulStudy.R wrappers.R fitch.mvsl.R
ouch2slouch.mvsl.R make.states.mvsl.R evolmodelest.R getESS.R
'iedb_consensus_mhci_nmer.R' 'abcpred_nmer.R' 'algpred.R'
'allermatch.R' 'bcepred_nmer.R' 'bimas_nmer.R'
'filter.firstlayer.R' 'iedb_arb_mhcii_nmer.R'
'iedb_consensus_mhcii_nmer.R' 'netmhcNN_nmer.R'
'netmhcWT_nmer.R' 'propred_nmer.R' 'iedb_arb_mhci_nmer.R'
'zzz.R'
'MyrrixClientConfiguration.R' 'ClientRecommender.R'
'ServerRecommender.R' 'RecommenderMethods.R'
'HyperParameters.R' 'utils.R' 'namespace_imports.R'
All.R summary.R cen.R km.R reg.R mle.R ken.R ros.R icmle.R
'add-cols.R' 'add-n-prop-miss.R' 'cast-shadows.R'
'data-common-na-numbers.R' 'data-common-na-strings.R'
'data-oceanbuoys.R' 'data-pedestrian.R' 'data-riskfactors.R'
'legend-draw.R' 'geom-miss-point.R' 'gg-miss-case-cumsum.R'
'gg-miss-case.R' 'gg-miss-fct.R' 'gg-miss-span.R'
'gg-miss-upset.R' 'gg-miss-var-cumsum.R' 'gg-miss-var.R'
'gg-miss-which.R' 'helpers.R' 'impute-median.R'
'impute_below.R' 'impute_mean.R' 'label-miss.R'
'miss-complete-x-pct-prop.R' 'miss-prop-pct-summary.R'
'miss-scan-count.R' 'miss-x-cumsum.R' 'miss-x-run.R'
'miss-x-span.R' 'miss-x-summary.R' 'miss-x-table.R'
'n-prop-miss-complete-rows.R' 'n-prop-miss-complete.R'
'n-var-miss.R' 'nabular.R' 'naniar-ggproto.R'
'naniar-package.R' 'prop-pct-var-case-miss-complete.R'
'replace-to-na.R' 'replace-with-na.R'
'scoped-replace-with-na.R' 'shade.R' 'shadow-recode.R'
'shadow-shifters.R' 'shadow-verifiers.R' 'shadows.R'
'stat-miss-point.R' 'utils.R' 'where-na.R'
'alphashape3d.R' 'amiralandmarks-io.R' 'amiramesh-io.R'
'cmtk-reformat.R' 'cmtk.R' 'cmtk_geometry.R' 'cmtk_io.R'
'cmtkreg.R' 'coordinates.R' 'dist3D_Segment_to_Segment.R'
'neuron.R' 'dotprops.R' 'graph-nodes.R' 'hxsurf.R' 'im3d.R'
'nat-data.R' 'nat-package.R' 'ndigest.R' 'neuron-io-amira.R'
'neuron-io-fiji.R' 'neuron-io-neuroml.R' 'neuron-io.R'
'neuron-plot.R' 'neuronlist.R' 'neuronlist_interactive_3d.R'
'neuronlist_sets.R' 'neuronlistfh.R' 'ngraph.R' 'nrrd-io.R'
'pop3d.R' 'potential_synapses.R' 'reglist.R' 'seglist.R'
'summary.R' 'utils.R' 'vaa3draw-io.R' 'vtk-io.R' 'xform.R'
'xformimage.R' 'xformpoints.R' 'zzz.R'
'nbc4va.R' 'nbc4va_data.R' 'nbc4va_validation.R'
'nbc4va_internal.R' 'nbc4va_main.R' 'nbc4va_extra.R'
'nbc4va_utility.R' 'nbc4va_wrapper.R'
'FindBestTransform.R' 'all_classes.R' 'all_generics.R'
'control.R' 'generateCountData.R' 'getShrinkedDispersions.R'
'helper_functions.R' 'normalize_counts.R'
'package_and_supplementary.R' 'plot.nblda.R' 'predict.nblda.R'
'trainNBLDA.R' 'zzz_methods.R'
'distancematrix.R' 'nonbimatch.R' 'assign.grp.R'
'fill.missing.R' 'gendistance.R' 'make.phantoms.R'
'nbpmatching-package.R' 'qom.R' 'quantile.R' 'subsetMatches.R'
'utils.R'
'RcppExports.R' 'imports.r' 'graph_data.r' 'adjmat.r' 'bass.r'
'bootnet.r' 'data.r' 'diffnet-c.R' 'diffnet-class.r'
'diffnet-indexing.r' 'diffnet-methods.r' 'egonets.R'
'formula.r' 'igraph.r' 'infect_suscept.r' 'mentor.r' 'misc.r'
'moran.r' 'netmatch.r' 'network.r' 'options.R' 'package-doc.r'
'plot_diffnet2.r' 'rewire.r' 'random_graph.R' 'rdiffnet.r'
'read_write_foreign.r' 'select_egoalter.R' 'spatial.R'
'stats.R' 'struct_equiv.R' 'struct_test.R'
'survey_to_diffnet.R'
'Agent-classes.R' 'Classes.R' 'NetLogoR-package.R'
'worldNLR-classes-methods.R' 'agentMatrix-Class-methods.R'
'agentset-functions.R' 'function-arguments.R'
'patch-functions.R' 'plot.R' 'world-functions.R' 'quickPlot.R'
'spades-functions.R' 'turtle-functions.R'
'0_helper_net.r' 'NetOrigin.r' 'origin_helper.r'
'origin_methods.r' 'distance.r' 'data.r' 'data_handling.r'
'robustness.r'
'AFNI_IO.R' 'AllGeneric.R' 'AllClass.R' 'Axis.R' 'BinaryIO.R'
'BrainData.R' 'common.R' 'NIFTI_IO.R' 'BrainFileDescriptor.R'
'BrainMetaInfo.R' 'BrainRegion3D.R' 'BrainSlice.R'
'BrainSpace.R' 'BrainSurface.R' 'SparseBrainVector.R'
'BrainVector.R' 'BrainVolume.R' 'Display.R' 'FREESURFER_IO.R'
'IndexLookupVolume.R' 'Ops.R' 'RcppExports.R' 'conncomp.R'
'datadoc.R' 'neuroim.R'
'ngram.r' 'babble.r' 'checks.r' 'concatenate.r' 'getseed.r'
'getters.r' 'multiread.r' 'ngram-package.R' 'ngram_asweka.r'
'phrasetable.r' 'preprocess.r' 'print.r' 'rcorpus.r'
'splitter.r' 'string.summary.r' 'wordcount.r'
config.R all_utils.R options.R distributions_inputList.R
distributions_processInputList.R
distributions_implementations.R BUGS_BUGSdecl.R BUGS_contexts.R
BUGS_nimbleGraph.R BUGS_modelDef.R BUGS_model.R
BUGS_graphNodeMaps.R BUGS_readBUGS.R BUGS_macros.R
BUGS_testBUGS.R BUGS_getDependencies.R BUGS_utils.R
BUGS_mathCompatibility.R externalCalls.R genCpp_exprClass.R
genCpp_operatorLists.R genCpp_RparseTree2exprClasses.R
genCpp_initSizes.R genCpp_buildIntermediates.R
genCpp_processSpecificCalls.R genCpp_sizeProcessing.R
genCpp_toEigenize.R genCpp_insertAssertions.R genCpp_maps.R
genCpp_liftMaps.R genCpp_eigenization.R genCpp_TFize.R
genCpp_addDebugMarks.R genCpp_generateCpp.R RCfunction_core.R
RCfunction_compile.R nimbleFunction_util.R
nimbleFunction_core.R nimbleFunction_nodeFunction.R
nimbleFunction_nodeFunctionNew.R nimbleFunction_Rexecution.R
nimbleFunction_compile.R nimbleFunction_keywordProcessing.R
nimbleList_core.R types_util.R types_symbolTable.R
types_modelValues.R types_modelValuesAccessor.R
types_modelVariableAccessor.R types_nimbleFunctionList.R
types_nodeFxnVector.R types_numericLists.R cppDefs_utils.R
cppDefs_variables.R cppDefs_core.R cppDefs_namedObjects.R
cppDefs_ADtools.R cppDefs_BUGSmodel.R cppDefs_RCfunction.R
cppDefs_nimbleFunction.R cppDefs_nimbleList.R
cppDefs_modelValues.R cppDefs_cppProject.R
cppDefs_outputCppFromRparseTree.R cppInterfaces_utils.R
cppInterfaces_models.R cppInterfaces_modelValues.R
cppInterfaces_nimbleFunctions.R cppInterfaces_otherTypes.R
nimbleProject.R initializeModel.R CAR.R MCMC_utils.R
MCMC_configuration.R MCMC_build.R MCMC_run.R MCMC_samplers.R
MCMC_conjugacy.R MCMC_suite.R MCMC_comparisons.R
MCMC_autoBlock.R MCEM_build.R crossValidation.R
filtering_resamplers.R filtering_auxiliary.R
filtering_liuwest.R filtering_enkf.R filtering_bootstrap.R
BNP_distributions.R BNP_samplers.R NF_utils.R miscFunctions.R
makevars.R setNimbleInternalFunctions.R registration.R
nimble-package.r zzz.R
'logLik1.R' 'nlmeU-package.R' 'Pwr.R' 'simulateY.R' 'varia.R'
'colorcode.R' 'options.R' 'grid.R' 'atracks.R' 'aheatmap.R'
'algorithmic.R' 'nmf-package.R' 'rmatrix.R' 'utils.R'
'versions.R' 'NMF-class.R' 'transforms.R' 'Bioc-layer.R'
'NMFstd-class.R' 'NMFOffset-class.R' 'heatmaps.R'
'NMFns-class.R' 'nmfModel.R' 'fixed-terms.R' 'NMFfit-class.R'
'NMFSet-class.R' 'NMFStrategy-class.R' 'registry.R'
'NMFSeed-class.R' 'NMFStrategyFunction-class.R'
'NMFStrategyIterative-class.R' 'NMFplots.R'
'registry-algorithms.R' 'algorithms-base.R' 'algorithms-lnmf.R'
'algorithms-lsnmf.R' 'algorithms-pe-nmf.R' 'algorithms-siNMF.R'
'algorithms-snmf.R' 'data.R' 'extractFeatures.R' 'parallel.R'
'registry-seed.R' 'nmf.R' 'rnmf.R' 'run.R' 'seed-base.R'
'seed-ica.R' 'seed-nndsvd.R' 'setNMFClass.R' 'simulation.R'
'tests.R'
aaa.R ifVerboseCat.R sesp-pv-NNT.R achievable.se.sp.R
binom.confint.R NNTintervals.R run.R ROCplots.R argmin.R zzz.R
'nofrills.R' 'exprs.R' 'fn.R' 'as-fn.R' 'make-fn-aware.R'
'curry.R'
npsCommon.R npsInvalid.R npsMain.R npsNecessary.R npsValid.R
RNested.R
'arg.R' 'caller.R' 'quo.R' 'dots.R' 'getpromise.R'
'conversions.R' 'format.R' 'force.R' 'shortcut.R' 'missing.R'
'util.R' 'CNPJ.R' 'CPF.R' 'RENAVAN.R' 'numbersBR.R'
'alignment.R' 'as_factor.R' 'constants.R' 'f_12_hour.R'
'utils.R' 'f_abbreviation.R' 'f_affirm.R' 'f_affix.R' 'f_bin.R'
'f_comma.R' 'f_data.R' 'f_date.R' 'f_degree.R' 'f_denom.R'
'f_dollar.R' 'f_list.R' 'f_logical.R' 'f_month.R' 'f_num.R'
'f_ordinal.R' 'f_pad_zero.R' 'f_parenthesis.R' 'f_percent.R'
'f_pval.R' 'f_quarter.R' 'f_replace.R' 'f_sign.R' 'f_state.R'
'f_title.R' 'f_weekday.R' 'f_wrap.R' 'f_year.R'
'fv_num_percent.R' 'fv_percent.R' 'fv_percent_diff.R'
'fv_percent_lead.R' 'fv_runs.R' 'glue-reexports.R'
'highlight_cells.R' 'numform-package.R'
'odbc.R' 'Driver.R' 'Connection.R' 'DataTypes.R'
'RcppExports.R' 'Result.R' 'Table.R' 'Viewer.R' 'db.R'
'hidden.R' 'utils.R' 'zzz.R'
'OIdata-internal.R' 'getTables.R' 'mapvar.R'
'print.htmlTables.R' 'processTable.R' 'rowScrape.R'
'scrapeTable.R' 'stripTags.R'
'0ClassUnion.R' 'cache.R' 'MxBaseNamed.R' 'MxData.R'
'MxDataWLS.R' 'DefinitionVars.R' 'MxReservedNames.R'
'MxNamespace.R' 'MxSearchReplace.R' 'MxFlatSearchReplace.R'
'MxUntitled.R' 'MxAlgebraFunctions.R' 'MxExponential.R'
'MxMatrix.R' 'DiagMatrix.R' 'FullMatrix.R' 'IdenMatrix.R'
'LowerMatrix.R' 'SdiagMatrix.R' 'StandMatrix.R' 'SymmMatrix.R'
'UnitMatrix.R' 'ZeroMatrix.R' 'MxMatrixFunctions.R'
'MxAlgebra.R' 'MxCycleDetection.R' 'MxDependencies.R'
'MxAlgebraConvert.R' 'MxAlgebraTransform.R' 'MxSquareBracket.R'
'MxEval.R' 'MxRename.R' 'MxPath.R' 'MxObjectiveMetaData.R'
'MxRAMMetaData.R' 'MxExpectation.R' 'MxExpectationNormal.R'
'MxExpectationRAM.R' 'MxExpectationLISREL.R' 'MxFitFunction.R'
'MxFitFunctionAlgebra.R' 'MxFitFunctionML.R'
'MxFitFunctionMultigroup.R' 'MxFitFunctionRow.R'
'MxFitFunctionWLS.R' 'MxRAMObjective.R' 'MxLISRELObjective.R'
'MxFIMLObjective.R' 'MxMLObjective.R' 'MxRowObjective.R'
'MxAlgebraObjective.R' 'MxBounds.R' 'MxConstraint.R'
'MxInterval.R' 'MxTypes.R' 'MxCompute.R' 'MxModel.R'
'MxRAMModel.R' 'MxLISRELModel.R' 'MxModelDisplay.R'
'MxFlatModel.R' 'MxMultiModel.R' 'MxModelFunctions.R'
'MxModelParameters.R' 'MxUnitTesting.R' 'MxApply.R' 'MxRun.R'
'MxRunHelperFunctions.R' 'MxSummary.R' 'MxCompare.R'
'MxSwift.R' 'MxOptions.R' 'MxThreshold.R' 'OriginalMx.R'
'MxGraph.R' 'MxGraphviz.R' 'MxDeparse.R' 'MxCommunication.R'
'MxRestore.R' 'MxVersion.R' 'MxPPML.R' 'MxRAMtoML.R' 'MxDiff.R'
'MxErrorHandling.R' 'MxDetectCores.R' 'MxSaturatedModel.R'
'omxBrownie.R' 'omxConstrainThresholds.R' 'omxGetNPSOL.R'
'MxFitFunctionR.R' 'MxRObjective.R'
'MxExpectationHiddenMarkov.R' 'MxExpectationMixture.R'
'MxExpectationStateSpace.R' 'MxExpectationBA81.R'
'MxFitFunctionGREML.R' 'MxExpectationGREML.R' 'MxMI.R'
'MxFactorScores.R' 'MxRobustSE.R' 'MxAvailableOptimizers.R'
'MxTryHard.R' 'MxSE.R' 'MxAutoStart.R' 'MxRetro.R' 'MxMMI.R'
'zzz.R'
'bertin.r' 'calc.r' 'data-openrepgrid.r' 'dev-functions.r'
'distance.R' 'double-entry.R' 'export.r' 'gmMain.r' 'import.r'
'measures.r' 'onair.r' 'openrepgrid.r' 'repgrid.r'
'repgrid-basicops.r' 'repgrid-constructs.r'
'repgrid-elements.r' 'repgrid-output.r' 'repgrid-plots.r'
'repgrid-ratings.r' 'rgl-3d.r' 'settings.r' 'utils-import.r'
'utils.r' 'zzz.r'
'CommentClass.R' 'HyperlinkClass.R' 'RcppExports.R'
'class_definitions.R' 'StyleClass.R' 'WorkbookClass.R'
'baseXML.R' 'borderFunctions.R' 'chartsheet_class.R'
'conditional_formatting.R' 'helperFunctions.R' 'loadWorkbook.R'
'onUnload.R' 'openXL.R' 'openxlsx.R' 'openxlsxCoerce.R'
'readWorkbook.R' 'sheet_data_class.R'
'workbook_column_widths.R' 'workbook_read_workbook.R'
'workbook_write_data.R' 'worksheet_class.R' 'wrappers.R'
'writeData.R' 'writeDataTable.R' 'writexlsx.R'
'optim.functions.R' 'vectorize.R' 'sampling.R'
'single.objective.R' 'functions.R'
'DenseMatrix.R' 'InfinitySparseMatrix.R'
'Ops.optmatch.dlist.R' 'Optmatch-package.R' 'Optmatch.R'
'RcppExports.R' 'abs.optmatch.dlist.R' 'boxplotMethods.R'
'caliper.R' 'data.R' 'deprecated.R' 'distUnion.R'
'exactMatch.R' 'feasible.R' 'fill.NAs.R' 'fmatch.R'
'fullmatch.R' 'makedist.R' 'match_on.R' 'matched.R'
'matched.distances.R' 'matchfailed.R' 'max.controls.cap.R'
'mdist.R' 'min.controls.cap.R' 'pairmatch.R' 'print.optmatch.R'
'print.optmatch.dlist.R' 'relaxinfo.R' 'scores.R'
'stratumStructure.R' 'subDivStrat.R' 'summary.ism.R'
'summary.optmatch.R' 'utilities.R' 'zzz.R'
'zzzDistanceSpecification.R'
