c(person("Anita M.", "Thieler", role = "aut"), person("Jonathan", "Rathjens", role = c("aut", "cre"), email = "jonathan.rathjens@tu-dortmund.de"), person("Roland", "Fried", role = "aut"), person("Brenton R.", "Clarke", role = "ctb", comment = "function betaCvMfit()"), person("Uwe", "Ligges", role = "ctb", comment = "function TK95()"), person("Matias", "Salibian-Barrera", role = "ctb", comment = "functions FastS() and FastTau()"), person("Gert", "Willems", role = "ctb", comment = "function FastTau()"), person("Victor", "Yohai", role = "ctb", comment = "function FastS()"))
c(person(given="Mengyang",family="Gu",role=c("aut","cre"),email="mg211@stat.duke.edu"),
    person(given="Jesus",family="Palomo", role=c("aut")),
    person(given="James",family="Berger", role="aut"))
person("Andreas", "Alfons",
    email = "alfons@ese.eur.nl",
    role = c("aut", "cre"))
c(person("Brian", "Ripley", role = c("aut", "cre"),
                    email = "ripley@stats.ox.ac.uk"),
             person("Michael", "Lapsley", role = "aut", 
	            comment = "1999 to Oct 2002"))
c(
  person("Nagi", "Teramo", , "teramonagi@gmail.com", c("aut", "cre")),
  person("Shinichi", "Takayanagi", , "shinichi.takayanagi@gmail.com", "aut"))
c(person("Mateusz", "Zoltak", role = c("aut", "cre"),
    email = "zozlak@zozlak.org"),
    person("Brian", "Ripley", role = "aut"),
    person("Michael", "Lapsley", role = "aut"),
    person("Will", "Beasley", role = "ctb",
    email = "wibeasley@hotmail.com"))
c(person("Roman", "Guchenko", role = c("aut", "cre"), email = "RomanGuchenko@yandex.ru"))
c(
    person("John", "Coene", , "jcoenep@gmail.com", role = c("aut", "cre")))
c( person("Kurt", "Hornik", role = "aut"),
              person("David", "Meyer", role = "aut"),
              person("Florian", "Schwendinger", role = "aut"),
              person("Stefan", "Theussl", role = c("aut", "cre"), email = "Stefan.Theussl@R-Project.org"),
              person("Diethelm", "Wuertz", role = "ctb") )
c(person("Stefan", "Theussl", role = c("aut", "cre"), email = "Stefan.Theussl@R-Project.org"))
c( person("Florian", "Schwendinger", role = "ctb"),
              person("Stefan", "Theussl", role = c("aut", "cre"), email = "Stefan.Theussl@R-Project.org"),
              person("Diethelm", "Wuertz", role = "ctb") )
person("Andrew", "Kane", email = "andrew@chartkick.com", role = c("aut", "cre"))
c(person("Karline","Soetaert", role = c("aut", "cre"), 
                     email = "karline.soetaert@nioz.nl"),
             person("yale sparse matrix package authors", role = "cph"))
person("Maxime", "Turgeon",
 email = "maxime.turgeon@mail.mcgill.ca", role = c("aut", "cre"))
c(person("Maëlle", "Salmon", email = "maelle.salmon@yahoo.se", role = c("aut", "cre")),
             person("Andrew", "MacDonald", role = c("ctb"), comment = "Andrew MacDonald reviewed the package for rOpenSci, see https://github.com/ropensci/onboarding/issues/24"),
             person("Andy ", "Teucher", role = c("ctb"), comment = "Andy Teucher reviewed the package for rOpenSci, see https://github.com/ropensci/onboarding/issues/24"))
c(person("Matthias", "Kohl", role=c("cre", "cph"), email="Matthias.Kohl@stamats.de"), 
		person("Mykhailo", "Pupashenko", role="ctb", comment="contributed wrapper functions for diagnostic plots"), 
		person("Gerald", "Kroisandt", role="ctb", comment="contributed testing routines"), 
		person("Peter", "Ruckdeschel", role=c("aut", "cph")))
c(
    person("Keerthi", "Chandra", email = "kckeerthi@gmail.com",role = c("aut", "cre")),
    person("Chris", "Johannessen", email = "",role = c("aut")),
    person("Siddharth", "Somayajula", email = "",role = c("ctb")))
c(person("Dewey", "Dunnington", role = c("aut", "cre"),
				email = "dewey@fishandwhistle.net"),
			person("Timothée", "Giraud", role = "ctb"))
c(
  person("Francois", "Michonneau", role=c("aut", "cre"),
         email="francois.michonneau@gmail.com"),
  person("Joseph", "Brown", role="aut"),
  person("David", "Winter", role="aut"))
c(person("Lala Septem", "Riza", role = "aut",
                     email = "lala_s_riza@yahoo.com"),
             person("Andrzej", "Janusz", role = "aut",
                    email = "janusza@mimuw.edu.pl"),
             person("Dominik", "Ślęzak", role = "ctb"),
             person("Chris", "Cornelis", role = "ctb"),
             person("Francisco", "Herrera", role = "ctb"),
             person("Jose Manuel", "Benitez", role = "ctb"),
             person("Christoph", "Bergmeir", role = c("ctb", "cre"),
                    email = "c.bergmeir@decsai.ugr.es"),
             person("Sebastian", "Stawicki", role = "ctb"))
c(
    person("Hadley", "Wickham",, "hadley@rstudio.com", c("aut", "cre", "cph")),
    person("Peter", "Danenberg",, "pcd@roxygen.org", c("aut", "cph")),
    person("Manuel", "Eugster", role = c("aut", "cph")),
    person("RStudio", role = "cph")
    )
c(person("Hélène", "Morlon", role = c("aut", "cre", "cph"), email = "morlon@biologie.ens.fr"))
c(person("Terry", "Therneau", role = "aut",
	             email = "therneau@mayo.edu"),
             person("Beth", "Atkinson", role = "aut",	
	             email = "atkinson@mayo.edu"),
             person("Brian", "Ripley", role = c("aut", "trl", "cre"),
                    email = "ripley@stats.ox.ac.uk",
		    comment = "author of initial R port"))
person("Maciek", "Sykulski", email = "macieksk@gmail.com", role = c("aut", "cre"))
c(person("David", "Springate", email = "daspringate@gmail.com",
    role = c("aut", "cre")),
    person("Evangelos", "Kontopantelis",
    role = c("ctb")),
    person("Ivan", "Olier",
    role = c("ctb")))
c(
    person("Joe", "Thorley", email = "joe@poissonconsulting.ca", role = c("aut", "cre")),
    person("Nathan", "Mantua", email = "nate.mantua@noaa.gov", role = c("aut", "dtc")),
    person("Steven R.", "Hare", email = "hare@iphc.washington.edu", role = c("aut", "dtc")))
c(person("Joshua",family="Pritikin", email="jpritikin@pobox.com",
    role=c('cre','aut')), person("Jonathan",family="Weeks",
    email="weeksjp@gmail.com", role=c('ctb')),
    person("Li", family="Cai", role=c('ctb')), person("Carrie",
    family="Houts", role=c('ctb')), person("Phil", "Chalmers", email =
    "rphilip.chalmers@gmail.com", role = c("ctb")),
    person(c("Michael", "D."), "Hunter", role="ctb"),
    person(c("Carl", "F."), "Falk", email =
    "falkcarl@msu.edu", role = c("ctb"))
    )
c(
    person("Clayton","Barrows", , "clayton.barrows@nrel.gov", c("cre","ctb")),
    person("Eduardo", "Ibanez", , "edu.ibanez@gmail.com", c("aut")),
    person("Jef", "Daniels", , " ", role=c("aut")),
    person("Marcin", "Kalicinski", , "kalita@poczta.onet.pl", "ctb", "for the included RapidXml source"),
    person("National Renewable Energy Laboratory", role = "cph")
    )
c(
    person("Scott", "Chamberlain", role = c("aut", "cre"), email = "myrmecocystus@gmail.com"),
    person("Carl", "Boettiger", role = "aut", email = "cboettig@gmail.com"),
    person("Karthik", "Ram", role = "aut", email = "karthik.ram@gmail.com")
    )
c(person(given="Pedro-Pablo", family="Garrido Abenza", role=c("aut","cre"), email="pgarrido@umh.es"))
person("daniell", "toth", email="danielltoth@yahoo.com",
    role=c("aut", "cre"))
c(
    person("Mathieu", "Basille", email = "basille@ufl.edu", role =
           c("aut")),
    person("David", "Bucklin", email = "dbucklin@ufl.edu", role =
           c("aut", "cre")) )
c(
    person("Balázs", "Dukai", email = "balazs.dukai@gmail.com", role = c("aut"),
          comment =  "Package creator during Google Summer of Code 2016"),
    person("Mathieu", "Basille", email = "basille@ufl.edu", role =
           c("aut")),
    person("David", "Bucklin", email = "dbucklin@ufl.edu", role =
           c("aut", "cre")),
    person("Clément", "Calenge", email = "clement.calenge@oncfs.gouv.fr",
          role = c("aut")) )
c(
    person('Onur Ismail', 'Filiz', , 'onur@fb.com', role=c('aut', 'cre')),
    person('Sergey', 'Goder', , 'sgoder@fb.com', role='aut'),
    person('John Myles', 'White', , 'johnmyleswhite@fb.com', role='ctb')
    )
c(
        person("Simon", "Crouch", email="simon.crouch@york.ac.uk", role='aut'),
        person("Stephanie", "Lax", email="stephanie.lax@york.ac.uk", role='aut'),
        person("Stuart", "Lacy", email="stuart.lacy@york.ac.uk", role=c('cre', 'aut'))
        )
c(person("Simon","Bond", email="simon.bond@addenbrookes.nhs.uk", role = c("aut","cre"), comment = "primary author of code, secondary author of vignette"),person("Annabel", "Allison", role = "aut", comment = "primary author of vignette, secondary author of code"))
c(
  person("Rajen", "Shah", email = "r.shah@statslab.cam.ac.uk", role = c("aut", "cre")),
  person("Peter", "Buhlmann", email="peter.buehlmann@stat.math.ethz.ch ", role="aut"))
c(person("Thomas J.", "Leeper", role = c("aut", "cre"),
                    email = "thosjleeper@gmail.com"),
             person("Jeroen","Ooms", role = "ctb"))
c(
    person("Jannes", "Muenchow", , "jannes.muenchow@uni-jena.de", c("aut", "cre")),
    person("Patrick", "Schratz", , "patrick.schratz@uni-jena.de", role = "aut"))
c(person(given = "Torben", family = "Schulz-Streeck", email
        = "torben.schulz-streeck@uni-hohenheim.de", role = "aut"),
        person(given = "Boubacar", family = "Estaghvirou", email =
        "S.Ould@uni-hohenheim.de", role = "aut"), person(given =
        "Frank",family = "Technow", email =
        "Frank.Technow@uni-hohenheim.de", comment = "University of
        Hohenheim, Institute of Plant Breeding, Seed Science and
        Population Genetics, Stuttgart, Germany", role =
        c("aut","cre")))
c(person("Ludovik","Çoba", role=c("aut","cre","cph"), email="lcoba@unishk.edu.al"), 
             person("Markus","Zanker", role="ctb",email="Markuz.Zanker@aau.at"))
c(person("VP", "Nagraj", role = c("aut", "cre"), email = "vpnagraj@virginia.edu"))
c(person(given="Natalia", family="Belgorodski", email="belgorodski@stat-up.de",
             role=c("aut"), comment="STAT-UP Statistical Consulting"),
      person(given="Matthias", family="Greiner", email="matthias.greiner@bfr.bund.de",
             role=c("aut", "cre"), comment="Federal Institute for Risk Assessment, Germany"),
      person(given="Kristin", family="Tolksdorf", email="kristin.tolksdorf@bfr.bund.de",
             role=c("aut"), comment="Federal Institute for Risk Assessment, Germany"),
      person(given="Katharina", family="Schueller", email="schueller@stat-up.de",
             role=c("aut"), comment="STAT-UP Statistical Consulting"),
      person(given="Matthias", family="Flor", email="Matthias.Flor@bfr.bund.de",
             role=c("ctb"), comment="Federal Institute for Risk Assessment, Germany"),  
      person(given="Lutz", family="Göhring", email="lutz.goehring@gmx.de",
             role=c("ctb"), comment="Lutz Göhring Consulting"))
c(person( given="Daniel
    W.",family="Heck",email="dheck@mail.uni-mannheim.de", role=c("aut","cre")),
    person(given="Morten",family="Moshagen",email="moshagen@uni-mannheim.de",
    rol=c("aut")))
c(
	   person("Paulo", "Prado", , "prado@ib.usp.br", role = c("aut", "cre")),
	   person("Andr\'e", "Chalom", , "andrechalom@gmail.com", role = c("aut")),
	   person("Alexandre", "Oliveira", , "adalardo@usp.br", role = c("aut"))
	   )
person("Ken", "Kleinman", email= "ken.kleinman@gmail.com", role=c("aut", "cre"))
c(person("John", "Harrison", , "johndharrison0@gmail.com", 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")
	)
c(person("Bernd", "Bischl", email = "bernd_bischl@gmx.net",
    role = c("aut")), person("Jakob", "Bossek", email = "j.bossek@gmail.com", role = c("aut", "cre")))
person("Benjamin", "Skinner", email = "b.skinner@vanderbilt.edu",
    role = c("aut", "cre"))
c(
  person("Emmanuel", "Blondel", role = c("aut", "cre"), email = "emmanuel.blondel1@gmail.com"),
  person("Matthieu", "Stigler", role = c("ctb")))
c(
  person("Kevin","Wright", email="kw.stat@gmail.com", role=c("aut","cre")),
  person("Jean-Louis","Laffont", role=c("aut")))
c(person("Jeff", "Dumont", role = c("aut", "cre"), email = "Jeff.Dumont@rsginc.com"),
             person("Jeff", "Keller", role = "aut", email = "Jeff.Keller@rsginc.com"),
             person("Chase", "Carpenter", role = "ctb", email = "Chase.Carpenter@rsginc.com"))
c(person('Iván', 'Ayala-Bizarro', role=c('aut','cre'),
	email='ivan.ayala@unh.edu.pe'),
	person('Jessica','Zúñiga-Mendoza', role='aut',
	email='zumeje@gmail.com'))
c(person('Philippe', 'Marchand', email = 'pmarchand@sesync.org', 
             role = c('aut', 'cre')), 
             person('Mike', 'Smorul', role = 'ctb'))
c(person("Russell V", "Lenth", role = c("aut", "cre"), 
    email = "russell-lenth@uiowa.edu"))
c(
    person("Scott", "Chamberlain", role = c("aut", "cre"), email = "myrmecocystus@gmail.com"),
    person("Kevin", "Ushey", role = "aut", email = "kevinushey@gmail.com"),
    person("Hao", "Zhu", role = "aut", email = "haozhu233@gmail.com")
    )
c(person(given = "Peter", family = "Wittek", role = c("aut"), email = ""),
             person(given = "Shichao", family = "Gao", role = c("cre"), email = "xgdgsc@gmail.com"))
c(
    person("Yixuan", "Qiu", , "yixuan.qiu@cos.name", c("aut", "cre")),
    person("Jiali", "Mei", , "vermouthmjl@gmail.com", "aut",
           comment = "Function interface of matrix operation"),
    person("Gael", "Guennebaud", , "gael.guennebaud@inria.fr", "ctb",
           comment = "Eigenvalue solvers from the 'Eigen' library"),
    person("Jitse", "Niesen", , "jitse@maths.leeds.ac.uk", "ctb",
           comment = "Eigenvalue solvers from the 'Eigen' library")
    )
c(person(given = "Jose", family = "Gama", role = c("aut","cre"),email = "rxprtgama@gmail.com"),
    person(given = "John", family = "Fuller", role = c("aut","cph")),
    person(given = "Paolo", family = "Leva", role = c("aut","cph")))
as.person(c(
    "Hadley Wickham <hadley@rstudio.com> [aut, cre]",
    "David A. James [aut]",
    "Seth Falcon [aut]",
    "SQLite Authors (for the included SQLite sources) [ctb]",
    "Liam Healy (for the include SQLite extensions) [ctb]",
    "RStudio [cph]"
    ))
c(person("Imanuel", "Costigan", role = c("aut", "cre"),
                    email = "i.costigan@me.com"),
             person("The jTDS Project (for MSSQL Server driver)",
                    role = "aut"),
             person("Simon", "Urbanek", role = "ctb",
                    email = "Simon.Urbanek@r-project.org"),
             person("Hadley", "Wickham", email = "hadley@rstudio.com",
                    role = "ctb"),
             person("Romain", "Francois", email = "romain@r-enthusiasts.com",
                    role = "ctb"),
             person("RStudio", role = "cph"),
             person("The Legion Of The Bouncy Castle", role = c("cph", "ctb")))
c(person("Jesse", "Krijthe", role = c("aut", "cre"),
    email = "jkrijthe@gmail.com"))
c(person("Jiqiang", "Guo", email = "guojq28@gmail.com", role = "aut"),
             person("Jonah", "Gabry", email = "jsg2201@columbia.edu", role = "aut"),
             person("Ben", "Goodrich", email = "benjamin.goodrich@columbia.edu", role = c("cre", "aut")),
             person("Daniel", "Lee", role = "ctb"),
             person("Krzysztof", "Sakrejda", role = "ctb"),
             person("Trustees of", "Columbia University", role = "cph"),
             person("Oleg", "Sklyar", role = "cph", comment = "R/cxxfunplus.R"),
             person("The R", "Core Team", role = "cph", comment = "R/pairs.R"),
             person("Jens", "Oehlschlaegel-Akiyoshi", role = "cph", comment = "R/pairs.R"),
             person("Hadley", "Wickham", role = "cph", comment = "R/rtools.R"),
             person("Joel", "de Guzman", role = "cph", comment = "Boost"),
             person("John", "Fletcher", role = "cph",  comment = "Boost"),
             person("Thomas", "Heller", role = "cph",  comment = "Boost"),
             person("Eric", "Niebler", role = "cph",   comment = "Boost"))
c(person("Jonah", "Gabry", email = "jsg2201@columbia.edu", role = "aut"),
             person("Trustees of", "Columbia University", role = "cph"),
             person("R Core", "Deveopment Team", role = "cph", 
                     comment = "R/stan_aov.R"),
             person("Douglas", "Bates", role = "cph", comment = "R/pp_data.R"),
             person("Martin", "Maechler", role = "cph", comment = "R/pp_data.R"),
             person("Ben", "Bolker", role = "cph", comment = "R/pp_data.R"),
             person("Steve", "Walker", role = "cph", comment = "R/pp_data.R"),
             person("Brian", "Ripley", role = "cph", 
                    comment = "R/stan_aov.R, R/stan_polr.R"),
             person("William", "Venables", role = "cph", comment = "R/stan_polr.R"),
             person("Ben", "Goodrich", email = "benjamin.goodrich@columbia.edu", 
                    role = c("cre", "aut")))
c(person("Jonah", "Gabry", email = "jsg2201@columbia.edu",
                    role = c("aut", "cre")),
             person("Ben", "Goodrich", email = "benjamin.goodrich@columbia.edu",
                    role = "aut"),
             person("Trustees of", "Columbia University", role = "cph"))
c(
    person("Benjamin", "Leutner", role=c("cre", "aut"), email="benjamin.leutner@uni-wuerzburg.de"),
    person("Ned", "Horning", role ="aut", email="horning@amnh.org")
    )
c(person("Mark", "Clements", role = c("aut", "cre"),
		     email = "mark.clements@ki.se"),
	      person("Xing-Rong", "Liu", role = "aut",
		     email = "xingrong.liu@ki.se"),
	      person("Paul", "Lambert", role = "ctb", email="pl4@leicester.ac.uk"))
c(
    person("Hadley", "Wickham", role = c("aut"), email = "hadley@rstudio.com"),
    person("JJ", "Allaire", role = c("aut", "cre"), email = "jj@rstudio.com"),
    person("Kevin", "Ushey", role = c("ctb"), email = "kevin@rstudio.com"),
    person(family = "RStudio", role = "cph")
    )
c(person("Scott", "Chamberlain", role = c("aut", "cre"),
    email = "myrmecocystus@gmail.com"),
    person("Thomas J.", "Leeper", role=c("ctb"),
    email="thosjleeper@gmail.com"))
c(person("N. Benjamin", "Erichson", role = c("aut", "cre"), email = "nbe@st-andrews.ac.uk"))
c(person("Reinhard", "Harter", role = "aut"),
             person("Kurt", "Hornik", role = c("aut", "cre"),
                    email = "Kurt.Hornik@R-project.org"),
             person("Stefan", "Theussl", role = "aut"),
             person("Cyrille", "Szymanski", role = "ctb",
                    email = "cnszym@gmail.com"))
c(
    person("David", "Gohel", role = c("aut", "cre"),
    email = "david.gohel@lysis-consultants.fr") )
c( person(given="Henrik", family="Singmann", email="singmann+rtdists@gmail.com", role=c("aut", "cre")),
             person(given="Scott", family="Brown", role=c("aut")), person(given="Matthew", family="Gretton",
             role=c("aut")), person(given="Andrew", family="Heathcote", role=c("aut")), person(given="Andreas",
             family="Voss", role=c("ctb")), person(given="Jochen", family="Voss", role=c("ctb")),
             person(given="Andrew", family="Terry", role=c("ctb")) )
c(person(c("Michael","E."), "Schaffer", role=c("aut", "cre", "cph"), email = "mschaff@gmail.com"))
c(
  person("JJ", "Allaire", role = c("aut", "cre"), email = "jj@rstudio.com"),
  person(family = "R Foundation", role = c("aut", "cph")),
  person("Hadley", "Wickham", role = "aut", email = "hadley@rstudio.com"),
  person(family = "Journal of Statistical Software", role = c("aut", "cph")),
  person("Yihui", "Xie", role = "aut", email = "yihui@rstudio.com"),
  person(family = "RStudio", role = "cph"),
  person("Ramnath ", "Vaidyanathan", role = c("aut", "cph"), email = "ramnath.vaidya@gmail.com"),
  person(family = "Assocation for Computing Machinery", role = c("aut", "cph")),
  person("Carl", "Boettiger", role = c("aut", "cph"), email = "cboettig@gmail.com"),
  person(family = "Elsevier", role = c("aut", "cph")),
  person("Karl", "Broman", role = c("aut", "cph"), email = "kbroman@gmail.com"),
  person("Kirill", "Mueller", role = c("aut", "cph"), email = "kirill.mueller@ivt.baug.ethz.ch"),
  person("Bastiaan", "Quast", role = c("aut", "cph"), email = "bquast@gmail.com"),
  person("Randall ", "Pruim", role = c("aut", "cph"), email = "rpruim@gmail.com"),
  person("Ben", "Marwick", role = c("aut", "cph"), email = "bmarwick@uw.edu"),
  person("Charlotte", "Wickham", role = c("aut", "cph"), email = "cwickham@gmail.com"),
  person("Oliver", "Keyes", role = c("aut", "cph"), email = "okeyes@wikimedia.org"),
  person("Miao", "Yu", role = c("aut", "cph"), email = "yufreecas@gmail.com")
  )
c(
    person("Joe", "Thorley", email = "joe@poissonconsulting.ca", role = c("aut", "cre")),
    person("Luke", "Miller", email = "contact@lukemiller.org", role = "aut"))
c(person("Angel", "Berihuete", email = "angel.berihuete@uca.es", role = c("aut", "cre")), person("Carmen Dolores", "Ramos", email = "carmen.ramos@uca.es", role = "aut"), person("Miguel Angel", "Sordo", email = "mangel.sordo@uca.es", role = "aut"))
c(person("Serge", "Iovleff", role=c("aut","cre"),
    email="serge.iovleff@stkpp.org"),
    person("Parmeet", "Bhatia", role="ctb",
    email="bhatia.parmeet@gmail.com"))
c(person("Serge", "Iovleff", role=c("aut","cre"),
    email="serge.iovleff@stkpp.org"),
    person("Vincent", "Kubicki", role="ctb",
    email="Vincent.Kubicki@inria.fr"),
    person("Quentin", "Grimonprez", role="ctb",
    email="Quentin.Grimonprez@inria.fr"),
    person("Parmeet", "Bhatia", role="ctb",
    email="bhatia.parmeet@gmail.com"))
c( person(given = c("John", "C"), family = "Nash", role =
        c("aut", "cre", "cph"), email = "nashjc@uottawa.ca"),
          person(given = c("Stephen", "G"), family = "Nash", role =
        c("aut", "cph"), email = "sgn@gmu.edu") )
c(person("Jonathan Richard", "Shewchuk" , role=c("ctb", "cph")),
             person("David C.", "Sterratt", role=c("cph","aut","cre"),
               email="david.c.sterratt@ed.ac.uk"),
             person("Elias", "Pipping", role=c("ctb")))
c(
    person("Jesse", "Krijthe", ,"jkrijthe@gmail.com", role = c("aut", "cre")),
    person("Laurens", "van der Maaten", role = c("cph"), comment = "Author of original C++ code")
    )
c(person("Philipp", "Boersch-Supan", email = "pboesu@gmail.com", role = c("aut", "cre")),
             person("Thanawin", "Rakthanmanon", role = "aut"),
             person("Bilson", "Campana", role = "aut"),
             person("Abdullah", "Mueen", role = "aut"),
             person("Gustavo", "Batista", role = "aut"),
             person("Eamonn", "Keogh", role = "aut")
             )
c(person("Matthew", "Denwood", role=c("aut","cre"), email="md@sund.ku.dk"),
             person("Martyn", "Plummer", role="cph", comment="Copyright holder of the code in /src/distributions/jags, src/distributions/DPar1.*, configure.ac, and original copyright holder of some modified code where indicated"))
c(person("Nicholas", "Henderson", email = "nhenders@stat.wisc.edu", role = "cre"), person("Michael", "Newton", email = "newton@stat.wisc.edu", role = "aut"))
c(
    person("Stefan", "Schlager",, "zarquon42@gmail.com", c("aut", "cre", "cph")),
    person("Girinon", "Francois",,, c("ctb"))
    )
c(
    person("Gábor", "Csárdi", , "csardi.gabor@gmail.com", role = c("aut", "cre")),
    person("Jeroen", "Ooms", , "jeroen.ooms@stat.ucla.edu", role = "ctb"))
c(
    person("David", "Gohel", role = c("aut", "cre"),
      email = "david.gohel@ardata.fr"),
    person("Bob", "Rudis", role = c("ctb"),
      comment = "the javascript code used by function set_attr"),
    person("Francois", "Brunetti", role = c("ctb"),
      email = "francois.brunetti@lysis-consultants.fr",
      comment = "clipping algorithms")
    )
c(person("Karim", "Oualkacha", email =
             "oualkacha.karim@uqam.ca", role = c("aut", "cre")),
             person("M'Hamed Lajmi", "Lakhal-Chaieb", email =
             "Lajmi.Lakhal@mat.ulaval.ca", role = "aut"),
             person("Celia M.T.", "Greenwood", email =
             "celia.greenwood@mcgill.ca", role = "aut"),
             person("Lennart C.", "Karssen", email =
             "l.c.karssen@polyomica.com", role = "aut"),
             person("Sodbo", "Sharapov", email =
             "sharapovsodbo@gmail.com", role = "aut")
             )
c(person("Edmund", "Hart", role = c("aut", "cre"),
    email = "edmund.m.hart@gmail.com"))
c(person("Kurt", "Hornik", role = c("aut", "cre"),
                    email = "Kurt.Hornik@R-project.org"),
             person("Christian", "Buchta", role = "ctb"),
             person("Torsten", "Hothorn", role = "ctb"),
             person("Alexandros", "Karatzoglou", role = "ctb"),
             person("David", "Meyer", role = "ctb"),
             person("Achim", "Zeileis", role = "ctb"))
c(person("Kurt", "Hornik", role = c("aut", "cre"),
                    email = "Kurt.Hornik@R-project.org"),
             person("University of Waikato", role = c("ctb", "cph"),
                    comment = "Weka Java library"))
c(person("Dominik", "Wabersich", 
                    role=c("aut", "cre"),
                    email="dominik.wabersich@gmail.com"))
c(person("Paolo", "Sonego", role = c("aut", "cph", "cre"),
        email = "paolo.sonego@gmail.com"), person("Mikko", "Korpela",
        role = "ctb"))
person("Varun", "Giri", role=c("aut", "cre"), email="varungiri@gmail.com")
c(
   person("Melissa", "Hallow", 
      role = "aut", email = "hallowkm@uga.edu"),
   person("Wenping", "Wang", 
      role = c("aut", "cre"), email = "wwang8198@gmail.com")
   )
person("Mark", "Heckmann", 
                  role = c("aut", "cre"),
                  email="heckmann.mark@gmail.com")
c(
    person("Marcin", "Kosinski", role = c("aut", "cre"),
    email = "m.p.kosinski@gmail.com")
    )
c(
    person('Ege', 'Rubak', email='rubak@math.aau.dk', role = c('aut', 'cre')),
    person("Jeroen", "Ooms", , "jeroen.ooms@stat.ucla.edu", role = "aut", comment = "configure script"),
    person(family = "Google, Inc.", role = "cph", comment = "Original s2 source code")
    )
c(
    person("Virginie", "Guemas", , "virginie.guemas@bsc.es", role = "aut"),
    person("Nicolau", "Manubens", , "nicolau.manubens@bsc.es", role = c("aut", "cre")),
    person("Louis-Philippe", "Caron", , "louis-philippe.caron@bsc.es", role = "aut"),
    person("Verónica", "Torralba", , "veronica.torralba@bsc.es", role = "aut"), 
    person("Chloé", "Prodhomme", , "chloe.prodhomme@bs.es", role = "aut"),
    person("Martin", "Ménégoz", , "martin.menegoz@bsc.es", role = "aut"),
    person("Javier", "Garcia-Serrano", , "javier.garcia@bsc.es", role = "aut"),
    person("Fabian", "Lienert", , "fabian.lienert@bsc.es", role = "aut"),
    person("Ludovic", "Auger", , "ludovic.auger@meteo.fr", role = "aut"),
    person("Isabel", "Andreu-Burillo", , "isabel.andreu.burillo@ic3.cat", role = "aut"))
c(person("Titus", "von der Malsburg", email="malsburg@posteo.de",
    role=c("aut", "cph", "cre")))
c(person("Stephen", "Hogg", role = c("aut", "cre"),
		     email = "saenet.r@gmail.com"),
	      person("Eugene", "Dubossarsky", role = "aut"))
c(person("Sebastian", "Warnholz", role = c("aut", "cre"),
                    email = "Sebastian.Warnholz@fu-berlin.de"),
             person("Timo", "Schmid", role = "aut",
                    email = "Timo.Schmid@fu-berlin.de"))
c(person("Rianne", "de Heide", role = c("aut", "cre"),
		     email = "r.de.heide@cwi.nl"),
	      person("Gustavo", "de los Campos", role = "ctb"),
	      person("Paulino", "Perez Rodriguez", role = "ctb"),
	      person("Bob", "Wheeler", role = "ctb"))
c(person(given = "Thomas", family = "Lumley", role = "aut", email = "tlumley@u.washington.edu"),
             person(given = "Achim", family = "Zeileis", role = c("aut", "cre"), email = "Achim.Zeileis@R-project.org"))
person( "Anthony Joseph" , "Damico" , role = c( "aut" ,
        "cre" ) , email = "ajdamico@gmail.com" )
c(person(given="Jesus",family="Palomo", role=c("aut",
        "cre"),email="jesus.palomo@urjc.es"),
        person(given="Gonzalo",family="Garcia-Donato",
        role="aut",email="gonzalo.garciadonato@uclm.es"),
        person(given="Rui",family="Paulo", role="aut",email="rui@iseg.utl.pt"),
	person(given="James",family="Berger",
        role="ctb",email="berger@stat.duke.edu"),
        person(given="Maria Jesus",family="Bayarri",
        role="ctb",email="susie.bayarri@uv.es"),
        person(given="Jerome",family="Sacks",
        role="ctb",email="sacks@niss.org"))
c(person("Luca", "Weihs", role=c("aut","cre"), email="lucaw@uw.edu"),
             person("Martyn", "Plummer", role="ctb"))
c(person("Paul", "Fischer", role = c("aut", "cre","cph"),
		     email = "pafi@dtu.dk"),person("Astrid", "Hilbert", role = c("ctb","cph"),
		     email = "astrid.hilbert@lnu.se"))
c(person("Hongyu", "Yang", role = c("aut", "cre"),
		     email = "hongyuy@mit.edu"),
	      person("Morris", "Chen", role = "ctb",
		     email = "Morris_CM_Chen@wistron.com"),
	      person("Cynthia", "Rudin", role = c("aut", "ctb"),
		     email = "rudin@cs.duke.edu"),
	      person("Margo", "Seltzer", role = c("aut", "ctb"),
		     email = "margo@eecs.harvard.edu"))
c(person("Tim", "Kern", role=c("cre"),
    email = "kernt@usgs.gov"),
    person("Luke", "Winslow", role = c("aut"),
    email = "lwinslow@usgs.gov"),
    person("Scott", "Chamberlain", role = c("aut"),
    email = "scott@ropensci.org"),
    person("Alison", "Appling", role = c("aut"),
    email = "aappling@usgs.gov"),
    person("Jordan", "Read", role = c("aut"),
    email = "jread@usgs.gov"))
person("Benjamin", "Kjellson", email = "benjak@math.su.se",
  role = c("aut", "cre"))
c(
    person(
      "Julien", "Barnier",
      email = "julien.barnier@ens-lyon.fr",
      role = c("aut", "cre")
    ),
    person(
        "Kent", "Russell"
        , role = c("aut", "ctb")
        , email = "kent.russell@timelyportfolio.com"
    ),
    person(
        "Mike", "Bostock"
        , role = c("aut", "cph")
        , comment = "d3.js library, http://d3js.org"
    ),
    person(
        "Susie", "Lu"
        , role = c("aut", "cph")
        , comment = "d3-legend library, http://d3-legend.susielu.com/"
    ),
    person(
        "Speros", "Kokenes"
        , role = c("aut", "cph")
        , comment = "d3-lasso-plugin library, https://github.com/skokenes/D3-Lasso-Plugin"
    )
  )
c(
    person("Sean", "Turner", , "swd.turner@gmail.com", c("aut", "cre")),
    person("Stefano", "Galelli", , "stefano_galelli@sutd.edu.sg", "aut")
    )
c(person("Matteo", "Dell'Omodarme", role = c("aut", "cre"), email
        = "mattdell@fastmail.fm"), person("Giada", "Valle",
        role = "aut"))
c(
  person("Guillaume", "Cornu", role=c("cre","aut"),email="gcornu@cirad.fr"),
  person("Frederic", "Mortier", role="aut", email="fmortier@cirad.fr"),
  person("Catherine", "Trottier", role="aut", email="catherine.trottier@univ-montp3.fr"),
  person("Xavier", "Bry", role="aut", email="xavier.bry@univ-montp2.fr"))
person("Michael", "North", email = "north@anl.gov", role = c("aut", "cre"))
person("Daniel", "Marcelino", email = "dmarcelino@live.com", role = c("aut", "cre"))
c(person("Mikko", "Vihtakari", role=c("aut", "cre"), email="mikko.vihtakari@gmail.com"))
c(person(given = "Borja", family = "Calvo", role = c("aut", "cre") , email ="borja.calvo@ehu.eus"), 
   person(given = "Guzman", family = "Santafe", role = c("aut"), email = "guzman.santafe@unavarra.es"))
c(person("Daniel", "Edsgärd", email = 'edsgard@hotmail.com', role = c("aut", "cre")))
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@uwa.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("Yichi", "Zhang", role = c("aut", "cre"),
		     email = "yzhang52@ncsu.edu"))
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(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  = '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("Jean-Pierre", "Gattuso", role = c("aut", "cre", "cph"), email = "gattuso@obs-vlfr.fr"),
  person("Jean-Marie", "Epitalon", role = "aut", email = "Jean-Marie.Epitalon@cerfacs.fr"),
  person("Heloise", "Lavigne", role = "aut", email = "lavigne@obs-vlfr.fr"),
  person("James", "Orr", role = "aut"),
  person("Bernard", "Gentili", 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"))
c(person("Anne Corinne", "Huggins-Manley", role = c("aut", "cre"), email = "ahuggins@coe.ufl.edu"), person("Douglas", "Whitaker", role = "aut"))
c(person("Justin", "de Benedictis-Kessner", email = "justindbk@gmail.com", role = c("aut", "cre")),
  person("Christian", "Lemp", email = "christianlemp@gmail.com", role = c("ctb","ccp")))
c(person("Seong-Yun", "Hong", role = c("aut", "cre"), email
        = "hong.seongyun@gmail.com"), person("David", "O'Sullivan",
        role = "ctb"))
c(person("Frank","Papenmeier", role=c("aut","cre"),
    email="frank.papenmeier@uni-tuebingen.de"),
    person("Konstantin","Sering", role="ctb"))
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("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")))
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("Patrick", "Mair", role = c("aut", "cre"), email = "mair@fas.harvard.edu"), person("Jose Fernando", "Vera", role = "aut", email = "jfvera@ugr.es"))
c(person("Armin", "Monecke", role=c("aut", "cph","cre"),
        email="armin.monecke@stat.uni-muenchen.de"),
        person("Friedrich", "Leisch", role=c("aut", "ths")))
c(person("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"))
c(person(given="Rune Haubo Bojesen", family="Christensen",
	   email="rhbc@dtu.dk", role=c("aut")),
	   person(given="Per Bruun", family="Brockhoff",
	   email="perbb@dtu.dk", role=c("aut", "cre")))
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("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 =
    "peter.teunis@rivm.nl", 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("ctb", "cre"), comment = "Project
    manager and the package maintainer"))
c(
  person("Yihui", "Xie", role = c("aut", "cre"), email = "xie@yihui.name"),
  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("Edzer", "Pebesma", role = c("aut", "cre"), email = "edzer.pebesma@uni-muenster.de"),
	person("Roger", "Bivand", role = "ctb"),
	person("Ian", "Cook", role = "ctb"),
	person("Tim", "Keitt", role = "ctb"),
	person("Michael", "Sumner", role = "ctb"),
	person("Robin", "Lovelace", role = "ctb"),
	person("Hadley", "Wickham", role = "ctb"),
	person("Jeroen", "Ooms", 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("Dustin", "Tran", email = "dustin@cs.columbia.edu", role = c("aut", "cre")),
    person("Panos", "Toulis", role = "aut"),
    person("Tian", "Lian", role = "ctb"),
    person("Ye", "Kuang", role = "ctb"),
    person("Edoardo", "Airoldi", role = "ctb")
    )
c(
    person("Balasubramanian", "Narasimhan",
    email = "naras@stat.stanford.edu", role = c("aut", "cre")),
    person("Mei-Chiung", "Shih",
    email = "meichiun@stanford.edu", role = "aut"))
c(person(given=c("Damian", "W."), family="Betebenner", email="dbetebenner@nciea.org", role=c("aut", "cre")),
            person(given="Adam", family="Van Iwaarden", email="Vaniwaarden@colorado.edu", role="aut"),
            person(given="Ben", family="Domingue", email="ben.domingue@gmail.com", role="aut"))
c(person("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("Steven", "Kleinstein", role=c("aut", "cph"), 
                    email="steven.kleinstein@yale.edu"))
c(
  person(family="Trestle Technology, LLC", role="aut", email="cran@trestletechnology.net"),
  person("Jeff", "Allen", role="cre", 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"))
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("Winston", "Chang", role = c("aut", "cre"), email = "winston@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("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(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")
    )
person("Gerhard", "Burger", email = "burger.ga@gmail.com", role = c("aut", "cre"))
c(
  person(family="Trestle Technology, LLC", role="aut", email="cran@trestletechnology.net"),
  person("Jeff", "Allen", role="cre", email="cran@trestletechnology.net"),
  person(family="Institut de Radioprotection et de S<c3><bb>ret<c3><a9> Nucl<c3><a9>aire", 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")))
c(person("Daniela", "Dunkler", email = "daniela.dunkler@meduniwien.ac.at", role = c("aut", "cre")),
    person("Georg", "Heinze", role = "aut"))
c(person("Liran","Lerman",role =
        c("cre","aut"),email="llerman@ulb.ac.be"),person("Gianluca","Bontempi",role
        =
        c("aut"),email="gbonte@ulb.ac.be"),person("Olivier","Markowitch",role
        = c("aut"),email="olivier.markowitch@ulb.ac.be"))
c(person("Uwe", "Ligges", role = c("aut", "cre"), email="ligges@statistik.tu-dortmund.de", comment = "new maintainer"),
 person("Tom", "Short", role = "aut", email = "tshort@eprisolutions.com", comment="port to R"),
 person("Paul", "Kienzle", role = "aut", comment="majority of the original sources"),
 person("Sarah", "Schnackenberg", role = "ctb", comment="various test cases and bug fixes"),
 person("David", "Billinghurst", role = "ctb"),
 person("Hans-Werner", "Borchers", role = "ctb"),
 person("Andre", "Carezia", role = "ctb"),
 person("Pascal", "Dupuis", role = "ctb"),
 person("John W.", "Eaton", role = "ctb"),
 person("E.", "Farhi", role = "ctb"),
 person("Kai", "Habel", role = "ctb"),
 person("Kurt", "Hornik", role = "ctb"),
 person("Sebastian", "Krey", role = "ctb"),
 person("Bill", "Lash", role = "ctb"),
 person("Friedrich", "Leisch", role = "ctb"),
 person("Olaf", "Mersmann", role = "ctb"),
 person("Paulo", "Neis", role = "ctb"),
 person("Jaakko", "Ruohio", role = "ctb"),
 person("Julius O.", "Smith III", role = "ctb"),                   	   
 person("Doug", "Stewart", role = "ctb"),
 person("Andreas", "Weingessel", role = "ctb"))
c(
    person("Michal", "Burdukiewicz",
    email = "michalburdukiewicz@gmail.com", role = c("cre", "aut")),
    person("Piotr", "Sobczyk",
    role = "aut"))
c(
    person("Oleg", "Sofrygin", role=c("aut", "cre"), email="oleg.sofrygin@gmail.com"),
    person(c("Mark", "J."), "van der Laan", role="aut", email="laan@berkeley.edu"),
    person("Romain", "Neugebauer", role="aut", email="Romain.S.Neugebauer@kp.org"))
c(person("Anestis", "Touloumis", role = c("aut", "cre"), email = "A.Touloumis@brighton.ac.uk"))
person("Phil", "Chalmers", email = "rphilip.chalmers@gmail.com",
           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"))
person("Andreas", "Alfons", email = "alfons@ese.eur.nl",
                  role = c("aut", "cre"))
c(person("Markus Baaske", role = c("aut", "cre"),
			 email = "markus.baaske@math.tu-freiberg.de"),
			 person("Felix", "Ballani", role = c("aut","ctb"),
			 email = "ballani@math.tu-freiberg.de"))
c(
    person("Iñaki", "Ucar", email="i.ucar86@gmail.com", role=c("aut", "cph", "cre")),
    person("Bart", "Smeets", email="bartsmeets86@gmail.com", role=c("aut", "cph")))
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"))
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("Oliver", "Flasch", role = c("aut", "cre"),
                    email = "of@sourcewerk.de"),
             person("Felix", "Gorschlueter", role = "aut",
                    email = "fg@sourcewerk.de"),
             person("Leo", "Liberti", role = "ctb"))
c( person("Mark", "van der Loo",
        role=c("aut","cre"),email="mark.vanderloo@gmail.com"))
c(
    person("Green", "Peter", role=c("aut", "cre"), email="greenp@landcareresearch.co.nz"),
    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(given = "Sunthud", family = "Pornprasertmanit", role = c("aut", "cre"), 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(given = "Terry", family = "Jorgensen", role = "ctb", email="TJorgensen314@gmail.com"))
person("Keith", "Goldfeld", email = "Keith.Goldfeld@nyumc.org",
                  role = c("aut","cre"))
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"))
person("Mikko", "Korpela", role = c("aut", "cre"),
        email = "mvkorpel@iki.fi")
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")),
                      person("Thijs","van den Berg", email = "thijs@sitmo.com", role = c("aut")))
c(person("Josymar", "Torrejon-Magallanes", email = "ejosymart@gmail.com", role = c("cre", "aut")))
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("Kurt", "Hornik", role = c("aut", "cre"),
                    email = "Kurt.Hornik@R-project.org"),
             person("Ingo", "Feinerer", role = "aut"),
             person("Martin", "Kober", role = "aut"))
as.person(c(
    "W. Gregory Alvord <greg.alvord@nih.gov> [aut, cre]",
    "Nick Carchedi <nick.carchedi@gmail.com> [aut]"
    ))
c(person("Kurt", "Hornik", role = c("aut", "cre"),
                     email = "Kurt.Hornik@R-project.org"),
             person("David", "Meyer", role = "aut"),
             person("Christian", "Buchta", role = "aut"))
person("Daniel", "Farewell",
    email = "farewelld@cf.ac.uk", 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("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")
    )
person("Jonathan", "Bartlett", email = "jwb133@googlemail.com",
    role = c("aut", "cre"))
c(person("Landon", "Sego", role = c("aut", "cre"), email = "Landon.Sego@pnnl.gov"))
c(person("Jakob", "Bossek", email = "j.bossek@gmail.com", role =
    c("aut", "cre")), person("Pascal", "Kerschke", email = "kerschke@uni-muenster.de",
    role = "ctb"))
person("Ivan", "Svetunkov", email = "ivan@svetunkov.ru", role = c("aut", "cre"),
                  comment="Research Associate at Lancaster Centre for Forecasting, UK")
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("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("Mickael", "Canouil", role = c("aut", "cre"), email = "mickael.canouil@cnrs.fr"),
    person("Loic", "Yengo", role = "ctb", email = "loic.yengo@good.ibl.fr"))
c(
    person("Jorge", "Gonzalez Burgos", email = "jgonzale@mat.puc.cl", role = c("cre","aut")),
    person("Daniel", "Acu<c3><b1>a Leon", email = "dnacuna@uc.cl", role = "ctb"))
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(given = "Jose", family = "Gama", role = c("aut","cre"),
 email = "rxprtgama@gmail.com"),
    person(given = "Dennis", family = "Milbert", role = c("aut","cph")))
c(person("Laura", "Bendhaiba", role = "aut", email="laurabendhaiba@gmail.com"),
             person("Julien", "Boelaert", role= "aut", email="julien.boelaert@gmail.com"),
             person("Jerome", "Mariette", role= "aut", email="jerome.mariette@toulouse.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("Nathalie", "Villa-Vialaneix", role = c("aut", "cre"), email="nathalie.villa@toulouse.inra.fr"))
person(given = "Daniel", family = "Nuest", role = c("cre",
        "aut"), email = "daniel.nuest@uni-muenster.de")
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")),
  person("Chris", "Jewell", role = "aut"),
  person("Jonathan", "Marshall", role = "aut"),
  person("Nigel", "French", 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"))
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("George", "Fisher",
        email = "george@georgefisher.com",
        role = c("aut", "cre"))
    )
c(person("Edzer", "Pebesma", role = c("cre", "aut"),
		 email = "edzer.pebesma@uni-muenster.de"),
	person("Benedikt", "Graeler", role = "ctb"),
	person("Tom", "Gottfried", role = "ctb"),
	person("Robert J.", "Hijmans", role = "ctb"))
c(person("Till", "Junge", role = c("aut", "cre"),
                    email = "till.junge@altermail.ch"),
             person("Sandra", "Penic", role = "aut",
                    email = "sandra.penic@unil.ch"),
             person("Mathieu", "Cossuta", role = "aut",
                    email = "mcossutta@gmail.com"),
             person("Guy", "Elcheroth", role = "aut",
                    email = "guy.elcheroth@unil.ch"),
             person("Stephanie", "Glaeser", role = "ctb",
                    email = "stephanie.glaeser@unil.ch"),
             person("Davide", "Morselli", role = "ctb",
                    email = "davide.morselli@unil.ch"))
person("Vilmantas", "Gegzna",
                    email = "GegznaV@gmail.com",
                    role = c("aut", "cre","cph")
            )
c(
    person(c("Alex", "M"), "Chubaty", email = "alexander.chubaty@canada.ca",
    role = c("aut", "cre")),
    person(c("Eliot", "J", "B"), "McIntire", email = "eliot.mcintire@canada.ca",
    role = c("aut")),
    person("Yong", "Luo", email = "yong.luo@canada.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")
    )
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 = "ctb",
	             email = "florian.gerber@math.uzh.ch"))
c(person("François", "Rousset", role = c("aut", "cre", "cph"),
                     email = "francois.rousset@umontpellier.fr"),
              person("Jean-Baptiste", "Ferdy", role = c("aut","cph")),
              person("Alexandre", "Courtiol", role = "ctb"),
              person("Dirk", "Eddelbuettel", role = "ctb", comment = "ziggurat rnorm sources"),
              person("GSL authors", role = "ctb", comment = "src/gsl_bessel.*")
              )
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", "Ushey", role = "aut", email = "kevin@rstudio.com"),
    person("JJ", "Allaire", role = "aut", email = "jj@rstudio.com"),
    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"))
person("Bryon", "Aragam", email = "sparsebn@gmail.com", role = c("aut", "cre"))
c(
    person("Bryon", "Aragam", email = "sparsebn@gmail.com", role = c("aut", "cre")),
    person("Jiaying", "Gu", role = c("aut"))
    )
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("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(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", "wdc"))
    )
c(person("Mathieu", "Ribatet", role = c("aut", "cre"),
	   email = "mathieu.ribatet@univ-montp2.fr"),
	   person("Richard", "Singleton", role = "ctb"),
	   person("R Core team", role = "ctb"))
c(
      person("Timothée", "Giraud", email = "timothee.giraud@ums-riate.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@uvigo.es", role = c("aut", "cre")),
	person("Martin Boer", role = "aut"),
	person("Paul Eilers", role = "aut"),
	person("Fred van Eeuwijk", role = "ctb"))
c(person("Michael D.","Sumner", role = c("aut", "cre"), email = "mdsumner@gmail.com"))
c( person(given = "Dennis", family = "Walvoort", email =
        "dennis.Walvoort@wur.nl", role = c("aut", "cre", "cph")),
        person(given = "Dick", family = "Brus", email =
        "dick.brus@wur.nl", role = c("aut", "cph")), person(given =
        "Jaap", family = "de Gruijter", email =
        "Jaap.degruijter@wur.nl", role = c("aut", "cph")))
c(person("Roger", "Bivand", role = c("cre", "aut"), email = "Roger.Bivand@nhh.no"),	person("Micah", "Altman", role = "ctb"),
	person("Luc", "Anselin", role = "ctb"),
	person("Renato", "Assunção", role = "ctb"),
	person("Olaf", "Berke", role = "ctb"),
	person("Andrew", "Bernat", role = "ctb"),
	person("Guillaume", "Blanchet", role = "ctb"),
	person("Eric", "Blankmeyer", role = "ctb"),
	person("Marilia", "Carvalho", role = "ctb"),
	person("Bjarke", "Christensen", role = "ctb"),
	person("Yongwan", "Chun", role = "ctb"),
	person("Carsten", "Dormann", role = "ctb"),
	person("Stéphane", "Dray", role = "ctb"),
	person("Virgilio", "Gómez-Rubio", role = "ctb"),
        person("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("Danlin", "Yu", role = "ctb"))
c(person("Michael D.","Sumner", role = c("aut", "cre"), email = "mdsumner@gmail.com"), 
             person("Joscha", "Legewie", role = "ctb"))
c(
    person("Andreas", "Beger", role = c("aut", "cre"), email = "adbeger@gmail.com"),
    person("Daina", "Chiba", role = c("aut"), email = "dchiba@essex.ac.uk"),
    person("Daniel", "Hill", role = c("aut"), email = "dwhill@uga.edu"),
    person("Nils", "Metternich", role = c("aut"), email = "n.metternich@ucl.ac.uk"),
    person("Shahryar", "Minhas", role = c("aut"), email = "hermes829@gmail.com"),
    person("Michael", "Ward", role = c("cph"), email = "michael.don.ward@gmail.com")
    )
c(person("Stefan", "Siegert", role = c("aut", "cre"),
                     email = "s.siegert@exeter.ac.uk"))
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('Santiago', 'Beguería', role=c('aut','cre'),
	email='santiago.begueria@csic.es'),
	person(c('Sergio','M.'), 'Vicente-Serrano', role='aut',
	email='svicen@ipe.csic.es'))
c(
	person("Roger", "Bivand", role = c("cre", "aut"), email = "Roger.Bivand@nhh.no"),
	person("Rainer", "Krug", role = "ctb", 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"),
	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("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(given = "Wenjie", family = "Wang",
           email = "wenjie.2.wang@uconn.edu",
           role = c("aut", "cre")),
    person(given = "Jun", family = "Yan", role = "aut")
    )
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"))
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("Scott", "Chamberlain", role = c("aut", "cre"), email = "myrmecocystus@gmail.com"),
    person("Karthik", "Ram", role = "ctb"),
    person("Ted", "Hart", role = "ctb")
    )
c(
  person("Thomas", "Bartz-Beielstein", role = "aut"),
  person("Martin", "Zaefferer", role = c("cre","ctb"), email = "martin.zaefferer@gmx.de"),
  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")
  )
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("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"))
person("Greg", "Freedman Ellis", email = "greg.freedman@gmail.com", role = c("aut", "cre"))
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@uclv.edu.cu"),
             person("José Daniel", "Rodríguez", role = "aut",
                    email = "josedaniel@uclv.cu"),
             person("Osmani", "Rosado", role = "aut",
                    email = "osmanir@uclv.cu"),
             person("José Manuel", " Benítez", role = "ths"),
             person("Christoph", "Bergmeir", role = c("ths", "cre"),
                    email = "c.bergmeir@decsai.ugr.es"),
             person("Isaac", "Triguero", role = "ctb"))
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("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"))
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("Andrie", "de Vries", role=c("aut", "cre"),
    email="apdevries@gmail.com")
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"))
    )
c(person("Benjamin", "Hofner", role = c("aut", "cre"),
	            email = "benjamin.hofner@fau.de"),
       	     person("Torsten", "Hothorn", role = c("aut")))
c(person("Cedric", "Briand", role = c("aut", "cre"), email = "cedric.briand00@gmail.com"))
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("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")))
person("Matthieu","Gomez", email = "mattg@princeton.edu", role =
    c("aut", "cre"))
c(person("Sebastian", "Sippel", , "ssippel@bgc-jena.mpg.de", role = c("aut", "cre")), person("Holger", "Lange", , "Holger.Lange@nibio.no", role = c("aut")), person("Fabian", "Gans", , "fgans@bgc-jena.mpg.de", role = c("aut")))
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(
             "Richard", "Iannone",
             role = c("aut", "cre"),
             email = "riannone@me.com")
c(
	     person("Mark S.", "Handcock", role=c("aut"), email="handcock@stat.ucla.edu"),
	     person("David R.", "Hunter", role=c("aut"), email="dhunter@stat.psu.edu"),
	     person("Carter T.", "Butts", role=c("aut"), email="buttsc@uci.edu"),
	     person("Steven M.", "Goodreau", role=c("aut"), email="goodreau@u.washington.edu"),
	     person("Pavel N.", "Krivitsky", role=c("aut"), email="pavel@uow.edu.au"),
	     person("Skye", "Bender-deMoll", role=c("aut"), email="skyebend@skyeome.net"),
	     person("Martina", "Morris", role=c("aut","cre"), email="morrism@u.washington.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("Emily", "Beylerian", email = "ebey@uw.edu", role = c("cre", "aut")),
  person("Kirk", "Li", email = "kirkli@uw.edu", role = "ctb"),
  person("Samuel", "Jenness", email = "sjenness@uw.edu", role = "ctb"),
  person("Martina", "Morris", email = "morrism@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("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("Timo", "Aspelmeier", role = c("cre", "aut"), email = "timo.aspelmeier@mathematik.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("Pein", "Florian", role = "ctb", email = "fpein@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@tno.nl"),
        person(given = "Claudio", family = "Conversano", role = c("aut", "cph"), email
        = "conversa@unica.it"),person("Cor","Ninaber",role="ctb"),person("Kristof","Meers",role="ctb"),person("Peter","Neufeglise",role="trl"))
person("Ryan", "Hafen", email = "rhafen@gmail.com", role = c("aut", "cre"))
person("Gregor", "Kastner", role = c("aut", "cre"), email = "gregor.kastner@wu.ac.at")
c(
    person("Robin", "Lovelace", email = "rob00x@gmail.com", role = c("aut", "cre")),
    person("Richard", "Ellison", role = c("aut")),
    person("Barry", "Rowlingson", role = c("aut"), comment = "Author of overline"),
    person("Nick", "Bearman", role = c("aut"), comment = "Co-author of gclip"),
    person("Malcolm", "Morgan", role = c("aut"), comment = "Co-author of angle_diff")
    )
person("Xiang", "Zhou", email = "xiang_zhou@fas.harvard.edu",
  role = c("aut", "cre"))
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")))
c(person("Michael", "Hahsler", role = c("aut", "cre", "cph"),
                email = "mhahsler@lyle.smu.edu"),
            person("Matthew", "Bolanos", role = c("aut", "cph")),
            person("John", "Forrest", role = c("aut", "cph")))
c(person("Michael", "Hahsler", role = c("aut", "cre", "cph"),
	    email = "mhahsler@lyle.smu.edu"),
	person("Matthew", "Bolanos", role = c("aut", "cph")),
	person("John", "Forrest", role = c("aut", "cph")))
c(
    person(
      "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=c("ctb")),person("Nick","Logan",role="ctb"))
c(
    person("Hadley", "Wickham", , "hadley@rstudio.com", c("aut", "cre", "cph")),
    person("RStudio", role = "cph")
    )
person("Bert", "Gunter", email = "bgunter@comcast.net",
                  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(given = "Elizabeth M.", "Sweeney",  
  email = "elizabethmargaretsweeney@gmail.com", role = c("aut", "cre")),
  person(given = "John", "Muschelli",  
  email = "muschellij2@gmail.com", 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(
        "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(
        "Kent", "Russell"
        , role = c("aut", "cre")
        , comment = "R interface"
        , email = "kent.russell@timelyportfolio.com"
      ),
      person(
        "Florian", "Breitwieser"
        , role = c("ctb")
        , comment = "R interface"
      )
    )
c(
  person("Federico", "Rotolo", email="federico.rotolo@gustaveroussy.fr", 
          role=c("aut", "cre")),
  person("Xavier", "Paoletti", role="ctr"),
  person("Marc", "Buyse", role="ctr"),
  person("Tomasz", "Burzykowski", role="ctr"),
  person("Stefan", "Michiels", role="ctr"))
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")),
             SM = person("Sebastian", "Meyer",
                  email = "seb.meyer@fau.de",
                  role = c("aut", "cre")),
             MP = person("Michaela", "Paul", role = "aut"),
             LH = person("Leonhard", "Held",
                  email = "Leonhard.Held@uzh.ch",
                  role = c("ctb", "ths")),
             person("Thais", "Correa", role = "ctb"),
             person("Mathias", "Hofmann", role = "ctb"),
             person("Christian", "Lang", role = "ctb"),
             person("Juliane", "Manitz", role = "ctb"),
             person("Andrea", "Riebler", role = "ctb"),
             person("Daniel", "Sabanés Bové", role = "ctb"),
             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"))
c(person("Juris", "Breidaks", email = "rcsb@csb.gov.lv",  role = c("aut", "cre")), person("Martins", "Liberts", role = "aut"), person("Janis", "Jukams", role = "aut"))
c(person(c("Terry", "M"), "Therneau",
                    email="therneau.terry@mayo.edu",
	            role=c("aut", "cre")),
               person("Thomas", "Lumley", role=c("ctb", "trl"),
	       comment="original S->R port and maintainer until 2009"))
c(
    person("Alboukadel", "Kassambara", role = c("aut", "cre"), email = "alboukadel.kassambara@gmail.com"),
    person("Marcin", "Kosinski", role = c("aut"), email = "m.p.kosinski@@gmail.com")
    )
c(person("Michael", "Laimighofer", role = c("aut", "cre","ctb"),
    email = "michael.laimighofer@helmholtz-muenchen.de")
    )
c(person("Philippe", "Grosjean", role = c("aut", "cre"),
  email = "phgrosjean@sciviews.org"))
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("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"))
c(person("Philippe", "Grosjean", role = c("aut", "cre"),
  email = "phgrosjean@sciviews.org"),
  person("Romain", "Francois", role = "aut",
  email = "romain@r-enthusiasts.com"))
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(c("C.", "Jonathan"), "Fritzemeier", role = c("cre", "ctb"), email = "clausjonathan.fritzemeier@uni-duesseldorf.de"),
	     person("Gabriel", "Gelius-Dietrich", role = c("aut")),
             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(c("C.", "Jonathan"), "Fritzemeier", role = c("cre", "ctb"), email = "clausjonathan.fritzemeier@uni-duesseldorf.de"),
			 person("Gabriel", "Gelius-Dietrich", role = c("aut", "ctb")),
             person("Deya", "Alzoubi", role = "ctb"))
c(person("Martin", "Rimmler", role = c("aut", "cre"),
	   email = "martin.rimmler@gmail.com"),
	person("Thomas", "Mueller", role = "aut"))
person("Alex", "Whitworth",,email = "whitworth.alex@gmail.com", c("aut", "cre"))
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("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("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"))
c(person(given = "Edgar", family = "Kublin", role = "aut"),
             person(given = "Johannes", family = "Breidenbach", role = c("ctb","cre"),
					email = "job@nibio.no"))
c(person("Christian", "Buchta", role = "aut"),
             person("Kurt", "Hornik", role = c("aut", "cre"),
                    email = "Kurt.Hornik@R-project.org"),
             person("Ingo", "Feinerer", role = "aut"),
             person("David", "Meyer", role = "aut"))
c(person("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."))
c(
    person("Scott", "Chamberlain", role = c("aut", "cre"), email = "myrmecocystus@gmail.com"),
    person("Eduard", "Szoecs", role = "aut", email = "eduardszoecs@gmail.com"),
    person("Zachary", "Foster", role = "aut", email = "zacharyfoster1989@gmail.com"),
    person("Carl", "Boettiger", role = "aut", email = "cboettig@gmail.com"),
    person("Karthik", "Ram", role = "aut", email = "karthik@ropensci.org"),
    person("Ignasi", "Bartomeus", role = "aut", email = "nacho.bartomeus@gmail.com"),
    person("John", "Baumgartner", role = "aut", email = "johnbb@student.unimelb.edu.au"),
    person("James", "O'Donnell", role = "aut", email = "jodonnellbio@gmail.com")
  )
c(person("Boris P.", "Hejblum", role = c("aut", "cre"),
		     email = "bhejblum@hsph.harvard.edu"), 
		     person("Damien", "Chimits", 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")))
person("Oscar", "Perpinan Lamigueiro", email="oscar.perpinan@gmail.com", role=c('cre', '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("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"))
c(person("David", "Gerard", email = "dcgerard@uchicago.edu", role = c("aut", "cre")),
            person("Peter", "Hoff", email = "pdhoff@uw.edu", role = "aut"))
c(
	person("Pavel N.", "Krivitsky", role=c("aut","cre"), email="pavel@uow.edu.au"),
        person("Mark S.", "Handcock", role=c("aut","ths"), email="handcock@stat.ucla.edu"),
        person("David R.", "Hunter", role=c("ctb"), email="dhunter@stat.psu.edu"),
        person("Steven M.", "Goodreau", role=c("ctb","ths"), email="goodreau@u.washington.edu"),
        person("Martina", "Morris", role=c("ctb","ths"), email="morrism@u.washington.edu"),
	person("Nicole Bohme", "Carnegie", role=c("ctb"), email="nicole.carnegie@nyu.edu"),
        person("Carter T.", "Butts", role=c("ctb"), email="buttsc@uci.edu"),
	person("Ayn", "Leslie-Cook", role=c("ctb"), email="aynlc3@uw.edu"),
	person("Skye", "Bender-deMoll", role=c("ctb"), email="skyebend@skyeome.net"),
  person("Li","Wang",role=c("ctb"),email="lxwang@gmail.com"),
  person("Kirk","Li",role=c("ctb"),email="kirkli@uw.edu"))
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"))
c(person("Dmitriy", "Selivanov", role = c("aut", "cre"),
    email = "selivanov.dmitriy@gmail.com"),
    person("Lincoln", "Mullen", role = c("ctb"),
    email = "lincoln@lincolnmullen.com"))
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"))
person("Patrick", "Reidy", email = "patrick.francis.reidy@gmail.com", 
    role = c("aut", "cre"))
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("Lincoln", "Mullen", role = c("aut", "cre"),
    email = "lincoln@lincolnmullen.com"))
c(
  person("Tom", "Shott", role = c("aut", "cre"), email = "tshott@pdx.edu"),
  person("Dong-Joon", "Lim", role = "aut", email="tgno3.com@gmail.com"))
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("Rob", "Hyndman", role=c("aut", "cre", "cph"), email="Rob.Hyndman@monash.edu"),
  person("Nikolaos", "Kourentzes", role=c("aut","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("David", "Piegza", role="cph")
    )
c(
    person("Hadley", "Wickham", , "hadley@rstudio.com", "aut"),
    person("Romain", "Francois", , "romain@r-enthusiasts.com", "aut"),
    person("Kirill", "Müller", , "krlmlr+r@mailbox.org", c("aut", "cre")),
    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"))
c(person("Gabriela", "De Queiroz", email = "gabidequeiroz@gmail.com", role = c("ctb")),
             person("David", "Robinson", email = "admiral.david@gmail.com", role = c("aut")),
             person("Julia", "Silge", email = "julia.silge@gmail.com", role = c("aut", "cre")))
c(person("Daniel", "Gadala-Maria", role=c("aut", "cre"), 
                    email="daniel.gadala-maria@yale.edu"),
             person("Jason", "Vander Heiden", role=c("ctb"), 
                    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", "cre"), email =
        "krlmlr+r@mailbox.org"), person("Yihui", "Xie", role = "ctb") )
person("Peter", "Humburg", email="peter.humburg@gmail.com",
		role=c("aut", "cre"))
c(person("Gianluca", "Campanella", role=c("aut", "cre"),
                    email="gianluca@campanella.org"),
             person("Marc", "Chadeau-Hyam", role="aut",
                    email="m.chadeau@imperial.ac.uk"),
             person("Maria", "De Iorio", role="ctb",
                    email="m.deiorio@ucl.ac.uk"),
             person("Ajay", "Jasra", role="ctb",
                    email="staja@nus.edu.sg"))
c(
    person("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")
      )
person("Nathan", "Teetor", email = "nathanteetor@gmail.com", role = c("aut", "cre"))
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")))
person("David","Harvey", email = "harvey@depauw.edu", role = c("aut", "cre"))
c(
  person("David", "Strebler", role= c("aut","cre"), email= "david.strebler@uni-koeln.de"),
  person("Dominik", "Brill", role = c("ths")),
  person("Helmut", "Brückner", role=c("ths"))
  )
c(person("Ingo", "Feinerer", role = c("aut", "cre"),
                    email = "feinerer@logic.at"),
             person("Kurt", "Hornik", role = "aut"),
             person("Artifex Software, Inc.", role = c("ctb", "cph"),
                    comment = "pdf_info.ps taken from GPL Ghostscript"))
c(
	person("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"))
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("Cole","Monnahan",role="ctb") )
c(
    person("Oleg", "Sofrygin", role=c("aut", "cre"), email="oleg.sofrygin@gmail.com"),
    person(c("Mark", "J."), "van der Laan", role="aut", email="laan@berkeley.edu"))
c(person("Milan", "Bouchet-Valat", email="nalimilan@club.fr",
                    role=c("aut", "cre")),
             person("Grigorij", "Ljubownikow",
                    email="g.ljubownikow@auckland.ac.nz",
                    role="ctb"))
c(person("Ingo", "Feinerer", role = c("aut", "cre"),
                    email = "feinerer@logic.at"),
             person("Wolfgang", "Mauerer", role = c("aut")))
c(person("Mario", "Annau", role = c("aut", "cre"),
    email = "mario.annau@gmail.com"))
c(person(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"))
c(
    person("Gregory", "Kanevsky", , "gregory.kanevsky@teradata.com", c("aut","cre"))
    )
c(person("Lincoln", "Mullen", role = c("aut", "cre"),
        email = "lincoln@lincolnmullen.com"),
        person("Dmitriy", "Selivanov", role = c("ctb"),
        email = "selivanov.dmitriy@gmail.com"))
c(person("Derek S.", "Young", role = c("aut", "cre"),
	email = "derek.young@uky.edu"))
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"))
person(c("Eric", "D."), "Watt",
                  email = "eric@ericdwatt.com",
                  role = c("aut", "cre"))
c(person(given = "Hannah", family = "Frick", role = c("aut", "cre"), email = "h.frick@ucl.ac.uk"),
             person(given = "Ioannis", family = "Kosmidis", role = "aut", email = "i.kosmidis@ucl.ac.uk"))
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("Edzer", "Pebesma", role = c("cre", "aut"),
		 email = "edzer.pebesma@uni-muenster.de"),
	person("Benedikt", "Klus", role = "aut"),
	person("Benedikt", "Graeler", role = "ctb"),
	person("Nikolai", "Gorte", role = "ctb"))
c(
  person("Alexis", "Gabadinho", role = c("aut", "cph")),
  person("Matthias", "Studer", role = c("aut", "cph")),
  person("Nicolas", "Muller", role = "aut"),
  person("Reto", "Buergin", role = "aut"),
  person("Gilbert", "Ritschard", role = c("aut", "cre", "cph"),
  email = "gilbert.ritschard@unige.ch"))
c(
  person("Gilbert", "Ritschard", role = c("aut", "cre", "ths", "cph"),
  email = "gilbert.ritschard@unige.ch"),
  person("Matthias", "Studer", role = "aut"),
  person("Reto", "Buergin", role = "aut"),
  person("Alexis", "Gabadinho", role = "ctb"),
  person("Nicolas", "Muller", role = "ctb"),
  person("Patrick", "Rousset", role = "ctb"))
c(person("Andreas","Wygrabek", role=c("aut","cre"),
            email= "Andreas.Wygrabek@eoda.de"),
            person("Bastian","Wiessner",role=c("aut"),
            email= "Bastian.Wiessner@eoda.de"),
			      person(family = "eoda GmbH", role = "cph"))
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"))
person(given = "Jeremy Thoms", family = "Hetzel", email =
        "jthetzel@gmail.com", role = c("aut", "cre"))
c(person("Nicole Bohme", "Carnegie", role = c("aut", "cre"), email = "carnegin@uwm.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")
c(person("Carl", "Boettiger", role = c("aut", "cre"),
    email = "cboettig@gmail.com"),
    person("Duncan", "Temple Lang", role = "aut"))
c(person("Daniel","Heck",email="heck@uni-mannheim.de",role=c("aut","cre")),
             person("Nina", "Arnold", email="arnold@uni-mannheim.de", role = c("aut","dtc")),
             person("Denis", "Arnold", email="denis.arnold@uni-tuebingen.de", role = c("aut")))
c(person("Christian", "Zang", role = c("aut", "cre", "cph", "trl"),
    email = "christian.zang@wzw.tum.de"), person("Franco", "Biondi", role =
    c("ctb", "cph")))
c(TJ=person("Thibaut", "Jombart", email="thibaut.jombart@gmail.com", role = c("aut")),
 	     MK=person("Michelle", "Kendall", email="m.kendall@imperial.ac.uk", role = c("aut", "cre")),
	     JAG=person("Jacob", "Almagro-Garcia", role = c("aut")),
	     CC=person("Caroline", "Colijn", role = c("aut")))
c(person("Ryan", "Hafen", email = "rhafen@gmail.com", role = c("aut", "cre")),
    person("Jeremiah", "Rounds", role = "ctb"),
    person("Barret", "Schloerke", role = "ctb"),
    person("Landon", "Sego", role = "ctb"))
c(
    person("Jakub", "Chromec", , "chromec.jakub@gmail.com", role = c("aut", "cre")),
    person("Irina", "Matusevich", role = "ctb")
    )
person("Mathias", "Milfeldt", email = "mathias@milfeldt.dk", role = c("aut", "cre"))
c(person("Gokce Mehmet", "AY", role = c("aut", "cre"),
		     email = "gmehmetay@gmail.com"), person("Osman Nuri","CELIK", role="ths", email = "celikon@gmail.com"))
person("James", "Grange", ,"grange.jim@gmail.com",  c("aut", "cre"))
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"))
c(person("Yves", "Croissant", role = c("aut", "cre"), email = "yves.croissant@univ-reunion.fr"),
             person("Achim", "Zeileis", role = "aut", email = "Achim.Zeileis@R-project.org"))
c(
  person("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"))
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("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("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@gmail.com", comment = "CEFET/RJ"),person("Eduardo","Ogasawara", role = c("ths"),email = "eogasawara@ieee.org", comment = "CEFET/RJ"))
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" ) )
person("Jasmine", "Dumas", email = "jasmine.dumas@gmail.com", role = c("aut", "cre"))
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("Yihui", "Xie", email = "xie@yihui.name", role = c("aut", "cre")),
    person("JJ", "Allaire", role = "aut"),
    person("Dave", "Liepmann", role = "ctb", comment = "Tufte CSS in inst/rmarkdown/templates/tufte_html/resources"),
    person(family = "RStudio, Inc.", role = "cph")
    )
c( person(given = c("Jennifer", "F."), family = "Bobb", role =
        "aut", email = "jbobb@hsph.harvard.edu"), person(given =
        "Ravi", family = "Varadhan", role = c("aut", "cre"), email =
        "ravi.varadhan@jhu.edu"), person(given = "Hui", family =
        "Zhao", role = "ctb"))
person("Jack", "Horne", role = c("aut", "cre"), email = "jack@jackhorne.net")
c(
    person("Anna", "Cena", role = c("aut")),
    person("Marek", "Gagolewski", role = c("aut")),
    person("Marcin", "Kosinski", role = c("aut")),
    person("Natalia", "Potocka", role = c("aut")),
    person("Barbara", "Zogala-Siudem", role = c("aut", "cre"),
    email = "zogala@rexamine.com")
    )
c(person("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"))
c(person("James", "Dai", role = c("aut", "cre"),
		     email = "jdai@fredhutch.org"),
	      person("Xiaoyu", "Wang", role = c("aut")))
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("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(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" , , "balamut2@illinois.edu", 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"))
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@math.tu-freiberg.de"),
			 person("Felix", "Ballani", role = "ctb",
			 email = "ballani@math.tu-freiberg.de")
			)
person("Shaun", "Jackman", , "sjackman@gmail.com", c("cre"))
person("Long", "Qu", role=c("aut", "cre"), email="long.qu@wright.edu")
c(person("Edzer", "Pebesma", role = c("aut", "cre"),
		email = "edzer.pebesma@uni-muenster.de"),
		person("Thomas", "Mailund", role = "aut", email = "mailund@birc.au.dk"))
c(
    person("Jake", "Conway", email = "jake_conway@hms.harvard.edu", role = "cre"),
    person("Nils", "Gehlenborg", email = "nils@hms.harvard.edu", role = "aut"))
person("Gabriele", "Baldassarre", role = c("aut", "cre"), email = "gabriele@gabrielebaldassarre.com")
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")),
    person("Dr. William M. Scholl Center for American History and Culture,
    The Newberry Library", role = "cph"),
    person("United States Census Bureau", role = "cph"))
c(person(given="Neil J.", family="Gunther", role
    = "aut"), person(given="Stefan", family="Moeding", role
    = c("aut", "cre"), email = "stm@moeding.net"))
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("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("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'),
    person('Kent', 'Riemondy', role = c('aut'), email = 'kent.riemondy@ucdenver.edu'),
    person('Ryan', 'Sheridan', role = c('ctb'), email = 'ryan.sheridan@ucdenver.edu')
    )
person("Guo", "Yu", email = "gy63@cornell.edu", role = c("aut", "cre"))
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("Wanghuan","Chu", email = "dqchuwh@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("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 = c("aut")),
             person(given = "Kurt", family = "Hornik", role = c("aut")),
             person(given = "Florian", family = "Gerber", role = c("ctb")),
             person(given = "Michael", family = "Friendly", role = "ctb"))
c(person(given = "Michael", family = "Friendly", role=c("aut", "cre"), email="friendly@yorku.ca"),
            person(given = "Heather", family = "Turner", role="ctb"),
            person(given = "Achim", family = "Zeileis", role="ctb"),
            person(given = "Duncan", family = "Murdoch", role="ctb"),
            person(given = "David", family = "Firth", role="ctb")
            )
c(person(c('Brian', 'J.'), 'Knaus', role = c('cre', 'aut'),
    email = 'briank.lists@gmail.com'),
    person(c('Niklaus', 'J.'), 'Grunwald', role = 'aut',
    email = 'grunwaln@science.oregonstate.edu'),
    person(c('Eric', 'C.'), 'Anderson', role = 'ctb',
    email = 'eric.anderson@noaa.gov'),
    person(c('David', 'J.'), 'Winter', role = 'ctb',
    email = 'david.winter@gmail.com'))
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("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="jari.oksanen@oulu.fi"),
             person("Roeland", "Kindt", role="aut"),
             person(given=c("Gavin", "L."), family="Simpson", role="aut",
             email="ucfagls@gmail.com"))
c(
    person("Ian", "Lyttle", email = "ian.lyttle@schneider-electric.com", role = c("aut", "cre", "cph"))
  )
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(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("Thomas", "Yee", role = c("aut", "cre", "cph"),
                    email = "t.yee@auckland.ac.nz"),
             person("James", "Gray", role = "dtc"))
person("Anh", "Le", email = "anh.le@duke.edu", role = c("aut", "cre"))
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")
    )
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("Simon", "Garnier", email = "garnier@njit.edu", role = c("aut", "cre")),
      person("Noam", "Ross", email = "noam.ross@gmail.com", role = c("ctb", "cph"), comment = "Continuous scale"),
      person("Bob", "Rudis", email = "bob@rudis.net", role = c("ctb", "cph"), comment = "Combined scales")
  )
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@rudis.net", role = c("ctb", "cph"))
  )
c(
    person("Nastasia", "Fouret", , "nastasia.fouret@chu-reunion.fr", c("aut", "cre", "cph")),
    person( "Mederic", "Descoins", , "mederic.descoins@chu-reunion.fr", role=, c("aut", "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")
    )
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@UnusualSolutionsThatWork.com"))
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")
             )
c(person("Robin", "Genuer", email = "Robin.Genuer@isped.u-bordeaux2.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"))
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(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("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("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"))
c(
    person("Winston", "Chang", email = "winston@rstudio.com", role = c("aut", "cre")),
    person("Yihui", "Xie", role = "ctb"),
    person("Nicolas", "Perriault", role = "ctb", comment = "The CasperJS library")
    )
c(person("Thomas J.", "Leeper", 
                    role = c("aut", "cre"), 
                    email = "thosjleeper@gmail.com"))
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"),
    person("Hadley", "Wickham", role = "aut",
    email = "h.wickham@gmail.com"),
    person("Clark", "Richards", role = "ctb",
    email = "crichards@whoi.edu"))
person("Iaroslav", "Domin", email = "ya.domin@gmail.com", role = c("aut", "cre"))
c(person("Chris", "Wallace", role=c("aut","cre"),
    email = "chris.wallace@cimr.cam.ac.uk"),
    person("Olly","Burren", role="ctb",
    email="oliver.burren@cimr.cam.ac.uk"))
c(	person("Ulrich", "Wittelsbuerger", role = c("aut", "cre"),email = "ulrich.wittelsbuerger@uni-duesseldorf.de"),
				person("Heng", "Li", role = "ctb"),
				person("Bob", "Handsaker", role = "ctb")			)
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("Jim", "Hester", , "james.f.hester@gmail.com", role = c("aut", "cre")),
    person("Kirill", "Müller", , "krlmlr+r@mailbox.org", role = "aut"),
    person("Hadley", "Wickham", , "hadley@rstudio.com", role = "aut"),
    person("Winston", "Chang", role = "aut"),
    person("RStudio", role = "cph"))
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")
    )
c(person("Ingo", "Feinerer", role = "aut"),
             person("Kurt", "Hornik", role = c("aut", "cre"),
                    email = "Kurt.Hornik@R-project.org"),
             person("Mike", "Wallace", role = c("ctb", "cph"),
                    comment = "Jawbone Java WordNet API library"))
person(given = "Michael", family = "Friendly", role=c("aut",
        "cre"), email="friendly@yorku.ca")
c(
    person("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(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("Qinghan", "Meng", email="qinghan.meng@gmail.com", role="aut"),
             person("He", "Zhao", email="Simon.Yansen.Zhao@gmail.com", role=c("aut", "cre")),
             person("Graham", "Williams", email="graham.williams@togaware.com", role="aut"),
             person("Junchao", "Lv", role="ctb"),
             person("Baoxun", "Xu", role="aut"))
c(
    person("John", "Malc", email = "cincenko@outlook.com", role = c("aut", "cre"), comment = "@dmpe"),
    person("Maksim", "Pecherskiy", role = c("ctb"), comment = "@MrMaksimize")
    )
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("Nicolas", "Turenne", role = "aut", email = "nturenne.inra@yahoo.fr"),
    person("Tien T.","Phan", role = c("aut","cre"),  email = "phantien84@gmail.com"),
    person("John", "Resig", role=c("ctb","cph"), comment = "the JavaScript file at inst/www/jquery-1.11.1.min.js"),
    person("Jeroen", "Ooms", role= "ctb", comment = "the JavaScript file at inst/www/opencpu-0.5.js"))
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(given="Meik", family="Michalke", email="meik.michalke@hhu.de", role=c("aut", "cre")))
c(person("Mirai Solutions GmbH", role = "aut",
                    email = "xlconnect@mirai-solutions.com"),
             person("Martin", "Studer", role = "cre",
                    email = "martin.studer@mirai-solutions.com"),
             person("The Apache Software Foundation", role = c("ctb", "cph"),
                    comment = "Apache POI, Apache Commons Codec"),
             person("Stephen", "Colebourne", role = c("ctb", "cph"),
                    comment = "Joda-Time Java library"),
             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"))
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("Aurele", "Morvan", role = c("aut", "cre"),
		email = "aurele@saagie.com"))
c(person("Yong", "Chen", role=c("aut", "cre"),
                     email ="ychen.upenn@gmail.com"),
		     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", c("aut", "cre")),
    person("James", "Hester", , "james.hester@rstudio.com", "aut"),
    person("Jeroen", "Ooms", role = "ctb"),
    person("RStudio", role = "cph"),
    person("R Foundation", role = "ctb",
      comment = "Copy of R-project homepage cached as example")
    )
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"))
c(
    person("Roy", "Mendelssohn", email = "roy.mendelssohn@noaa.gov", role = c("aut","cre")),
    person("Cindy", "Bessey",  role = "ctb"),
    person("Dave", "Foley", role = "ctb"))
person("Nathaniel", "Phillips", email = "yarrr.book@gmail.com",
                  role = c("aut", "cre"))
c(
    person("Olivier M. F.", "Martin", email = "olivmrtn@gmail.com", role = c("aut", "cre")),
    person("Michel", "Ducher", email = "michel.ducher@chu-lyon.fr", role = "aut"))
c(person(given = "Achim", family = "Zeileis", role = c("aut", "cre"), email = "Achim.Zeileis@R-project.org"),
             person(given = "Gabor", family = "Grothendieck", role = "aut", email = "ggrothendieck@gmail.com"),
             person(given = c("Jeffrey", "A."), family = "Ryan", role = "aut", email = "jeff.a.ryan@gmail.com"),
             person(given = "Felix", family = "Andrews", role = "ctb", email = "felix@nfrac.org"))
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"))
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"))
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 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 spca.rtests.R dapc.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
servers.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.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 add.R
ade4-kplot.R ade4-scatter.R ade4-plot.R multiplot.R s.Spatial.R
utilskey.R
utils.R partition.R table.phylo4d.R distances.R proximities.R
orthobasis.R ppca.R orthogram.R abouheif.R moran.R dibas.R
bullseye.R zzz.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'
'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'
'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
'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'
'APBinary.R' 'APtools.R' 'CompareAP.R' 'sum.I.R' 'APSurv.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-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-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'
'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'
'RcppExports.R' 'bayou-utilities.R' 'probability.R'
'bayou-weight_matrix.R' 'bayou-moves.R' 'bayou-likelihood.R'
'bayou-prior.R' 'conversion-utilities.R'
'bayou-mcmc-utilities.R' 'bayou-mcmc.R' 'bayou-plotting.R'
'bayou-simulation.r' 'bayou-steppingstone.R' 'bayou-package.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
'algperf-beplot0.R' 'testprocedure.R' 'proto.R'
'algperf-paircomp.R' 'algperf-preference.R'
'algperf-visualizations.R' 'warehouse.R' 'algperf.R'
'as.psychobench.R' 'as.warehouse.R' 'benchmark.R' 'bsgraph.R'
'bsplot.R' 'datachar-visualizations.R'
'dataset-characteristics.R' 'dataset.R'
'dataset-characterization.R' 'testres-analysis.R'
'testres-visualizations.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'
'auth.r' 'bigrquery.r' 'datasets.r' 'upload.r' 'dbi-driver.r'
'dbi-connection.r' 'dbi-result.r' 'dplyr.r' 'id.R' 'jobs.r'
'projects.r' 'query.r' 'request.r' 'tabledata.r' 'tables.r'
'utils.r' 'zzz.r'
'allClasses.R' 'allPackage.R' 'IO-methods.R' 'BIOM-class.R'
'validity-methods.R'
'R-bizdays-package.r' 'datasets.R' 'calendar.R'
'adjust.date.R' 'bizdays.options.R' 'bizseq.R' 'is.bizday.R'
'offset.R' 'bizdays.R' 'create-calendars.R'
'coclusterStrategy.R' 'optionclasses.R' 'onattach.R'
'RCocluster.R' 'cocluster.R' 'coclusterBinary.R'
'coclusterCategorical.R' 'coclusterContingency.R'
'coclusterContinuous.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'
rblockdata.R init.R RcppExports.R blockseg-class.R blockseg.R
criteria-class.R stabsel-class.R stabsel.R blockseg-package.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'
'makeScene.R' 'write4D.file.R' 'write4D.R'
'writeWebGL_split.R' 'brainR-package.R' 'scene4d.R'
'writeTrianglesSTL.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'
'BuyseTest-package.R' 'FCT_BuyseTest.R' 'FCT_Valid.R'
'FCT_simul.R' 'FCT_support.R' 'FCTi-computation.R'
'FCTi-initilization.R' 'FCTi-print.R' 'OBJECT_BuyseTest.R'
'METHOD_get.R' 'METHOD_summary.R' 'METHOD_print.R'
'RcppExports.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'
'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' 'checkCharacter.R' 'checkChoice.R'
'checkClass.R' 'checkComplex.R' 'checkCount.R'
'checkDataFrame.R' 'checkDataTable.R' 'checkDate.R'
'checkDirectoryExists.R' 'checkEnvironment.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'
'checkPathForOutput.R' 'checkScalar.R' 'checkScalarNA.R'
'checkSetEqual.R' 'checkString.R' 'checkSubset.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'
'startup_messages.R' 'state.R'
'bugs.R' 'classify.R' 'gpcm.rc.R' 'prob_functions.R'
'scores.R' 'w_lord.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'
'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'
'sample_seed.R' 'search_executable.R' 'segsites.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'
'AllClasses.R' 'bma.R' 'coloc-package.R' 'coloc.combine.R'
'coloc.test.R' 'pcs.R' 'private.R' 'claudia.R'
'other_package_compatibility.R' 'colorplane.R'
'ScaleColorPlane.R' 'colorplaner.R' 'guide_colorplane.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'
AllClass.R Classes.R Copula.R Auxiliaries.R aux-acopula.R
special-func.R cop_objects.R nacopula.R dC-dc.R amhCopula.R
amhExpr.R An.R archmCopula.R asymCopula.R cCopula.R
claytonCopula.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 frankCopula.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 matrix_tools.R
normalCopula.R opower.R plackettCopula.R plackettExpr.R
rotCopula.R rstable1.R safeUroot.R schlatherCopula.R stable.R
timing.R tCopula.R tawnCopula.R tawnExpr.R tevCopula.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'
'add_sub.R' 'cowplot.R' 'draw.R' 'get_legend.R' 'gtable.R'
'plot_grid.R' 'save.R' 'setup.R' 'switch_axis.R' 'themes.R'
generics.R cplexConst.R cplexErrorClass.R cplexPtrClass.R
cplex.R cplexAPI.R cplex_checkAPI.R cplex_longparamAPI.R zzz.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'
'ami.R' 'CTcrqa.R' 'calcphi.R' 'checkts.R' 'crqa.R'
'drpdfromts.R' 'lorenzattractor.R' 'optimizeParam.R'
'runcrqa.R' 'simts.R' 'spdiags.R' 'takephi.R' 'theiler.R'
'tt.R' 'wincrqa.R' 'windowdrp.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'
'RcppExports.R' 'autosave.R' 'net.Class.R' 'darch.Class.R'
'backpropagation.R' 'benchmark.R' 'bootstrap.R' 'caret.R'
'compat.R' 'config.R' 'darch.Add.R' 'darch.Getter.R'
'dataset.R' 'darch.Learn.R' 'darch.R' 'darch.Setter.R'
'darchUnitFunctions.R' 'dropout.R' 'errorFunctions.R'
'rbm.Class.R' 'generateRBMs.R' 'generateWeightsFunctions.R'
'loadDArch.R' 'log.R' 'makeStartEndPoints.R' 'minimize.R'
'minimizeAutoencoder.R' 'minimizeClassifier.R' 'mnist.R'
'momentum.R' 'net.Getter.R' 'newDArch.R' 'params.R' 'plot.R'
'predict.R' 'print.R' 'rbm.Learn.R' 'rbm.Reset.R'
'rbmUnitFunctions.R' 'rbmUpdate.R' 'rpropagation.R'
'runDArch.R' 'saveDArch.R' 'test.R' 'util.R'
'weightUpdateFunctions.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'
'SystemMetadata.R' 'dmsg.R' 'DataObject.R' 'DataPackage.R'
'ResourceMap.R' 'datapack-package.r'
avelinkdis.R centdis.R distable.R randdis.R valuep.R
functionall.R
'DBObject.R' 'DBDriver.R' 'DBConnection.R' 'ANSI.R'
'DBI-package.R' 'DBResult.R' 'data-types.R' 'data.R'
'deprecated.R' 'hidden.R' 'interpolate.R' 'quote.R'
'rownames.R' 'table-create.R' 'table-insert.R' 'table.R'
'transactions.R' 'util.R'
'DBItest.R' 'context.R' 'expectations.R' 'import-dbi.R'
'import-testthat.R' 'run.R' 'test-all.R'
'test-getting-started.R' 'test-driver.R' 'test-connection.R'
'test-result.R' 'test-sql.R' 'test-meta.R' 'test-compliance.R'
'tweaks.R' 'utf8.R' 'utils.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'
'RcppExports.R' 'ddR.R' 'dobject.R' 'ops.R' 'pdriver.R'
'pobject.R' 'zzz.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
'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'
'plainNames2data.frameNames.R' 'plsr.mcSimulation.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' 'classes.R' 'collate.R'
'constants.R' 'deps.R' 'description.R' 'encoding.R' 'latex.R'
'non-oo-api.R' 'read.R' 'str.R' 'syntax_checks.R' 'utils.R'
'validate.R'
'conversions.R' 'dielectric.r' 'plot.r' 'zzz.r' 'drude.r'
'metals.r'
'capt.R' 'check.R' 'pager.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'
'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'
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'
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'
'AUCtest.R' 'BioradCNV.R' 'White.R' 'adpcr2panel.R'
'adpcr2ppp.R' 'analyze_pooled.R' 'binarize.R' 'classes.R'
'bind_dpcr.R' 'bioamp.R' 'calc_breaks.R' 'calc_coordinates.R'
'calc_lambda.R' 'compare_dens.R' 'count_test-class.R'
'create_dpcr.R' 'dPCRmethyl.R' 'dpcR-package.R'
'dpcReport_gui.R' 'dpcr_calculator.R' 'dpcr_density.R'
'dpcr_density_gui.R' 'dpcr_density_table.R' 'extract_dpcr.R'
'fit_adpcr.R' 'fl.R' 'get_k_n.R' 'limit_cq.R' 'many_peaks.R'
'modlist.R' 'moments.R' 'num2int.R' 'pds.R' 'pds_raw.R'
'plot_distr.R' 'plot_panel.R' 'plot_vf_circ.R' 'plot_vic_fam.R'
'print_summary.R' 'qdpcr.R' 'qpcr2pp.R' 'qpcr_analyser.R'
'read_dpcr.R' 'rename_dpcr.R' 'rtadpcr.R' 'safe_efficiency.R'
'show_dpcr.R' 'sim_adpcr.R' 'sim_ddpcr.R' 'sim_ddpcr_bkm.R'
'simulations.R' 'six_panels.R' 'summary_dpcr.R' 'test_counts.R'
'test_counts_gui.R' 'test_panel.R' 'test_peaks.R'
'test_ratio.R' 'valid_amp.R' 'y_val_conf.R'
'RcppExports.R' 'all-equal.r' 'bench-compare.r' 'bind.r'
'case_when.R' 'coalesce.R' 'colwise.R' 'compute-collect.r'
'copy-to.r' 'data-lahman.r' 'data-nasa.r' 'data-nycflights13.r'
'data-temp.r' 'data.r' 'dataframe.R' 'dbi-s3.r' 'desc.r'
'distinct.R' 'do.r' 'dplyr.r' 'explain.r' 'failwith.r' 'funs.R'
'group-by.r' 'group-indices.R' 'group-size.r' 'grouped-df.r'
'id.r' 'if_else.R' 'inline.r' 'join.r' 'lazy-ops.R'
'lead-lag.R' 'location.R' 'manip.r' 'na_if.R' 'near.R'
'nth-value.R' 'order-by.R' 'over.R' 'partial-eval.r'
'progress.R' 'query.r' 'rank.R' 'recode.R' 'rowwise.r'
'sample.R' 'select-utils.R' 'select-vars.R' 'sets.r'
'sql-build.R' 'sql-escape.r' 'sql-generic.R' 'sql-query.R'
'sql-render.R' 'sql-star.r' 'src-local.r' 'src-mysql.r'
'src-postgres.r' 'src-sql.r' 'src-sqlite.r' 'src-test.r'
'src.r' 'tally.R' 'tbl-cube.r' 'tbl-df.r' 'tbl-lazy.R'
'tbl-sql.r' 'tbl.r' 'tibble-reexport.r' 'top-n.R'
'translate-sql-helpers.r' 'translate-sql-base.r'
'translate-sql-window.r' 'translate-sql.r' 'utils-format.r'
'utils-replace-with.R' 'utils.r' 'view.r' 'zzz.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'
'dspoint.R' 'dsguessing.R' 'dsmodel.R' 'dsrange.R' 'dsregion.R'
'features.R' 'modelConstruction.R'
'generic.functions.R' 'DDF.Data.R' 'DDF.Analysis.R'
'DS.Analysis.R' 'Survey.Design.R' 'PT.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' 'add.dist.error.R'
'add.summary.results.R' 'calc.area.R'
'calc.poss.detect.dists.lines.R'
'calc.poss.detect.dists.points.R' 'check.LinkID.order.R'
'check.intersection.R' 'check.shapefile.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'
'modify.strata.for.analysis.R' 'rename.duplicates.R'
'save.sim.results.R' 'simulate.detections.R'
'single.simulation.loop.R' 'store.ddf.results.R'
'store.dht.results.R' 'uf.detect.R'
'bca.R' 'belplau.R' 'combmasses.R' 'dempster.R' 'dotprod.R'
'doubles.R' 'dsrwon.R' 'dstCombListfacts.R'
'dstCombListfacts2.R' 'identRow.R' 'initsing.R' 'inters.R'
'nzdsr.R' 'plautrans.R' 'probCombListfacts.R' 'rplau.R'
'smppmexample.R' 'tabresul.R' 'tabresulSingl.R'
'DBA.R' 'NCCc.R' 'SBD.R' 'TADPole.R' 'all-cent.R' 'cvi.R'
'ddist.R' 'dtw-basic.R' 'dtw-lb.R' 'dtw2.R'
'dtwclust-classes.R' 'dtwclust-methods.R' 'dtwclust.R'
'fuzzy.R' 'lb-improved.R' 'lb-keogh.R' 'partitional.R'
'utils.R' 'pkg.R' 'reinterpolate.R' 'shape-extraction.R'
'uciCT.R' 'zscore.R'
'class-twdtwMatches.R' 'class-twdtwRaster.R'
'class-twdtwTimeSeries.R' 'createPatterns.R' 'data.R' 'dtw.R'
'dwtSat.R' 'getInternals.R' 'getTimeSeries.R' 'linearWeight.R'
'logisticWeight.R' 'methods.R' 'miscellaneous.R' 'plot.R'
'plotAlignments.R' 'plotArea.R' 'plotChanges.R'
'plotClassification.R' 'plotCostMatrix.R' 'plotDistance.R'
'plotMaps.R' 'plotMatches.R' 'plotPaths.R' 'plotPatterns.R'
'plotTimeSeries.R' 'resampleTimeSeries.R' 'subset.R' 'twdtw.R'
'twdtwApply.R' 'twdtwClassify.R' 'zzz.R'
'dynrData.R' 'dynrRecipe.R' 'dynrModelInternal.R'
'dynrModel.R' 'dynrCook.R' 'dynrPlot.R' 'dynrFuncAddress.R'
'dynrVersion.R' 'dataDoc.R'
clsEqcReader4.r clsRCD7.r clsGWADATA45.r clsEASYMERGE.r
fnEasyPLOT10.r clsREPORT9.r clsMERGE11.r clsSPLOT21.r
clsADDCOL11.r clsADJUSTALLELES15.r clsANNOTATE8.r
clsBONFERRONI3.r clsCALCPDIFF6.r clsCALCPHET3.r clsCALCULATE3.r
clsCLEAN5.r clsCLUMP5.r clsCRITERION6.r clsEDITCOL.r
clsEVALSTAT7.r clsEXTRACTSNPS3.r clsFDR3.r clsFILTER6.r
clsGC5.r clsGETCOLS.r clsGETNUM2.r clsINDEP7.r clsJOINTTEST3.r
clsMERGEEASYIN4.r clsMETAANALYSIS5.r clsMHPLOT15.r
clsMIAMIPLOT15.r clsQQPLOT17.r clsREMOVECOL.r clsRENAMECOL2.r
clsRPLOT18.r clsSTRSPLITCOL.r clsWRITE7.r EasyX86.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' 'auxiliar.R' 'int.genind.R' 'ecogen.1OF5.definition.R'
'ecogen.2OF5.constructor.R' 'ecogen.3OF5.basic.methods.R'
'ecogen.4OF5.brackets.R' 'ecogen.5OF5.get&set.R' 'accesors.R'
'classes.R' 'control.R' 'deprecated.R' 'eco.2geneland.R'
'eco.2genepop.R' 'eco.2gstudio.R' 'eco.2hierfstat.R'
'eco.2spagedi.R' 'eco.NDVI.R' 'eco.NDVI.post.R' 'eco.alfreq.R'
'eco.association.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.genepop2df.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.order.R' 'eco.pairtest.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.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.random.test.R'
'miscellaneous.R' 'plot.generic.R' 'plot.methods.R'
'roxygen.auxiliar.R' 'show_summary.methods.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'
'eqs2lavaan.R' 'eqsCorr.R' 'eqsCov.R' 'eqsDesc.R'
'out2lavaan.R' 'plotCov.R'
calEdgeCorScore.R ESEA.Main.R PlotGlobEdgeCorProfile.R
PlotPathwayGraph.R PlotRunEnrichment.R SavePathway2File.R
getEnvironmentData.R GetExampleData.R GetEdgesBackgrandData.R
GetPathwayEdgeData.R
'cluster_utilities.R' 'decathlon2.R' 'eigenvalue.R'
'utilities.R' 'dist.R' 'fviz_dend.R' 'hcut.R'
'print.factoextra.R' 'get_pca.R' 'fviz_cluster.R' 'eclust.R'
'facto_summarize.R' 'fviz_add.R' 'fviz_ca.R' 'fviz_contrib.R'
'fviz_cos2.R' 'get_hmfa.R' 'fviz_hmfa.R' 'get_mca.R'
'fviz_mca.R' 'get_mfa.R' 'fviz_mfa.R' 'fviz_nbclust.R'
'fviz_pca.R' 'fviz_silhouette.R' 'get_ca.R'
'get_clust_tendency.R' 'hkmeans.R' 'housetasks.R'
'multishapes.R' 'poison.R'
'FastImputation.R' 'TrainFastImputation.R' 'UnfactorColumns.R'
'BoundNormalizedVariable.R' 'NormalizeBoundedVariable.R'
'LimitToSet.R' 'CovarianceWithMissing.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'
'FDboost.R' 'baselearners.R' 'baselearnersX.R'
'constrainedX.R' 'crossvalidation.R' 'FDboostLSS.R' 'hmatrix.R'
'methods.R' 'stabsel.R' 'utilityFunctions.R'
'aaa.R' 'HandlerStack.R' 'Fire.R' 'FutureStack.R'
'fake_request.R' 'fiery-package.R'
'polarCoord.R' 'stratified.R' 'univariate.tests.R'
'string.to.color.R' 'spearman.plot.R' 'r.crit.R' 'boxcoxR.R'
'missing.vals.R' 'make.formula.R' 'scaleB.R' 'par1.R'
'make.null.R' 'demographics.R' 'chisqPostHoc.R' 'cor2cov.R'
'random.correlation.R' 'scale.it.R' 'densityPlotR.R'
'excelMatch.R' 'prism.plots.R' 'hash.R' 'printx.R' 'roxtemp.R'
'intersperse.R' 'subsetString.R' 'auto.layout.R' 'contents.R'
'mvrnorm.R' 'kruskalmc.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'
'RcppExports.R' 'acfARp.R' 'f.R' 'forecastSNSTS-package.R'
'models.R' 'mspe.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' 'catcont.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' 'terms.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
'distances.R' 'expressions.R' 'franc.R' 'ngrams.R'
'normalize.R' 'script.R' 'speakers.R' 'trigrams.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'
'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-calculateAHP.R' 'function-calculateFuzzyAHP.R'
'function-calculateWeights.R' 'function-compare.R'
'function-consistencyIndex.R' 'function-consistencyRatio.R'
'function-defuzzify.R' 'function-getFuzzyNumbers.R'
'function-insertInto.R' 'function-internal-optimization.R'
'function-internal-weights.R' 'function-print.R'
'function-strictConsistency.R' 'function-weakConsistency.R'
'package-FuzzyAHP.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
'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'
'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'
'desc_statby.R' 'diff_express.R' 'utilities.R' 'ggpar.R'
'ggbarplot.R' 'ggboxplot.R' 'ggdensity.R' 'stat_conf_ellipse.R'
'stat_chull.R' 'ggdotchart.R' 'ggdotplot.R' 'ggecdf.R'
'ggerrorplot.R' 'gghistogram.R' 'ggline.R' 'ggmaplot.R'
'ggpie.R' 'ggqqplot.R' 'ggscatter.R' 'ggstripchart.R'
'ggviolin.R' 'show_line_types.R' 'show_point_shapes.R'
'stat_mean.R' 'theme_pubr.R'
'flip-aes.R' 'geom-barh.R' 'legend-draw.R' 'geom-boxploth.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-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-showsecondary.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-interpolate-tern.R' 'stat-interpolate-tern.R'
'stat-interpolate-methods.R' 'geom-crosshair-tern.R'
'geom-point-swap.R' 'annotation-tern.R'
'annotation-raster-tern.R'
'banking.R' 'base.R' 'calc.R' 'colorblind.R' 'economist.R'
'excel.R' 'few.R' 'ggthemes-data.R' 'ggthemes-package.R'
'fivethirtyeight.R' 'gdocs.R' 'geom-rangeframe.R'
'geom-tufteboxplot.R' 'hc.R' 'igray.R' 'pander.R' 'ptol.R'
'scale-tufte.R' 'shapes.R' 'show.R' 'solarized.R'
'stat-fivenumber.R' 'stata.R' 'tableau.R' 'theme-foundation.R'
'theme-map.R' 'theme-solid.R' 'tufte.R' 'utils.R' 'wsj.R'
'S4_classes.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' 'refspec.r' 'push.r' 'reference.r'
'reflog.r' 'remote.r' 'repository.r' 'reset.r' 'revparse.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'
generics.R glpk_ptrClass.R glpk.R glpkAPI.R zzz.R
'GMD-package.R' 'imports.R' 'util.R' 'GMD-internal.R'
'GMD-data.R' 'ghist.R' 'gdist.R' 'css.R' 'heatmap3.R' 'gmdp.R'
'gmdm.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'
'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'
'granovaGG-package.R' 'granovagg.1w-helpers.R'
'theme-defaults.R' 'shared-functions.R' 'granovagg.1w.R'
'granovagg.contr.R' 'granovagg.ds.R'
gtable.r grob-utils.r arrangeGrob.r gridExtra-package.r
tableGrob.r ngonGrob.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' 'bactgrowth.R' 'cost.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' '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'
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'
'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'
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'
'All_classes.R' 'Fuzzy_cmeans.R' 'H_time_series.R'
'HistDAWass-package.R' 'Kohonen_maps.R' 'Met_HTS.R'
'Met_MatH.R' 'Met_distributionH.R' 'Utility.R'
'principal_components.R' 'regression.R'
'unsuperv_classification.R' 'Plotting_with_ggplot.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' 'barbiturates.R'
'paracetamol.R' 'laser.R' 'chondro.R' 'flu.R'
'hyperspec-package.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' 'bind.R'
'call.list.R' 'chk.hy.R' 'colMeans.R' 'collapse.R' 'cov.R'
'decomposition.R' 'dim.R' 'dimnames.R' 'empty.R' 'wl2i.R'
'extract.R' 'factor2num.R' 'fileio.optional.R' 'getbynames.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' 'options.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.ini.R' 'read.jdx.R' 'read.jdx.Shimadzu.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.Shimadzu.R' 'read.txt.long.R' 'read.txt.wide.R'
'replace.R' 'sample.R' 'scale.R' 'scan.txt.Witec.R'
'scan.asc.Andor.R' 'scan.txt.Renishaw.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' 'unittest.R' 'vandermonde.R'
'wc.R' 'wl.R' 'wleval.R' 'write.txt.long.R' 'write.txt.wide.R'
'y-pastenames.R' 'zzz.R'
misc.R encoding.R dialect.R table_area.R row_function.R
col_function.R data_type.R quality_assessment.R parser_full.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' 'printr.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' 'mgclust.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' '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'
ImageClass.R MethodDefinitions.R ComplexImageClass.R
OrthogonalImageClass.R MultipleImagesClass.R OrthMoments.R
MultiCmplxWrapper.R gpzmWrapper.R frWrapper.R fmWrapper.R
fcWrapper.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'
'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'
Class-Metric.R BSSUtils.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'
'zzz.R' 'hello.R' 'JASPAR-package.R' 'JASPAR-internal.R'
'make_template.R'
'kitagawa-package.R' 'alpha_constants.R' 'omega_constants.R'
'open_well_response.R' 'well_response.R' 'utilities.R'
'methods.R' 'zzz.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'
'Kmisc-md2html.R' 'Kmisc-package.R' 'RcppExports.R'
'Rcpp_gen_makevars.R' 'anat.R' 'any_na.R' 'apply.R' 'awk.R'
'bwplot2.R' 'chunk.R' 'clean_doc.R' 'copy.R' 'counts.R'
'extract.R' 'extract_rows_from_file.R' 'fast_factor.R'
'grid.text2.R' 'html.R' 'htmlTable.R' 'html_attach.R'
'html_extras.R' 'html_tags.R' 'in_interval.R' 'is_sorted.R'
'labeller.R' 'list_to_dataframe.R' 'manhattan_plot.R'
'mat2df.R' 'matches.R' 'melt_.R' 'misc.R' 'nametree.R'
'pMerge.R' 'pad.R' 'par.reset.R' 'pp_plot.R'
'prepare_package.R' 'pymat.R' 'rcpp_apply_generator.R'
'rcpp_tapply_generator.R' 'read.R' 'regex.R'
'registerFunctions.R' 'remove_char_digit.R' 'simp.R' 'size.R'
'split_file.R' 'split_runs.R' 'stack_list.R' 'str_collapse.R'
'str_rev.R' 'str_slice.R' 'str_sort.R' 'str_split.R' 'swap.R'
'sys.R' 'tapply_.R' 'transpose.R' 'unmelt.R' 'update_date.R'
'value_matching.R' 'wrap.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'
'VAR.R' 'break.ties.R' 'coef.l2boost.R' 'cv.l2boost.R'
'diabetes.R' 'elasticNetSim.R' 'error.bars.R' 'eval.fold.R'
'extract.corr.R' 'fitted.l2boost.R' 'get.discrete.solution.R'
'get.hybrid.solution.R' 'get.lars.solution.R' 'help.R'
'l2boost.R' 'mstep.long.R' 'mvnorm.l2boost.R' 'nu.limit.R'
'plot.l2boost.R' 'plot.lines.R' 'predict.l2boost.R'
'print.l2boost.R' 'print.summary.l2boost.R' 'resample.R'
'residuals.l2boost.R' 'summary.l2boost.R' 'which.max.ind.R'
'which.min.ind.R'
'generics.R' 'laf.R' 'laf_column.R' 'meta.R' 'open.R'
'read_dm_blaise.R' 'stats.R' 'textutils.R' 'types.R'
'utility.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'
'any.R' 'lambda.tools-package.R' 'logic.R' 'sequence.R'
'fold.R' 'map.R' 'sample.R' 'transform.R' 'ntry.R'
'binary.R' 'clprobit.R' 'hooks.R' 'onload.R' 'plotres.R'
'rbind.Surv.R' 'tobit.R' 'weighted.R' 'lava.tobit-package.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'
'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'
'ColumnLinkedMatrix.R' 'RowLinkedMatrix.R' 'LinkedMatrix.R'
'package.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'
'ltmle-package.R' 'GeneralUtilities.R' 'ltmle.R' 'ltmle_sg.R'
'BinaryToCensoring.R' 'DeterministicFunctions.R' 'zzz.R'
'Dates.r' 'POSIXt.r' 'util.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' 'help.r' 'hidden.r' 'instants.r'
'leap-years.r' 'ops-addition.r' 'ops-%m+%.r' 'ops-compare.r'
'ops-division.r' 'ops-integer-division.r' 'ops-modulo.r'
'ops-multiplication.r' 'ops-subtraction.r' 'parse.r' 'pretty.r'
'round.r' 'stamp.r' 'update.r'
'class-CategoryLabel.R' 'class-PredictiveModelList.R'
'class-ObsLulcRasterStack.R' 'class-NeighbRasterStack.R'
'class-ExpVarRasterList.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'
'Analyse_SAR.OSLdata.R' 'CW2pHMi.R' 'CW2pLM.R' 'CW2pLMi.R'
'CW2pPMi.R' 'Luminescence-package.R' 'RcppExports.R'
'replicate_RLum.R' 'RLum-class.R' 'names_RLum.R'
'structure_RLum.R' 'length_RLum.R' 'set_RLum.R' 'get_RLum.R'
'RLum.Analysis-class.R' 'RLum.Data-class.R' 'bin_RLum.Data.R'
'RLum.Data.Curve-class.R' 'RLum.Data.Image-class.R'
'RLum.Data.Spectrum-class.R' 'RLum.Results-class.R'
'Risoe.BINfileData2RLum.Analysis.R'
'Risoe.BINfileData2RLum.Data.Curve.R' 'set_Risoe.BINfileData.R'
'get_Risoe.BINfileData.R' 'RisoeBINfileData-class.R'
'Second2Gray.R' 'analyse_IRSAR.RF.R' 'analyse_SAR.CWOSL.R'
'analyse_SAR.TL.R' 'analyse_baSAR.R' 'analyse_pIRIRSequence.R'
'app_RLum.R' 'apply_CosmicRayRemoval.R'
'apply_EfficiencyCorrection.R' 'calc_AliquotSize.R'
'calc_CentralDose.R' 'calc_CommonDose.R'
'calc_CosmicDoseRate.R' 'calc_FadingCorr.R' 'calc_FastRatio.R'
'calc_FiniteMixture.R' 'calc_FuchsLang2001.R'
'calc_HomogeneityTest.R' 'calc_IEU.R' 'calc_MaxDose.R'
'calc_MinDose.R' 'calc_OSLLxTxRatio.R' 'calc_SourceDoseRate.R'
'calc_Statistics.R' 'calc_TLLxTxRatio.R'
'calc_ThermalLifetime.R' 'calc_gSGC.R'
'extract_IrradiationTimes.R' 'fit_CWCurve.R' 'fit_LMCurve.R'
'get_Layout.R' 'get_Quote.R' 'get_rightAnswer.R'
'internal_as.latex.table.R' 'internals_RLum.R'
'merge_RLum.Analysis.R' 'merge_RLum.Data.Curve.R'
'merge_RLum.R' 'merge_RLum.Results.R'
'merge_Risoe.BINfileData.R' 'methods_DRAC.R' 'methods_RLum.R'
'model_LuminescenceSignals.R' 'plot_AbanicoPlot.R'
'plot_DRTResults.R' 'plot_DetPlot.R'
'plot_FilterCombinations.R' 'plot_GrowthCurve.R'
'plot_Histogram.R' 'plot_KDE.R' 'plot_NRt.R'
'plot_RLum.Analysis.R' 'plot_RLum.Data.Curve.R'
'plot_RLum.Data.Image.R' 'plot_RLum.Data.Spectrum.R'
'plot_RLum.R' 'plot_RLum.Results.R' 'plot_RadialPlot.R'
'plot_Risoe.BINfileData.R' 'plot_ViolinPlot.R' 'read_BIN2R.R'
'read_Daybreak2R.R' 'read_SPE2R.R' 'read_XSYG2R.R'
'report_RLum.R' 'template_DRAC.R' 'tune_Data.R' 'use_DRAC.R'
'verify_SingleGrainData.R' 'write_R2BIN.R' 'zzz.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' 'bind.r' 'blockrep.r' 'coerce.r' 'det.r' 'diag.r'
'eigen.r' 'elwise.r' 'madness_pkg.r' 'matwise.r' 'max.r'
'norm.r' 'numderiv.r' 'Ops.r' 'reshape.r' 'solve.r' 'sum.r'
'sums.r' 'theta.r' 'to_objective.r' 'trace.r' 'twomoments.r'
'utils.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
'assertion_funcions.R' 'expectations.R' 'mark_my_assignment.R'
'mark_my_file.R' 'markmyassignment.R' 'set_assignment.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'
AggregateFireFracs.R AggregateVegFracs.R
ReportMeanVegChanges.R SaveFireAreaFracs.R SaveMatrix.R
SaveRatios.R SaveSequence.R SaveVegFracs.R VTnames.R
VegTypeChanges.R SaveVegChangeProbabilityMultipliers.R
SaveFireProbabilityMultipliers.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'
'gini.R' 'migration.indices.R' 'crude.R' 'others.R' 'acv.R'
'graph.R'
'RcppExports.R' 'milr-package.R' 'DGP.R' 'softmax.R' 'milr.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' 'ClusterInit.R' 'ClusterStrategy.R'
'IClusterModel.R' 'ClusterModelNames.R' 'global.R'
'ClusterCategorical.R' 'ClusterDiagGaussian.R' 'ClusterGamma.R'
'ClusterKernel.R' 'ClusterMixedData.R' 'ClusterPlot.R'
'ClusterPoisson.R' 'LearnAlgo.R' 'Learners.R' 'MixAll.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' 'NS_species_params-data.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'
'expect_called.R' 'expectations.R' 'function_source_utils.R'
'mockery.R' 'mock-object.R' 'stub.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
'NAMESPACE.R' 'getSearchPath.R' 'module-class.R'
'module-coercion.R' 'module-helper.R' 'module.R'
AllClasses.R AllGenerics.R alapl.R bms_ortho.R
derivatives_nlps.R msPriorSpec.R modelsearch.R modelSelection.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 embedded.R
mapi.R dbi.R dbapply.R dplyr.R control.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'
'add3pt.R' 'calc.genoprob2.R' 'calcmpprob.R' 'check-ped.R'
'check-QTL.R' 'clean.mpcross.R' 'cleanmap.R' 'cleanrf.R'
'combine-chr.R' 'combine-ibd.R' 'combine-ld.R' 'combine-rf.R'
'compare-orders.R' 'compute-bnld.R' 'compute-bnrf.R'
'computemap.R' 'convertped.R' 'CR-calcLD.R' 'CR-cross.R'
'CR-estrf.R' 'CR-gen-geno.R' 'fill.R' 'fillmiss.R' 'findqtl2.R'
'fit.mpqtl.R' 'gen4ped.R' 'gen8ped.R' 'generate-error.R'
'generate-obs.R' 'generate-pheno.R' 'getpval.R' 'mapcomp.R'
'maporder.R' 'mp-mapdist.R' 'mpadd.R' 'mpcalcld.R' 'mpcross.R'
'mpestrf.R' 'mpgroup.R' 'mpIM.R' 'mpMap-output.R' 'mporder.R'
'mpprob.R' 'mpsegrat.R' 'nai.R' 'plot.mpcross.R'
'plot.mpprob.R' 'plot.mpqtl.R' 'plotlink.map.R'
'print.mapcomp.R' 'print.mpcross.R' 'print.mpprob.R'
'print.mpqtl.R' 'qtlmap.R' 'read.mpcross.R'
'reorgRIgenoprob2.R' 'sim.mpcross.R' 'sim.mpped.R'
'sim.sigthr.R' 'subset.mpcross.R' 'subset.mpprob.R'
'summary.mpcross.R' 'summary.mpprob.R' 'summary.mpqtl.R'
'supportinterval.R' 'waldTests.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
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'
classes.R mardiaTest.R hzTest.R roystonTest.R mvOutlier.R
mvnPlot.R uniNorm.R uniPlot.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
'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' '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_help.R' 'nbc4va_utility.R' 'nbc4va_wrapper.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' 'data.R'
'diffnet-c.R' 'diffnet-class.R' 'diffnet-indexing.R'
'diffnet-methods.R' 'egonets.R' 'igraph.R' 'infect_suscept.R'
'misc.R' 'moran.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'
'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' '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_testBUGS.R
BUGS_getDependencies.R BUGS_utils.R BUGS_mathCompatibility.R
genCpp_exprClass.R genCpp_operatorLists.R
genCpp_RparseTree2exprClasses.R genCpp_initSizes.R
genCpp_buildIntermediates.R genCpp_processSpecificCalls.R
genCpp_sizeProcessing.R genCpp_insertAssertions.R genCpp_maps.R
genCpp_liftMaps.R genCpp_eigenization.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 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_BUGSmodel.R
cppDefs_RCfunction.R cppDefs_nimbleFunction.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 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 filtering_auxiliary.R
filtering_liuwest.R filtering_enkf.R filtering_bootstrap.R
optimTools.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'
'rmatrix.R' 'nmf-package.R' 'utils.R' 'versions.R'
'algorithmic.R' 'options.R' 'grid.R' 'colorcode.R' 'atracks.R'
'aheatmap.R' 'NMF-class.R' 'transforms.R' 'Bioc-layer.R'
'NMFstd-class.R' 'NMFOffset-class.R' 'registry.R' 'heatmaps.R'
'NMFns-class.R' 'nmfModel.R' 'fixed-terms.R' 'NMFfit-class.R'
'NMFSet-class.R' 'NMFStrategy-class.R' 'NMFSeed-class.R'
'NMFStrategyFunction-class.R' 'NMFStrategyIterative-class.R'
'NMFStrategyOctave-class.R' 'NMFplots.R'
'registry-algorithms.R' 'algorithms-base.R'
'algorithms-brunet.R' 'algorithms-lnmf.R' 'algorithms-lsnmf.R'
'algorithms-pe-nmf.R' 'algorithms-siNMF.R' 'algorithms-snmf.R'
'data.R' 'extractFeatures.R' 'registry-seed.R' 'parallel.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
'global.R' 'NpdeData.R' 'NpdeRes.R' 'NpdeObject.R'
'func_methods.R' 'func_plots.R' 'main.R' 'zzz.R'
'data.R' 'helper.funs.R' 'pl.smooth.gam.R'
'calc.oddsratio.gam.R' 'calc.oddsratio.glm.R'
'add.oddsratio.into.plot.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' 'MxExpectationStateSpace.R'
'MxExpectationBA81.R' 'MxFitFunctionGREML.R'
'MxExpectationGREML.R' 'MxMI.R' 'MxFactorScores.R'
'MxRobustSE.R' 'MxAvailableOptimizers.R' 'MxTryHard.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'
'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
'andalusia.R' 'as.R' 'loca.p.R' 'orloca-package.R'
'plot.loca.p.R' 'plot.zsum.R' 'rloca.p.R' 'split.R'
'zsuml2min.R' 'zsuml2.R' 'zsumlpmin.R' 'zsumlp.R' 'zsummin.R'
'zsum.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' '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' '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' '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'
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'
'aggplot.R' 'aggspec.R' 'as.rspec.r' 'coldist.R'
'data-bgandilum.R' 'data-sicalis.R' 'data-teal.R'
'data-ttvertex.R' 'data-vissyst.R' 'explorespec.R' 'getspec.R'
'internal.R' 'pavo-package.R' 'peakshape.R' 'plot.rspec.r'
'plotsmooth.R' 'procspec.R' 'projplot.R' 'segclass.R'
'sensmodel.R' 'spec2rgb.R' 'summary.rspec.R' 'summary.tcs.R'
'tcs.R' 'tcsplot.R' 'tcspoints.R' 'tcsvol.R' 'vismodel.R'
'voloverlap.R' 'merge.rspec.R' 'summary.vismodel.R'
'subset.rspec.R' 'irrad2flux.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
'formula.R' 'linear.predictors.R' 'phmm.R' 'pseudoPoisPHMM.R'
'traceHat.R'
'oldclasses-class.R' 'internal-constructors.R'
'phylo4-methods.R' 'RcppExports.R' 'checkdata.R'
'phylo4-class.R' 'getNode-methods.R' 'formatData.R'
'phylo4d-class.R' 'phylo4d-methods.R' 'MRCA-methods.R'
'addData-methods.R' 'ancestors.R' 'phylo4-accessors.R'
'root-methods.R' 'nodeId-methods.R' 'edgeLength-methods.R'
'setAs-methods.R' 'extractTree.R' 'labels-methods.R'
'multiphylo4-class.R' 'pdata.R' 'phylo4d-accessors.R'
'phylobase-package.R' 'phylobase.options.R' 'phylomats-class.R'
'print-methods.R' 'readNCL.R' 'reorder-methods.R'
'shortestPath-methods.R' 'subset-methods.R' 'summary-methods.R'
'tbind.R' 'tdata-methods.R' 'treePlot.R' 'treestruc.R' 'zzz.R'
'ARTP-GE.R' 'data.to.PIGE.R' 'permutation.snp.R'
'PIGE-package.R' 'compute.p.snp.obs.R' 'LR.inter.cat.R'
'LR.inter.cont.R' 'permutation.wrapper.cont.R' 'LR.cont.R'
'permutation.wrapper.cat.inter.R'
'permutation.wrapper.cont.inter.R'
'permutation.wrapper.cat.inter.Y.and.X.R'
'permutation.wrapper.cont.inter.Y.and.X.R'
'permutation.wrapper.cont.Y.and.X.R' 'LR.cont.surv.R'
'LR.inter.cat.surv.R' 'LR.inter.cont.surv.R'
'data.to.list.gene.snp.R'
'CreateScoreFun.R' 'DysData.R' 'EngelData.R' 'Estimators.R'
'pim.poset-class.R' 'pim.environment-class.R' 'pim-package.R'
'pim.formula-class.R' 'pim-class.R' 'pim.summary-class.R'
'Extract.pim.summary.R' 'FEVData.R' 'Getters.R' 'Getters_pim.R'
'Getters_pim.formula.R' 'InternalFunctions.R'
'InternalObjects.R' 'LR.R' 'MHData.R' 'P.R' 'SUData.R'
'add.poset.R' 'as.data.frame.pim.environment.R'
'as.matrix.pim.summary.R' 'coef.R' 'confint.pim.R'
'create.poset.R' 'formula.R' 'has.intercept.R' 'is.complete.R'
'make.posfun.R' 'model.matrix.pim.R' 'new.pim.R'
'new.pim.env.R' 'new.pim.formula.R' 'new.pim.poset.R' 'nobs.R'
'penv.R' 'pim.R' 'pim.fit.R' 'pimdata.R' 'poset.R' 'print.R'
'response.R' 'sandwich.estimator.R' 'summary.R' 'vcov.R'
'vcov.estimators.R' 'vcov.internal.R' 'zzz.R'
'utils.R' 'logging.R' 'unitTests.R' 'data.R' 'namespace.R'
'devutils.R' 'package.R' 'options.R' 'is.R' 'registry.R'
'bibtex.R' 'packages.R' 'vignette.R' 'files.R'
'package-extra.R' 'colors.R' 'graphics.R' 'rd.R' 'project.R'
'CLI.R' 'knitr.R' 'repositories.R'
global.r psnCdd.r psnBootstrap.r functionOK.r proto.r ggobi.r
function.r handler.r PKgraphlayout.r PKgraph.r
zzz.R debug.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' 'globals.R'
'serializer-json.R' 'plumber.R' 'default-handlers.R'
'includes.R' 'paths.R' 'plumber-step.R' 'post-body.R'
'processor.R' 'processor-image.R' 'query-string.R' 'response.R'
'serializer-content-type.R' 'serializer-html.R'
'serializer-xml.R' 'serializer.R' 'session-cookie.R'
'accessors.r' 'bindings.r' 'coercion.r' 'constructor.r'
'dimensions.r' 'events.r' 'linking.r' 'mutalist.R' 'names.r'
'print.r' 'proxy-filter.r' 'selection.r' 'utils.r' 's4.r'
'import.r' 'globals.r'
'create.R' 'holdout.R' 'kernel.R' 'learn.R' 'pnn-package.r'
'data-norms.R' 'guess-category.R' 'guess-probabilities.R'
'perf.r' 'guess.r' 'smooth.R' 'what-else.R'
'CQI.R' 'CQI.Rcpp.R' 'CQI.cqpserver.R' 'CQI.perl.R'
'CQI.rcqp.R' 'TermDocumentMatrix_methods.R'
'adjustEncoding_method.R' 'generics.R' 'textstat_class.R'
'bundle_class.R' 'polmineR_package.R' 'partition_class.R'
'partitionBundle_class.R' 'as.DocumentTermMatrix_method.R'
'as.markdown_method.R' 'comp_class.R' 'context_class.R'
'contextBundle_class.R' 'cooccurrences_class.R'
'as.sparseMatrix_method.R' 'as.speeches_method.R'
'blapply_method.R' 'kwic_class.R' 'browse_method.R'
'chisquare_method.R' 'ngrams_method.R' 'compare_method.R'
'contextBundle_methods.R' 'context_method.R'
'cooccurrencesBundle_methods.R'
'cooccurrencesReshaped_methods.R' 'cooccurrences_method.R'
'cooccurrences_methods.R' 'corpus_method.R'
'dispersion_class.R' 'count_method.R' 'tempcorpus.R'
'cpos_method.R' 'hits_class.R' 'dispersion_method.R'
'dispersion_methods.R' 'divide_method.R' 'dotplot_method.R'
'encoding_method.R' 'enrich_method.R' 'freq_method.R'
'frequencies_method.R' 'getEncoding_method.R'
'getTermFrequencies_method.R' 'getTerms_method.R'
'getTokenStream_method.R' 'highlight_method.R' 'html_method.R'
'html_methods.R' 'kwic_method.R' 'kwic_methods.R' 'll_method.R'
'mail_method.R' 'means_method.R' 'meta_method.R'
'noise_method.R' 'pAttributes_method.R'
'partitionBundle_method.R' 'partitionBundle_methods.R'
'sAttributes2cpos_method.R' 'partition_method.R'
'partition_methods.R' 'plot_method.R' 'pmi_method.R'
'read_method.R' 'registry.R' 'rustyard.R'
'sAttributes_method.R' 'scatterplot_method.R' 'size_method.R'
'tTest.R' 'terms_method.R' 'trim_method.R' 'use_method.R'
'utils.R' 'view_method.R' 'weigh_method.R' 'zzz.R'
generic.R UtilPolychaos.R analyticsPolyLeg.R PCEdesign-class.R
PCEfit-class.R PCEpoly-class.R PCESI.R polyLeg.R
aaa.R authors.R bake.R generics.R eulermultinom.R csnippet.R
safecall.R pomp_fun.R plugins.R builder.R parmat.R logmeanexp.R
slice_design.R profile_design.R sobol.R bsplines.R sannbox.R
pomp_class.R load.R pomp.R pomp_methods.R rmeasure_pomp.R
rprocess_pomp.R initstate_pomp.R dmeasure_pomp.R
dprocess_pomp.R skeleton_pomp.R dprior_pomp.R rprior_pomp.R
simulate_pomp.R trajectory_pomp.R plot_pomp.R pfilter.R
pfilter_methods.R minim.R traj_match.R bsmc.R bsmc2.R kalman.R
kalman_methods.R mif.R mif_methods.R mif2.R mif2_methods.R
proposals.R pmcmc.R pmcmc_methods.R nlf_funcs.R nlf_guts.R
nlf_objfun.R nlf.R probe.R probe_match.R basic_probes.R spect.R
spect_match.R abc.R abc_methods.R covmat.R example.R
'ES.anova.oneway.R' 'ES.chisq.assoc.R' 'ES.chisq.gof.R'
'ES.proportions.R' 'ES.t.one.R' 'ES.t.paired.R' 'ES.t.two.R'
'power.chisq.R' 'power.plot.chisq.R' 'power.t.R'
'power.anova.oneway.R' 'power.proportions.R'
'aaa_all_classes.R' 'AllGenerics.R' 'bootstrap.R'
'bootstrap_p.R' 'checks.R' 'compare_distributions.R'
'ctn_helper_functions.R' 'data_help_files.R' 'def_conexp.R'
'def_conlorm.R' 'def_conpl.R' 'def_conweibull.R' 'def_disexp.R'
'def_dislnorm.R' 'def_displ.R' 'def_dispois.R' 'def_template.R'
'dist_data_cdf_methods.R' 'estimate_pars.R' 'estimate_xmin.R'
'get_n.R' 'get_ntail.R' 'lines_methods.R' 'plcon.R' 'pldis.R'
'plot_methods.R' 'points_methods.R' 'poweRlaw-package.R'
'show_methods.R' 'timer.R'
'adjective.R' 'adverb.R' 'exclamation.R' 'verb.R' 'rpackage.R'
'package.R'
'app.r' 'as.precintcon.annual.r' 'as.annual.r'
'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'
'anova.critpat.R' 'cpa.R' 'mpa.R' 'pams.R' 'paos.R' 'pbg.R'
'pcv.R' 'plot.critpat.R' 'plot.prof.R' 'pr.R' 'print.critpat.R'
'print.prof.R' 'print.profg.R' 'profileplot.R'
'summary.critpat.R' 'summary.profg.R' 'wprifm.R'
'misc.R' 'browsegWidgetsFiles.R' 'browseQtFiles.R'
'browseRGtk2Files.R' 'browseTclTkFiles.R' 'showGtkWidgetInfo.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' 'create.project.R' 'translate.dcf.R'
'default.config.R' 'get.project.R' 'help.R' 'load.project.R'
'migrate.project.R' 'new.config.R' 'reload.project.R'
'run.project.R' 'show.project.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 findNN.R
fragmentIon.R genMod.R iTRAQ2GroupAnalysis.R lcmsmap.R mdp.R
parentIonMass.R peakplot.R pgImporter.R pgLFQ.R pp.R psm.R
PTM_MarkerFinder.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'
'utils.R' 'style_default.R' 'style_apa.R' 'style.R'
'out_plain.R' 'out_default.R' 'out_latex.R' 'out_markdown.R'
'out_html.R' 'out.R' 'defaults.R' 'io.R' 'pubprint.R' 'zzz.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'
'RcppExports.R' 'dictionaries.R' 'corpus.R' 'collocations.R'
'converters.R' 'dataDocs.R' 'describe-texts.R' 'dfm-classes.R'
'dfm-main.R' 'dfm-methods.R' 'dfm-weighting.R' 'encoding.R'
'findSequences.R' 'joinTokens.R' 'kwic.R' 'lexdiv.R' 'ngrams.R'
'phrases.R' 'plots.R' 'quanteda.R' 'readability.R' 'resample.R'
'selectFeatures-old.R' 'selectFeatures.R' 'settings.R'
'similarity.R' 'stopwords.R' 'syllables.R' 'textfile.R'
'textmodel-NB.R' 'textmodel-ca.R' 'textmodel-generics.R'
'textmodel-wordfish.R' 'textmodel-wordscores.R' 'toLower.R'
'tokenize.R' 'tokenize_outtakes.R' 'util.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'
'R2admb-package.R' 'base-funs.r' 'check_section.R' 'do_admb.r'
'mcmc.control.r' 'plot.admb_hist.r' 'read-funs.r' 'readfit.R'
'run.control.r' 'setup_admb.R' 'std_accessor.r'
'utility-funs.R' 'write-funs.r' 'extract_gradient.r'
'find_large_cor.r' 'get_version.r'
functions.R logical.R factor.R ordered.R discrete.R
continuous.R rtlu.R rtlbDisplay.R rtlb.R
'GLSZM.R' 'MGLSZM.R' 'GLRLM.R' 'GLCM.R' 'CalculateFeatures.R'
'FirstOrder.R' 'GLCMFeatures.R' 'GLRLMFeatures.R'
'GLSZMFeatures.R' 'ImageQuantize.R' 'RcppExports.R' 'data.R'
'image.R' 'radiomics.R' 'startupMessage.R'
'base_data.R' 'base_startupMessage.R' 'chart_amBarplot.R'
'chart_amBoxplot.R' 'chart_amBullet.R' 'chart_amCandlestick.R'
'chart_amDataset.R' 'chart_amFloatingBar.R' 'chart_amFunnel.R'
'chart_amGauge.R' 'chart_amHist.R' 'chart_amMekko.R'
'chart_amOHLC.R' 'chart_amPie.R' 'chart_amPlot.R'
'chart_amRadar.R' 'chart_amTimeSeries.R' 'chart_amWaterfall.R'
'chart_amWind.R' 'utils_sharedGenerics.R' 'class_AmObject.R'
'class_AxisBase.R' 'class_ValueAxis.R'
'utils_basicClassUnions.R' 'class_TrendLine.R' 'class_Title.R'
'class_AmChart.R' 'class_StockPanel.R' 'class_StockEvent.R'
'class_PeriodSelector.R' 'class_Label.R' 'class_Guide.R'
'class_GaugeAxis.R' 'class_GaugeBand.R' 'class_GaugeArrow.R'
'class_DataSet.R' 'class_ChartScrollbar.R'
'class_ChartCursor.R' 'class_AmLegend.R' 'class_AmGraph.R'
'class_AmBalloon.R' 'classUnions.R'
'class_AmChart_constructors.R' 'class_CategoryAxis.R'
'class_AmChart_setters.R' 'class_AmChart_shinyUtils.R'
'class_AmStockChart.R' 'class_AmStockChart_setters.R'
'class_AxisBase_setters.R' 'class_ChartCursor_setters.R'
'class_ChartScrollbar_setters.R' 'class_DataSetSelector.R'
'class_DataSet_setters.R' 'class_GaugeArrow_setters.R'
'class_GaugeAxis_setters.R' 'class_Guide_setters.R'
'class_StockEvent_setters.R' 'class_StockPanel_setters.R'
'class_TrendLine_setters.R' 'shiny_examples.R'
'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' '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' 'ebcSeq.R' 'gbcdPar.R' 'gbcdSeq.R' 'hadaPar.R'
'hadaSeq.R' 'mpPar.R' 'mpSeq.R' 'pbrPar.R' 'pbrSeq.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'
'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'
'zzz.R' 'rChoiceDialogs-package.R' 'rFileChooser.R'
'rListChooser.R' 'rMiscFunctions.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-nonFactors.r'
'zzz.r'
'Octave-class.R' 'OctaveFunction-class.R' 'base-functions.R'
'config-vars.R' 'config.R' 'eval.R' 'utils.R' 'interface.R'
'package.R' 'random.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'
'RDML.types.R' 'RDML.asserts.R' 'RDML.R' 'RDML.AsDendrogram.R'
'RDML.AsTable.R' 'RDML.GetFData.R' 'RDML.Merge.R'
'RDML.SetFData.R' 'RDML.init.R' 'functional_wrappers.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'
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
'class.R' 'aic.R' 'baseline.R' 'coef.R' 'data.R' 'dataCheck.R'
'fit.R' 'mcf.R' 'plot.R' 'reda.R' 'show.R' 'summary.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'
'correlation.r' 'element.r' 'freq.r' 'funs.r' 'na.r' 'parse.r'
'rbind.r' 'remix.r' 'summarize.by.r' 'summarize.r' 'survival.r'
'table.r' 'tabular.r' 'test.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' 'utils.r'
'repr_function.r' 'repr_help_files_with_topic.r'
'repr_htmlwidget.r' 'repr_list.r' 'repr_matrix_df.r'
'repr_packageIQR.r' 'repr_recordedplot.r' 'repr_vector.r'
'utils.R' 'eutil-error.R' 'eutil.R' 'ecitmatch.R'
'parse-params.R' 'efetch.R' 'egquery.R' 'einfo.R' 'elink.R'
'epost.R' 'esearch.R' 'espell.R' 'esummary.R' 'parse-docsum.R'
'reutils-package.R' 'zzz.R'
'aaa.R' 'utils.R' 'escape.R' 'capture.R' 'character_class.R'
'counts.R' 'lookarounds.R' 'match.R' 'or.R' 'rex-mode.R'
'rex.R' 'shortcuts.R' 'wildcards.R' 'zzz.R'
'RForcecom.R' 'rforcecom.abortBulkJob.R' 'rforcecom.api.R'
'rforcecom.closeBulkJob.R' 'rforcecom.getBulkQueryResult.R'
'rforcecom.getBatchDetails.R' 'rforcecom.checkBatchStatus.R'
'rforcecom.submitBulkQuery.R' 'rforcecom.createBulkJob.R'
'rforcecom.bulkQuery.R' 'rforcecom.create.R'
'rforcecom.createBulkBatch.R' 'rforcecom.debug.R'
'rforcecom.delete.R' 'rforcecom.getObjectDescription.R'
'rforcecom.getObjectList.R' 'rforcecom.getServerTimestamp.R'
'rforcecom.insertBulkAttachments.R' 'rforcecom.login.R'
'rforcecom.logout.R' 'rforcecom.query.R'
'rforcecom.queryMore.R' 'rforcecom.retrieve.R'
'rforcecom.search.R' 'rforcecom.update.R' 'rforcecom.upsert.R'
'rforcecom.utils.R' 'rforcecom.write.csv.R'
'auth.R' 'utils.R' 'request.R' 'url.R' 'convert.R'
'get-data.R' 'mgmt.R' 'accounts.R' 'customDataSources.R'
'customDimensions.R' 'customMetrics.R' 'date-ranges.R'
'experiments.R' 'filters.R' 'profiles.R' 'query.R' 'report.R'
'ga.R' 'firstdate.R' 'goals.R' 'mcf.R' 'metadata.R' 'package.R'
'realtime.R' 'segments.R' 'unsampledReports.R' 'uploads.R'
'webproperties.R' 'zzz.R'
AAA.R options.R rgrass.R bin_link.R vect_link.R initGRASS.R
xml1.R
'rhoR.R' 'rho.R' 'rhoK.R' 'rhoSet.R' 'rhoCT.R' 'kappa.R'
'kappaSet.R' 'kappaCT.R' 'rhoMin.R' 'baserate.R'
'baserateSet.R' 'baserateCT.R' 'calcKappa.R' 'calcRho.R'
'checkBRPKcombo.R' 'contingencyTable.R' 'codeSet.R'
'contingencyToSet.R' 'createRandomSet.R'
'createSimulatedCodeSet.R' 'genPKcombo.R' 'genPcombo.R'
'generateKPs.R' 'getBootPvalue.R' 'getHandSet.R' 'getR.R'
'getTestSet.R' 'prset.R'
readJSON.R asVars.R isValid.R json.R simpleHandler.R stream.R
RLumModel-package.R calc_signal.R calc_concentrations.R
create_DRT.sequence.R create_SAR.sequence.R
model_LuminescenceSignals.R read_SEQ2R.R set_ODE.R
set_ODE_LM_OSL.R set_pars.R simulate_CW_OSL.R simulate_LM_OSL.R
simulate_RF.R simulate_TL.R simulate_heating.R
simulate_illumination.R simulate_irradiation.R simulate_pause.R
translate_sequence.R
'app_RLum.R' 'jscolor.R' 'tooltip.R' 'popover.R' 'RLumShiny.R'
'zzz.R'
rmgarch-var.R rmgarch-functions.R rmgarch-classes.R
rmgarch-ica.R rmgarch-series.R rmgarch-mmean.R
gogarch-classes.R gogarch-distributions.R gogarch-main.R
gogarch-methods.R rdcc-classes.R rdcc-main.R rdcc-likelihoods.R
rdcc-plots.R fdcc-likelihoods.R fdcc-main.R rdcc-methods.R
rdcc-mdistributions.R rdcc-postestimation.R rdcc-solver.R
copula-classes.R copula-distributions.R copula-likelihoods.R
copula-solver.R copula-fn.R copula-transformations.R