workspace.R file.R dict.R ore.R match.R es.R zzz.R
'auditTrail.R' 'niftiS4.R' 'analyzeS4.R' 'afniS4.R' 'is.R'
'nifti_assign.R' 'coerce.R' 'writeS4.R' 'convert_anlz.R'
'convert_nifti.R' 'cal_img.R' 'drop_img_dim.R' 'hotmetal.R'
'miscellaneous.R' 'plot.R' 'slice.R' 'slice_overlay.R'
'blend.R' 'readS4.R' 'remove.R' 'tim_colors.R' 'transform.R'
'wrappers.R' 'onefile.R' 'voxdim.R' 'anlz_Operators.R'
'Operators.R' 'zero_trans.R' 'aux_file.R' 'cal_max.R'
'cal_min.R' 'descrip.R' 'glmax.R' 'glmin.R' 'pixdim.R'
'qform_code.R' 'scl_inter.R' 'scl_slope.R' 'sform_code.R'
'vox_offset.R' 'bitpix.R' 'data_type.R' 'datatype.R'
'db_name.R' 'dim_.R' 'dim_info.R' 'extender.R' 'extents.R'
'img_data.R' 'intent_code.R' 'intent_name.R' 'intent_p1.R'
'intent_p2.R' 'intent_p3.R' 'magic.R' 'qoffset_x.R'
'qoffset_y.R' 'qoffset_z.R' 'quatern_b.R' 'quatern_c.R'
'quatern_d.R' 'quaternion.R' 'regular.R' 'session_error.R'
'sizeof_hdr.R' 'slice_code.R' 'slice_duration.R' 'slice_end.R'
'slice_start.R' 'srow_x.R' 'srow_y.R' 'srow_z.R' 'toffset.R'
'xyzt_units.R' 'cal_units.R' 'compressed.R' 'dim_un0.R'
'exp_date.R' 'exp_time.R' 'field_skip.R' 'funused1.R'
'funused2.R' 'funused3.R' 'generated.R' 'hist_un0.R'
'hkey_un0.R' 'nii2oro.R' 'oro2nii.R' 'omax.R' 'omin.R'
'orient.R' 'origin.R' 'patient_id.R' 'scannum.R' 'smax.R'
'smin.R' 'start_field.R' 'unused1.R' 'verified.R' 'views.R'
'vols_added.R' 'vox_units.R' 'voxres.R' 'zzz.R'
'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'
'RcppExports.R' 'osqp-package.R' 'solve.R' 'osqp.R' 'params.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
'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'
'pivot.R' 'unpivot.R' 'tidyr.R' 'utils.R' 'testing-utils.R'
'bibtex.R' 'colors.R' 'data.R' 'namespace.R' 'utils.R'
'devutils.R' 'files.R' 'graphics.R' 'options.R' 'logging.R'
'unitTests.R' 'is.R' 'knitr.R' 'registry.R' 'package-extra.R'
'package.R' 'packages.R' 'project.R' 'rd.R' 'user.R'
'vignette.R' 'with.r'
global.r psnCdd.r psnBootstrap.r functionOK.r proto.r ggobi.r
function.r handler.r PKgraphlayout.r PKgraph.r
zzz.R generic.R designfactors.R keymatrix.R keyring.R
listofkeyrings.R planor.R basep.R randomize.R designkey.R
listofdesignkeys.R planordesign.R makedesignkey.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' 'new-rstudio-project.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' 'Labels.R' 'polmineR.R' 'S4classes.R' 'p_attributes.R'
'textstat.R' 'bundle.R' 'corpus.R' 'count.R' 'partition.R'
'partition_bundle.R' 'ngrams.R' 'features.R' 'context.R'
'TermDocumentMatrix.R' 'as.VCorpus.R' 'as.markdown.R'
'cooccurrences.R' 'as.sparseMatrix.R' 'as.speeches.R'
'blapply.R' 'kwic.R' 'browse.R' 'chisquare.R' 'hits.R'
'tempcorpus.R' 'cpos.R' 'cqpserver.R' 'decode.R' 'dispersion.R'
'dotplot.R' 'encoding.R' 'enrich.R' 'highlight.R' 'html.R'
'label.R' 'll.R' 'mail.R' 'means.R' 'noise.R' 'pmi.R'
'regions.R' 'read.R' 'registry.R' 'reindex.R' 'renamed.R'
's_attributes.R' 'size.R' 'store.R' 't_test.R' 'templates.R'
'terms.R' 'token_stream.R' 'tooltips.R' 'trim.R' 'type.R'
'use.R' 'utils.R' 'view.R' 'weigh.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'
'prais_winsten.R' 'print.prais.R' 'summary.prais.R'
'print.summary.prais.R' 'pw_transform.R'
'adjective.R' 'adverb.R' 'exclamation.R' 'verb.R' 'rpackage.R'
'package.R'
'app.r' 'as.precintcon.annual.r' 'as.annual.r'
'as.precintcon.daily.r' 'as.daily.r' 'as.precintcon.decade.r'
'as.decade.r' 'deciles.r' 'as.precintcon.deciles.r'
'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'
'precintcon.plot.lorenz.r' 'pplot.ci.r'
'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'
'precintcon.plot.rai.r' 'pplot.rai.r' 'precintcon.plot.spi.r'
'pplot.spi.r' 'precintcon.H.r' 'precintcon.Z.r'
'precintcon.b.r' 'precintcon.classification.r'
'precintcon.gamma.r' 'precintcon.ln.a.r' 'precintcon.max.r'
'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'
'00Utils.R' '01DataClass.R' '02PhaseClass.R'
'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'
'RcppExports.R' 'bias_correct_curve.R' 'get.lam.R' 'package.R'
'periodic_lowess.R' 'smoother_functions.R' 'principal_curve.R'
'principal.curve.R' 'start_circle.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 pepxml.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' 'co_occurrence_fn.R'
'space_quasher.R' 'readabsnew.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' 'directionchange-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_old.R'
'textstat_frequency.R' 'textstat_keyness.R' 'textstat_lexdiv.R'
'textstat_readability.R' 'textstat_simil.R'
'textstat_simil_old.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' 'tokens_sample.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'
'amChartsAPI.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' 'cwb.R' 'checks.R'
'count.R' 'RcppExports.R' 'decode.R' 'cbow.R' 'region_matrix.R'
'misc.R' 'zzz.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.mfrd.R' 'summary.mrd.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_phypartition.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'
'CheckVal.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' 'logitnorm.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' 'checksums.R' 'consistentPaths.R'
'convertPaths.R' 'download.R' 'gis.R' 'git.R' 'helpers.R'
'objectSize.R' 'packages.R' 'pipe.R' 'postProcess.R'
'preProcess.R' 'prepInputs.R' 'reproducible-package.R'
'search.R' 'zzz.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'
'getHandSetIndices.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' 'module_aboutTab.R'
'module_exportTab.R' 'module_printCode.R' 'zzz.R'
'MariaDBConnection.R' 'MariaDBDriver.R' 'MariaDBResult.R'
'RMariaDB.R' 'RcppExports.R' 'coerce.R' 'connect.R' 'default.R'
'export.R' 'names.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'
'A_NAMESPACE.R' 'global.R' 'Model.R' 'MultinomialModel.R'
'GaussianModel.R' 'CompositeModel.R' 'Parameter.R'
'MultinomialParameter.R' 'GaussianParameter.R'
'CompositeParameter.R' 'MixmodResults.R' 'Mixmod.R'
'Strategy.R' 'MixmodCluster.R' 'MixmodDAResults.R'
'MixmodLearn.R' 'MixmodPredict.R' 'MixmodXml.R' 'Rmixmod.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'
'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'
'f_core_constants.R' 'class_core_parameter_set.R'
'class_core_plot_settings.R' 'class_analysis_dataset.R'
'class_design.R' 'class_analysis_stage_results.R'
'class_analysis_results.R' 'class_design_plan.R'
'class_design_power_and_asn.R' 'class_design_set.R'
'f_analysis.R' 'f_analysis_means.R' 'f_analysis_rates.R'
'f_analysis_survival.R' 'f_core_assertions.R'
'f_core_output_formats.R' 'f_core_utilities.R'
'f_design_fisher_combination_test.R'
'f_design_group_sequential.R'
'f_design_sample_size_calculator.R' 'pkgname.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'
'db-lib.R' 'polyhedra-lib.R' 'ledger-lib.R'
'serialization-lib.R' 'zzz.R'
'PqDriver.R' 'PqConnection.R' 'PqResult.R' 'RPostgres-pkg.R'
'RcppExports.R' 'default.R' 'export.R' 'names.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' 'src.presto.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'
'parse.response.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' 'sql_translate_env.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'
'RSelenium.R' 'errorHandler.R' 'remoteDriver.R' 'rsDriver.R'
'selKeys-data.R' 'util.R' 'webElement.R' 'zzz.R'
APITools.R displayTools.R MlxEnvironment.R APIManager.R
ProjectManagement.R Scenario.R Settings2.R
PopulationParameters.R IndividualModel.R CovariateModel.R
ObservationModel.R Results.R MlxCore.R bootstrap.R buildmlx.R
confintmlx.R correlationModelSelection.R
covariateModelSelection.R covariateSearch.R
errorModelSelection.R llp.R newConnectors.R setSettings.R
testmlx.R initializeRsmlx.R RsmlxTools.R readdatamlx.R
pkbuild.R pkpopini.R whichPKmodel.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' 'names.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
'AllClass.R' 'AllGeneric.R' 'methods-CramerLundberg.R'
'methods-CramerLundbergCapitalInjections.R'
'methods-SparreAndersen.R'
'methods-SparreAndersenCapitalInjections.R' 'plot_path.R'
'ruin_probability.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
'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
RcppExports.R utils.R generics.R filterClasses.R
modelClasses.R sarima.R autocovariances.R armacalc.R fit.R
wrapKFAS.R arma_Q0dotdotstats.R fkf.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' 'conversion.R' 'as.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'
'not_used_yet.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' 'nearest.R' 'deprecated.R'
'SharpeR.r' 'utils.r' 'distributions.r' 'sr.r' 'estimation.r'
'sr_bias.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' 'globals.R' 'conditions.R' 'map.R'
'utils.R' 'bootstrap.R' 'cache-context.R' 'cache-disk.R'
'cache-memory.R' 'cache-utils.R' 'diagnose.R' 'fileupload.R'
'font-awesome.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-cached-plot.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'
'aaa.R' 'filechoose.R' 'dirchoose.R' 'filesave.R'
'shinyFiles-package.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'
'windowed_sindy.R' 'sindy.R' 'features.R'
'finite_differences.R' 'finite_difference.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' 'netImport.R'
'netDir.R' 'netUnd.R' 'netInt.R' 'netPath.R' 'skynet.R'
'nodeStats.R' 'import_t100.R' 'import_db1b.R' 'findCarrier.R'
'plotMap.R' 'skynet_example.R' 'bootnet.R' 'summary.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'
'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' 'code-checking.R' 'copy.R'
'downloadData.R' 'experiment.R' 'simulation-parseModule.R'
'simulation-simInit.R' 'load.R' 'module-define.R'
'module-dependencies-methods.R' 'module-repository.R'
'module-template.R' 'moduleCoverage.R' 'moduleMetadata.R'
'times.R' 'simList-accessors.R' 'plotting-diagrams.R'
'plotting.R' 'progress.R' 'save.R' 'simulation-spades.R'
'spades-classes.R' 'spades-core-deprecated.R'
'spades-core-package.R' 'suppliedElsewhere.R' 'zzz.R'
'RcppExports.R' 'heading.R' 'SELES.R'
'distanceFromEachPoint.R' 'environment.R' 'initialize.R'
'mapReduce.R' 'mergeRaster.R' 'movement.R' 'neighbourhood.R'
'numerical-comparisons.R' 'probability.R' 'resample.R'
'rings.R' 'spades-tools-deprecated.R' 'spades-tools-package.R'
'splitRaster.R' 'spread.R' 'spread2.R' 'zzz.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'
'internalHelpers.R' 'allGenericFunctions.R'
'objectSpectraInTime.R' 'objectProcessTimes.R'
'objectLinking.R' 'alignmentFunctions.R'
'dataManagementTools.R' 'defaults.R' 'readSPC.R'
'saveSpectraInTime.R' 'spectralAnalysis.R'
'spectralIntegration.R' 'spectralNMF.R'
'spectralPreprocessing.R' 'spectralVisualization.R'
'subsetting.R'
'create_neig.r' 'extract_extrema.r' 'extrema_irr.r'
'mean_enveloppe.r' 'spemd.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'
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'
'stacomirtools-package.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'
'init.R' 'stars.R' 'read.R' 'sf.R' 'dimensions.R' 'plot.R'
'tidyverse.R' 'transform.R' 'ops.R' 'write.R' 'raster.R'
'spacetime.R' 'ncdf.R' 'proxy.R' 'factors.R' 'rasterize.R'
'warp.R'
'stutil.R' 'stwin.R' 'stsubpop.R' 'stmodelKM.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'
'ten.R' 'nc.R' 'sf.R' 'ci.R' 'autoplotTAP.R' 'autoplotTen.R'
'print.R' 'asWide.R' 'COV.R' 'predict.R' 'comp.R' 'cutp.R'
'gastric.R' 'gof.R' 'onAttach.R' 'profLik.R' 'rsq.R'
'survMisc_package.R' 'xtable.R'
generics.R validmodelorg.R validoptsol.R validreactId.R