copula-main.R copula-postestimation.R copula-methods.R
rmgarch-tests.R rmgarch-scenario.R zzz.R
'CI.R' 'STDERR.R' 'group.UCL.R' 'group.CI.R' 'group.STDERR.R'
'lr.glover.R' 'multiplot.R' 'panel.circle.R' 'rounder.R'
'rsi.R' 'summarySE.R'
'global.R' 'Model.R' 'Parameter.R' 'GaussianParameter.R'
'Strategy.R' 'MixmodResults.R' 'MixmodDAResults.R'
'MultinomialModel.R' 'GaussianModel.R' 'MultinomialParameter.R'
'Mixmod.R' 'CompositeModel.R' 'CompositeParameter.R'
'MixmodCluster.R' 'MixmodLearn.R' 'Rmixmod.R' 'MixmodPredict.R'
'zzz.R'
'mysql.R' 'driver.R' 'connection.R' 'data-type.R' 'default.R'
'escaping.R' 'result.R' 'extension.R' 'is-valid.R' 'table.R'
'transaction.R'
'AllGenerics.R' 'ng_data.R' 'ng_graph.R' 'ng_path.R'
'ng_image.R' 'SettingClasses.R' 'NavGraphHandler.R'
'GraphDisplay.R' 'Visualization.R' 'Visualization2D.R'
'Viz2D_Axis.R' 'Viz2D_tk2d.R' 'FunctionNavGraph.R'
'graphtools.R' 'navGraph.R' 'scagNav.R' 'zzz.R'
'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'
'ghq.R' 'AllClass.R' 'rlmer.R' 'accessors.R' 'fromLme4.R'
'DAS-scale.R' 'fit.effects.R' 'helpers.R' 'AllGeneric.R'
'lmer.R' 'mutators.R' 'psiFunc2.R' 'plot.R'
AllGenerics.R AllClasses.R show-methods.R rococo.R
rococo.test-methods.R gauss.cor.R gauss.cor.test-methods.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'
'RcppExports.R' 'alias.R' 'default-data-format.R'
'description.R' 'family.R' 'inherit-params.R' 'minidesc.R'
'object-defaults.R' 'object-from-call.R' 'object.R'
'order-params.R' 'parse-preref.R' 'parse.R' 'rc.R'
'rd-escape.R' 'rd-file-api.R' 'rd-parse.R' 'rd-tag-api.R'
'tag-registry.R' 'roclet-collate.R' 'roclet-namespace.R'
'roclet-rd.R' 'roclet-vignette.R' 'roclet.R' 'roxygen-tag.R'
'roxygen.R' 'roxygenize.R' 's3.R' 'safety.R' 'source.R'
'template.R' 'topic-name.R' 'topo-sort.R' 'usage.R'
'util-locale.R' 'utils.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'
S4R.R zzz.R PostgreSQLSupport.R dbObjectId.R PostgreSQL.R
'PrestoDriver.R' 'Presto.R' 'PrestoConnection.R'
'utility_functions.R' 'PrestoCursor.R' 'PrestoResult.R'
'copy.to.src.presto.R' 'db.data.type.PrestoConnection.R'
'db.explain.PrestoConnection.R'
'db.query.rows.PrestoConnection.R' 'dbHasCompleted.R'
'dbClearResult.R' 'dbConnect.R' 'dbDataType.R' 'dbDisconnect.R'
'dbSendQuery.R' 'json.tabular.to.data.frame.R' 'extract.data.R'
'dbFetch.R' 'dbGetQuery.R' 'dbListTables.R' 'dbExistsTable.R'
'dbGetInfo.R' 'dbGetRowCount.R' 'dbGetStatement.R'
'dbIsValid.R' 'dbListFields.R' 'dbUnloadDriver.R'
'db_query_fields.R' 'fetch.R' 'help.R'
'src.translate.env.src.presto.R' 'sql_translate_env.R'
'src.desc.src.presto.R' 'src.presto.R' 'tbl.src.presto.R'
'rrmake.R' 'criterion.R' 'file.R' 'has-file.R' 'root.R'
'rprojroot-package.R' 'shortcut.R'
'gridtools.R' 'RSAGA-core.R' 'RSAGA-modules.R' 'RSAGA-utils.R'
'RSAGA-package.R' 'landslides.R'
'errorHandler.R' 'remoteDriver.R' 'selKeys-data.R' 'util.R'
'webElement.R' 'zzz.R'
'zzz.R' 'helper.R' 'sampling.R' 'models.R' 'rsig.R'
'evaluate.R' 'rsig.all.R'
'ConnectionExtensions.R' 'Connection.R' 'Driver.R' 'Connect.R'
'ConnectionRead.R' 'ConnectionTransactions.R'
'ConnectionWrite.R' 'Constants.R' 'Escaping.R' 'Result.R'
'Object.R' 'Summary.R' 'Utils.R' 'datasetsDb.R' 'dbGetInfo.R'
'extensions.R' 'zzz.R'
'RSQLServer.R' 'Utils.R' 'dbi-classes.R' 'dbi-methods.R'
'dbi-shims.R' 'dplyr-imports.R' 'dplyr.R' 'jdbc-methods.R'
'onLoad.R' 'sql-backends.R' 'sql-methods.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-csgarch.R rugarch-fgarch.R rugarch-egarch.R
rugarch-gjrgarch.R rugarch-aparch.R rugarch-igarch.R
rugarch-mcsgarch.R rugarch-realgarch.R rugarch-multi.R
rugarch-plots.R rugarch-rolling.R rugarch-uncertainty.R
rugarch-bootstrap.R rugarch-methods.R rugarch-benchmarks.R
arfima-classes.R arfima-multi.R arfima-main.R arfima-methods.R
rugarch-cv.R zzz.R
unuran_distr.R unuran_cont.R unuran_discr.R unuran_cmv.R
Runuran.R universal.R distributions.R deprecated.R zzz.R
AllGenerics.R AllClasses.R methods-pedigree.R methods-trio.R
methods-RVsharingProb.R functions.R approx2_functions.R
ped2trio.R PFU.R RVsharing.fn.R extract_carriers.R
pvalue_functions.R RVgene.R zzz.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'
'runExample.r' 'sadists.r' 'sumchisqpow.r' 'sumlogchisq.r'
'upsilon.r' 'utils.r'
global.R SaemixData.R SaemixModel.R SaemixRes.R SaemixObject.R
main.R zzz.R
'NAMESPACE.R' 'RcppExports.R' 'bootstrap.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
'AFirst_lib.R' 'SASformat.R' 'SASiformat.R' 'SAStype.R'
'all.is.numeric.R' 'blanks.R' 'fromSASDate.R' 'fstr.R'
'importConvertDateTime.R' 'in.opererator.R' 'lookup.xport.R'
'make.formats.R' 'makeNames.R' 'makeSASNames.R' 'package.R'
'parseFormat.R' 'process.formats.R' 'rawToDisplay.R'
'read.xport.R' 'scat.R' 'testDateTime.R' 'toSAS.R'
'write.xport.R' 'xport.NA.R' 'xport.R' 'xport.character.R'
'xport.dateFMT.R' 'xport.file.header.R' 'xport.fill.R'
'xport.member.header.R' 'xport.namestr.R'
'xport.namestr.header.R' 'xport.numeric.R' 'xport.obs.header.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'
'Anonymize.R' 'Atkinson.R' 'Bootstrap.R' 'CI.R'
'CMDcheckVars.R' 'CalibratePlot.R' 'Clear.R' 'Condorcet.R'
'CountMissing.R' 'CrossValidation.R' 'Crosstable.R' 'DATA.R'
'DEPRECATED.R' 'Describe.R' 'Dirichlet.R' 'Dummy.R'
'Frequency.R' 'Geoms.R' 'Gini.R' 'Herfindahl.R'
'HighestAverages.R' 'Jackknife.R' 'Label.R'
'LargestRemainders.R' 'Lorenz.R' 'MOMENTS.R' 'MeanFromRange.R'
'Normalize.R' 'Operators.R' 'Outlier.R' 'Palettes.R'
'Permutate.R' 'PoliticalDiversity.R' 'Proportionality.R'
'RcppExports.R' 'Recode.R' 'Rosenbluth.R' 'RunShinyApp.R'
'ScatterPlot.R' 'SciencesPo.R' 'Stratify.R' 'TESTS.R'
'Tableplot.R' 'Ternaryplot.R' 'Themes.R' 'Transform.R'
'UTILS.R' 'Untable.R' 'Voronoi.R' 'Waffleplot.R' 'Winsorize.R'
'flag.R' 'print.SciencesPo.R' 'zTree.R' 'zzz.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' 'addNoise.r' 'aux_functions.r' 'dataGen.r'
'dataSets.R' 'dRisk.R' 'dRiskRMD.R' 'dUtility.R' 'freqCalc.r'
'globalRecode.R' 'GUIfunctions.R' 'indivRisk.R'
'LLmodGlobalRisk.R' 'LocalRecProg.R' 'localSupp.R'
'localSuppression.R' 'mdav.R' 'measure_risk.R' 'methods.r'
'microaggregation.R' 'modRisk.R' 'plotFunctions.R'
'plotMicro.R' 'pram.R' 'rankSwap.R' 'RcppExports.R' 'report.R'
'sdcMicro-package.R' 'shuffle.R' 'suda2.R' 'timeEstimation.R'
'topBotCoding.R' 'valTable.R' 'zzz.R' 'printFunctions.R'
'mafast.R' 'maG.R' 'show_sdcMicroObj.R'
'aux_functions.R' 'plot.indivRisk.r' 'sdcGUI.r' 'zzz.r'
'HelpHelperFunctions.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
RcppExports.R init.R bbox.R read.R db.R sfc.R sfg.R sf.R wkb.R
wkt.R plot.R geom.R transform.R sp.R crs.R grid.R arith.R
tidy.R
'SharpeR.r' 'utils.r' 'distributions.r' 'sr.r' 'estimation.r'
'tests.r' 'unified.r'
'Shazam.R' 'RegionDefinitions.R' 'Baseline.R' 'Core.R'
'DistToNearest.R' 'MutationDefinitions.R' 'MutationProfiling.R'
'Shmulate.R' 'TargetingModels.R'
'app.R' 'bookmark-state-local.R' 'stack.R' 'bookmark-state.R'
'bootstrap-layout.R' 'conditions.R' 'map.R' 'globals.R'
'utils.R' 'bootstrap.R' 'cache.R' 'diagnose.R' 'fileupload.R'
'graph.R' '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-ui.R'
'jqueryui.R' 'middleware-shiny.R' 'middleware.R' 'modal.R'
'modules.R' 'notifications.R' 'priorityqueue.R' 'progress.R'
'react.R' 'reactive-domains.R' 'reactives.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' 'tar.R'
'test-export.R' 'timer.R' 'update-input.R'
'Announcement.R' 'ApplicationCharge.R' 'Article.R' 'Asset.R'
'Blog.R' 'CarrierService.R' 'Checkout.R' 'Collect.R'
'Comment.R' 'Country.R' 'CustomCollection.R' 'Customer.R'
'CustomerGroup.R' 'Event.R' 'Fulfillment.R'
'FulfillmentService.R' 'Location.R' 'MetaField.R' 'Order.R'
'OrderRisks.R' 'Page.R' 'Product.R' 'ProductImage.R'
'ProductVariant.R' 'Province.R' 'RecurringApplicationCharge.R'
'Redirect.R' 'Refund.R' 'ScriptTag.R' 'Shop.R' 'private.R'
'Webhook.R' 'Transaction.R' 'Theme.R' 'SmartCollection.R'
'ShopifyShop.R' 'shopfyr-package.R'
'sig.R' 'list_sigs.R' 'as.R' 'is.R' 'sig_report.R'
'indexing.R' 'utils.R' 'write_sigs.R'
'AllGenerics.R' 'check_arguments.R' 'scheduled_events.R'
'siminf_model.R' 'SISe.R' 'SISe3.R' 'SISe3_sp.R' 'SISe_sp.R'
'SimInf.R' 'demo_model.R' 'dependencies.R' 'distance.R'
'run_outer.R'
'0classes.R' 'addKnownMargins.R' 'addWeights.r' 'calibPop.R'
'calibSample.R' 'calibVars.R' 'contingencyWt.R' 'fitGPD.R'
'getBreaks.R' 'getCat.R' 'ipu.r' 'meanWt.R' 'printFunctions.R'
'quantileWt.R' 'sampHH.R' 'silcTools.R' 'silcTools2.R'
'simCategorical.R' 'simComponents.R' 'simContinuous.R'
'simEUSILC.R' 'simGPD.R' 'simInitSpatial.R' 'simple_dis.R'
'simPop-package-package.R' 'simRelation.R' 'simStructure.R'
'spBwplot.R' 'spBwplotStats.R' 'spCdf.R' 'spCdfplot.R'
'spMosaic.R' 'spPredict.R' 'spSample.R' 'spTable.R'
'specifyInput.R' 'sprague.R' 'tableWt.R' 'utility.R' 'utils.R'
'whipple.R' 'dataSets.R' 'zzz.R'
'evals-class.R' 'output-class.R' 'component-class.R'
'model-class.R' 'draws-class.R' 'reference-classes.R'
'simulation-class.R' 'add-to-simulation.R' 'aggregator-class.R'
'utils.R' 'create.R' 'draws.R' 'evaluate.R' 'metric-class.R'
'method-class.R' 'examples.R' 'extended-method-class.R'
'get-from-simulation.R' 'import_from.R' 'load.R' 'manage.R'
'method-extension-class.R' 'methods.R' 'models.R'
'parallel-draws.R' 'parallel-methods.R' 'parallel.R'
'plot_eval.R' 'plot_eval_by.R' 'plot_evals.R' 'tables.R'
'zzz.R'
skeleSim-package.R skeleSim.classes.R fsc.classes.R
rms.classes.R skeleSim.internals.R skeleSimGUI.R
analysis.funcs.R fsc.loadScenario.R fsc.run.R
fsc.scenarioCheck.R rms.convert.R rms.init.land.function.R
rms.init.landscape.R rms.internals.R rms.run.R
rms.scenarioCheck.R runSim.R skeleSim.checks.R
getGammaMutRates.R
'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 depository.R depository_version.R dataset.R
dataset_field.R utils.R zzz.R
'counting_function.R' 'f_ackley.R' 'f_bbob2009.R' 'f_branin.R'
'f_discus.R' 'f_double_sum.R' 'f_ellipsoidal.R'
'f_goldstein_price.R' 'f_griewank.R' 'f_kotancheck.R'
'f_mexican_hat.R' 'f_rastrigin.R' 'f_rosenbrock.R' 'f_sphere.R'
'f_weierstrass.R' 'generics.R' 'is_soo_function.R'
'persp3d_soo_function.R' 'plot_soo_function.R'
'print_soo_function.R' 'rotate_parameter_space.R'
'check-arguments.r' 'sortinghat-package.r'
'helper-partition-data.r' 'helper-which-min.r'
'simdata-normal.r' 'simdata-uniform.r' 'covariance-matrices.r'
'helpers.r' 'simdata-t.r' 'simdata-contaminated-normal.r'
'simdata-guo.r' 'simdata-friedman.r' 'simdata-wrapper.r'
'errorest-632.r' 'errorest-632plus.r' 'errorest-apparent.r'
'errorest-bcv.r' 'errorest-boot.r' 'errorest-cv.r'
'errorest-loo-boot.r' 'errorest-wrapper.r'
Constants.R Class-OWS.R Class-GML.R Class-SWE.R Class-OM.R
Class-SA.R Class-OGC.R Class-SOS.R Class-SML.R
Generic-methods.R OWS-methods.R OWS-methods-parsing.R
SOS-methods-parsing.R OM-methods.R OM-methods-coercion.R
OM-methods-parsing.R SA-methods.R GML-methods.R SWE-methods.R
SML-methods.R GML-methods-parsing.R SA-methods-parsing.R
SWE-methods-parsing.R OGC-methods.R SOS-methods-accessor.R
PrintShowStructureSummmary-methods.R SOS-methods-util.R
SOS-methods.R SOS-methods-plotting.R SOS-methods-coercion.R
SML-methods-util.R SML-methods-coercion.R Defaults.R
'NPC.R' 'PermSpace.R' 'RankResults.R' 'SOUP.R'
'multiplicity.R' 'simpleAD.R' 'simpleHotelling.R'
'simpleMeanDiff.R' 'simpleTtest.R' 'strataAD.R'
'strataLmCoef.R' 'strataMeanDiff.R' 'strataTtest.R' 't2p.R'
'utilities.R' 'PValueMat.R' 'SoupObject.R' 'rankingRule.R'
'iterNPC.r'
bpy.colors.R AAA.R Class-CRS.R CRS-methods.R Class-Spatial.R
Spatial-methods.R projected.R Class-SpatialPoints.R
SpatialPoints-methods.R Class-SpatialPointsDataFrame.R
SpatialPointsDataFrame-methods.R Class-SpatialMultiPoints.R
SpatialMultiPoints-methods.R
Class-SpatialMultiPointsDataFrame.R
SpatialMultiPointsDataFrame-methods.R Class-GridTopology.R
Class-SpatialGrid.R Class-SpatialGridDataFrame.R
Class-SpatialLines.R SpatialLines-methods.R
Class-SpatialLinesDataFrame.R SpatialLinesDataFrame-methods.R
Class-SpatialPolygons.R Class-SpatialPolygonsDataFrame.R
SpatialPolygons-methods.R SpatialPolygonsDataFrame-methods.R
GridTopology-methods.R SpatialGrid-methods.R
SpatialGridDataFrame-methods.R SpatialPolygons-internals.R
point.in.polygon.R SpatialPolygons-displayMethods.R zerodist.R
image.R stack.R bubble.R mapasp.R select.spatial.R gridded.R
asciigrid.R spplot.R over.R spsample.R recenter.R dms.R
gridlines.R spdists.R rbind.R flipSGDF.R chfids.R loadmeuse.R
compassRose.R surfaceArea.R spOptions.R subset.R disaggregate.R
sp_spat1.R merge.R aggregate.R
Class-xts.R Class-ST.R Class-STFDF.R Class-STSDF.R
Class-STIDF.R Class-STTDF.R interval.R endtime.R ST-methods.R
STFDF-methods.R STSDF-methods.R STIDF-methods.R STTDF-methods.R
apply.R coerce.R stconstruct.R plot.R stplot.R timematch.R
over.R aggregate.R eof.R mnf.R bind.R na.R raster.R tgrass.R
stinteraction.R
all_classes.R common.R moran.R SpawML.R Descriptives.R
ResExSpawML.R ResAggSpawML.R Weights.R ExplorSpawML.R
'priority.R' 'environment.R' 'misc-methods.R'
'module-dependencies-class.R' 'helpers.R' 'simList-class.R'
'POM.R' 'agent.R' 'SELES.R' 'cache.R' 'check.R' 'checkpoint.R'
'copy.R' 'experiment.R' 'initialize.R' 'simulation.R' 'load.R'
'mapReduce.R' 'mergeRaster.R' 'module-dependencies-methods.R'
'moduleMetadata.R' 'module-repository.R' 'module-template.R'
'moduleCoverage.R' 'movement.R' 'neighbourhood.R'
'numerical-comparisons.R' 'plotting-classes.R'
'plotting-colours.R' 'times.R' 'simList-accessors.R'
'plotting-diagrams.R' 'plotting-helpers.R' 'plotting-other.R'
'plotting.R' 'probability.R' 'progress.R' 'rstudio-addins.R'
'save.R' 'shine.R' 'spades-classes.R' 'spades-package.R'
'splitRaster.R' 'spread-process.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
'sperrorest-package.R' 'sperrorest.R' 'sperrorest_misc.R'
'sperrorest_resampling.R' 'sperrorest_error.R'
'sperrorest_data.R'
AAA.R options.R spgrass6.R bin_link.R vect_link.R initGRASS.R
xml1.R
'utils.R' 'spikeAndSlab.R' 'terms.R' 'ssGAMDesign.R'
'spikeSlabGAM.R' 'bugs.R' 'plot.R' 'predict.R' 'summary.R'
'zzz.R'
Swrappers.R peaks.R bits.per.integer.R doTest.R muS2RC.R
showStructure.R sigseriesS3.R sigseriesS4.R splus2R_is.R
splus2R_pkg.R
'Algorithm.SDM.R' 'Ensemble.SDM.R' 'Env.R' 'Occurrences.R'
'SSDM.R' 'Stacked.SDM.R' 'checkargs.R' 'ensemble.R'
'modelling.R' 'ensemble_modelling.R' 'gui.R' 'load_model.R'
'load_occ.R' 'load_var.R' 'plot.model.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
'ConnectionODBC.r' 'RequeteODBC.r' 'RequeteODBCwhere.r'
'RequeteODBCwheredate.r' 'utilitaires.r' 'stacomirtools.r'
'SolverOptions.R' 'addNamesToList.R' 'checkICs.R'
'checkSolution.R' 'deathFunction.R' 'reproductionFunction.R'
'immigrationFunction.R' 'emigrationFunction.R'
'durationFunction.R' 'developmentFunction.R'
'defaultRateFunctions.R' 'derivDede.R' 'derivPBS.R'
'genericPlot.R' 'initConditions.R' 'namingVariables.R'
'plotStrains.R' 'popModel.R' 'rateFuncCheck.R' 'rateFunction.R'
'runStagePopExample.R' 'stagePop-package.R' 'sumStrains.R'
'vecDivideFunc.R' 'vecToMatList.R'
remoteDB.R localDB.R init.R util.R valid.R zzz.R
'stutil.R' 'stwin.R' 'stsubpop.R' 'stmodelKM.R' 'stmodelCOX.R'
'stmodelCI.R' 'stmodelGLM.R' 'stmodel.R' 'steppes.R' 'stepp.R'
'init.R' 'sticky.R' 'append.R' 'extract1.R' 'print.R'
'subset.R' 'utils.R' 'zzz.R'
strataG-package.R gtypes.class.R gtypes.accessors.R
is.gtypes.R initialize.gtypes.R strataG-internal.R
as.matrix.gtypes.R as.data.frame.gtypes.R alleleFreqFormat.R
alleleFreqs.R alleleSplit.R allelicRichness.R as.multidna.R
baseFreqs.R clumpp.R createConsensus.R dupGenotypes.R evanno.R
expectedNumAlleles.R fasta.R fastsimcoal.input.R fastsimcoal.R
fixedDifferences.R fixedSites.R freq2GenData.R fstToNm.R
fusFs.R gelato.R genepop.R haplotypeLikelihoods.R
heterozygosity.R Hstats.R hweTest.R idRows.R iupacCode.R
jackHWE.R labelHaplotypes.R LDgenepop.R lowFreqSubs.R mRatio.R
mafft.R mega.R mostDistantSequences.R
mostRepresentativeSequences.R neiDa.R nucleotideDivergence.R
nucleotideDiversity.R numAlleles.R numGensEq.R numMissing.R
permuteStrata.R phase.R popStructStat.R popStructTest.R
privateAlleles.R propUniqueAlleles.R qaqc.R RcppExports.R
readGenData.R removeSequences.R sharedLoci.R show.gtypes.R
simGammaHaps.R statChi2.R statFis.R statFst.R statGst.R
statJostD.R statPhist.R strataSplit.R stratify.R structure.R
structurePlot.R summarizeLoci.R summarizeSeqs.R
summary.gtypes.R tajimasD.R theta.R TiTvRatio.R trimNs.R
validIupacCodes.R variableSites.R wrightFst.R write.arlequin.R
write.gtypes.R write.nexus.snapp.R gtypes2genind.R
gtypes2loci.R df2gtypes.R sequence2gtypes.R gtypes2phyDat.R
'filterStream.R' 'parseTweets.R' 'sampleStream.R'
'userStream.R' 'streamR-package.R' 'readTweets.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
'surveydata-package.R' 'surveydata.R' 'merge.R' 'varlabels.R'
'questions.R' 'strings.R' 'extract.R' 'cleandata.R'
'encoding.R' 'tools.R' 'pattern.R'
'ggcoxdiagnostics.R' 'utilities.R' 'ggcoxfunctional.R'
'ggcoxzph.R' 'theme_classic2.R' 'ggsurvplot.R'
'sf.R' 'nc.R' 'ci.R' 'autoplotTAP.R' 'autoplotTen.R' 'print.R'
'asWide.R' 'ten.R' 'COV.R' 'predict.R' 'comp.R' 'cutp.R'
'gastric.R' 'gof.R' 'onAttach.R' 'plotSurv.R' 'profLik.R'
'rsq.R' 'survMisc_package.R' 'xtable.R'
generics.R validmodelorg.R validoptsol.R validreactId.R
validreactId_Exch.R validsysBiolAlg.R addAlgorithm.R
addExchReact.R addReact.R addSolver.R blockedReact.R
bracket_pairs.R ceilValues.R changeBounds.R changeGPR.R
changeObjFunc.R checkAlgorithm.R checkDefaultMethod.R
checkEmptyField.R checkReactId.R check_brackets.R
createReactionString.R deadEndMetabolite.R doInRound.R
doubleFluxDel.R doubleGeneDel.R doubleReact.R editEnvir.R
findExchReact.R floorValues.R fluxVar.R geneDel.R
geneDeletion.R generateFluxdels.R generateModKey.R generateWT.R
getsybilenv.R makeLPcompatible.R mod2irrev.R modelorg2ExPA.R
modelorg2text.R modelorg2tsv.R multiDel.R oneFluxDel.R
oneGeneDel.R onlyChangeGPR.R onlyCheckGPR.R optObj_basicfunc.R
optObj_lpSolveAPIcompat.R optimizer.R parseBoolean.R phpp.R
ppProcessing.R prepareSubSysMatrix.R printLogComment.R
printNamedList.R progress.R promptSysBiolAlg.R recodeMatrix.R
readTEXTmod.R readTSVmod.R reassignFwBwMatch.R rmReact.R
robAna.R settings.R singletonMetabolite.R sybilStack.R ypd.R
zzz.R modelorgClass.R modelorg_irrevClass.R optObj_pointer.R
optObjClass.R optObj_clpAPIClass.R optObj_cplexAPIClass.R
optObj_glpkAPIClass.R optObj_lpSolveAPIClass.R
sybilErrorClass.R ppProcClass.R netFluxClass.R
fluxDistributionClass.R reactIdClass.R reactId_ExchClass.R
optsolClass.R optsol_blockedReactClass.R
optsol_optimizeProbClass.R optsol_fluxVarClass.R
optsol_fluxdelClass.R optsol_robAnaClass.R optsol_phppClass.R
optsol_genedelClass.R checksolClass.R summaryOptsolClass.R
sysBiolAlgClass.R sysBiolAlg_fbaClass.R
sysBiolAlg_fbaEasyConstraintClass.R sysBiolAlg_fvClass.R
sysBiolAlg_lmomaClass.R sysBiolAlg_momaClass.R
sysBiolAlg_mtfClass.R sysBiolAlg_mtfEasyConstraintClass.R
sysBiolAlg_roomClass.R sybilLogClass.R upgradeModelorg.R
generics.R sbmlPtrClass.R sbmlErrorClass.R sybilSBML.R
uglyHack.R readSBMLmod.R zzz.R
'Other.R' 'helpFunctions.R' 'EstimationBR.R'
'EstimationGumbel.R' 'EstimationML.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'
'makeTdmRandomSeed.r' 'printTDMclassifier.r'
'printTDMregressor.r' 'tdmBigLoop.r' 'tdmClassify.r'
'tdmClassifyLoop.r' 'tdmDefaultsFill.r' 'tdmDispatchTuner.r'
'tdmEmbedDataFrame.r' 'tdmEnvTMakeNew.r' 'tdmGeneralUtils.r'
'tdmGetObj.r' 'tdmGraphicUtils.r' 'tdmMapDesign.r'
'tdmMetacostRf.r' 'tdmModelingUtils.r' 'tdmOptsDefaults.r'
'tdmParaBootstrap.r' 'tdmPlotResMeta.r' 'tdmPreprocUtils.r'
'tdmReadData.r' 'tdmReadData2.r' 'tdmROCR.r' 'tdmRegress.r'
'tdmRegressLoop.r' 'tdmSplitTestData.r' 'tdmStartSpot.r'
'tdmStartOther.r' 'unbiasedBestRun_O.r' 'unbiasedRun.r'
'tdmExecSpotStep.r' 'tdmTuneIt.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' '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-equal-to-reference.R'
'expect-equality.R' 'expect-inheritance.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' 'reporter-check.R'
'reporter-fail.R' 'reporter-list.R' 'reporter-minimal.R'
'reporter-multi.R' 'reporter-rstudio.R' 'reporter-silent.R'
'reporter-stop.R' 'stack.R' 'reporter-summary.R'
'reporter-tap.R' 'reporter-teamcity.R' 'reporter-zzz.R'
'skip.R' 'source.R' 'test-compiled-code.R' 'test-example.R'
'test-files.R' 'test-package.R' 'test-path.R' 'test-that.R'
'traceback.R' 'try-again.R' 'utils.R' 'watcher.R'
'AIC.evm.R' 'Dcond.R' 'Profile_likelihood_HT_unc.R'
'Profile_likelihood_cd_nm_joint_D_KT.R'
'Profile_likelihood_cd_nm_joint_D_KT_neg.R' 'bootmex.R' 'chi.R'
'coef.evmSim.R' 'coefficients.evm.R' 'coefficients.migpd.R'
'constructObject.R' 'copulas.R' 'degp3.R' 'dgev.R' 'dgpd.R'
'efficient.closures.R' 'texmexFamily.R' 'egp3.R'
'egp3RangeFit.R' 'endPoint.R' 'estimate_HT.R'
'estimate_HT_KPT_joint_posneg_nm.R' 'evm.R' 'evm.simSetSeed.R'
'evmBoot.R' 'evmFit.R' 'evmSim.R' 'extremalIndex.R' 'gev.R'
'ggplot.bayes.R' 'ggplot.boot.R' 'ggplot.cluster.R'
'ggplot.evm.R' 'ggplot.mex.R' 'ggplot.migpd.R'
'ggplot.predict.R' 'ggplot.thresh.R' 'globalVariables.R'
'gpd.sandwich.R' 'gpd.info.R' 'gpd.R' 'gpdRangeFit.R'
'hist.evm.R' 'initial_posneg.R' 'internal-roxygen.R'
'inv_Laplace.R' 'jointExceedanceCurves.R' 'mex.R'
'mexDependence.R' 'mexDependenceLowLevelFunctions.R'
'mexMonteCarlo.R' 'mexRangeFit.R' 'mexTransform.R' 'migpd.R'
'migpdCoefs.R' 'mrl.R' 'mspearman.R' 'multivariate.cauchy.R'
'pegp3.R' 'pgev.R' 'pgpd.R' 'plot.bootmex.R' 'plot.evm.R'
'plot.evmSim.R' 'plot.mex.R' 'plot.mexPrediction.R'
'plot.migpd.R' 'plot.predict.link.evm.R' 'plotrl.evm.R'
'ppevm.R' 'predict.evm.R' 'predict.mex.R' 'predictWorkers.R'
'print.bootmex.R' 'print.evm.R' 'print.evmSim.R'
'print.mexDependence.R' 'print.mexPrediction.R' 'print.migpd.R'
'profile_minmax_joint_posneg_KT.R' 'qegp3.R' 'qgev.R' 'qgpd.R'
'qgpd2.R' 'qqevm.R' 'rMaxAR.R' 'regp3.R' 'residuals.evm.R'
'revTransform.R' 'rgev.R' 'rgpd.R' 'roots.R' 'simulate.R'
'sombrero-internal.R' 'special.functions.R' 'summary.evm.R'
'summary.evmSim.R' 'summary.migpd.R' 'summary.predict.mex.R'
'texmex-package.R' 'texmexWorkers.R' 'thinAndBurn.evm.sim.R'
'u2gpd.R'
'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'
'aalenC.r' 'aalen-des.r' 'aalen.r' 'additive-compSs.r'
'base.r' 'clusterindex-reshape.r' 'comprisk.r' 'comprisk.ipw.r'
'cox-aalen.r' 'cox.ipw.r' 'cox.marg.r' 'Cpred.r' 'Csmooth.r'
'dynadd.r' 'event.r' 'event.split.r' 'Gprop-odds.r'
'krylow.pls.r' 'kmplot.r' 'mgresid.r' 'new.aalen.r'
'new.cox-aalen.r' 'new.dynreg.r' 'new.pe-sasieni.r'
'new.prop-excess.r' 'new.timecox.r' 'plots.r'
'predict-timereg.r' 'residualsTimereg.r' 'glm-comprisk.r'
'prop-excess.r' 'prop-odds.r' 'prop-odds-subdist.r'
'read-design.r' 'restricted.residual.mean.r'
'ipcw-residualmean.r' 'two-stage-reg.r' 'timecox.r' 'qcut.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'
'toaster.R' 'misc.R' 'utils.R' 'tools.R'
'computeCorrelations.R' 'computeHistogram.R' 'computeHeatmap.R'
'maps.R' 'showData.R' 'computeAggregates.R' 'computeBarchart.R'
'computeSample.R' 'computePercentiles.R' 'computeLm.R'
'computeTfIdf.R' 'textParsers.R' 'computeKmeans.R' 'plotting.R'
'plottingKmeans.R' 'showGraph.R' 'computeGraph.R'
'computeGraphClusters.R' 'validateGraph.R'
'gui_andrews.r' 'gui_density.r' 'gui_faces.r' 'gui_image.r'
'gui_pcp.r' 'gui_scatmat.r' 'gui_stars.r' 'gui_stereo.r'
'gui_tour.r' 'gui_xy.r' 'tour_andrews.r' 'tour_density.r'
'tour_faces.r' 'tour_pcp.r' 'tour_scatmat.r' 'tour_stars.r'
'tour_stereo.r' 'tour_xy.r'
'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
RcppExports.R metrics.R medTree.R treescape.R findGroves.R
plotGroves.R plotTreeDiff.R servers.R transmissionTrees.R
data.R
'avatar.R' 'dashboard.R' 'def.postParams.R' 'follow.R'
'followers.R' 'http.connection.R' 'info.blog.R' 'like.post.R'
'likes.R' 'post.R' 'post.delete.R' 'post.edit.R'
'post.reblog.R' 'posts.R' 'posts.draft.R' 'posts.queue.R'
'posts.submission.R' 'tagged.R' 'unfollow.R' 'unlike.post.R'
'user.following.R' 'user.info.R' 'user.likes.R' 'compact.R'
'nonce.R' 'oauth.encode.R' 'oauth.encode1.R' 'sort.names.R'
'df_to_blocks.r' 'dummy_to_list.r' 'factor_to_dummy.r'
'from_to.r' 'funlist.r' 'indexify.r' 'lengths.r' 'list_ones.r'
'list_to_dummy.r' 'list_to_matrix.r' 'listify.r' 'listsize.r'
'matrix_to_blocks.r' 'vector_to_dummy.r' 'turner-package.r'
'RcppExports.R' 'aaa.R' 'tween_numeric.R' 'display_ease.R'
'interpolate.R' 'tween_colour.R' 'tween_datetime.R'
'tween_date.R' 'tween.R' 'tween_appear.R' 'tween_constant.R'
'tween_elements.R' 'tween_states.R' 'tweenr_package.R'
allGenerics.R base.R account.R statuses.R users.R trends.R
s4methods.R convert.R dm.R oauth.R comm.R followers.R search.R
db.R df_columns.R db_connections.R db_utils.R db_search.R
toys.R utils.R zzz.R
'class-01-Points.R' 'class-02-UncertainPoints.R'
'class-03-UncertainInterpolation.R'
'class-04-FuzzyInterpolation.R' 'class-05-Variogram.R'
'methods-as.data.frame.R' 'methods-as.UncertainPoints.R'
'methods-uncertaintyConstant.R' 'methods-uncertaintyError.R'
'methods-uncertaintyPercent.R'
'methods-uncertaintyRandomDeviate.R'
'methods-uncertaintyRandomNumber.R'
'methods-uncertaintyRandomPercent.R' 'methods-Grid.R'
'methods-idwUncertain.R' 'methods-splineUncertain.R'
'methods-krigingUncertain.R' 'methods-variogram.R'
'methods-plot.R'
'simRedCard.R' 'getLineup.R' 'formation.R'
'penaltyGoalsProb.R' 'summary.R' 'unitedRPackage.R'
'unitedRoverview.R' 'unitedSimClass.R' 'unitedSimResults.R'
'unitedSimOne.R' 'unitedSim.R'
'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'
'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' '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' 'DataCstr.R' 'VSLCMGrlClasses.R' 'ICLexact.R'
'DesignOutput.R' 'Summary.R' 'Print.R' 'VarSelLCM.R'
'RcppExports.R' 'Imputation.R' 'withoutmixture.R'
Class-Cartesian2DCoordinateTransformation.R
Cartesian2DCoordinateTransformation-methods.R
Class-SimilarityTransformation.R Class-AffineTransformation.R
SimilarityTransformation-methods.R
AffineTransformation-methods.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'
'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'
'Z.value.R' 'genperms.R' 'pairtest.R' 'plotting.R'
'scoretest.R' 'wilcoxon.R' 'wgsea-package.R'
'whisker.R' 'section.R' 'parseTemplate.R' 'partials.R'
'inverted.R' 'pkg.R' 'iteratelist.R' 'delim.R' 'markdown.R'
'rowsplit.R'
'with_.R' 'collate.R' 'dir.R' 'env.R' 'libpaths.R' 'locale.R'
'makevars.R' 'options.R' 'par.R' 'path.R' 'wrap.R' 'sink.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'
'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'
'00_class_01_XiMpLe.node.R' '00_class_02_XiMpLe.doc.R'
'01_method_01_pasteXML.R' 'XiMpLe-internal.R'
'01_method_02_node.R' '01_method_03_show.R' 'XMLNode.R'
'XMLTree.R' 'XiMpLe-package.R' 'parseXMLTree.R' 'pasteXMLTag.R'
'zzz_is_get_utils.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
'CSAb.R' 'CalcNGR.R' 'CalcPD.R' 'CalcSimulatedExposure.R'
'CalcVA.R' 'Curve.R' 'GenerateTimeGrid.R' 'HashTable.R'
'Trade.R' 'IRD.R' 'calcCVACapital.R' 'calcDefCapital.R'
'calcEAD.R' 'calcEffectiveMaturity.R' 'calcKVA.R'
'xVACalculator.R' 'xVACalculatorExample.R'
'AIC.r' 'carbon.model.r' 'carcass.EMI.model.r'
'carcass.model.r' 'carcass_data.r' 'chicks_data.r'
'cotton.model.r' 'epirice.model.r' 'evaluation.criteria.r'
'exponential.model.r' 'goodness.of.fit.r'
'lactation.calf.model.r' 'lactation.machine.model.r'
'magarey.model.r' 'maize.data_EuropeEU.r' 'maize.model.r'
'population.age.model.r' 'prey.predator.model.r' 'sample.r'
'seedweight.data_data.r' 'seedweight.model.r'
'Sunflower_Phomopsis_data.r' 'verhulst.model.r'
'watbal.model.r' 'watbal.simobsdata.r'
'weather_EuropeEU_data.r' 'weather_FranceWest_data.r'
'weather_SouthAsia_data.r' 'weed.model.r' 'Wheat_GPC_data.r'
'WheatYieldGreece_data.r' 'ZeBook.r'
'utils.R' 'model-zelig.R' 'model-weibull.R' 'model-tobit.R'
'model-bayes.R' 'model-tobit-bayes.R' 'model-glm.R'
'model-gee.R' 'model-gamma.R' 'model-binchoice.R'
'model-logit.R' 'model-relogit.R' 'model-quantile.R'
'model-probit.R' 'model-binchoice-gee.R' 'model-probit-gee.R'
'model-probit-bayes.R' 'model-poisson.R' 'model-poisson-gee.R'
'model-poisson-bayes.R' 'model-oprobit-bayes.R'
'model-normal.R' 'model-normal-gee.R' 'model-normal-bayes.R'
'model-negbinom.R' 'model-mlogit-bayes.R' 'model-ls.R'
'model-lognorm.R' 'model-logit-gee.R' 'model-logit-bayes.R'
'model-gamma-gee.R' 'model-factor-bayes.R' 'model-exp.R'
'create-json.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' 'model-timeseries.R' 'model-arima.R'
'model-ar.R' 'model-ma.R' 'plots.R' 'wrappers.R'
'model-obinchoice.R' 'model-oprobit.R' 'model-ologit.R'
'model-mlogit.R' 'model-bbinchoice.R' 'model-bprobit.R'
'model-blogit.R' 'create-json.R'
'create-json.R' 'model-ei.R' 'model-dynamic-ei.R'
'model-hier-ei.R' 'model-ml-ei.R' 'model-rxc-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
GenABEL project developers <genabel.project at gmail.com>
Tan Dang <dangt@purdue.edu>, Bartek Rajwa
<rajwa@cyto.purdue.edu>
Predictive Analytics Team at Pivotal Inc.
<user@madlb.incubator.apache.org>
Austin Chungath Vincent <austincv@gmail.com>,Vikas
Raguttahalli <vikas.r@mu-sigma.com>, Sachin Sudarshana
<sachin.sudarshana@gmail.com>
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.
The code for apriori and eclat in src/rapriori.c was
obtained from http://www.borgelt.net/ and is Copyright (C)
1996-2003 Christian Borgelt. All other code is Copyright (C)
Michael Hahsler, Christian Buchta, Bettina Gruen and Kurt
Hornik.
(C) 2011 Michael Hahsler and Sudheer Chelluboina
This package is Copyright (C) 2005-2016 Weierstrass
Institute for Applied Analysis and Stochastics.
This package is Copyright (C) 2012-16 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
(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)
MNI152_T1_2mm_brain.nii.gz from Copyright (C) 1993-2009
Louis Collins, McConnell Brain Imaging Centre, Montreal
Neurological Institute, McGill University 6th generation
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(c) 2012 NYU School of Medicine; (c) 2012-14 Jeff Binder
(C) 2012 Decision Analyst, Inc. (ChoiceModelR is a trademark
of Decision Analyst, Inc.)
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
ANN library is copyright by University of Maryland, Sunil
Arya and David Mount. All other code is copyright by Michael
Hahsler.
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
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.
Authors and Aalto University (for work of M. Korpela)
This library is Copyright (C) 2006-2016 Weierstrass
Institute for Applied Analysis and Stochastics.
The R package and code, and the main programs, were written
by and are Copyright by David Bolin and Finn Lindgren, and are
redistributable under the GNU Public License, version 3 or
later.  The package also includes code from the libraries 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).
See the individual source files for copyrights information
ANN Copyright (c) 1997-2010 University of Maryland and Sunil
Arya and David Mount. All Rights Reserved.
Copyright (C) 2006 University of Duisburg-Essen, Essen,
Germany, University of Bristol, Bristol, United Kingdom
The Pennsylvania State University and Northeastern
University
see inst/COPYRIGHTS for the files in src/.
(C) 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) 2014 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
Dan Kelley and Clark Richards
Under the allowance of MIT license, smhash
(https://code.google.com/p/smhasher/) codes have been adopted.
RStudio, Inc.; Joyent, Inc.; Nginx Inc.; Igor Sysoev; Niels
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(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
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.
Original C/C++ code (GPL) by Leon Bottou
(http://leon.bottou.org), Yann Le Cun, AT&T Corp, NECI/NEC Labs
America and Neuristique.
The LIBLINEAR Project; Thibault Helleputte
<thibault.helleputte@dnalytics.com>; Pierre Gramme
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'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
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terms of the GNU General Public License as published by the
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any later version. This program is distributed in the hope that
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file inst/COPYRIGHTS for src/*.f obtained from LAPACK
All files are copyright Institut National de la Sante Et de
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(C) 2014 National Institute of Advanced Industrial Science
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libnabo is copyright 2010--2011, Stephane Magnenat, ASL,
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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
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The fortran routines for the function HW.original are from
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published material). The fortran routines for the function
'bestlm' are from Alan Miller and were obtained from his R
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2006-2011, Bank of Canada. 2012-2016, Paul Gilbert
Pokemon, pokedex and all pokemon names are trademarks of
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Copyright (c) 2010, David Weisman and Dan Simovici, All
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Most files are originally designed by David Pierce under GPL
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Implementation of the binary heap is by Hewlett-Packard
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ANN library is copyright University of Maryland and Sunil
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Original python appdirs module copyright (c) 2010
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(C) 2014 Coherent Logic Limited; ALL RIGHTS RESERVED.
The functionos in EZR package were programmed by Y. Kanda,
except for the following functions. dot.plot(), prop.conf(),
Cochran.Q.test(), pairwise.prop2.test(),
pairwise.pairedt.test(), pairwise.kruskal.test(),
pairwise.friedman.test(), pairwise.logrank.test(),
pairwise.gray.test(), Steel.Dwass(), Steel(), pdunnet(),
Mantel.Byar() were modified from the corresponding functions
distributed by professor Aoki
(http://aoki2.si.gunma-u.ac.jp/R). crr() and cuminc() were
derived from cmprsk package. nrisk() was modified from survplot
package (http://www.cbs.dtu.dk/~eklund/survplot/). epi.test()
and epi.kappa() were derived from epiR package.
jonckheere.test(), ph2simon() and ph2single() were derived from
clinfun package. Functions of meta-analysis were derived from
meta and metatest packages. Functions of matched-pair analysis
were derived from optmatch package, and its license restricts
to academic use. See
http://cran.r-project.org/web/packages/optmatch/LICENSE for
details. The package must be installed before performing
matched-pair analysis, assuming the user is covered by the
license. Functions of receiver-operating characteristics curve
analyses were derived from pROC package. Many dialog functions
in EZR were modified from the corresponding functions in Rcmdr
package and RcmdrPlugin.survivalT package.
See the file COPYRIGHTS for various Eigen copyright details
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.
MaxMind, Inc. for the underlying libmaxmminddb library, and
the package authors for all other content.
Code for the wavelet transform is based on Brandon
Whitcher's work. See file COPYRIGHT for details
Copyright (c) 2008-2016 LabKey Corporation
The package includes the source code of matio written by
Christopher Hulbert (http://sourceforge.net/projects/matio/)
(License: Simplified BSD). The matio io routines have been
adopted to use R printing and error routines.
Authors, and Mixmod use Newmat C++ library
Code is Copyright (C) Jan Wijffels and BNOSAC
The data in star_groj0422.32 have been kindly provided by
the NASA. The data in Mrk421 and Mrk501 have been kindly
provided by the Deutsches Elektronen-Synchrotron. See the
respective documentation.
All code is Copyright (c) 2002-2016 University of
California, Cornell University, Cold Spring Harbor Laboratory.
Authors listed above, PostgreSQL Global Development Group,
and The Regents of the University of California
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see files COPYRIGHTS for detailed information
(c) 2010-2015 Madeleine Thompson, except dchud.f and
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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
MOA code in inst/java/moa.jar is Copyright (C) The
University of Waikato and distributed under the Apache License,
version 2.0. All other code is Copyright (C) Matthew Bolanos,
John Forrest and Michael Hahsler
Copyright 2012. The Regents of the University of California
(Regents). All Rights Reserved.
2007-2011 Bank of Canada.  2012-2015 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
2009-2014 Andrew C. Hooker, E. Niclas Jonsson and Mats O.
Karlsson. 2005-2008 Andrew C. Hooker, Justin J. Wilkins, Mats
O. Karlsson and E. Niclas Jonsson. 1998-2004 E. Niclas Jonsson
and Mats Karlsson.
ANN library is copyright University of Maryland and Sunil
Arya and David Mount. See file COPYRIGHTS for details.
Original GPL-2 C++ code by Naoki Yoshinaga (see
http://www.tkl.iis.u-tokyo.ac.jp/~ynaga/yakmo/)
(C) 2014 John W. Emerson and Walton Green
Check NEWS file for changes: news(package='simSummary')
Supplies tools for tabulating and analyzing the results of predictive models. The methods employed are applicable to virtually any predictive model and make comparisons between different methodologies straightforward.
Get text from images of text using Abbyy Cloud Optical Character
    Recognition (OCR) API. Easily OCR images, barcodes, forms, documents with
    machine readable zones, e.g. passports. Get the results in a variety of formats
    including plain text and XML. To learn more about the Abbyy OCR API, see http://
    ocrsdk.com/.
Implements several ABC algorithms for
        performing parameter estimation, model selection, and goodness-of-fit.
        Cross-validation tools are also available for measuring the
        accuracy of ABC estimates, and to calculate the
        misclassification probabilities of different models.
For a given data set, the package provides a novel method of computing precise limits to acquire subsets which are easily interpreted. Closely related to the Lorenz curve, the ABC curve visualizes the data by graphically representing the cumulative distribution function. Based on an ABC analysis the algorithm calculates, with the help of the ABC curve, the optimal limits by exploiting the mathematical properties pertaining to distribution of analyzed items. The data containing positive values is divided into three disjoint subsets A, B and C, with subset A comprising very profitable values, i.e. largest data values ("the important few"), subset B comprising values where the yield equals to the effort required to obtain it, and the subset C comprising of non-profitable values, i.e., the smallest data sets ("the trivial many"). Package is based on "Computed ABC Analysis for rational Selection of most informative Variables in multivariate Data", PLoS One. Ultsch. A., Lotsch J. (2015) <DOI:10.1371/journal.pone.0129767>.
Contains data which are used by functions of the 'abc' package.
Functions for Constraint Based Simulation using Flux
        Balance Analysis and informative analysis of the data generated
        during simulation.
An implementation of Karaboga (2005) Artificial Bee Colony
    Optimization algorithm <http://mf.erciyes.edu.tr/abc/pub/tr06_2005.pdf>.
    This (working) version is a Work-in-progress, which is
    why it has been implemented using pure R code. This was developed upon the basic
    version programmed in C and distributed at the algorithm's official website.
Tests the goodness of fit of a distribution of offspring to the Normal, Poisson, and Gamma distribution and estimates the proportional paternity of the second male (P2) based on the best fit distribution.
It aims to identify candidate genes that are “differentially
    methylated” between cases and controls. It applies Student’s t-test and delta beta analysis to
    identify candidate genes containing multiple “CpG sites”.
Performs Approximate Bayesian Computation (ABC) model choice and parameter inference via random forests.
Tools for approximate Bayesian computation including summary statistic selection and assessing coverage.
The abd package contains data sets and sample code for The
    Analysis of Biological Data by Michael Whitlock and Dolph Schluter (2009;
    Roberts & Company Publishers).
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.
Bayesian network analysis is a form of probabilistic  graphical models which derives from empirical data a directed acyclic graph, DAG, describing the dependency structure between  random variables. An additive Bayesian network model consists of a form of a DAG where each node comprises a generalized linear model, GLM. Additive Bayesian network models are equivalent to Bayesian multivariate regression using graphical modelling, they generalises the usual multivariable regression, GLM, to multiple dependent variables. 'abn' provides routines to help determine optimal Bayesian network models for a given data set, where these models are used to identify statistical dependencies in messy, complex data. The additive formulation of these models is equivalent to multivariate generalised linear modelling (including mixed models with iid random effects). The usual term to describe this model selection process is structure discovery. The core functionality is concerned with model selection - determining the most robust empirical model of data from interdependent variables. Laplace approximations are used to estimate goodness of fit metrics and model parameters, and wrappers are also included to the INLA package which can be obtained from <http://www.r-inla.org>. It is recommended the testing version, which can be downloaded by running: source("http://www.math.ntnu.no/inla/givemeINLA-testing.R"). A comprehensive set of documented case studies, numerical accuracy/quality assurance exercises, and additional documentation are available from the 'abn' website.
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.
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.
Fit and predict with the high-dimensional principal fitted
        components model.
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.
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.
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).
This data package contains 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.
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.
Provides a general toolkit for downloading, managing,
  analyzing, and presenting data from the U.S. Census, 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. Current version is 2.0 +/- .033.
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.
This package includes functions used to perform cluster analysis for cognitive 
  diagnosis based on the Asymptotic Classification Theory (Chiu, Douglas & Li, 2009). Given the 
  sample statistic of sum-scores and the item-by-attribute association matrix (Q-matrix; Tatsuoka, 1985), 
  cluster analysis techniques can be used to classify examinees into latent 
  classes based on their attribute patterns. In addition to the algorithms used to classify data, three 
  labeling approaches are proposed to label clusters so that examinees' attribute profiles can be obtained.
Functional linear modeling and analysis for actigraphy data.
Provides functions to fit kernel density functions
    to animal activity time data; plot activity distributions;
    quantify overall levels of activity; statistically compare
    activity metrics through bootstrapping; and evaluate variation
    in linear variables with time (or other circular variables).
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.
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.
Computes the probability density function, hazard rate
        function, integrated hazard rate function and the quantile
        function for 44 commonly used survival models
Performs discrete, real, and gentle boost under both exponential and 
             logistic loss on a given data set.  The package ada provides a straightforward, 
             well-documented, and broad boosting routine for classification, ideally suited 
             for small to moderate-sized data sets.
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.
The R package 'adagio' will provide methods and algorithms for
    discrete optimization and (evolutionary) global optimization.
Provides new approaches to variable selection for AFT model.
Tracks reading and writing within R scripts that are organized into a directed acyclic graph. Contains an interactive shiny application adaprApp(). Uses Git and file hashes to track version histories of input and output.
The adaptive mixture discriminant analysis (AMDA) allows to adapt a model-based classifier to the situation where a class represented in the test set may have not been encountered earlier in the learning phase.
Based on the function "spm" of the SemiPar package fits
        semiparametric regression models with spatially adaptive
        penalized splines.
Fits semiparametric regression models with spatially adaptive penalized splines and computes simultaneous confidence bands.
Multimodal distributions can be modelled as a mixture of components. The model is derived using the Pareto Density Estimation (PDE) for an estimation of the pdf. PDE has been designed in particular to identify groups/classes in a dataset. Precise limits for the classes can be calculated using the theorem of Bayes. Verification of the model is possible by QQ plot, Chi-squared test and Kolmogorov-Smirnov test.
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.
This package provides an implementation of the generic
        adaptive Monte Carlo Markov chain sampler proposed by Vihola
        (2011).
This package contains R functions for estimating the blood
        oxygenation level dependent (BOLD) effect by using functional
        Magnetic Resonance Imaging (fMRI) data, based on adaptive Gauss
        Markov random fields, for real as well as simulated data. The
        implemented simulations make use of efficient Markov Chain
        Monte Carlo methods.
The functions defined in this program serve for
        implementing adaptive two-stage tests. Currently, four tests
        are included: Bauer and Koehne (1994), Lehmacher and Wassmer
        (1999), Vandemeulebroecke (2006), and the horizontal
        conditional error function. User-defined tests can also be
        implemented. Reference: Vandemeulebroecke, An investigation of
        two-stage tests, Statistica Sinica 2006.
Existing adaptive design methods in clinical trials. The package
    includes power, stopping boundaries (sample size) calculation functions for
    two-group group sequential designs, adaptive design with coprimary endpoints,
    biomarker-informed adaptive design, etc.
Functions to fit the binomial and multinomial additive hazards models and to calculate the contribution of diseases/conditions to the disability prevalence, as proposed by Nusselder and Looman (2004) <DOI:10.1353/dem.2004.0017>.
Contains tools to fit additive hazards model to random sampling, two-phase sampling and two-phase sampling with auxiliary information. 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.
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).
Multivariate data analysis and graphical display.
A Tcl/Tk GUI for some basic functions in the 'ade4' package.
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 analysis of habitat selection by animals.
A collection of tools for the estimation of animals home range.
A collection of tools for the analysis of habitat selection.
A collection of tools for the analysis of animal movements.
A collection of tools to deal with raster maps.
Multivariate tools to analyze comparative data, i.e. a phylogeny and some traits measured for each taxa.
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).
Anderson-Darling GoF test with p-value calculation based on Marsaglia's 2004 paper "Evaluating the Anderson-Darling Distribution"
An ad hoc distance threshold can be calculated for each particular reference library of DNA barcodes. Specimen identification at this ad hoc distance threshold (using the best close match method) will produce identifications with an estimated relative error probability that can be fixed by the user (e.g. 5%)
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>.
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>.
Adaptive Wavelet transforms for signal denoising
Robust change point detection using ADM3 algorithm.
This package provides functions to perform the fitting of an
  adaptive mixture of Student-t distributions to a target density through its kernel function.
  The mixture approximation can then be used as the importance density
  in importance sampling or as the candidate density in the Metropolis-Hastings algorithm to
  obtain quantities of interest for the target density itself.
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.
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.
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.
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.
Implements wavelet-based approaches for describing population admixture. Principal Components Analysis (PCA) is used to define the population structure and produce a localized admixture signal for each individual. Wavelet summaries of the PCA output describe variation present in the data and can be related to population-level demographic processes. For more details, see Sanderson et al. (2015).
Package to incorporate change point analysis in ARIMA forecasting.
Download and process real time trading prices and demand data
    freely provided by the Australian Energy Market Operator (AEMO). Note that
    this includes a sample data set.
Functions, data sets, examples, demos, and vignettes for the book
             Christian Kleiber and Achim Zeileis (2008),
	     Applied Econometrics with R, Springer-Verlag, New York.
	     ISBN 978-0-387-77316-2. (See the vignette 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) and Cox proportional hazard regression (cohort design with time-to-event outcome). The variance of the estimator is obtained by combining the delta method with the the sandwich formula.
Provides convenience functions for analyzing factorial
    experiments using ANOVA or mixed models. aov_ez(), aov_car(), and aov_4() allow
    specification of between, within (i.e., repeated-measures), or mixed between-
    within (i.e., split-plot) ANOVAs for data in long format (i.e., one observation
    per row), potentially 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 approximation for
    degrees of freedom (LMM only), parametric bootstrap (LMMs and GLMMs), or
    likelihood ratio tests (LMMs and GLMMs). afex uses type 3 sums of squares as
    default (imitating commercial statistical software).
Hybrid simulation functions for dominant genetic data and genome scan methods.
Provides Atomic Force Microscope images analysis such as Power
    Spectral Density, roughness against lengthscale, experimental variogram and variogram models,
    fractal dimension and scale. The AFM images can be exported to STL format for 3D
    printing.
Run an R function in the background, possibly after a delay.
    The current version uses the Tcl event loop and was ported from the
    'tcltk2' package.
This package features both rank-based estimates and least
		 square estimates to the Accelerated Failure Time (AFT) model. 
		 For rank-based estimation, it provides approaches that include 
		 the computationally efficient Gehan's weight and the general's 
		 weight such as the logrank weight. 
		 For the least square estimation, the estimating equation is 
		 solved  with Generalized Estimating Equations (GEE). 
		 Moreover, in multivariate cases, the dependence working 
		 correlation structure can be specified in GEE's setting.
Tools for NIHES course EP18 'Analysis of Growth Data', May 22-23
    2012, Rotterdam.
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.
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.
Bland-Altman plot and scatter plot with identity line 
             for visualization and point and 
             interval estimates for different metrics related to 
             reproducibility/repeatability/agreement including
             the concordance correlation coefficient, 
             intraclass correlation coefficient,
             within-subject coefficient of variation,
             smallest detectable difference, 
             and mean normalized smallest detectable difference.
This package computes several statistics for measuring
        agreement, for example, mean square deviation (MSD), total
        deviation index (TDI) or concordance correlation coefficient
        (CCC). It can be used for both continuous data and categorical
        data for multiple raters and multiple readings cases.
Original idea was presented in the thesis "A statistical analysis tool for agricultural research" to obtain the degree of Master on science, National Engineering University (UNI), Lima-Peru. Some experimental data for the examples come from the CIP and others research. Agricolae offers extensive functionality on experimental design especially for agricultural and plant breeding experiments, which can also be useful for other purposes. It supports planning of lattice, Alpha, Cyclic, Complete Block, Latin Square, Graeco-Latin Squares, augmented block, factorial, split and strip plot designs. There are also various analysis facilities for experimental data, e.g. treatment comparison procedures and several non-parametric tests comparison, biodiversity indexes and consensus cluster.
Datasets from books, papers, and websites related to agriculture.
    Example analyses are included.  Includes functions for plotting field
    designs and GGE biplots.
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.
Aho-Corasick is an optimal algorithm for finding many
    keywords in a text. It can locate all matches in a text in O(N+M) time; i.e.,
    the time needed scales linearly with the number of keywords (N) and the size of
    the text (M). Compare this to the naive approach which takes O(N*M) time to loop
    through each pattern and scan for it in the text. This implementation builds the
    trie (the generic name of the data structure) and runs the search in a single
    function call. If you want to search multiple texts with the same trie, the
    function will take a list or vector of texts and return a list of matches to
    each text. By default, all 128 ASCII characters are allowed in both the keywords
    and the text. A more efficient trie is possible if the alphabet size can be
    reduced. For example, DNA sequences use at most 19 distinct characters and
    usually only 4; protein sequences use at most 26 distinct characters and usually
    only 20. UTF-8 (Unicode) matching is not currently supported.
Model and analyse complex decision making problems
    using the Analytic Hierarchy Process (AHP) by Thomas Saaty.
Methods for estimation of multivariate average hazard ratios as
    defined by Kalbfleisch and Prentice. The underlying survival functions of the
    event of interest in each group can be estimated using either the (weighted)
    Kaplan-Meier estimator or the Aalen-Johansen estimator for the transition
    probabilities in Markov multi-state models. Right-censored and left-truncated
    data is supported. Moreover, the difference in restricted mean survival can be
    estimated.
Functions to implement model selection and multimodel inference based on Akaike's information criterion (AIC) and the second-order AIC (AICc), as well as their quasi-likelihood counterparts (QAIC, QAICc) from various model object classes.  The package implements classic model averaging for a given parameter of interest or predicted values, as well as a shrinkage version of model averaging parameter estimates or effect sizes.  The package includes diagnostics and goodness-of-fit statistics for certain model types including those of 'unmarkedFit' classes estimating demographic parameters after accounting for imperfect detection probabilities.  Some functions also allow the creation of model selection tables for Bayesian models of the 'bugs' and 'rjags' classes.  Functions also implement model selection using BIC.  Objects following model selection and multimodel inference can be formatted to LaTeX using 'xtable' methods included in the package.
Power transformation parameter is estimated for different purposes.
Read objects from the AIDA file and make them available
    as dataframes in R
R functions for adaptively constructing index models for
        continuous, binary and survival outcomes. Implementation
        requires loading R-pacakge "survival"
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.
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 526 and ACM 761, regular data: ACM 433) and univariate case (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.
Adaptive K-means algorithm with various threshold settings.
             It support two distance metric: 
             Euclidean distance, Cosine distance (1 - cosine similarity)
             In version 1.1, it contains one more threshold condition.
The Atlas of Living Australia (ALA) provides tools to enable users
    of biodiversity information to find, access, combine and visualise data on
    Australian plants and animals; these have been made available from
    <http://ala.org.au/>. ALA4R provides a subset of the tools to be
    directly used within R. It enables the R community to directly access data
    and resources hosted by the ALA. Our goal is to enable outputs (e.g.
    observations of species) to be queried and output in a range of standard
    formats.
Augmented Lagrangian Adaptive Barrier Minimization
        Algorithm for optimizing smooth nonlinear objective functions
        with constraints. Linear or nonlinear equality and inequality
        constraints are allowed.
Provides immunoglobulin (Ig) sequence lineage reconstruction,
    diversity profiling, and amino acid property analysis. Also provides
    core functionality for other tools in the Change-O suite.
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 (2016) <http://www.ime.unicamp.br/sites/default/files/rp07-16.pdf> useful for quantile regression.
EM algorithm for estimation of parameters and other methods in a quantile regression.
Determination of absolute protein quantities is necessary for multiple applications, such as mechanistic modeling of biological systems. Quantitative liquid chromatography tandem mass spectrometry (LC-MS/MS) proteomics can measure relative protein abundance on a system-wide scale. To estimate absolute quantitative information using these relative abundance measurements requires additional information such as heavy-labeled references of known concentration. Multiple methods have been using different references and strategies; some are easily available whereas others require more effort on the users end. Hence, we believe the field might benefit from making some of these methods available under an automated framework, which also facilitates validation of the chosen strategy. We have implemented the most commonly used absolute label-free protein abundance estimation methods for LC-MS/MS modes quantifying on either MS1-, MS2-levels or spectral counts together with validation algorithms to enable automated data analysis and error estimation. Specifically, we used Monte-carlo cross-validation and bootstrapping for model selection and imputation of proteome-wide absolute protein quantity estimation. Our open-source software is written in the statistical programming language R and validated and demonstrated on a synthetic sample.
Algorithmic experimental designs. Calculates exact and
        approximate theory experimental designs for D,A, and I
        criteria. Very large designs may be created. Experimental
        designs may be blocked or blocked designs created from a
        candidate list, using several criteria.  The blocking can be
        done when whole and within plot factors interact.
Two unordered pairs of data of two different snips positions is haplotyped by resolving a small number ob closed equations.
The company, Algorithmia, houses the largest marketplace of online
    algorithms. This package essentially holds a bunch of REST wrappers that
    make it very easy to call algorithms in the Algorithmia platform and access
    files and directories in the Algorithmia data API. To learn more about the
    services they offer and the algorithms in the platform visit
    <http://algorithmia.com>. More information for developers can be found at
    <http://developers.algorithmia.com>.
algstat provides functionality for algebraic statistics in R.
    Current applications include exact inference in log-linear models for
    contingency table data, analysis of ranked and partially ranked data, and
    general purpose tools for multivariate polynomials, building on the mpoly
    package. To aid in the process, algstat has ports to Macaulay2, Bertini,
    LattE-integrale and 4ti2.
Methods for comparing two alternative multiple 
    sequence alignments (MSAs) to determine whether they align homologous residues in 
    the same columns as one another. It then classifies similarities and differences 
    into conserved gaps, conserved sequence, merges, splits or shifts of one MSA relative 
    to the other. Summarising these categories for each MSA column yields information 
    on which sequence regions are agreed upon my both MSAs, and which differ. Several 
    plotting functions enable easily visualisation of the comparison data for analysis.
Functions are provided to calculate the 'ALINE' Distance between words. 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.
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.
An R interface to the open source article level metrics platform
    <https://github.com/articlemetrics/lagotto/> created by the Public Library
    of Science (PLOS). A number of publishers are using the open source app 
    created by PLOS, so you can drop in a different base URL to the functions 
    in this package to get to not only PLOS data, but data for Crossref, 
    and more as the open source PLOS software is used.
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.
This package is a companion to the textbook S. Weisberg (2005), 
 "Applied Linear Regression," 3rd edition, Wiley. It includes all the
 data sets discussed in the book (except one), and a few functions that 
 are tailored to the methods discussed in the book.  As of version 2.0.0,
 this package depends on the car package. Many functions formerly 
 in alr3 have been renamed and now reside in car.  
 Data files have beeen lightly modified to make some data columns row labels.
This package is a companion to the textbook S. Weisberg (2014), 
 "Applied Linear Regression," 4rd edition, Wiley. It includes all the
 data sets discussed in the book and one function to access the textbook's
 website.  
 This package depends on the car package.  Many data files in this package
 are included in the alr3 package as well, so only one of them should be
 loaded.
Alternating least squares is often used to resolve
        components contributing to data with a bilinear structure; the
        basic technique may be extended to alternating constrained
        least squares.  Commonly applied constraints include
        unimodality, non-negativity, and normalization of components.
        Several data matrices may be decomposed simultaneously by
        assuming that one of the two matrices in the bilinear
        decomposition is shared between datasets.
Using of the accelerated line search algorithm  for simultaneously diagonalize a set of symmetric positive definite matrices.
Functions and Data set presented in Applied Linear Statistical Models Fifth Edition (Chapters 1-9 and 16-18), 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.
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
Accompanies "Designing experiments and analyzing data: A model comparison perspective"" (3rd ed.) Maxwell, Delaney, & Kelley (forthcoming). This package contains all of the data sets in the book. Information about the book is available here <http://www.DesigningExperiments.com>.
Annual macro-economic database provided by the European Commission.
This package provides a flexible statistical framework for generating optimal 
 epidemiological interventions that are designed to minimize the total expected
 cost of an emerging epidemic while simultaneously propagating uncertainty regarding 
 underlying disease parameters through to the decision process via Bayesian posterior
 inference.  The strategies produced through this framework are adaptive: vaccination 
 schedules are iteratively adjusted to reflect the anticipated trajectory of the 
 epidemic given the current population state and updated parameter estimates.
A tool that "multiply imputes" missing data in a single cross-section
  (such as a survey), from a time series (like variables collected for
  each year in a country), or from a time-series-cross-sectional data
  set (such as collected by years for each of several countries).
  Amelia II implements our bootstrapping-based algorithm that gives
  essentially the same answers as the standard IP or EMis approaches,
  is usually considerably faster than existing approaches and can
  handle many more variables.  Unlike Amelia I and other statistically
  rigorous imputation software, it virtually never crashes (but please
  let us know if you find to the contrary!).  The program also
  generalizes existing approaches by allowing for trends in time series
  across observations within a cross-sectional unit, as well as priors
  that allow experts to incorporate beliefs they have about the values
  of missing cells in their data.  Amelia II also includes useful
  diagnostics of the fit of multiple imputation models.  The program
  works from the R command line or via a graphical user interface that
  does not require users to know R.
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), Wong (1982)), and multiplicative factor
   models (Hoff(2009)). Four different link functions accommodate different
   relational data structures, including binary/network data (bin), normal
   relational data (nrm), ordinal relational data (ord) and data from
   fixed-rank nomination schemes (frn).  Several of these link functions are
   discussed in Hoff, Fosdick, Volfovsky and Stovel (2013). Development of this
   software was supported in part by 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.
AMGET allows to simply and rapidly creates highly informative diagnostic plots for ADAPT 5 models. Features include data analysis prior any modeling form either NONMEM or ADAPT shaped dataset, goodness-of-fit plots (GOF), posthoc-fits plots (PHF), parameters distribution plots (PRM) and visual predictive check plots (VPC) based on ADAPT output.
This package implements the adaptive mixed lasso (AML) method proposed by Wang et al.(2011).  AML applies adaptive lasso penalty to a large number of predictors,  thus producing a sparse model, while accounting for the population structure in the linear mixed model framework.  The package here is primarily designed for application to genome wide association studies or genomic prediction in plant breeding populations, though it could be applied to other settings of linear mixed models.
A function to calculate spatial clusters using the Getis-Ord local statistic. It searches
   irregular clusters (ecotopes) on a map.
This package was born to release the TAO robust neural
        network algorithm to the R users. It has grown and I think it
        can be of interest for the users wanting to implement their own
        training algorithms as well as for those others whose needs lye
        only in the "user space".
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.
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.
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.
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.
Class with methods to read and execute R commands described as steps in a CSV file.
Functions for I/O, visualisation and analysis of
        functional Magnetic Resonance Imaging (fMRI) datasets stored in
        the ANALYZE or NIFTI format.
Analyze fuzzy time series by Chen, Singh, Heuristic and Chen-Hsu models. The Abbasov-Mamedova and NFTS models is included as well.
Offers a set of functions to upload/download data and metadata on Anametrix API <http://anametrix.com/>.
This package contains functions for
        normalisation,differentially analysis of microarray data and
        others functions implementing recent methods developed by the
        Statistic and Genom Team from UMR 518 AgroParisTech/INRA Appl.
        Math. Comput. Sc.
Assigns genetic ancestry to an individual and
    studies relationships between local and global populations.
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.
Create an angler population, sample the population with a user-specified survey times, and calculate metrics from a bus route-type creel survey.
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,
    nonparametric 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 (saveSWF(), saveGIF(),
    saveHTML(), saveLatex(), and saveVideo() respectively). PDF animations can
    be inserted into Sweave/knitr easily.
Simple animated versions of basic R plots, using the 'animation'
    package. Includes animated versions of plot, barplot, persp, contour,
    filled.contour, hist, curve, points, lines, text, symbols, segments, and
    arrows.
Library to sample and clean text data, build N-gram model, Backoff algorithm etc.
Annotate multiple lists from a specific annotation file.
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.
Fits ideal point model described in Carroll, Lewis, Lo, Poole and Rosenthal, "The Structure of Utility in Models of Spatial Voting," American Journal of Political Science 57(4): 1008--1028.
Allows users to quickly and easily anonymize data containing
    Personally Identifiable Information (PII) through convenience functions.
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.
Offers a complete and interactive GUI to work out Mean Measures of Divergence, especially for anthropologists.
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 mergers under different competitive regimes.
A complete programmatic interface to the AntWeb database from the
    California Academy of Sciences.
Convert input in any one of character, integer, numeric, factor,
 or ordered type into 'POSIXct' (or 'Date') objects, using one of a number of
 predefined formats, and relying on Boost facilities for date and time parsing.
This package provides a set of functions to analyse
        overdispersed counts or proportions. Most of the methods are
        already available elsewhere but are scattered in different
        packages. The proposed functions should be considered as
        complements to more sophisticated methods such as generalized
        estimating equations (GEE) or generalized linear mixed effect
        models (GLMM).
This package provides functions to analyse overdispersed
        counts or proportions.  These functions should be considered as
        complements to more sophisticated methods such as generalized
        estimating equations (GEE) or generalized linear mixed effect
        models (GLMM). aods3 is an S3 re-implementation of the
        deprecated S4 package aod.
Another implementation of object-orientation in R. It provides
    syntactic sugar for the S4 class system and two alternative new
    implementations. One is an experimental version built around S4
    and the other one makes it more convenient to work with lists as objects.
Provides tools for analyzing adverse outcome pathways
    (AOPs) for pharmacological and toxicological research. Functionality
    includes the ability to perform causal network analysis of networks
    developed in and exported from Cytoscape or existing as R graph objects, and
    identifying the point of departure/screening/risk value from concentration-
    response data.
'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 a function to process Apache Log Common or Combined files. The main
    functionality is to extract data from log files to data frames.
Most psychological journals require that tables in a manuscript comply to APA (American Association of Psychology) standards. Creating APA tables manually is often time consuming and prone to transcription errors. This package generates tables for MS Word ('.docx' extension) in APA format automatically with just a few lines of code.
A common task faced by researchers is the creation of APA style (i.e., American Psychological Association style) tables from statistical output. In R a large number of function calls are often needed to obtain all of the desired information for a single APA style table. As well, the process of manually creating APA style tables in a word processor is prone to transcription errors. This package creates Word files (.doc files) containing APA style tables for several types of analyses. Using this package minimizes transcription errors and reduces the number commands needed by the user.
Functions for age-period-cohort analysis. The data can be organised in matrices indexed by age-cohort, age-period or cohort-period. The data can include dose and response or just doses.  The statistical model is a generalized linear model (GLM) allowing for 3,2,1 or 0 of the age-period-cohort factors. The canonical parametrisation of Kuang, Nielsen and Nielsen (2008) is used. Thus, the analysis does not rely on ad hoc identification.
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 allelic and nucleotide data, 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. 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.
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.
An unofficial companion to "Applied
    Logistic Regression" by D.W. Hosmer, S. Lemeshow and
    R.X. Sturdivant (3rd ed., 2013) containing the dataset used in the book.
set of functions for drawing some special plots:
   stem.leaf plots a stem and leaf plot,
   stem.leaf.backback plots back-to-back versions of stem and leafs,
   bagplot plots a bagplot,
   skyline.hist plots several histgramm in one plot of a one dimensional data set,
   plotsummary plots a graphical summary of a data set with one or more variables,
   plothulls plots sequentially hulls of a bivariate data set,
   faces plots chernoff faces,
   spin3R for an inspection of a 3-dim point cloud,
   slider functions for interactive graphics.
apmsWAPP provides a complete workflow for the analysis of AP-MS data (replicate single-bait purifications including negative controls) based on spectral counts. 
		It comprises pre-processing, scoring and postprocessing of protein interactions.
		A final list of interaction candidates is reported: it provides a ranking of the candidates according 
		to their p-values which allow estimating the number of false-positive interactions.
This package wraps Fortran code by F. D. Colavecchia and
        G. Gasaneo for computing the Appell's F1 hypergeometric
        function. Their program uses Fortran code by L. F. Shampine and
        H. A. Watts. Moreover, the hypergeometric function with complex
        arguments is computed with Fortran code by N. L. J. Michel and
        M. V. Stoitsov or with Fortran code by R. C.  Forrey. See the
        function documentations for the references and please cite them
        accordingly.
Approximate Path for Penalized Likelihood Estimators for
        Generalized Linear Models penalized by LASSO or MCP
A few functions and several data set for the Springer book 'Applied Predictive Modeling'
Amyloid propensity prediction neural network (APPNN) is an amyloidogenicity propensity predictor based on a machine learning approach through recursive feature selection and feed-forward neural networks, taking advantage of newly published sequences with experimental, in vitro, evidence of amyloid formation.
Performs Bayesian prediction of complex computer codes
  when fast approximations are available: M. C. Kennedy and A. O'Hagan
  2000, Biometrika 87(1):1-13
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 organises 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.
We provide tools to estimate two prediction performance metrics,
    the average positive predictive values (AP) as well as the well-known AUC
    (the area under the receiver operator characteristic curve) for risk scores
    or marker. The outcome of interest is either binary or censored event time.
    Note that for censored event time, our functions estimate the AP and the
    AUC are time-dependent for pre-specified time interval(s). A function that
    compares the APs of two risk scores/markers is also included. Optional
    outputs include positive predictive values and true positive fractions at
    the specified marker cut-off values, and a plot of the time-dependent AP
    versus time (available for event time data).
apTreeshape is mainly dedicated to simulation and analysis
        of phylogenetic tree topologies using statistical indices. It
        is a companion library of the 'ape' package. It provides
        additional functions for reading, plotting, manipulating
        phylogenetic trees. It also offers convenient web-access to
        public databases, and enables testing null models of
        macroevolution using corrected test statistics.  Trees of class
        "phylo" (from 'ape' package) can be converted easily.
This package contains functions to help display air quality model output and monitoring data, such as creating color scatterplots, color legends, etc.
A collection of algorithms related to modeling of soil resources, soil classification, soil profile aggregation, and visualization.
This R extension provides methods to use a standalone ActiveQuant
    Master Server from within R. Currently available features include fetching
    and storing historical data, receiving and sending live data.  Several
    utility methods for simple data transformations are included, too. For
    support requests, please join the mailing list at
    https://r-forge.r-project.org/mail/?group_id=1518
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.
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) 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.
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.
The archdata package provides several types of data that are typically used in archaeological research.
The main function archetypes implements a
    framework for archetypal analysis supporting arbitrary
    problem solving mechanisms for the different conceptual
    parts of the algorithm.
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.
Package ArDec implements autoregressive-based
        decomposition of a time series based on the constructive
        approach in West (1997). Particular cases include the
        extraction of trend and seasonal components.
Activated Region Fitting (ARF) is an analysis method for fMRI data.
Simulates, fits, and predicts long-memory and anti-persistent time
    series, possibly mixed with ARMA, regression, transfer-function components.
    Exact methods (MLE, forecasting, simulation) are used.
Functions to 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.
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.
R functions for processing 'lm', 'glm', 'svy.glm', 'merMod' and 'polr' outputs.
Arnold Schwarzenegger movie weekend box office records from
    1982-2014
A cross-platform R framework that facilitates processing of any number of Affymetrix microarray samples regardless of computer system.  The only parameter that limits the number of chips that can be processed is the amount of available disk space.  The Aroma Framework has successfully been used in studies to process tens of thousands of arrays.  This package has actively been used since 2006.
DEPRECATED. Do not start building new projects based on this package. (The (in-house) APD file format was initially developed to store Affymetrix probe-level data, e.g. normalized CEL intensities.  Chip types can be added to APD file and similar to methods in the affxparser package, this package provides methods to read APDs organized by units (probesets).  In addition, the probe elements can be arranged optimally such that the elements are guaranteed to be read in order when, for instance, data is read unit by unit.  This speeds up the read substantially.  This package is supporting the Aroma framework and should not be used elsewhere.)
Methods for analyzing DNA copy-number data.  Specifically,
  this package implements the multi-source copy-number normalization (MSCN)
  method for normalizing copy-number data obtained on various platforms and
  technologies.  It also implements the TumorBoost method for normalizing
  paired tumor-normal SNP data.
Core methods and classes used by higher-level aroma.* packages
        part of the Aroma Project, e.g. aroma.affymetrix and aroma.cn.
Tools for 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.
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 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>.
A package 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 interfaces to 
    C implementations of the association mining algorithms Apriori and Eclat
    by C. Borgelt.
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, 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.
The as.color function takes an R vector of any class as an input,
    and outputs a vector of unique hexadecimal color values that correspond to the
    unique input values. This is most handy when overlaying points and lines for
    data that correspond to different levels or factors. The function will also
    print the random seed used to generate the colors. If you like the color palette
    generated, you can save the seed and reuse those colors.
Package runs simulations for adaptive seamless designs with and without early outcomes 
                           for treatment selection and subpopulation type designs.
A simple driver that reads binary data created by the ASD Inc.
    portable spectrometer instruments, such as the FieldSpec (for more information,
    see <http://www.asdi.com/products/fieldspec-spectroradiometers>). Spectral data
    can be extracted from the ASD files as raw (DN), white reference, radiance, or
    reflectance. Additionally, the metadata information contained in the ASD file
    header can also be accessed.
David Scott's ASH routines ported from S-PLUS to R.
Some hypothesis test functions with a focus on non-asymptotic methods that have matching confidence intervals.
'Asio' is a cross-platform C++ library for network and low-level
 I/O programming that provides developers with a consistent asynchronous model
 using a modern C++ approach.

    'Asio' is also included in Boost but requires linking when used with
 Boost. Standalone it can be used header-only provided a recent-enough
 compiler. 'Asio' is written and maintained by Christopher M. Kohlhoff.
 'Asio' is released under the 'Boost Software License', Version 1.0.
Provides an interface to the algorithm selection benchmark library
    at <http://www.aslib.net> and the 'LLAMA' package
    (<https://cran.r-project.org/web/packages/llama/index.html>) for building
    algorithm selection models.
Functions for Accurate and Speedy linkage map construction, manipulation and diagnosis of Doubled Haploid, Backcross and Recombinant Inbred 'R/qtl' objects. This includes extremely fast linkage map clustering and optimal marker ordering using 'MSTmap' (see Wu et al.,2008).
Implements several tools that are used in animal social network analysis. In particular, this package provides the tools to infer groups and generate networks from observation data, perform permutation tests on the data, calculate lagged association rates, and performed multiple regression analysis on social network data.
A collection of functions for computing centrographic
        statistics (e.g., standard distance, standard deviation
        ellipse, standard deviation box) for observations taken at
        point locations. Separate plotting functions have been
        developed for each measure. Users interested in writing results
        to ESRI shapefiles can do so by using results from aspace
        functions as inputs to the convert.to.shapefile and
        write.shapefile functions in the shapefiles library. The aspace
        library was originally conceived to aid in the analysis of
        spatial patterns of travel behaviour (see Buliung and Remmel,
        2008). Major changes in the current version include (1) removal
        of dependencies on several external libraries (e.g., gpclib,
        maptools, sp), (2) the separation of plotting and estimation
        capabilities, (3) reduction in the number of functions, and (4)
        expansion of analytical capabilities with additional functions
        for descriptive analysis and visualization (e.g., standard
        deviation box, centre of minimum distance, central feature).
This package allows to make inference on the properties of causal genetic
             variants in linkage disequilibrium with genotyped markers. In a first step, 
			 we select a subset of variants using a score statistic for affected 
			 sib-pairs. In a second step, on the selected subset, we make 
             inference on causal genetic variants in the considered region.
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.
Lots of predicates (is_* functions) to check the state of your
    variables, and assertions (assert_* functions) to throw errors if they
    aren't in the right form.
A minimal set of predicates and assertions used by the assertive
    package.  This is mainly for use by other package developers who want to
    include run-time testing features in their own packages.  End-users will
    usually want to use assertive directly.
A set of predicates and assertions for checking the properties of
    code. This is mainly for use by other package developers who want to include
    run-time testing features in their own packages. End-users will usually want to
    use assertive directly.
A set of predicates and assertions for checking the properties of
    (country independent) complex data types.  This is mainly for use by other
    package developers who want to include run-time testing features in
    their own packages.  End-users will usually want to use assertive directly.
A set of predicates and assertions for checking the properties of
    UK-specific complex data types.  This is mainly for use by other package
    developers who want to include run-time testing features in their own
    packages.  End-users will usually want to use assertive directly.
A set of predicates and assertions for checking the properties of
    US-specific complex data types.  This is mainly for use by other package
    developers who want to include run-time testing features in their own
    packages.  End-users will usually want to use assertive directly.
A set of predicates and assertions for checking the properties of
    dates and times.  This is mainly for use by other package developers who
    want to include run-time testing features in their own packages.  End-users
    will usually want to use assertive directly.
A set of predicates and assertions for checking the properties of
    files and connections.  This is mainly for use by other package developers
    who want to include run-time testing features in their own packages.
    End-users will usually want to use assertive directly.
A set of predicates and assertions for checking the properties of
    matrices.  This is mainly for use by other package developers who want to
    include run-time testing features in their own packages.  End-users will
    usually want to use assertive directly.
A set of predicates and assertions for checking the properties of
    models.  This is mainly for use by other package developers who want to
    include run-time testing features in their own packages.  End-users will
    usually want to use assertive directly.
A set of predicates and assertions for checking the properties of
    numbers.  This is mainly for use by other package developers who want to
    include run-time testing features in their own packages.  End-users will
    usually want to use assertive directly.
A set of predicates and assertions for checking the properties of
    variables, such as length, names and attributes.  This is mainly for use by
    other package developers who want to include run-time testing features in
    their own packages.  End-users will usually want to use assertive directly.
A set of predicates and assertions for checking the state and
    capabilities of R, the operating system it is running on, and the IDE
    being used.  This is mainly for use by other package developers who
    want to include run-time testing features in their own packages.
    End-users will usually want to use assertive directly.
A set of predicates and assertions for checking the properties of
    sets.  This is mainly for use by other package developers who want to
    include run-time testing features in their own packages.  End-users will
    usually want to use assertive directly.
A set of predicates and assertions for checking the properties of
    strings.  This is mainly for use by other package developers who want to
    include run-time testing features in their own packages.  End-users will
    usually want to use assertive directly.
A set of predicates and assertions for checking the types of
    variables.  This is mainly for use by other package developers who want to
    include run-time testing features in their own packages.  End-users will
    usually want to use assertive directly.
Provides functionality to assert conditions
    that have to be met so that errors in data used in
    analysis pipelines can fail quickly. Similar to
    'stopifnot()' but more powerful, friendly, and easier
    for use in pipelines.
assertthat is an extension to stopifnot() that makes it
    easy to declare the pre and post conditions that you code should
    satisfy, while also producing friendly error messages so that your
    users know what they've done wrong.
Calculates the optimal price of assets (such as
	airline flight seats, hotel room bookings) whose value
	becomes zero after a fixed ``expiry date''.  Assumes
	potential customers arrive (possibly in groups) according
	to a known inhomogeneous Poisson process.  Also assumes a
	known time-varying elasticity of demand (price sensitivity)
	function.  Uses elementary techniques based on ordinary
	differential equations.  Uses the package deSolve to effect
	the solution of these differential equations.
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.
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-Wisdom 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
	exact test), MAX3 (a procedure for testing for the association
	between a single nucleotide polymorphism and a binary phenotype
	using the maximum value of the three test statistics derived for
	the recessive, additive, and dominant models), nonparametric trend
	test (a procedure for testing for the association between a genetic
	variant and a non-normal distributed quantitative trait based on the
	nonparametric risk), and nonparametric MAX3 (a procedure for testing
	for the association between a biallelic single nucleotide polymorphism
	and a quantitative trait using the maximum value of the three
	nonparametric trend tests derived for the recessive, additive, and
	dominant models), which are commonly used in genetic association studies.
Functions to calculate the assortment of vertices in social networks.  This can be measured on both weighted and binary networks, with discrete or continuous vertex values.
R package with statistical tests and methods for genetic
    association studies with emphasis on rare variants and binary (dichotomous)
    traits
Aster models are exponential family regression models for life
    history analysis.  They are like generalized linear models except that
    elements of the response vector can have different families (e. g.,
    some Bernoulli, some Poisson, some zero-truncated Poisson, some normal)
    and can be dependent, the dependence indicated by a graphical structure.
    Discrete time survival analysis, zero-inflated Poisson regression, and
    generalized linear models that are exponential family (e. g., logistic
    regression and Poisson regression with log link) are special cases.
    Main use is for data in which there is survival over discrete time periods
    and there is additional data about what happens conditional on survival
    (e. g., number of offspring).  Uses the exponential family canonical
    parameterization (aster transform of usual parameterization).
Aster models are exponential family regression models for life
    history analysis.  They are like generalized linear models except that
    elements of the response vector can have different families (e. g.,
    some Bernoulli, some Poisson, some zero-truncated Poisson, some normal)
    and can be dependent, the dependence indicated by a graphical structure.
    Discrete time survival analysis, zero-inflated Poisson regression, and
    generalized linear models that are exponential family (e. g., logistic
    regression and Poisson regression with log link) are special cases.
    Main use is for data in which there is survival over discrete time periods
    and there is additional data about what happens conditional on survival
    (e. g., number of offspring).  Uses the exponential family canonical
    parameterization (aster transform of usual parameterization).
    Unlike the aster package, this package does dependence groups (nodes of
    the graph need not be conditionally independent given their predecessor
    node), including multinomial and two-parameter normal as families.  Thus
    this package also generalizes mark-capture-recapture analysis.
The astro package provides a series of functions, tools and routines in everyday use within astronomy. Broadly speaking, one may group these functions into 7 main areas, namely: cosmology, FITS file manipulation, the Sersic function, plotting, data manipulation, statistics and general convenience functions and scripting tools.
Routines for astrochronologic testing, astronomical time scale construction, and time series analysis. Also included are a range of statistical analysis and modeling routines that are relevant to time scale development and paleoclimate analysis.
A collection of 19 datasets from contemporary astronomical research.  They are described the textbook `Modern Statistical Methods for Astronomy with R Applications' by Eric D. Feigelson and G. Jogesh Babu (Cambridge University Press, 2012, Appendix C) or on the website of Penn State's Center for Astrostatistics (http://astrostatistics.psu.edu/datasets).  These datasets can be used to exercise methodology involving: density estimation; heteroscedastic measurement errors; contingency tables; two-sample hypothesis tests; spatial point processes; nonlinear regression; mixture models; censoring and truncation; multivariate analysis; classification and clustering; inhomogeneous Poisson processes; periodic and stochastic time series analysis.
Miscellaneous astronomy functions, utilities, and data.
Several dozen low-level utilities and codes from the Interactive Data Language (IDL) Astronomy Users Library (http://idlastro.gsfc.nasa.gov) are implemented in R.  They treat: time, coordinate and proper motion transformations; terrestrial precession and nutation, atmospheric refraction and aberration, barycentric corrections, and related effects; utilities for astrometry, photometry, and spectroscopy; and utilities for planetary, stellar, Galactic, and extragalactic science.
Contains data sets and scripts to accompany Time Series Analysis and Its Applications: With R Examples by Shumway and Stoffer, fourth edition.
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.
Models and methods for the visualization for asymmetric data.
Estimation of regression models for sparse asynchronous longitudinal observations, where time-dependent response and covariates are mismatched and observed intermittently within subjects. Kernel weighted estimating equations are used for generalized linear models with either time-invariant or time-dependent coefficients.
A set of routines written in the S language
             that calculate power and related quantities utilizing asymptotic
             likelihood ratio methods.
Nonparametric estimation and inference for average treatment effects based on covariate balancing.
A collection of statistical simulation and computation tools with a GTK GUI, to help teach statistical concepts and compute probabilities. Two domains are covered: I. Understanding (Central-Limit Theorem and the Normal Distribution, Distribution of a sample mean, Distribution of a sample variance, Probability calculator for common distributions), and II. Elementary Bayesian Statistics (bayesian inference on proportions, contingency tables, means and variances, with informative and noninformative priors).
Uses adaptive diagnostics to tune and run a random walk Metropolis MCMC algorithm, to converge to a specified target distribution and estimate means of functionals.
This package provides functions for smart sampling and sensitivity analysis for metrology applications, including computationally expensive problems.
Calculates acoustic traveltimes and ray paths in 1-D,
        linear atmospheres.  Later versions will support arbitrary 1-D
        atmospheric models, such as radiosonde measurements and
        standard reference atmospheres.
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.
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.
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.
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
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.
An interface to the 'AutoDesk' 'APIs' 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.
Functions for displaying (multiple) images with scaled colors, possibly with projected coordinates and for an irregular grid.
This package performs an automatic interpolation by automatically estimating the variogram and then calling gstat.
A set of functions that automates the process and produces reasonable output for hierarchical multiple regression models.  It allows you to specify predictor blocks, from which it generates all of the linear models, and checks the assumptions of the model, producing the requisite plots and statistics to allow you to judge the suitability of the model.
Contains functions for autologistic variable selection and parameter estimation for spatially correlated categorical data (including k>2). The main function is MPLE. Capable of fitting the centered autologistic model described in Caragea and Kaiser (2009)<doi:10.1198/jabes.2009.07032>, as well as the traditional autologistic model of Besag (1974)<http://www.jstor.org/stable/2984812>.
Some convenience functions for pls regression, including backward 
        variable selection and validation procedures, image based predictions
		and plotting.
Consider autoregressive model of order p where the distribution function of innovation is unknown, but innovations are independent and symmetrically distributed. The package contains a function named ARMDE which takes X (vector of n observations) and p (order of the model) as input argument and returns minimum distance estimator of the parameters in the model.
General-to-Specific (GETS) modelling of the mean and variance of a regression. NOTE: The package has been succeeded by gets, also available on the CRAN, which is more user-friendly, faster and easier to extend. Users are therefore encouraged to consider gets instead.
Implements multiple heuristic search algorithms for
    automatically creating structural equation models.
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 day 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 day dummies improves the model fit.
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.
Collection of R-functions implementing the
    Propagation-Separation Approach to adaptive smoothing as
    described in "J. Polzehl and V. Spokoiny (2006)
    <DOI:10.1007/s00440-005-0464-1>"
    and "J. Polzehl and V. Spokoiny (2004) <DOI:10.20347/WIAS.PREPRINT.998>".
Use the Amazon Alexa Web Information Services API to 
    find information about domains, including the kind of content 
    that they carry, how popular are they---rank and traffic history, 
    sites linking to them, among other things. See <https://aws.amazon.com/awis/> 
    for more information.
Defines the method extract and provides 'openMP' support as needed in several packages.
Generates request signatures for Amazon Web Services (AWS) APIs.
A generalization of Fisher's exact test that allows for
        structural zeros.
Functions and datasets to support Azure Machine Learning. This
    allows you to interact with datasets, as well as publish and consume R functions
    as API services.
This package fits the Bayesian two-Zone Models.
This package contains b6e6rl algorithm, adaptive
        differential evolution for global optimization.
Babar is designed to use nested sampling (a Bayesian analysis technique) to compare possible models for bacterial growth curves, as well as extracting parameters. It allows model evidence and parameter likelihood values to be extracted, and also contains helper functions for comparing distributions as well as direct access to the underlying nested sampling code.
Included here are babel routines for identifying unusual ribosome protected fragment counts given mRNA counts.
Included are two variants of Bayesian Bootstrap
        Predictive Mean Matching to multiply impute missing data. The
        first variant is a variable-by-variable imputation combining
        sequential regression and Predictive Mean Matching (PMM) that
        has been extended for unordered categorical data. The Bayesian
        Bootstrap allows for generating approximately proper multiple
        imputations. The second variant is also based on PMM, but the
        focus is on imputing several variables at the same time. The
        suggestion is to use this variant, if the missing-data pattern
        resembles a data fusion situation, or any other
        missing-by-design pattern, where several variables have
        identical missing-data patterns. Both variants can be run as
        'single imputation' versions, in case the analysis objective is
        of a purely descriptive nature.
US baby names provided by the SSA. This package contains all
    names used for at least 5 children of either sex.
R-based graphical tool to concisely visualise and compare biological annotations queried from the DAVID web service. It provides R functions to perform enrichment analysis (via DAVID - http://david.abcc.ncifcrf.gov) on several gene lists at once, and then visualizing all the results in one generated figure that allows R users to compare the annotations found for each list.
Can be used for simulation of organisms living in
    communities. Each organism is represented individually and genome scale
    metabolic models determine the uptake and release of compounds. Biological
    processes such as movement, diffusion, chemotaxis and kinetics are available
    along with data analysis techniques.
The BACCO bundle of packages is replaced by the BACCO
 package, which provides a vignette that illustrates the constituent
 packages (emulator, approximator, calibrator) in use.
Implements the Bayesian Augmented Control (BAC, a.k.a. Bayesian historical data borrowing) method under clinical trial setting by calling 'Just Another Gibbs Sampler' ('JAGS') software. In addition, the 'BACCT' package evaluates user-specified decision rules by computing the type-I error/power, or probability of correct go/no-go decision at interim look. The evaluation can be presented numerically or graphically. Users need to have 'JAGS' 4.0.0 or newer installed due to a compatibility issue with 'rjags' package. Currently, the package implements the BAC method for binary outcome only. Support for continuous and survival endpoints will be added in future releases. We would like to thank AbbVie's Statistical Innovation group and Clinical Statistics group for their support in developing the 'BACCT' package.
Provides convenience functions for the Backblaze B2 cloud storage
    API (see https://www.backblaze.com/b2/docs/). All B2 API calls are mapped
    to equivalent R functions. Files can be easily uploaded, downloaded and
    deleted from B2, all from within R programs.
Provides a backward-pipe operator for 'magrittr' (%<%) or 
    'pipeR' (%<<%) that allows for a performing operations from right-to-left. 
    This indispensable for writing clear code where there is natural 
    right-to-left ordering common with nested structures 
    and hierarchies such as trees/directories or markup languages such as HTML 
    and XML.
Implementations of functions which have been introduced in
    R since version 3.0.0. The backports are conditionally exported which
    results in R resolving the function names to the version shipped with R (if
    available) and uses the implemented backports as fallback. This way package
    developers can make use of the new functions without worrying about the
    minimum required R version.
Code for 'backShift', an algorithm to estimate the connectivity 
  matrix of a directed (possibly cyclic) graph with hidden variables. The 
  underlying system is required to be linear and we assume that observations 
  under different shift interventions are available. For more details, 
  see <http://arxiv.org/abs/1506.02494>.
The backtest package provides facilities for exploring
        portfolio-based conjectures about financial instruments
        (stocks, bonds, swaps, options, et cetera).
Creates an interactive graphics 
             interface to visualize backtest results of different financial 
             instruments, such as equities, futures, and credit default swaps.
             The package does not run backtests on the given data set but 
             displays a graphical explanation of the backtest results. Users can
             look at backtest graphics for different instruments, investment 
             strategies, and portfolios. Summary statistics of different 
             portfolio holdings are shown in the left panel, and interactive 
             plots of profit and loss (P\&L), net market value (NMV) and 
             gross market value (GMV) are displayed in the right panel.
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.
Implements sample size calculations following the approach
        described in "Bayesian Average Error Based Approach to
        Hypothesis Testing and Sample Size Determination."
This is the companion package of a PhD thesis entitled "Bases Giving Distances. A new paradigm for investigating functional data with applications for spectroscopy" by Timmermans (2012). See references for details and related publications. The core of the BAGIDIS methodology is a functional wavelet based semi-distance that has been introduced by Timmermans and von Sachs (2010, 2015) and Timmermans, Delsol and von Sachs (2013). This semi-distance allows for comparing curves with sharp local patterns that might not be well aligned from one curve to another. It is data-driven and highly adaptive to the curves being studied.  Its main originality is its ability to consider simultaneously horizontal and vertical variations of patterns, which proofs highly useful when used together with clustering algorithms or visualization method. BAGIDIS is an acronym for BAsis GIving DIStances. The extension of BAGIDIS to image data relies on the same principles and has been described in Timmermans and Fryzlewicz (2012), Fryzlewicz and Timmermans (2015).
bagRboostR is a set of ensemble classifiers for multinomial
    classification. The bagging function is the implementation of Breiman's
    ensemble as described by Opitz & Maclin (1999). The boosting function is
    the implementation of Stagewise Additive Modeling using a Multi-class
    Exponential loss function (SAMME) created by Zhu et al (2006). Both bagging
    and SAMME implementations use randomForest as the weak classifier and
    expect a character outcome variable.  Each ensemble classifier returns a
    character vector of predictions for the test set.
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.
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).
Functions for Bayesian meta-analysis of diagnostic test data which
    are based on a scale mixtures bivariate random-effects model.
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.
Provides a full package of posterior inference, model comparison, and graphical illustration of model fitting. A parallel computing algorithm for the Markov chain Monte Carlo (MCMC) based posterior inference and an Expectation-Maximization (EM) based algorithm for posterior approximation are are developed, both of which greatly reduce the computational time for model inference.
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 sciences and related fields. The package includes: Hierarchical Bayes ANOVA models with normal response, t response, Binomial(Bernoulli) response, Poisson response, ordered multinomial response and multinomial response variables. All models accommodate unobserved heterogeneity by including a normal distribution of the parameters across individuals. Outputs of the package include tables of sums of squares, effect sizes and p-values, and tables of means, which are easily interpretable for behavioral researchers. BANOVA uses JAGS as the computational platform.
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.
This package includes the function \code{barcode()}, which
        produces a histogram-like plot of a distribution that shows
        granularity in the data.
To perform species identification using DNA barcodes.
Barnard's unconditional test for 2x2 contingency tables.
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.
Computation of a Wasserstein Barycenter. The package implements a method described in Cuturi (2014) "Fast Computation of Wasserstein Barycenters". The paper is available at <http://jmlr.org/proceedings/papers/v32/cuturi14.pdf>. To speed up the computation time the main iteration step is based on 'RcppArmadillo'.
Package for Bayesian Model Averaging in linear models and
    generalized linear models using stochastic or
    deterministic sampling without replacement from posterior
    distributions.  Prior distributions on coefficients are
    from Zellner's g-prior or mixtures of g-priors
    corresponding to the Zellner-Siow Cauchy Priors or the
    mixture of g-priors from Liang et al (2008)
    <DOI:10.1198/016214507000001337>
    for linear models or mixtures of g-priors in GLMs of Li and Clyde (2015)
    <http://arxiv.org/abs/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.
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.
Some very simple infrastructure for basis functions.
Collection of baseline correction algorithms, along with a framework and a GUI for optimising baseline algorithm parameters. Typical use of the package is for removing background effects from spectra originating from various types of spectroscopy and spectrometry, possibly optimizing this with regard to regression or classification results. Correction methods include polynomial fitting, weighted local smoothers and many more.
Conducts Aldrich-McKelvey and Blackbox Scaling to Recover
        Latent Dimensions of Judgment.
BASIX provides some efficient C/C++ implementations to speed up calculations in R.
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.
Provides consistent batch means estimation of Monte
    Carlo standard errors.
As a successor of the packages 'BatchJobs' and 'BatchExperiments',
    this package provides a parallel implementation of the Map function for high
    performance computing systems managed by schedulers 'IBM Spectrum LSF'
    (<http://www-03.ibm.com/systems/spectrum-computing/products/lsf/>),
    'OpenLava' (<http://www.openlava.org/>), 'Univia 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.
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.
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.
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 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).
Provides graphical user interface for the packages 'bayesTFR', 'bayesLife' and 'bayesPop'.
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.
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.
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.
A 'Shiny' web application for creating interactive Bayesian Network models,
    learning the structure and parameters of Bayesian networks, and utilities for classical
    network analysis.
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. Latent labels are sampled using chequerboard updating or Swendsen-Wang.
    Algorithms for the smoothing parameter include pseudolikelihood, path sampling,
    the exchange algorithm, and approximate Bayesian computation (ABC).
Bayesian Latent Class Analysis using several different
        methods.
Making probabilistic projections of life expectancy for all countries of the world, using a Bayesian hierarchical model.
The BayesLogit package does posterior simulation for binomial and
 multinomial logistic regression using the Polya-Gamma latent variable
 technique. This method is fully automatic, exact, and fast.  A routine to
 efficiently sample from the Polya-Gamma class of distributions is included.
Covers many important models used
  in marketing and micro-econometrics applications. 
  The package includes:
  Bayes Regression (univariate or multivariate dep var),
  Bayes Seemingly Unrelated Regression (SUR),
  Binary and Ordinal Probit,
  Multinomial Logit (MNL) and Multinomial Probit (MNP),
  Multivariate Probit,
  Negative Binomial (Poisson) Regression,
  Multivariate Mixtures of Normals (including clustering),
  Dirichlet Process Prior Density Estimation with normal base,
  Hierarchical Linear Models with normal prior and covariates,
  Hierarchical Linear Models with a mixture of normals prior and covariates,
  Hierarchical Multinomial Logits with a mixture of normals prior
     and covariates,
  Hierarchical Multinomial Logits with a Dirichlet Process prior and covariates,
  Hierarchical Negative Binomial Regression Models,
  Bayesian analysis of choice-based conjoint data,
  Bayesian treatment of linear instrumental variables models,
  Analysis of Multivariate Ordinal survey data with scale
     usage heterogeneity (as in Rossi et al, JASA (01)),
  Bayesian Analysis of Aggregate Random Coefficient Logit Models as in BLP (see
  Jiang, Manchanda, Rossi 2009)
  For further reference, consult our book, Bayesian Statistics and
  Marketing by Rossi, Allenby and McCulloch (Wiley 2005) and Bayesian Non- and Semi-Parametric
  Methods and Applications (Princeton U Press 2014).
Calculating Bayesian sample sizes for multi-arm trials where several experimental treatments are compared to a common control, perhaps even at multiple stages.
This package provides various Markov Chain Monte Carlo
        (MCMC) sampler for model-based clustering of discrete-valued
        time series obtained by observing a categorical variable with
        several states (in a Bayesian approach).  In order to analyze
        group membership, we provide also an extension to the
        approaches by formulating a probabilistic model for the latent
        group indicators within the Bayesian classification rule using
        a multinomial logit model.
Default Bayesian hypothesis tests for correlation, partial correlation, and mediation
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, 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.
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. 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), Benoit & Van den Poel (2012) and Al-Hamzawi, Yu & Benoit (2012). To speed up the calculations, the Markov Chain Monte Carlo core of all algorithms is programmed in Fortran and called from R.
Fits linear or logistic regression model using Bayesian continuous
    shrinkage prior distributions. Handles ridge, lasso, horseshoe and horseshoe+
    regression with logistic, Gaussian, Laplace or Student-t distributed targets.
bayess contains a collection of functions that allows the
        reenactment of the R programs used in the book "Bayesian
        Essentials with R" (revision of "Bayesian Core") without
        further programming. R code being available as well, they can
        be modified by the user to conduct one's own simulations.
This package provides a variety of functions to deal with several specific small area area-
  level models in Bayesian context. Models provided range from the basic Fay-Herriot model to 
  its improvement such as You-Chapman models, unmatched models, spatial models and so on. 
  Different types of priors for specific parameters could be chosen to obtain MCMC posterior 
  draws. The main sampling function is written in C with GSL lab so as to facilitate the 
  computation. Model internal checking and model comparison criteria are also involved.
The BayesSingleSub package is a suite of functions for computing various Bayes factors for interrupted time-series, based on the models described in de Vries and Morey (2013).
Methods for generating Markov Chain Monte Carlo (MCMC) posterior samples of Bayesian linear regression model parameters that require only summary statistics of data as input. Summary statistics are useful for systems with very limited amounts of physical memory. The package provides two functions:  one function that computes summary statistics of data and one function that carries out the MCMC posterior sampling for Bayesian linear regression models where summary statistics are used as input. The function read.regress.data.ff utilizes the R package 'ff' to handle data sets that are too large to fit into a user's physical memory, by reading in data in chunks.
Contains Bayesian implementations of Mixed-Effects Accelerated Failure Time (MEAFT) models
             for censored data. Those can be not only right-censored but also interval-censored,
	     doubly-interval-censored or misclassified interval-censored.
Making probabilistic projections of total fertility rate for all countries of the world, using a Bayesian hierarchical model.
This package fits a linear mixed model for ordinal
        categorical responses using Bayesian inference via Monte Carlo
        Markov Chains. Default is Nandran & Chen algorithm using
        Gaussian link function and saving just the summaries of the
        chains.  Among the options, package allow for two other options
        of algorithms, for using Student's "t" link function and for
        saving the full chains.
This is an implementation of BART:Bayesian Additive Regression Trees,
        by Chipman, George, McCulloch (2010).
Provides a way to simulate from the prior distribution of Bayesian trees by Chipman et al. (1998) <DOI:10.2307/2669832>. The prior distribution of Bayesian trees is highly dependent on the design matrix X, therefore using the suggested hyperparameters by Chipman et al. (1998) <DOI:10.2307/2669832> is not recommended and could lead to unexpected prior distribution. This work is part of my master thesis (expected 2016).
BayesValidate implements the software validation method
        described in the paper "Validation of Software for Bayesian
        Models using Posterior Quantiles" (Cook, Gelman, and Rubin,
        2005).  It inputs a function to perform Bayesian inference as
        well as functions to generate data from the Bayesian model
        being fit, and repeatedly generates and analyzes data to check
        that the Bayesian inference program works properly.
Conceived to calculate Bayes factors in linear models and then to provide a formal Bayesian answer to testing and variable selection problems. From a theoretical side, the emphasis in this package is placed on the prior distributions and it allows a wide range of them: Jeffreys (1961); Zellner and Siow(1980)<DOI:10.1007/bf02888369>; Zellner and Siow(1984); Zellner (1986)<DOI:10.2307/2233941>; Fernandez et al. (2001)<DOI:10.1016/s0304-4076(00)00076-2>; Liang et al. (2008)<DOI:10.1198/016214507000001337>  and Bayarri et al. (2012)<DOI:10.1214/12-aos1013>. The interaction with the package is through a friendly interface that syntactically mimics the well-known lm() command of R. The resulting objects can be easily explored providing the user very valuable information (like marginal, joint and conditional inclusion probabilities of potential variables; the highest posterior probability model, HPM; the median probability model, MPM) about the structure of the true -data generating- model. Additionally, this package incorporates abilities to handle problems with a large number of potential explanatory variables through parallel and heuristic versions of the main commands, Garcia-Donato and Martinez-Beneito (2013)<DOI:10.1080/01621459.2012.742443>.
This package provides functionality for exploring and visualising estimation results
	     obtained with the software package BayesX for structured additive regression. It also provides
	     functions that allow to read, write and manipulate map objects that are required in spatial analyses
	     performed with BayesX, a free software for estimating structured additive regression models 
       (http://www.bayesx.org).
BayesX performs Bayesian inference in structured additive regression (STAR) models.
	The R package BayesXsrc provides the BayesX command line tool for easy installation.
	A convenient R interface is provided in package R2BayesX.
The package BayHap performs simultaneous estimation of
        uncertain haplotype frequencies and association with haplotypes
        based on generalized linear models for quantitative, binary and
        survival traits. Bayesian statistics and Markov Chain Monte
        Carlo techniques are the theoretical framework for the methods
        of estimation included in this package. Prior values for model
        parameters can be included by the user. Convergence diagnostics
        and statistical and graphical analysis of the sampling output
        can be also carried out.
A suite of R functions for Bayesian estimation of smooth
        hazard rates via Compound Poisson Process (CPP) and Bayesian
        Penalized Spline (BPS) priors.
Functions and data used for Baylor University Educational
        Psychology Quantitative Courses
Tools for fitting and simulating multi-optima Ornstein-Uhlenbeck
    models to phylogenetic comparative data using Bayesian reversible-jump
    methods.
This R package is the software implementation of the
        algorithm BaySIC, a Bayesian approach toward analysis of
        significantly mutated genes in cancer data.
The manuscript introduces the BAYSTAR package, which
        provides the functionality for Bayesian estimation in
        autoregressive threshold models.
Barzilai-Borwein spectral methods for solving nonlinear
        system of equations, and for optimizing nonlinear objective
        functions subject to simple constraints. A tutorial style
        introduction to this package is available in a vignette on the
        CRAN download page or, when the package is loaded in an R
        session, with vignette("BB").
Estimating optimal bandwidths for the regression mean function approximated by the functional Nadaraya-Watson estimator and the error density approximated by a kernel density of residuals simultaneously in a scalar-on-function regression. As a by-product of Markov chain Monte Carlo, the optimal choice of semi-metric is selected based on largest marginal likelihood.
Bayesian bandwidth estimation for Nadaraya-Watson type
        multivariate kernel regression with Gaussian error density
We implemented a Bayesian-statistics approach for 
        subtraction of incoherent scattering from neutron total-scattering data. 
        In this approach, the estimated background signal associated with 
        incoherent scattering maximizes the posterior probability, which combines 
        the likelihood of this signal in reciprocal and real spaces with the prior 
        that favors smooth lines.
Miscellaneous helper functions for and from B. Bischl and
    some other guys at TU Dortmund, mainly for package development.
Methods and functions for fitting maximum likelihood models in R.
    This package modifies and extends the 'mle' classes in the 'stats4' package.
The model provides an empirical estimate of a movement
        path using discrete location data obtained at relatively short
        time intervals.
This package provides an R implementation of
        Biogeography-Based Optimization (BBO), originally invented by
        Prof. Dan Simon, Cleveland State University, Ohio. This method
        is an application of the concept of biogeography, a study of
        the geographical distribution of biological organisms, to
        optimization problems. More information about this method can
        be found here: http://academic.csuohio.edu/simond/bbo/.
Model fitting of flexible behavioural recapture models based on conditional probability reparameterization and meaningful partial capture history quantification also referred to as meaningful behavioural covariate
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).
Underlying support functions for RcmdrPlugin.BCA and a
        companion to the book Customer and Business Analytics: Applied
        Data Mining for Business Decision Making Using R by Daniel S.
        Putler and Robert E. Krider
Fully Bayesian Classification with a subset of high-dimensional features, such as expression levels of genes. The data are modeled with a hierarchical Bayesian models using heavy-tailed t distributions as priors. When a large number of features are available, one may like to select only a subset of features to use, typically those features strongly correlated with the response in training cases. Such a feature selection procedure is however invalid since the relationship between the response and the features has be exaggerated by feature selection. This package provides a way to avoid this bias and yield better-calibrated predictions for future cases when one uses F-statistic to select features.
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.
B Cell Receptor Somatik Hyper Mutation Analysis provide analysis
    of nucleotide frequency, transition, transversion, reconstruction of germline
    sequences based on IMGT outputs, calculate and plot the percentage difference
    at 6 positions to a nucleotide mutation to identify the hotspot motifs and
    calculate and plot average mutation frequency of nucleotide mutations resulting
    in amino acid substitution.
Implements a full Bayesian cost-effectiveness analysis in the case where the cost variable is characterised by structural zeros. The package implements the Gamma, log-Normal and Normal models for the cost variable and the Gamma, Beta, Bernoulli and Normal models for the measure of clinical effectiveness.
Enables quick calibration of radiocarbon dates under various calibration curves (including user generated ones); Age-depth modelling as per the algorithm of Haslett and Parnell (2008); Relative sea level rate estimation incorporating time uncertainty in polynomial regression models; and non-parametric phase modelling via Gaussian mixtures as a means to determine the activity of a site (and as an alternative to the Oxcal function SUM). The package includes a vignette which can be accessed via the vignette.
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, A. C., 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.
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.
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.
An R interface to the OpenBSD 'blowfish' password hashing algorithm,
    as described in "A Future-Adaptable Password Scheme" by Niels Provos. The
    implementation is derived from the 'py-bcrypt' module for Python which is a
    wrapper for 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 (or pre-binned) 
        data.
A collection of S4 classes which implements different methods to estimate and deal with densities in bounded domains. That is, densities defined within the interval [lower.limit, upper.limit], where lower.limit and upper.limit are values that can be set by the user.
Provides statistical tools for Bayesian structure learning in undirected graphical models with both continuous and discrete variables.
Analyze differences among time series curves with p-value adjustment for multiple comparisons introduced in Oleson et al (2015) <DOI:10.1177/0962280215607411>.
Optimisation of the expected utility in single-stage and multi-stage Bayesian decision problems. The expected utility is estimated by simulation. For single-stage problems, JAGS is used to draw MCMC samples.
Computation of asymptotic confidence intervals for negative and positive predictive values in binary diagnostic tests in case-control studies. Experimental design for hypothesis tests on predictive values.
Beta drift poses a serious challenge to asset managers   
    and financial researchers. Beta drift causes problems in asset 
    pricing models and can have serious ramifications for hedging 
    attempts. Providing users with a tool that allows them to 
    quantify beta drift and form educated opinions about it is 
    the primary purpose of this package.
    This package contains the BDA() function that performs a beta 
    drift analysis, typically for multi-factor asset pricing models. 
    The BDA() function tests the underlying model parameters for 
    drift across time, drift across model horizon, and applies a 
    jackknife procedure to the baseline model. This allows the users 
    to draw conclusions about the stability of model parameters or 
    make inferences about the behavior of funds. For example, the 
    drift of parameters for active funds could be interpreted as 
    implicit style drift or, in the case of passive funds, management's 
    inability to track a benchmark completely.
Provides a continuous date scale, omitting weekends and holidays.
This is a special case of sparse matrices, used by coxme
Provides a set of functions to create basic visualizations to quickly
    preview different aspects of biodiversity information such as inventory 
    completeness, extent of coverage (taxonomic, temporal and geographic), gaps
    and biases.
A Bayesian regression model for discrete response, where the conditional distribution is modelled via a discrete Weibull distribution. This package provides an implementation of Metropolis-Hastings and Reversible-Jumps algorithms to draw samples from the posterior. It covers a wide range of regularizations through any two parameter prior. Examples are Laplace (Lasso), Gaussian (ridge), Uniform, Cauchy and customized priors like a mixture of priors. An extensive visual toolbox is included to check the validity of the results as well as several measures of goodness-of-fit.
The package bdynsys for panel/longitudinal data combines methods to model 
             changes in up to four indicators over times as a function of the indicators
             themselves and up to three predictors using ordinary differential equations 
             (ODEs) with polynomial terms that allow to model complex and nonlinear 
             effects. A Bayesian model selection approach is implemented. The package 
             provides also visualisation tools to plot phase portraits of the dynamic 
             system, showing the complex co-evolution of two indicators over time with the
             possibility to highlight trajectories for specified entities (e.g. countries, 
             individuals). Furthermore the visualisation tools allow for making 
             predictions of the trajectories of specified entities with respect to the 
             indicators.
A platform is provided for interactive analyses with a goal of totally easy to develop, deploy, interact, and explore (TEDDIE). Using this package, users can create customized analyses and make them available to end users who can perform interactive analyses and save analyses to RTF or HTML files. It allows developers to focus on R code for analysis, instead of dealing with html or shiny code.
This package contains functions to fit the filtering model
        of Forcheh et al., (2012) which is used to derive the
        intra-cluster correlation (ICC). Model fitting is done using
        the modified version of the ``MLM.beadarray" function of Kim
        and Lin (2011).
Imports bead-summary data from Illumina scanner.
        Pre-processes using a suite of optional normalizations and
        transformations. Clusters and automatically calls genotypes,
        critically able to handle markers in duplicated regions of the
        genome (multisite variants; MSVs). Interactive clustering if
        needed. MSVs with variation in both paralogs may be resolved
        and mapped to their respective chromosomes. Quality control
        including pedigree checking and visual assessment of clusters.
        Too large data-sets are handled by working on smaller subsets
        of the data in sequence.
Plots univariate comparison graphs, an alternative to
        boxplot/stripchart/violin plot.
Computing age specific nest survival rates based on a Bayesian hierarchical model. This package can handle unknown nest age, irregular visiting schedule and nest-specific covariates.
It is vital to assess the heterogeneity of treatment effects
    (HTE) when making health care decisions for an individual patient or a group
    of patients. Nevertheless, it remains challenging to evaluate HTE based
    on information collected from clinical studies that are often designed and
    conducted to evaluate the efficacy of a treatment for the overall population.
    The Bayesian framework offers a principled and flexible approach to estimate
    and compare treatment effects across subgroups of patients defined by their
    characteristics. This package allows users to explore a wide range of Bayesian
    HTE analysis models, and produce posterior inferences about HTE.
provides 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 (http://pngu.mgh.harvard.edu/~purcell/plink/), the whole
    genome association analysis toolset, without loading the entire file into
    memory.
Genomic regions processing using open-source command line tools such as BEDtools, BEDOPS and Tabix. 
    These tools offer scalable and efficient utilities to perform genome arithmetic e.g indexing, formatting and merging.
    bedr API enhances access to these tools as well as offers additional utilities for genomic regions processing.
The sole function of this package is beep(), with the purpose to
    make it easy to play notification sounds on whatever platform you are on.
    It is intended to be useful, for example, if you are running a long analysis
    in the background and want to know when it is ready.
The bee swarm plot is a one-dimensional scatter plot like "stripchart", but with closely-packed, non-overlapping points.
Tools for downloading historical financial data from the www.belex.rs.
Full implementation of the 28 distributions introduced as
        benchmarks for nonparametric density estimation by Berlinet and
        Devroye (1994). Includes densities, cdfs, quantile functions
        and generators for samples as well as additional information on
        features of the densities. Also contains the 4 histogram
        densities used in Rozenholc/Mildenberger/Gather (2010).
The benchmark package provides a toolbox for setup, execution
    and analysis of benchmark experiments. Main focus is the analysis of
    data accumulating during the execution -- one primary objective is the
    statistical correct computation of the candidate algorithms' order.
Methods for frontier
	analysis, Data Envelopment Analysis (DEA), under different
	technology assumptions (fdh, vrs, drs, crs, irs, add/frh, and fdh+),
	and using different efficiency measures (input based, output based,
	hyperbolic graph, additive, super, and directional efficiency). Peers
	and slacks are available, partial price information can be included,
	and optimal cost, revenue and profit can be calculated. Evaluation of
	mergers is also supported.  Methods for graphing the technology sets
	are also included. There is also support comparative methods based
	on Stochastic Frontier Analyses (SFA). In general, the methods can be
	used to solve not only standard models, but also many other model
	variants. It complements the book, Bogetoft and Otto,
	Benchmarking with DEA, SFA, and R, Springer-Verlag, 2011, but can of
	course also be used as a stand-alone package.
Benchmark your CPU and compare against other CPUs. Also provides 
    functions for obtaining system specifications, such as
    RAM, CPU type, and R version.
Crowd sourced benchmarks from running the 'benchmarkme' package.
Provides infrastructure to accurately measure and compare
    the execution time of R expressions.
Provides tools that make it easier to validate data using Benford's Law.
Several specialized statistical tests and support functions 
			for determining if numerical data could conform to Benford's law.
Included are two main interfaces for fitting and diagnosing
	bent-cable regressions for autoregressive time-series data or
	independent data (time series or otherwise): 'bentcable.ar()' and
	'bentcable.dev.plot()'. Some components in the package can also be
	used as stand-alone functions. The bent cable
	(linear-quadratic-linear) generalizes the broken stick
	(linear-linear), which is also handled by this package. Version 0.2
	corrects a glitch in the computation of confidence intervals for the
	CTP. References that were updated from Versions 0.2.1 and 0.2.2 appear
	in Version 0.2.3 and up. Version 0.3.0 improves robustness of the
	error-message producing mechanism. It is the author's intention to
	distribute any future updates via GitHub.
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, 
    use the unit hydrograph in a linear storage cascade, convert lists to data.frames, 
    fit multiple functions by regression (power, reciprocal, exponential, logarithmic,
    polynomial, rational).
Bessel Function Computations for complex and real numbers;
  notably interfacing TOMS 644; approximations for large arguments,
  experiments, etc.
An alternative to t-tests, producing posterior estimates
  for group means and standard deviations and their differences and
  effect sizes.
Best subset glm using AIC, BIC, EBIC, BICq or Cross-Validation. For the normal case, the 'leaps' is used. Otherwise, a slower exhaustive search. The 'xtable' package is needed for vignette 'SimExperimentBICq.Rnw' accompanying this package.
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>.
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.
betapart allows computing pair-wise dissimilarities (distance matrices) and multiple-site dissimilarities, separating the turnover and nestedness-resultant components of taxonomic (incidence and abundance based), functional and phylogenetic beta diversity.
Permutational method to incorporate taxonomic uncertainty
        and some functions to assess its effects on parameters of some
        widely used multivariate methods in ecology
Beta regression for modeling beta-distributed dependent variables, e.g., rates and proportions.
  In addition to maximum likelihood regression (for both mean and precision of a beta-distributed
  response), bias-corrected and bias-reduced estimation as well as finite mixture models and
  recursive partitioning for beta regressions are provided.
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.
Bayesian inference procedures for evaluating evidence of involvement of variants in Mendelian disorders based on intuitive models of dominant and recessive inheritance.
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).
Wrappers for fork()/waitpid() meant to allow R users to quickly
    and easily fork child processes and wait for them to finish.
Implements the Bayesian paradigm for fractional
 polynomial models under the assumption of normally distributed error terms.
Routine for fitting regression models for binary rare events with linear and nonlinear covariate effects when using the quantile function of the Generalized Extreme Value random variable.
Implementation of Bayesian estimation in generalized linear models following Gamerman (1997).
A collection of box-geometry model (BGM) files for the Atlantis 
    ecosystem model. Atlantis is a deterministic, biogeochemical, 
    whole-of-ecosystem model (see <http://atlantis.cmar.csiro.au/> for more information).
Two partially supervised mixture modeling methods: 
        soft-label and belief-based modeling are implemented.
        For completeness, we equipped the package also with the
        functionality of unsupervised, semi- and fully supervised
        mixture modeling.  The package can be applied also to selection
        of the best-fitting from a set of models with different
        component numbers or constraints on their structures.
        For detailed introduction see:
        Przemyslaw Biecek, Ewa Szczurek, Martin Vingron, Jerzy
        Tiuryn (2012), The R Package bgmm: Mixture Modeling with
        Uncertain Knowledge, Journal of Statistical Software.
Computes the hazard rate estimate as described by Nieto-Barajas and Walker (2002) and Nieto-Barajas (2003).
Implement an efficient block Gibbs sampler with incomplete
        data from a multinomial distribution taking values from the k
        categories 1,2,...,k, where data are assumed to miss at random
        and each missing datum belongs to one and only one of m
        distinct non-empty proper subsets A1, A2,..., Am of 1,2,...,k
        and the k categories are labelled such that only consecutive
        A's may overlap.
Fits a Bayesian group-sparse multi-task regression model using Gibbs
    sampling. The hierarchical prior encourages shrinkage of the estimated regression
    coefficients at both the gene and SNP level. The model has been applied
    successfully to imaging phenotypes of dimension up to 100; it can be used more
    generally for multivariate (non-imaging) phenotypes.
Boost provides free peer-reviewed portable C++ source 
 libraries.  A large part of Boost is provided as C++ template code
 which is resolved entirely at compile-time without linking.  This 
 package aims to provide the most useful subset of Boost libraries 
 for template use among CRAN package. By placing these libraries in 
 this package, we offer a more efficient distribution system for CRAN 
 as replication of this code in the sources of other packages is 
 avoided. As of release 1.62.0-1, the following Boost libraries are
 included: 'algorithm' 'any' 'atomic' 'bimap' 'bind' 'circular_buffer'
 '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' '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.
Biomarker threshold models are tools to fit both predictive and prognostic biomarker effects. Both generalized linear models and Cox proportional hazards models can be fitted using either Bayesian method or profile likelihood method.
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 tidy tibble (trimmed down version of data.frame) to make it accessible for further analysis and visualization.
Tool for quantitative research in scientometrics and bibliometrics.
    It provides various routines for importing bibliographic data from SCOPUS and 
    Thomson Reuters' ISI Web of Knowledge databases, performing bibliometric analysis 
    and building data matrices for co-citation, coupling and scientific collaboration analysis.
R class to access 'sqlite', 'BiblioSpec' generated, mass spectrometry search result files,
    containing detailed information about peptide spectra matches.
    Convert 'Mascot' '.dat' or e.g. 'comet' '.pep.xml' files with 'BiblioSpec' into 'sqlite' files and than 
    access them with the 'CRAN' 'bibliospec' package to analyse with the R-packages 'specL' to generate
    spectra libraries, 'protViz' to annotate spectra, or 'prozor' for false discovery rate 
    estimation and protein inference.
The main function biclust provides several algorithms to
        find biclusters in two-dimensional data: Cheng and Church,
        Spectral, Plaid Model, Xmotifs and Bimax. In addition, the
        package provides methods for data preprocessing (normalization
        and discretisation), visualisation, and validation of bicluster
        solutions.
An S3 class with a method for calculates the bidimensional regression between two 2D configurations following the approach by Tobler (1965).
This package makes global and multiple inferences for
        given bi- and trifactorial clinical trial designs using
        bootstrap methods and a classical approach.
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). Descriptive statistics, linear
    and logistic regression, path models for manifest variables
    with measurement error correction and two-level hierarchical
    regressions for weighted samples are included. Statistical 
    inference can be conducted for multiply imputed datasets and
    nested multiply imputed datasets.    
    This package is developed by BIFIE (Federal Institute for 
    Educational Research, Innovation and Development of the Austrian 
    School System; Salzburg, Austria).
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'.
The big data package is a collection of scalable methods
        for large-scale data analysis.
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.
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.
Regression for data too large to fit in memory
Create, store, access, and manipulate massive matrices.
    Matrices are allocated to shared memory and may use memory-mapped
    files.  Packages 'biganalytics', 'bigtabulate', 'synchronicity', and
    'bigalgebra' provide advanced functionality.
This package provides a shared resource interface for the bigmemory and synchronicity packages.
The 'bigml' package contains bindings for the BigML API.
    The package includes methods that provide straightforward access
    to basic API functionality, as well as methods that accommodate
    idiomatic R data types and concepts.
Adds wrappers to add functionality for big.matrix objects (see the bigmemory project).
 This allows fast scalable principle components analysis (PCA), or singular value decomposition (SVD).
 There are also functions for transposing, using multicore 'apply' functionality, data importing 
 and for compact display of big.matrix objects. Most functions also work for standard matrices if 
 RAM is sufficient.
Interface with 'Google BigQuery',
    see <https://cloud.google.com/bigquery/> for more information.
    This package uses 'googleAuthR' so is compatible with similar packages, 
    including 'Google Cloud Storage' (<https://cloud.google.com/storage/>) for result extracts.
Use Newton's method, coordinate descent, and METIS clustering
    to solve the L1 regularized Gaussian MLE inverse covariance
    matrix estimation problem.
Allows the user to carry out GLM on very large
    data sets. Data can be created using the data_frame() function and appended
    to the object with object$append(data); data_frame and data_matrix objects
    are available that allow the user to store large data on disk. The data is
    stored as doubles in binary format and any character columns are transformed
    to factors and then stored as numeric (binary) data while a look-up table is
    stored in a separate .meta_data file in the same folder. The data is stored in
    blocks and GLM regression algorithm is modified and carries out a MapReduce-
    like algorithm to fit the model. The functions bglm(), and summary()
    and bglm_predict() are available for creating and post-processing of models.
    The library requires Armadillo installed on your system. It probably won't 
    function on windows since multi-core processing is done using mclapply() 
    which forks R on Unix/Linux type operating systems.
Easily talk to Google's 'BigQuery' database from R.
The package fits large-scale (generalized) ridge regression for various distributions of response. The shrinkage parameters (lambdas) can be pre-specified or estimated using an internal update routine (fitting a heteroscedastic effects model, or HEM). It gives possibility to shrink any subset of parameters in the model. It has special computational advantage for the cases when the number of shrinkage parameters exceeds the number of observations. For example, the package is very useful for fitting large-scale omics data, such as high-throughput genotype data (genomics), gene expression data (transcriptomics), metabolomics data, etc.
Construct large systems of structural equations using the two-stage penalized least squares (2SPLS) method proposed by Chen, Zhang and Zhang (2016).
Fits smoothing spline regression models using scalable algorithms designed for large samples. Six marginal spline types are supported: cubic, different cubic, cubic periodic, cubic thin-plate, ordinal, and nominal. Random effects and parametric effects are also supported. Response can be Gaussian or non-Gaussian: Binomial, Poisson, Gamma, Inverse Gaussian, or Negative Binomial.
Selecting linear models for large data sets using modified
    stepwise procedure and modern selection criteria (like modifications of
    Bayesian Information Criterion). Selection can be performed on data which
    exceed RAM capacity. Special selection strategy is available, faster than
    classical stepwise procedure.
Extend the bigmemory package with 'table', 'tapply', and 'split'
    support for 'big.matrix' objects. The functions may also be used with native R
    matrices for improving speed and memory-efficiency.
For studying recurrent disease and death with competing
    risks, comparisons based on the well-known cumulative incidence function
    can be confounded by different prevalence rates of the competing events.
    Alternatively, comparisons of the conditional distribution of the survival
    time given the failure event type are more relevant for investigating the
    prognosis of different patterns of recurrence disease. This package implements
    a nonparametric estimator for the conditional cumulative incidence function
    and a nonparametric conditional bivariate cumulative incidence function for the
    bivariate gap times proposed in Huang et al. (2016) <doi:10.1111/biom.12494>.
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.
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.
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.
Provides methods for the binarization of one-dimensional data and some visualization functions.
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).
Convert to binary numbers (Base2). Shift, rotate, summary. Based on logical vector.
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.
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.
This package provides methods for estimation and
        hypothesis testing of proportions in group testing designs. It
        involves methods for estimating a proportion in a single
        population (assuming sensitivity and specificity 1 in designs
        with equal group sizes), as well as hypothesis tests and
        functions for experimental design for this situation. For
        estimating one proportion or the difference of proportions, a
        number of confidence interval methods are included, which can
        deal with various different pool sizes. Further, regression
        methods are implemented for simple pooling and matrix pooling
        designs.
Binomial Haar-Fisz transforms for Gaussianization
Asymptotic simultaneous confidence intervals for comparison of many treatments with one control,
 for the difference of binomial proportions, allows for Dunnett-like-adjustment, Bonferroni or unadjusted intervals.
 Simulation of power of the above interval methods, approximate calculation of any-pair-power, and sample size
 iteration based on approximate any-pair power. 
 Exact conditional maximum test for many-to-one comparisons to a control.
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.
This package provides a range of diagnostic methods for
        binomial regression models.
Generation of samples from a mix of binary, ordinal and continuous random variables with a pre-specified correlation matrix and marginal distributions.
Implementation of algorithms for cutting numerical values
    exhibiting a potentially highly skewed distribution into evenly distributed
    groups (bins). This functionality can be applied for binning discrete
    values, such as counts, as well as for discretization of continuous values,
    for example, during generation of features used in machine learning
    algorithms.
For a series of binary responses, create stopping boundary with exact results after stopping, allowing updating for missing assessments.
Provides several methods for generating density functions
    based on binned data. Data are assumed to be nonnegative, but the bin widths
    need not be uniform, and the top bin may be unbounded. All PDF smoothing methods
    maintain the areas specified by the binned data. (Equivalently, all CDF
    smoothing methods interpolate the points specified by the binned data.) An
    estimate for the mean of the distribution may be supplied as an optional
    argument, which greatly improves the reliability of statistics computed from
    the smoothed density functions. Methods include step function, recursive
    subdivision, and optimized spline.
Various supervised and unsupervised binning tools
    including using entropy, recursive partition methods
    and clustering.
Utilities to process, organize and explore protein structure,
    sequence and dynamics data. Features include the ability to read and write
    structure, sequence and dynamic trajectory data, perform sequence and structure
    database searches, data summaries, atom selection, alignment, superposition,
    rigid core identification, clustering, torsion analysis, distance matrix
    analysis, structure and sequence conservation analysis, normal mode analysis,
    principal component analysis of heterogeneous structure data, and correlation
    network analysis from normal mode and molecular dynamics data. In addition,
    various utility functions are provided to enable the statistical and graphical
    power of the R environment to work with biological sequence and structural data.
    Please refer to the URLs below for more information.
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.
 Biocomb 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.
 Biocomb has 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 data frames 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 functions ringApply(), modelFrame(), and muleMan().
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.
Functions for working with biogas data. Both low- and high-level functions are included for carrying out common tasks for analysis of biogas and related data. 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 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. Lastly, biogas and methane production can be predicted from substrate composition and additional, optional data.
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.
Prediction and adjustment to experimental data of microbial
    inactivation. Several models available in the literature are implemented.
Imports benthic count data, reformats this data, and
        computes environmental inferences from this data.
This is an R package for interfacing with the
    BIOM format. This package includes basic tools for
    reading biom-format files, accessing and subsetting
    data tables from a biom object (which is more complex
    than a single table), as well as limited support for
    writing a biom-object back to a biom-format file. The
    design of this API is intended to match the python API
    and other tools included with the biom-format project,
    but with a decidedly "R flavor" that should be familiar
    to R users. This includes S4 classes and methods, as
    well as extensions of common core functions/methods.
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 meta-genomic data retrieval and functional annotation retrieval with
    R.
Contains data analysis functions to estimate above-ground biomass and its uncertainty in tropical forests. BIOMASS 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 at the stand level with associated uncertainty.
Functions for species distribution modeling, calibration and
    evaluation, ensemble of models.
Provides utilities to facilitate import, export and computation with the 
 BIOM (Biological Observation Matrix) format (http://biom-format.org).
Data Package that includes several examples of chemical and biological data networks, i.e. data graph structured.
Prognostic Enrichment is a clinical trial strategy of evaluating an intervention in a patient population with a higher rate of the unwanted event than the broader patient population (R. Temple (2010) <DOI:10.1038/clpt.2010.233>). A higher event rate translates to a lower sample size for the clinical trial, which can have both practical and ethical advantages. This package is a tool to help evaluate biomarkers for prognostic enrichment of clinical trials.
Utilities and functions to investigate the relation
        between biomolecular structures, their interactions, and the
        evolutionary information revealed in sequence alignments of
        these molecules.
Visualization of complex biological datasets is
    essential to understand complementary spects of biology
    in big data era.
    In addition, analyzing of multiple datasets enables to
    understand biologcal processes deeply and accurately.
    Multiple datasets produce multiple analysis results, and
    these overlappings are usually visualized in Venn diagram.
    bioplots is a tiny R package that generates a heatmap to
    visualize overlappings instead of using Venn diagram.
bioPN is a package suited to perform simulation of deterministic and stochastic systems of biochemical reaction
  networks.
  Models are defined using a subset of Petri Nets, in a way that is close at how chemical reactions
  are defined.
  For deterministic solutions, bioPN creates the associated system of differential equations "on the fly", and
  solves it with a Runge Kutta Dormand Prince 45 explicit algorithm.
  For stochastic solutions, bioPN offers variants of Gillespie algorithm, or SSA.
  For hybrid deterministic/stochastic,
  it employs the Haseltine and Rawlings algorithm, that partitions the system in fast and slow reactions.
  bioPN algorithms are developed in C to achieve adequate performance.
The bioRxiv preprint server (http://www.biorxiv.org) is a website where scientists can post preprints of scholarly texts in biology. Users can search and download PDFs in bulk from the preprint server. The text of abstracts are stored as raw text within R, and PDFs can easily be saved and imported for text mining with packages such as 'tm'.
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.
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.
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.
Functions to visualise webs and calculate a series of indices commonly used to describe pattern in (ecological) webs. It focuses on webs consisting of only two levels (bipartite), e.g. pollination webs or predator-prey-webs. Visualisation is important to get an idea of what we are actually looking at, while the indices summarise different aspects of the web's topology.
A GUI with which the user can construct and interact with Bootstrap methods on Classical Biplots and with Clustering and/or Disjoint Biplot.
Provides a GUI with which users can construct and interact
        with biplots.
Bayesian parameter estimation for (partially observed)
    two-dimensional diffusions.
A connector to the API for 'Wordnik' <https://whoapi.com>, a dictionary service that also provides
             bigram generation, word frequency data, and a whole host of other functionality.
R functions to read EURING data and analyse re-encounter data of birds marked by metal rings. For a tutorial, go to http://www.tandfonline.com/doi/full/10.1080/03078698.2014.933053.
Collection of tools to make R more convenient. Includes tools to
    summarize data using statistics not available with base R and manipulate
    objects for analyses.
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.
bitmapped vectors of booleans (no NAs), 
  coercion from and to logicals, integers and integer subscripts; 
  fast boolean operators and fast summary statistics. 