validreact.R reactClass.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
mergeReact2Modelorg.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'
'available.sylly.lang.R' 'get.sylly.env.R'
'install.sylly.lang.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-csv.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' 'transform-proc-tab.R'
'NULLing.R''auxiliary_functions.R''deprecated-functions.R''dissect_name.R'
'taxlist-class.R''clean.R''as.list.R''taxon_views.R''add_view.R'
'taxon_names.R''taxon_relations.R''taxon_traits.R'
'levels.R''add_concept.R''update_concept.R''add_synonym.R'
'accepted_name.R''synonyms.R''basionym.R''update_name.R''delete_name.R'
'replace_view.R''get_children.R'
'change_concept.R''Extract.R''subset.R'
'merge_taxa.R''backup_object.R''load_last.R''summary.R'
'df2taxlist.R''tv2taxlist.R''tnrs.R''tax2traits.R''match_names.R''print_name.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'
'defaultSC.R' 'defaultOpts.R' 'makeTdmRandomSeed.r'
'printTDMclassifier.r' 'printTDMregressor.r' 'tdmBigLoop.r'
'tdmClassify.r' 'tdmClassifyLoop.r' 'tdmDefaultsFill.r'
'tdmDispatchTuner.r' 'tdmEnvTMakeNew.r' 'tdmGeneralUtils.r'
'tdmGraphicUtils.r' 'tdmMapDesign.r' 'tdmMetacostRf.r'
'tdmModelingUtils.r' 'tdmOptsDefaults.r' 'tdmParaBootstrap.r'
'tdmPreprocUtils.r' 'tdmReadAndSplit.r' 'tdmReadDataset.r'
'tdmRegress.r' 'tdmRegressLoop.r' 'tdmROCR.r' 'tdmStartSpot2.r'
'tdmStartOther.r' 'tdmTuneIt.r' 'unbiasedRun.r'
'InitConstraint.R' 'InitErgmTerm.duration.R'
'InitMHP.DynMLE.R' 'InitMHP.DynMLE.blockdiag.R'
'InitMHP.DynMoME.R' 'coef.stergm.R' 'combine.networks.R'
'control.logLik.stergm.R' 'control.simulate.stergm.R'
'control.stergm.R' 'ergm.godfather.R' 'gof.stergm.R'
'impute.network.list.R' 'is.lasttoggle.R' 'logLik.stergm.R'
'mcmc.diagnostics.stergm.R' 'print.stergm.R'
'simulate.stergm.R' 'stergm.CMLE.R' 'stergm.EGMME.GD.R'
'stergm.EGMME.R' 'stergm.EGMME.SA.R'
'stergm.EGMME.initialfit.R' 'stergm.R' 'tergm-deprecated.R'
'stergm.getMCMCsample.R' 'stergm.utils.R'
'summary.statistics.networkDynamic.R' 'summary.stergm.R'
'zzz.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' 'RcppExports.R'
'bootmex.R' 'gpd.sandwich.R' 'gpd.info.R' 'texmexFamily.R'
'cgpd.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' '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.R' 'gpdProfileLikRetLevels.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' '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'
'add_comma_space.R' 'add_missing_endmark.R' 'utils.R'
'replace_html.R' 'check_text_logicals.R' 'check_text.R'
'drop_element.R' 'drop_row.R' 'fgsub.R' 'filter_element.R'
'filter_row.R' 'glue-reexports.R' 'has_endmark.R'
'make_plural.R' 'match_tokens.R' 'mgsub.R'
'replace_contraction.R' 'replace_date.R' 'replace_email.R'
'replace_emoji.R' 'replace_emoticon.R' 'replace_grade.R'
'replace_hash.R' 'replace_incomplete.R'
'replace_internet_slang.R' 'replace_kerning.R'
'replace_money.R' 'replace_names.R' 'replace_non_ascii.R'
'replace_number.R' 'replace_ordinal.R' 'replace_rating.R'
'replace_symbol.R' 'replace_tag.R' 'replace_time.R'
'replace_to.R' 'replace_tokens.R' 'replace_url.R'
'replace_white.R' 'replace_word_elongation.R' 'strip.R'
'sub_holder.R' 'swap.R' 'textclean-package.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'
'bind_list.R' 'bind_table.R' 'bind_vector.R' 'change_index.R'
'cluster_matrix.R' 'column_to_rownames.R' 'combine.R'
'duration.R' 'flatten.R' 'from_to.R' 'mtabulate.R'
'split_index.R' 'split_match.R'
'split_match_regex_to_transcript.R' 'split_portion.R'
'split_run.R' 'split_sentence.R' 'split_sentence_token.R'
'split_speaker.R' 'split_token.R' 'split_transcript.R'
'split_word.R' 'textshape-package.R' 'tidy_colo_dtm.R'
'utils.R' 'tidy_dtm.R' 'tidy_list.R' 'tidy_matrix.R'
'tidy_table.R' 'tidy_vector.R' 'unique_pairs.R' 'unnest_text.R'
'thrift.R' 'transport.R' 'rpc.R' 'parser.R'
'protocol_binary.R' 'server.R' 'transport_buffered.R'
'transport_memory.R' 'transport_socket.R' 'utils.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'
'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' 'recordFile.R' 'shiny.R' 'showMethods.R'
'QuotaClasses.R' 'TariffClasses.R' 'TariffCournot-methods.R'
'summary-methods.R' 'ps-methods.R' 'bertrand_quota.R'
'bertrand_tariff.R' 'cournot_tariff.R' 'initialize-methods.R'
'trade_shiny.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 rtracks.R Trackstat.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
'RcppExports.R' 'append.R' 'as-tsibble.R' 'data.R'
'dplyr-verbs.R' 'dplyr-join.R' 'error.R' 'fill-na.R' 'format.R'
'holiday.R' 'index-by.R' 'interval.R' 'parse-key.R' 'period.R'
'reexports.R' 'slide.R' 'split-by.R' 'stretch.R' 'subset.R'
'tidyr-verbs.R' 'tile.R' 'time-wise.R' 'ts2tsibble.R'
'tsibble-pkg.R' 'tsibble2ts.R' 'type-sum.R' 'update.R'
'utils-format.R' 'utils.R' 'zzz.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'
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'
'exec.R' 'prompt.R' 'browse.struct.R' 'browse.R' 'demo.R'
'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'
'call.R' 'firmly.R' 'validate.R' 'scope.R' 'checkers.R'
'valaddin.R'
'rule.R' 'sugar.R' 'validate_pkg.R' 'parse.R'
'expressionset.R' 'indicator.R' 'validator.R' 'confrontation.R'
'barplot.R' 'compare.R' 'factory.R' 'functions.R'
'lumberjack.R' 'retailers.R' 'utils.R' 'yaml.R'
'model_gen.R' 'refit.R' 'varband_cv.R' 'varband_path.R'
'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' 'Extractors.R'
'Predict.R'
Class-Cartesian2DCoordinateTransformation.R
Cartesian2DCoordinateTransformation-methods.R
Class-SimilarityTransformation.R Class-AffineTransformation.R
SimilarityTransformation-methods.R
AffineTransformation-methods.R
'NULLing.R'
'coverconvert-class.R''vegtable-class.R''shaker-class.R'
'cross2db.R''clean.R''as.list.R''merge_taxa.R''add_releves.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'
'layers2samples.R' 'shaker-methods.R''make_cocktail.R'
'summary.R''match_names.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
'whisker.R' 'section.R' 'parseTemplate.R' 'partials.R'
'inverted.R' 'pkg.R' 'iteratelist.R' 'delim.R' 'markdown.R'
'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'
'par.R' 'path.R' 'seed.R' 'sink.R' 'tempfile.R' 'torture.R'
'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'
'xEnricher.r' 'xEnricherGenes.r' 'xEnricherSNPs.r'
'xEnricherYours.r' 'xEnrichViewer.r' 'xEnrichBarplot.r'
'xEnrichDAGplot.r' 'xEnrichNetplot.r' 'xEnrichCompare.r'
'xEnrichDAGplotAdv.r' 'xSocialiser.r' 'xSocialiserGenes.r'
'xSocialiserSNPs.r' 'xSocialiserDAGplot.r'
'xSocialiserDAGplotAdv.r' 'xSocialiserNetplot.r' 'xCircos.r'
'xSubneterGenes.r' 'xSubneterSNPs.r' 'xVisNet.r'
'xVisKernels.r' 'xSNPscores.r' 'xSNPlocations.r'
'xSNP2nGenes.r' 'xSparseMatrix.r' 'xSM2DF.r'
'xSNP2GeneScores.r' 'xGRviaGeneAnno.r' 'xGRviaGenomicAnno.r'
'xGRviaGenomicAnnoAdv.r' 'xGRsampling.r' 'xLiftOver.r'
'xEnrichConciser.r' 'xColormap.r' 'xGR2nGenes.r' 'xGRscores.r'
'xGR2GeneScores.r' 'xSubneterGR.r' 'xGR.r' 'xCheckParallel.r'
'xSymbol2GeneID.r' 'xDAGpropagate.r' 'xVisInterp.r'
'xVisInterpAnimate.r' 'xDefineNet.r' 'xCombineNet.r'
'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'
'xRPS.r' 'xGRmanhattan.r' 'xLDblock.r' 'xLDsampling.r'
'xLDenricher.r' 'xDefineGenomicAnno.r' 'xDefineOntology.r'
'xDefineEQTL.r' 'xDefineHIC.r' 'xPCHiCplot.r' 'xBiproject.r'
'xRWkernel.r' 'xRWenricher.r' 'xGRkaryogram.r' 'xRepurpose.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>
Tan Dang <dangt@purdue.edu>, Bartek Rajwa
<rajwa@cyto.purdue.edu>
Andrea Rodriguez-Martinez
<andrea.rodriguez-martinez13@imperial.ac.uk>, Rafael Ayala
<r.ayala14@imperial.ac.uk>
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/
credit_usgs.html#copyright. This software is in the public
domain because it contains materials that originally came from
the U.S. Geological Survey, an agency of the United States
Department of Interior. Although this software program has been
used by the U.S. Geological Survey (USGS), no warranty,
expressed or implied, is made by the USGS or the U.S.
Government as to the accuracy and functioning of the program
and related program material nor shall the fact of distribution
constitute any such warranty, and no responsibility is assumed
by the USGS in connection therewith. This software is provided
"AS IS."
The mdlp2.R script reuses parts of the code from the R
`discretization` package by HyunJi Kim (GPL license).
See inst/COPYRIGHTS for files in src/argon2.
The code for apriori and eclat in src/rapriori.c was
obtained from http://www.borgelt.net/ and is Copyright (C)
1996-2003 Christian Borgelt. All other code is Copyright (C)
Michael Hahsler, Christian Buchta, Bettina Gruen and Kurt
Hornik.
(C) 2018 Michael Hahsler, Tyler Giallanza and Sudheer
Chelluboina
This package is Copyright (C) 2005-2018 Weierstrass
Institute for Applied Analysis and Stochastics.
This package is Copyright (C) 2012-18 Weierstrass Institute
for Applied Analysis and Stochastics.
(C) 2012 John W. Emerson and Walton A. Green and John A.
Hartigan
hyp2f1 is from Cephes Math Library Release 2.3, Copyright
1995 by Stephen L. Moshier. Newmat is Copyright 1991-2005 by
Robert B. Davies.
(C) 2014 Michael J. Kane, Bryan Lewis, and John W. Emerson
(C) 2013 John W. Emerson and Michael J. Kane
The included METIS library is Copyright 1997, Regents of the
University of Minnesota
(C) 2015 Michael J. Kane and John W. Emerson
Copyright (C) Collective, Inc. | file inst/COPYRIGHTS
Natural Environment Research Council (NERC) and Centre for
Ecology and Hydrology (CEH)
(c) 2012 NYU School of Medicine; (c) 2012-14 Jeff Binder
(C) 2012 Decision Analyst, Inc. (ChoiceModelR is a trademark
of Decision Analyst, Inc.)
2016 Merck Sharp & Dohme Corp. a subsidiary of Merck & Co.,
Inc., Kenilworth, NJ, USA.
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.
ANN library is copyright by University of Maryland, Sunil
Arya and David Mount. All other code is copyright by Michael
Hahsler and Matthew Piekenbrock.
Fushing Lab & McCowan Lab, University of California, Davis
src/arms.c and src/arms.h are copyright Wally Gilks. All
other files are copyright Institut de Recherches
Internationales Servier and Institut national de la sante et de
la recherche medicale.
Instituto Colombiano para la Evaluacion de la Educacion -
ICFES; Victor H. Cervantes
All files in src/CppBugs are copyright Whit Armstrong. All
other files are copyright Institut de Recherches
Internationales Servier and Institut national de la sante et de
la recherche medicale.
stanmodels.R is copyright 2015, 2016, 2017 Trustees of
Columbia University. All other files are copyright French
National Institute of Health and Medical Research.
This package is Copyright (C) 2005-2016 Weierstrass
Institute for Applied Analysis and Stochastics.
This library 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
https://www.usgs.gov/visual-id/credit_usgs.html#copyright
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/credit_usgs.html#copyright
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
from the SuiteSparse collection of Tim Davis, as well as
functions from the GNU Scientific library and the RngStreams
library by Pierre L'Ecuyer.  For details see the COPYRIGHTS
file.
Mayank Lahiri for 'easyexif', Oliver Keyes for the
integration with R, and Penelope Hopkins and Robyn Temple-Wood
for the included test images (which are licensed CC-BY 4.0).
Until package version 1.1.23: © 2011 Daniel Müllner
<http://danifold.net>. All changes from version 1.1.24 on: ©
Google Inc. <http://google.com>.