  With 'bit' vectors you can store true binary booleans {FALSE,TRUE} at the 
  expense of 1 bit only, on a 32 bit architecture this means factor 32 less 
  RAM and ~ factor 32 more speed on boolean operations. Due to overhead of 
  R calls, actual speed gain depends on the size of the vector: expect gains 
  for vectors of size > 10000 elements. Even for one-time boolean operations 
  it can pay-off to convert to bit, the pay-off is obvious, when such 
  components are used more than once. 
  Reading from and writing to bit is approximately as fast as accessing 
  standard logicals - mostly due to R's time for memory allocation. The package 
  allows to work with pre-allocated memory for return values by calling .Call() 
  directly: when evaluating the speed of C-access with pre-allocated vector 
  memory, coping from bit to logical requires only 70% of the time for copying 
  from logical to logical; and copying from logical to bit comes at a 
  performance penalty of 150%. the package now contains further classes for 
  representing logical selections: 'bitwhich' for very skewed selections and 
  'ri' for selecting ranges of values for chunked processing. All three index 
  classes can be used for subsetting 'ff' objects (ff-2.1-0 and higher).
Package 'bit64' provides serializable S3 atomic 64bit (signed) integers 
 that can be used in vectors, matrices, arrays and data.frames. Methods are 
 available for coercion from and to logicals, integers, doubles, characters 
 and factors as well as many elementwise and summary functions. Many fast 
 algorithmic operations such as 'match' and 'order' support interactive data
 exploration and manipulation and optionally leverage caching.
Functions for bitwise operations on integer vectors.
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.
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)
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.
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 (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.
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 organizations. Relational (network) data may be incorporated for additional analyses.
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.
Function for drawing the coastline of the British Isles
Allows data to be processed by multiple machine learning algorithms
    at the same time, enables feature selection of data by single a algorithm or
    combinations of multiple. Easy to use tool for k-fold cross validation and
    nested cross validation.
This package is an extension to the "ergm" package which implements the block ergms.
Implements regression models for binary data on the absolute risk scale. These models are applicable to cohort and population-based case-control data.
Maximum a posteriori estimation for linear and generalized
             linear mixed-effects models in a Bayesian setting. Extends
             'lme4' by Douglas Bates, Martin Maechler, Ben Bolker, and Steve Walker.
Data files and functions accompanying the book Korner-Nievergelt, Roth, von Felten, Guelat, Almasi, Korner-Nievergelt (2015) "Bayesian Data Analysis in Ecology using R, BUGS and Stan", Elsevier, New York.
Simultaneous clustering of rows and columns, usually designated by
    biclustering, co-clustering or block clustering, is an important technique
    in two way data analysis. It consists of estimating a mixture model which
    takes into account the block clustering problem on both the individual and
    variables sets. The blockcluster package provides a bridge between the C++
    core library and the R statistical computing environment. This package
    allows to co-cluster binary, contingency, continuous and categorical
    data-sets. It also provides utility functions to visualize the results.
    This package may be useful for various applications in fields of Data
    mining,Information retrieval, Biology, computer vision and many more. More
    information about the project and comprehensive tutorial can be found on
    the link mentioned in URL.
Some elementary matrix algebra tools are implemented to manage
    block matrices or partitioned matrix, i.e. "matrix of matrices"
    (http://en.wikipedia.org/wiki/Block_matrix). The block matrix is here
    defined as a new S3 object. In this package, some methods for "matrix"
    object are rewritten for "blockmatrix" object. New methods are implemented.
    This package was created to solve equation systems with block matrices for
    the analysis of environmental vector time series .
    Bugs/comments/questions/collaboration of any kind are warmly welcomed.
Creates strings that show a text message in 8 by 8 block
        letters
The package is primarly ment as an implementation of
        Generalized blockmodeling for valued networks. In addition,
        measurese of similarity or dissimilarity based on structural
        equivalence and regular equivalence (REGE algorithem) can be
        computed and partitioned matrices can be ploted.
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 nested and crossed block designs 
  for unstructured treatment sets with arbitrary replication not necessarily all equal.
  Blocks can be nested to any required depth of nesting and blocks are optimized 
  hierarchically from the top down with successive nested blocks strata optimized within
  the levels of all preceding blocks strata. Block sizes are all equal in any one stratum if the number 
  of blocks in that stratum exactly divides the number of plots, otherwise they differ by at most one plot. 
  The design output is a data table showing a randomised allocation of treatments to blocks together with
  plans showing the allocation of treatments to blocks for each block in the bottom stratum of the design. 
  D and A-efficiency factors are shown for each stratum together with A-efficiency upper bounds,
  where available.
Segments a matrix in blocks with constant values.
Blocks units into experimental blocks, with one unit per treatment condition, by creating a measure of multivariate distance between all possible pairs of units.  Maximum, minimum, or an allowable range of differences between units on one variable can be set.  Randomly assign units to treatment conditions.  Diagnose potential interference between units assigned to different treatment conditions.  Write outputs to .tex and .csv files.
Provides tools for making statistical comparisons with distance-function based permutation tests developed by P. W. Mielke, Jr. and colleagues at Colorado State University (Mielke, P. W. & Berry, K. J. Permutation Methods: A Distance Function Approach (Springer, New York, 2001)) and for testing parameters estimated in linear models with permutation procedures developed by B. S. Cade and colleagues at the Fort Collins Science Center, U. S. Geological Survey.
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
    <http://www.bls.gov/developers/api_signature.htm> and the function returns a JSON
    string.
Scrapes various data from the Bureau of Labor Statistics, which is the statistical branch of the United States Department of Labor. The package has additional functions to help parse, analyze and visualize the data.
Package for Bayesian model averaging and variable selection for linear models,
        generalized linear models and survival models (cox
        regression).
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.
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.
This is a bare-bones implementation of sampling algorithms
        for a variety of Bayesian stick-breaking (marginally DP)
        mixture models, including particle learning and Gibbs sampling
        for static DP mixtures, particle learning for dynamic BAR
        stick-breaking, and DP mixture regression.  The software is
        designed to be easy to customize to suit different situations
        and for experimentation with stick-breaking models.  Since
        particles are repeatedly copied, it is not an especially
        efficient implementation.
MCMC diagnostic package that contains tools to diagnose
        convergence as well as to evaluate sensitivity studies,
        Includes summary functions which output mean, median,
        95percentCI, Gelman & Rubin diagnostics and the Hellinger
        distance based diagnostics, Also contains functions to
        determine when an MCMC chain has converged via Hellinger
        distance, A function is also provided to compare outputs from
        identically dimensioned chains for determining sensitivy to
        prior distribution assumptions
Easy estimation of Bayesian multilevel mediation models with Stan.
This package implements approximate and exact methods for
        pairwise binary markov models. The exact method uses an
        implementation of the junction tree algorithm for binary
        graphical models. For more details see the help files
Reads Windows BMP format images. Currently limited to 8 bit
    greyscale images and 24,32 bit (A)RGB images. Pure R implementation without
    external dependencies.
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.
Data generator based on Bayesian network model
Bayesian network structure learning, parameter learning and
  inference.
  This package implements constraint-based (GS, IAMB, Inter-IAMB, Fast-IAMB,
  MMPC, Hiton-PC), pairwise (ARACNE and Chow-Liu), score-based (Hill-Climbing
  and Tabu Search) and hybrid (MMHC and RSMAX2) structure learning algorithms
  for 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 www.bnlearn.com.
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).
Modeling mixtures with normalized random measures by a Ferguson-Klass type algorithm for posterior normalized random measures.
Implements the Bayesian nonparametric monotonic regression
        method described in Bornkamp & Ickstadt (2009), Biometrics, 65,
        198-205.
Performs the algorithm for time series clustering described in Nieto-Barajas and Contreras-Cristan (2014).
Markov chain Monte Carlo algorithms for non- and semi-parametric models.
Package for the 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 (agreement 283093).
Bayesian Network Structure Learning from Data with Missing Values.
    The package implements the Silander-Myllymaki complete search,
    the Max-Min Parents-and-Children, the Hill-Climbing, the
    Max-Min Hill-climbing heuristic searches, and the Structural
    Expectation-Maximization algorithm. Available scoring functions are
    BDeu, AIC, BIC. The package also implements methods for generating and using
    bootstrap samples, imputed data, inference.
A menu-driven program and library of functions for carrying out
    convergence diagnostics and statistical and graphical analysis of Markov
    chain Monte Carlo sampling output.
Tools for constructing board/grid based games, as well as readily available game(s) for your entertainment.
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 for genetic 'barcode' data. Functions include methods for
    searching by sequences by taxonomic names, ids, collectors, and
    institutions; as well as a function for searching for specimens, and
    downloading trace files.
A set of R functions and data sets for the book Introduction to
    Bayesian Statistics, Bolstad, W.M. (2007), John Wiley & Sons ISBN 0-471-27020-2.
A set of R functions and data sets for the book
        Understanding Computational Bayesian Statistics, Bolstad, W.M.
        (2009), John Wiley & Sons ISBN 978-0470046098
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.
This package implements a
    partial-observability procedure for testing Boolean
    hypotheses that generalizes the binary response GLM as
    outlined in Braumoeller (2003).
Provides methods to reconstruct and generate synchronous,
        asynchronous, 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.
Calculate robust measures of effect sizes using the bootstrap.
Computes appropriate confidence intervals for the likelihood ratio tests commonly used in medicine/epidemiology.  It is particularly useful when the sensitivity or specificity in the sample is 100%.  Note that this does not perform the test on nested models--for that, see 'epicalc::lrtest'.
Selection of informative features like genes, transcripts, RNA seq, etc. using Bootstrap Maximum Relevance and Minimum Redundancy technique from a given high dimensional genomic dataset. Informative gene selection involves identification of relevant genes and removal of redundant genes as much as possible from a large gene space. Main applications in high-dimensional expression data analysis (e.g. microarray data, NGS expression data and other genomics and proteomics applications).
Bootstrap methods to assess accuracy and stability of estimated network structures
              and centrality indices. Allows for flexible specification of any undirected network 
              estimation procedure in R, and offers default sets for 'qgraph', 'IsingFit', 'IsingSampler',
              'glasso', 'huge' and 'parcor' packages.
Bias-Corrected Forecasting and Bootstrap Prediction Intervals for Autoregressive Time Series
Calculation of Bootstrapped Response and Correlation
        Functions for Use in Dendroclimatology
This package provides a framework for testing the probability of ruin in the classical (compound Poisson) risk process. It also includes some procedures for assessing and comparing the performance between the bootstrap test and the test using asymptotic normality.
Bootstrap for testing the hypothesis that the spectral
        densities of a number m, m>=2, not necessarily independent time
        series are equal
Applicable to any classification problem with more than 2 classes. It relies on bootstrap subsamplings of sPLS-DA and provides tools to select the most stable variables (defined as the ones consistently selected over the bootstrap subsamplings) and to predict the class of test samples.
Model selection by bootstrapping the stepAIC() procedure.
Software (bootstrap, cross-validation, jackknife) and data
        for the book "An Introduction to the Bootstrap" by B. Efron and
        R. Tibshirani, 1993, Chapman and Hall.
        _____________________________________________________________
        This package is primarily provided for projects already based
        on it, and for support of the book.  New projects should
        preferentially use the recommended package "boot".
Implements fast, exact bootstrap Principal Component Analysis and
    Singular Value Decompositions for high dimensional data, as described in
    <http://arxiv.org/abs/1405.0922>. For data matrices that are too large to operate
    on in memory, users can input objects with class 'ff' (see the 'ff'
    package), where the actual data is stored on disk. In response, this
    package will implement a block matrix algebra procedure for calculating the
    principal components (PCs) and bootstrap PCs. Depending on options set by
    the user, the 'parallel' package can be used to parallelize the calculation of
    the bootstrap PCs.
Applied researchers often test for the difference of the Sharpe
    ratios of two investment strategies. A very popular tool to this end is the test
    of Jobson and Korkie, which has been corrected by Memmel. Unfortunately, this
    test is not valid when returns have tails heavier than the normal distribution
    or are of time series nature. Instead, we propose the use of robust inference
    methods. In particular, we suggest to construct a studentized time series
    bootstrap confidence interval for the difference of the Sharpe ratios and
    to declare the two ratios different if zero is not contained in the obtained
    interval. This approach has the advantage that one can simply resample from the
    observed data as opposed to some null-restricted data.
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.
Provides significance tests for second-order stationarity
	for time series using bootstrap wavelet packet tests.
Bayesian approaches for analyzing multivariate data in ecology. Estimation is performed using Markov Chain Monte Carlo (MCMC) methods via JAGS. Three types of models may be fitted: 1) With explanatory variables only, boral fits independent column GLMs to each column of the response matrix; 2) With latent variables only, boral fits a purely latent variable model for model-based unconstrained ordination; 3) With explanatory and latent variables, boral fits correlated column GLMs with latent variables to account for any residual correlation between the columns of the response matrix.
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.
Reads and plots placements obtained using pplacer/guppy (see http://matsen.fhcrc.org/pplacer/ for details). Read PDB file (protein 3D structure file).
This package is a collection of R functions implemented
        from published and available analytic solutions for the
        One-Dimensional Boussinesq Equation (ground-water). In
        particular, the function "beq.lin" is the analytic solution of
        the linearized form of Boussinesq Equation between two
        different head-based boundary (Dirichlet) conditions;
        "beq.song" is the non-linear power-series analytic solution of
        the motion of a wetting front over a dry bedrock (Song at al,
        2007, see complete reference on function documentation).
        Bugs/comments/questions/collaboration of any kind are warmly
        welcomed.
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.
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.
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 is best suited to data from a Freesurfer analysis (cortical
    thickness, volumes, local gyrification index, surface area), but also works
    with e.g., tractography data from FSL. It contains a graphical user
    interface for graph visualization and data exploration.
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.
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().
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.
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)).
A collection of functions to analyse, visualize and interpret wind data
         and to calculate the potential energy production of wind turbines.
brew implements a templating framework for mixing text and
        R code for report generation. brew template syntax is similar
        to PHP, Ruby's erb module, Java Server Pages, and Python's psp
        module.
Retrieves and parses graduate admissions survey data from the Grad Cafe website (http://thegradcafe.com).
Fit generalized linear models with binomial responses using either an adjusted-score approach to bias reduction or maximum penalized likelihood where penalization is by Jeffreys invariant prior. These procedures return estimates with improved frequentist properties (bias, mean squared error) that are always finite even in cases where the maximum likelihood estimates are infinite (data separation). Fitting takes place by fitting generalized linear models on iteratively updated pseudo-data. The interface is essentially the same as 'glm'.  More flexibility is provided by the fact that custom pseudo-data representations can be specified and used for model fitting. Functions are provided for the construction of confidence intervals for the reduced-bias estimates.
Decomposes the empirical Brier score into reliability, resolution and uncertainty. Two different estimators for the components are provided: The original estimators proposed by Murphy (1974), and the bias-corrected estimators proposed by Ferro and Fricker (2012). Sampling variances of all the components are estimated. This package applies only to probabilistic predictions of binary events.
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.
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.
Fits novel models for the conditional relative risk, risk difference and odds ratio.
Fit Bayesian generalized (non-)linear multilevel models 
    using Stan for full Bayesian inference.
Bayesian regularization for feed-forward neural networks.
Handles very large numbers in R.  Real numbers are held
        using their natural logarithms, plus a logical flag indicating
        sign.  The package includes a vignette that gives a
        step-by-step introduction to using S4 methods.
Miscellaneous R functions, including functions related to
    graphics (mostly for base graphics), permutation tests, running
    mean/median, and general utilities.
Convert statistical analysis objects from R into tidy data frames,
    so that they can more easily be combined, reshaped and otherwise processed
    with tools like 'dplyr', 'tidyr' and 'ggplot2'. The package provides three
    S3 generics: tidy, which summarizes a model's statistical findings such as
    coefficients of a regression; augment, which adds columns to the original
    data such as predictions, residuals and cluster assignments; and glance, which
    provides a one-row summary of model-level statistics.
A lossless compressed data format that uses a combination of the
    LZ77 algorithm and Huffman coding. Brotli is similar in speed to deflate (gzip)
    but offers more dense compression.
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.
Fully-interactive R interface to the 'OpenBUGS' software for Bayesian analysis using MCMC sampling.  Runs natively and stably in 32-bit R under Windows.  Versions running on Linux and on 64-bit R under Windows are in "beta" status and less efficient.
Collection of functions, data sets and code examples 
 for evaluations of field trials with the objective of equivalence assessment.
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'.
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.
Bayes screening and model discrimination follow-up designs.
Bayesian inference on the (discrete) power spectrum of time series.
Some functions for nonparametric and semiparametric
        Bayesian models for random effects meta-analysis
This package models the quantile process as a function of
        predictors.
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.
It provides the density, distribution function, quantile function, random number generator, reliability function, failure rate, likelihood function,
             moments and EM algorithm for Maximum Likelihood estimators, also empirical quantile and generated envelope for a given sample, all this for the three parameter
             Birnbaum-Saunders model based on Skew-Normal Distribution.
             Additionally, it provides the random number generator for the mixture of Birnbaum-Saunders model based on Skew-Normal distribution.
Functional gradient descent algorithm for a variety of convex and non-convex loss functions, for both classical and robust regression and classification problems.
Time series regression using dynamic linear models fit using MCMC.
Kernel density estimation dedicated to urban geography.
Temporal Exponential Random Graph Models (TERGM) estimated by maximum pseudolikelihood with bootstrapped confidence intervals or Markov Chain Monte Carlo maximum likelihood. Goodness of fit assessment for ERGMs, TERGMs, and SAOMs. Micro-level interpretation of ERGMs and TERGMs.
Trend filtering uses the generalized
    lasso framework to fit an adaptive polynomial of degree k to
    estimate the function f_0 at each input x_i in the model: y_i =
    f_0(x_i) + epsilon_i, for i = 1, ..., n, and epsilon_i
    is sub-Gaussian with E(epsilon_i) = 0.  Bayesian trend filtering adapts
    the genlasso framework to a fully Bayesian hierarchical model, estimating
    the penalty parameter lambda within a tractable Gibbs sampler.
Performs 'BTLLasso', 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.
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.
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.
Basic tools to analyze forest fire history data (e.g. FHX2) 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.
Implementation of the Generalized Pairwise Comparisons. This test
    enables to compare two groups of observations in randomized trials(e.g treated
    vs. control patients) on several prioritized outcomes. Pairwise comparisons
    require consideration of all possible pairs of individuals, one taken from the
    treatment group and the other taken from the control group. The outcomes of the
    two individuals forming a pair are compared. Thresholds of minimal clinically
    significant differences can be defined. It is possible to analyse simultaneously
    several outcomes by prioritizing the variables that capture them. The highest
    priority is assigned to the variable considered the most clinically relevant.
    A natural way of handling uninformative or neutral pairs is to consider the
    outcomes in descending order of priority: whenever a pair is uninformative or
    neutral for an outcome of higher priority, the outcomes of lower priority are
    examined In the case of time-to-event endpoint, four methods to handle censored
    observations are available in this package (Gehan, Peto, Efron, and Peron).
R/C++ implementation of the model proposed by Primiceri ("Time Varying Structural Vector Autoregressions and Monetary Policy", Review of Economic Studies, 2005), with functionality for computing posterior predictive distributions and impulse responses.
This package implements a simple alternative to the
        traditional Venn diagram. It depicts each overlap as a separate
        bubble with area proportional to the overlap size. Relation of
        the bubbles to input sets is shown by their their arrangement.
An R interface to the Stark-Parker implementation of an
        algorithm for bounded-variable least squares
Functions that solve boundary value problems ('BVP') of systems of ordinary
             differential equations ('ODE') and differential algebraic equations ('DAE').
             The functions provide an interface to the FORTRAN functions
             'twpbvpC', 'colnew/colsys', and an R-implementation of the shooting method.
The functions in this package focus on analyzing
        case-control association studies involving a group of genetic
        variants.  In particular, we are interested in modeling the
        outcome variable as a function of a multivariate genetic
        profile using Bayesian model uncertainty and variable selection
        techniques.  The package incorporates functions to analyze data
        sets involving common variants as well as extensions to model
        rare variants via the Bayesian Risk Index (BRI) as well as
        haplotypes.  Finally, the package also allows the incorporation
        of external biological information to inform the marginal
        inclusion probabilities via the iBMU.
Designed for whole-genome regressions using Bayesian models fitted via EM or Gibbs sampling with optional resampling techniques (Bagging) and polygenic term with reduced dimensionality.
Performs the 'Baumgartner-Weiss-Schindler' two-sample test of equal
   probability distributions.
Shows statistics about bytes contained in a file 
  as a circle graph of deviations from mean in sigma increments. 
  The function can be useful for statistically analyze the content of files 
  in a glimpse: text files are shown as a green centered crown, compressed 
  and encrypted files should be shown as equally distributed variations with 
  a very low CV (sigma/mean), and other types of files can be classified between 
  these two categories depending on their text vs binary content, which can be 
  useful to quickly determine how information is stored inside them (databases, 
  multimedia files, etc).
c060 provides additional functions to perform stability selection, model validation and parameter tuning for glmnet models
This package allows inferring gene regulatory networks
        with direct physical interactions from microarray expression
        data using C3NET.
C5.0 decision trees and rule-based models for pattern recognition.
Computation and visualization of simple, multiple and joint correspondence analysis.
A wrapper for the CanIStream.It API for searching across the most
    popular streaming, rental, and purchase services to find where a movie is
    available.
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, and the like.
This package performs Hansen's (1995) Covariate-Augmented
        Dickey-Fuller (CADF) test. The only required argument is y, the
        Tx1 time series to be tested. If no stationary covariate X is
        passed to the procedure, then an ordinary ADF test is
        performed. The p-values of the test are computed using a
        procedure proposed in Costantini, Lupi and Popp (2007),
        illustrated in Lupi (2009).
Using JGR, provides a GUI to several statistical methods -
    scatterplot, boxplot, linear regression, generalized linear
    regression, quantile, regression, conditional probability
    calculations, and regression trees.
Provides functions for the estimation of the conditional Akaike 
			 information in generalized mixed-effects models fitted with (g)lmer 
			 form lme4.
Cairo graphics device that can be use to create high-quality vector (PDF, PostScript and SVG) and bitmap output (PNG,JPEG,TIFF), and high-quality rendering in displays (X11 and Win32). Since it uses the same back-end for all output, copying across formats is WYSIWYG. Files are created without the dependence on X11 or other external programs. This device supports alpha channel (semi-transparent drawing) and resulting images can contain transparent and semi-transparent regions. It is ideal for use in server environments (file output) and as a replacement for other devices that don't have Cairo's capabilities such as alpha support or anti-aliasing. Backends are modular such that any subset of backends is supported.
This device uses Cairo and GTK to draw to the screen,
        file (png, svg, pdf, and ps) or memory (arbitrary GdkDrawable
        or Cairo context). The screen device may be embedded into RGtk2
        interfaces and supports all interactive features of other graphics
        devices, including getGraphicsEvent().
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 Zadeh 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, i.e. Calculator.LR.FNs 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 a greedy algorithm for coarse approximation linear
    function.
Functions to impute using Random Forest under Full Conditional Specifications (Multivariate Imputation by Chained Equations). The CALIBER programme is funded by the Wellcome Trust (086091/Z/08/Z) and the National Institute for Health Research (NIHR) under its Programme Grants for Applied Research programme (RP-PG-0407-10314). The author is supported by a Wellcome Trust Clinical Research Training Fellowship (0938/30/Z/10/Z).
Automated parameter estimation for complex (ecological) models in R. 
  This package allows the parameter estimation or calibration of complex models, 
  including stochastic ones. It is a generic tool that can be used for fitting 
  any type of models, especially those with non-differentiable objective functions. 
  It supports multiple phases and constrained optimization. 
  It implements maximum likelihood estimation methods and automated construction 
  of the objective function from simulated model outputs. 
  See <http://roliveros-ramos.github.io/calibrar> for more details.
Package for drawing calibrated scales with tick marks on (non-orthogonal) 
             variable vectors in scatterplots and biplots.
Function to calculate weights and estimates for panel data with non-response.
Performs Bayesian calibration of computer models as per
 Kennedy and O'Hagan 2001.  The package includes routines to find the
 hyperparameters and parameters; see the help page for stage1() for a
 worked example using the toy dataset.  A tutorial is provided in the
 calex.Rnw vignette; and a suite of especially simple one dimensional
 examples appears in inst/doc/one.dim/.
It is sometimes useful to perform a computation in a
    separate R process, without affecting the current R process at all.
    This packages does exactly that.
A multi-array post-processing method of allele-specific copy-number estimates (ASCNs).
The code takes an n x p data matrix and fits a Causal Additive Model (CAM) for estimating the causal structure of the underlying process. The output is a p x p adjacency matrix (a one in entry (i,j) indicates an edge from i to j). Details of the algorithm can be found in: P. Bühlmann, J. Peters, J. Ernest: "CAM: Causal Additive Models, high-dimensional order search and penalized regression", Annals of Statistics 42:2526-2556, 2014.
Tools for the analysis of finite semiparametric mixtures.
        These are useful when data is heterogeneous, e.g. in
        pharmacokinetics or meta-analysis. The NPMLE and VEM algorithms
        (flexible support size) and EM algorithms (fixed support size)
        are provided for univariate and bivariate data.
The package "camel" provides the implementation of a family of high-dimensional calibrated machine learning tools, including (1) LAD, SQRT Lasso and Calibrated Dantzig Selector for estimating sparse linear models; (2) Calibrated Multivariate Regression for estimating sparse multivariate linear models; (3) Tiger, Calibrated Clime for estimating sparse Gaussian graphical models. We adopt the combination of the dual smoothing and monotone fast iterative soft-thresholding algorithm (MFISTA). The computation is memory-optimized using the sparse matrix output, and accelerated by the path following and active set tricks.
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.
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.
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.
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.
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 and Datasets to Accompany J. Fox and S. Weisberg, 
  An R Companion to Applied Regression, Second Edition, Sage, 2011.
Implements a class of univariate and multivariate spatial generalised linear mixed models for areal unit data, with inference in a Bayesian setting using Markov chain Monte Carlo (McMC) simulation. The response variable can be binomial, Gaussian or Poisson. Spatial autocorrelation is modelled by  a set of random effects, which are assigned a conditional autoregressive (CAR) prior distribution. A number of different CAR priors are available for the random effects, including a multivariate CAR (MCAR) model for multivariate spatial data. 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 was supported by the Engineering and Physical Science Research Council (EPSRC) grant EP/J017442/1.
Spatio-temporal data from Scotland used in the vignettes accompanying the CARBayes (spatial modelling) and CARBayesST (spatio-temporal modelling) packages. For the CARBayes vignette the data include the Scottish lip cancer data and property price and respiratory hospitalisation data from the Greater Glasgow and Clyde health board. For the CARBayesST vignette the data include spatio-temporal data on property sales and respiratory hospitalisation and air pollution from the Greater Glasgow and Clyde health board.
Implements a class of spatio-temporal generalised linear mixed models for areal unit data, with inference in a Bayesian setting using Markov chain Monte Carlo (McMC) simulation. The response variable can be binomial, Gaussian or Poisson. 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, and full details are given in the vignette accompanying this package and the references in the help files. The creation of this package was supported by the Engineering and Physical Sciences Research Council (EPSRC) grant EP/J017442/1 and the Medical Research Council (MRC) grant MR/L022184/1.
The number of bird or bat fatalities from collisions with buildings, towers or wind energy turbines can be estimated based on carcass searches and experimentally assessed carcass persistence times and searcher efficiency. Functions for estimating the probability that a bird or bat that died is found by a searcher are provided. Further functions calculate the posterior distribution of the number of fatalities based on the number of carcasses found and the estimated detection probability.
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.
  CAR scores measure the correlation between the response and the 
  Mahalanobis-decorrelated  predictors.  The squared CAR score is a 
  natural measure of variable importance and provides a canonical 
  ordering of variables. This package provides functions for estimating 
  CAR scores, for variable selection using CAR scores, and for estimating 
  corresponding regression coefficients. Both shrinkage as well as 
  empirical estimators are available.
The R package CARE1, the first part of the program CARE
        (Capture-Recapture) in
        http://chao.stat.nthu.edu.tw/softwareCE.html, can be used to
        analyze epidemiological data via sample coverage approach (Chao
        et al. 2001a). Based on the input of records from several
        incomplete lists (or samples) of individuals, the R package
        CARE1 provides output of population size estimate and related
        statistics.
Misc functions for training and plotting classification and
    regression models.
Functions for creating ensembles of caret models: caretList
    and caretStack.  caretList is a convenience function for fitting multiple
    caret::train models to the same dataset. caretStack will make linear or
    non-linear combinations of these models, using a caret::train model as a
    meta-model, and caretEnsemble will make a robust linear combination of
    models using a glm.
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.
Construct continuous and non-contiguous area cartograms.
Create and integrate maps in your R workflow. This package allows
    various cartographic representations such as proportional symbols, chroropleth,
    typology, flows or discontinuities. In addition, it also proposes some useful
    features like cartographic palettes, layout (scale, north arrow, title...), labels,
    legends or access to cartographic API to ease the graphic presentation of maps.
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.
Allows users to identify similar cases for qualitative case studies using statistical matching methods.
Analysis of categorical-variable with missing values
Categorical data analysis program package.
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.
Formal psychological models, independently-replicated data sets against which to test them, and simulation archives.
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.
Provides routines for the generation of response patterns under unidimensional dichotomous and polytomous computerized adaptive testing (CAT) framework. It holds many standard functions to estimate ability, select the first item(s) to administer and optimally select the next item, as well as several stopping rules. Options to control for item exposure and content balancing are also available (Magis and Raiche, Random Generation of Response Patterns under Computerized Adaptive Testing with the R Package catR, Journal of Statistical Software, 48 (8), 2012, 1-31 <doi:10.18637/jss.v048.i08>).
`ctab' creates (multiway) percentage tables. `sqtab'
        contains a set of functions for estimating models for square
        tables such as quasi-independence, symmetry, uniform
        association. Examples show how to use these models in a
        loglinear model using glm or in a multinomial logistic model
        using mlogit or clogit
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 Ilya Shpitser and Judea Pearl, an algorithm for transportability from multiple domains with limited experiments by Elias Bareinboim and Judea Pearl and a selection bias recovery algorithm by Elias Bareinboim and Jin Tian.  All of the previously mentioned algorithms are based on a causal effect identification algorithm by Jin Tian.
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.
This package implements various estimators for average
        treatment effects---an inverse probability weighted (IPW)
        estimator, an augmented inverse probability weighted (AIPW)
        estimator, and a standard regression estimator---that make use
        of generalized additive models for the treatment assignment
        model and/or outcome model.
The causalsens package provides functions to perform sensitivity analyses and to study how various assumptions about selection bias affects estimates of causal effects.
The Causata package provides utilities for 
    extracting data from the Causata application, training binary classification 
    models, and exporting models as PMML for scoring.
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.
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) <http://imai.princeton.edu/research/files/CBGPS.pdf>, 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 add the optimal CBPS which chooses the optimal balancing function and results in doubly robust and efficient estimator for the treatment effect.
The data and meta data from Statistics
    Netherlands (www.cbs.nl) can be browsed and downloaded. The client uses
    the open data API of Statistics Netherlands.
The package provide a set of functions that extend the
        cancor function with new numerical and graphical outputs. It
        also include a regularized extension of the cannonical
        correlation analysis to deal with datasets with more variables
        than observations.
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.
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 that classifies a sample according
        to the class with the maximum mean (or any other function of)
        correlation between the test and training samples with known
        classes.
Tools to simulate networks based on Congruence Class models.
Significance tests for canonical correlation analysis,
        including asymptotic tests and a Monte Carlo method
Tests of association between SNPs or pairs of SNPs and binary phenotypes, in case-control / case-population / case-control-population studies.
A user-friendly GUI for the application of Cultural Consensus Theory (CCT) models and methods to consensus data. The models are applied using hierarchical Bayesian inference. The current package version supports binary, ordinal, and continuous data formats.
Coupled-dipole simulations for electromagnetic scattering of light by sub-wavelength particles in arbitrary 3-dimensional configurations. Scattering and absorption spectra are simulated by inversion of the interaction matrix, or by an order-of-scattering approximation scheme. High-level functions are provided to simulate spectra with varying angles of incidence, as well as with full angular averaging.
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. The Flash
    interface makes it difficult and time-consuming to select and retrieve
    influenza data. This package provides functions 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.
Designed by and for the community of differential privacy algorithm developers. It can be used to empirically evaluate and visualize Cumulative Distribution Functions incorporating noise that satisfies differential privacy, with numerous options made to streamline collection of utility measurements across variations of key parameters, such as epsilon, domain size, sample size, data shape, etc. Developed by researchers at Harvard PSI.
Employs a two-parameter family of
    distributions for modelling random variables on the (0, 1) interval by
    applying the cumulative distribution function (cdf) of one parent
    distribution to the quantile function of another.
This package proposes a statistical downscaling method for
        cumulative distribution functions (CDF), as well as the
        computation of the Cram\`er-von Mises statistics U, and the
        Kolmogorov-Smirnov statistics KS.
Coordinate Descent Algorithms for Lasso Penalized L1, L2,
        and Logistic Regression
Downloads USDA National Agricultural Statistics Service (NASS) 
    cropscape data for a specified state. Utilities for fips, abbreviation, 
    and name conversion are also provided. Full functionality requires an 
    internet connection, but data sets can be cached for later off-line use.
Functions for cognitive diagnosis modeling
    and multidimensional item response modeling for
    dichotomous and polytomous data. This package
    enables the estimation of the DINA and DINO model,
    the multiple group (polytomous) GDINA model,
    the multiple choice DINA model, the general diagnostic
    model (GDM), the multidimensional linear compensatory
    item response model and the structured latent class
    model (SLCA).
Components of Canadian Credit Aggregates and Monetary Aggregates with continuity adjustments.
Wrapper functions to model and extract various quantitative information from absorption spectra of chromophoric dissolved organic matter (CDOM).
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).
Cross-Entropy Clustering (CEC) divides the data into Gaussian type clusters. It performs the automatic reduction of unnecessary clusters, while at the same time allows the simultaneous use of various type Gaussian mixture models.
This package is a wrapper for the C implementation of the 25 benchmark functions for the CEC 2005 Special Session on Real-Parameter Optimization. The original C code by Santosh Tiwari and related documentation are available at http://www.ntu.edu.sg/home/EPNSugan/index_files/CEC-05/CEC05.htm.
This package provides R wrappers for the C implementation of 28 benchmark functions defined for the  Special Session and Competition on Real-Parameter Single Objective Optimization at CEC-2013. The focus of this package is to provide an open-source and multi-platform implementation of the CEC2013 benchmark functions, in order to make easier for researchers to test the performance of new optimization algorithms in a reproducible way. The original C code (Windows only) was provided by Jane Jing Liang, while GNU/Linux comments were made by Janez Brest. This package was gently authorised for publication on CRAN by Ponnuthurai Nagaratnam Suganthan. The official documentation is available at http://www.ntu.edu.sg/home/EPNSugan/index_files/CEC2013/CEC2013.htm. Bugs reports/comments/questions are very welcomed (in English, Spanish or Italian).
Model building, surrogate model
    based optimization and Efficient Global Optimization in combinatorial
    or mixed search spaces.
Contains a number of common astronomy conversion routines, particularly the HMS and degrees schemes, which can be fiddly to convert between on mass due to the textural nature of the former. It allows users to coordinate match datasets quickly. It also contains functions for various cosmological calculations.
Helper functions to work with spreadsheets and the "A1:D10" style
    of cell range specification.
This package is an object-oriented implementation of one-dimensional cellular automata. It supports many of the features offered by Mathematica, including elementary rules, user-defined rules, radii, user-defined seeding, and plotting.
This package implements a methodology for using cell
        volume distributions to estimate cell growth rates and division
        times that is described in the paper entitled "Cell Volume
        Distributions Reveal Cell Growth Rates and Division Times", by
        Michael Halter, John T. Elliott, Joseph B. Hubbard, Alessandro
        Tona and Anne L. Plant, which is in press in the Journal of
        Theoretical Biology.  In order to reproduce the analysis used
        to obtain Table 1 in the paper, execute the command
        "example(fitVolDist)".
Implementation of the Coarsened Exact Matching algorithm.
Conditional expectation manifolds are an approach to compute principal curves and surfaces.
Fit censored linear regression models where the random errors follow a finite mixture of Normal or Student-t distributions.
             Fit censored linear models of finite mixture multivariate Student-t and Normal distributions.
Implement an interval censor method 
 to break ties when using data with ties to fitting a 
 bivariate copula.
Estimation of censored regression (Tobit) models
   with cross-section and panel data.
Fits univariate censored linear regression model under Normal or Student-t distribution
It fits linear regression models for censored spatial data. It 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. It also offers graphical tools for assessing the fitted model.
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.
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.
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) and Green and Martin (2014).
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.
Numerical integration of cause-specific survival curves to arrive at cause-specific cumulative incidence functions,
  with three usage modes: 1) Convenient API for parametric survival regression followed by competing-risk analysis, 2) API for
  CFC, accepting user-specified survival functions in R, and 3) Same as 2, but accepting survival functions in C++.
Estimate quantiles using formula (18) from
        http://www.jaschke-net.de/papers/CoFi.pdf (Yaschke; 2001)
This project provides a group of new functions to calculate the
    outputs of the two main components of the Canadian Forest Fire Danger Rating
    System (CFFDRS) at various time scales: the Fire Weather Index (FWI) System and
    the Fire Behaviour Prediction (FBP) System. Some functions have two versions,
    table and raster based.
Comprehensive data analysis software, and the name "cg" stands for "compare groups." Its genesis and evolution are driven by common needs to compare administrations, conditions, etc. in medicine research and development. The current version provides comparisons of unpaired samples, i.e. a linear model with one factor of at least two levels. It also provides comparisons of two paired samples. Good data graphs, modern statistical methods, and useful displays of results are emphasized.
A constrained generalized additive model is fitted by the cgam routine. Given a set of predictors, each of which may have a shape or order restrictions, the maximum likelihood estimator for the constrained generalized additive model is found using an iteratively re-weighted cone projection algorithm. The ShapeSelect routine chooses a subset of predictor variables and describes the component relationships with the response. For each predictor, the user need 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 wps routine implements a two-dimensional isotonic regression without additivity assumptions.
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).
Efficient procedures for fitting an entire regression
        sequences with different model types.
Functions to analyze microarray comparative genome
        hybridization data using the Smith-Waterman algorithm
Spatial smoothing and hot spot detection using the fused
        lasso regression
cghseg is an R package dedicated to the analysis of CGH
        profiles using segmentation models.
Fit composite Gaussian process (CGP) models as described in Ba and Joseph (2012) "Composite Gaussian Process Models for Emulating Expensive Functions", Annals of Applied Statistics. The CGP model is capable of approximating complex surfaces that are not second-order stationary.  Important functions in this package are CGP, print.CGP, summary.CGP, predict.CGP and plotCGP.
A set of tools the author has found useful for performing quick observations or evaluations of data, including a variety of ways to list objects by size, class, etc. Several other tools mimic Unix shell commands, including 'head', 'tail' ,'pushd' ,and 'popd'. The functions 'seqle' and 'reverse.seqle' mimic the base 'rle' but can search for linear sequences. The function 'splatnd' allows the user to generate zero-argument commands without the need for 'makeActiveBinding' .
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.
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}.
A multiple testing procedure for clustered alternative hypotheses. It is assumed that the p-values under the null hypotheses follow U(0,1) and that the distributions of p-values from the alternative hypotheses are stochastically smaller than U(0,1). By aggregating information, this method is more sensitive to detecting signals of low magnitude than standard methods. Additionally, sporadic small p-values appearing within a null hypotheses sequence are avoided by averaging on the neighboring p-values.
Advertisers use a variety of online marketing channels to reach consumers and they want to know the degree each channel contributes to their marketing success. It's 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 identifying 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 plot 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.
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 (IFSP) 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.
This package provides functions to compute Marcus, Marcus-Levich-Jortner or Landau-Zener charge transfer rates. These rates can then be used to perform kinetic Monte Carlo simulations to estimate charge carrier mobilities in molecular materials. The preparation of this package was supported by the the Fondazione Cariplo (PLENOS project, ref. 2011-0349).
CHAT is a collection of tools developed for tumor subclonality analysis using high density DNA SNP array data and sequencing data. The pipeline consists of four major compartments: 1) tumor aneuploid genome proportion (AGP) calculation and ploidy estimation. 2) segment-specific AGP calculation and absolute copy number estimation for somatic CNAs. 3) cancer cell fraction correction for somatic SNVs in clonal or subclonal sCNA regions. 4) number of subclones estimation using Dirichlet process prior followed by MCMC approach.
A compilation of historical through contemporary climate
        measurements scraped from the Environment Canada Website
        Including tools for scraping data, creating metadata and
        formating temperature files.
Contains methods for creating multivariate Chebyshev
  approximation of functions on a hypercube. Some methods for
  non-Chebyshev grids are also provided.
A set of functions to calculate check digits according to
        various algorithms and to verify whether a string ends in a
        valid check digit
Tests and assertions to perform frequent argument checks. A
    substantial part of the package was written in C to minimize any worries
    about execution time overhead.
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.
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).
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).
The package provides data sets used in the book
        "Chemometrics with R - Multivariate Data Analysis in the
        Natural Sciences and Life Sciences" by Ron Wehrens, Springer
        (2011).
A collection of functions for top-down exploratory data analysis of spectral data obtained via nuclear magnetic resonance (NMR), infrared (IR) or Raman spectroscopy.  Includes functions for plotting and inspecting spectra, peak alignment, hierarchical cluster analysis (HCA), principal components analysis (PCA) and model-based clustering. Robust methods appropriate for this type of high-dimensional data are available.  ChemoSpec is designed with metabolomics data sets in mind, where the samples fall into groups such as treatment and control.  Graphical output is formatted consistently for publication quality plots.  ChemoSpec is intended to be very user friendly and help you get usable results quickly.  A vignette covering typical operations is available.
Provides an alternative approach to multiple testing
        by calculating a simultaneous upper confidence bounds for the
        number of true null hypotheses among any subset of the hypotheses of interest. 
	Some of the functions in this package are optionally enhanced by the 'gurobi'
	software and its accompanying R package. For their installation, please follow the 
	instructions at www.gurobi.com and http://www.gurobi.com/documentation, respectively.
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.
Calculation of standard deviation scores from different
    growth standards
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. '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.
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.
Change point regression models are also called two-phase regression, break-point regression, split-point regression, structural change models and threshold regression models. Hypothesis testing in change point logistic regression with or without interaction terms. Several options are provided for testing in models with interaction, including a maximum of likelihood ratios test that determines p-value through Monte Carlo. Estimation under change point model is also included, but less developed at this point.
Functions and data sets to support chemical thermodynamic modeling in biochemistry
  and low-temperature geochemistry. The features include calculation of the standard molal
  thermodynamic properties and chemical affinities of reactions involving minerals and/or
  biomolecules; a database of thermodynamic properties of aqueous, crystalline and gaseous
  species; amino acid group additivity for the standard molal thermodynamic properties of
  neutral and ionized proteins; use of the revised Helgeson-Kirkham-Flowers equations of state
  for aqueous species; construction of equilibrium activity diagrams as a function of
  temperature, pressure, and chemical activities or fugacities of basis species.
This package consists of functions to design DCMs and other types of choice 
  studies (including MaxDiff and other tradeoffs)
Implements an MCMC algorithm to estimate a hierarchical
        multinomial logit model with a normal heterogeneity
        distribution. The algorithm uses a hybrid Gibbs Sampler with a
        random walk metropolis step for the MNL coefficients for each
        unit. Dependent variable may be discrete or continuous.
        Independent variables may be discrete or continuous with
        optional order constraints. Means of the distribution of
        heterogeneity can optionally be modeled as a linear function of
        unit characteristics variables.
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.
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. Please see the vignettes for more details.
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.
A programmatic interface to the Chromosome Counts Database
    (http://ccdb.tau.ac.il/). This package is part of the rOpenSci suite
    (http://ropensci.org)
chromoR provides users with a statistical pipeline for analysing chromosomal interactions data (Hi-C data).It combines wavelet methods and a Bayesian approach for correction (bias and noise) and comparison (detecting significant changes between Hi-C maps) of Hi-C contact maps.In addition, it also support detection of change points in 1D Hi-C contact profiles.
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.
Generative models for complex networks with conditionally independent dyadic structure. Now supports directed arcs!
Estimate of cumulative incidence function in two samples. Provide weighted summary statistics based on various methods and weights.
Many experiments in neuroscience involve randomized and fast stimulation while the continuous outcome measures respond at much slower time scale, for example event-related fMRI.  This package provide valid statistical tools with causal interpretation under these challenging settings, without imposing model assumptions.
This package provides functions to compute a method for identifying the instar of Curculionid larvae from the observed distribution of the headcapsule size of mature larvae.
Detecting and visualizing nonlinear interaction effects of single nucleotide polymorphisms or epistatic interactions, especially high-order epistatic interactions, are important topics in bioinformatics because of their significant mathematical and computational challenges. We present CINOEDV (Co-Information based N-Order Epistasis Detector and Visualizer) for detecting, visualizing, and analyzing high-order epistatic interactions by introducing virtual vertices into the construction of a hypergraph. CINOEDV was developed as an alternative to existing software to build a global picture of epistatic interactions and unexpected high-order epistatic interactions, which might provide useful clues for understanding the underlying genetic architecture of complex diseases.
This package contains functions for fitting circumplex
        structural models for correlation matrices (with negative
        correlation) by the method of maximum likelihood.
Circular layout is an efficient way for the visualization of huge 
    amounts of information. Here the circlize package provides an implementation 
    of circular layout generation in R as well as an enhancement of available 
    software. The flexibility of this package is based on the usage of low-level 
    graphics functions such that self-defined high-level graphics can be easily 
    implemented by users for specific purposes. Together with the seamless 
    connection between the powerful computational and visual environment in R, 
    circlize gives users more convenience and freedom to design figures for 
    better understanding complex patterns behind multi-dimensional data.
Includes functions for the analysis of circular data using distributions based on Nonnegative Trigonometric Sums (NNTS). The package includes functions for calculation of densities and distributions, for the estimation of parameters, for plotting and more.
Detection of outliers in circular-circular regression models, modifying its and estimating of models parameters.
Circular Statistics, from "Topics in circular Statistics"
        (2001) S. Rao Jammalamadaka and A. SenGupta, World Scientific.
Circular Statistics, from "Topics in circular Statistics" (2001) S. Rao Jammalamadaka and A. SenGupta, World Scientific.
Jointly model the accuracy of cognitive responses and item choices within a bayesian hierarchical framework as described by Culpepper and Balamuta (2015) <doi:10.1007/s11336-015-9484-7>. In addition, the package contains the datasets used within the analysis of the paper.
A likelihood-based hypothesis testing approach is implemented for assessing causal mediation. For example, it could be used to test for mediation of a known causal association between a DNA variant, the 'instrumental variable', and a clinical outcome or phenotype by gene expression or DNA methylation, the potential mediator. Another example would be testing mediation of the effect of a drug on a clinical outcome by the molecular target. The hypothesis test generates a p-value or permutation-based FDR value with confidence intervals to quantify uncertainty in the causal inference. The outcome can be represented by either a continuous or binary variable, the potential mediator is continuous, and the instrumental variable can be continuous or binary and is not limited to a single variable but may be a design matrix representing multiple variables.
Supports quantitative
    research in scientometrics and bibliometrics. Provides
    various tools for preprocessing bibliographic
    data retrieved, e.g., from Elsevier's SciVerse Scopus,
    computing bibliometric impact of individuals,
    or modeling many phenomena encountered in the social sciences.
This package implements the approach to assign tumor gene expression dataset to the 6 CIT Breast Cancer Molecular Subtypes described in Guedj et al 2012.
This package implements the approach to assign tumor gene expression dataset to the 6 CIT Colon Cancer Molecular Subtypes described in Marisa et al 2013.
Contain an RStudio addin to insert BibTex citation in Rmarkdown documents with a minimal user interface.
Functions and an 'RStudio' add-in that search a 'BibTeX'-file to create and
  insert formatted Markdown citations into the current document.
Input: a csv-file for each database table and a
        controlfile describing relations between tables. Output: An
        extended ER diagram
An R implementation of the Average Marginal Component-specific Effects (AMCE) estimator presented in Hainmueller, J., Hopkins, D., and Yamamoto T. (2014) Causal Inference in Conjoint Analysis: Understanding Multi-Dimensional Choices via Stated Preference Experiments. Political Analysis 22(1):1-30.
Client for 'CKAN' 'API' (http://ckan.org/). Includes interface
    to 'CKAN' 'APIs' for search, list, show for packages, organizations, and
    resources. In addition, provides an interface to the 'datastore' 'API'.
A dynamic programming algorithm for optimal univariate k-means
 clustering. Minimizing the sum of squares of within-cluster distances, the
 algorithm guarantees optimality and reproducibility. Its advantage over
 heuristic k-means algorithms in efficiency and accuracy is increasingly
 pronounced as the number of clusters k increases. It provides an alternative
 to heuristic k-means algorithms for univariate clustering.
This package implements in C++/Rcpp various cladogenesis-related calculations that are slow in pure R.  These include the calculation of the probability of various scenarios for the inheritance of geographic range at the divergence events on a phylogenetic tree, and other calculations necessary for models which are not continuous-time markov chains (CTMC), but where change instead occurs instantaneously at speciation events.  Typically these models must assess the probability of every possible combination of (ancestor state, left descendent state, right descendent state).  This means that there are up to (# of states)^3 combinations to investigate, and in biogeographical models, there can easily be hundreds of states, so calculation time becomes an issue.  C++ implementation plus clever tricks (many combinations can be eliminated a priori) can greatly speed the computation time over naive R implementations.  CITATION INFO: This package is the result of my Ph.D. research, please cite the package if you use it!  Type: citation(package="cladoRcpp") to get the citation information.
Implementation of the Wilkinson and Ivany (2002) approach to paleoclimate analysis, applied to isotope data extracted from clams.
Get description of images from Clarifai API. For more information,
    see http://clarifai.com. Clarifai uses a large deep learning cloud to come
    up with descriptive labels of the things in an image. It also provides how
    confident it is about each of the labels.
Various functions for classification, including k-nearest
  neighbour, Learning Vector Quantization and Self-Organizing Maps.
Construct directed graphs of S4 class hierarchies and
  visualize them.  In general, these graphs typically are DAGs (directed
  acyclic graphs), often simple trees in practice.
Given $p$-dimensional training data containing
    $d$ groups (the design space), a classification
    algorithm (classifier) predicts which group new data
    belongs to.  Generally the input to these algorithms is
    high dimensional, and the boundaries between groups
    will be high dimensional and perhaps curvilinear or
    multi-faceted. This package implements methods for
    understanding the division of space between the groups.
IRT classification uses the probability that candidates of
    a given ability, will answer correctly questions of a specified
    difficulty to calculate the probability of their achieving
    every possible score in a test. Due to the IRT assumption of
    conditional independence (that is every answer given is assumed
    to depend only on the latent trait being measured) the
    probability of candidates achieving these potential scores can
    be expressed by multiplication of probabilities for item
    responses for a given ability. Once the true score and the
    probabilities of achieving all other scores have been
    determined for a candidate the probability of their score lying
    in the same category as that of their true score
    (classification accuracy), or the probability of consistent
    classification in a category over administrations
    (classification consistency), can be calculated.
Selected commonly used methods for choosing univariate class intervals for mapping or other graphics purposes.
A collection of functions for the creation and application of highly optimised, robustly evaluated ensembles of support vector machines (SVMs). The package takes care of training individual SVM classifiers using a fast parallel heuristic algorithm, and combines individual classifiers into ensembles. Robust metrics of classification performance are offered by bootstrap resampling and permutation testing.
Provides a set of utility tools to inspect spatial objects, facilitate
  handling and reporting of topology errors and geometry validity issues.
  Finally, it provides a geometry cleaner that will fix all geometry problems,
  and eliminate (at least reduce) the likelihood of having issues when doing
  spatial data processing.
Provides a set of fast tools for converting a textual corpus into a set of normalized tables. The underlying natural language processing pipeline utilizes the Stanford's CoreNLP library. Exposed annotation tasks include tokenization, part of speech tagging, named entity recognition, entity linking, sentiment analysis, dependency parsing, coreference resolution, and information extraction. Several datasets containing token unigram, part of speech tag, and dependency type frequencies are also included to assist with analyses. Currently supports parsing text in English, French, German, and Spanish.
Implements an empirical Bayes approach for simultaneous variable clustering and regression. This version also (re)implements in C++ an R script proposed by Howard Bondell that fits the Pairwise Absolute Clustering and Sparsity (PACS) methodology (see Sharma et al (2013) <DOI:10.1080/15533174.2012.707849>).
Conditioned Latin hypercube sampling, as published by Minasny and McBratney (2006) <DOI:10.1016/j.cageo.2005.12.009>. This method proposes to stratify sampling in presence of ancillary data. An extension of this method, which propose to associate a cost to each individual and take it into account during the optimisation process, is also proposed (Roudier et al., 2012, <DOI:10.1201/b12728-46>).
Clustering categorical sequences by means of finite mixtures with Markov model components is the main utility of ClickClust. The package also allows detecting blocks of equivalent states by forward and backward state selection procedures.
Fixes data errors in numerical, factor and date variables and performs report tables from models and summaries.
A set of tools to read, analyze and write lists of click sequences
    on websites (i.e., clickstream). A click can be represented by a number,
    character or string. Clickstreams can be modeled as zero- (only computes
    occurrence probabilities), first- or higher-order Markov chains.
CliFlo is a web portal to the New Zealand National Climate
    Database and provides public access (via subscription) to around 6,500
    various climate stations. Collating and manipulating data from CliFlo
    (hence clifro) and importing into R for further analysis, exploration and
    visualisation is now straightforward and coherent. The user is required to
    have an internet connection, and a current CliFlo subscription (free) if
    data from stations, other than the public Reefton electronic weather
    station, is sought.
A profile likelihood based method of estimation and inference on the correlation coefficient of bivariate data with different types of censoring and missingness.
Functions to homogenize climatological series and to produce climatological summaries and grids from the homogenized results, plus functions to draw wind-roses and Walter&Lieth diagrams.
Calculates first, and second order, average minimal depth of a
    maximal subtree for a forest object produced by the R 'ranger'
    package. This variable importance metric is implemented as described in
    Ishwaran et. al. ("High-Dimensional Variable Selection for Survival Data",
    March 2010, <doi:10.1198/jasa.2009.tm08622>).
Classification of climate according to Koeppen - Geiger, of aridity
    indices, of continentality indices, of water balance after Thornthwaite, of
    viticultural bioclimatic indices. Drawing climographs: Thornthwaite, Peguy,
    Bagnouls-Gaussen.
PCIC's implementation of Climdex routines for computation of
    extreme climate indices.
A robust constrained L1 minimization method for estimating
        a large sparse inverse covariance matrix (aka precision
        matrix), and recovering its support for building graphical
        models.  The computation uses linear programming.
Functions for fitting GEV and POT (via point process fitting)
    models for extremes in climate data, providing return values, return
    probabilities, and return periods for stationary and nonstationary models.
    Also provides differences in return values and differences in log return
    probabilities for contrasts of covariate values. Functions for estimating risk
    ratios for event attribution analyses, including uncertainty. Under the hood,
    many of the functions use functions from extRemes, including for fitting the
    statistical models.
Absolute homogeneity tests SNHT absolute 1-breaks, 1-break, 
        SD different from 1, 2-breaks, Buishand, Pettitt, von Neumann ratio and 
        ratio-rank, Worsley, and Craddock, Relative homogeneity tests SNHT 
        absolute 1-breaks, 1-break SD different from 1, 2-breaks, Peterson 
        and Easterling, and Vincent, Differences in scale between two groups Siegel–Tukey, 
        Create reference time series mean, weights/correlation, finding outliers Grubbs, 
        ESD, MAD, Tietjen Moore, Hampel, etc.
Contains functions to detect and visualise periods of climate
    sensitivity (climate windows) for a given biological response.
Utilities to make your clinical collaborations easier if not fun.
Functions for calculating clinical significance.
This package provides the estimation of an index measuring
        the clinical utility of DNA testing in the context of
        gene-environment interactions on a disease. The corresponding
        gene-environment interaction effect on the additive scale can
        also be obtained. The estimation is based on case-control or
        cohort data. The method was developed by Nguyen et al. 2013.
Simple utility functions to read from and write to the Windows,
    OS X, and X11 clipboards.
A small subset of Unicode symbols, that are useful
    when building command line applications. They fall back to
    alternatives on terminals that do not support Unicode.
    Many symbols were taken from the 'figures' 'npm' package
    (see <https://github.com/sindresorhus/figures>).
Estimation and inference for linear models where some or all of the
    fixed-effects coefficients are subject to order restrictions. This package uses
    the robust residual bootstrap methodology for inference, and can handle some
    structure in the residual variance matrix.
A set of functions to fit a boosting conditional logit model.
Tools for the fitting and cross validation of exact conditional logistic regression models with lasso and elastic net penalties. Uses cyclic coordinate descent and warm starts to compute the entire path efficiently.
Fit a sequence of conditional logistic regression models with lasso, for small to large sized samples.
Provides means of plots for comparing utilization data of compute systems.
R Interface to C API of COIN-OR Clp, depends on COIN-OR Clp Version >= 1.12.0.
This package provides and implementation of a one step
        smoothing newton method for the solution of second order cone
        programming problems, originally described by Xiaoni Chi and
        Sanyang Liu.
Central limit theorem experiments presented by data frames or plots. Functions include generating theoretical sample space, corresponding probability, and simulated results as well.
Provides several cluster-robust variance estimators
    (i.e., sandwich estimators) for ordinary and weighted least squares linear
    regression models. Several adjustments are incorporated to improve small-
    sample performance. The package includes functions for estimating the variance-
    covariance matrix and for testing single- and multiple-contrast hypotheses
    based on Wald test statistics. Tests of single regression coefficients use
    Satterthwaite or saddle-point corrections. Tests of multiple-contrast hypotheses 
    use an approximation to Hotelling's T-squared distribution.  Methods are
    provided for a variety of fitted models, including lm(), plm() (from package 'plm'),
    gls() and lme() (from 'nlme'), robu() (from 'robumeta'), and rma.uni() and rma.mv() (from
    'metafor').
CLUE is an R package for identifying optimal number of clusters in a given time-course dataset clustered by cmeans or kmeans algorithms.
We developed the clues R package to provide functions 
        for automatically estimating the number of clusters and 
        getting the final cluster partition without any input 
        parameter except the stopping rule for convergence. 
        The package also provides functions to
        evaluate and compare the performances of partitions of a data
        set both numerically and graphically.
Provides utilities for clustering subjects and variables of mixed data types. Similarities between subjects are measured by Gower's general similarity coefficient with an extension of Podani for ordinal variables. Similarities between variables are assessed by combination of appropriate measures of association for different pairs of data types. Alternatively, variables can also be clustered by the 'ClustOfVar' approach. The main feature of the package is the generation of a mixed-data heatmap. For visualizing similarities between either subjects or variables, a heatmap of the corresponding distance matrix can be drawn. Associations between variables can be explored by a 'confounderPlot', which allows visual detection of possible confounding, collinear, or surrogate factors for some variables of primary interest. Distance matrices and dendrograms for subjects and variables can be derived and used for further visualizations and applications.
Non-parametric tests (Wilcoxon rank sum test and Wilcoxon signed rank test) for clustered data.
Tests, utilities, and case studies for analyzing significance in clustered binary matched-pair
  data. The central function clust.bin.pair uses one of several tests to calculate a Chi-square 
  statistic. Implemented are the tests Eliasziw, Obuchowski, Durkalski, and Yang with McNemar
  included for comparison. The utility functions nested.to.contingency and paired.to.contingency
  convert data between various useful formats. Thyroids and psychiatry are the canonical
  datasets from Obuchowski and Petryshen respectively.
Methods for Cluster analysis.  Much extended the original from
	Peter Rousseeuw, Anja Struyf and Mia Hubert,
	based on Kaufman and Rousseeuw (1990) "Finding Groups in Data".
A collection of data sets for teaching cluster analysis.
Identification and visualization of groups of closely spaced mutations in the DNA sequence of cancer genome. The extremely mutated zones are searched in the symmetric dissimilarity matrix using the anti-Robinson matrix properties. Different data sets are obtained to describe and plot the clustered mutations information.
Visualise clustering algorithms with GGobi.  Contains both
    general code for visualising clustering results and specific
    visualisations for model-based, hierarchical and SOM clustering.
We developed the clusterGeneration package to provide functions 
        for generating random clusters, generating random 
        covariance/correlation matrices,
        calculating a separation index (data and population version)
        for pairs of clusters or cluster distributions, and 1-D and 2-D
        projection plots to visualize clusters.  The package also
        contains a function to generate random clusters based on
        factorial designs with factors such as degree of separation,
        number of clusters, number of variables, number of noisy
        variables.
The Partitioning Algorithm based on Recursive Thresholding
        (PART) is used to recursively uncover clusters and subclusters
        in the data. Functionality is also available for visualization
        of the clustering.
One haplotype is a combination of SNP
  (Single Nucleotide Polymorphisms) within the QTL (Quantitative Trait Loci).
  clusterhap groups together all individuals of a population with the same haplotype.
  Each group contains individual with the same allele in each SNP,
  whether or not missing data. Thus, clusterhap groups individuals,
  that to be imputed, have a non-zero probability of having the same alleles
  in the entire sequence of SNP's. Moreover, clusterhap calculates such
  probability from relative frequencies.
A dynamic programming algorithm for optimal clustering multidimensional data with sequential constraint. The algorithm minimizes the sum of squares of within-cluster distances. The sequential constraint allows only subsequent items of the input data to form a cluster. The sequential constraint is typically required in clustering data streams or items with time stamps such as video frames, GPS signals of a vehicle, movement data of a person, e-pen data, etc. The algorithm represents an extension of Ckmeans.1d.dp to multiple dimensional spaces. Similarly to the one-dimensional case, the algorithm guarantees optimality and repeatability of clustering. Method clustering.sc.dp can find the optimal clustering if the number of clusters is known. Otherwise, methods findwithinss.sc.dp and backtracking.sc.dp can be used.
Integrative context-dependent clustering for heterogeneous
    biomedical datasets. Identifies local clustering structures in related
    datasets, and a global clusters that exist across the datasets.
This package enables researchers to calculate power for cluster-randomized crossover trials by employing a simulation-based approach.  A particular study design is specified, with fixed sample sizes for all clusters and an assumed treatment effect, and the empirical power for that study design is calculated by simulating hypothetical datasets.
Gaussian mixture models, k-means, mini-batch-kmeans and k-medoids
    clustering with the option to plot, validate, predict (new data) and estimate the
    optimal number of clusters. The package takes advantage of 'RcppArmadillo' to
    speed up the computationally intensive parts of the functions.
Nonparametric rank based tests (rank-sum tests and signed-rank tests) for clustered data, especially useful for clusters having informative cluster size and intra-cluster group size.
A function for validating microarry clusters via
        reproducibility
Calculate p-values and confidence intervals using cluster-adjusted
    t-statistics (based on Ibragimov and Muller (2010) <DOI:10.1198/jbes.2009.08046>, pairs cluster bootstrapped t-statistics, and wild cluster bootstrapped t-statistics (the latter two techniques based on Cameron, Gelbach, and Miller (2008) <DOI:10.1162/rest.90.3.414>. Procedures are included for use with GLM, ivreg, plm (pooling or fixed effects), and mlogit models.
Distance measures (GDM1, GDM2,	Sokal-Michener, Bray-Curtis, for symbolic interval-valued data), cluster quality indices (Calinski-Harabasz, Baker-Hubert, Hubert-Levine, Silhouette, Krzanowski-Lai, Hartigan, Gap,	Davies-Bouldin),	data normalization formulas, data generation (typical and non-typical data), HINoV method,	replication analysis, linear ordering methods, spectral clustering, agreement indices between two partitions, plot functions (for categorial and symbolic interval-valued data).
Allows one to assess the stability of individual objects, clusters 
    and whole clustering solutions based on repeated runs of the K-means and K-medoids 
    partitioning algorithms.
Calculate some statistics aiming to help analyzing the clustering tendency of given data. In the first version, Hopkins' statistic is implemented.
An R package that provides a suite of tools to evaluate
        clustering algorithms, clusterings, and individual clusters.
Functions which allow to integrate geographical constraints in Ward hierarchical clustering. Geographical maps of typologies obtained can be displayed with the use of shapefiles.
Model-based clustering of mixed data (i.e. data which consist of continuous, binary, ordinal or nominal variables) using a parsimonious mixture of latent Gaussian variable models.
Functions to perform k-prototypes partitioning clustering for
    mixed variable-type data according to Z.Huang (1998): Extensions to the k-Means
    Algorithm for Clustering Large Data Sets with Categorical Variables, Data Mining
    and Knowledge Discovery 2, 283-304, <DOI:10.1023/A:1009769707641>.
An implementation of a variable selection procedure in clustering by mixture models for discrete data (clustMMDD). Genotype data are examples of such data with two unordered observations (alleles) at each locus for diploid individual. The two-fold problem of variable selection and clustering is seen as a model selection problem where competing models are characterized by the number of clusters K, and the subset S of clustering variables. Competing models are compared by penalized maximum likelihood criteria. We considered asymptotic criteria such as Akaike and Bayesian Information criteria, and a family of penalized criteria with penalty function to be data driven calibrated.
Cluster analysis of a set of variables. Variables can be
    quantitative, qualitative or a mixture of both.
A class of methods that combine dimension reduction and clustering of continuous or categorical data. For continuous data, the package contains implementations of factorial K-means (Vichi and Kiers 2001; <DOI:10.1016/S0167-9473(00)00064-5>) and reduced K-means (De Soete and Carroll 1994; <DOI:10.1007/978-3-642-51175-2_24>); both methods that combine principal component analysis with K-means clustering. For categorical data, the package provides MCA K-means (Hwang, Dillon and Takane 2006; <DOI:10.1007/s11336-004-1173-x>), i-FCB (Iodice D'Enza and Palumbo 2013, <DOI:10.1007/s00180-012-0329-x>) and Cluster Correspondence Analysis (van de Velden, Iodice D'Enza and Palumbo 2016; <DOI:10.1007/s11336-016-9514-0>), which combine multiple correspondence analysis with K-means.
A complimentary package for use with hclust; simprof tests
        to see which (if any) clusters are statistically different. The
        null hypothesis is that there is no a priori group structure.
        See Clarke, K.R., Somerfield, P.J., and Gorley R.N. 2008.
        Testing of null hypothesis in exploratory community analyses:
        similarity profiles and biota-environment linkage. J. Exp. Mar.
        Biol. Ecol. 366, 56-69.
Functions for the clustering of variables around Latent Variables. Each cluster of variables, which may be defined as a local or directional cluster, is associated with a latent variable.
    External variables measured on the same observations or/and additional information on the variables can be taken into account. A "noise" cluster or sparse latent variables can also de defined.
A function which implements variable selection methodology for model-based clustering which allows to find the (locally) optimal subset of variables  in a data set that have group/cluster information. A greedy or headlong search can be used, either in a forward-backward or backward-forward direction, with or without sub-sampling at the hierarchical clustering stage for starting MCLUST models. By default the algorithm uses a sequential search, but parallelisation is also available.
Package contains most of the popular internal and external
        cluster validation methods ready to use for the most of the
        outputs produced by functions coming from package "cluster".
        Package contains also functions and examples of usage for
        cluster stability approach that might be applied to algorithms
        implemented in "cluster" package as well as user defined
        clustering algorithms.
Statistical and biological validation of clustering results.
Single objective optimization using a CMA-ES.
Pure R implementation of the Covariance Matrix Adaptation -
    Evolution Strategy (CMA-ES) with optional restarts (IPOP-CMA-ES).
Calculation and plot of the stepwise Cronbach-Mesbah Curve
Collective matrix factorization (CMF) finds joint low-rank representations for a collection of matrices with shared row or column entities. This code learns variational Bayesian approximation for CMF, supporting multiple likelihood potentials and missing data, while identifying both factors shared by multiple matrices and factors private for each matrix.
Quite extensive package for the estimation of marginal models for categorical data.
Provides the source and examples for James P. Howard, II, 
             "Computational Methods for Numerical Analysis with R," 
			 <https://jameshoward.us/cmna>, a forthcoming book on
			 numerical methods in R.
The main purpose of this package is to juxtapose the different control limits obtained by modelling a data set through the COM-Poisson distribution vs. the classical Poisson distribution. Accordingly, this package offers the ability to compute the COM-Poisson parameter estimates and plot associated Shewhart control charts for a given data set.
Manhattan plot, a type of scatter plot, was widely used to display the association results. However, it is usually time-consuming and laborious for a
 non-specialist user to write scripts and adjust parameters of an elaborate plot. Moreover, the ever-growing traits measured have necessitated the 
 integration of results from different Genome-wide association study researches. Circle Manhattan Plot is the first open R package that can lay out 
 Genome-wide association study P-value results in both traditional rectangular patterns, QQ-plot and novel circular ones. United in only one bull's eye style 
 plot, association results from multiple traits can be compared interactively, thereby to reveal both similarities and differences between signals.
Estimation, testing and regression modeling of
 subdistribution functions in competing risks, as described in Gray
 (1988), A class of K-sample tests for comparing the cumulative
 incidence of a competing risk, Ann. Stat. 16:1141-1154, and Fine JP and
 Gray RJ (1999), A proportional hazards model for the subdistribution
 of a competing risk, JASA, 94:496-509.
Estimation, testing and regression modeling of
 subdistribution functions in competing risks using quantile regressions,
 as described in Peng and Fine (2009) <DOI:10.1198/jasa.2009.tm08228>.
A collection of useful helper routines developed by
  students of the Center for the Mathematical Research, Stankin,
  Moscow.
The Satellite Application Facility on Climate Monitoring (CM SAF) 
  is a ground segment of the European Organization for the Exploitation of 
  Meteorological Satellites (EUMETSAT) and one of EUMETSATs Satellite Application 
  Facilities. The CM SAF contributes to the sustainable observing of the climate 
  system by providing Essential Climate Variables related to the energy and water 
  cycle of the atmosphere (www.cmsaf.eu). It is a joint cooperation of seven 
  National Meteorological and Hydrological Services, including the Deutscher
  Wetterdienst (DWD).
  The cmsaf R-package provides a small collection of R-functions, which are 
  inspired by the Climate Data Operators (cdo). This gives the opportunity to  
  analyse and manipulate CM SAF data without the need of installing cdo. 