See the individual source files for copyrights information
The copyright for the single precision BLAS/LAPACK
distribution located in src/lapack is given in the file
src/lapack/LICENSE.
ANN Copyright (c) 1997-2010 University of Maryland and Sunil
Arya and David Mount. All Rights Reserved.
This package includes sources from the LZ4 library written
by Yann Collet, sources of the ZSTD library owned by Facebook,
Inc. and sources of the fstlib library owned by Mark Klik
Copyright (C) 2006 University of Duisburg-Essen, Essen,
Germany, University of Bristol, Bristol, United Kingdom
The Pennsylvania State University and Northeastern
University
see inst/COPYRIGHTS for the files in src/.
(C) 2016 by Helmholtz Institute Freiberg for Resource
Technology
(C) 2014 John W. Emerson and Walton A. Green
Copyright 2010, Merck Research Laboratories
Original algorithms and 'Matlab'/C library (c) 2015-2017
WG127 SCOR/IAPSO (Scientific Committee on Oceanic Research /
International Association for the Physical Sciences of the
Oceans, Working Group 127); C wrapper code and R code (c)
2015-2017 Dan Kelley and Clark Richards
Under the allowance of MIT license, smhash
(https://code.google.com/p/smhasher/) codes have been adopted.
For the hdf5r package: Novartis Institute for BioMedical
Research Inc. For HDF5: see the HDF5_COPYRIGHTS file.
RStudio, Inc.; Joyent, Inc.; Nginx Inc.; Igor Sysoev; Niels
Provos; Internet Systems Consortium, Inc.; Alexander Chemeris
(c) John Curtin Research School of Medical Sciences and
Centre for Mathematics and Its Applications, The Australian
National University
Oleg Sklyar, 2005-2010 and other authors per their commits
This software is in the public domain because it contains
materials that originally came from the 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 holders of FFTW3: Copyright (c) 2003, 2007-11
Matteo Frigo; Copyright (c) 2003, 2007-11 Massachusetts
Institute of Technology
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/credit_usgs.html#copyright. Data
are part of the associated publication. See
citation('laketemps') for dataset attribution.
The LIBLINEAR Project; Thibault Helleputte
<thibault.helleputte@dnalytics.com>; Pierre Gramme
<pierre.gramme@dnalytics.com>
Ingo Steinwart, Philipp Thomann, Mohammad Farooq
'lm.br' uses the design and some R-code of 'lm' copyright
(C) 2015 The R Foundation for Statistical Computing, and of
'lm.gls' copyright (C) 1994-2005 W. N. Venables and B. D.
Ripley.
The portion of numerical integration is based on Cubature,
Copyright (c) 2005-2013 Steven G. Johnson
(http://ab-initio.mit.edu/wiki/index.php/Cubature), which in
turn is based on HIntLib (also distributed under * the GNU GPL,
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/).
Copyright Notice GENERAL DISCLAIMER This program is free
software; you can redistribute it and/or modify it under the
terms of the GNU General Public License as published by the
Free Software Foundation; either version 2, or (at your option)
any later version. This program is distributed in the hope that
it will be useful, but WITHOUT ANY WARRANTY; without even the
implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR
PURPOSE. See the GNU General Public License for more details.
In short: You may use it any way you like, as long as you do
not charge money for it, remove this notice, or hold anyone
liable for its results. Also, please acknowledge the source and
communicate changes to the author. If this software is used in
work presented for publication, kindly reference it using for
example: Krebs AJ (2016): makeFlow: Visualizing Sequential
Classifications. Be sure to reference R itself and other
libraries used.
Segments of the package are based upon Will G. Hopkins'
work. See vignette and COPYRIGHT file for details.
All files are copyright Institut National de la Sante Et de
la Recherche Medicale.
The Institute of Information Theory and Automation of the
Czech Academy of Sciences, Pod Vodárenskou věží 4, CZ-18208
Prague 8, Czech Republic, email: utia@utia.cas.cz, phone: +420
266 053 111
(C) 2014 National Institute of Advanced Industrial Science
and Technology (AIST)
libnabo is copyright 2010--2011, Stephane Magnenat, ASL,
ETHZ, Switzerland <stephane at magnenat dot net>
ANN library is copyright University of Maryland and Sunil
Arya and David Mount. R wrapper is based on the ANN library,
copyright Samuel Kemp 2005-9 and Gregory Jefferis 2009-2013.
See file COPYRIGHT for details
Nozzle is copyright 2013 The Fellows and President of
Harvard College and The Broad Institute, Inc. All rights
reserved. jQuery is licensed under the MIT license. Copyright
2012 jQuery Foundation. jQuery Tablesorter plugin is licensed
under the MIT license. Copyright 2008 Christian Bach.
The fortran routines for the function HW.original are from
Jonathan R. M. Hosking and are available at the url
http://www.research.ibm.com/people/h/hosking/lmoments.html. The
functions `MLlaio2004.R', `GOFlaio2004.R' and `MSClaio2008.R'
are derived from original Francesco Laio Matlab code (non
published material). The fortran routines for the function
'bestlm' are from Alan Miller and were obtained from his R
package 'leaps'.
2006-2011, Bank of Canada. 2012-2016, Paul Gilbert
OPUS Miner algorithm and C++ implementation Copyright (C)
2012-2017 Geoffrey I Webb, R interface Copyright (C) 2017 Angus
Dempster.
Pokemon, pokedex and all pokemon names are trademarks of
Nintendo.
Copyright (c) 2010, David Weisman and Dan Simovici, All
rights reserved.
Most files are originally designed by David Pierce under GPL
(>= 3).
See pbdPROF/inst/fpmpi_LICENSE.txt for the files in
src/fpmpi/ and src/fpmpi_win.
See 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)
Copyright 2018, Center for Mathematical Sciences, Merck &
Co., Inc.
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 Eigen3 C++ library
Code is Copyright (C) Jan Wijffels and BNOSAC
Institut National de la Recherche Agronomique (INRA)
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.
Jack Dongarra, Jim Bunch, Cleve Moler, and Gilbert Stewart
due to the LINPACK routines `dchdd` and `dchud`.
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
This software is in the public domain because it contains
materials that originally came from the U.S. Bureau of
Reclamation, an agency of the U.S. Department of Interior.
(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
See the file COPYRIGHTS for copyright, authorship and
license details
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.
Contains data which are used by functions of the 'abc' package.
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”.
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>.
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.
Performs Approximate Bayesian Computation (ABC) model choice and parameter inference via random forests.
  Pudlo, P., Marin, J.-M., Estoup, A., Cornuet, J.-M., Gautier, M. and Robert, C.P. (2016) <doi:10.1093/bioinformatics/btv684>.
  Raynal, L., Marin, J.-M., Pudlo, P., Ribatet, M., Robert, C.P. and Estoup, A. (2017) <arXiv:1605.05537>.
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>. 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
              <doi: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.
'Actigraphy' is a cost-effective and convenient tool for activity-based monitoring. 'ActisoftR' was designed for parsing 'actigraphy' outputs and to summarise scored data across user-defined intervals. It consists of several functions for importing, generating reports and statistics, and for data visualization.
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, e.g. knapsack and subset sum procedures,
	derivative-free Nelder-Mead and Hooke-Jeeves minimization, and
	some (evolutionary) global optimization functions.
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.
Infer the code style (which style rules are followed and which ones are 
    not) from one package and use it to check another. This makes it easier to
    find and correct the most important problems first.
Based on the function "spm" of the SemiPar package fits
        semiparametric regression models with spatially adaptive
        penalized splines.
Fits semiparametric additive regression models with spatially adaptive penalized splines and computes simultaneous confidence bands and associated specification (lack-of-fit) tests. Simultaneous confidence bands cover the entire curve with a prescribed level of confidence and allow us to assess the estimation uncertainty for the whole curve. In contrast to pointwise confidence bands, they permit statements about the statistical significance of certain features (e.g. bumps) in the underlying curve.The method allows for handling of spatially heterogeneous functions and their derivatives as well as heteroscedasticity in the data. See Wiesenfarth et al (2012) <doi:10.1080/01621459.2012.682809>.
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.
Implementation of adaptive p-value thresholding (AdaPT), including both a framework that allows the user to specify any 
  algorithm to learn local false discovery rate and a pool of convenient functions that implement specific 
  algorithms. See Lei, Lihua and Fithian, William (2016) <arXiv:1609.06035>.
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.
Implements the adaptive sampling procedure, a framework for both positive unlabeled learning and learning with class label noise. Yang, P., Ormerod, J., Liu, W., Ma, C., Zomaya, A., Yang, J. (2018) <doi:10.1109/TCYB.2018.2816984>.
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 hazard models and to estimate the contribution of diseases/conditions to the disability prevalence, as proposed by Nusselder and Looman (2004) and extended by Yokota et al (2017).
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>.
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.
Provides the functions for planning and conducting a
  clinical trial with adaptive sample size determination. Maximal statistical
  efficiency will be exploited even when dramatic or multiple adaptations
  are made. Such a trial consists of adaptive determination of sample size
  at an interim analysis and implementation of frequentist statistical test at the
  interim and final analysis with a prefixed significance level. The required
  assumptions for the stage-wise test statistics are independent and stationary
  increments and normality. Predetermination of adaptation rule is not required.
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>. In this version, it is possible to include a source as a function depending on space and time, that is, s(x,t).
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 J Sanderson, H Sudoyo, TM Karafet, MF Hammer and MP Cox. 2015. Reconstructing past admixture processes from local genomic ancestry using wavelet transformation. Genetics 200:469-481 <doi:10.1534/genetics.115.176842>.
Allows access to selected services that are part of the 
    'Google Adwords' API <https://developers.google.com/adwords/api/docs/guides/start>.
	'Google Adwords' is an online advertising service by 'Google', that delivers Ads 
	to users. This package offers a authentication process using 'OAUTH2'.  Currently, 
	there are two methods of data of accessing the API, depending on the type of 
	request. One method uses 'SOAP' requests which require building an 'XML' structure 
	and then sent to the API. These are used for the 'ManagedCustomerService' and 
	the 'TargetingIdeaService'. The second method is by building 'AWQL' queries for 
	the reporting side of the 'Google Adwords' API.
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 (i.e., split-plot) 
         ANOVAs for data in long format (i.e., one observation per row),
         automatically 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_plot() provides a high-level interface for interaction or one-way 
         plots using ggplot2, combining raw data and model estimates. 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.
Provides Atomic Force Microscope images analysis such as Power
    Spectral Density, roughness against lengthscale, experimental variogram and variogram models,
    fractal dimension and scale, 2D network analysis. 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 the analysis of growth data: to extract an 
    LMS table from a gamlss object, to calculate the standard 
    deviation scores and its inverse, and to superpose two wormplots 
    from different models. The package contains a some varieties of 
    reference tables, especially for The Netherlands.
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>].
Computation of A (pedigree), G (genomic-base), and H (A corrected
    by G) relationship matrices for diploid and autopolyploid species considering additive and non-additive effects as in Amadeu et al (2016) <doi:10.3835/plantgenome2016.01.0009>.
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.
A tool for calculating agreement interval of two measurement methods (Jason Liao (2015) <DOI:10.1515/ijb-2014-0030>) and present results in plots with discordance rate and/or clinically meaningful limit to quantify agreement quality.
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.
The Analytic Hierarchy Process is a versatile multi-criteria decision-making tool introduced by Saaty (1987) <doi:10.1016/0270-0255(87)90473-8> that allows decision-makers to weigh attributes and evaluate alternatives presented to them.    This package provides a consistent methodology for researchers to reformat data and run analytic hierarchy process in R on data that are formatted using the survey data entry mode. It is optimized for performing the analytic hierarchy process with many decision-makers, and provides tools and options for researchers to aggregate individual preferences and test multiple options. It also allows researchers to quantify, visualize and correct for inconsistency in the decision-maker's comparisons.
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 (HYCAR 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 and references.
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.
Retrieves open source airport data and provides tools to look up information, translate names into codes and vice-verse, as well as some basic calculation functions for measuring distances.
Schema definitions and read, write and validation tools for data 
    formatted in accordance with the AIRR Data Representation schemas defined 
    by the AIRR Community <http://docs.airr-community.org>.
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.
Augmented Lagrangian Adaptive Barrier Minimization
        Algorithm for optimizing smooth nonlinear objective functions
        with constraints. Linear or nonlinear equality and inequality
        constraints are allowed.
Provides methods for high-throughput adaptive immune 
    receptor repertoire sequencing (AIRR-Seq; Rep-Seq) analysis. In 
    particular, immunoglobulin (Ig) sequence lineage reconstruction, 
    lineage topology analysis, diversity profiling, amino acid property 
    analysis and gene usage.
    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.
Create extensible figures of multiple sequence alignments, using the 'ggplot2' plotting engine. 'alignfigr' will create a baseline figure of a multiple sequence alignment which can be fully customized to the user's liking with standard 'ggplot2' features.
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 a routine to concentrate out factors with many levels during the
  optimization of the log-likelihood function of the corresponding generalized linear model (glm).
  The package is based on the algorithm proposed by Stammann (2018) <arXiv:1707.01815> and is
  restricted to glm's that are based on maximum likelihood estimation and non-linear. It also offers
  an efficient algorithm to recover estimates of the fixed effects in a post-estimation routine.
  The package also includes robust and multi-way clustered standard errors.
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.
The successor to the 'AlphaSim' software for breeding program 
  simulation (Faux et al., 2016, <doi:10.3835/plantgenome2016.02.0013>). 
  Used for stochastic simulations of breeding programs to the level of DNA 
  sequence for every individual. Contained is a wide range of functions for 
  modeling common tasks in a breeding program, such as selection and crossing. 
  These functions allow for constructing simulations of highly complex plant and 
  animal breeding programs via scripting in the R software environment. Such 
  simulations can be used to evaluate overall breeding program performance and 
  conduct research into breeding program design, such as implementation of 
  genomic selection. Included is the 'Markovian Coalescent Simulator' ('MaCS') 
  for fast simulation of biallelic sequences according to a population 
  demographic history (Chen et al., 2009, <doi:10.1101/gr.083634.108>).
Developed to perform the tasks given by the following. 
             1-computing the probability density function and distribution function of a univariate 
             stable distribution; 2- generating realization from univariate stable, truncated stable,
             multivariate elliptically contoured stable, and bivariate strictly stable distributions; 
             3- estimating the parameters of univariate symmetric stable, skew stable, Cauchy, 
             multivariate elliptically contoured stable, and multivariate strictly stable distributions;
             4- estimating the parameters of the mixture of symmetric stable and mixture of Cauchy 
             distributions.
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 and a few functions used in the textbook S. Weisberg (2005), 
 'Applied Linear Regression,' 3rd edition, Wiley are presented.  
 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.  Many 
 of these files are also in the newer alr4 package.
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.
Generation of natural looking noise has many application within 
    simulation, procedural generation, and art, to name a few. The 'ambient' 
    package provides an interface to the 'FastNoise' C++ library and allows for
    efficient generation of perlin, simplex, worley, cubic, value, and white 
    noise with optional pertubation in either 2, 3, or 4 (in case of simplex and
    white noise) dimensions.