  The cmsaf R-package is tested for CM SAF netcdf data, which are structured 
  in three-dimensional arrays (longitude, latitude, time) on a rectangular grid. 
  Layered CM SAF data have to be converted with the provided `levbox_mergetime` 
  function. The cmsaf R-package functions have only minor checks for deviations 
  from the recommended data structure, and give only few specific error messages. 
  Thus, there is no warranty of accurate results.
  Scripts for an easy application of the functions are provided at the CM SAF homepage 
  (http://www.cmsaf.eu/EN/Products/Tools/Tools_node.html).
Various utilities for the complex multivariate Gaussian distribution.
Provides functions for performing Coincidence Analysis (CNA).
A GUI with which users can construct and interact
        with Canonical Correspondence Analysis and Canonical Non-Symmetrical Correspondence Analysis and provides inferential results by using Bootstrap Methods.
Contains functions for computing the nonparametric maximum
	     likelihood estimate of a log-concave density function from
	     univariate observations. The log-density estimate is always a
	     piecewise linear function.
Methods for assigning copy number states and breakpoints in resequencing experiments of prokaryotic organisms.
This package evaluates DNA copy number data, using both their initial form (copy number as a noisy function of genomic position) and their approximation by a piecewise-constant function (segmentation), for the purpose of identifying genomic regions where the copy number differs from the norm.
Carries out analysis of common 
             Copy Number Variants (CNVs) and imputed Single Nucleotide 
             Polymorphisms (SNPs) in population-based studies. 
             It includes tools for estimating association under a series 
             of study designs (case-control, cohort, etc), using several 
             dependent variables (class status, censored data, counts) 
             as response, adjusting for covariates and considering 
             various inheritance models. Moreover, it is possible to 
             perform epistasis studies with pairs of CNVs or imputed SNPs.
             It has been optimized in order to make feasible the analyses 
             of Genome Wide Association studies (GWAs) with hundreds of 
             thousands of genetic variants (CNVs / imputed SNPs). Also, 
             it incorporates functions for inferring copy number (CNV 
             genotype calling). Various classes and methods for generic 
             functions (print, summary, plot, anova, ...) have been 
             created to facilitate the analysis.
This package contains example data sets with Copy Number Variants and imputed SNPs to be used by CNVassoc package.
Coalescent simulators can rapidly simulate biological sequences
    evolving according to a given model of evolution.
    You can use this package to specify such models, to conduct the simulations
    and to calculate additional statistics from the results.
    It relies on existing simulators for doing the simulation, and currently
    supports the programs 'ms', 'msms' and 'scrm'. It also supports finite-sites
    mutation models by combining the simulators with the program 'seq-gen'.
Flexible framework for coalescent analyses in R. It includes a main function running the  MCMC algorithm, auxiliary functions for tree rearrangement, and some functions to compute population genetic parameters.
Functions to analyze coarse data.
    Specifically, it contains functions to (1) fit parametric accelerated
    failure time models to interval-censored survival time data, and (2)
    estimate the case-fatality ratio in scenarios with under-reporting.
    This package's development was motivated by applications to infectious
    disease: in particular, problems with estimating the incubation period and
    the case fatality ratio of a given disease.  Sample data files are included
    in the package.
Generate balance tables and plots for covariates of groups
    preprocessed through matching, weighting or subclassification, for example,
    using propensity scores. Includes integration with 'MatchIt', 'twang', 'Matching', and
    'CBPS' for assessing balance on the output of their preprocessing functions. Users
    can also specify data for balance assessment not generated through the above packages.
This package performs prediction for regression-oriented problems, aggregating in a nonlinear scheme any basic regression machines suggested by the context and provided by the user. If the user has no valuable knowledge on the data, four defaults machines wrappers are implemented so as to cover a minimal spectrum of prediction methods. If necessary, the computations may be parallelized. The method is described in Biau, Fischer, Guedj and Malley (2013), "COBRA: A Nonlinear Aggregation Strategy".
Qualitatively Constrained (Regression) Smoothing Splines via
        Linear Programming and Sparse Matrices.
Allows for pooled analysis of microarray data by batch-correcting control samples, and then applying the derived correction parameters to non-control samples to obtain bias-free, inter-dataset corrected data.
Statistical tests for the comparison between two correlations
    based on either independent or dependent groups. Dependent correlations can
    either be overlapping or nonoverlapping. A web interface is available on the
    website http://comparingcorrelations.org. A plugin for the R GUI and IDE RKWard
    is included. Please install RKWard from https://rkward.kde.org to use this
    feature. The respective R package 'rkward' cannot be installed directly from a
    repository, as it is a part of RKWard.
The cocoreg algorithm extracts shared variation from a collection of datasets using regression models.
Fits predictive and symmetric co-correspondence analysis (CoCA) models to relate one data matrix
  to another data matrix. More specifically, CoCA maximises the weighted covariance 
  between the weighted averaged species scores of one community and the weighted averaged species
  scores of another community. CoCA attempts to find patterns that are common to both communities.
Statistical tests for the comparison between two or more alpha
    coefficients based on either dependent or independent groups of individuals.
    A web interface is available at http://comparingcronbachalphas.org. A plugin
    for the R GUI and IDE RKWard is included. Please install RKWard from https://
    rkward.kde.org to use this feature. The respective R package 'rkward' cannot be
    installed directly from a repository, as it is a part of RKWard.
Provides functions for summarizing and plotting the
	output from Markov Chain Monte Carlo (MCMC) simulations, as
	well as diagnostic tests of convergence to the equilibrium
	distribution of the Markov chain.
Markov chain Monte Carlo burn-in based on "bridge" statistics, in the way of coda::heidel.diag, but including non asymptotic tabulated statistics.
Generates all necessary C functions allowing the user to work with
    the compiled-code interface of ode() and bvptwp(). The implementation supports
    "forcings" and "events". Also provides functions to symbolically compute
    Jacobians, sensitivity equations and adjoint sensitivities being the basis for
    sensitivity analysis.
Computation of Multiscale Codependence Analysis and spatial eigenvector maps, as an additional feature.
A collection of coding functions as alternatives to the standard
    functions in the stats package, which have names starting with 'contr.'.  Their
    main advantage is that they provide a consistent method for defining marginal
    effects in multi-way factorial models.  In a simple one-way ANOVA model the
    intercept term is always the simple average of the class means.
A toolbox of ecological community dynamics metrics that are
    explicitly temporal. Functions fall into two categories: temporal diversity
    indices and community stability metrics. The diversity indices are temporal
    analogs to traditional diversity indices such as richness and rank-abundance
    curves. Specifically, functions are provided to calculate species turnover, mean
    rank shifts, and lags in community similarity between time points. The community
    stability metrics calculate overall stability and patterns of species covariance
    and synchrony over time.
Cronbach's alpha and McDonald's omega are widely used reliability or internal consistency measures in social, behavioral and education sciences. Alpha is reported in nearly every study that involves measuring a construct through multiple test items. The package 'coefficientalpha' calculates coefficient alpha and coefficient omega with missing data and non-normal data. Robust standard errors and confidence intervals are also provided. A test is also available to test the tau-equivalent and homogeneous assumptions. Version 0.5 added the bootstrap confidence intervals.
Plots the coefficients from model objects.  This very quickly shows the user the point estimates and confidence intervals for fitted models.
Simulate species occurrence and abundances (counts) along
    gradients.
Simulates the composition of samples of vegetation
        according to gradient-based vegetation theory.  Features a
        flexible algorithm incorporating competition and complex
        multi-gradient interaction.
species coexistence modeling under asymmetric dispersal
        and fluctuating source-sink dynamics;testing the proportion of
        coexistence scenarios driven by neutral and niche processes
Generate cofeature (feature by sample) matrices. The package 
  utilizes ggplot2::geom_tile() to generate the matrix allowing for easy
  additions from the base matrix.
Copula based imputation method. A semiparametric imputation procedure for missing multivariate data based on conditional copula specifications.
Conditional inference procedures for the general independence
  problem including two-sample, K-sample (non-parametric ANOVA), correlation,
  censored, ordered and multivariate problems.
Methods for obtaining simultaneous confidence interval for
        multinomial proportion have been proposed by many authors and
        the present study include a variety of widely applicable
        procedures. Seven classical methods (Wilson, Quesenberry and
        Hurst, Goodman, Wald with and without continuity correction,
        Fitzpatrick and Scott, Sison and Glaz) and Bayesian Dirichlet
        models are included in the package. The advantage of MCMC pack
        has been exploited to derive the Dirichlet posterior directly
        and this also helps in handling the Dirichlet prior parameters.
        This package is prepared to have equal and unequal values for
        the Dirichlet prior distribution that will provide better scope
        for data analysis and associated sensitivity analysis.
We propose a consistent monitoring procedure to detect a
    structural change from a cointegrating relationship to a spurious
    relationship. The procedure is based on residuals from modified least
    squares estimation, using either Fully Modified, Dynamic or Integrated
    Modified OLS. It is inspired by Chu et al. (1996) <DOI:10.2307/2171955> in
    that it is based on parameter estimation on a pre-break "calibration" period
    only, rather than being based on sequential estimation over the full sample.
    See the discussion paper <DOI:10.2139/ssrn.2624657> for further information.
    This package provides the monitoring procedures for both the cointegration
    and the stationarity case (while the latter is just a special case of the
    former one) as well as printing and plotting methods for a clear
    presentation of the results.
Cointegration methods are widely used in empirical macroeconomics
    and empirical finance. It is well known that in a cointegrating
    regression the ordinary least squares (OLS) estimator of the
    parameters is super-consistent, i.e. converges at rate equal to the
    sample size T. When the regressors are endogenous, the limiting
    distribution of the OLS estimator is contaminated by so-called second
    order bias terms, see e.g. Phillips and Hansen (1990) <DOI:10.2307/2297545>.
    The presence of these bias terms renders inference difficult. Consequently,
    several modifications to OLS that lead to zero mean Gaussian mixture
    limiting distributions have been proposed, which in turn make
    standard asymptotic inference feasible. These methods include
    the fully modified OLS (FM-OLS) approach of Phillips and Hansen
    (1990) <DOI:10.2307/2297545>, the dynamic OLS (D-OLS) approach of Phillips
    and Loretan (1991) <DOI:10.2307/2298004>, Saikkonen (1991)
    <DOI:10.1017/S0266466600004217> and Stock and Watson (1993)
    <DOI:10.2307/2951763> and the new estimation approach called integrated
    modified OLS (IM-OLS) of Vogelsang and Wagner (2014)
    <DOI:10.1016/j.jeconom.2013.10.015>. The latter is based on an augmented
    partial sum (integration) transformation of the regression model. IM-OLS is
    similar in spirit to the FM- and D-OLS approaches, with the key difference
    that it does not require estimation of long run variance matrices and avoids
    the need to choose tuning parameters (kernels, bandwidths, lags). However,
    inference does require that a long run variance be scaled out.
    This package provides functions for the parameter estimation and inference
    with all three modified OLS approaches. That includes the automatic
    bandwidth selection approaches of Andrews (1991) <DOI:10.2307/2938229> and
    of Newey and West (1994) <DOI:10.2307/2297912> as well as the calculation of
    the long run variance.
Performs regression analysis for longitudinal count
  data using a self-decomposable probability distribution,
  a random intercept term is also allowed. Estimation is via maximization
  of the exact likelihood of a suitably defined model. Missing values and
  unbalanced data are allowed.
Implements a generalized version of the CDH test
    <DOI:10.1371/journal.pone.0028145> for detecting compound heterozygosity on a
    genome-wide level, due to usage of generalized linear models it allows flexible
    analysis of binary and continuous traits with covariates.
These functions implement collocation-inference
    for continuous-time and discrete-time stochastic processes.
    They provide model-based smoothing, gradient-matching,
    generalized profiling and forwards prediction error methods.
Provides some low level functions for processing PLINK input and output files.
Performs the colocalisation tests described in Plagnol et al
    (2009), Wallace et al (2013) and Giambartolomei et al (2013).
It implements colored Independent Component Analysis (Lee et al., 2011) and spatial colored Independent Component Analysis (Shen et al., 2014). They are two algorithms to perform ICA when sources are assumed to be temporal or spatial stochastic processes, respectively.
Given 2-,3- or 4-dimensional data, plots a Venn diagram, i.e. 'crossing circles'. The user can specify values, labels for each circle-group and unique colors for each plotted part. Here is what it would look like for a 3-dimensional plot: http://elliotnoma.files.wordpress.com/2011/02/venndiagram.png. To see what the 4-dimensional plot looks like, go to http://elliotnoma.files.wordpress.com/2013/03/4dplot.png.
This function takes a hierarchical cluster-class object and a factor describing the groups as arguments and generates colorful dendrograms in which leaves belonging to different groups are identified by colors.
Allows to generate colors from palettes defined in the colormap module of 'Node.js'. (see <https://github.com/bpostlethwaite/colormap> for more information). In total it provides 44 distinct palettes made from sequential and/or diverging colors. In addition to the pre defined palettes you can also specify your own set of colors. There are also scale functions that can be used with 'ggplot2'.
Different methods to generate a color palette based on a specified base color and a number of colors that should be created.
A 'ggplot2' extension to visualize two
    variables through one color aesthetic via mapping to a color space
    projection. With this technique for 2-D color mapping, one can create a
    bivariate choropleth in R as well as other visualizations with multivariate
    color scales. Includes two new scales and a new guide for 'ggplot2'.
Methods and data for color science - color conversions by observer,
        illuminant and gamma. Color matching functions and chromaticity diagrams.
        Color indices, color differences and spectral data conversion/analysis.
Carries out mapping between assorted color spaces including
             RGB, HSV, HLS, CIEXYZ, CIELUV, HCL (polar CIELUV),
	     CIELAB and polar CIELAB. Qualitative, sequential, and
	     diverging color palettes based on HCL colors are provided
	     along with an interactive palette picker (with either a Tcl/Tk
	     or a shiny GUI).
Calculate with spectral properties of light sources, materials, cameras, eyes, and scanners.
    Build complex systems from simpler parts using a spectral product algebra. For light sources,
    compute CCT and CRI.  For object colors, compute optimal colors and Logvinenko coordinates.
    Work with the standard CIE illuminants and color matching functions, and read spectra from 
    text files, including CGATS files.  Sample text files, and 4 vignettes are included.
R package with handy functions to help users select and play with
    color schemes in an HSV color model
Provides access to the COLOURlovers <http://www.colourlovers.com/>
    API, which offers color inspiration and color palettes.
A colour picker that can be used as an input in Shiny apps
    or R-markdown documents. A colour picker RStudio addin is provided to let
    you select colours for use in your R code. The colour picker is also
    available as an 'htmlwidgets' widget.
Colour vision models and colour spaces for dichromatic, trichromatic and tetrachromatic animals.
Provides methods for the import/export and automated analysis of concept maps.
A comprehensive synergy/antagonism  analyses of drug combinations with
    quality graphics and data. The analyses can be performed by Bliss independence and Loewe
    additivity models. COMBIA provides improved statistical analysis and makes only very weak assumption of data variability 
    while calculating bootstrap intervals (BIs). Finally, package saves analyzed data, 
    2D and 3D plots ready to  use in research publications. COMBIA does not require manual
    data entry. Data can be directly input from wetlab experimental platforms 
    for example fluostar, automated robots etc. One needs to call a single function only  
    to perform all analysis (examples are provided with sample data).
Suite of R functions for combination of probabilities using a game-theoretic method.
Estimation of optimal portfolio weights as combination of simple portfolio strategies, like the tangency, global minimum variance (GMV) or naive (1/N) portfolio. It is based on a utility maximizing 8-fund rule. Popular special cases like the Kan-Zhou(2007) 2-fund and 3-fund rule or the Tu-Zhou(2011) estimator are nested.
We offer two statistical tests to combine p-values: selfcontained.test vs competitive.test. The goal is to test whether a vector of pvalues are jointly significant when we combine them together.
Series of partially balanced incomplete block designs (PBIB) based on the combinatory method (S) introduced in (Imane Rezgui et al, 2014) <doi:10.3844/jmssp.2014.45.48>; and it gives their associated U-type design.
Provides iterators for combinations, permutations, and subsets,
    which allow one to go through all elements without creating a huge set of
    all possible values.
Functions for computing optimal convex combinations of
        model selection criteria based on ranks, along with utility
        functions for constructing model lists, MSCs, and priors on
        model lists.
Computes community climate statistics for volume and mismatch using species' climate niches either unscaled or scaled relative to a regional species pool. These statistics can be used to describe biogeographic patterns and infer community assembly processes. Includes a vignette outlining usage.
An algorithm for identifying combinations of mutually exclusive alterations in cancer genomes. CoMEt represents the mutations in a set M of k genes with a 2^k dimensional contingency table, and then computes the tail probability of observing T(M) exclusive alterations using an exact statistical test.
Functions to calculate various common and less common thermal comfort indices, convert physical variables, and evaluate the performance of thermal comfort indices.
Provided are Computational methods for Immune Cell-type Subsets, including:(1) DCQ (Digital Cell Quantifier) to infer global dynamic changes in immune cell quantities within a complex tissue; and (2) VoCAL (Variation of Cell-type Abundance Loci) a deconvolution-based method that utilizes transcriptome data to infer the quantities of immune-cell types, and then uses these quantitative traits to uncover the underlying DNA loci.
An S4 representation of the Command design pattern. The
  Operation class is a simple implementation using closures and supports
  forward and reverse (undo) evaluation. The more complicated Protocol
  framework represents each type of command (or analytical protocol) by
  a formal S4 class. Commands may be grouped and consecutively executed
  using the Pipeline class. Example use cases include logging, do/undo,
  analysis pipelines, GUI actions, parallel processing, etc.
Autosimilarity curves, dissimilarity indexes that overweight rare species, phylogenetic and functional (pairwise and multisample) dissimilarity indexes and nestedness for phylogenetic, functional and other diversity metrics. This should be a complement to available packages, particularly 'vegan'.
Functions to
    produce nicely formatted comments to use in R-scripts (or
    Latex/HTML/markdown etc). A comment with formatting is printed to the
    console and can then be copied to a script.
Useful libraries for building a Java based GUI under R
The CommonMark specification defines a rationalized version of markdown
    syntax. This package uses the libcmark reference implementation for converting
    markdown text into various formats including html, latex and groff man. In
    addition it exposes the markdown parse tree in xml format.
Directly extract and plot stochastic common trends from
        a cointegration system using different approaches, currently
        including Kasa (1992) and Gonzalo and Granger (1995). 
	The approach proposed by Gonzalo and Granger, also known as
        Permanent-Transitory Decomposition, is widely used in
        macroeconomics and market microstructure literature. 
	Kasa's approach, on the other hand, has a nice property that it only
        uses the super consistent estimator: the cointegration vector
        'beta'. 
	This package also provides functions calculate P-value
        from Johansen Statistics according to the approximation method
        proposed by Doornik (1998).
	Update:
	0.7-1: Fix bugs in calculation alpha. Add formulas and more explanations.
        0.6-1: Rewrite the description file.
        0.5-1: Add functions to calculate P-value from Johansen statistic, and vice versa.
Provides functions to measure the difference between constrained and unconstrained gene tree distributions using various tree distance metrics. Constraints are enforced prior to this analysis via the estimation of a tree under the community tree model.
Facilitates optimal clustering of a data set. Provides a framework to run a wide range of clustering algorithms to determine the optimal number (k) of clusters in the data. Then analyzes the cluster assignments from each clustering algorithm to identify samples that repeatedly classify to the same group. We call these 'core clusters', providing a basis for later class discovery.
The CommunityCorrelogram package is designed for the geostatistical analysis of ecological community datasets with either a spatial or temporal distance component.
Comparison of two ROC curves through the methodology proposed by Ana C. Braga.
Creates empty plots with compact axis notation to which users can
    add whatever they like (points, lines, text, etc.) The notation is more
    compact in the sense that the axis-labels and tick-labels are closer to the
    axis and the tick-marks are shorter. Also, if the plot appears as part of a
    matrix, the x-axis notation is suppressed unless the plot appears along the
    bottom row and the y-axis notation is suppress unless the plot appears
    along the left column.
Functions to compare a model object to a comparison object.
  If the objects are not identical, the functions can be instructed to
  explore various modifications of the objects (e.g., sorting rows,
  dropping names) to see if the modified versions are identical.
Proposed by Harrell, the C index or concordance C, is considered an overall measure of discrimination in survival analysis between a survival outcome that is possibly right censored and a predictive-score variable, which can represent a measured biomarker or a composite-score output from an algorithm that combines multiple biomarkers. This package aims to statistically compare two C indices with right-censored survival outcome, which commonly arise from a paired design and thus resulting two correlated C indices.
Unified interface for the estimation of causal networks, 
 including the methods 'backShift' (from package 'backShift'), 
 'bivariateANM' (bivariate additive noise model), 
 'bivariateCAM' (bivariate causal additive model), 
 'CAM' (causal additive model) (from package 'CAM'), 
 'hiddenICP' (invariant causal prediction with hidden variables), 
 'ICP' (invariant causal prediction) (from package 'InvariantCausalPrediction'), 
 'GES' (greedy equivalence search), 
 'GIES' (greedy interventional equivalence search), 
 'LINGAM', 'PC' (PC Algorithm), 
 'RFCI' (really fast causal inference)
 (all from package 'pcalg') and regression.
Compares two dataframes which have the same column
    structure to show the rows that have changed. Also gives a git style diff format
    to quickly see what has changes in addition to summary statistics.
Create data summaries for quality control, extensive reports for exploring data, as well as publication-ready univariate or bivariate tables in several formats (plain text, HTML,LaTeX, PDF, Word or Excel. Perform figures to quickly visualise the distribution of your data (boxplots, barplots, normality-plots, etc.). Display statistics (mean, median, frequencies, incidences, etc.). Perform the appropriate tests (t-test, Analysis of variance, Kruskal-Wallis, Fisher, log-rank, ...) depending on the nature of the described variable (normal, non-normal or qualitative). Summarize genetic data (Single Nucleotids Polymorphisms) data displaying Allele Frequencies and performing Hardy-Weinberg Equilibrium tests among other typical statistics and tests for these kind of data.
Input: 2 ODM files (ODM version 1.3) Output: list of
        identical, matching, similar and differing data items
A standard test is observed on all specimens.  We treat the second test (or sampled test) as being conducted on only a stratified sample of specimens.  Verification Bias is this situation when the specimens for doing the second (sampled) test is not under investigator control.  We treat the total sample as stratified two-phase sampling and use inverse probability weighting.  We estimate diagnostic accuracy (category-specific classification probabilities; for binary tests reduces to specificity and sensitivity, and also predictive values) and agreement statistics (percent agreement, percent agreement by category, Kappa (unweighted), Kappa (quadratic weighted) and symmetry tests (reduces to McNemar's test for binary tests)).  See: Katki HA, Li Y, Edelstein DW, Castle PE.  Estimating the agreement and diagnostic accuracy of two diagnostic tests when one test is conducted on only a subsample of specimens. Stat Med. 2012 Feb 28; 31(5): 10.1002/sim.4422.
Functions for calculating and evaluating likelihood ratios from uni/multivariate continuous observations
The package enables to compute event-specific incidence
        rates for competing risks data, to compute rate ratios,
        event-specific incidence proportions and cumulative incidence
        functions from these, and to plot these in a comprehensive
        multi-state type graphic.
Package for the systematic retrieval and storage of
    functional genomics data via a MySQL database.
Organizing and Analyzing Social Dominance
    Hierarchy Data.
The package contains a function (which uses a similar interface to
    the `glm' function) for the fitting of a Conway-Maxwell-Poisson GLM. There
    are also various methods for analysis of the model fit. The package also
    contains functions for the Conway-Maxwell-Poisson distribution in a similar
    interface to functions `dpois', `ppois' and `rpois'. The functions are
    generally quick, since the workhorse functions are written in C++ (thanks
    to the Rcpp package).
Performs the complementary hierarchical clustering procedure and returns X' (the expected residual matrix) and a vector of the relative gene importances.
Contains several functions to enhance approaches to the Composite Indicators methods, focusing, in particular, on the normalisation and weighting-aggregation steps.
Evaluate complex integrals along a disc or a straight line. 
 Evaluate higher-order derivatives by employing the Cauchy integral formula.
 Evaluate residues.
 Search for simple poles given a function.
 Evaluate limits.
Extension of several functions to the complex domain, including the matrix exponential and logarithm, and the determinant.
Provides functionality to perform robust regression
    on compositional data. To get information on the distribution of the
    estimates, various bootstrapping methods are implemented for the
    compositional as well as for standard robust regression models, to provide
    a direct comparison between them.
Computes the probability density function, cumulative distribution function, quantile function, random numbers of any composite model based on the lognormal distribution.
Provides routines for density and moments of the
        Conway-Maxwell-Poisson distribution as well as functions for
        fitting the COM-Poisson model for over/under-dispersed count
        data.
Fit a CMP regression model to count data. The code provides functions for model estimation, dispersion testing, and diagnostics.
A collection of functions for compositional data analysis.
The package provides functions for the consistent analysis of
  compositional data (e.g. portions of substances) and positive numbers
  (e.g. concentrations) in the way proposed by Aitchison and Pawlowsky-Glahn.
Calculate regression coefficients and their standard deviations under the Cox proportional hazard model with a large number of covariates. A shrinkage type estimation based on the compound covariate method (Emura, Chen & Chen, 2012 PLoS ONE) and a univariate gene selection method under dependent censoring (Emura & Chen, 2014 Statistical Methods in Medical Research) are implemented. Algorithms for simulating gene expressions in these references are also given.
Computing Continuous Distributions Obtained by Compounding
        a Continuous and a Discrete Distribution
Computes the distribution function of quadratic forms in normal variables using Imhof's method, Davies's algorithm, Farebrother's algorithm or Liu et al.'s algorithm.
Different tools for describing and analysing paired comparison data are presented. Main methods are estimation of products scores according Bradley Terry Luce model. A segmentation of the individual could be conducted on the basis of a mixture distribution approach. The number of classes can be tested by the use of Monte Carlo simulations. This package deals also with multi-criteria paired comparison data.
A set of procedures for the analysis of Random Fields using likelihood and non-standard likelihood methods is provided. Spatial analysis often involves dealing with large dataset. Therefore even simple studies may be too computationally demanding. Composite likelihood inference is emerging as a useful tool for mitigating such computational problems. This methodology shows satisfactory results when compared with other techniques such as the tapering method. Moreover, composite likelihood (and related quantities) have some useful properties similar to those of the standard likelihood.
This package contains several functions for calculating the most
    widely used effect sizes (ES), along with their variances, confidence
    intervals and p-values.  The output includes ES's of d (mean difference), g
    (unbiased estimate of d), r (correlation coefficient), z' (Fisher's z), and
    OR (odds ratio and log odds ratio). In addition, NNT (number needed to
    treat), U3, CLES (Common Language Effect Size) and Cliff's Delta are
    computed. This package uses recommended formulas as described in The
    Handbook of Research Synthesis and Meta-Analysis (Cooper, Hedges, &
    Valentine, 2009).
Continuous smoothing of probability density function on a compact or semi-infinite support is performed using four continuous associated kernels: extended beta, gamma, lognormal and reciprocal inverse Gaussian. The cross-validation technique is also implemented for bandwidth selection.
Simple functions for joining strings. Construct human-friendly messages whose elements aren't known in advance, like in stop, warning, or message, from clean code.
There are 4 main functions in this package: ckmeans(), lcvqe(), mpckm() and ccls(). They take an unlabeled dataset and two lists of must-link and cannot-link constraints as input and produce a clustering as output.
Continuous convex piecewise linear (ccpl) resp. quadratic (ccpq) functions can be implemented with sorted breakpoints and slopes. This includes functions that are ccpl (resp. ccpq) on a convex set (i.e. an interval or a point) and infinite out of the domain. These functions can be very useful for a large class of optimisation problems. Efficient manipulation (such as log(N) insertion) of such data structure is obtained with map standard template library of C++ (that hides balanced trees). This package is a wrapper on such a class based on Rcpp modules.
The four functions svdcp (cp for column partitioned),
        svdbip or svdbip2 (bip for bi-partitioned), and svdbips (s for
        a simultaneous optimization of one set of r solutions),
        correspond to a "SVD by blocks" notion, by supposing each block
        depending on relative subspaces, rather than on two whole
        spaces as usual SVD does. The other functions, based on this
        notion, are relative to two column partitioned data matrices x
        and y defining two sets of subsets xi and yj of variables and
        amount to estimate a link between xi and yj for the pair (xi,
        yj) relatively to the links associated to all the other pairs.
A set of utilities for matching products in different classification codes used in international trade research. It supports concordance between HS (Combined), ISIC Rev. 2,3, and SITC1,2,3,4 product classification codes, as well as BEC, NAICS, and SIC classifications. It also provides code nomenclature / descriptions look-up, Rauch classification look-up (via concordance to SITC2) and trade elasticity look-up (via concordance to SITC2/3 or HS3.ss).
Higher order likelihood-based inference for logistic and 
  loglinear models
Apply and visualize conditional formatting to data frames in R.
    It renders a data frame with cells formatted according to
    criteria defined by rules, using a syntax similar to 'ggplot2'. The table is
    printed either opening a web browser or within the 'RStudio' viewer if
    available. The conditional formatting rules allow to highlight cells
    matching a condition or add a gradient background to a given column. This
    package supports both 'HTML' and 'LaTeX' outputs in 'knitr' reports, and
    exporting to an 'xlsx' file.
Solves for the mean parameters, the variance parameter, and their asymptotic variance in a conditional GEE for recurrent event gap times, as described by Clement and Strawderman (2009) in the journal Biostatistics. Makes a parametric assumption for the length of the censored gap time.
Conditional density estimation with mixtures for
        heavy-tailed distributions
Computes conditional multivariate normal probabilities, random deviates and densities.
An implementation of the computational strategy for the
    comprehensive analysis of condition-dependent operon maps in prokaryotes
    proposed by Fortino et al. (2014) <DOI:10.1186/1471-2105-15-145>. 
    It uses RNA-seq transcriptome profiles to improve prokaryotic operon map inference.
Method to implement some newly developed methods for the
    estimation of the conditional survival function.
Exploring fitted models by interactively taking 2-D and 3-D
  sections in data space.
Routines doing cone projection and quadratic programming, as well as doing estimation and inference for constrained parametric regression and shape-restricted regression problems.
This small library contains a series of simple tools for
        constructing and manipulating confounded and fractional
        factorial designs.
Functions for estimating and reporting multiyear averages and
    corresponding confidence intervals and distributions. A potential use case
    is reporting the chemical and ecological status of surface waters according
    to the European Water Framework Directive.
Manage configuration values across multiple environments (e.g.
  development, test, production). Read values using a function that determines
  the current environment and returns the appropriate value.
Implementation of conformal prediction using caret models for classification and regression.
Offers several functions for Configural Frequencies
    Analysis (CFA), which is a useful statistical tool for the analysis of
    multiway contingency tables. CFA was introduced by G. A. Lienert as
    'Konfigurations Frequenz Analyse - KFA'.
Geometric circle fitting with Levenberg-Marquardt (a, b, R), Levenberg-Marquardt reduced (a, b), Landau, Spath and Chernov-Lesort. Algebraic circle fitting with Taubin, Kasa, Pratt and Fitzgibbon-Pilu-Fisher. Geometric ellipse fitting with ellipse LMG (geometric parameters) and conic LMA (algebraic parameters). Algebraic ellipse fitting with Fitzgibbon-Pilu-Fisher and Taubin.
plot conics (ellipses, hyperbolas, parabolas)
Provides a set of tools that can be used across 'data.frame' and
    'imputationList' objects.
Conjoint is a simple package that implements a conjoint
        analysis method to measure the preferences.
Implementation of a procedure (Domingue, 2012; see also
        Karabatsos, 2001 and Kyngdon, 2011) to test the single and
        double cancellation axioms of conjoint measure in data that is
        dichotomously coded and measured with error.
Converts 'LaTeX' files (with extension '.tex') generated by R Sweave using package 'knitr' to Rich Text Format files (with extension '.rtf'). Rich Text Format files can be read and written by most word processors.
Collects several different methods for analyzing and
    working with connectivity data in R.  Though primarily oriented towards
    marine larval dispersal, many of the methods are general and useful for
    terrestrial systems as well.
Computes the Conover-Iman test (1979) for stochastic dominance and reports the results among multiple pairwise comparisons after a Kruskal-Wallis test for stochastic dominance among k groups (Kruskal and Wallis, 1952). The interpretation of stochastic dominance requires an assumption that the CDF of one group does not cross the CDF of the other. conover.test makes k(k-1)/2 multiple pairwise comparisons based on Conover-Iman t-test-statistic of the rank differences. The null hypothesis for each pairwise comparison is that the probability of observing a randomly selected value from the first group that is larger than a randomly selected value from the second group equals one half; this null hypothesis corresponds to that of the Wilcoxon-Mann-Whitney rank-sum test. Like the rank-sum test, if the data can be assumed to be continuous, and the distributions are assumed identical except for a difference in location, Conover-Iman test may be understood as a test for median difference. conover.test accounts for tied ranks. The Conover-Iman test is strictly valid if and only if the corresponding Kruskal-Wallis null hypothesis is rejected.
Multi-species estimation of geographical range parameters
	for preliminary assessment of conservation status following Criterion B of the 
	International Union for Conservation of Nature (IUCN, 
	see <http://www.iucnredlist.org>).
Given response y, continuous predictor x, and covariate matrix, the relationship between E(y) and x is estimated with a shape constrained regression spline.  Function outputs fits and various types of inference.
Compute the median ranking according the Kemeny's axiomatic approach. Rankings can or cannot contain ties, rankings can be both complete or incomplete.
Provides functions for
        efficient computations of nonlinear spatial predictions with
        local change of support. This package supplies functions for
        tow-dimensional spatial interpolation by constrained,
        covariance-matching constrained and universal (external drift)
        kriging for points or block of any shape for data with a
        nonstationary mean function and an isotropic weakly stationary
        variogram. The linear spatial interpolation methods,
        constrained and covariance-matching constrained kriging,
        provide approximately unbiased prediction for nonlinear target
        values under change of support. This package
        extends the range of geostatistical tools available in R and
        provides a veritable alternative to conditional simulation for
        nonlinear spatial prediction problems with local change of
        support.
Fits mixtures of multivariate contaminated normal distributions
        (with eigen-decomposed scale matrices) via the expectation conditional-
	maximization algorithm under a clustering or classification paradigm.
Various utilities for evaluating continued fractions.
Bayesian analysis of complete and incomplete contingency tables.
Create contour lines for a non regular series of points, potentially from a non-regular canvas.
One degree of freedom contrasts for lm, glm, gls, and geese objects.
Nonparametric two-sample procedure for comparing the median survival time.
Converts between the Date class and d/m/y for several
        calendars, including Persian, Islamic, and Hebrew
This package provides a way to investigate various modes of convergence of random variables.
Converts graphical file formats (SVG,
    PNG, JPEG, BMP, GIF, PDF, etc) to one another. The exceptions are the
    SVG file format that can only be converted to other formats and in contrast,
    PDF format, which can only be created from others graphical formats.
    The main purpose of the package was to provide a solution for converting SVG
    file format to PNG which is often needed for exporting graphical files
    produced by R widgets.
Provides conversion functionality between a broad range of
    scientific, historical, and industrial unit types.
Quantifies and assesses the significance of convergent evolution.
Variance estimation on indicators of income concentration and
    poverty using linearized or replication-based survey designs. Wrapper around the
    survey package.
Fits convolution-based nonstationary
    Gaussian process models to point-referenced spatial data. The nonstationary
    covariance function allows the user to specify the underlying correlation
    structure and which spatial dependence parameters should be allowed to
    vary over space: the anisotropy, nugget variance, and process variance.
    The parameters are estimated via maximum likelihood, using a local
    likelihood approach. Also provided are functions to fit stationary spatial
    models for comparison, calculate the Kriging predictor and standard errors,
    and create various plots to visualize nonstationarity.
This R package applies the probabilistic model of species co-occurrence (Veech 2013) to a set of species distributed among a set of survey or sampling sites. The algorithm calculates the observed and expected frequencies of co-occurrence between each pair of species. The expected frequency is based on the distribution of each species being random and independent of the other species. The analysis returns the probabilities that a more extreme (either low or high) value of co-occurrence could have been obtained by chance. The package also includes functions for visualizing species co-occurrence results and preparing data for downstream analyses.
Read and preprocess fasta format data, and construct the co-occurrence network for downstream analyses. This R package is to construct the co-occurrence network with the algorithm developed by Du (2008) <DOI:10.1101/gr.6969007>. It could be used to transform the data with high-dimension, such as DNA or protein sequence, into co-occurrence networks. Co-occurrence network could not only capture the co-variation pattern between variables, such as the positions in DNA or protein sequences, but also reflect the relationship between samples. Although it is originally used in DNA and protein sequences, it could be also used to other kinds of data, such as SNP.
Fast implementations of the co-operations: covariance,
    correlation, and cosine similarity.  The implementations are
    fast and memory-efficient and their use is resolved
    automatically based on the input data, handled by R's S3
    methods.  Full descriptions of the algorithms and benchmarks
    are available in the package vignettes.
Computes several cooperative games and allocation rules associated with minimum cost spanning tree problems and minimum cost arborescence problems.
Extensive functions for bivariate copula (bicopula) computations and related operations concerning oft cited bicopula theory described by Nelsen (2006), Joe (2014), and other selected works. The lower, upper, product, and select other bicopula are implemented. Individual copula support is deliberately limited to keep the package focused on an API to bicopula theory. Arbitrary bicopula expressions include the diagonal, survival copula, the dual of a copula, co-copula, numerical bicopula density, and maximum likelihood estimation. Level curves/sets, horizontal/vertical sections also are supported. Numerical derivatives and inverses of a bicopula are provided; simulation by the conditional distribution method thus is supported. Bicopula Ccomposition, convex combination, and products are provided.  Support extends to Kendall Function and L-moments thereof, Kendall Tau, Spearman Rho and Footrule, Gini Gamma, Blomqvist Beta, Hoeffding Phi, Schweizer-Wolff Sigma, tail dependency (including pseudo-polar representation) and tail order, skewness, and bivariate L-moments. Evaluators of positively/negatively quadrant dependency, left(right)-tail decreasing(increasing) are available. Kullback-Leibler divergence, Vuong's procedure, Spectral Measure, and L-comoments for copula inference are available. Quantile and median regressions for V with respect to U and U with respect to V are available. Empirical copulas (EC) are supported.
Provides tools for fitting the copCAR regression model for discrete areal data. Three types of estimation are supported: continuous extension, composite marginal likelihood, and distributional transform.
Provides functions to  compute and plot Coverage
    Probability Excursion (CoPE) sets
    for real valued functions on a 2-dimensional domain. CoPE sets are obtained
    from repeated noisy observations of the function on the entire domain.
    They are designed to bound the excursion
    set of the target function at a given level from above and below with
    a predefined probability. The target
    function can be a parameter in spatially-indexed linear regression.
Classes (S4) of commonly used elliptical, Archimedean, extreme
 value and some more copula families.  Methods for density, distribution,
 random number generation, bivariate dependence measures, perspective and
 contour plots.  Fitting copula models including variance estimates.
 Independence and serial (univariate and multivariate) independence tests,
 and other copula related tests. Empirical copula and multivariate CDF.
 Goodness-of-fit tests for copulas based on multipliers, the parametric
 bootstrap with several transformation options.
 Merged former package 'nacopula' for nested Archimedean copulas: Efficient
 sampling algorithms, various estimators, goodness-of-fit tests and related
 tools and special functions.
Modelling of sensitivity and specificity on their natural scale
    using copula based bivariate beta-binomial distribution to yield marginal mean sensitivity
    and specificity. The intrinsic negative correlation between sensitivity and
    specificity is modelled using a copula function. A forest plot can be obtained
    for categorical covariates or for the model with intercept only.
Provides a platform where EDAs (estimation of
    distribution algorithms) based on copulas can be implemented and
    studied. The package offers complete implementations of various
    EDAs based on copulas and vines, a group of well-known
    optimization problems, and utility functions to study the
    performance of the algorithms. Newly developed EDAs can be easily
    integrated into the package by extending an S4 class with generic
    functions for their main components.
Estimation and statistical process control are performed under copula-based time-series models.
This R-packages presents a bivariate, copula-based model
        for the joint distribution of a pair of continuous and discrete
        random variables. The two marginal random variables are modeled
        via generalized linear models, and their joint distribution
        (represented by a parametric copula family) is estimated using
        maximum-likelihood techniques.
It has functions to implement the copula mixed models for bivariate and trivariate meta-analysis of diagnostic test accuracy studies.
Contains several IRT and non-IRT based statistical indices proposed in the literature for detecting answer copying on multiple-choice examinations. Includes the indices that have been shown as effective and reliable based on the simulation studies. Provides results for the Omega index, Generalized Binomial Test, K index, K1 and K2 indices, and S1 and S2 indices.
The 450k arrays are frequently used in the epigenetic studies, the copy number calling from the 450k data is possible but it faces some difficulties in cancer samples regarding the determination of the copy number status due to the false sample centering and baseline shifting. Without solving this issue the CN calling will be inaccurate. CopyNumber450kCancer-package was designed to correct the baseline in cancer samples using the Maximum Density Peak Estimation (MDPE) method.
 The main advantages for CopyNumber450kCancer-package are: Fast (few seconds per sample), high accuracy rate, in-sample correction, no input parameters needed, low computer sources required, and adaptable for 450k-similar technologies.
Calculates the co-ranking matrix to assess the
    quality of a dimensionality reduction.
Provides a bundle of basic and fundamental bioinformatics tools,
    such as network querying and alignment, subnetwork extraction and search,
    network biomarker identification.
Perform a correlational class analysis of the data, resulting in a partition of the data into separate modules.
Generate high-dimensional correlated count random
        variables with a prespecified Pearson correlation.
Partition data points (variables) into communities/clusters, similar to clustering algorithms, such as k-means and hierarchical clustering.  This package implements a clustering algorithm based on a new metric CORD, defined for high dimensional parametric or semi-parametric distributions.  Read http://arxiv.org/abs/1508.01939 for more details.
given a collection of intervals with integer start and end positions, find recurrently targeted regions and estimate the significance of finding. Randomization is implemented by parallel methods, either using local host machines, or submitting grid engine jobs.
Core Hunter is a tool to sample diverse, representative subsets from large germplasm
    collections, with minimum redundancy. Such so-called core collections have applications in plant
    breeding and genetic resource management in general. Core Hunter can construct cores based on
    genetic marker data, phenotypic traits or precomputed distance matrices, optimizing one of many
    provided evaluation measures depending on the precise purpose of the core (e.g. high diversity,
    representativeness, or allelic richness). In addition, multiple measures can be simultaneously
    optimized as part of a weighted index to bring the different perspectives closer together.
    The Core Hunter library is implemented in Java 8 as an open source project (see
    <http://www.corehunter.org>).
This is a suite of machine learning algorithms written in C++ with R 
 interface. It contains several machine learning model learning techniques in 
 classification and regression, for example classification and regression trees with
 optional constructive induction and models in the leaves, random forests, kNN, 
 naive Bayes, and locally weighted regression. All predictions obtained with these
 models can be explained and visualized with ExplainPrediction package.  
 The package is especially strong in feature evaluation where it contains several variants of
 Relief algorithm and many impurity based attribute evaluation functions, e.g., Gini, 
 information gain, MDL, and DKM. These methods can be used for example to discretize 
 numeric attributes.
 Its additional feature is OrdEval algorithm and its visualization used for evaluation
 of data sets with ordinal features and class, enabling analysis according to the 
 Kano model of customer satisfaction. 
 Several algorithms support parallel multithreaded execution via OpenMP.  
 The top-level documentation is reachable through ?CORElearn.
Provides a minimal interface for applying
    annotators from the 'Stanford CoreNLP' java library. Methods
    are provided for tasks such as tokenisation, part of speech
    tagging, lemmatisation, named entity recognition, coreference
    detection and sentiment analysis.
Core functions for simulating quantities of interest
    from generalised linear models (GLM). This package will form the backbone of
    a series of other packages that improve the interpretation of GLM estimates.
Use to analysis case-parent trio sequencing studies. Test the compound heterozygous and recessive disease models
Fits a hidden rates model that allows different transition rate classes on different portions of a phylogeny by treating rate classes as hidden states in a Markov process and various other functions for evaluating models of binary character evolution.
Includes binning categorical variables into lesser number of categories based on t-test, converting categorical variables into continuous features 
	using the mean of the response variable for the respective categories, understanding the relationship between the response variable and predictor variables 
	using data transformations.
A matrix of agreement patterns and counts for record pairs is the input for the procedure.  An EM algorithm is used to impute plausible values for missing record pairs.  A second EM algorithm, incorporating possible correlations between per-field agreement, is used to estimate posterior probabilities that each pair is a true match - i.e. constitutes the same individual.
We proposed a new model-based clustering method, called the clustering of 
             regression models method(CORM), which groups genes that share a similar 
             relationship to the covariate(s). This method provides a unified approach 
             for a family of clustering procedures and can be applied to data collected 
             with various experimental designs. This package includes the implementation 
             for two such clustering procedures: (1) the Clustering of Linear Models 
             (CLM) method, and (2) the Clustering of Linear Mixed Models (CLMM) method.
Implements a James-Stein-type shrinkage estimator for 
  the covariance matrix, with separate shrinkage for variances and correlations.  
  The details of the method are explained in Sch\"afer and Strimmer (2005) and 
  Opgen-Rhein and Strimmer (2007).  The approach is both computationally as well
  as statistically very efficient, it is applicable to "small n, large p" data, 
  and always returns a positive definite and well-conditioned covariance matrix.  
  In addition to inferring the covariance matrix the package also provides 
  shrinkage estimators for partial correlations and partial variances.  
  The inverse of the covariance and correlation matrix 
  can be efficiently computed, as well as any arbitrary power of the 
  shrinkage correlation matrix.  Furthermore, functions are available for fast 
  singular value decomposition, for computing the pseudoinverse, and for 
  checking the rank and positive definiteness of a matrix.
Utility functions and data sets for the statistical
        analysis of corpus frequency data, used in the SIGIL statistics
        course.
Implementation of two-dimensional (2D) correlation analysis based
    on the Fourier-transformation approach described by Isao Noda (I. Noda
    (1993) <DOI:10.1366/0003702934067694>). Additionally there are two plot
    functions for the resulting correlation matrix: The first one creates
    colored 2D plots, while the second one generates 3D plots.
This package implements non-parametric analyses for clustered
    binary and multinomial data. The elements of the cluster are assumed
    exchangeable, and identical joint distribution (also known as marginal
    compatibility, or reproducibility) is assumed for clusters of different
    sizes. A trend test based on stochastic ordering is implemented.
Correcting area under ROC (AUC) for measurement error based on probit-shift model.
Analyzes and modifies metabolomics raw data (generated using GC-APCI-MS, Gas Chromatography-Atmospheric Pressure Chemical Ionization-Mass Spectrometry) to correct overloaded signals, i.e. ion intensities exceeding detector saturation leading to a cut-off peak. Data in xcmsRaw format are accepted as input and mzXML files can be processed alternatively. Overloaded signals are detected automatically and modified using an Gaussian or Isotopic-Ratio approach, QC plots are generated and corrected data are stored within the original xcmsRaw or mzXML respectively to allow further processing.
Linear regression based on a recursive structural equation model
    (explicit multiples correlations) found by a MCMC algorithm. It permits to face
    highly correlated covariates. Variable selection is included (by lasso,
    elastic net, etc.). It also provides some graphical tools for basic
    statistics (Boxplot with confidence intervals, etc) and regression trees.
A collection of tools for correspondence regression, i.e. the
    correspondence analysis of the crosstabulation of a categorical variable Y
    in function of another one X, where X can in turn be made up of the
    combination of various categorical variables. Consequently, correspondence
    regression can be used to analyze the effects for a polytomous or
    multinomial outcome variable.
Correlplot contains diverse routines for the construction of different plots for representing correlation matrices.
Calculates correlation of variables and displays the results
    graphically. Included panel functions can display points, shading, ellipses, and
    correlation values with confidence intervals.
In clinical practice and research settings in medicine and the behavioral sciences, it is often of interest to quantify the correlation of a continuous endpoint that was repeatedly measured (e.g., test-retest correlations, ICC, etc.). This package allows for estimating these correlations based on mixed-effects models. Part of this software has been developed using funding provided from the European Union's 7th Framework Programme for research, technological development and demonstration under Grant Agreement no 602552.
A graphical display of a correlation matrix or general matrix.
    It also contains some algorithms to do matrix reordering.
A tool for exploring correlations.
    It makes it possible to easily perform routine tasks when
    exploring correlation matrices such as ignoring the diagonal,
    focusing on the correlations of certain variables against others,
    or rearranging and visualising the matrix in terms of the
    strength of the correlations.
Statistical summary of Structure output.
Modeling the correlation transitions under specified distributional assumptions within the realm of discretization in the context of the latency and threshold concepts.
Designed for analysis of the results of a Genome Wide Association Study. Includes tools to pull lists of Chromosome number and SNP position below a certain significance threshold, refine gene networks (including data I/O for Cytoscape), and check SNP base pair changes.
To identify the globally most discriminative subnetwork from gene 
             expression profiles using an optimization model and genetic algorithm
cosinor is a set of simple functions that transforms longitudinal
    data to estimate the cosinor linear model as described in Tong (1976).
    Methods are given to summarize the mean, amplitude and acrophase, to
    predict the mean annual outcome value, and to test the coefficients.
Package encapsulates standard expressions for distances,
        times, luminosities, and other quantities useful in
        observational cosmology, including molecular line observations.
        Currently coded for a flat universe only.
User-friendly interfaces to perform fast and reliable photometric
    redshift estimation. The code makes use of generalized linear models and
    can adopt gamma or inverse gaussian families, either from a frequentist or
    a Bayesian perspective. The code additionally provides a Shiny application
    providing a simple user interface.
COSSO is a new regularization method that automatically
        estimates and selects important function components by a
        soft-thresholding penalty in the context of smoothing spline
        ANOVA models. Implemented models include mean regression,
        quantile regression, logistic regression and the Cox regression
        models.
Contains functions that can determine whether a time series
	is second-order stationary or not (and hence evidence for
	locally stationarity). Given two non-stationary series (i.e.
	locally stationary series) this package can then discover
	time-varying linear combinations that are second-order stationary.
Density, distribution function, quantile function, random
    generation and survival function for the Cosine Weibull Distribution as defined
    by SOUZA, L. New Trigonometric Class of Probabilistic Distributions. 219 p.
    Thesis (Doctorate in Biometry and Applied Statistics) - Department of Statistics
    and Information, Federal Rural University of Pernambuco, Recife, Pernambuco,
    2015 (available at <http://www.openthesis.org/documents/New-trigonometric-classes-probabilistic-distributions-602633.html>) and BRITO, C. C. R. Method Distributions generator and
    Probability Distributions Classes. 241 p. Thesis (Doctorate in Biometry and
    Applied Statistics) - Department of Statistics and Information, Federal Rural
    University of Pernambuco, Recife, Pernambuco, 2014 (available upon request).
Implements cointegration/cotrending rank selection
        algorithm in Guo and Shintani(2011). Paper: "Consistant
        Cotrending rank selection when both stochastic and nonlinear
        deterministic trends are present", Preprint, Feb 2011.
Interface to the couchDB document database <http://couchdb.apache.org>.
Functions, data and code for Hilbe, J.M. 2011. Negative Binomial Regression, 2nd Edition (Cambridge University Press) and Hilbe, J.M. 2014. Modeling Count Data (Cambridge University Press).
Implements the estimation and inference methods for counterfactual analysis described in Chernozhukov, Fernandez-Val and Melly (2013) <DOI:10.3982/ECTA10582> "Inference on Counterfactual Distributions," Econometrica, 81(6). The counterfactual distributions considered are the result of changing either the marginal distribution of covariates related to the outcome variable of interest, or the conditional distribution of the outcome given the covariates. They can be applied to estimate quantile treatment effects and wage decompositions.
Flexible univariate count models based on renewal
    processes. The models may include covariates and can be specified
    with familiar formula syntax as in glm().
Standardize country names, convert them into one of
    eleven coding schemes, convert between coding schemes, and
    assign region descriptors.
Modeling under- and over-dispersed count data using extended Poisson process models (EPPM).
Inputs date-times in Coordinated Universal Time (UTC)
    and converts to a local date-time and local date for US counties, based on
    each county's Federal Information Processing Standard (FIPS) code.
    This work was supported in part by grants from the National Institute of
    Environmental Health Sciences (R00ES022631) and the National Science
    Foundation (1331399).
Interacts with NOAA data sources (including the NCDC API at
  <http://www.ncdc.noaa.gov/cdo-web/webservices/v2> and ISD data) using
  functions from the 'rnoaa' package to obtain and compile weather time
  series for U.S. counties. This work was supported in part by grants from the
  National Institute of Environmental Health Sciences (R00ES022631) and the
  Colorado State University Water Center.
Contact prediction using shrinked covariance (COUSCOus). COUSCOus is a residue-residue contact detecting method approaching the contact inference using the glassofast implementation of Matyas and Sustik (2012, The University of Texas at Austin UTCS Technical Report 2012:1-3. TR-12-29.) that solves the L_1 regularised Gaussian maximum likelihood estimation of the inverse of a covariance matrix. Prior to the inverse covariance matrix estimation we utilise a covariance matrix shrinkage approach, the empirical Bayes covariance estimator, which has been shown by Haff (1980) <DOI:10.1214/aos/1176345010> to be the best estimator in a Bayesian framework, especially dominating estimators of the form aS, such as the smoothed covariance estimator applied in a related contact inference technique PSICOV.
Facilitates local polynomial regression for state dependent covariates in state-space models. The functionality can also be used from 'C++' based model builder tools such as 'Rcpp'/'inline', 'TMB', or 'JAGS'.
Allows Brownian motion, fractional Brownian motion,
    and integrated Ornstein-Uhlenbeck process components to
    be added to linear and non-linear mixed effects models
    using the structures and methods of the 'nlme' package.
Estimation of latent class models with covariate effects
        on underlying and measured variables. The measured variables
        are dichotomous or polytomous, all with the same number of
        categories.
We implement a collection of techniques for estimating covariance matrices. 
              Covariance matrices can be built using missing data. Stambaugh Estimation and 
              FMMC methods can be used to construct such matrices. Covariance matrices can 
              be built by denoising or shrinking the eigenvalues of a sample covariance 
              matrix. Such techniques work by exploiting the tools in Random Matrix Theory 
              to analyse the distribution of eigenvalues. Covariance matrices can also 
              be built assuming that data has many underlying regimes. Each regime is 
              allowed to follow a Dynamic Conditional Correlation model. Robust covariance 
              matrices can be constructed by multivariate cleaning and smoothing of noisy data.
Track and report code coverage for your package and (optionally)
    upload the results to a coverage service like 'Codecov' (http://codecov.io) or
    'Coveralls' (http://coveralls.io). Code coverage is a measure of the amount of
    code being exercised by a set of tests. It is an indirect measure of test
    quality and completeness. This package is compatible with any testing
    methodology or framework and tracks coverage of both R code and compiled
    C/C++/FORTRAN code.
This package fits a simultaneous regression model for the mean vectors and covariance matrices of multivariate response variables, as described in Hoff and Niu (2012). The explanatory variables can be continuous or discrete. The current version of the package provides the Bayesian estimates.
The cov.nnve() function for robust covariance estimation
        by the nearest neighbor variance estimation (NNVE) method of
        Wang and Raftery (2002,JASA)
Model-free selection of covariates under unconfoundedness for situations where the parameter of interest is an average causal effect. This package is based on  model-free backward elimination algorithms proposed in de Luna, Waernbaum and Richardson (2011). Marginal co-ordinate hypothesis testing is used in situations where all covariates are continuous while kernel-based smoothing appropriate for mixed data is used otherwise.
Model-free selection of covariates in high dimensions under unconfoundedness for situations where the parameter of interest is an average causal effect. This package is based on  model-free backward elimination algorithms proposed in de Luna, Waernbaum and Richardson (2011) <DOI:10.1093/biomet/asr041> and VanderWeele and Shpitser (2011) <DOI:10.1111/j.1541-0420.2011.01619.x>. Confounder selection can be performed via either Markov/Bayesian networks, random forests or LASSO.
Functions for testing if the covariance structure of 2-dimensional data
    (e.g. samples of surfaces X_i = X_i(s,t)) is separable, i.e. if covariance(X) = C_1 x C_2.
    A complete descriptions of the implemented tests can be found in the paper
    arXiv:1505.02023.
This package computes covariance test for the lasso.
Some helpful extensions and modifications to the 'ggplot2'
    package. In particular, this package makes it easy to combine multiple
    'ggplot2' plots into one and label them with letters, e.g. A, B, C, etc.,
    as is often required for scientific publications. The package also provides
    a streamlined and clean theme that is used in the Wilke lab, hence the
    package name, which stands for Claus O. Wilke's plot package.
Allows printing of character strings as messages/warnings/etc. 
    with ASCII animals, including cats, cows, frogs, chickens, ghosts, and more.
This package provides routines for fitting Cox models by
        likelihood based boosting for a single endpoint or in presence
        of competing risks
Fits Cox-type models based on interval-censored data from a survival
             or illness-death process.
Cox proportional hazards models containing Gaussian random 
  effects, also known as frailty models.
Cox model 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. Moreover, it treats the number of non-zero coefficients as another tuning parameter and simultaneously selects with the regularization parameter \code{lambda}. In addition, it fits a varying coefficient Cox model by kernel smoothing, incorporated with the aforementioned penalties. The package uses one-step coordinate descent algorithm and runs extremely fast by taking into account the sparsity structure of coefficients.
Cox regression with Firth's penalized likelihood
This package implements weighted estimation in Cox regression as proposed by 
             Schemper, Wakounig and Heinze (Statistics in Medicine, 2009). Weighted Cox regression 
             provides unbiased average hazard ratio estimates also in case of non-proportional hazards. 
             Approximated generalized concordance probability an effect size measure for clear-cut
             decisions can be obtained. 
             Additionally estimation of nonlinear effects using fractional polynomials similar to the 
             MFP algorithm (Royston, Sauerbrei, 2008) is provided. This feature can also be used to 
             estimate the interaction of a covariate with a nonlinear function of time.
A high performance package estimating Cox Model when an even has more than one causes. It also supports random and fixed effects, tied events, and time-varying variables.
A package for fitting Cox models with penalized ridge-type partial likelihood. The package includes functions for fitting simple Cox models with all covariates controlled by a ridge penalty. The weight of the penalty is optimised by using a REML type-algorithm. Models with time varying effects of the covariates can also be fitted. Some of the covariates may be allowed to be fixed and thus not controlled by the penalty. There are three different penalty functions, ridge, dynamic and weighted dynamic. Time varying effects can be fitted without the need of an expanded dataset.
Fit robustly proportional hazards regression model
It fits a CoxSEI (Cox type Self-Exciting Intensity) model to right-censored counting process data.
Functions for calculating the conditional power
             for different models in survival time analysis
             within randomized clinical trials
             with two different treatments to be compared
             and survival as an endpoint.
Functions to check whether a vector of p-values respects the assumptions of FDR (false discovery rate) control procedures and to compute adjusted p-values.
The package includes functions to test and compare causal models.
This package contains methods to perform Common Principal
    Component Analysis (CPCA). The stepwise method by Trendafilov is published
    in the current version. Please see Trendafilov (2010). Stepwise estimation
    of common principal components. Computational Statistics & Data Analysis,
    54(12), 3446-3457. doi:10.1016/j.csda.2010.03.010
Functions to calculate concordance probability estimates
        in survival analysis
Is designed to test for association between methylation at CpG sites across the genome and a phenotype of interest, adjusting for any relevant covariates. The package can perform standard analyses of large datasets very quickly with no need to impute the data. It can also handle mixed effects models with chip or batch entering the model as a random intercept. Also includes tools to apply quality control filters, perform permutation tests, and create QQ plots, manhattan plots, and scatterplots for individual CpG sites.
Frequently used methods in genomic applications with emphasis on parallel computing (OpenMP).
 At its core, the package has a Gibbs Sampler that allows running univariate linear
 mixed models that have both, sparse and dense design matrices. The parallel sampling method
 in case of dense design matrices (e.g. Genotypes) allows running Ridge Regression or BayesA for
 a very large number of individuals. The Gibbs Sampler is capable of running Single Step Genomic Prediction models.
 In addition, the package offers parallelized functions for common tasks like genome-wide
 association studies and cross validation in a memory efficient way.
Filter CpGs based on Intra-class Correlation Coefficients (ICCs) when replicates are available. ICCs are calculated by fitting linear mixed effects models to all samples including the un-replicated samples. Including the large number of un-replicated samples improves ICC estimates dramatically. The method accommodates any replicate design.
This package computes the maximum likelihood estimator (MLE) of the shape-constrained hazard baseline and the effect parameters in the Cox proportional hazards model under IID sampling.  We assume that the data are continuous and allow for right censoring.  The function 'find.shapeMLE' allows for four different shape constraints: increasing, decreasing, unimodal, and u-shaped.
The package cpk provides simplified clinical pharmacokinetic functions for dose regimen design and modification at the point-of-care. Currently, the following functions are available: (1) ttc.fn for target therapeutic concentration, (2) dr.fn for dose rate, (3) di.fn for dosing interval, (4) dm.fn for maintenance dose, (5) bc.ttc.fn for back calculation, (6) ar.fn for accumulation ratio, (7) dpo.fn for orally administered dose, (8) cmax.fn for peak concentration, (9) css.fn for steady-state concentration, (10) cmin.fn for trough,(11) ct.fn for concentration-time predictions, (12) dlcmax.fn for calculating loading dose based on drug's maximum concentration, (13) dlar.fn for calculating loading dose based on drug's accumulation ratio, and (14) R0.fn for calculating drug infusion rate. Reference: Linares O, Linares A. Computational opioid prescribing: A novel application of clinical pharmacokinetics. J Pain Palliat Care Pharmacother 2011;25:125-135.
This is the R Interface to the C API of IBM ILOG CPLEX. It necessarily depends on IBM ILOG CPLEX (>= 12.1).
Likelihood-based and Bayesian methods for various compound Poisson linear models.
Sequential and batch change detection for univariate data streams, using the change point model framework. Functions are provided to allow nonparametric distribution-free change detection in the mean, variance, or general distribution of a given sequence of observations. Parametric change detection methods are also provided for Gaussian, Bernoulli and Exponential sequences. Both the batch (Phase I) and sequential (Phase II) settings are supported, and the sequences may contain either a single or multiple change points.
We propose to determine the correction of the significance level after multiple coding of an explanatory variable in Generalized Linear Model. The different methods of correction of the p-value are the Single step Bonferroni procedure, and resampling based methods developped by P.H.Westfall in 1993. Resampling methods are based on the permutation and the parametric bootstrap procedure. If some continuous, and dichotomous transformations are performed this package offers an exact correction of the p-value developped by B.Liquet & D.Commenges in 2005. The naive method with no correction is also available.
Estimates the conditional probability function of a competing event, and fits, using the temporal process regression or the pseudo-value approach, a proportional-odds model to the conditional probability function
Estimate quantile regression(QR) and composite quantile regression (cqr) and with adaptive lasso penalty using interior point (IP), majorize and minimize(MM), coordinate descent (CD), and alternating direction method of multipliers algorithms(ADMM).
Estimation, based on conditional maximum likelihood, of the quadratic exponential model proposed by Bartolucci, F. & Nigro, V. (2010, Econometrica) and of a simplified and a modified version of this model. The quadratic exponential model is suitable for the analysis of binary longitudinal data when state dependence (further to the effect of the covariates and a time-fixed individual intercept) has to be taken into account. Therefore, this is an alternative to the dynamic logit model having the advantage of easily allowing conditional inference in order to eliminate the individual intercepts and then getting consistent estimates of the parameters of main interest (for the covariates and the lagged response). The simplified version of this model does not distinguish, as the original model does, between the last time occasion and the previous occasions. The modified version formulates in a different way the interaction terms and it may be used to test in a easy way state dependence as shown in Bartolucci, F., Nigro, V. & Pigini, C. (2013, Econometric Reviews). The package also includes estimation of the dynamic logit model by a pseudo conditional estimator based on the quadratic exponential model, as proposed by Bartolucci, F. & Nigro, V. (2012, Journal of Econometrics).
This package contains R-functions to perform power
        calculation in a group sequential clinical trial with censored
        survival data and possibly unequal patient allocation between
        treatment and control groups. The fuctions can also be used to
        determine the study duration in a clinical trial with censored
        survival data as the sum of the accrual duration, which
        determines the sample size in a traditional sense, and the
        follow-up duration, which more or less controls the number of
        events to be observed. This package also contains R functions
        and methods to display the computed results.
R functions for cosmological research.
  The main functions are similar to the python library, cosmolopy.
Using a sampling-based approach (either sequential importance sampling or explicit Monte Carlo), this package can be used to perform a probabilistic damage tolerance for aircraft structures. It can model a single crack, or two simultaneously growing fatigue cracks (the so-called continuing damage problem). With a single crack, multiple types of future repairs are possible.
Provides R routine for the so called two-sample
        Cramer-Test.  This nonparametric two-sample-test on equality
        of the underlying distributions can be applied to 
        multivariate data as well as univariate data. It offers two 
        possibilities to approximate the critical value both of which 
        are included in this package.
Provides a data.frame listing of known data-only and data-heavy
    packages available on CRAN.
Functions for completing and recalculating rankings.
'API' to the database of 'CRAN' package downloads from the
    'RStudio' 'CRAN mirror'. The database itself is at <http://cranlogs.r-pkg.org>,
    see <https://github.com/metacran/cranlogs.app> for the raw 'API'.
Various R tools for http://crantastic.org/
The Correlated Random Walk Library of
    R functions was designed for fitting continuous-time
    correlated random walk models with time indexed
    covariates. The model is fit using the Kalman-filter on
    a state space version of the continuous-time stochastic
    movement process.
Colored terminal output on terminals that support 'ANSI'
    color and highlight codes. It also works in 'Emacs' 'ESS'. 'ANSI'
    color support is automatically detected. Colors and highlighting can
    be combined and nested. New styles can also be created easily.
    This package was inspired by the 'chalk' 'JavaScript' project.
Implements a statistical test for comparing bar plots or
        histograms of categorical data derived from a randomized block
        repeated measures layout.
Different approaches to censored or truncated regression with 
  conditional heteroscedasticity are provided. First, continuous 
  distributions can be used for the (right and/or left censored or truncated)
  response with separate linear predictors for the mean and variance. 
  Second, cumulative link models for ordinal data
  (obtained by interval-censoring continuous data) can be employed for
  heteroscedastic extended logistic regression (HXLR). In the latter type of
  models, the intercepts depend on the thresholds that define the intervals.
A set of functions for computing the CreditMetrics risk model
Provides tools for pricing credit default swaps using
             C code for the International Swaps and Derivatives
             Association (ISDA) CDS Standard Model. See
             <http://www.cdsmodel.com/cdsmodel/documentation.html>
             for more information about the model and 
             <http://www.cdsmodel.com/cdsmodel/cds-disclaimer.html>
             for license details for the C code.
It provides functions to bootstrap Credit Curves from market quotes (Credit Default Swap - CDS - spreads) and price Credit Default Swaps - CDS.
Implements modeling and computational tools for conditional
    random fields (CRF) model as well as other probabilistic undirected
    graphical models of discrete data with pairwise and unary potentials.
Tools for analyzing performances of cricketers based on stats in
    ESPN Cricinfo Statsguru. The toolset can  be used for analysis of Tests,ODIs 
    and Twenty20 matches of both batsmen and bowlers.
This package fits discrete mixtures of Zero-Inflated
        Poisson (ZIP) models for analyzing criminal trajectories.
Statistical Methods for Crime Series Linkage. This package provides 
      code for criminal case linkage, crime series identification, crime series 
      clustering, and suspect identification.
CRM simulator for Phase I Clinical Trials
Implements the Cross-contribution Compensating Multiple
    standard Normalization (CCMN) method and other normalization
    algorithms.
Implements a wide range of model-based dose
    escalation designs, ranging from classical and modern continual
    reassessment methods (CRMs) based on dose-limiting toxicity endpoints to
    dual-endpoint designs taking into account a biomarker/efficacy outcome. The
    focus is on Bayesian inference, making it very easy to setup a new design
    with its own JAGS code. However, it is also possible to implement 3+3
    designs for comparison or models with non-Bayesian estimation. The whole
    package is written in a modular form in the S4 class system, making it very
    flexible for adaptation to new models, escalation or stopping rules.
The crn package provides the core functions required to
        download and format data from the Climate Reference Network.
        Both daily and hourly data are downloaded from the ftp, a
        consolidated file of all stations is created, station metadata
        is extracted. In addition functions for selecting individual
        variables and creating R friendly datasets for them is
        provided.
A device closing function which is able to crop graphics (e.g.,
  PDF, PNG files) on Unix-like operating systems with the required underlying
  command-line tools installed.
Computes a partial clustering algorithm that combines
    the Ward's minimum variance and Complete Linkage algorithms, providing
    automatic estimation of a suitable number of clusters and identification of
    outlier elements.
Contains functions for the construction of carryover
        balanced crossover designs. In addition contains functions to
        check given designs for balance.
This package performs a test for comparing two
        multivariate distributions by using the distance between
        observations.  The input is a distance matrix and the labels of
        the two groups to be compared, the output is the number of
        cross-matches and a p-value.
Package Crossover provides different crossover designs from
    combinatorial or search algorithms as well as from literature and a GUI to
    access them.
This package provides functions to calculate confidence intervals for crossover points of two simple linear regression lines using 
 the non-linear regression, the delta method, the Fieller method, and the bootstrap methods.
Enables transformation of Verbal Autopsy data collected with the WHO 2016 questionnaire
  for automated coding of Cause of Death using different computer algorithms. Currently supports user-supplied mappings,
  and provides unvalidated mapping definitions to transform to InterVA4, Tariff 2, and InSilicoVA.
Contains generic functions for performing 
  cross validation and for computing diagnostic errors.
Modelling credit risks based on the concept of "CreditRisk+", First Boston Financial Products, 1997 and "CreditRisk+ in the Banking Industry", Gundlach & Lehrbass, Springer, 2003.
Cross-recurrence quantification analysis 
    of two time-series, of either categorical or
    continuous values. It provides different methods
    for profiling cross-recurrence, i.e., only looking
    at the diagonal recurrent points, as well as more
    in-depth measures of the whole cross-recurrence plot,
    e.g., recurrence rate.