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.
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.
Computes various stability parameters from Additive Main Effects
    and Multiplicative Interaction (AMMI) analysis results such as  Modified
    AMMI Stability Value (MASV), Sums of the Absolute Value of the Interaction
    Principal Component Scores (SIPC), Sum Across Environments of
    Genotype-Environment Interaction Modelled by AMMI (AMGE), Sum Across 
    Environments of Absolute Value of Genotype-Environment Interaction Modelled
    by AMMI (AV_(AMGE)), AMMI Stability Index (ASI), Modified ASI (MASI), AMMI
    Based Stability Parameter (ASTAB), Annicchiarico's D Parameter (DA), Zhang's
    D Parameter (DZ), Averages of the Squared Eigenvector Values (EV), Stability
    Measure Based on Fitted AMMI Model (FA), Absolute Value of the Relative
    Contribution of IPCs to the Interaction (Za). Further calculates the
    Simultaneous Selection Index for Yield and Stability from the computed
    stability parameters. See the vignette for complete list of citations for
    the methods implemented.
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, missing value imputation, column/rows sums/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. Note that the latest version of XQuartz seems to be necessary under MacOS.
Analyze fuzzy time series by Chen, Singh, Heuristic and Chen-Hsu models. The Abbasov-Mamedova and NFTS models is included as well.
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
Provides a comprehensive system for selecting
    variables and weighting data to match the specifications of the American
    National Election Studies. The package includes methods for identifying
    discrepant variables, raking data, and assessing the effects of the raking
    algorithm. It also allows automated re-raking if target variables fall
    outside identified bounds and allows greater user specification than other
    available raking algorithms. A variety of simple weighted statistics that
    were previously in this package (version .55 and earlier) have been moved to
    the package 'weights.'
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.
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.
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.
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.
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.
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.
An implementation of CAPA (Collective And Point Anomaly) for the detection of anomalies in time series data. The package also contains Kepler lightcurve data and shows how CAPA can be applied to detect exoplanets.
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.
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.
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.
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 open source software developed by
    RTE (Réseau de Transport d’Électricité) to simulate and study electric power systems (more information about
    'Antares' here: <https://github.com/AntaresSimulatorTeam/Antares_Simulator>). You can see the results of several ANTARES studies here : <http://bpnumerique.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 open source software developed by RTE (Réseau de Transport d’Électricité) to simulate and study electric power systems
    (more information about 'Antares' here : <https://github.com/AntaresSimulatorTeam/Antares_Simulator>). You can see the results of several ANTARES studies here : <http://bpnumerique.rte-france.com/>.
Visualize results generated by Antares, a powerful open source software
    developed by RTE to simulate and study electric power systems
    (more information about Antares here: <https://github.com/AntaresSimulatorTeam/Antares_Simulator>).
    This package provides functions that create interactive charts to help
    Antares users visually explore the results of their simulations. 
    You can see the results of several ANTARES studies here : <http://bpnumerique.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.
Supplies a set of functions to query air travel data for user-
    specified years and airports. Datasets include on-time flights, airlines,
    airports, and weather.
Made to make your life simpler with packages, by
    installing and loading a list of packages, whether they are on CRAN,
    Bioconductor or github. For github, if you do not have the full path, with 
    the maintainer name in it (e.g. "achateigner/topReviGO"), it will be able to
    load it but not to install it.
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).
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.
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.
Some functions for drawing some special plots:
   The function 'bagplot' plots a bagplot,
   'faces' plots chernoff faces,
   'iconplot' plots a representation of a frequency table or a data matrix,
   'plothulls' plots hulls of a bivariate data set,
   'plotsummary' plots a graphical summary of a data set,
   'puticon' adds icons to a plot,
   'skyline.hist' combines several histograms of a one dimensional data set in one plot,
   'slider' functions supports some interactive graphics,
   'spin3R' helps an inspection of a 3-dim point cloud,
   'stem.leaf' plots a stem and leaf plot,
   'stem.leaf.backback' plots back-to-back versions of stem and leaf plot.
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.  It uses a hierarchical version of the Gaussian process, originally proposed by Kennedy and O'Hagan (2000), Biometrika 87(1):1.
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 accuracy 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. 
    The outcome of interest is either binary or censored event time.
    Note that for censored event time, our functions' estimates, 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.
Reads Arena <https://www.arenasimulation.com/> CSV output files and generates nice tables and plots. The package contains a Shiny App that can be used to interactively visualize Arena's results.
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.
It performs All-Resolutions Inference (ARI) on functional Magnetic Resonance Image (fMRI) data. As a main feature, 
 it estimates lower bounds for the proportion of active voxels in a set of clusters as, for example, given by a cluster-wise analysis. 
 The method is described in Rosenblatt, Finos, Weeda, Solari, Goeman (2018) <doi:10.1016/j.neuroimage.2018.07.060>.
Implements an efficient O(n) algorithm based on bucket-sorting for 
    fast computation of standard clustering comparison measures. Available measures
    include adjusted Rand index (ARI), normalized information distance (NID), 
    normalized mutual information (NMI), normalized variation information (NVI) and
    entropy, as described in Vinh et al (2009) <doi:10.1145/1553374.1553511>.
Flat text files provide a robust, compressible, and portable
  way to store tables from databases.  This package provides convenient
  functions for exporting tables from relational database connections
  into compressed text files and streaming those text files back into
  a database without requiring the whole table to fit in working memory.
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
Estimates the covariate-adjusted Receiver Operating Characteristic (AROC) curve and pooled (unadjusted) ROC curve by different methods. Inacio de Carvalho, V., and Rodriguez-Alvarez, M. X. (2018) <arXiv:1806.00473>.
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, 2018 <doi:10.1002/ajpa.23493>), 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; paired(), a Table-1-like summary of multiple variable types paired across
  two time points; 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.
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).
Gives 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 [see Fay and Kim <doi:10.1002/bimj.201600111>], 
    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 effect estimates and confidence intervals, 
    see Fay and Malinovsky <doi:10.1002/sim.7890>], two-sample melding tests 
    [see Fay, Proschan, and Brittain <doi:10.1111/biom.12231>],
    one-way ANOVA allowing var.equal=FALSE [see Brown and Forsythe, 1974, Biometrics]). 
    The focus is on methods that have compatible 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. It is also included in Boost but requires linking
 when used with Boost. Standalone it can be used header-only (provided a recent
 compiler). 'Asio' is written and maintained by Christopher M. Kohlhoff, and
 released under the 'Boost Software License', Version 1.0.
Efficiently perform complex satellite image processes automatically with minimum inputs. 
  Functions are providing more control on the user to specify how the function needs to be executed by offering more customization options and facilitate more functionalities. 
  The functions are designed to identify the type of input satellite images and perform accordingly. 
  Also, some functions are giving options to perform multiple satellite data (even from different types) in single run.
  Package currently supports satellite images from most widely used Landsat 4,5,7 and 8 and Sentinel-2 MSI data. 
  The primary applications of this package are given below.
  1. Conversion of optical bands to top of atmosphere reflectance.
  2. Conversion of thermal bands to corresponding temperature images. 
  3. Derive application oriented products directly from source satellite image bands.
  4. Compute user defined equation and produce corresponding image product.
  5. Other basic tools for satellite image processing.
  REFERENCES.
  i. Chander and Markham (2003) <doi:10.1109/TGRS.2003.818464>.
  ii. Roy et.al, (2014) <doi:10.1016/j.rse.2014.02.001>.
  iii. Abrams (2000) <doi:10.1080/014311600210326>.
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.
Functions to model and decompose time series into principal components using singular spectrum analysis (de Carvalho and Rua (2017) <doi:10.1016/j.ijforecast.2015.09.004>; de Carvalho et al (2012) <doi:10.1016/j.econlet.2011.09.007>).
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.
Functions for the estimation of intra- and inter-cohort correlations in the Vasicek credit portfolio model. For intra-cohort correlations, the package covers the two method of moments estimators of Gordy (2000) <doi:10.1016/S0378-4266(99)00054-0>, the method of moments estimator of Lucas (1995) <http://jfi.iijournals.com/content/4/4/76> and a Binomial approximation extension of this approach. Moreover, the maximum likelihood estimators of Gordy and Heitfield (2010) <http://elsa.berkeley.edu/~mcfadden/e242_f03/heitfield.pdf> and Duellmann and Gehde-Trapp (2004) <http://hdl.handle.net/10419/19729> are implemented. For inter-cohort correlations, the method of moments estimator of Bluhm and Overbeck (2003) <doi:10.1007/978-3-642-59365-9_2>/Bams et al. (2016) <https://ssrn.com/abstract=2676595> is provided and the maximum likelihood estimators comprise the approaches of Gordy and Heitfield (2010)/Kalkbrener and Onwunta (2010) <ISBN: 978-1906348250> and Pfeuffer et al. (2018). Bootstrap and Jackknife procedures for bias correction are included as well as the method of moments estimator of Frei and Wunsch (2018) <doi:10.21314/JCR.2017.231> for auto-correlated time series.
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
Fits a model to adjust and consider additional variations in three dimensions of age groups, time, and space on residuals excluded from a prediction model that have residual such as: linear regression, mixed model and so on. Details are given in Foreman et al. (2015) <doi:10.1186/1478-7954-10-1>.
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).
    There are also random effects versions of these models.
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 <doi:10.1111/j.2044-8317.1996.tb01078.x>. An asymmetric matrix has the same number of rows and columns, and these rows and columns refer to the same set of objects. At least some elements in the upper-triangle are different from the corresponding elements in the lower triangle. 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 <doi:10.1007/BF02294474>, 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.
One and two sample mean and variance
 tests (differences and ratios) are considered.
 The test statistics are all expressed in the same
 form as the Student t-test, which facilitates their
 presentation in the classroom. This contribution
 also fills the gap of a robust (to non-normality)
 alternative to the chi-square single variance test
 for large samples, since no such procedure is implemented
 in standard statistical software.
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.
Create Tables For Reporting Clinical Trials.
	Calculates descriptive statistics and hypothese tests, 
	arranges the results in a table ready for reporting with LaTeX or Word.
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.
Stanford 'ATLAS' (Advanced Temporal Search Engine) is a powerful tool that allows constructing
             cohorts of patients extremely quickly and efficiently. This package is designed to interface directly
             with an instance of 'ATLAS' search engine and facilitates API queries and data dumps. Prerequisite
             is a good knowledge of the temporal language to be able to efficiently construct a query.
             More information available at <https://shahlab.stanford.edu/start>.
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.
Provides functions for implementing the Analysis-of-marginal-Tail-Means (ATM) method, a robust optimization method for discrete black-box optimization. Technical details can be found in Mak and Wu (2018+) <arXiv:1712.03589>. This work was supported by USARO grant W911NF-17-1-0007.
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
Provides an easy to use unified interface for creating validation plots for any model. 
  The 'auditor' helps to avoid repetitive work consisting of writing code needed to create residual plots. 
  This visualizations allow to asses and compare the goodness of fit, performance, and similarity of models.
Functions for analysis of data generated from experiments in
    augmented randomised complete block design according to Federer, W.T. (1961)
    <doi:10.2307/2527837>. Computes analysis of variance, adjusted means,
    descriptive statistics, genetic variability statistics etc. Further includes
    data visualization and report generation functions.
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.
Uses Auth0 API (see <https://auth0.com> for more 
    information) to use a simple and secure authentication system. It provides 
    tools to log in and out a shiny application using social networks or a list
    of e-mails.
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.
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.
Automatically calculates cognostic groups for plot objects and list column plot objects.  Results are returned in a nested data frame.
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.
Fits from simple regression to highly customizable deep neural networks 
    either with gradient descent or metaheuristic, using automatic hyper parameters 
    tuning and custom cost function.
    A mix inspired by the common tricks on Deep Learning and Particle Swarm Optimization.
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.
Fits the autologistic model described in Besag's famous 1974 paper on auto- models <http://www.jstor.org/stable/2984812>. Fits a multicategory generalization of the autologistic model when there are more than 2 response categories. Provides support for both asymptotic and bootstrap confidence intervals. For full model descriptions and a guide to the use of this package, please see the vignette.
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.
Static code compilation of a 'shiny' app given an R function (into 'ui.R' and 'server.R' files or into a 'shiny' app object). See examples at <https://github.com/alekrutkowski/autoshiny>.
Algorithms for automatically finding appropriate thresholds for 
    numerical data, with special functions for thresholding images. 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.
Bindings to 'FFmpeg' <http://www.ffmpeg.org/> AV library for working with audio 
    and video in R. Generate high quality videos files by capturing images from the R graphics
    device combined with custom audio stream. This package interfaces directly to the C API
    and does not require any command line utilities.
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.
We provide a collection of R-functions implementing 
    adaptive smoothing procedures in 1D, 2D and 3D. This includes the
    Propagation-Separation Approach to adaptive smoothing as
    described in "J. Polzehl and V. Spokoiny (2006)
    <DOI:10.1007/s00440-005-0464-1>",
   "J. Polzehl and V. Spokoiny (2004) <DOI:10.20347/WIAS.PREPRINT.998>"
   and "J. Polzehl, K. Papafitsoros, K. Tabelow (2018) <DOI:10.20347/WIAS.PREPRINT.2520>",
   the Intersecting Confidence Intervals (ICI), variational approaches and a non-local means filter.
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/>.
Client package for the 'AWS Key Management Service' <https://aws.amazon.com/kms/>, a cloud service for managing encryption keys.
A simple client package for the Amazon Web Services ('AWS') Lambda 'API' <https://aws.amazon.com/lambda/>.
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' instances, 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.
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/>.
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.
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 the design of multi-group phase
    II clinical trials with binary outcomes using the hierarchical Bayesian
    classification and information sharing (BaCIS) model. Subgroups are classified
    into two clusters on the basis of their outcomes mimicking the hypothesis
    testing framework. Subsequently, information sharing takes place within
    subgroups in the same cluster, rather than across all subgroups. This method can
    be applied to the design and analysis of multi-group clinical trials with binary
    outcomes.
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 allows writing more legible code where right-to-left ordering is 
    natural. This is common with hierarchies and nested structures such as 
    trees, directories or markup languages (e.g. HTML and XML). 
    The package also includes a R-Studio add-in that can be bound to a keyboard 
    shortcut.
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."
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.
Balances have become a cornerstone of compositional data analysis. However,
 conceptualizing balances is difficult, especially for high-dimensional data. Most often,
 investigators visualize balances with "balance dendrograms". However, this visualization
 tool does not scale well for large data. This package provides an alternative scheme for
 visualizing balances, described in [Quinn (2018) <DOI:10.12688/f1000research.15858.1>].
 This package also provides a method for principal balance analysis.
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.
Bayesian analysis of loss development on insurance triangles or 'BALD' is a Bayesian model of developing aggregate loss triangles in property casualty insurance. This actuarial model makes use of a heteroskedastic and skewed t-likelihood with endogenous degrees of freedom, employs model averaging by means of Reversible Jump MCMC, and accommodates a structural break in the path of the consumption of benefits. Further, the model is capable of incorporating expert information in the calendar year effect. In an accompanying vignette, this model is applied to two widely studied General Liability and Auto Bodily Injury Liability loss triangles. For a description of the methodology, see Frank A. Schmid (2010) <doi:10.2139/ssrn.1501706>.
Hypothesis tests and sure independence screening (SIS) procedure based on ball statistics, including ball divergence <doi:10.1214/17-AOS1579>, ball covariance, and ball correlation <doi:10.1080/01621459.2018.1462709>, 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 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>.
Create a hierarchical acoustic event species classifier out of  
  multiple call type detectors as described in Rankin et al (2017) <doi:10.1111/mms.12381>.
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.
Tools to generate unique identifiers and printable barcoded labels for sample management. 
    The creation of unique ID codes and printable PDF files can be initiated by standard commands, 
    user prompts, or through a GUI addin for R Studio. Both single-level and hierarchical labels can 
    be created in the command line interactively or non-interactively.
To perform species identification using DNA barcodes.
Barnard's unconditional test for 2x2 contingency tables.
Implements plots of discrete functions (bar plots) and continuous functions (surface plots) in 3D. Uses orthographic projection, a nonstandard fixed viewing angle, does not support detailed axes and is designed to be relatively minimal and simple, from a programming perspective. However, does support mixed bar sizes and multiple colors.
Bayesian Additive Regression Trees (BART) provide flexible nonparametric modeling of covariates for continuous, binary, categorical 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 entropy regularized Wasserstein Distances, a.k.a. dual-Sinkhorn divergences, and entropy 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>.
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 (2018)
    <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.
Provides a function for examining posterior MCMC samples
    from a single chain using trace plots and density plots, and from
    multiple chains by comparing posterior medians and credible intervals
    from each chain.  These plotting functions have a variety of options,
    such as figure sizes, legends, parameters to plot, and saving plots to file.
    Functions interface with the NIMBLE software package, see
    de Valpine, Turek, Paciorek, Anderson-Bergman, Temple Lang and Bodik (2017)
    <doi:10.1080/10618600.2016.1172487>.
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'.
Plot, draw regression line and confidence interval, and show regression equation, R-square and P-value,  as simple as possible, by using different models ("line2P", "line3P", "log2P", "exp2P", "exp3P", "power2P", "power3P") built in the 'trendline()' function.
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 four data present in the 'BASiNET' package, "sequences", "sequences2", "sequences-predict" and "sequences2-predict" with 11, 10, 11 and 11 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. The BASiNET was published on Nucleic Acids Research, (ITO, Eric; KATAHIRA, Isaque; VICENTE, Fábio; PEREIRA, Felipe; LOPES, Fabrício, 2018) <doi:10.1093/nar/gky462>.
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'
    (<https://www.ibm.com/us-en/marketplace/hpc-workload-management>),
    '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'.
Implements Bayesian dynamic factor analysis with 'Stan'. Dynamic 
    factor analysis is a dimension reduction tool for multivariate time series.
    'bayesdfa' extends conventional dynamic factor models in several ways. 
    First, extreme events may be estimated in the latent trend by modeling
    process error with a student-t distribution. Second, autoregressive and 
    moving average components can be optionally included. Third, the estimated
    dynamic factors can be analyzed with hidden Markov models to evaluate
    support for latent regimes.
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 paper "Generalized Empirical Bayes via Frequentist Goodness of Fit" (<https://www.nature.com/articles/s41598-018-28130-5 >).
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 Moores et al. (2015) <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, Hahn, He and Hedibert (2018) <arXiv:1806.05738>.
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).
An implementation of the Bayesian version of the Mallows rank model (Vitelli et al., Journal of Machine Learning Research, 2018 <http://jmlr.org/papers/v18/15-481.html>). Both Cayley, footrule, Kendall, and Spearman distances are supported in the models. The rank data to be analyzed can be the form of complete rankings, top-k rankings, partially missing rankings, as well as consistent pairwise preferences. Several functions for plotting and studying the posterior distributions of parameters are provided. The package also provides functions for estimating the partition function (normalizing constant) of the Mallows rank model, both with the importance sampling algorithm of Vitelli et al. and asymptotic approximation with the IPFP algorithm (Mukherjee, Annals of Statistics, 2016 <doi:10.1214/15-AOS1389>).
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).
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 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>.
Functions for exploring and visualising estimation results
  obtained with BayesX, a free software for estimating structured additive
  regression models (<http://www.BayesX.org>). In addition,
  functions that allow to read, write and manipulate map objects that are required in spatial
  analyses performed with BayesX.
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 C-14 age estimates. 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.
Enable users to evaluate long-term trends using a Generalized 
    Additive Modeling (GAM) approach. The model development includes selecting a 
    GAM structure to describe nonlinear seasonally-varying changes over time, 
    incorporation of hydrologic variability via either a river flow or salinity, 
    the use of an intervention to deal with method or laboratory changes 
    suspected to impact data values, and representation of left- and 
    interval-censored data. The approach has been applied to water quality data 
    in the Chesapeake Bay, a major estuary on the east coast of the United 
    States to provide insights to a range of management- and research-focused 
    questions.
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', 'normalize').
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.
Set of functions to calculate Benthic Biotic Indices from
	composition data, obtained whether from morphotaxonomic inventories or
	sequencing data. Based on reference ecological weights publicly available for
	a set of commonly used marine biotic indices, such as AMBI (A Marine Biotic Index, Borja et al., 2000) <doi:10.1016/S0025-326X(00)00061-8>
	NSI (Norwegian Sensitivity Index) and ISI (Indicator Species Index) (Rygg 2013, <ISBN:978-82-577-6210-0>). It provides the ecological quality status of the samples based on each BBI as well as the normalized Ecological Quality Ratio.
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
Computation of bootstrap confidence intervals in an almost automatic fashion.
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.
Causal inference for a binary treatment and continuous outcome using Bayesian Causal Forests. See Hahn, Murray and Carvalho (2017) <arXiv:1706.09523> for additional information. This implementation relies on code originally accompanying Pratola et. al. (2013) <arXiv:1309.1906>.
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.
The 'shiny' application 'bdDwC' makes biodiversity data field names Darwin Core compatible.
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) <doi:10.1214/14-BA889>, Mohammadi et al. (2017) <doi:10.1111/rssc.12171>, and Dobra and Mohammadi (2018) <doi:10.1214/18-AOAS1164>. 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>.
Tools and workflow to choose design parameters in Bayesian adaptive single-arm phase II trial designs with binary endpoint (response, success) with possible stopping for efficacy and futility at interim analyses. Also contains routines to determine and visualize operating characteristics. See Kopp-Schneider et al. (2018) <doi:10.1002/bimj.201700209>.
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.
Implements Bayesian inference to detect signal from blinded 
    clinical trial when total number of adverse events of special 
    concerns and total risk exposures from all patients are available in the study.  
    For more details see the article by Mukhopadhyay et. al. (2018) 
    titled 'Bayesian Detection of Potential Risk Using Inference on Blinded Safety Data', 
    in Pharmaceutical Statistics (to appear).
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.
Analyze bioequivalence study data with industrial strength. Sample size could be determined for various crossover designs, such as 2x2 design, 2x4 design, 4x4 design, Balaam design, Two-sequence dual design, and William design.
             Reference: Chow SC, Liu JP. Design and Analysis of Bioavailability and Bioequivalence Studies. 3rd ed. (2009, ISBN:978-1-58488-668-6).
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> .
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. See Wang et al.
    (2018) <DOI:10.18637/jss.v085.i07> for further details.
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 main 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.
Facilitates many of the analyses performed in studies of
    behavioral economic demand. The package supports commonly-used options for
		modeling operant demand including (1) data screening proposed by Stein,
		Koffarnus, Snider, Quisenberry, & Bickel (2015; <doi:10.1037/pha0000020>),
		(2) fitting models of demand such as linear (Hursh, Raslear, Bauman,
		& Black, 1989, <doi:10.1007/978-94-009-2470-3_22>), exponential	(Hursh & Silberberg, 2008,
		<doi:10.1037/0033-295X.115.1.186>) and modified exponential (Koffarnus,
		Franck, Stein, & Bickel, 2015, <doi:10.1037/pha0000045>), and (3) calculating
		numerous measures	relevant to applied behavioral economists (Intensity,
		Pmax, Omax). Also	supports plotting and comparing data.
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.
Contains specialised analyses and
    visualisation tools for behavior change science.
    These facilitate conducting determinant studies
    (for example, using confidence interval-based
    estimation of relevance, CIBER, or CIBERlite
    plots) and systematically developing, reporting,
    and analysing interventions (for example, using
    acyclic behavior change diagrams). This package
    is especially useful for researchers in the field of
    behavior change or health psychology and to
    behavior change professionals such as intervention
    developers and prevention workers.
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.
    This package uses the analytical method created by Gao, P., Zhang, H.
    and Li, Z., 2018 (<doi:10.1111/tgis.12315>). It also extend the original 
    idea by allowing calculations on data with missing values.
Tools to accurately benchmark and analyze execution times for R expressions.
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).
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. Implements PCR and PLS using AIC/BIC. Implements one-standard deviation rule for use with the `caret` package.
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. It also estimates the normalization efficacy of other
    commonly used transformations.
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>.
Implements boosting beta regression for potentially high-dimensional data (Mayr et al., 2018 <doi:10.1093/ije/dyy093>). 
  The 'betaboost' package uses the same parametrization as 'betareg' (Cribari-Neto and Zeileis, 2010 <doi:10.18637/jss.v034.i02>) to make results directly comparable. 
  The underlying algorithms are implemented via the R add-on packages 'mboost' (Hofner et al., 2014 <doi:10.1007/s00180-012-0382-5>) and 
  'gamboostLSS' (Mayr et al., 2012 <doi:10.1111/j.1467-9876.2011.01033.x>).
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>.
Derived from the work of Kruschke (2015, <ISBN:9780124058880>),
    the present package aims to provide a framework for conducting Bayesian
    analysis using Markov chain Monte Carlo (MCMC) sampling utilizing the
    Just Another Gibbs Sampler ('JAGS', Plummer, 2003, <http://mcmc-jags.sourceforge.net/>).
    The initial version includes several modules for conducting Bayesian
    equivalents of chi-squared tests, analysis of variance (ANOVA),
    multiple (hierarchical) regression, softmax regression, and for fitting data
    (e.g., structural equation modeling).
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>.
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 extended to a 
    spatial model that allows for two type correlation in neuroimaging genetics data and 
    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.
Can be used for joint identification of candidate compound hits from multiple assays, in drug discovery. This package implements the framework of I. D. Shterev, D. B. Dunson, C. Chan and G. D. Sempowski. "Bayesian Multi-Plate High-Throughput Screening of Compounds", Scientific Reports 8(1):9551, 2018. This project was funded by the Division of Allergy, Immunology, and Transplantation, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Department of Health and Human Services, under contract No. HHSN272201400054C entitled "Adjuvant Discovery For Vaccines Against West Nile Virus and Influenza", awarded to Duke University and lead by Drs. Herman Staats and Soman Abraham.
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.
Parse a BibTeX file to a data.frame to make it accessible for further analysis and visualization.
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>),
    Clarivate Analytics Web of Science (<http://www.webofknowledge.com/>), Cochrane Library (<http://www.cochranelibrary.com/>) 
	and PubMed (<https://www.ncbi.nlm.nih.gov/pubmed/>) databases, performing bibliometric analysis 
    and building networks for co-citation, coupling, scientific collaboration and co-word analysis.
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.
The main function biclust() provides several algorithms to
        find biclusters in two-dimensional data: Cheng and Church (2000, ISBN:1-57735-115-0),
        spectral (2003) <doi:10.1101/gr.648603>, plaid model (2005) <doi:10.1016/j.csda.2004.02.003>, xmotifs (2003) <doi:10.1142/9789812776303_0008> and bimax (2006) <doi:10.1093/bioinformatics/btl060>. In addition, the
        package provides methods for data preprocessing (normalization
        and discretisation), visualisation, and validation of bicluster
        solutions.
Prior transcription factor binding knowledge and target gene expression data are integrated in a Bayesian framework for functional cis-regulatory module inference. Using Gibbs sampling, we iteratively estimate transcription factor associations for each gene, regulation strength for each binding event and the hidden activity for each transcription factor.
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>.
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; 
    Shao, 1996, <doi:10.1080/02331889708802523>). 
    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
    and is in particularly suited for the analysis of plausible values
    (for details see George, Oberwimmer & Itzlinger-Bruneforth, 2016; 
    Bruneforth, Oberwimmer & Robitzsch, 2016; Robitzsch, Pham &
    Yanagida, 2016; <doi:10.17888/fdb-demo:bistE813I-16a>).    
    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'.
Provides functions for reading *.PGN files with more than one game, including large files without copying it into RAM (using 'ff' package). Handle chess data and chess aggregated data, count figure moves statistics, create player profile, plot winning chances, browse openings.
Functions to compute distances between probability measures or any other data object than can be posed in this way, entropy measures for samples of curves, distances and depth measures for functional data, and the Generalized Mahalanobis Kernel distance for high dimensional data. For further details about the metrics please refer to Martos et al (2014) <doi:10.3233/IDA-140706>; Martos et al (2018) <doi:10.3390/e20010033>; Hernandez et al (2018, submitted); Martos et al (2018, submitted).
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
Regression for data too large to fit in memory. This package functions exactly like the 'biglm' package, but works with later versions of R.
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. The main functions in the 'bigmatch' package are optcal() to find the optimal caliper, optconstant() to find the optimal number of nearest neighbors and nfmatch() to find a near-fine balance match with a caliper and a restriction on number of nearest neighbors. 
    Glover, F. (1967).  <DOI:10.1002/nav.3800140304>.
    Lipski, W., Jr, and Preparata, F. P. (1981). <DOI:10.1007/BF00264533>.
    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.
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.
Read large text files by splitting them in smaller files.
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.
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.
Easy-to-use, efficient, flexible and scalable statistical tools.
  Package bigstatsr provides and uses Filebacked Big Matrices via memory-mapping.
  It provides for instance matrix operations, Principal Component Analysis,
  sparse linear supervised models, utility functions and more
  <doi:10.1093/bioinformatics/bty185>.
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.
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/>,
  <doi:10.4137/CIN.S2846>.
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 Faddy and 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.
A collection of 'LaTeX' styles using 'Beamer' customization for
 pdf-based presentation slides in 'RMarkdown'. At present it contains
 'RMarkdown' adaptations of the LaTeX themes 'Metropolis' (formerly 'mtheme')
 theme by Matthias Vogelgesang and others (now included in 'TeXLive'), the
 'IQSS' by Ista Zahn (which is included here), and the 'Monash' theme by
 Rob J Hyndman. Additional (free) fonts may be needed: 'Metropolis' prefers
 'Fira', and 'IQSS' requires 'Libertinus'.