In competing risks regression, the proportional subdistribution hazards(PSH) model is popular for its direct assessment of covariate effects on the cumulative incidence function. This package allows for penalized variable selection for the PSH model. Penalties include LASSO, SCAD, MCP, and their group versions.
Extension of cmprsk to Stratified and Clustered data.
        Goodness of fit test for Fine-Gray model.
Performs forward and backwards stepwise regression for the Proportional subdistribution hazards model in competing risks (Fine & Gray 1999). Procedure uses AIC, BIC and BICcr as selection criteria. BICcr has a penalty of k = log(n*), where n* is the number of primary events.
Regression splines that handle a mix of continuous and categorical (discrete) data often encountered in applied settings. I would like to gratefully acknowledge support from  the Natural Sciences and Engineering Research Council of Canada (NSERC:www.nserc.ca), the Social Sciences and Humanities Research Council of Canada (SSHRC:www.sshrc.ca), and the Shared Hierarchical Academic Research Computing Network (SHARCNET:www.sharcnet.ca).
Provides the implementation of analytical and graphical approaches for checking the assumptions of the Fine and Gray model.
Functions for nonlinear regression parameters estimation by algorithms based on Controlled Random Search algorithm.
  Both functions (crs4hc(), crs4hce()) adapt current search strategy by four heuristics competition. In addition, crs4hce() improves adaptability by adaptive stopping condition.
Provides wrapper functions for running classification and
    regression tests using different machine learning techniques, such as Random
    Forests and decision trees. The package provides standardized methods for
    preparing data to suit the algorithm's needs, training a model, making
    predictions, and evaluating results. Also, some functions are provided to run
    multiple instances of a test.
Implements a semi-parametric GEE estimator accounting for missing data with Inverse-probability weighting (IPW) and for imbalance in covariates with augmentation (AUG). The estimator IPW-AUG-GEE is Doubly robust (DR).
Sample size estimation in cluster (group) randomized trials.  Contains traditional power-based methods, empirical smoothing (Rotondi and Donner, 2009), and updated meta-analysis techniques (Rotondi and Donner, 2012).
A simple HTTP client, with tools for making HTTP requests,
    and mocking HTTP requests. The package is built on R6, and takes 
    inspiration from Ruby's 'faraday' gem (<https://rubygems.org/gems/faraday>).
    The package name is a play on curl, the widely used command line tool
    for HTTP, and this package is built on top of the R package 'curl', an
    interface to 'libcurl' (<https://curl.haxx.se/libcurl>).
The Crunch.io service <http://crunch.io/> provides a cloud-based
    data store and analytic engine, as well as an intuitive web interface.
    Using this package, analysts can interact with and manipulate Crunch
    datasets from within R. Importantly, this allows technical researchers to
    collaborate naturally with team members, managers, and clients who prefer a
    point-and-click interface.
Functions for reading in and manipulating CRU TS3.21: Climatic
    Research Unit (CRU) Time-Series (TS) Version 3.21 data.
Exploratory tools to identify closely related subjects using autosomal genetic marker data.
Performs cryptographic randomness tests on a sequence of random
    integers or bits. Included tests are greatest common divisor, birthday spacings,
    book stack, adaptive chi-square, topological binary, and three random walk
    tests. Tests except greatest common divisor and birthday spacings are not
    covered by standard test suites. In addition to the chi-square goodness-of-fit
    test, results of Anderson-Darling, Kolmogorov-Smirnov, and Jarque-Bera tests are
    also generated by some of the cryptographic randomness tests.
Functions to calculate the relative crystallinity of starch by X-ray Diffraction (XRD) and Infrared Spectroscopy (FTIR). Starch is biosynthesized by plants in the form of granules semicrystalline. For XRD, the relative crystallinity is obtained by separating the crystalline peaks from the amorphous scattering region. For FTIR, the relative crystallinity is achieved by setting of a Gaussian holocrystalline-peak in the 800-1300 cm-1 region of FTIR spectrum of starch which is divided into amorphous region and crystalline region. The relative crystallinity of native starch granules varies from 14 of 45 percent. This package was supported by FONDECYT 3150630 and CIPA Conicyt-Regional R08C1002 is gratefully acknowledged.
Monte Carlo conditional inference for the parameters of a
  linear nonnormal regression model
Germline and somatic locus data which contain the total read depth and B allele 
    read depth using Bayesian model (Dirichlet Process) to cluster. Meanwhile, the cluster 
    model can deal with the SNVs mutation and the CNAs mutation.
cSFM is a method to model skewed functional data when considering covariates via a copula-based approach.
Package for CShapes, a GIS dataset of country boundaries (1946-2015). Includes functions for data extraction and the computation of distance matrices and -lists.
This package contains functions for likelihood and
        posterior analysis of conditionally specified logistic
        regression models.  All calculus and simulation is done in
        compiled FORTRAN.
Provides functions for computing the density
    and the log-likelihood function of closed-skew normal variates,
    and for generating random vectors sampled from this distribution.
    See Gonzalez-Farias, G., Dominguez-Molina, J., and Gupta, A. (2004).
    The closed skew normal distribution, 
    Skew-elliptical distributions and their applications: a journey beyond normality,
    Chapman and Hall/CRC, Boca Raton, FL, pp. 25-42.
Provides the Correlates of State Policy data set for easy use in R.
Includes two functions to evaluate the hypothesis of complete spatial randomness (csr) in point processes.  The function 'mwin' calculates quadrat counts to estimate the intensity of a spatial point process through the moving window approach proposed by Bailey and Gatrell (1995).  Event counts are computed within a window of a set size over a fine lattice of points within the region of observation.  The function 'pielou' uses the nearest neighbor test statistic and asymptotic distribution proposed by Pielou (1959) to compare the observed point process to one generated under csr.  The value can be compared to that given by the more widely used test proposed by Clark and Evans (1954).
Cell-type specific differential expression of a microarray
        experiment of heterogeneous tissue samples, using SAM.
A collection of tools for estimating a network from a random sample of cognitive social structure (CSS) slices. Also contains functions for evaluating a CSS in terms of various error types observed in each slice.
Functions that allow a researcher to examine the robustness of the substantive significance of their findings. Implements ideas set out in Esarey and Danneman (2014).
Functions for loading large (10M+ lines) CSV
    and other delimited files, similar to read.csv, but typically faster and
    using less memory than the standard R loader. While not entirely general,
    it covers many common use cases when the types of columns in the CSV file
    are known in advance. In addition, the package provides a class 'int64',
    which represents 64-bit integers exactly when reading from a file. The
    latter is useful when working with 64-bit integer identifiers exported from
    databases. The CSV file loader supports common column types including
    'integer', 'double', 'string', and 'int64', leaving further type
    transformations  to the user.
Support for import from and export to the CSVY file format. CSVY is a file format that combines the simplicity of CSV (comma-separated values) with the metadata of other plain text and binary formats (JSON, XML, Stata, etc.) by placing a YAML header on top of a regular CSV.
Estimation and inference methods for the continuous threshold expectile regression.
    It can fit the continuous threshold expectile regression and test the existence of change point,
    for the paper, "Feipeng Zhang and Qunhua Li (2016). A continuous threshold expectile regression, submitted."
Analysis of genetical genomic data to identify genetic loci associated with correlation changes in quantitative traits (CTL).
The CTM package is designed to solve problems of text mining and is specific for Chinese document.
Functions for estimating Markov generator matrices from discrete-time observations. The implemented approaches comprise diagonal adjustment, weighted adjustment and quasi-optimization of matrix logarithm based candidate solutions, an expectation-maximization algorithm as well as a Gibbs sampler.
Software to facilitates taking movement data in xyt format and pairing it with raster covariates within a continuous time Markov chain (CTMC) framework.  As described in Hanks et al. (2015) <DOI:10.1214/14-AOAS803> , this allows flexible modeling of movement in response to covariates (or covariate gradients) with model fitting possible within a Poisson GLM framework.
Functions for identifying, fitting, and applying continuous-space, continuous-time stochastic movement models to animal tracking data.
Estimation of quantile regression models for survival data.
Functions to Fit Continuous Time Autoregressive Models with the Kalman Filter.
A multivariate continuous (and discrete) time dynamic modelling 
    package for panel and time series data, using linear stochastic differential 
    equations. Contains a faster frequentist set of functions using OpenMx for 
    single subject and mixed-effects (random intercepts only) structural 
    equation models, or a hierarchical Bayesian implementation using Stan that 
    allows for random effects over all model parameters. Allows for modelling of 
    multiple noisy measurements of multiple stochastic processes, time varying 
    input / event covariates, and time invariant covariates used to predict the 
    parameters.
Interactive shiny application for running classical test theory (item analysis).
Infrastructure for task views to CRAN-style repositories: Querying task views and installing the associated
             packages (client-side tools), generating HTML pages and storing task view information in the repository
	     (server-side tools).
Estimate and test models for ordinal data concerning the family of
    CUB models and their extensions (where CUB stands for Combination of a 
	discrete Uniform and a shifted Binomial distributions).
Adaptive multivariate integration over hypercubes
Estimating mutation and selection coefficients on synonymous
       codon bias usage based on models of ribosome overhead cost (ROC).
       Multinomial logistic regression and Markov Chain Monte Carlo are used to
       estimate and predict protein production rates with/without the presence
       of expressions and measurement errors. Work flows with examples for
       simulation, estimation and prediction processes are also provided
       with parallelization speedup. The whole framework is tested with
       yeast genome and gene expression data of Yassour (2009).
Regression modeling using rules with added instance-based corrections
Compute Unified Device Architecture (CUDA) is a software
        platform for massively parallel high-performance computing on
        NVIDIA GPUs. This package provides a CUDA implementation of a
        Bayesian multilevel model for the analysis of brain fMRI data.
        A fMRI data set consists of time series of volume data in 4D
        space. Typically, volumes are collected as slices of 64 x 64
        voxels. Analysis of fMRI data often relies on fitting linear
        regression models at each voxel of the brain. The volume of the
        data to be processed, and the type of statistical analysis to
        perform in fMRI analysis, call for high-performance computing
        strategies. In this package, the CUDA programming model uses a
        separate thread for fitting a linear regression model at each
        voxel in parallel. The global statistical model implements a
        Gibbs Sampler for hierarchical linear models with a normal
        prior. This model has been proposed by Rossi, Allenby and
        McCulloch in `Bayesian Statistics and Marketing', Chapter 3,
        and is referred to as `rhierLinearModel' in the R-package
        bayesm. A notebook equipped with a NVIDIA `GeForce 8400M GS'
        card having Compute Capability 1.1 has been used in the tests.
        The data sets used in the package's examples are available in
        the separate package cudaBayesregData.
FMRI data sets used in the examples of "cudaBayesreg".
        Data sets have been separated from the main package
        "cudaBayesreg" for convenience.
Reconstruct individual-level values from aggregate-level
        summaries.
Utility functions that provides wrapper to descriptive base functions
  like cor, mean and table.  It makes use of the formula interface to pass
  variables to functions.  It also provides operators to concatenate (%+%), to
  repeat (%n%) and manage character vectors for nice display.
Combining Univariate Association Test Results of Multiple Phenotypes for Detecting Pleiotropy.
Extends ddply to allow calculation of cumulative
        quantities.
Estimation of number and location of change points in
        mean-shift (piecewise constant) models. Particularly useful to
        model genomic sequences of continuous measurements.
The curl() and curl_download() functions provide highly
    configurable drop-in replacements for base url() and download.file() with
    better performance, support for encryption (https, ftps), gzip compression,
    authentication, and other 'libcurl' goodies. The core of the package implements a
    framework for performing fully customized requests where data can be processed
    either in memory, on disk, or streaming via the callback or connection
    interfaces. Some knowledge of 'libcurl' is recommended; for a more-user-friendly
    web client see the 'httr' package which builds on this package with http
    specific tools and logic.
Deciphering web/'REST' 'API' and 'XHR' calls can be tricky, which is one reason
    why internet browsers provide "Copy as cURL" functionality within their "Developer Tools"
    pane(s). These 'cURL' command-lines can be difficult to wrangle into an 'httr' 'GET' or
    'POST' request, but you can now "straighten" these 'cURLs' either from data copied to the
    system clipboard or by passing in a vector of 'cURL' command-lines and getting back a
    list of parameter elements which can be used to form 'httr' requests. You can also make a
    complete/working/callable 'httr::VERB' function right from the tools provided.
The currentSurvival package contains functions for the
        estimation of the current cumulative incidence (CCI) and the
        current leukaemia-free survival (CLFS). The CCI is the
        probability that a patient is alive and in any disease
        remission (e.g. complete cytogenetic remission in chronic
        myeloid leukaemia) after initiating his or her therapy (e.g.
        tyrosine kinase therapy for chronic myeloid leukaemia). The
        CLFS is the probability that a patient is alive and in any
        disease remission after achieving the first disease remission.
Partial application is the process of reducing the arity of a
    function by fixing one or more arguments, thus creating a new function
    lacking the fixed arguments. The curry package provides three different ways
    of performing partial function application by fixing arguments from either
    end of the argument list (currying and tail currying) or by fixing multiple
    named arguments (partial application). This package provides this
    functionality through the %<%, %-<%, and %><% operators which allows for
    a programming style comparable to modern functional languages. Compared
    to other implementations such a purrr::partial() the operators in curry
    composes functions with named arguments, aiding in autocomplete etc.
Test Equality of Curves with Homoscedastic or
        Heteroscedastic Errors.
Filtering, also known as gating, of 
             flow cytometry samples using the curvHDR method.
Cobb's maximum likelihood method for cusp-catastrophe modeling
        (Grasman, van der Maas, & Wagenmakers, 2009, JSS, 32:8;
        Cobb, L, 1981, Behavioral Science, 26:1, 75--78).
        Includes a cusp() function for model fitting, and several
        utility functions for plotting, and for comparing the
        model to linear regression and logistic curve models.
Customized training is a simple technique for transductive
    learning, when the test covariates are known at the time of training. The
    method identifies a subset of the training set to serve as the training set
    for each of a few identified subsets in the training set. This package
    implements customized training for the glmnet() and cv.glmnet() functions.
Computation of decision intervals (H) and average run lengths (ARL) for CUSUM charts.
This package provides a set of tools for spatio-temporal imputation 
          in R. It includes the implementation for then CUTOFF imputation method, 
          a useful cross-validation function that can be used not only by the 
          CUOTFF method but also by some other imputation  functions to help 
          choosing an optimal value for relevant parameters, such as the number 
          of k-nearest neighbors for the KNN imputation method, or the number of
          components for the SVD imputation method. It also contains tools for 
          simulating data with missing values with respect to some specific 
          missing pattern, for example, block missing. Some useful visualisation
          functions for imputation purposes are also provided in the package.
This package can be used to standardize abundance indices using the delta-GLM method and to model the English Channel cuttlefish stock using a two-stage biomass model
This package contains various tools for working with and evaluating cross-validated area under the ROC curve (AUC) estimators.  The primary functions of the package are ci.cvAUC and ci.pooled.cvAUC, which report cross-validated AUC and compute confidence intervals for cross-validated AUC estimates based on influence curves for i.i.d. and pooled repeated measures data, respectively.  One benefit to using influence curve based confidence intervals is that they require much less computation time than bootstrapping methods.  The utility functions, AUC and cvAUC, are simple wrappers for functions from the ROCR package.
Statistical inferences for estimating the calibration equation with error-in observations
The package uses majorization minimization by coordinate
        descent (MMCD) algorithm to compute the solution surface for
        concave penalized logistic regression model.  The SCAD and MCP
        (default) are two concave penalties considered in this
        implementation.  For the MCP penalty, the package also provides
        the local linear approximation by coordinate descant (LLA-CD)
        and adaptive rescaling algorithms for computing the solutions.
        The package also provides a Lasso-concave hybrid penalty for
        fast variable selection. The hybrid penalty applies the concave
        penalty only to the variables selected by the Lasso.  For all
        the implemented methods, the solution surface is computed along
        kappa, which is a more smooth fit for the logistic model.
        Tuning parameter selection method by k-fold cross-validated
        area under ROC curve (CV-AUC) is implemented as well.
The external prediction capability of quantitative structure-activity relationship (QSAR) models is often quantified using the predictive squared correlation coefficient. This value can be calculated with an external data set or by cross validation.
This package implements the fast cross-validation via sequential testing (CVST) procedure. CVST is an improved cross-validation procedure which uses non-parametric testing coupled with sequential analysis to determine the best parameter set on linearly increasing subsets of the data. By eliminating underperforming candidates quickly and keeping promising candidates as long as possible, the method speeds up the computation while preserving the capability of a full cross-validation. Additionally to the CVST the package contains an implementation of the ordinary k-fold cross-validation with a flexible and powerful set of helper objects and methods to handle the overall model selection process. The implementations of the Cochran's Q test with permutations and the sequential testing framework of Wald are generic and can therefore also be used in other contexts.
This package carries out level-dependent cross-validation
        method for the selection of thresholding value in wavelet
        shrinkage. This procedure is implemented by coupling a
        conventional cross validation with an imputation method due to
        a limitation of data length, a power of 2. It can be easily
        applied to classical leave-one-out and k-fold cross validation.
        Since the procedure is computationally fast, a level-dependent
        cross validation can be performed for wavelet shrinkage of
        various data such as a data with correlated errors.
Tools that allow developers to write functions for
        cross-validation with minimal programming effort and assist
        users with model selection.
This is a package for selecting tuning parameters based on cross-validation (CV) in regularized estimators of large covariance matrices. Four regularized methods are implemented: banding, tapering, hard-thresholding and soft-thresholding. Two types of matrix norms are applied: Frobenius norm and operator norm. Two types of CV are considered: K-fold CV and random CV. Usually K-fold CV use K-1 folds to train a model and the rest one fold to validate the model. The reverse version trains a model with 1 fold and validates with the rest with K-1 folds. Random CV randomly splits the data set to two parts, a training set and a validation set with user-specified sizes.
An iterative algorithm for solving a convex
    formulation of the biclustering problem.
Alternating Minimization Algorithm (AMA) and Alternating Direction
    Method of Multipliers (ADMM) splitting methods for convex clustering.
Miscellaneous useful or interesting functions. Some parameters of functions may have changed, so beware!
This package estimates gaussian cluster weighted linear regressions by EM algorithm.
extend cxxfunction by saving the dynamic shared objects
        for reusing across R sessions
A suite of functions for analysing cycling data.
Cyclomatic complexity is a software metric (measurement),
    used to indicate the complexity of a program. It is a quantitative
    measure of the number of linearly independent paths through a program's
    source code. It was developed by Thomas J. McCabe, Sr. in 1976.
Tools for calculating coordinate representations of
    hypocycloids, epicyloids, hypotrochoids, and epitrochoids
    (altogether called 'cycloids' here) with different scaling
    and positioning options. The cycloids can be visualised with
    any appropriate graphics function in R.
This model fitting tool incorporates cyclic coordinate descent and
    majorization-minimization approaches to fit a variety of regression models
    found in large-scale observational healthcare data.  Implementations focus
    on computational optimization and fine-scale parallelization to yield
    efficient inference in massive datasets.
A toolkit for querying 'Team Cymru' <http://team-cymru.org> 'IP'
    address, Autonomous System Number ('ASN'), Border Gateway Protocol ('BGP'), Bogon
    and 'Malware' Hash Data Services.
This library contains a primary function that divides
        chewing sequences in cycles and cycles into phases.  See
        get.all.breaks for an example.
The cystiSim package provides an agent-based model for Taenia solium transmission and control. cystiSim was developed within the framework of CYSTINET, the European Network on taeniosis/cysticercosis, COST ACTION TD1302.
Calculates ecological diversity indices for a microbial
        community
The relationship between statistical dependency and causality lies
    at the heart of all statistical approaches to causal inference. The D2C
    package implements a supervised machine learning approach to infer the
    existence of a directed causal link between two variables in multivariate
    settings with n>2 variables. The approach relies on the asymmetry of some
    conditional (in)dependence relations between the members of the Markov
    blankets of two variables causally connected. The D2C algorithm predicts
    the existence of a direct causal link between two variables in a
    multivariate setting by (i) creating a set of of features of the
    relationship based on asymmetric descriptors of the multivariate dependency
    and (ii) using a classifier to learn a mapping between the features and the
    presence of a causal link
Create interactive heat maps that are usable from the R console, in
    the 'RStudio' viewer pane, in 'R Markdown' documents, and in 'Shiny' apps. Hover
    the mouse pointer over a cell to show details, drag a rectangle to zoom, and
    click row/column labels to highlight.
Two sample test based on Wasserstein metric. This is motivated from detection of differential DNA-methylation sites based on underlying distributions.
This packages is intended to make it easy to create D3 JavaScript
    network, tree, dendrogram, and Sankey graphs from R using data frames.
    !!! NOTE: Active development has moved to the networkD3 package. !!!
Plotting hierarchical plots in R such as Sunburst, Treemap, Circle Treemap, Partition Chart, collapsible indented tree and collapsible tree.
Helper functions for using 'd3.js' in R.
Various data sets used in examples and exercises in the
        book Maindonald, J.H. and Braun, W.J. (2003, 2007, 2010) "Data
        Analysis and Graphics Using R".
Data sets and functions, for the display of gene expression array (microarray) data, and for demonstrations with such data.
various data sets used in additional exercises for
        the book Maindonald, J.H. and Braun, W.J. (3rd edn 2010)
        "Data Analysis and Graphics Using R", and for a
        'Data Mining' course.  Note that a number of datasets
        that were in earlier versions of this package have been
        transferred to the DAAG package.
The three-way data consists of a set of variables measured on several groups of individuals. To each group is associated an estimated probability density function. The package provides functional methods (principal component analysis, multidimensional scaling, discriminant analysis...) for such probability densities.
The content falls into the following groupings: (i) Data, (ii)
    Factor manipulation functions, (iii) Design functions, (iv) ANOVA functions, (v)
    Matrix functions, (vi) Projector and canonical efficiency functions, and (vii)
    Miscellaneous functions. A document 'daeDesignRandomization.pdf', available
    in the doc subdirectory of the installation directory for 'dae', describes the
    use of the package for generating randomized layouts for experiments. The ANOVA
    functions facilitate the extraction of information when the 'Error' function has
    been used in the call to 'aov'.
Contains Data frames and functions used in the book "Design and Analysis of Experiments with R".
Diff, patch and merge for data frames. Document changes in data
    sets and use them to apply patches. Changes to data can be made visible by using
    render_diff. The V8 package is used to wrap the 'daff.js' JavaScript library
    which is included in the package.
Data and miscellanea to support the book "Introduction to
        Data analysis with R for Forensic Scientists", Curran, J.M.
        2010 CRC Press ISBN: 978-1-4200-8826-7
dagbag is a set of methods that learn DAGs via bootstrap aggregating.
Integrates the information from multiple linkage maps to
        create a consensus directed graph, which is then linearized to
        produce a consensus map.
A port of the web-based software 'DAGitty', available at 
    <http://dagitty.net>, for analyzing structural causal models 
    (also known as directed acyclic graphs or DAGs).
    This package computes covariate adjustment sets for estimating causal
    effects, enumerates instrumental variables, derives testable
    implications (d-separation and vanishing tetrads), generates equivalent
    models, and includes a simple facility for data simulation.
Functions to draw, manipulate, evaluate directed
        acyclic graphs and simulate corresponding data.
Several functions for evaluating the accuracy of
        classification models. The package provides the following
        performance measures: repeated k-fold cross-validation, 
		0.632 and 0.632+ bootstrap estimation of the misclassification rate, 
        sensitivity, specificity and AUC. If an application is 
        computationally intensive, parallel execution can be used 
        to reduce the computational effort.
Simulates and computes the (maximum) likelihood of a dynamical model of island biota assembly through speciation, immigration and extinction. See Valente et al. 2015. Ecology Letters 18: 844-852, <DOI:10.1111/ele.12461>.
Functions and an example dataset for the psychometric theory of
  knowledge spaces.  This package implements data analysis methods and
  procedures for simulating data and quasi orders and transforming different
  formulations in knowledge space theory.  See package?DAKS for an overview.
The DALY Calculator is a free, open-source Graphical User
             Interface (GUI) for stochastic disability-adjusted life year
             (DALY) calculation.
A collection of functions which aim to assist common computational workflow for analysis of matabolomic data..
Interactively train neural networks on Numerai, <https://numer.ai/>, data. Generate tournament predictions and write them to a CSV.
Miscellaneous set of functions I use in my teaching either at the University of Wisconsin-Milwaukee or the Inter-university Consortium for Political and Social Research Summer Program in Quantitative Methods.  Broadly, the functions help with presentation and interpretation of GLMs.
Simulates and computes (maximum) likelihood of a dynamical model of community assembly that takes into account phylogenetic history.
The single largest source of dams in the United States is the
    National Inventory of Dams (NID) <http://nid.usace.army.mil> from the US
    Army Corps of Engineers. Entire data from the NID cannot be obtained all at
    once and NID's website limits extraction of more than a couple of thousand
    records at a time. Moreover, selected data from the NID's user interface
    cannot not be saved to a file. In order to make the analysis of this data
    easier, all the data from NID was extracted manually. Subsequently, the raw
    data was checked for potential errors and cleaned. This package provides
    sample cleaned data from the NID and provides functionality to access the
    entire cleaned NID data.
Contains the function used to create the Dandelion Plot. Dandelion Plot is a visualization method for R-mode Exploratory Factor Analysis.
The darch package is built on the basis of the code from G. E.
    Hinton and R. R. Salakhutdinov (available under Matlab Code for deep belief
    nets). This package is for generating neural networks with many layers (deep
    architectures) and train them with the method introduced by the publications
    "A fast learning algorithm for deep belief nets" (G. E. Hinton, S. Osindero,
    Y. W. Teh (2006) <DOI:10.1162/neco.2006.18.7.1527>) and "Reducing the
    dimensionality of data with neural networks" (G. E. Hinton, R. R.
    Salakhutdinov (2006) <DOI:10.1126/science.1127647>). This method includes a
    pre training with the contrastive divergence method published by G.E Hinton
    (2002) <DOI:10.1162/089976602760128018> and a fine tuning with common known
    training algorithms like backpropagation or conjugate gradients.
    Additionally, supervised fine-tuning can be enhanced with maxout and
    dropout, two recently developed techniques to improve fine-tuning for deep
    learning.
The recovery of visual sensitivity in a dark environment is known
    as dark adaptation. In a clinical or research setting the recovery is typically
    measured after a dazzling flash of light and can be described by the Mahroo,
    Lamb and Pugh (MLP) model of dark adaptation. The functions in this package take
    dark adaptation data and use nonlinear regression to find the parameters of the
    model that 'best' describe the data. They do this by firstly, generating rapid
    initial objective estimates of data adaptation parameters, then a multi-start
    algorithm is used to reduce the possibility of a local minimum. There is also a
    bootstrap method to calculate parameter confidence intervals. The functions rely
    upon a 'dark' list or object. This object is created as the first step in the
    workflow and parts of the object are updated as it is processed.
Provides programmatic access to the Dark Sky API 
    <https://darksky.net/dev/docs>, which provides current or historical global 
    weather conditions.
Are you aiming at the right spot in darts? Maybe not! Use
        this package to find your optimal aiming location. For a better
        explanation, go to
        http://www-stat.stanford.edu/~ryantibs/darts/ or see the paper
        "A Statistician Plays Darts".
The dashboard package allows users to create web pages which display 
	interactive data visualizations working in a standard modern browser. It displays them locally 
	using the Rook server. Nor knowledge about web technologies nor Internet connection are 
	required. D3.js is a JavaScript library for manipulating documents based on data. 
	D3 helps the dashboard package bring data to life using HTML, SVG and CSS.
Provides an interface to the C++ FILEVECTOR library
    facilitating analysis using large (giga- to tera-bytes) matrices.
    Matrix storage is organized in a way that either columns or rows
    are quickly accessible. DatABEL is primarily aimed to support
    genome-wide association analyses e.g. using GenABEL, MixABEL and
    ProbABEL.
Functions to check variables against a
    set of data quality rules. A rule file can be accompanied by look-up tables. In
    addition, there are some convenience functions that may
    serve as an example for defining clearer 'data rules'. An
    HTML based user interface facilitates initial exploration of the
    functionality.
Checks column names,
    column classes, values, keys, joins, vectors and scalars.
    If the user-defined conditions are met the function
    returns an invisible copy of the data frame, vector or scalar.
    Otherwise the function throws an informative error.
Includes functions that researchers or practitioners may use to clean
    raw data, transferring html, xlsx, txt data file into other formats. And it
    also can be used to manipulate text variables, extract numeric variables from
    text variables and other variable cleaning processes. It is originated from a
    author's project which focuses on creative performance in online education
    environment. The resulting paper of that study will be published soon.
Tools for combining and cleaning data sets, particularly
    with grouped and time series data.
Methods for dividing data into subsets, applying analytical
    methods to the subsets, and recombining the results.  Comes with a generic
    MapReduce interface as well.  Works with key-value pairs stored in memory,
    on local disk, or on HDFS, in the latter case using the R and Hadoop
    Integrated Programming Environment (RHIPE).
Data exploration process for data analysis and model building, so
    that users could focus on understanding data and extracting insights. The
    package automatically scans through each variable and does data profiling.
    Typical graphical techniques will be performed for both discrete and
    continuous features.
Writes data frames to xls files. It supports 
        multiple sheets and basic formatting.
Our method automatically generates models of dynamic decision-making 
    that both have strong predictive power and are interpretable in human terms.
    We use an efficient model representation and a genetic algorithm-based
    estimation process to generate simple deterministic approximations that
    explain most of the structure of complex stochastic processes.
    We have applied the software to empirical data, and demonstrated it's 
    ability to recover known data-generating processes by simulating
    data with agent-based models and correctly deriving the underlying decision
    models for multiple agent models and degrees of stochasticity.
Functions to import multiple files of multiple data file types ('.xlsx', '.xls', '.csv', '.txt')
             from a given directory into R data frames.
datamap utilizes variable bindings and objects of class
        "UserDefinedDatabase" to provide a simple mapping system to
        foreign objects. Maps can be used as environments or attached
        to the search path, and changes to either are persistent.
        Mapped foreign objects are fetched in real-time and are never
        cached by the mapping system.
Provides an S4 infrastructure for unified handling
    of internal datasets and web based data sources. The package is
    currently in beta; things may break, change or go away without
    warning.
Post-GFC derivatives reforms have lifted the veil off
  over-the-counter (OTC) derivative markets. Swap Execution Facilities (SEFs)
  and Swap Data Repositories (SDRs) now publish data on swaps that are traded
  on or reported to those facilities (respectively). This package provides you
  the ability to get this data from supported sources.
Provides read and write access to data and metadata from
    the DataONE network <https://www.dataone.org> of data repositories.  
    Each DataONE repository implements a consistent repository application 
    programming interface. Users call methods in R to access these remote 
    repository functions, such as methods to query the metadata catalog, get 
    access to metadata for particular data packages, and read the data objects 
    from the data repository. Users can also insert and update data objects on 
    repositories that support these methods.
Provides a flexible container to transport and
    manipulate complex sets of data. These data may consist of multiple data files and
    associated meta data and ancillary files. Individual data objects have
    associated system level meta data, and data files are linked together using
    the OAI-ORE standard resource map which describes the relationships between the files. 
    The OAI-ORE standard is described at <https://www.openarchives.org/ore>. Data packages 
    can be serialized and transported as structured files that have been created following 
    the BagIt specification. The BagIt specification is described at 
    <https://tools.ietf.org/html/draft-kunze-bagit-08>.
The package performs variable level data quality checks including
    missing values, unique values, frequency tables, and generates summary
    statistics
Collection of functions to help retrieve U.S. Geological Survey
    (USGS) and U.S. Environmental Protection Agency (EPA) water quality and
    hydrology data from web services. USGS web services are discovered from the
    National Water Information System (NWIS) tools. Both EPA and USGS water
    quality data are obtained from the Water Quality Portal.
For working with the DataRobot predictive modeling platform's API. See http://www.datarobot.com/ for more information.
Based on a SAS data step. This allows for row-wise dynamic building
    of data, iteratively importing slices of existing dataframes, conducting
    analyses, and exporting to a results frame. This is particularly useful for
    differential or time-series analyses, which are often not well suited to vector-
    based operations.
Fast aggregation of large data (e.g. 100GB in RAM), fast ordered joins, fast add/modify/delete of columns by group using no copies at all, list columns, a fast friendly file reader and parallel file writer. Offers a natural and flexible syntax, for faster development.
Create tree structures from hierarchical data, and traverse the
    tree in various orders. Aggregate, cumulate, print, plot, convert to and from
    data.frame and more. Useful for decision trees, machine learning, finance,
    conversion from and to JSON, and many other applications.
Support functions for packages VBmix, semisupKernelPCA,
        and patchPlot.
Tools for deciphering the contents of
	unknown objects or environments from within the terminal,
	a problem often encountered when working with unfamiliar packages or debugging complex functions.
	If working in xterm256 or ANSI terminals the output is coloured by default
	to improve readability (e.g. the standard Ubuntu terminal).
This package performs voxel-wise deconvolution analysis of
        DCE-MRI contrast agent concentration versus time data and
        generates the Impulse Response Function, which can be used to
        approximate commonly utilized kinetic parameters such as Ktrans
        and ve.  An interactive advanced voxel diagnosis tool (AVDT) is
        also provided to facilitate easy navigation of voxel-wise data.
A collection of functions accompanying the book "Data Analysis in Vegetation Ecology", Second edition. 2013, Wiley-Blackwell, Chichester
Various utilities for the Davies distribution.
In applications it is usual that some additional information is available. This package dawai (an acronym for Discriminant Analysis With Additional Information) performs linear and quadratic discriminant analysis with additional information expressed as inequality restrictions among the populations means. It also computes several estimations of the true error rate.
Fits Bayesian additive regression trees (BART) while allowing the updating of predictors or response so that BART can be incorporated as a conditional model in a Gibbs/MH sampler. Also serves as a drop-in replacement for package 'BayesTree'.
Using widgets provided by gWidgets this creates a nice
        usable interface to help facilitate the learning of SQL.  It
        also just gives a nice interface for more advanced users.  Not
        all features are provided in the GUI but it is possible for
        more advanced users to achieve these goals inside the GUI.
Goodness-of-fit and two sample comparison tests using sample entropy
A program for analyzing vegetation time series, with two algorithms: 1) change detection algorithm that detects trend changes, determines their type (abrupt or non-abrupt), and estimates their timing, magnitude, number, and direction; 2) generalization algorithm that simplifies the temporal trend into main features. The user can set the number of major breakpoints or magnitude of greatest changes of interest for detection, and can control the generalization process by setting an additional parameter of generalization-percentage.
A tool to ensure the validity of database writes.
  It provides a set of utilities to analyze and type check the properties
  of data frames that are to be written to databases with SQL support.
This package provides methods and examples to support a
        method of Gene Set Analysis (GSA).  DBGSA is a novel
        distance-based gene set enrichment analysis method. We consider
        that, the distance between 2 groups with different phenotype by
        focusing on the gene expression should be larger, if a certain
        gene functional set was significantly associated with a
        particular phenotype.
Client for programmatic access to the South Florida Water
  Management District's 'DBHYDRO' database at 
  <http://sfwmd.gov/dbhydro>, with functions
  for accessing hydrologic and water quality data.
A database interface definition for communication
    between R and relational database management systems.  All
    classes in this package are virtual and need to be extended by
    the various R/DBMS implementations.
A helper that tests 'DBI' back ends for conformity
    to the interface, currently work in progress.
This package allows for nonparametric graduation of mortality rates using fixed or adaptive discrete beta kernel estimator.
Use EM algorithm to compute the NPMLE of CDF and also the
        two censoring distributions. For doubly censored data (as
        described in Chang and Yang (1987) Ann. Stat. 1536-47). You can
        also specify a constraint, it will return the constrained NPMLE
        and the -2 log empirical likelihood ratio. This can be used to
        test the hypothesis about the constraint and find confidence
        intervals for probability or quantile via empirical likelihood
        ratio theorem. Influence function of hat F may also be
        calculated (but may be slow).
Simple computation of spatial statistic functions of distance to characterize the spatial structures of mapped objects, including classical ones (Ripley's K and others) and more recent ones used by spatial economists (Duranton and Overman's Kd, Marcon and Puech's M). Relies on spatstat for some core calculation.
A fast reimplementation of several density-based algorithms of
    the DBSCAN family for spatial data. Includes the DBSCAN (density-based spatial
    clustering of applications with noise) and OPTICS (ordering points to identify
    the clustering structure) clustering algorithms and the LOF (local outlier
    factor) algorithm. The implementations uses the kd-tree data structure (from
    library ANN) for faster k-nearest neighbor search. An R interface to fast kNN
    and fixed-radius NN search is also provided.
Prediction methods where explanatory information is coded as a matrix of distances between individuals. Distances can either be directly input as a distances matrix, a squared distances matrix, an inner-products matrix or computed from observed predictors.
Functions for analyzing dichotomous choice contingent valuation (CV) data. It provides 
   functions for estimating parametric and nonparametric models for single-, one-and-one-half-, 
   and double-bounded CV data.
A collection of routines and documentation that allows one to
  perform voxel-wise quantitative analysis of dynamic contrast-enhanced MRI 
  (DEC-MRI) and diffusion-weighted imaging (DWI) data, with emphasis on 
  oncology applications.
Data cloud geometry (DCG) applies random walks in finding community structures for social networks.
Functions for 1) gene filtration; 2) link filtration; 3) differential co-expression analysis: DCG (Differential Coexpressed
        Gene) identification and DCL (Differentially Coexpressed Link)
        identification; and 4) differential regulation analysis: DRG (Differential Regulated
        Gene) identification, DRL (Differential Regulated Link)
        identification, DRL visualization and regulator ranking.
There lacks a package for analysing domain-centric ontologies and annotations, particularly those in the dcGO database. The dcGO (http://supfam.org/SUPERFAMILY/dcGO) is a comprehensive domain-centric database for annotating protein domains using a panel of ontologies including Gene Ontology. With the package, users are expected to analyse and visualise domain-centric ontologies and annotations. Supported analyses include but are not limited to: easy access to a wide range of ontologies and their domain-centric annotations; able to build customised ontologies and annotations; domain-based enrichment analysis and visualisation; construction of a domain (semantic similarity) network according to ontology annotations; significance analysis for estimating a contact (statistical significance) network via Random Walk with Restart; and high-performance parallel computing. The new functionalities are: 1) to create domain-centric ontologies; 2) to predict ontology terms for input protein sequences (precisely domain content in the form of architectures) plus to assess the predictions; 3) to reconstruct ancestral discrete characters using maximum likelihood/parsimony.
Functions for reading DCP and CDF.bin files generated by the dChip software.
Statistical modelling and forecasting in claims reserving in non-life insurance under the Double Chain Ladder framework by Martinez-Miranda, Nielsen and Verrall (2012).
Low level functions for implementing
    maximum likelihood estimating procedures for
    complex models using data cloning and Bayesian
    Markov chain Monte Carlo methods.
    Sequential and parallel MCMC support
    for JAGS, WinBUGS and OpenBUGS.
A set of functions for the detection of spatial clusters
        of disease using count data. Bootstrap is used to estimate
        sampling distributions of statistics.
S4 classes around infrastructure provided by the
    'coda' and 'dclone' packages to make package development easy as a breeze
    with data cloning for hierarchical models.
Analysis of dichotomous response data to obtain attribute profile
    estimates for respondents using Diagnostic Classification Model (DCM) and
    Markov Chain Monte Carlo (MCMC) method. The estimation procedure uses a
    loglinear cognitive diagnostic modeling (LDCM) framework that allows for
    the estimation of a host of DCMs such as NIDO, NIDA, NC-RUM etc.
Traversal graph algorithm for listing linear n-peptide constraints for overlapping protein regions. (Lebre and Gascuel, The combinatorics of overlapping genes, freely available from arXiv at : http://arxiv.org/abs/1602.04971).
Computing and plotting the distance covariance and correlation
             function of a univariate or a multivariate time series. Both versions
             of biased and unbiased estimators of distance covariance and correlation are provided.
             Test statistics for testing pairwise independence are also implemented. 
             Some data sets are also included.
This package performs several conventional
        Cross-validation statistical methods for climate-growth model
        in the climate reconstruction from tree rings, including Sign
        Test statistic, Reduction of Error statistic, Product Mean
        Test, Durbin-Watson statistic etc. This package is at its
        primary stage, the functions have not been tested exhaustively
        and more functions would be added in the comming days.
Contains procedures for depth-based supervised learning, which are entirely non-parametric, in particular the DDalpha-procedure (Lange, Mosler and Mozharovskyi, 2014). The training data sample is transformed by a statistical depth function to a compact low-dimensional space, where the final classification is done. It also offers an extension to functional data and routines for calculating certain notions of statistical depth functions. 50 multivariate and 5 functional classification problems are included.
Implements maximum likelihood and bootstrap methods based on the diversity-dependent birth-death process to test whether speciation or extinction are diversity-dependent, under various models including various types of key innovations.
   See Etienne et al. 2012, Proc. Roy. Soc. B 279: 1300-1309, <DOI:10.1098/rspb.2011.1439>, Etienne & Haegeman 2012, Am. Nat. 180: E75-E89, <DOI:10.1086/667574> and Etienne et al. 2016. Meth. Ecol. Evol. 7: 1092-1099, <DOI:10.1111/2041-210X.12565>.
   Also contains functions to simulate the diversity-dependent process.
A wrapper for the Duke Analytics model deployment API. See <http://www.dukeanalytics.com/products/dukedeployR.php> for more details.
Contains the normalizing and variance stabilizing
	Data-Driven Haar-Fisz algorithm. Also contains related algorithms
	for simulating from certain microarray gene intensity models and
	evaluation of certain transformations. Contains cDNA and shipping
	credit flow data.
This package provides useful functions for various DDI (Data Documentation Initiative) related outputs.
An interface to explore, analyze, and visualize droplet digital PCR
    (ddPCR) data in R. This is the first non-proprietary software for analyzing
    two-channel ddPCR data. An interactive tool was also created and is available
    online to facilitate this analysis for anyone who is not comfortable with
    using R.
Provides distributed data structures and simplifies
    distributed computing in R.
Project data into a reduced dimensional space and construct a principal graph from the reduced dimension.
Smooth testing of goodness of fit. These tests are data
    driven (alternative hypothesis is dynamically selected based on data). In this
    package you will find various tests for exponent, Gaussian, Gumbel and uniform
    distribution.
Statistical deadband algorithms are based on the Send-On-Delta concept as in Miskowicz(2006,<doi:10.3390/s6010049>). A collection of functions compare effectiveness and fidelity of sampled signals using statistical deadband algorithms.
Bayesian networks with continuous and/or discrete
        variables can be learned and compared from data.
deamer provides deconvolution algorithms for the
        non-parametric estimation of the density f of an error-prone
        variable x with additive noise e. The model is y = x + e where
        the noisy variable y is observed, while x is unobserved.
        Estimation may be performed for i) a known density of the error
        ii) with an auxiliary sample of pure noise and iii) with an
        auxiliary sample of replicate (repeated) measurements.
        Estimation is performed using adaptive model selection and
        penalized contrasts.
A Bayesian framework for parameter inference in differential equations.
    This approach offers a rigorous methodology for parameter inference as well as
    modeling the link between unobservable model states and parameters, and
    observable quantities. Provides templates for the DE model, the
    observation model and data likelihood, and the model parameters and their prior
    distributions. A Markov chain Monte Carlo (MCMC) procedure processes these inputs
    to estimate the posterior distributions of the parameters and any derived
    quantities, including the model trajectories. Further functionality is provided
    to facilitate MCMC diagnostics and the visualisation of the posterior distributions
    of model parameters and trajectories.
Debugger for R functions, with code display, graceful
        error recovery, line-numbered conditional breakpoints, access
        to exit code, flow control, and full keyboard input.
Specify debug messages as special string constants, and
    control debugging of packages via environment variables.
Calculates various estimates for measures of educational
        differentials, the relative importance of primary and secondary
        effects in the creation of such differentials and compares the
        estimates obtained from two datasets.
Contains a function called dmur() which accepts four parameters like possible values, probabilities of the values, selling cost and preparation cost. The dmur() function generates various numeric decision parameters like MEMV (Maximum (optimum) expected monitory value), best choice, EPPI (Expected profit with perfect information), EVPI (Expected value of the perfect information), EOL (Expected opportunity loss), which facilitate effective decision-making.
Decision curves are a useful tool to evaluate the population impact
 of adopting a risk prediction instrument into clinical practice. Given one or more
 instruments (risk models) that predict the probability of a binary outcome, this
 package calculates and plots decision curves, which display estimates of the
 standardized net benefit by the probability threshold used to categorize observations
 as 'high risk.' Curves can be estimated using data from an observational cohort, or
 from case-control studies when an estimate of the population outcome prevalence
 is available. Confidence intervals calculated using the bootstrap can be displayed and
 a wrapper function to calculate cross-validated curves using k-fold cross-validation is
 also provided.
Supporting the quantitative analysis of binary welfare based
    decision making processes using Monte Carlo simulations. Decision support
    is given on two levels: (i) The actual decision level is to choose between
    two alternatives under probabilistic uncertainty. This package calculates
    the optimal decision based on maximizing expected welfare. (ii) The meta
    decision level is to allocate resources to reduce the uncertainty in the
    underlying decision problem, i.e to increase the current information to
    improve the actual decision making process. This problem is dealt with
    using the Value of Information Analysis. The Expected Value of
    Information for arbitrary prospective estimates can be calculated as
    well as Individual Expected Value of Perfect Information.
    The probabilistic calculations are done via Monte Carlo
    simulations. This Monte Carlo functionality can be used on its own.
Integrated differential expression (DE) and differential co-expression (DC) analysis on gene expression data based on DECODE (DifferEntial CO-expression and Differential Expression) algorithm.
Main function "decode" is used to decode coded key values to plain
    text. Function "code"" can be used to code plain text to code if there is a
    1:1 relation between the two. The concept relies on "keyvalue" objects used
    for translation. There are several "keyvalue"" objects included in the areas
    of geographical regional codes, administrative health care unit codes,
    diagnosis codes et cetera but it is also easy to extend the use by arbitrary 
    code sets.
Two global-value-chain decompositions are implemented. Firstly, the
    Wang-Wei-Zhu (Wang, Wei, and Zhu, 2013) algorithm splits bilateral gross exports
    into 16 value-added components. Secondly, the Leontief decomposition (default)
    derives the value added origin of exports by country and industry, which is also
    based on Wang, Wei, and Zhu (Wang, Z., S.-J. Wei, and K. Zhu. 2013. "Quantifying
    International Production Sharing at the Bilateral and Sector Levels.").
This package contains a collection of functions to deal
        with nonparametric measurement error problems using
        deconvolution kernel methods. We focus two measurement error
        models in the package: (1) an additive measurement error model,
        where the goal is to estimate the density or distribution
        function from contaminated data; (2) nonparametric regression
        model with errors-in-variables. The R functions allow the
        measurement errors to be either homoscedastic or
        heteroscedastic. To make the deconvolution estimators
        computationally more efficient in R, we adapt the "Fast Fourier
        Transform" (FFT) algorithm for density estimation with
        error-free data to the deconvolution kernel estimation. Several
        methods for the selection of the data-driven smoothing
        parameter are also provided in the package. See details in:
        Wang, X.F. and Wang, B. (2011). Deconvolution estimation in
        measurement error models: The R package decon. Journal of
        Statistical Software, 39(10), 1-24.
Takes sample information in the form of the fraction of mutations
    in each of 96 trinucleotide contexts and identifies the weighted combination
    of published signatures that, when summed, most closely reconstructs the
    mutational profile.
An intuitive, cross-platform graphical data analysis system. It uses menus and dialogs to guide the user efficiently through the data manipulation and analysis process, and has an excel like spreadsheet for easy data frame visualization and editing. Deducer works best when used with the Java based R GUI JGR, but the dialogs can be called from the command line. Dialogs have also been integrated into the Windows Rgui.
Added functionality for Deducer. This package includes
        additional dialogs for calculating distribution function
        values, cluster analysis and more.
A example GUI plug-in package to serve as a template.
A Deducer plug-in for factor analysis, reliability
        analysis and discriminant analysis, using psych, GPArotation
        and mvnormtest packages.
A Deducer plug-in for spatial data analysis. Includes The
        ability to plot and explore open street map and Bing satellite
        images.
Adds Kaplan-Meier Survival Analysis to the Deducer GUI
A collection of methods for automated data cleaning where all actions are logged.
Attempt to repair inconsistencies and missing values in data 
    records by using information from valid values and validation rules 
    restricting the data.
Provides deep boosting models training, evaluation, predicting and
 hyper parameter optimising using grid search and cross validation.
 Based on Google's Deep Boosting algorithm, and Google's C++ implementation.
 Cortes, C., Mohri, M., & Syed, U. (2014) <URL:
 http://machinelearning.wustl.edu/mlpapers/papers/icml2014c2_cortesb14>.
An implementation of deep neural network with rectifier linear units trained
	with stochastic gradient descent method and batch normalization. A combination of these methods have
	achieved state-of-the-art performance in ImageNet classification by overcoming the gradient saturation
	problem experienced by many deep architecture neural network models in the past. In addition,
	batch normalization and dropout are implemented as a means of regularization. The deeplearning package is
	inspired by the darch package and uses its class DArch.
Implement some deep learning architectures and neural network
    algorithms, including BP,RBM,DBN,Deep autoencoder and so on.
Tests for, and describe differences in event count profiles in groups of reconstructed cophylogenies
Numerical quadrature of functions of one variable over a finite or infinite interval
	with double exponential formulas.
Detection of differentially expressed genes between two
        distinct groups of samples.
Likelihood-based inference for skewed count distributions used in network modeling. "degreenet" is a part of the "statnet" suite of packages for network analysis.
Provides probability mass, distribution, quantile, random-variate generation, and method-of-moments parameter-estimation functions for the Delaporte distribution. The Delaporte is a discrete probability distribution which can be considered the convolution of a negative binomial distribution with a Poisson distribution. Alternatively, it can be considered a counting distribution with both Poisson and negative binomial components. It has been studied in actuarial science as a frequency distribution which has more variability than the Poisson, but less than the negative binomial.
Calculates the Delaunay triangulation and the Dirichlet
	or Voronoi tessellation (with respect to the entire plane) of
	a planar point set. Plots triangulations and tessellations in
	various ways.  Clips tessellations to sub-windows. Calculates
	perimeters of tessellations.
Implements the DeLorean model to estimate pseudotimes for
    single cell expression data. The DeLorean model uses a Gaussian process
    latent variable model to model uncertainty in the capture time of
    cross-sectional data.
We implement methods for estimating multivariate densities.
  We include a discretized kernel estimator,
  an adaptive histogram (a greedy histogram and a CART-histogram), 
  stagewise minimization, and bootstrap aggregation.
The deltaPlotR package proposes an implementation of Angoff's Delta Plot method to detect dichotomous DIF. Several detection thresholds are included, either from multivariate normality assumption or by prior determination. Item purification is supported.
Functions to calculate pairwise relatedness on diploid genetic datasets. Different estimators for relatedness can be combined with information on geographical distances. Information on heterozygosity, allele- and genotype diversity as well as genetic F-statistics are provided for each population.
This package allows to calculate the fixation index Gst
  (Nei, 1973) and the differentiation index D (Jost, 2008) pairwise
  between or averaged over several populations.  P-values, stating the
  significance of differentiation, and 95 percent confidence intervals
  can be estimated using bootstrap resamplings. In the case that more
  than two populations are compared pairwise, the p-values are
  adjusted by bonferroni correction and in several other ways due to
  the multiple comparison from one data set.
Implementation of the DEMI method for the analysis of high-density microarray data.
Generalized Deming regression, Theil-Sen regression and Passing-Bablock regression functions.
Functions for demographic analysis including lifetable
        calculations; Lee-Carter modelling; functional data analysis of
        mortality rates, fertility rates, net migration numbers; and
        stochastic population forecasting.
Demonstration code showing how (univariate) kernel
	     density estimates are computed, at least conceptually,
	     and allowing users to experiment with different kernels,
	     should they so wish.  NOTE: the density function in the
	     stats package should be used for computational efficiency.
Tool for the development of multi-linear QSPR/QSAR models (Quantitative structure-property/activity relationship). Theses models are used in chemistry, biology and pharmacy to find a relationship between the structure of a molecule and its property (such as activity, toxicology but also physical properties). The various functions of this package allows: selection of descriptors based of variances, intercorrelation and user expertise; selection of the best multi-linear regression in terms of correlation and robustness; methods of internal validation (Leave-One-Out, Leave-Many-Out, Y-scrambling) and external using test sets.
Offers a set of functions for extending
    dendrogram objects in R, letting you visualize and compare trees of
    hierarchical clusterings. You can (1) Adjust a tree's graphical parameters
    - the color, size, type, etc of its branches, nodes and labels. (2)
    Visually and statistically compare different dendrograms to one another.
Offers faster manipulation of dendrogram objects in
    R. A dendrogram object in R is a list structure with attributes in its
    nodes and leaves. Working with dendrogram objects often require a function
    to recursively go through all (or most) element in the list object.
    Naturally, such function are rather slow in R, but can become much faster
    thanks to 'Rcpp'.
This package provides extra functions to manipulate dendrograms
    that build on the base functions provided by the stats package. The main
    functionality it is designed to add is the ability to colour all the edges
    in an object of class dendrogram according to cluster membership i.e. each
    subtree is coloured, not just the terminal leaves. In addition it provides
    some utility functions to cut dendrogram and hclust objects and to set/get
    labels.
Various functions to import, verify, process and plot high-resolution dendrometer using daily and stem-cycle approaches.
Re-arranges a dendrogram to optimize visualisation-based cost functions
An implementation of functions to optimize ordering of nodes in
    a dendrogram, without affecting the meaning of the dendrogram. A dendrogram can
    be sorted based on the average distance of subtrees, or based on the smallest
    distance value. These sorting methods improve readability and interpretability
    of tree structure, especially for tasks such as comparison of different distance
    measures or linkage types and identification of tight clusters and outliers. As
    a result, it also introduces more meaningful reordering for a coupled heatmap
    visualization.
Provides functions for parameter estimation and real-time predictions  of a single dengue 
 outbreak taking into account model uncertainty using  model averaging.
Estimate a low rank matrix from noisy data using singular values
    thresholding and shrinking functions. Impute missing values with matrix completion.
An integrated toolset for the analysis of de novo (sporadic)
    genetic sequence variants. denovolyzeR implements a mutational model that
    estimates the probability of a de novo genetic variant arising in each human
    gene, from which one can infer the expected number of de novo variants in a
    given population size. Observed variant frequencies can then be compared against
    expectation in a Poisson framework. denovolyzeR provides a suite of functions
    to implement these analyses for the interpretation of de novo variation in human
    disease.
We provide tools to 
  (1) visualize multivariate density functions and density estimates 
      with level set trees,
  (2) visualize level sets with shape trees,
  (3) visualize multivariate data with tail trees, 
  (4) visualize scales of multivariate density estimates with 
      mode graphs and branching maps, and
  (5) visualize anisotropic spread with 2D volume functions and
      2D probability content functions.
  Level set trees visualize mode structure,
  shape trees visualize shapes of level sets of unimodal densities,
  and tail trees visualize connected data sets.
  The kernel estimator is implemented
  but the package may  also be applied for visualizing other density estimates.
An implementation of the clustering algorithm described by Alex
    Rodriguez and Alessandro Laio (Science, 2014 vol. 344), along with tools to
    inspect and visualize the results.
Implementation in the density framework of the non-combinatorial algorithm and its greedy version, introduced by Magalhães and Rozenholc (2014), for T-estimation Hold-Out proposed in Birgé (2006, Section 9). The package provide an implementation which uses several families of estimators (regular and irregular histograms, kernel estimators) which may be used alone or combined. As a complement, provides also a comparison with other Held-Out derived from least-squares and maximum-likelihood. This package implements also the T-estimation Hold-Out derived from the test introduced in Baraud (2011).
Provide a Dens-based method for estimating functional connection in large scale brain networks using partial correlation.
Density ratio estimation.
    The estimated density ratio function can be used in many applications such as
    the inlier-based outlier detection, covariate shift adaptation and etc.
Graphical methods for compactly illustrating probability distributions, including density strips, density regions, sectioned density plots and varying width strips.
Implements the differential evolution algorithm for global optimization of a real-valued function of a real-valued parameter vector.
Differential Evolution (DE) stochastic algorithms for global
  optimization of problems with and without constraints.
  The aim is to curate a collection of its state-of-the-art variants that
  (1) do not sacrifice simplicity of design,
  (2) are essentially tuning-free, and
  (3) can be efficiently implemented directly in the R language.
  Currently, it only provides an implementation of the 'jDE' algorithm by
  Brest et al. (2006) <doi:10.1109/TEVC.2006.872133>.
Suppose that one can observe bivariate random variables (X, Y) only when X<=Y holds. Data (Xj, Yj), subject to Xj<=Yj, for all j=1,...,n, are called truncated data. For truncated data, several different approaches are implemented for statistical inference on (X, Y), when X and Y are dependent. Also included is truncated data on the number of deaths at each year (1963-1980) for Japanese male centenarians.
Traditional phasing programs are limited to diploid organisms.
 Our method modifies Li and Stephens algorithm with Markov chain Monte Carlo
 (MCMC) approaches, and builds a generic framework that allows haplotype searches
 in a multiple infection setting. This package is primarily developed as part of
 the Pf3k project, which is a global collaboration using the latest
 sequencing technologies to provide a high-resolution view of natural variation
 in the malaria parasite Plasmodium falciparum. Parasite DNA are extracted from
 patient blood sample, which often contains more than one parasite strain, with
 unknown proportions. This package is used for deconvoluting mixed haplotypes,
 and reporting the mixture proportions from each sample.
Fits (multigroup) mixtures of latent or hidden Markov models on mixed categorical and continuous (timeseries) data. The Rdonlp2 package can optionally be used for optimization of the log-likelihood and is available from R-forge.
Fits latent (hidden) Markov models on mixed categorical and continuous (time series) data, otherwise known as dependent mixture models.
Depth functions methodology applied to multivariate
        analysis. Besides allowing calculation of depth values and
        depth-based location estimators, the package includes functions
        for drawing contour plots and perspective plots of depth
        functions. Euclidian and spherical depths are supported.
Could be used to obtain spatial depths, spatial ranks and outliers of multivariate random variables. Could also be used to visualize DD-plots (a multivariate generalization of QQ-plots).
Data depth concept offers a variety of powerful and user friendly tools for robust exploration and inference for multivariate data. The offered techniques may be successfully used in cases of lack of our knowledge on parametric models generating data due to their nonparametric nature. The package consist of among others implementations of several data depth techniques involving multivariate quantile-quantile plots, multivariate scatter estimators, multivariate Wilcoxon tests and robust regressions.
depthTools is a package that implements different
        statistical tools for the description and analysis of gene
        expression data based on the concept of data depth, namely, the
        scale curves for visualizing the dispersion of one or various
        groups of samples (e.g. types of tumors), a rank test to decide
        whether two groups of samples come from a single distribution
        and two methods of supervised classification techniques, the DS
        and TAD methods. All these techniques are based on the Modified
        Band Depth, which is a recent notion of depth with a low
        computational cost, what renders it very appropriate for high
        dimensional data such as gene expression data.
Queues, stacks, and 'deques' are list-like, abstract data types. 
    These are meant to be very cheap to "grow", or insert new objects into.
    A typical use case involves storing data in a list in a streaming fashion,
    when you do not necessarily know how may elements need to be stored.
    Unlike R's lists, the new data structures provided here are not
    necessarily stored contiguously, making insertions and deletions at the
    front/end of the structure much faster.  The underlying implementation
    is new and uses a head/tail doubly linked list; thus, we do not rely on R's
    environments or hashing.  To avoid unnecessary data copying, most operations
    on these data structures are performed via side-effects.
R-based solution for symbolic differentiation. It admits
    user-defined function as well as function substitution
    in arguments of functions to be differentiated. Some symbolic
    simplification is part of the work.
A set of pricing and expository functions that should
    be useful in teaching a course on financial derivatives.
Tools to read, write, create, and manipulate DESCRIPTION files.
    It is intended for packages that create or manipulate other packages.
Decompose a time series into seasonal, trend and irregular components using transformations to amplitude-frequency domain.
Weighted frequency and contingency tables of categorical
       variables and of the comparison of the mean value of a numerical
       variable by the levels of a factor, and methods to produce xtable
       objects of the tables and to plot them. There are also functions to
       facilitate the character encoding conversion of objects, to quickly
       convert fixed width files into csv ones, and to export a data.frame to
       a text file with the necessary R and SPSS codes to reread the data.
Produce publication quality graphics from output of GGobi's
    describe display plugin.
Allows users to quickly and easily describe data using
    common descriptive statistics.
A collection of miscellaneous basic statistic functions and convenience wrappers for efficiently describing data. The author's intention was to create a toolbox, which facilitates the (notoriously time consuming) first descriptive tasks in data analysis, consisting of calculating descriptive statistics, drawing graphical summaries and reporting the results. The package contains furthermore functions to produce documents using MS Word (or PowerPoint) and functions to import data from Excel. Many of the included functions can be found scattered in other packages and other sources written partly by Titans of R. The reason for collecting them here, was primarily to have them consolidated in ONE instead of dozens of packages (which themselves might depend on other packages which are not needed at all), and to provide a common and consistent interface as far as function and arguments naming, NA handling, recycling rules etc. are concerned. Google style guides were used as naming rules (in absence of convincing alternatives). The 'camel style' was consequently applied to functions borrowed from contributed R packages as well.
Deseasonalize daily or monthly time series.
The package provides R scripts for designing genetical
        genomics experiments.
Use simulated annealing to find optimal designs for Poisson regression models with blocks.
Includes functions for the construction of matched samples that are balanced and representative by design.  Among others, these functions can be used for matching in observational studies with treated and control units, with cases and controls, in related settings with instrumental variables, and in discontinuity designs.  Also, they can be used for the design of randomized experiments, for example, for matching before randomization.  By default, 'designmatch' uses the 'GLPK' optimization solver, but its performance is greatly enhanced by the 'Gurobi' optimization solver and its associated R interface.  For their installation, please follow the instructions at http://user.gurobi.com/download/gurobi-optimizer and http://www.gurobi.com/documentation/6.5/refman/r_api_overview.html.  We have also included directions in the gurobi_installation file in the inst folder.
Functions for (1) ranking, selecting, and prioritising  genes,
    proteins, and metabolites from high dimensional biology experiments, (2)
    multivariate hit calling in high content screens, and (3) combining data
    from diverse sources.
S3 classes for multivariate optimization using the desirability function by Derringer and Suich (1980).
Harrington and Derringer-Suich type desirability functions
This package implements a statistical data mining method to
    compare whole genome gene expression profiles, with respect to the presence
    of a recurrent genetic disturbance event, to identify the affected target
    genes.
Functions that solve initial value problems of a system
        of first-order ordinary differential equations (ODE), of
        partial differential equations (PDE), of differential
        algebraic equations (DAE), and of delay differential
        equations.  The functions provide an interface to the FORTRAN
        functions lsoda, lsodar, lsode, lsodes of the ODEPACK
        collection, to the FORTRAN functions dvode and daspk and a
        C-implementation of solvers of the Runge-Kutta family with
        fixed or variable time steps.  The package contains routines
        designed for solving ODEs resulting from 1-D, 2-D and 3-D
        partial differential equations (PDE) that have been converted
        to ODEs by numerical differencing.
Several estimators of the diagonal elements of a sparse precision
	(inverse covariance) matrix from a sample of Gaussian vectors for a
	given matrix of estimated marginal regression coefficients. Moreover, a robust estimator
  of the precision matrix is proposed.
	To install package 'gurobi', instructions at 
	http://user.gurobi.com/download/gurobi-optimizer and 
	http://www.gurobi.com/documentation/6.0/refman/r_api_overview.html.
A function for plotting maps of agricultural field experiments that
    are laid out in grids.
Models for analyzing site occupancy and count data models
  with detection error, including single-visit based models,
  conditional distance sampling and time-removal models.
  Package development was supported by the
  Alberta Biodiversity Monitoring Institute (www.abmi.ca)
  and the Boreal Avian Modelling Project (borealbirds.ca).
Allows users to quickly and easily detect data containing
    Personally Identifiable Information (PII) through convenience functions.
Solvers and testset for stiff and nonstiff differential equations, and 
  differential algebraic equations
Implementation of DetMCD, a new algorithm for robust and deterministic estimation of location and scatter. The benefits of robust and deterministic estimation are explained in Hubert, Rousseeuw and Verdonck (2012) <doi:10.1080/10618600.2012.672100>.
DetLTS, DetMM (and DetS) Algorithms for Deterministic, Robust
    Linear Regression.
Simple GUI to perform some standard tree-ring analyses.
In the new era of population genomics, surveys of genetic
        polymorphism ("genome scans") offer the opportunity to
        distinguish locus-specific from genome wide effects at many
        loci. Identifying presumably neutral regions of the genome that
        are assumed to be influenced by genome-wide effects only, and
        excluding presumably selected regions, is therefore critical to
        infer population demography and phylogenetic history reliably.
        Conversely, detecting locus-specific effects may help identify
        those genes that have been, or still are, targeted by natural
        selection. The software package DetSel has been developed to
        identify markers that show deviation from neutral expectation
        in pairwise comparisons of diverging populations.
Compare detrital zircon suites by uploading univariate,
      U-Pb age, or bivariate, U-Pb age and Lu-Hf data, in a 'shiny'-based
      user-interface. Outputs publication quality figures using 'ggplot2', and
      tables of statistics currently in use in the detrital zircon geochronology
      community.
Output graphics to EMF (enhanced metafile).
Data sets and sample analyses from Jay L. Devore (2008),
        "Probability and Statistics for Engineering and the Sciences
        (7th ed)", Thomson.
A set of functions to ease quantifying the relationship between developmental
    rate and temperature. The package comprises a set of models and estimated parameters
    borrowed from a literature review in ectotherms.
Collection of package development tools.
It handles numerics, characters, factors, and logicals.
Phase I/II adaptive dose-finding design for combination
   studies. Several methods are proposed depending on the type of
   combinations: (1) the combination of two cytotoxic agents, and (2)
   combination of a molecularly targeted agent with a cytotoxic agent.
This package provides functions to run the CRM and
        TITE-CRM in phase I trials and calibration tools for trial
        planning purposes.
Quickly and graphically show missing values and classes of each variable and each observation of a data.frame. The aim is to show pattern of missing values and if there is wrong class attribution.
A set of functions to perform Raju, van der Linden and Fleer's
    (1995) Differential Functioning of Items and Tests (DFIT) analyses. It includes
    functions to use the Monte Carlo Item Parameter Replication  approach
    (Oshima, Raju, & Nanda, 2006) for obtaining the associated statistical
    significance tests cut-off points. They may also be used for a priori and
    post-hoc power calculations.
Phase I/II adaptive dose-finding design for single-agent
   Molecularly Targeted Agent (MTA), according to the paper "Phase
   I/II Dose-Finding Design for Molecularly Targeted Agent: Plateau
   Determination using Adaptive Randomization" (in review).
Derivative-Free optimization algorithms. These algorithms do not require gradient information. More importantly, they can be used to solve non-smooth optimization problems.
Statistical methods for retrospectively detecting changes in location and/or dispersion of univariate and multivariate variables. Data can be individual (one observation at each instant of time) or subgrouped (more than one observation at each instant of time). Control limits are computed, often using a permutation approach, so that a prescribed false alarm probability is guaranteed  without making any parametric assumptions on the stable (in-control) distribution.
Includes methods involving PK measures in the dose allocation process during a Phase I clinical trials. These methods enter PK in the dose finding designs in different ways, including covariates models, dependent variable or hierarchical models. This package provides functions to generate scenarios, and to run simulations which their objective is to determine the maximum tolerated dose (MTD) and to investigate PK of a drug or a combination of drugs.
Performs Bayesian model averaging for capture-recapture. This includes code to stratify records, check the strata for suitable overlap to be used for capture-recapture, and some functions to plot the estimated population size.
Feature selection from high dimensional data using a diploid
    genetic algorithm with Incomplete Dominance for genotype to phenotype mapping
    and Random Assortment of chromosomes approach to recombination.
Performs differential correlation analysis on input
    matrices, with multiple conditions specified by a design matrix. Contains
    functions to filter, process, save, visualize, and interpret differential
    correlations of identifier-pairs across the entire identifier space, or with
    respect to a particular set of identifiers (e.g., one). Also contains several
    functions to perform differential correlation analysis on clusters (i.e., modules)
    or genes. Finally, it contains functions to generate empirical p-values for the
    hypothesis tests and adjust them for multiple comparisons. Although the package
    was built with gene expression data in mind, it is applicable to other types of
    genomics data as well, in addition to being potentially applicable to data from
    other fields entirely. It is described more fully in the manuscript
    introducing it, freely available at <doi:10.1186/s12918-016-0349-1>.
Spatial analyses involving binning require that every bin have the same area, but this is impossible using a rectangular grid laid over the Earth or over any projection of the Earth. Discrete global grids use hexagons, triangles, and diamonds to overcome this issue, overlaying the Earth with equally-sized bins. This package provides utilities for working with discrete global grids, along with utilities to aid in plotting such data.
dglars implements the differential geometric extension
        of the least angle regression method for logistic and Poisson 
        regression models. To compute the implicitly defined solution 
        curve, two different algorithms are implemented, i.e. the 
        predictor-corrector method and the cyclic coordinate descent
        method.
Model fitting and evaluation tools for double generalized linear
    models (DGLMs). This class of models uses one generalized linear model (GLM)
    to fit the specified response and a second GLM to fit the deviance of the first
    model.
A set of functions have been implemented to generate random data to perform Monte Carlo simulations on structural models with formative constructs and interaction and nonlinear effects (Two-Step PLS Mode B structural models). The setup of the true model considers a simple structure with three formative exogenous constructs related to one formative endogenous construct. The routines take into account the interaction and nonlinear effects of the exogenous constructs on the endogenous construct.
This package contains a proposed revision to the
        stats::ks.test() function and the associated ks.test.Rd help
        page.  With one minor exception, it does not change the
        existing behavior of ks.test(), and it adds features necessary
        for doing one-sample tests with hypothesized discrete
        distributions.  The package also contains cvm.test(), for doing
        one-sample Cramer-von Mises goodness-of-fit tests.
The 'DHARMa' package uses a simulation-based approach to create
    readily interpretable scaled (quantile) residuals from fitted generalized linear mixed
    models. Currently supported are all 'merMod' classes from 'lme4' ('lmerMod', 'glmerMod'),
    'glm' (including 'negbin' from 'MASS', but excluding quasi-distributions) and 'lm' model
    classes. Alternatively, externally created simulations, e.g. posterior predictive simulations 
    from Bayesian software such as 'JAGS', 'STAN', or 'BUGS' can be processed as well. 