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 climate time series with different timescales. The idea is that autocorrelation (AR1 process) allows to correlate values obtained on different time points. '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 items in group
        testing designs: Optimal testing configurations can be found 
        for hierarchical and array-based algorithms. Operating 
        characteristics can be calculated for testing configurations 
        across a wide variety of situations.
Binomial Haar-Fisz transforms for Gaussianization as in Nunes and Nason (2009).
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.
Imports benthic count data, reformats this data, and
        computes environmental inferences from this data.
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.
Contains all the necessary tools to process audio recordings of
             various formats (e.g., WAV, WAC, MP3, ZC), filter noisy files, 
             display audio signals, detect and extract automatically acoustic
             features for further analysis such as classification.
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.
A convenient tool to install and update Bioconductor packages.
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.
Can be used to integrate massive bioinformatics resources, such as tool/script and database. It provides the R functions and Shiny web application. Hundreds of bioinformatics tool/script and database have been included.
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.
Provides utilities to facilitate import, export and computation with the 
 BIOM (Biological Observation Matrix) format (http://biom-format.org).
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 large scale genomic data retrieval and functional annotation retrieval. This package aims to provide users with a standardized
                way to automate genome, proteome, 'RNA', coding sequence ('CDS'), 'GFF', and metagenome
                retrieval from 'NCBI RefSeq', 'NCBI Genbank', 'ENSEMBL', 'ENSEMBLGENOMES',
                and 'UniProt' 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. In addition, users can download entire databases such
                as 'NCBI RefSeq' (Pruitt et al. (2007) <doi:10.1093/nar/gkl842>), 'NCBI nr',
                'NCBI nt', 'NCBI Genbank' (Benson et al. (2013) <doi:10.1093/nar/gks1195>), etc. 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.
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.
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.
Utility functions, datasets and extended examples for survival analysis. This includes a range of other packages, some simple wrappers for time-to-event analyses, datasets, and extensive examples in HTML with R scripts. The package also supports the course Biostatistics III entitled "Survival analysis for epidemiologists in R".
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.
A connector to the API for 'Wordnik' <https://www.wordnik.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>.
True boolean datatype (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.
Interactive shiny application for working with different kinds of
 data. Visuals for data are provided. Runtime examples are provided.
Contains convenience functions for constructing and plotting bivariate probability distributions (probability mass functions, probability density functions and cumulative distribution functions). Currently supports uniform (discrete and continuous), binomial, Poisson, normal, bimodal and kernel distributions.
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)
Computes the joint probability mass function, the joint cumulative distribution, the correlation coefficient and generate random deviates from the Basu-Dhar bivariate geometric distribution. The package also computes the joint survival function in presence of cure fraction using the mixture model approach (For more details, see Achcar et. al (2016) <doi:10.1080/02664763.2015.1117589> and de Oliveira et. al (2018) <doi:10.1080/02664763.2018.1495701>).
Implements Roy's bivariate geometric model (Roy (1993) <doi:10.1006/jmva.1993.1065>): joint probability mass function, distribution function, survival function, random generation, parameter estimation, and more.
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.
This is a port of the WTC MATLAB package written by Aslak Grinsted
    and the wavelet program written by Christopher Torrence and Gibert P.
    Compo. This package can be used to perform univariate and bivariate
    (cross-wavelet, wavelet coherence, wavelet clustering) analyses.
Compute multivariate location, scale, and correlation
        estimates based on Tukey's biweight M-estimator.
Business days calculations based on a list of holidays and
    nonworking weekdays. Quite useful for fixed income and derivatives pricing.
Drawing heavy influence from 'blscrapeR', this package scrapes crime data from <https://www.bjs.gov/>. Specifically, it scrapes data from the National Crime Victimization Survey which tracks personal and household crime in the USA. The idea is to utilize the 'tidyverse' methodology to create an efficient work flow when dealing with crime statistics.
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. This package is also available as a module
    for the 'jamovi' statistical spreadsheet (see <https://www.jamovi.org>
    for more information).
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.
Performs a joint analysis of experiments with mixtures and random effects, taking on a process variable represented by a covariable.
Estimate influence networks from longitudinal bipartite relational data, where the longitudinal relations are continuous. The outputs are estimates of weighted influence networks among each actor type in the data set. The generative model is the Bipartite Longitudinal Influence Network (BLIN) model, a linear autoregressive model for these type of data. The supporting paper is ``Inferring Influence Networks from Longitudinal Bipartite Relational Data'', which is in preparation by the same authors. The model may be estimated using maximum likelihood methods and Bayesian methods. For more detail on methods, see Marrs et. al. <arXiv:1809.03439>.
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.
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
    <doi:10.1016/j.csda.2007.09.007>, contingency <doi:10.1080/03610920903140197>,
    continuous <doi:10.1007/s11634-013-0161-3> and categorical data-sets
    <doi:10.1007/s11222-014-9472-2>. 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.
Computation of large covariance matrices having a block structure up to a permutation of their columns and rows from a small number of 
    samples with respect to the dimension of the matrix. For further details we refer the reader to the paper Perrot-Dockes and Lévy-Leduc (2018), <arXiv:1806.10093>.
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.
Implements a heuristic algorithm to build blocks of a given size
	aiming to maximize similarity within strata across multiple covariates. 
	The blocking structure can be used for causal inference and for sensitivity
	analysis to unmeasured confounding. A stratified structure gives more flexibility
	for using multiple instrumental variables and direct treatment vs. control 
	analysis as evidence factors.
	Karmakar, B., Small, D. S., and Rosenbaum, P. R. (2018). 
	Rosenbaum, P. R. (2010)<ISBN:978-1-4419-1213-8>.
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:
 Žiberna (2007)<doi:10.1016/j.socnet.2006.04.002>, Žiberna (2008)<doi:10.1080/00222500701790207>,
 Žiberna (2014)<doi:10.1016/j.socnet.2014.04.002>.
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.
Constructs D-optimal or near D-optimal nested and crossed 
  block designs for unstructured or general factorial treatment designs. 
  Where a structured treatment design is required, a D-optimal or near 
  D-optimal treatment design is found based on a suitable model
  matrix design formula. The required block design is then found for the 
  required treatment design based on a defined set of block factors. 
  The block factors are added in sequence and each added block factor
  is optimized conditional on all previously added block factors.
  The block design can have repeated nesting down to any required 
  depth of nesting with either simple nested blocks or a
  crossed blocks design at each level of nesting. Outputs include a table
  showing the allocation of treatments to blocks and tables showing
  the achieved D-efficiency factors for each block and treatment design.
Segments a matrix in blocks with constant values.
    BRAULT V, CHIQUET J. and LEVY-LEDUC C. (2017) <doi:10.1214/17-EJS1270>.
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>).
Methods for non-compartmental
        pharmacokinetic analysis with observations below the 
        limit of quantification (BLOQ) are implemented as 
        described in Barnett, Helen Yvette. 
        "Optimizing pharmacokinetic studies utilizing 
        microsampling." PhD diss., Lancaster University, 2017. 
        (available online: 
        <http://eprints.lancs.ac.uk/89163/1/2017barnettphd.pdf>). 
        It includes estimating the area under the concentrations
        versus time curve (AUC) and its standard error using two 
        approaches: direct estimation using censored maximum 
        likelihood, also by first imputing the BLOQ's 
        using various methods, then compute AUC and its standard 
        error using imputed data.
Tools designed to make it easier for beginner and intermediate users to build and validate 
    binary logistic regression models. Includes bivariate analysis, comprehensive regression output, 
    model fit statistics, variable selection procedures, model validation techniques and a 'shiny' 
    app for interactive model building.
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.
Provides a Bayesian latent space 
    model for complex networks, either weighted or unweighted.
    Given an observed input graph, the estimates for the latent coordinates 
    of the nodes are obtained through a Bayesian MCMC algorithm. 
    The overall likelihood of the graph depends on a fundamental probability 
    equation, which is defined so that ties are more likely to exist 
    between nodes whose latent space coordinates are close. 
    The package is mainly based on the model by Hoff, Raftery and Handcock (2002)
    <doi:10.1198/016214502388618906> and contains some extra features 
    (e.g., removal of the Procrustean step, weights implemented as 
    coefficients of the latent distances, 3D plots). 
    The original code related to the above model was retrieved from
    <https://www.stat.washington.edu/people/pdhoff/Code/hoff_raftery_handcock_2002_jasa/>.
    Users can inspect the MCMC simulation, create and customize insightful 
    graphical representations or apply clustering techniques.
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), to combine distribution functions based on some external weights, and to invert distribution functions.  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.
A Bayesian regression package supporting constrained coefficient estimation and
     variable selection using Stan. This includes a robust variable selection
    algorithm by a horseshoe prior  (<doi:10.1093/biomet/asq017>) that finds the optimal model considering main effects,
    interactions as well as powers of given variables under potential parameter constraints.
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.
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>, with functions for prediction, model evaluation and inspection.
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.
Interactive visualization of Bayesian Networks. 
             The 'bnviewer' package reads various structure learning algorithms provided by the 
             'bnlearn' package and allows you to view them interactively.
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.
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, historical and current weather data from stations,
    agriculture bulletin data, BOM 0900 or 1500 weather bulletins and
    downloading and importing radar and satellite imagery 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'.
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
Bootstrapped correlation and response functions are widely used in dendrochronology to calibrate tree-ring proxy data against multiple instrumental time series of climatic variables. With this package, we provide functionality similar to DENDROCLIM2002 (Biondi and Waikul (2004) <10.1016/j.cageo.2003.11.004>). A full description with examples is given in Zang and Biondi (2013) <10.1016/j.dendro.2012.08.001>.
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".
Identifies genome-related molecular traits with significant 
  evidence of genetic regulation and performs a bootstrap procedure to 
  correct estimated effect sizes for over-estimation present in cis-QTL
  mapping studies (The "Winner's Curse"), described in Huang QQ *et al.* 
  2018 <doi: 10.1093/nar/gky780>.
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, logistic, multinomial and cox 'glmnet' models as well as lm and glm models.
Provides significance tests for second-order stationarity
	for time series using bootstrap wavelet packet tests.
Transforms focal observations' data, where different types of social interactions
  can be recorded by multiple observers, into asymmetric data matrices.
  Each cell in these matrices provides counts on the number of times
  a specific type of social interaction was initiated by the row subject and
  directed to the column subject.
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.
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.
Box-Cox-type transformations for linear and logistic models
             with random effects using non-parametric profile maximum
             likelihood estimation. The main functions are optim.boxcox()
             and boxcoxtype().
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.
Bayesian purity model to estimate tumor purity using methylation 
    array data (DNA methylation Infinium 450K array data) without reference samples.
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.
It is designed to calculate connection between (among) brain regions and plot connection lines. Also, the summary function is included to summarize group-level connectivity network. Kang, Jian (2016) <doi:10.1016/j.neuroimage.2016.06.042>.
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.
Interact with the 'Brandwatch' API <https://developers.brandwatch.com/docs>. 
  Allows you to authenticate to the API and obtain data for projects, queries, query groups tags and categories.
  Also allows you to directly obtain mentions and aggregate data for a specified query or query group.
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.
Model agnostic tool for decomposition of predictions from black boxes.
    Break Down Table shows contributions of every variable to a final prediction. 
    Break Down Plot presents variable contributions in a concise graphical way. 
    This package 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'.
        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.
Analysis of relative cell type proportions in bulk gene expression data. Provides a well-validated set of brain cell type-specific marker genes derived from multiple types of experiments, as described in McKenzie (2018) <doi:10.1038/s41598-018-27293-5>. For brain tissue data sets, there are marker genes available for astrocytes, endothelial cells, microglia, neurons, oligodendrocytes, and oligodendrocyte precursor cells, derived from each of human, mice, and combination human/mouse data sets. However, if you have access to your own marker genes, the functions can be applied to bulk gene expression data from any tissue. Also implements multiple options for relative cell type proportion estimation using these marker genes, adapting and expanding on approaches from the 'CellCODE' R package described in Chikina (2015) <doi:10.1093/bioinformatics/btv015>. The number of cell type marker genes used in a given analysis can be increased or decreased based on your preferences and the data set. Finally, provides functions to use the estimates to adjust for variability in the relative proportion of cell types across samples prior to downstream analyses.
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.
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>).
Fits Bootstrap with univariate spatial regression models using Bootstrap for Rapid Inference on Spatial Covariances (BRISC) for large datasets using Nearest Neighbor Gaussian Processes detailed in Saha and Datta (2018) <doi:10.1002/sta4.184>.
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.
    Walker E, Leclerc M, Rey JF, Beaudouin R, Soubeyrand S, and Messean A, (2017),
    A Spatio-Temporal Exposure-Hazard Model for Assessing Biological Risk and Impact,
    Risk Analysis, <doi:10.1111/risa.12941>.
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.
Summarizes key information about statistical objects in tidy
    tibbles. This makes it easy to report results, create plots and consistently
    work with large numbers of models at once. Broom provides three verbs that 
    each provide different types of information about a model. tidy()
    summarizes information about model components such as coefficients of
    a regression. glance() reports information about an entire model, such as
    goodness of fit measures like AIC and BIC. augment() adds information
    about individual observations to a dataset, such as fitted values or
    influence measures.
Convert fitted objects from various R mixed-model packages
    into tidy data frames along the lines of the 'broom' package.
    The package provides three
    S3 generics for each model: 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>.
Make 'Bootstrap 4' dashboards. Use the full power
    of 'AdminLTE3', a dashboard template built on top of 'Bootstrap 4' 
    <https://github.com/almasaeed2010/AdminLTE/tree/v3-dev>.
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. See Wang (2011) <doi:10.2202/1557-4679.1304>, Wang (2012) <doi:10.3414/ME11-02-0020>, Wang (2018) <doi:10.1080/10618600.2018.1424635>, Wang (2018) <doi:10.1214/18-EJS1404>.
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.
The kernelSmoothing() function allows you to square and smooth geolocated data. It calculates a classical kernel smoothing (conservative) or a geographically weighted median. There are four major call modes of the function. 
        The first call mode is kernelSmoothing(obs, epsg, cellsize, bandwith) for a classical kernel smoothing and automatic grid.
        The second call mode is kernelSmoothing(obs, epsg, cellsize, bandwith, quantiles) for a geographically weighted median and automatic grid.
        The third call mode is kernelSmoothing(obs, epsg, cellsize, bandwith, centroids) for a classical kernel smoothing and user grid.
        The fourth call mode is kernelSmoothing(obs, epsg, cellsize, bandwith, quantiles, centroids) for a geographically weighted median and user grid.
        Geographically weighted summary statistics : a framework for localised exploratory data analysis, C.Brunsdon & al., in Computers, Environment and Urban Systems C.Brunsdon & al. (2002) <doi:10.1016/S0198-9715(01)00009-6>, 
        Statistical Analysis of Spatial and Spatio-Temporal Point Patterns, Third Edition, Diggle, pp. 83-86, (2003) <doi:10.1080/13658816.2014.937718>.
We apply Bradley-Terry Model to estimate teams' ability in paired comparison data. Exponential Decayed Log-likelihood function is applied for dynamic approximation of current rankings and Lasso penalty is applied for variance reduction and grouping. The main algorithm applies the Augmented Lagrangian Method described by Masarotto and Varin (2012) <doi:10.1214/12-AOAS581>.
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.
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 (2017; Psychological Science, 28, 1547-1562).
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 (GPC). GPC
    compare two groups of observations (intervention vs. control
    group) regarding several prioritized endpoints. The net benefit
    and win ratio statistics can then be estimated and corresponding
    confidence intervals and p-values can be estimated using
    resampling methods or the asymptotic U-statistic theory. The software enables the use of thresholds of
    minimal important difference as well as stratification. Censored observations
	can be handled using Gehan's modification of the Wilcoxon statistic, inverse
    probability of censoring weights, or, in the case of right
    censoring, using the empirical survival curves in each group to
    estimating the expected score of the censored pairs.
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 kernel term and sampling techniques as described
    by Xavier (2017) <doi:10.1186/s12859-017-1582-3>.
Performs the 'Baumgartner-Weiss-Schindler' two-sample test of equal
   probability distributions, <doi:10.2307/2533862>. Also performs
   similar rank-based tests for equal probability distributions due to
   Neuhauser <doi:10.1080/10485250108832874> and
   Murakami <doi:10.1080/00949655.2010.551516>.
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
Query different C14 date databases and apply basic data cleaning, merging and calibration steps.
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).
Create interactive charts with the 'C3.js' <http://c3js.org/> charting library. All plot 
    types in 'C3.js' are available and include line, bar, scatter, and mixed geometry plots. Plot 
    annotations, labels and axis are highly adjustable. Interactive web based charts can be embedded 
    in R Markdown documents or Shiny web applications.
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.
Provides three variants of three-way correspondence analysis (ca)
 three-way symmetrical ca, three-way non-symmetrical ca, three-way ordered symmetrical ca.
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'.
Allows to plot a number of information related to the interpretation of Correspondence Analysis' results. It provides the facility to plot the contribution of rows and columns categories to the principal dimensions, the quality of points display on selected dimensions, the correlation of row and column categories to selected dimensions, etc. It also allows to assess which dimension(s) is important for the data structure interpretation by means of different statistics and tests. The package also offers the facility to plot the permuted distribution of the table total inertia as well as of the inertia accounted for by pairs of selected dimensions. Different facilities are also provided that aim to produce interpretation-oriented scatterplots. Reference: <doi:10.1016/j.softx.2015.07.001>.
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.
Performs the calibration procedure proposed by Sung et al. (2018+) <arXiv:1806.01453>. This calibration method is particularly useful when the outputs of both computer and physical experiments are binary and the estimation for the calibration parameters is of interest.
Functions to calculate weights, estimates of changes and corresponding variance 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/.
Transforms your uncalibrated Machine Learning scores to well-calibrated prediction estimates that can be interpreted as probability estimates. The implemented BBQ (Bayes Binning in Quantiles) model is taken from Naeini (2015, ISBN:0-262-51129-0).
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).
Implements methods for querying data from CalPASS using its API.
 CalPASS Plus.  MMAP API V1. <https://mmap.calpassplus.org/docs/index.html>.
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>.
Provides functions to more easily analyze computerized adaptive tests. Currently, functions for computerized 
  adaptive tests (CAT), computer adaptive multistage tests (CMT), and mixed computer adaptive multistage tests (McaMST) 
  utilizing CAT item-level adaptation for the initial stage and traditional MST module-level adaptation for the subsequent 
  stages have been created, and a variation of Hybrid computer adaptive MST is planned as well. For an in-depth look at 
  CAT and MST, see Weiss & Kingsbury (1984) <doi:10.1111/j.1745-3984.1984.tb01040.x> and Luecht & Nungester 
  (2000) <doi:10.1007/0-306-47531-6_6> respectively.
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.
Performs Covariate Assisted Principal (CAP) Regression for covariance matrix outcomes. The method identifies the optimal projection direction which maximizes the log-likelihood function of the log-linear heteroscedastic regression model in the projection space. See Zhao et al. (2018), Covariate Assisted Principal Regression for Covariance Matrix Outcomes, <doi:10.1101/425033> for details.
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 demographic characterizations and population management 
    evaluations see: "Baquero, et al." (2018), 
    <doi:10.1016/j.prevetmed.2018.07.006>. For modelling of 
    population dynamics see: "Baquero et al." (2016),
    <doi:10.1016/j.prevetmed.2015.11.009>. For sampling methods
    see: "Levy PS & Lemeshow S" (2013), "ISBN-10: 0470040076"; 
    "Lumley" (2010), "ISBN: 978-0-470-28430-8".
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, multinomial, Poisson or zero-inflated Poisson (ZIP), 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, including the BYM model (Besag et al. (1991) <doi:10.1007/BF00116466>), the Leroux model (Leroux et al. (2000) <doi:10.1007/978-1-4612-1284-3_4>) and the localised model (Lee et al. (2015) <doi:10.1002/env.2348>). Additionally,  a multivariate CAR (MCAR) model for multivariate spatial data is available, as is a two-level hierarchical model for modelling data relating to 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 been supported by the Engineering and Physical Science Research Council (EPSRC) grant EP/J017442/1, ESRC grant ES/K006460/1, Innovate UK / Natural Environment Research Council (NERC) grant NE/N007352/1 and the TB Alliance.
Spatio-temporal data from Scotland used in the vignettes accompanying the CARBayes (spatial modelling) and CARBayesST (spatio-temporal modelling) packages. All data except for the Scottish lip cancer data relate to the set of 271 Intermediate Geographies (IG, but also known as Intermediate Zones (IZ))  that make up the 2001 definition of 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.
Create beautiful images of source code using 'carbon.js'<https://carbon.now.sh/about>.
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.
When taking online surveys, participants sometimes respond to items without regard to their content.
    These types of responses, referred to as careless or insufficient effort responding, constitute significant problems for data quality, leading to distortions in data analysis and hypothesis testing, such as spurious correlations. The 'R' package 'careless' provides solutions designed to detect such careless / insufficient effort responses by allowing easy calculation of indices proposed in the literature. It currently supports the calculation of longstring, even-odd consistency, psychometric synonyms/antonyms, Mahalanobis distance, and intra-individual response variability (also termed inter-item standard deviation). For a review of these methods, see Curran (2016) <doi:10.1016/j.jesp.2015.07.006>.
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.
Sending functions to remote processes can be wasteful of
    resources because they carry their environments with them. With
    the carrier package, it is easy to create functions that are
    isolated from their environment. These isolated functions, also
    called crates, print at the console with their total size and can
    be easily tested locally before being sent to a remote.
Predicts categorical or continuous outcomes while concentrating on four key points. These are Cross-validation, Accuracy, Regression and Rule of Ten or "one in ten rule" (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 Receiver 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, choropleth, 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>.
Analytical tools are designed to help people understand the complex relationships associated with freeway performance and traffic breakdown. Emphasis is placed on: (1) Traffic noise or volatility; (2) Driver behavior and safety; and (3) Stochastic modeling, models that explain breakdown and performance.
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.
Given a large set of problems and their individual solutions case based reasoning seeks to solve a new problem by referring to the solution of that problem which is "most similar" to the new problem. Crucial in case based reasoning is the decision which problem "most closely" matches a given new problem. The basic idea is to define a family of distance functions and to use these distance functions as parameters of local averaging regression estimates of the final result. Then that distance function is chosen for which the resulting estimate is optimal with respect to a certain error measure used in regression estimation. The idea is based on: Dippon J. et al. (2002) <DOI:10.1016/S0167-9473(02)00058-0>.
Allows users to identify similar cases for qualitative case studies using statistical matching methods.
A significant pattern mining-based toolbox for region-based genome-wide association studies and higher-order epistasis analyses, implementing the methods described in Llinares-López et al. (2017) <doi:10.1093/bioinformatics/btx071>.
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
Performs classification and variable selection on high-dimensional tensors (multi-dimensional arrays) after adjusting for additional covariates (scalar or vectors) as CATCH model in Pan, Mai and Zhang (2018) <arXiv:1805.04421>. The low-dimensional covariates and the high-dimensional tensors are jointly modeled to predict a categorical outcome in a multi-class discriminant analysis setting. The Covariate-Adjusted Tensor Classification in High-dimensions (CATCH) model is fitted in two steps: (1) adjust for the covariates within each class; and (2) penalized estimation with the adjusted tensor using a cyclic block coordinate descent algorithm. The package can provide a solution path for tuning parameter in the penalized estimation step. Special case of the CATCH model includes linear discriminant analysis model and matrix (or tensor) discriminant analysis without covariates.
Methods and utilities for testing, identifying, selecting and  
    mutating objects as categorical or continous types. These functions work on both 
    atomic vectors as well as recursive objects: data.frames, data.tables, 
    tibbles, lists, etc..
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) and population-mean cosinor parameter tests (PMCtest).
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 Barrada (2017) <doi:10.18637/jss.v076.c01>).
`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,
    median 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 Confidence Bound Target (CBT) algorithm is designed for infinite arms bandit problem. It is shown that CBT algorithm achieves the regret lower bound for general reward distributions. Reference: Hock Peng Chan and Shouri Hu (2018) <arXiv:1805.11793>.
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.
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.
CEC divides data into Gaussian type clusters. The implementation allows the simultaneous use of various type Gaussian mixture models, performs the reduction of unnecessary clusters and it's able to discover new groups. Based on Spurek, P. and Tabor, J. (2014) <doi:10.1016/j.patcog.2014.03.006>.
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 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.
The degree of correlation between centre and peripheral items are shown by the length of the line between them. You can self-define the length by inputing the "distance" parameter. For example, you can input (1 - Pearson's correlation coefficient) as "distance" so that the stronger the correlation between centre and peripheral item, the nearer they will be in this plot. Also, If you do a hypothesis test and the null hypothesis is centre and peripheral items are the same, you can input -log(P) as distance. To sum up, the stronger the correlation between centre and peripheral is, the smaller the "distance" parameter should be. Due to its high degree of freedom, it can be applied to many different circumstance.
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).
Ceteris Paribus Profiles (What-If Plots) are designed to present model 
    responses around selected points in a feature space. 
    For example around a single prediction for an interesting observation. 
    Plots are designed to work in a model-agnostic fashion, they are working 
    for any predictive Machine Learning model and allow for model comparisons.
    Ceteris Paribus Plots supplement the Break Down Plots from 'breakDown' package.
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.
Performs causal functional mediation analysis (CFMA) for functional treatment, functional mediator, and functional outcome. This package includes two functional mediation model types: (1) a concurrent mediation model and (2) a historical influence mediation model. See Zhao et al. (2018), Functional Mediation Analysis with an Application to Functional Magnetic Resonance Imaging Data, <arXiv:1805.06923> for details.
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>.
Functions to analyze microarray comparative genome
        hybridization data using the Smith-Waterman algorithm
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.
The l1-penalized censored Gaussian graphical model is an extension of the graphical lasso estimator developed to handle datasets with censored observations. An EM-like algorithm is implemented to estimate the parameters of the censored Gaussian graphical models.
This code provides several different functions for cleaning and analyzing continuous glucose monitor data. Currently it works with 'Dexcom' (<https://www.dexcom.com>), 'iPro 2' (<http://professional.medtronicdiabetes.com/ipro2-professional-cgm>), Diasend (<https://diasend.com//us>), Libre (<https://www.freestylelibre.us/>) or Carelink (<https://www.medtronicdiabetes.com/products/carelink-personal-diabetes-software>) data. The cleandata() function takes a directory of CGM data files and prepares them for analysis. 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). cgmreport() also iterates through a directory of cleaned data, and produces PDFs of individual and aggregate AGP plots.
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.
Allows to create, evaluate, and differentiate computational graphs in R. A computational graph is a graph representation of a multivariate function decomposed by its (elementary) operations. Nodes in the graph represent arrays while edges represent dependencies among the arrays. An advantage of expressing a function as a computational graph is that this enables to differentiate the function by automatic differentiation. The 'cgraph' package supports various operations including basic arithmetic, trigonometry operations, and linear algebra operations. It differentiates computational graphs by reverse automatic differentiation. The flexible architecture of the package makes it applicable to solve a variety of problems including local sensitivity analysis, gradient-based optimization, and machine learning.
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.  Nested models can be compared using an adjusted 
    likelihood ratio test.
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.
Changing the name of an existing R package is annoying but common task especially in the early stages of package development. This package (mostly) automates this task.
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.
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
  interpolations of functions on a hypercube. If available through fftw3, the DCT-II/FFT
  is used to compute coefficients for a Chebyshev interpolation.
  Other interpolation methods for arbitrary Cartesian grids are also provided, a piecewise multilinear,
  and the Floater-Hormann barycenter method. For scattered data polyharmonic splines with a linear term
  is provided. The time-critical parts are written in C for speed. All interpolants are parallelized if
  used to evaluate more than one point.
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
Confirms if the number is Luhn compliant.
    Can check if credit card, IMEI number or any other Luhn based number is correct. 
    For more info see: <https://en.wikipedia.org/wiki/Luhn_algorithm>.
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 functions 
  to check the properties of common R objects.
  In the case of failure the functions issue informative error messages.
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 including nuclear magnetic resonance (NMR), infrared (IR),
    Raman, X-ray fluorescence (XRF) and other similar types of 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 for structured experiments, such as
    metabolomics investigations, where the samples fall into treatment and
    control groups. Graphical output is formatted consistently for publication quality
    plots. ChemoSpec is intended to be very user friendly and to help you get usable
    results quickly. A vignette covering typical operations is available.
Functions supporting the common needs of packages 'ChemoSpec' and 'ChemoSpec2D'.
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).
Toolkit for processing and calling interactions in capture Hi-C data. Converts BAM files into counts of reads linking restriction fragments, and identifies pairs of fragments that interact more than expected by chance. Significant interactions are identified by comparing the observed read count to the expected background rate from a count regression model.
Tools for connecting to 'CHILDES', an open repository for
    transcripts of parent-child interaction. For more information on the
    underlying data, see <http://childes-db.stanford.edu>.
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.
Design functions for 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)
Provides interactive, configurable and elegant graphics visualization of the human chromosomes 
    allowing users to map chromosome elements (like genes, SNPs etc.) on the chromosome plot. It introduces 
    a special plot viz. the "chromosome heatmap" that, in addition to mapping elements, can visualize the data 
    associated with chromosome elements (like gene expression) in the form of heat colors which can be highly 
    advantageous in the scientific interpretations and research work. Because of the large size of the chromosomes, 
    it is impractical to visualize each element on the same plot. However, the plot provides a magnified view for each 
    of chromosome location to render additional information and visualization specific for that location. You can map 
    thousands of genes and can view all mappings easily. Users can investigate the detailed information about the mappings
    (like gene names or total genes mapped on a location) or can view the magnified single or double stranded view of the 
    chromosome at a location showing each mapped element in sequential order. The plots can be saved as HTML documents 
    that can be customized and shared easily. In addition, you can include them in R Markdown or in R 'Shiny' applications.
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/>.