    The resulting residuals are standardized to values between 0 and 1 and can be interpreted 
    as intuitively as residuals from a linear regression. The package also provides a number of 
    plot and test functions for typical model misspecification problem, such as 
    over/underdispersion, zero-inflation, and residual spatial and temporal autocorrelation.
Identification of hub genes in a gene co-expression network from gene expression data. The differential network analysis for two contrasting conditions leads to the identification of various types of hubs like Housekeeping, Unique to stress (Disease) and Unique to control (Normal) hub genes.
Double hierarchical generalized linear models in which the mean, dispersion parameters for variance of random effects, and residual variance (overdispersion) can be further modeled as random-effect models.
Contains an implementation of the
	d-variable Hilbert Schmidt independence criterion
	and several hypothesis tests based on it.
Fast functions for effective sample size, weighted multivariate mean and variance computation, 
    and weight diagnostic plot for generic importance sampling type results.
Several tools for handling block-matrix diagonals and similar constructs are implemented. Block-diagonal matrices can be extracted or removed using two small functions implemented here. In addition, non-square matrices are supported. Block diagonal matrices occur when two dimensions of a data set are combined along one edge of a matrix. For example, trade-flow data in the 'decompr' and 'gvc' packages have each country-industry combination occur along both edges of the matrix.
Visualises simple graphs (networks) based on a transition matrix, utilities to plot flow diagrams, 
     visualising webs, electrical networks, ...  
     Support for the books "A practical guide to ecological modelling -
     using R as a simulation platform"
     by Karline Soetaert and Peter M.J. Herman (2009). Springer.
     and the book "Solving Differential Equations in R"
     by Karline Soetaert, Jeff Cash and Francesca Mazzia. Springer.
     Includes demo(flowchart), demo(plotmat), demo(plotweb)
Create graph diagrams and flowcharts using R.
Allows for export of DiagrammeR Graphviz objects to SVG.
The R package provides utilities to estimate and make inference on two summary measures for diagnostic tests when there are three ordinal groups ----volume under ROC surface (VUS) and the extended Youden index, assuming that diagnostic markers increase monotonically and stochastically with disease severity. Point estimates, variance, confidence interval and optimal cut-points both under the normal assumption and also the non-parametric method(s) will be provided for the summary measures. Statistical tests are implemented to compare multiple diagnostic tests and two diagnostic test.  Sample size is calculated to estimate the summary measures within a user-specified margin of error for future study planning.
Performs Diallel Analysis with R using Griffing's and Hayman's approaches. Four different methods (1: Method-I (Parents + F1's + reciprocals); 2: Method-II (Parents and one set of F1's); 3: Method-III (One set of F1's and reciprocals); 4: Method-IV (One set of F1's only)) and two methods (1: Fixed Effects Model; 2: Random Effects Model) can be applied using Griffing's approach.
Visualize one-factor data frame. 
  Beads plot consists of diamonds of each factor of each data series. 
  A diamond indicates average and range. 
  Look over a data frame with many numeric columns and a factor column.
This package provides utilities to calculate the probabilities of various dice-rolling events, such as the probability of rolling a four-sided die six times and getting a 4, a 3, and either a 1 or 2 among the six rolls (in any order); the probability of rolling two six-sided dice three times and getting a 10 on the first roll, followed by a 4 on the second roll, followed by anything but a 7 on the third roll; or the probabilities of each possible sum of rolling five six-sided dice, dropping the lowest two rolls, and summing the remaining dice.
Space-Filling Designs and Uniformity Criteria.
Estimation, validation and prediction of models of different types : linear models, additive models, MARS,PolyMARS and Kriging.
Estimation, validation and prediction of kriging models.
        Important functions : km, print.km, plot.km, predict.km.
Efficient Global Optimization (EGO) algorithm and adaptations for
  parallel infill (multipoint EI), problems with noise, and problems with
  constraints.
View 2D/3D sections or contours of computer experiments designs, surrogates or test functions.
Collapse red-green or green-blue distinctions to simulate
        the effects of different types of color-blindness.
This package contains the dictionaries for reading microdata
        from IBGE (Brazilian Institute of Geography and Statistics)
        surveys PNAD, PME and POF.
A basic, clear implementation of tree-based gradient boosting
    designed to illustrate the core operation of boosting models. Tuning
    parameters (such as stochastic subsampling, modified learning rate, or
    regularization) are not implemented. The only adjustable parameter is the
    number of training rounds. If you are looking for a high performance boosting
    implementation with tuning parameters, consider the 'xgboost' package.
Physical constants. Gold, silver and glass permittivities,
    together with spline interpolation functions.
A wrapper for the ZEIT ONLINE Content API, available at <http://developer.zeit.de>. 'diezeit' gives access to articles and corresponding metadata from the ZEIT archive and from ZEIT ONLINE. A personal API key is required for usage.
Performs detection of Differential Item Functioning using the method DIFboost as proposed in Schauberger and Tutz (2015): Detection of Differential item functioning in Rasch models by boosting techniques, British Journal of Mathematical and Statistical Psychology.
Difference of two precision matrices is estimated by the d-trace loss with lasso penalty, given two sample classes.
A method for identifying pattern changes between 2 experimental
         conditions in correlation networks (e.g., gene co-expression networks),
         which builds on a commonly used association measure, such as Pearson's
         correlation coefficient. This package includes functions to calculate
         correlation matrices for high-dimensional dataset and to test
         differential correlation, which means the changes in the correlation
         relationship among variables (e.g., genes and metabolites) between 2
         experimental conditions.
The package includes functions to calculate confidence
        intervals for the difference of dependent proportions. There
        are two functions implemented to edit the data (dichotomising
        with the help of cutpoints, counting accordance and discordance
        of two tests or situations). For the calculation of the
        confidence intervals entries of the fourfold table are needed.
Functions and examples from the book Solving Differential 
   Equations in R by Karline Soetaert, Jeff R Cash and Francesca Mazzia.
   Springer, 2012.
Metrics of difference for comparing pairs of maps representing real or categorical variables at original and multiple resolutions.
Package to fit diffusion-based IRT models to response and 
	response time data. Models are fit using marginal maximum 
	likelihood. Parameter restrictions (fixed value and equality 
	constraints) are possible. In addition, factor scores (person drift 
	rate and person boundary separation) can be estimated. Model fit 
	assessment tools are also available. The traditional diffusion model 
	can be estimated as well.
Generate a colorized diff of two R objects for an intuitive
    visualization of their differences.
An R interface to the 'codediff' JavaScript library (a copy of which is included in the package).
  Allows for visualization of the difference between 2 files, usually text files or R scripts, in a browser.
Residual-based baseline identification and peak decomposition for x-ray diffractograms as introduced in Davies/Gather/Mergel/Meise/Mildenberger (2008).
Provides data structures and methods for measuring, coding, 
    and analysing text within text corpora. The package allows for manual as 
    well computer aided coding on character, token and text pair level.
Interactive visualizations and tabulations for diffrprojects. 
    All presentations are based on the htmlwidgets framework allowing for 
    interactivity via HTML and Javascript, Rstudio viewer integration, 
    RMarkdown integration, as well as Shiny compatibility.
Implements diffusion map method of data
          parametrization, including creation and visualization of
          diffusion map, clustering with diffusion K-means and
	  regression using adaptive regression model.
Tools for performing inference and analysis on a class of quadratic diffusion processes for both scalar and bivariate diffusion systems. For scalar diffusions, a module is provided for solving first passage time problems for both time-homogeneous and time-inhomogeneous GQDs.
Tools for performing inference and analysis using a data-imputation scheme and the method of lines.
Tools for performing inference and analysis on a class of quadratic jump diffusion processes.
Performs DIFlasso, a method to detect DIF (Differential Item Functioning) in Rasch Models. It can handle settings with many variables and also metric variables.
Detection of differential item functioning among dichotomously
    scored items with non-linear regression procedure.
Provides a collection of standard methods to detect differential item functioning among dichotomously scored items. Methods for uniform and non-uniform DIF, based on test-score or IRT methods, for comparing two or more than two groups of respondents, are available (Magis, Beland, Tuerlinckx and De Boeck,A General Framework and an R Package for the Detection of Dichotomous Differential Item Functioning, Behavior Research Methods, 42, 2010, 847-862 <doi:10.3758/BRM.42.3.847>).
Item focused recursive partitioning for simultaneous selection of
    items and variables that induce Differential Item Functioning (DIF) based on the
    Rasch Model or the Logistic Regression Approach for DIF detection.
Implementation of a function 'digest()' for the creation 
 of hash digests of arbitrary R objects (using the 'md5', 'sha-1', 'sha-256', 
 'crc32', 'xxhash' and 'murmurhash' algorithms) permitting easy comparison of R
 language objects, as well as a function 'hmac()' to create hash-based
 message authentication code. Please note that this package is not meant to
 be deployed for cryptographic purposes for which more comprehensive (and
 widely tested) libraries such as 'OpenSSL' should be used.
Digiroo2 is an R package developed by researchers at the
        University of Queensland to investigate association patterns
        and social structure in wild animal populations. Proximity
        between individuals is generally considered to be an
        appropriate proxy for associations and pairwise association
        indices are the most widely used technique for analysing animal
        social structure. However, little attention is given to
        identifying how patterns of spatial overlap affect these
        association patterns. For example, do individuals associate
        randomly with others with whom they share home ranges, or do
        some individuals go out of their way to associate with or avoid
        particular individuals? This program builds a null model of
        random associations based on an individual's space use
        determined using home range methodologies. Random points may be
        generated within a specified home range contour or according to
        the Utilization Distribution (UD). Expected associations of
        individuals are extracted based on probability of occurrence
        and the proximity between home range weighted random points.
        Association matrices can be generated from multiple
        permutations for analysis using SOCPROG 2.4 (Whitehead 2009) to
        create 'expected' pairwise half-weight association indices
        (HWIs). These may be compared with the 'observed' HWIs from
        field observations to reveal whether pairs of animals associate
        more (= attraction) or less (= avoidance) than expected by
        chance.
The assay sensitivity is the minimum number of copies that the digital PCR assay can detect. Users provide serial dilution results in the format of counts of positive and total reaction wells. The output is the estimated assay sensitivity and the copy number per well in the initial dilute.
Import data from a digital image; it requires user input for
    calibration and to locate the data points. The end result is similar to
    'DataThief' and other other programs that 'digitize' published plots or
    graphs.
Combine multiple-relationship networks into a single weighted
    network.  The approach is similar to factor analysis in the that
    contribution from each constituent network varies so as to maximize the
    information gleaned from the multiple-relationship networks.
    This implementation uses Principal Component Analysis calculated using
    'prcomp' with bootstrap subsampling.  Missing links are imputed using
    the method of Chen et al.
    (2012).
A robust differential identification method that considers an ensemble of finite mixture models combined with a local false discovery rate (fdr) to analyze ChIP-seq (high-throughput genomic)data comparing two samples allowing for flexible modeling of data.
Estimate the Deterministic Input, Noisy ``And'' Gate (DINA) cognitive diagnostic model parameters using the Gibbs sampler described by Culpepper (2015) DOI: 10.3102/1076998615595403.
This function implements the DiNAMIC procedure for
        assessing the statistical significance of recurrent DNA copy
        number aberrations (Bioinformatics (2011) 27(5) 678 - 685).
Compute Hartigan's dip test statistic for unimodality /
 multimodality and provide a test with simulation based p-values,  where
 the original public code has been corrected.
A Bayesian clustering method for replicated time series or replicated measurements from multiple experimental conditions, e.g., time-course gene expression data.  It estimates the number of clusters directly from the data using a Dirichlet-process prior.  See Fu, A. Q., Russell, S., Bray, S. and Tavare, S. (2013) Bayesian clustering of replicated time-course gene expression data with weak signals. The Annals of Applied Statistics. 7(3) 1334-1361. <doi:10.1214/13-AOAS650>.
A collection of R functions for directional data analysis.
An extensible framework
 for automatically placing direct labels onto multicolor lattice or
 ggplot2 plots.
 Label positions are described using Positioning Methods
 which can be re-used across several different plots.
 There are heuristics for examining "trellis" and "ggplot" objects
 and inferring an appropriate Positioning Method.
Direction analysis is a set of tools designed to identify
    combinatorial effects of multiple treatments and/or perturbations on pathways
    and kinases profiled by microarray, RNA-seq, proteomics, or phosphoproteomics
    data.
Calculate adjusted means and proportions of a variable by groups defined by another variable by direct standardisation, standardised to the structure of the dataset.
Implements Dirichlet regression models in R.
Estimate parameters in Dirichlet-Multinomial and compute
        profile log-likelihoods.
Discrete smoothing of probability mass function is performed using three discrete associated kernels: Dirac Discrete Uniform (DiracDU), Binomial and Discrete Triangular. Two automatic bandwidth selection procedures are implemented: the cross-validation method for the three kernels and the local Bayesian approach for Binomial kernel. Note that DiracDU is used for categorical data, Binomial kernel is appropriate for count data with small or moderate sample sizes, and Discrete Triangular kernel is recommended for count data with large sample sizes.
Client for the Discourse 'API'. Discourse is a open source
    discussion forum platform (<http://www.discourse.org/>). It comes with 'RESTful'
    API access to an installation. This client requires that you are authorized
    to access a Discourse installation, either yours or another.
Discrete Laplace exponential family for models such as a generalized linear model
Make inference in a mixture of discrete Laplace distributions using the EM algorithm. This can e.g. be used for modelling the distribution of Y chromosomal haplotypes as described in [1, 2] (refer to the URL section).
DiscML performs rate estimation using maximum likelihood with the 
 options to correct for unobservable data, to implement a Gamma-distribution 
 for rate variation, and to estimate the prior root probabilities from the 
 empirical data.
Structure learning of Bayesian network using coordinate-descent
    algorithm. This algorithm is designed for discrete network assuming a multinomial data set,
    and we use a multi-logit model to do the regression.
    The algorithm is described in Gu, Fu and Zhou (2016) <http://arxiv.org/abs/1403.2310>.
Probability mass function, distribution function, quantile function, random generation and parameter estimation for the discrete inverse Weibull distribution.
Probability mass function, distribution function, quantile function, random generation and estimation for the skew discrete Laplace distributions.
Multiple testing procedures for discrete test statistics,
        that use the known discrete null distribution of the p-values
        for simultaneous inference.
Create, manipulate, transform, and simulate from discrete random
    variables. The syntax is modeled after that which is used in mathematical
    statistics and probability courses, but with powerful support for more
    advanced probability calculations. This includes the creation of joint
    random variables, and the derivation and manipulation of their conditional
    and marginal distributions.
Probability mass function, distribution function, quantile function, random generation and parameter estimation for the type I and III discrete Weibull distributions.
This package is a collection of supervised discretization
        algorithms. It can also be grouped in terms of top-down or
        bottom-up, implementing the discretization algorithms.
'discrimARTs' discriminates with explicit confidence the alternative reproductive tactics (ARTs) in dimorphic systems with bimodal traits by computing the maximum likelihood estimate of a mixture of distributions of a measured ARTs trait. Supported distributions include a mixture of 2 normal distributions and a mixture of 2 facing gamma distributions.
Functions for Discriminant Analysis and Classification purposes
    covering various methods such as descriptive, geometric, linear, quadratic,
    PLS, as well as qualitative discriminant analyses
Provides data transformations, estimation utilities,
    predictive evaluation measures and simulation functions for discrete time
    survival analysis.
Formatting of population and case data, calculation of Standardized
    Incidence Ratios, and fitting the BYM model using INLA.
Implement the methods proposed by Ahmad & Dey (2007) <doi:10.1016/j.datak.2007.03.016> in calculating the dissimilarity matrix at the presence of mixed attributes. This Package includes functions to discretize quantitative variables, calculate conditional probability for each pair of attribute values, distance between every pair of attribute values, significance of attributes, calculate dissimilarity between each pair of objects.
A pipeline to analyze photographs of disk diffusion plates. This removes the need to analyze the plates themselves, and thus analysis can be done separate from the assay. Furthermore, diskImageR removes potential researcher bias, by quantitative assessment of drug resistance as the zone diameter at multiple cutoff values of growth inhibition. This method also extends the disk diffusion assay by measuring drug tolerance (in addition to drug resistance) as the fraction of the subpopulation that is able to grow above the resistance point ("FoG"), and drug sensitivity as the rate of change from no growth to full growth ("slope").
Functions for species distribution modeling, that is, predicting entire geographic distributions form occurrences at a number of sites and the environment at these sites.
An implementation of two exact algorithms for testing the
        Hausdorff and simplex dispersion orderings
The disparity filter algorithm is a network reduction technique to
    identify the 'backbone' structure of a weighted network without destroying
    its multi-scale nature. The algorithm is documented in M. Angeles Serrano,
    Marian Boguna and Alessandro Vespignani in "Extracting the multiscale
    backbone of complex weighted networks", Proceedings of the National Academy
    of Sciences 106 (16), 2009. This implementation of the algorithm supports
    both directed and undirected networks.
A package containing R functions for displaying data and
        results from high-throughput screening experiments.
Description: Functions for modelling dispersion in GLM.
Create disposable R packages for testing.
    You can create, install and load multiple R packages with a single
    function call, and then unload, uninstall and destroy them with another
    function call. This is handy when testing how some R code or an R package
    behaves with respect to other packages.
This package has extensible C++ code for computing dissimilarities between vectors. It also has a number of C++ functions for assembling collections of dissimilarities. In particular, it lets you find a matrix of dissimilarities between the rows of two input matrices. There are also functions for finding the nearest neighbors of each row of a matrix, either within the matrix itself or within another matrix.
A simple way of fitting detection functions to distance sampling
    data for both line and point transects. Adjustment term selection, left and
    right truncation as well as monotonicity constraints and binning are
    supported. Abundance and density estimates can also be calculated (via a
    Horvitz-Thompson-like estimator) if survey area information is provided.
Calculates the study size (either number of detections, or
    proportion of region that should be covered) to achieve a target precision for
    the estimated abundance. The calculation allows for the penalty due to unknown
    detection function, and for overdispersion. The user must specify a guess at the
    true detection function.
Implement DiSTATIS and CovSTATIS (three-way multidimensional scaling). For the  analysis of  multiple distance/covariance matrices collected on the same set of observations
Implementing algorithms and fitting models when sites (possibly remote) share
  computation summaries rather than actual data over HTTP with a master R process (using
  'opencpu', for example). A stratified Cox model and a singular value decomposition are
  provided. The former makes direct use of code from the R 'survival' package. (That is,
  the underlying Cox model code is derived from that in the R 'survival' package.)
  Sites may provide data via several means: CSV files, Redcap API, etc. An extensible
  design allows for new methods to be added in the future. Web applications are provided
  (via 'shiny') for the implemented methods to help in designing and deploying the
  computations.
Download data from the FLORKART database on the distribution of vascular plants in Germany in a convenient way.
An R package that is developed for constructing confidence
        regions without the need to know the sampling distribution of
        bivariate data.
Some very simple method functions for confidence interval calculation and to distill pertinent information from a potentially complex object; primarily used in common with packages extRemes and SpatialVx.
Geodesic distance between phylogenetic trees and
        associated functions.
S4-classes and methods for distributions.
Provides documentation in form of a common vignette to packages 'distr', 'distrEx',
        'distrMod', 'distrSim', 'distrTEst', 'distrTeach', and 'distrEllipse'.
Distribution (S4-)classes for elliptically contoured distributions (based on
        package 'distr').
Extends package 'distr' by functionals, distances, and conditional distributions.
This package contains utilities which are of use in the
        packages I have developed for dealing with distributions.
        Currently these packages are GeneralizedHyperbolic,
        VarianceGamma, and SkewHyperbolic and NormalLaplace. Each of
        these packages requires DistributionUtils. Functionality
        includes sample skewness and kurtosis, log-histogram, tail
        plots, moments by integration, changing the point about which a
        moment is calculated, functions for testing distributions using
        inversion tests and the Massart inequality. Also includes an
        implementation of the incomplete Bessel K function.
Implements S4 classes for probability models based on packages 'distr' and
        'distrEx'.
Estimation for a multinomial logistic regression factorized into independent  Poisson log regressions.  See the textir package for applications in multinomial inverse regression analysis of text.
S4-distribution classes based on package distr for distributions from packages
        'fBasics' and 'fGarch'.
S4-classes for setting up a coherent framework for simulation within the distr
        family of packages.
Provides flexible examples of LLN and CLT for teaching purposes in secondary
        school.
Evaluation (S4-)classes based on package distr for evaluating procedures
        (estimators/tests) at data/simulation in a unified way.
Provides tools for quality checks of georeferenced plant
        species accessions.
R-package DivE contains functions for the DivE estimator (Laydon, D. et al., Quantification of HTLV-1 clonality and TCR diversity, PLOS Comput. Biol. 2014). The DivE estimator is a heuristic approach to estimate the number of classes or the number of species (species richness) in a population.
Utilities to represent, visualize, filter, analyse, and summarize
	     time-depth recorder (TDR) data.  Miscellaneous functions for
	     handling location data are also provided.
Computes the most common diversity measures used in social and other sciences, and includes new measures from interdisciplinary research.
Contains a number of comparative 'phylogenetic' methods,
  mostly focusing on analysing diversification and character
  evolution.  Contains implementations of 'BiSSE' (Binary State
  'Speciation' and Extinction) and its unresolved tree extensions,
  'MuSSE' (Multiple State 'Speciation' and Extinction), 'QuaSSE',
  'GeoSSE', and 'BiSSE-ness' Other included methods include Markov
  models of discrete and continuous trait evolution and constant rate
  'speciation' and extinction.
Allows the calculation of both genetic diversity partition 
  statistics, genetic differentiation statistics, and locus informativeness 
  for ancestry assignment. 
  It also provides users with various option to calculate 
  bootstrapped 95\% confidence intervals both across loci, 
  for pairwise population comparisons, and to plot these results interactively. 
  Parallel computing capabilities and pairwise results without 
  bootstrapping are provided. 
  Weir and Cockerham's 1984 F-statistics are also calculated. 
  Various plotting features are also provided, as well as Chi-square tests of 
  genetic heterogeneity are also provided. 
  Functionality for the calculation of various diversity parameters is 
  possible for RAD-seq derived SNP data sets containing thousands of marker loci.
  A shiny application for the development of microsatellite multiplexes is 
  also available.
Predictions of alpha diversity are fitted from presence data, first abundance is modeled from occupancy models and then, several diversity indices are calculated and finally GLM models are used to predict diversity in different environments and select priority areas.
There are two functions in this package, which can be used
        together to estimate the Shannon's Diversity index at different
        levels of sample size. A Monte-Carlo procedure is used to
        re-sample a given observation at each level of sampling. The
        expectation being that the mean of the re-sampling will
        approach Shannon's diversity index at that sample level.
This package has tools for analyzing DNA melting data to
        generate HRM scores, the DNA diversity measure output of the
        HRM Diversity Assay.  For additional documentation visit
        http://code.google.com/p/divmelt/.
A set of tools for empirical analysis of diversity (a number and frequency of different types in population) and similarity (a number and frequency of shared types in two populations) in biological or ecological systems.
Function to test spatial segregation and association based
        in contingency table analysis of nearest neighbour counts
        following Dixon (2002). Some fortran code has been included to
        the original dixon2002 function of the ecespa package to
        improve speed.
Implements various decision support tools related to the new product development.
             Subroutines include correlation reliability test, Mahalanobis distance measure for outlier detection, combinatorial search (all possible subset regression), non-parametric efficiency analysis measures: DDF (directional distance function), DEA (data envelopment analysis), HDF (hyperbolic distance function), SBM (slack-based measure), and SF (shortage function), benchmarking, risk analysis, technology adoption model, new product target setting, etc.
Compute diffusion kernels on DNA polymorphisms, including SNP and bi-allelic genotypes.
An implementation of the differentiable lasso (dlasso) using iterative ridge algorithm. This package allows selecting the tuning parameter by AIC, BIC and GCV.
Maximum likelihood, Kalman filtering and smoothing, and Bayesian
  analysis of Normal linear State Space models, also known as 
  Dynamic Linear Models
QTL mapping in a mixed model framework with separate
        detection and localization stages. The first stage detects the
        number of QTL on each chromosome based on the genetic variation
        due to grouped markers on the chromosome; the second stage uses
        this information to determine the most likely QTL positions.
        The mixed model can accommodate general fixed and random
        effects, including spatial effects in field trials and pedigree
        effects. Applicable to backcrosses, doubled haploids,
        recombinant inbred lines, F2 intercrosses, and association
        mapping populations.
dlmodeler is a set of user-friendly functions to simplify the state-space modelling, fitting, analysis and forecasting of Generalized Dynamic Linear Models (DLMs). It includes functions to name and extract individual components of a DLM, build classical seasonal time-series models (monthly, quarterly, yearly, etc. with calendar adjustments) and provides a unified interface compatible with other state-space packages including: dlm, FKF and KFAS.
Simulation testing and implementation of data-limited fishery stock
    assessment methods.
Collection of functions for distributed lag linear and non-linear models.
Inference functionalities for structural equation models with second-order polynomial and gamma lag shapes.
Monthly download stats of 'CRAN' and 'Bioconductor' packages.
	     Download stats of 'CRAN' packages is from the 'RStudio' 'CRAN mirror', see <http://cranlogs.r-pkg.org>.
	     'Bioconductor' package download stats is at <https://bioconductor.org/packages/stats/>.
Dynamic model averaging for binary and continuous
        outcomes.
The state-of-the-art algorithms for distance metric learning, including global and local methods such as Relevant Component Analysis, Discriminative Component Analysis, Local Fisher Discriminant Analysis, etc. These distance metric learning methods are widely applied in feature extraction, dimensionality reduction, clustering, classification, information retrieval, and computer vision problems.
Dyadic mixed model analysis with multi-trait responses and
  pedigree-based partitioning of individual variation into a range of
  environmental and genetic variance components for individual and 
  maternal effects.
The framework provides functions to generate ODEs of reaction
    networks, parameter transformations, observation functions, residual functions,
    etc. The framework follows the paradigm that derivative information should be
    used for optimization whenever possible. Therefore, all major functions produce
    and can handle expressions for symbolic derivatives.
A backward selection procedure called delete or merge
        regressors (DMR) combines deleting continuous variables with
        merging levels of factors. The method assumes greedy search
        among linear models with set of constraints of two types:
        either a parameter for a continuous variable is set to zero or
        parameters corresponding to two levels of a factor are
        compared. DMR is a stepwise regression procedure, where in each
        step a new constraint is added according to ranking of the
        hypotheses based on squared t-statistics. As a result a nested
        family of linear models is obtained and the final decision is
        made according to minimization of the generalized information
        criterion (GIC, default BIC). The main function of the package
        is DMR, which is based on hierarchical clustering. Moreover,
        other functions for extensions of DMR method are given, such as
        stepDMR which is based on recalculation of t-statistics in each
        step and function DMR4glm for generalized linear models.
Perform differential analysis for
        methylation array data. Detect differentially
        methylated regions (DMRs) from array M-values. 
        The core is a Non-homogeneous Hidden Markov Model
        for estimating spatial correlation and a novel Constrained 
        Gaussian Mixture Model for modeling the M-value pairs of each individual locus.
This package includes functions and data accompanying the book 
	     "Data Mining with R, learning with case studies" by Luis Torgo, CRC Press 2010.
Functions and data accompanying the second edition of the book  "Data Mining with R, learning with case studies" by Luis Torgo, published by CRC Press.
Package for conducting differential network analysis from
        microarray data.
Methods for studying forensic DNA profiling, in particular familial database searches.
Generates DNA sequences based on Markov model techniques for matched sequences. This can be generalized to several sequences. The sequences (taxa) are then arranged in an evolutionary tree (phylogenetic tree) depicting how taxa diverge from their common ancestors. This gives the tests and estimation methods for the parameters of different models. Standard phylogenetic methods assume stationarity, homogeneity and reversibility for the Markov processes, and  often impose further restrictions on the parameters.
Computationally efficient tools for comparing all pairs of profiles in a DNA database. The expectation and covariance of the summary statistic
  is implemented for fast computing. Routines for estimating proportions of close related individuals are available. The use of wildcards (also
  called F-designation) is implemented. Dedicated functions ease plotting the results.
Community detection for dynamic networks, i.e., networks measured repeatedly over a sequence of discrete time points, using a latent space approach.
The DnE package involves functions to analyse the distribution of a set of given data. The basic idea of the analysis is chi-squared test. Functions which have the form as "is.xxdistribution" are used to analyse whether the data obeys the xxdistrbution. If you do not know which distribution to judge, use function is.dt().
The focus of the dnet is to make sense of omics data (such as gene expression and mutations) from different angles including: integration with molecular networks, enrichments using ontologies, and relevance to gene evolutionary ages. Integration is achieved to identify a gene subnetwork from the whole gene network whose nodes/genes are labelled with informative data (such as the significant levels of differential expression or survival risks). To help make sense of identified gene networks, enrichment analysis is also supported using a wide variety of pre-compiled ontologies and phylostratific gene age information in major organisms including: human, mouse, rat, chicken, C.elegans, fruit fly, zebrafish and arabidopsis. Add-on functionalities are supports for calculating semantic similarity between ontology terms (and between genes) and for calculating network affinity based on random walk; both can be done via high-performance parallel computing.
Discriminant Non-Negative Matrix Factorization aims to extend the Non-negative Matrix Factorization algorithm in order to extract features that enforce not only the spatial locality, but also the separability between classes in a discriminant manner. It refers to three article, Zafeiriou, Stefanos, et al. "Exploiting discriminant information in nonnegative matrix factorization with application to frontal face verification." Neural Networks, IEEE Transactions on 17.3 (2006): 683-695. Kim, Bo-Kyeong, and Soo-Young Lee. "Spectral Feature Extraction Using dNMF for Emotion Recognition in Vowel Sounds." Neural Information Processing. Springer Berlin Heidelberg, 2013. and Lee, Soo-Young, Hyun-Ah Song, and Shun-ichi Amari. "A new discriminant NMF algorithm and its application to the extraction of subtle emotional differences in speech." Cognitive neurodynamics 6.6 (2012): 525-535.
Provides Frequentist (EM) and Bayesian  (MCMC) Methods for Inference of  Birth-Death-Immigration Markov Chains.
The facilities can roughly be grouped as: 

   1) Facilities for groupwise computations of summary statistics and
 other facilities for working with grouped data: 'do' something to data 
 stratified 'by' some variables.  

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

   3) Miscellaneous other utilities.
Define a command-line interface by just giving it
    a description in the specific format.
A direct approach to optimal designs for copula models based on
    the Fisher information. Provides flexible functions for building joint PDFs,
    evaluating the Fisher information and finding optimal designs. It includes an
    extensible solution to summation and integration called 'nint', functions for
    transforming, plotting and comparing designs, as well as a set of tools for
    common low-level tasks.
Production of R packages from tagged comments introduced within the code 
             and a minimum of additional documentation files.
Microsoft Word docx files provide an XML structure that is fairly
    straightforward to navigate, especially when it applies to Word tables and
    comments. Tools are provided to determine table count/structure, comment count
    and also to extract/clean tables and comments from Microsoft Word docx documents.
Various sampling plans are able to be compared using evaluations of their OC, AOQ, ATI etc.
Detect Differences in rhythmic time series. Using linear
    least squares and the robust semi-parametric rfit() method. Differences in
    harmonic fitting could be detected as well as differences in scale of the
    noise distribution.
Package DoE.base creates full factorial experimental designs and designs based on orthogonal arrays for (industrial) experiments. Additionally, it provides utility functions for the class design, which is also used by other packages for designed experiments.
This package creates various kinds of designs for
        (industrial) experiments. It uses, and sometimes enhances,
        design generation routines from other packages. 
        So far, response surface designs from package rsm, latin hypercube
        samples from packages lhs and DiceDesign, and 
        D-optimal designs from package AlgDesign have been implemented.
Provides a '%dopar%' adaptor such that any type of futures can
    be used as backends for the 'foreach' framework.
Provides a parallel backend for the %dopar% function using
        the multicore functionality of the parallel package.
Can calculate ADI (Average Dominance Index), FDI (Frequency based Dominance Indexand) and can build social network graphs with dual directions, can build a Music Notation Graph.
A wrapper on top of the 'Domino Command-Line Client'. It lets you
    run 'Domino' commands (e.g., "run", "upload", "download") directly from your
    R environment. Under the hood, it uses R's system function to run the 'Domino'
    executable, which must be installed as a prerequisite. 'Domino' is a service
    that makes it easy to run your code on scalable hardware, with integrated
    version control and collaboration features designed for analytical workflows
    (see <http://www.dominodatalab.com> for more information).
Provides a parallel backend for the %dopar% function using
        the Rmpi package.
Provides a parallel backend for the %dopar% function using
        the parallel package.
A Redis parallel backend for the %dopar% function
This package provides functions to perform
    reproducible parallel foreach loops, using independent
    random streams as generated by L'Ecuyer's combined
    multiple-recursive generator [L'Ecuyer (1999)]. It
    enables to easily convert standard %dopar% loops into
    fully reproducible loops, independently of the number
    of workers, the task scheduling strategy, or the chosen
    parallel environment and associated foreach backend.
The DoseFinding package provides functions for the design and analysis
	     of dose-finding experiments (with focus on pharmaceutical Phase
	     II clinical trials). It provides functions for: multiple contrast
	     tests, fitting non-linear dose-response models (using Bayesian and
	     non-Bayesian estimation), calculating optimal designs and an
	     implementation of the MCPMod methodology.
Provides a parallel backend for the %dopar% function using
        Luke Tierney's snow package.
It estimates a dose-response relation from either a single or
    multiple summarized data. The trend estimation takes into account the
    correlation among sets of log relative risks and use it to efficiently
    estimate the dose-response relation. To obtain a pooled functional
    relation, the study-specific trends are combined according to principles of
    multivariate random-effects meta-analysis.
A small package containing helper utilities for creating function
    for computing statistics.
Renders DOT diagram markup language in R and also provides the possibility to
    export the graphs in PostScript and SVG (Scalable Vector Graphics) formats.
    In addition, it supports literate programming packages such as 'knitr' and
    'rmarkdown'.
Calculation of cluster typicality coefficients as being generated by fuzzy k-means clustering.
An alternative version of .C() and .Fortran() supporting long vectors and 64-bit integer type arguments. The provided interface .C64() features mechanisms the avoid unnecessary copies of read-only or write-only arguments. This makes it a convenient and fast interface to C/C++ and Fortran code.
Load configuration from a '.env' file, that is
    in the current working directory, into environment variables.
Quick and easy dot-and-whisker plots of regression results.
Performs hypothesis tests concerning a regression function in a least-squares model, where the null is a parametric function, and the alternative is the union of large-dimensional convex polyhedral cones.
Differential exon usage test for RNA-Seq data via an empirical Bayes shrinkage method for the dispersion parameter the utilizes inclusion-exclusion data to analyze the propensity to skip an exon across groups. The input data consists of two matrices where each row represents an exon and the columns represent the biological samples. The first matrix is the count of the number of reads expressing the exon for each sample. The second matrix is the count of the number of reads that either express the exon or explicitly skip the exon across the samples, a.k.a. the total count matrix. Dividing the two matrices yields proportions representing the propensity to express the exon versus skipping the exon for each sample.
Local linear estimator for the univariate hazard (hazard rate) and bandwidth parameter selection using the do-validation method and the standard least squares cross-validation method.
'Kevin Dowd's' book Measuring Market Risk is a widely read book 
          in the area of risk measurement by students and 
          practitioners alike. As he claims, 'MATLAB' indeed might have been the most 
          suitable language when he originally wrote the functions, but,
          with growing popularity of R it is not entirely 
	  valid. As 'Dowd's' code was not intended to be error free and were mainly 
	  for reference, some functions in this package have inherited those 
	  errors. An attempt will be made in future releases to identify and correct 
	  them. 'Dowd's' original code can be downloaded from www.kevindowd.org/measuring-market-risk/. 
          It should be noted that 'Dowd' offers both
          'MMR2' and 'MMR1' toolboxes. Only 'MMR2' was ported to R. 'MMR2' is more 
          recent version of 'MMR1' toolbox and they both have mostly similar 
          function. The toolbox mainly contains different parametric and non 
	  parametric methods for measurement of market risk as well as 
	  backtesting risk measurement methods.
Provides a wrapper for the download.file function,
    making it possible to download files over HTTPS on Windows, Mac OS X, and
    other Unix-like platforms. The 'RCurl' package provides this functionality
    (and much more) but can be difficult to install because it must be compiled
    with external dependencies. This package has no external dependencies, so
    it is much easier to install.
A set of functions that downscales species occupancy at
    coarse grain sizes to predict species occupancy at fine grain sizes.
Toggles the test and production versions of a large workflow.
A GUI or command-line operated data analysis tool, for
        analyzing time-dependent simulation data in which multiple
        instantaneous or time-lagged relations are assumed. This
        package uses Structural Equation Modeling (the sem package). It
        is aimed to deal with time-dependent data and estimate whether
        a causal diagram fits data from an (agent-based) simulation
        model.
Beta-binomial Mixture Model is used to infer the pattern from count data.
		It can be used for clustering of RNA methylation sequencing data.
Analysis, visualisation and simulation of digital polymerase chain
    reaction (dPCR).
fits the primal graphical lasso, via one-at-a-time
        block-coordinate descent.
Perform tree-ring analyses such as detrending, chronology
        building, and cross dating.  Read and write standard file formats
        used in dendrochronology.
The concordance method is a non-parametric method based on bootstrapping that is used to test the hypothesis that two subsets of time series are similar in terms of mean, variance or both. This method was developed to address a concern within dendroclimatology that young trees may produce a differing climate response to older more established trees. Details of this method are available in  Pirie, M. (2013). The Climate of New Zealand reconstructed from kauri tree rings: Enhancement through the use of novel statistical methodology. PhD. Dissertation, School of Environment and Department of Statistics, University of Auckland, New Zealand. This package also produces a figure with 3 panels, each panel is for a different climate variable. An example of this figure in included in "On the influence of tree size on the climate - growth relationship of New Zealand kauri (Agathis australis): insights from annual, monthly and daily growth patterns. J Wunder, AM Fowler, ER Cook, M Pirie, SPJ McCloskey. Trees 27 (4), 937-948". For further R functions for loading your own dendroclimatology datasets and performing dendrochronology analysis refer to the R package "dplR: Dendrochronology Program Library in R". The concordance procedure is intended to add to the standard dendrochronology techniques provided in "dplR".
A fast, consistent tool for working with data frame like objects,
    both in memory and out of memory.
The package implements a Dirichlet Process Mixture (DPM)
        model for clustering and image segmentation.  The DPM model is
        a Bayesian nonparametric methodology that relies on MCMC
        simulations for exploring mixture models with an unknown number
        of components.  The code implements conjugate models with
        normal structure (conjugate normal-normal DP mixture model).
        The package's applications are oriented towards the
        classification of magnetic resonance images according to tissue
        type or region of interest.
Create, install, and summarise data packages that follow
    the Open Knowledge Foundation's Data Package Protocol.
This package contains functions to perform inference via
        simulation from the posterior distributions for Bayesian
        nonparametric and semiparametric models. Although the name of
        the package was motivated by the Dirichlet Process prior, the
        package considers and will consider other priors on functional
        spaces. So far, DPpackage includes models considering Dirichlet
        Processes, Dependent Dirichlet Processes, Dependent Poisson-
        Dirichlet Processes, Hierarchical Dirichlet Processes, Polya
        Trees, Linear Dependent Tailfree Processes, Mixtures of
        Triangular distributions, Random Bernstein polynomials priors
        and Dependent Bernstein Polynomials.  The package also includes
        models considering Penalized B-Splines. Currently the package
        includes semiparametric models for marginal and conditional
        density estimation, ROC curve analysis, interval censored data,
        binary regression models, generalized linear mixed models, IRT
        type models, and generalized additive models. The package also
        contains functions to compute Pseudo-Bayes factors for model
        comparison, and to elicitate the precision parameter of the
        Dirichlet Process. To maximize computational efficiency, the
        actual sampling for each model is done in compiled FORTRAN. The
        functions return objects which can be subsequently analyzed
        with functions provided in the coda package.
Data preprocessing techniques for classification. Functions for normalization, handling of missing values,discretization, outlier detection, feature selection, and data visualization are included.
Functions, methods, and datasets for fitting dimension
 reduction regression, using slicing (methods SAVE and SIR), Principal
 Hessian Directions (phd, using residuals and the response), and an
 iterative IRE.  Partial methods, that condition on categorical
 predictors are also available.  A variety of tests, and stepwise
 deletion of predictors, is also included.  Also included is
 code for computing permutation tests of dimension.  Adding additional
 methods of estimating dimension is straightforward.
 For documentation, see the vignette in the package.   With version 3.0.4,
 the arguments for dr.step have been modified.
Creation and use of R Repositories via helper functions 
 to insert packages into a repository, and to add repository information 
 to the current R session. Two primary types of repositories are support:
 gh-pages at GitHub, as well as local repositories on either the same machine
 or a local network. Drat is a recursive acronym: Drat R Archive Template.
Graphical display of R expression, showing the
        interpretation of an expression by R and the various kind of R
        data structure. The steps of the interpretation of an
        expression are obtained through the parsed tree.
We present DRaWR, a network-based method for ranking genes or
    properties related to a given gene set. Such related genes or properties are
    identified from among the nodes of a large, heterogeneous network of biological
    information. Our method involves a random walk with restarts, performed on
    an initial network with multiple node and edge types, preserving more of the
    original, specific property information than current methods that operate
    on homogeneous networks. In this first stage of our algorithm, we find the
    properties that are the most relevant to the given gene set and extract a
    subnetwork of the original network, comprising only the relevant properties. We
    then rerank genes by their similarity to the given gene set, based on a second
    random walk with restarts, performed on the above subnetwork.
Feed longitudinal data into a Bayesian Latent Factor Model to obtain a low-rank representation. Parameters are estimated using a Hamiltonian Monte Carlo algorithm with STAN. See G. Weinrott, B. Fontez, N. Hilgert and S. Holmes, "Bayesian Latent Factor Model for Functional Data Analysis", Actes des JdS 2016.
Analysis of dose-response data is made available through a suite of flexible and versatile model fitting and after-fitting functions.
A penalized/non-penalized implementation for dynamic regression in the presence of autocorrelated residuals (DREGAR) using iterative penalized/ordinary least squares. It applies Mallows CP, AIC, BIC and GCV to select the tuning parameters.
A somewhat outdated package of basic and easy-to-use functions for
  fitting dose-response curves to continuous dose-response data, calculating some
  (eco)toxicological parameters and plotting the results. Please consider using
  the more powerful and actively developed 'drc' package.  Functions that are
  fitted are the cumulative density function of the lognormal distribution
  (probit fit), of the logistic distribution (logit fit), of the weibull
  distribution (weibull fit) and a linear-logistic model ('linlogit' fit),
  derived from the latter, which is used to describe data showing stimulation at
  low doses (hormesis). In addition, functions checking, plotting and retrieving
  dose-response data retrieved from a database accessed via 'RODBC' are included.
  As an alternative to the original fitting methods, the algorithms from the 'drc'
  package can be used.
Fit restricted mean models for the conditional association
  between an exposure and an outcome, given covariates. Three methods
  are implemented: O-estimation, where a nuisance model for the
  association between the covariates and the outcome is used;
  E-estimation where a nuisance model for the association
  between the covariates and the exposure is used, and doubly robust (DR)
  estimation where both nuisance models are used. In DR-estimation,
  the estimates will be consistent when at least one of the nuisance
  models is correctly specified, not necessarily both.
Provides a R driver for Apache Drill<https://drill.apache.org>, which could connect to the Apache Drill cluster<https://drill.apache.org/docs/installing-drill-on-the-cluster> or drillbit<https://drill.apache.org/docs/embedded-mode-prerequisites> and get result(in data frame) from the SQL query and check the current configuration status. This link <https://drill.apache.org/docs> contains more information about Apache Drill.
This is a collection of functions for discontinuous regression
	     analysis and image processing.
Contains quality control routines for multiplex immunoassay data, 
    including several approaches for: treating the background noise of the assay,  
    fitting the dose-response curves and estimating the limits of quantification.
Likelihood-based marginal regression and association
        modelling for repeated, or otherwise clustered, categorical
        responses using dependence ratio as a measure of the
        association
Dual empirical likelihood (DEL) inference under semiparametric density ratio models (DRM) in the presence of multiple samples, including population cumulative distribution function estimation, quantile estimation and comparison, density estimation, composite hypothesis testing for DRM parameters which encompasses testing for changes in population distribution functions as a special case, etc.
Drop out analysis for psychologists in a R based web application.
    Shiny is used to visualize and analyze drop outs tailored to the methods of
    online survey methodology. Concept and app presented at the SCIP Conference
    in Long Beach, California.
An Implementation of Dimensionality Reduction
    via Regression using Kernel Ridge Regression.
Provides tools for assessing the shape of a dose-response
    curve by testing linearity and non-linearity at user-defined cut-offs. It
    also provides two methods of estimating a threshold dose, or the dose at
    which the dose-response function transitions to significantly increasing:
    bi-linear (based on pkg 'segmented') and smoothed with splines (based on
    pkg 'mgcv').
An implementation of a Machine Learning Framework for prediction of new drugs Side Effects.
    Firstly drugs are clustered with respect to their features description and secondly predictions are made, according to Bayesian scores.
    Moreover it can perform protein enrichment considering the proteins clustered together in the first step of the algorithm.
    This last tool is of extreme interest for biologist and drug discovery purposes, given the fact that it can be used either as a validation of the clusters obtained, as well as for the possible discovery of new interactions between certain side effects and non targeted pathways.
    Clustering of the drugs in the feature space can be done using K-Means, PAM or K-Seeds (a novel clustering algorithm proposed by the author).
The package performs various analyzes of descriptive statistics, including correlations
Two discretization-based Monte Carlo algorithms, namely the Fu-Wang algorithm and the Wang-Lee algorithm, are provided for random sample generation from a high dimensional distribution of complex structure.  The normalizing constant of the target distribution needs not to be known.
Calculate posterior modes and credible intervals of parameters of the Dixon-Simon model for subgroup analysis (with binary covariates) in clinical trials.
Tools for multivariate, linear, time-invariant,
	time series models. This includes ARMA and state-space representations,
	and methods for converting between them. It also includes simulation
	methods and several estimation functions. The package has functions 
	for looking at model roots, stability, and forecasts at different 
	horizons. The ARMA model representation is general, so that VAR, VARX, 
	ARIMA, ARMAX, ARIMAX can all be considered to be special cases. Kalman
	filter and smoother estimates can be obtained from the state space
	model, and state-space model reduction techniques are implemented. 
	An introduction and User's Guide is available in a vignette.
An abstract DList class helps storing large list-type objects in a distributed manner. Corresponding high-level functions and methods for handling distributed storage (DStorage) and lists allows for processing such DLists on distributed systems efficiently. In doing so it uses a well defined storage backend implemented based on the DStorage class.
Dynamic slicing is a method designed for dependency detection between a categorical variable and a continuous variable. It could be applied for non-parametric hypothesis testing and gene set enrichment analysis.
Density surface modelling of line transect data. A Generalized
    Additive Model-based approach is used to calculate spatially-explicit estimates
    of animal abundance from distance sampling (also presence/absence and strip
    transect) data. Several utility functions are provided for model checking,
    plotting and variance estimation.
An expressive language to facilitate the creation and visualization
    of two-dimensional dynamical systems. The basic elements of the language are
    a model wrapping around a function(x,y) which outputs a list(x
    = xprime, y = yprime), and a range. The language supports three
    types of visual objects: visualizations, features, and backgrounds. Visualizations, including dots and arrows,
    depict the behavior of the dynamical system over the entire range.
    Features display
    user-defined curves and points, and their images under the system.
    Backgrounds define and color regions of interest, such as areas of convergence and divergence.
    The language
    can also automatically guess attractors and regions of convergence and divergence.
Fits inhomogeneous Poisson process spatial models
    to line transect sampling data and provides estimate of
    abundance within a region.
Performs distance sampling simulations. It repeatedly generates
    instances of a user defined population within a given survey region, generates
    realisations of a survey design (currently these must be generated using
    Distance software in advance <http://distancesampling.org/>) and simulates
    the detection process. The data are then analysed so that the results can
    be compared for accuracy and precision across all replications. This will
    allow users to select survey designs which will give them the best accuracy
    and precision given their expectations about population distribution. Any
    uncertainty in population distribution or population parameters can be
    included by running the different survey designs for a number of different
    population descriptions. An example simulation can be found in the help file for
    make.simulation.
This package allows you to make basic probability assignments on a set of
    possibilities (events) and combine these events with Dempster's rule of combination.
A collection of functions to estimate parameters of a diffusion model via a D*M analysis. Build in models are: the Ratcliff diffusion model, the RWiener diffusion model, and Linear Ballistic Accumulator models. Custom models functions can be specified as long as they have a density function.
Building discrete-time survival trees and bagged trees based on
    the functionalities of the rpart package. Splitting criterion maximizes the
    likelihood of a covariate-free logistic discrete time hazard model.
Elucidation of human disease similarities has emerged as an active research area, which is highly relevant to etiology, disease classification, and drug repositioning. This package was designed and implemented for identifying disease similarities. It contains five functions which are 'DCEA', 'DCpathway', 'DS', 'comDCGL' and 'comDCGLplot'. In 'DCEA' function, differentially co-expressed genes and differentially co-expressed links are extracted from disease vs. health samples. Then 'DCpathway' function assigns differential co-expression values of pathways to be the average differential co-expression value of their component genes. Then 'DS' employs partial correlation coefficient of pathways as the disease similarity for each disease pairs. And 'DS' contains a permutation process for evaluating the statistical significant of observed disease partial correlation coefficients. At last, 'comDCGL' and 'comDCGLplot' sort out shared differentially co-expressed genes and differentially co-expressed links with regulation information and visualize them.
Data objects in R can be rendered as HTML tables using the
    JavaScript library 'DataTables' (typically via R Markdown or Shiny). The
    'DataTables' library has been included in this R package. The package name
    'DT' is an abbreviation of 'DataTables'.
Towards automation of descriptive frequencies and statistics tables.
This package contains functions to compare the accuracy of two binary diagnostic tests in a "paired" study design, i.e. when each test is applied to each subject in the study.
This package implements different algorithms for analyzing
        randomly truncated data, one-sided and two-sided (i.e. doubly)
        truncated data. Two real data sets are included.
Diffusion Weighted Imaging (DWI) is a Magnetic Resonance Imaging
             modality, that measures diffusion of water in tissues like the human 
             brain. The package contains R-functions to process diffusion-weighted 
             data. The functionality includes diffusion tensor imaging (DTI),
             diffusion kurtosis imaging (DKI), modeling for high angular resolution 
             diffusion weighted imaging (HARDI) using Q-ball-reconstruction and 
             tensor mixture models, several methods for structural adaptive 
             smoothing including POAS and msPOAS, and a streamline fiber tracking 
             for tensor and tensor mixture models.
             The package provides functionality to manipulate and visualize results 
             in 2D and 3D.
This package was created to analyze multi-level one-way
        experimental designs. It is designed to handle vectorized
        observation and factor data where there are unequal sample
        sizes and population variance homogeneity can not be assumed.
        To conduct the Dunnett modified Tukey-Kramer test (a.k.a. the
        T3 Procedure), create two vectors: one for your observations
        and one for the factor level of each observation. The function,
        gl.unequal, provides a means to more conveniently produce a
        factor vector with unequal sample sizes. Next, use the DTK.test
        function to conduct the test and save the output as an object
        to input into the DTK.plot function, which produces a
        confidence interval plot for each of the pairwise comparisons.
        Lastly, the function TK.test conducts the original Tukey-Kramer
        test.
A series of functions which aid in both simulating and
        determining the properties of finite, discrete-time, discrete
        state markov chains.  Two functions (DTMC, MultDTMC) produce n
        iterations of a Markov Chain(s) based on transition
        probabilities and an initial distribution.  The function FPTime
        determines the first passage time into each state.  The
        function statdistr determines the stationary distribution of a
        Markov Chain.
This implements the data table back-end for 'dplyr' so that you
    can seamlessly use data table and 'dplyr' together.
Estimation and comparison of survival distributions of dynamic treatment regimes (DTRs) from sequentially randomized clinical trials.
Dynamic treatment regimens (DTRs) are sequential decision rules tailored at each stage by time-varying subject-specific features and intermediate outcomes observed in previous stages. This package implements three methods: O-learning (Zhao et. al. 2012,2014), Q-learning (Murphy et. al. 2007; Zhao et.al. 2009) and P-learning (Liu et. al. 2014, 2015) to estimate the optimal DTRs.
Dynamic treatment regime estimation and inference via G-estimation,
    dynamic weighted ordinary least squares (dWOLS) and Q-learning. Inference
    via bootstrap and (for G-estimation) recursive sandwich estimation. The authors
    thank Gabrielle Simoneau of McGill University for identifying (and fixing) some
    errors in an earlier version of the code.
This package provides functions for 1D and 2D Discrete
        Cosine Transform (DCT), Discrete Sine Transform (DST) and
        Discrete Hartley Transform (DHT).
A comprehensive implementation of dynamic time warping (DTW) algorithms in R.  DTW computes the optimal (least cumulative distance) alignment between points of two time series.  Common DTW variants covered include local (slope) and global (window) constraints, subsequence matches, arbitrary distance definitions, normalizations, minimum variance matching, and so on.  Provides cumulative distances, alignments, specialized plot styles, etc.
Time series clustering along with optimized techniques related
    to the Dynamic Time Warping distance and its corresponding lower bounds.
    Implementations of partitional, hierarchical, fuzzy, k-Shape and TADPole
    clustering are available. Functionality can be easily extended with
    custom distance measures and centroid definitions.
Provides an implementation of the Time-Weighted Dynamic Time
    Warping (TWDTW) method for land use and land cover mapping using satellite
    image time series. TWDTW is based on the Dynamic Time Warping technique and
    has achieved high accuracy for land use and land cover classification using
    satellite data. The method is based on comparing unclassified satellite image
    time series with a set of known temporal patterns (e.g. phenological cycles
    associated with the vegetation). Using 'dtwSat' the user can build temporal
    patterns for land cover types, apply the TWDTW analysis for satellite datasets,
    visualize the results of the time series analysis, produce land cover maps, and
    create temporal plots for land cover change analysis.
Functions to analyze multiple choice data using Dual Scaling
Expands factors, characters and other eligible classes
        into dummy/indicator variables.
Efficiently create dummies of all factors and character vectors in a data frame. Support is included for learning the categories on one data set (e.g., a training set) and deploying them on another (e.g., a test set).
For the implementation of the step-down or step-up Dunnett testing procedures, the package includes R functions to calculate critical constants and R functions to calculate adjusted P-values of the test statistics. In addition, the package also contains functions to evaluate testing powers and hence the necessary sample sizes specially for the classical problem of comparisons of several treatments with a control.
Computes Dunn's test (1964) for stochastic dominance and reports the results among multiple pairwise comparisons after a Kruskal-Wallis test for stochastic dominance among k groups (Kruskal and Wallis, 1952). The interpretation of stochastic dominance requires an assumption that the CDF of one group does not cross the CDF of the other. 'dunn.test' makes k(k-1)/2 multiple pairwise comparisons based on Dunn's z-test-statistic approximations to the actual rank statistics. The null hypothesis for each pairwise comparison is that the probability of observing a randomly selected value from the first group that is larger than a randomly selected value from the second group equals one half; this null hypothesis corresponds to that of the Wilcoxon-Mann-Whitney rank-sum test. Like the rank-sum test, if the data can be assumed to be continuous, and the distributions are assumed identical except for a difference in location, Dunn's test may be understood as a test for median difference. 'dunn.test' accounts for tied ranks.
Inference of population sizes using a binomial likelihood and least informative discrete uniform priors.
Hurst exponent estimation of a fractional Brownian motion
        by using discrete variations methods in presence of outliers
        and/or an additive noise
Functionality for analyzing dose-volume histograms (DVH)
        in radiation oncology: Read DVH text files, calculate DVH
        metrics as well as generalized equivalent uniform dose (gEUD),
        biologically effective dose (BED), equivalent dose in 2 Gy
        fractions (EQD2), normal tissue complication probability
        (NTCP), and tumor control probability (TCP). Show DVH
        diagrams, check and visualize quality assurance constraints
        for the DVH. Includes web-based graphical user interface.
Provides access to Dataverse version 3 APIs, enabling access to archived data (and metadata), and the ability to create and manipulate studies in a user's dataverse(s). For Dataverse server versions >= 4.0, please use the dataverse package <https://github.com/IQSS/dataverse-client-r> instead.
Regression for a discrete response, where the conditional distribution is modelled via a discrete Weibull distribution.
An R interface to the 'dygraphs' JavaScript charting library
    (a copy of which is included in the package). Provides rich facilities
    for charting time-series data in R, including highly configurable
    series- and axis-display and interactive features like zoom/pan and
    series/point highlighting.
Add a "Did You Mean" feature to the R interactive. With this
    package, error messages for misspelled input of variable names or package names
    suggest what you really want to do in addition to notification of the mistake.
Time series regression.  The dyn class interfaces ts,
        irts, its, zoo and zooreg time series classes to lm, glm,
        loess, quantreg::rq, MASS::rlm, MCMCpack::MCMCregress,
        quantreg::rq, randomForest::randomForest and other regression
        functions allowing those functions to be used with time series
        including specifications that may contain lags, diffs and
        missing values.
The package is aimed at dynamically visualizing probability
    distributions and their moments and all the commonly used distributions are
    included.
Interactive graphical tool for manipulating graphs
Contains methods for detection of clusters in hierarchical clustering dendrograms.
A native R implementation and enhancement of the Dynamic TOPMODEL
    semi-distributed hydrological model. Includes some pre-processsing and
    output routines.
Inference by sequential Monte Carlo for 
  dynamic tree regression and classification models
  with hooks provided for sequential design and optimization, 
  fully online learning with drift, variable selection, and 
  sensitivity analysis of inputs.  Illustrative 
  examples from the original dynamic trees paper are facilitated
  by demos in the package; see demo(package="dynaTree").
A GUI to solve dynamic biplots and classical biplot. Try matrices of 2-way and 3-way. The GUI can be run in multiple languages.
DynClust is a two-stage procedure for the denoising and clustering of stack of noisy images acquired over time. Clustering only assumes that the data contain an unknown but small number of dynamic features. The method first denoises the signals using local spatial and full temporal information. The clustering step uses the previous output to aggregate voxels based on the knowledge of their spatial neighborhood. Both steps use a single keytool based on the statistical comparison of the difference of two signals with the null signal. No assumption is therefore required on the shape of the signals. The data are assumed to be normally distributed (or at least follow a symmetric distribution) with a known constant variance. Working pixelwise, the method can be time-consuming depending on the size of the data-array but harnesses the power of multicore cpus.
Computes dynamical correlation estimates and percentile
        bootstrap confidence intervals for pairs of longitudinal
        responses, including consideration of lags and derivatives.
An anonymization algorithm to resist neighbor label attack in a dynamic network.
Fit dynamic intervention model using the arima() function.
Dynamic linear models and time series regression.
Demonstrate the results of a statistical model object as a dynamic nomogram in an 'RStudio' panel or web browser. DynNom supports a variety of model objects; 'lm', 'glm', 'coxph' and also 'Ols', 'Glm', 'lrm', 'cph' models.
Implements the fast Fourier transform to estimate delays of expression initiation between trajectories to integrate and
    analyse time course omics data.
Computes the first stage GMM estimate of a dynamic linear model with p lags of the dependent variables.
The dynpred package contains functions for dynamic prediction in survival analysis.
Dynamic modeling of all kinds in R. These include models of processes in 
    discrete time or continuous time.  They also include processes that are linear
    or nonlinear. Latent variables can be continuous (e.g. state space models) or 
    discrete (e.g. regime-switching models). The general approach involves maximum
    likelihood estimation of single- and multi-subject models of latent time series
    with the extended Kalman filter and Kim filter. The user provides recipes and
    data which are combined into a model that is then cooked to obtain free parameter
    estimates.
Improves the concept of multivariate range boxes, which is highly susceptible for outliers and does not consider the distribution of the data. The package uses dynamic range boxes to overcome these problems.
Dynamic simulations and graphical depictions of autoregressive
    relationships.
Functions to fit time-varying coefficient models for interval
    censored and right censored survival data. Three major approaches are
    implemented: 1) Bayesian Cox model with time-independent, time-varying or
    dynamic coefficients for right censored and interval censored data; 2)
    Spline based time-varying coefficient Cox model for right censored data; 3)
    Transformation model with time-varying coefficients for right censored data
    using estimating equations.
A comprehensive toolkit for estimating Dynamic Treatment Regimes. Available methods include Interactive Q-Learning, Q-Learning, and value-search methods based on Augmented Inverse Probability Weighted estimators and Inverse Probability Weighted estimators.
Small collection of functions for dyadic binary/dichotomous sequence analyses, e.g. transforming sequences into time-to-event data, implementation of Bakeman & Gottman's (1997) approach of aggregated logit-models, and simulating expected number of low/zero frequencies for state-transition tables. Further functions will be added in future releases. References: Bakeman, R., & Gottman, J. M. (1997) <DOI:10.1017/cbo9780511527685>.
Functions for latent class analysis, short time Fourier
	     transform, fuzzy clustering, support vector machines,
	     shortest path computation, bagged clustering, naive Bayes
	     classifier, ...
Plots of the empirical attainment function for two objectives.
A 'Shiny' web application for energy industry analytics.
      Take an overview of the industry, measure Key Performance Indicators,
      identify changes in the industry over time, and discover new relationships in the data.
The Early-Warning-Signals Toolbox provides methods for estimating
    statistical changes in timeseries that can be used for identifying nearby
    critical transitions. Based on Dakos et al (2012) Methods for Detecting
    Early Warnings of Critical Transitions in Time Series Illustrated Using
    Simulated Ecological Data. PLoS ONE 7(7):e41010
Build regression models using the techniques in Friedman's
    papers "Fast MARS" and "Multivariate Adaptive Regression
    Splines". (The term "MARS" is trademarked and thus not used in
    the name of the package.)
Downloads a satellite image via Google Maps/Earth (these are
    originally from a variety of aerial photography sources), 
    translates the image into a perceptually uniform color space,
    runs one of a few different clustering algorithms on the colors in the image 
    searching for a user-supplied number of colors,
    and returns the resulting color palette.
Enables launching a series of simulations of a computer code from the R session, and to retrieve the simulation outputs in an appropriate format for post-processing treatments. Five sequential sampling schemes and three coupled-to-MCMC schemes are implemented.
Perform analysis of variance and other important complementary analyzes. The functions are easy to use. Performs analysis in various designs, with balanced and unbalanced data.
Builds format strings that can be used with strptime() and sprintf().
It is a package that can be used to send HTML reports easily.
Contains a few utility functions for working with capture-history data, a function for simulating capture-history data, and a function to fit this data, using a Gibbs sampler.
The package fit and plot some nonlinear models
Easily load and install multiple packages from different sources, 
    including CRAN and GitHub. The libraries function allows you to load or attach 
    multiple packages in the same function call. The packages function will load one 
    or more packages, and install any packages that are not installed on your system 
    (after prompting you). Also included is a from_import function that allows you 
    to import specific functions from a package into the global environment.
Power analysis is used in the estimation of sample sizes for
    experimental designs. Most programs and R packages will only output the highest
    recommended sample size to the user. Often the user input can be complicated
    and computing multiple power analyses for different treatment comparisons can
    be time consuming. This package simplifies the user input and allows the user
    to view all of the sample size recommendations or just the ones they want to see.
    The calculations used to calculate the recommended sample sizes are from the
    'pwr' package.
Query NCBI Entrez and retrieve PubMed records in XML format. A large number of records can be easily downloaded via this simple-to-use interface to the NCBI PubMed API.
Performs analysis of regression in simple designs with quantitative treatments, 
             including mixed models and non linear models. Plot graphics (equations and data).
This is a pipelining tool that facilitates 
  evaluation and visualisation of stratified genome-wide 
  association meta-analyses (GWAMAs) results data. It 
  provides (i) statistical methods to test and to account 
  for between-strata difference and to clump genome-wide
  results into independent loci and (ii) extended graphical 
  features (e.g., Manhattan, Miami and QQ plots) tailored 
  for stratified GWAMA results.
Set of tools to simplify application of atomic forecast
    verification metrics for (comparative) verification of ensemble forecasts
    to large data sets. The forecast metrics are imported from the
    'SpecsVerification' package, and additional forecast metrics are provided
    with this package. Alternatively, new user-defined forecast scores can be
    implemented using the example scores provided and applied using the
    functionality of this package.
Fitting and testing multi-attribute probabilistic choice
  models, especially the Bradley-Terry-Luce (BTL) model (Bradley &
  Terry, 1952; Luce, 1959), elimination-by-aspects (EBA) models
  (Tversky, 1972), and preference tree (Pretree) models (Tversky &
  Sattath, 1979).
Package implements entropy balancing, a data preprocessing procedure that allows users to reweight a dataset such that the covariate distributions in the reweighted data satisfy a set of user specified moment conditions. This can be useful to create balanced samples in observational studies with a binary treatment where the control group data can be reweighted to match the covariate moments in the treatment group. Entropy balancing can also be used to reweight a survey sample to known characteristics from a target population.
We propose a new sample size calculation method for trial-based
    cost-effectiveness analyses. Our strategy is based on the value of perfect
    information that would remain after the completion of the study.
This package carries out Empirical Bayes thresholding
        using the methods developed by I. M. Johnstone and B. W.
        Silverman. The basic problem is to estimate a mean vector given
        a vector of observations of the mean vector plus white noise,
        taking advantage of possible sparsity in the mean vector.
        Within a Bayesian formulation, the elements of the mean vector
        are modelled as having, independently, a distribution that is a
        mixture of an atom of probability at zero and a suitable
        heavy-tailed distribution.  The mixing parameter can be
        estimated by a marginal maximum likelihood approach.  This
        leads to an adaptive thresholding approach on the original
        data.  Extensions of the basic method, in particular to wavelet
        thresholding, are also implemented within the package.
Infer the adjacency matrix of a
	network from time course data using an empirical Bayes
	estimation procedure based on Dynamic Bayesian Networks.
Provides the Empirical Bayesian Elastic Net for handling multicollinearity in generalized linear regression models.  As a special case of the 'EBglmnet'
  package (also available on CRAN), this package encourages a grouping effects to select relevant variables and estimate the corresponding non-zero effects.
Genotyping the population using next generation sequencing data is essentially important for the rare variant detection. In order to distinguish the genomic structural variation from sequencing error, we propose a statistical model which involves the genotype effect through a latent variable to depict the distribution of non-reference allele frequency data among different samples and different genome loci, while decomposing the sequencing error into sample effect and positional effect. An ECM algorithm is implemented to estimate the model parameters, and then the genotypes and SNPs are inferred based on the empirical Bayes method.
Provides empirical Bayesian lasso and elastic net algorithms for variable selection and effect estimation. Key features include sparse variable selection and effect estimation via generalized linear regression models, high dimensionality with p>>n, and significance test for nonzero effects. This package outperforms other popular methods such as lasso and elastic net methods in terms of power of detection, false discovery rate, and power of detecting grouping effects.
Functions for querying the EBI Metagenomics Portal (https://www.ebi.ac.uk/metagenomics/). The current main focus is on taxa abundance data, but the intention is that this package should evolve into a general purpose package for working with EBI Metagenomics data using R.
Performs an exact Bayesian segmentation on data and returns the probabilities of breakpoints, an ICL criteria, comparison of change-point location, etc.
Genotyping and SNP calling tool for single-sample next generation sequencing data analysis using an empirical Bayes method.
Provides an interface to the European Central Bank's Statistical
    Data Warehouse API, allowing for programmatic retrieval of a vast quantity
    of statistical data.
Draw sample paths for endpoint-conditioned continuous time Markov chains via modified rejection sampling or uniformization.
An implementation of the univariate elliptic distribution, and
    lambda option pricing model. It provides detailed functionality and data
    sets for the distribution and modelling. Especially, it contains functions
    for the computation of density, probability, quantile, fitting procedures,
    option prices, volatility smile. It also comes with sample financial data,
    and plotting routines.
Computes and plots a transformed empirical CDF (ecdf) as a
    diagnostic for heavy tailed data, specifically data with power law decay on the
    tails.  Routines for annotating the plot, comparing data to a model, fitting a
    nonparametric model, and some multivariate extensions are given.
Some wrappers, functions and data sets for for spatial point pattern analysis (mainly based on spatstat), used in the book "Introduccion al Analisis Espacial de Datos en Ecologia y Ciencias Ambientales: Metodos y Aplicaciones".
Functions to update data sets in Ecdat and to create,
          manipulate, plot and analyze those and similar data sets.
With this package, users can embed interactive charts to their Shiny applications. These charts will be generated by ECharts library developed by Baidu (http://echarts.baidu.com/). Current version supports line chart, bar chart, pie chart, scatter plot, gauge, word cloud, radar chart, tree map, and heat map.
Provides a function that quickly computes the fine structure
    isotope patterns of a set of chemical formulas to a given degree of
    accuracy (up to the limit set by errors in floating point arithmetic). A
    data-set comprising the masses and isotopic abundances of individual
    elements is also provided.
Functions for easy building of error correction models (ECM) for time series regression.
We implement the Bayesian and likelihood methods proposed 
  in Imai, Lu, and Strauss (2008, 2011) for ecological inference in 2 
  by 2 tables as well as the method of bounds introduced by Duncan and 
  Davis (1953).  The package fits both parametric and nonparametric 
  models using either the Expectation-Maximization algorithms (for 
  likelihood models) or the Markov chain Monte Carlo algorithms (for 
  Bayesian models).  For all models, the individual-level data can be 
  directly incorporated into the estimation whenever such data are available.
  Along with in-sample and out-of-sample predictions, the package also
  provides a functionality which allows one to quantify the effect of data
  aggregation on parameter estimation and hypothesis testing under the
  parametric likelihood models.
Dissimilarity-based analysis functions including ordination and Mantel test functions, intended for use with spatial and community data.
The ecoengine provides access to more than 5 million georeferenced
    specimen records from the University of California, Berkeley's Natural History
    Museums.
Geostatistical tools for analyzing spatial patterns in
    population biology. Easy integration of information from multiple sources
    with "ecogen" objects.
This package provides a flexible foundation for scientists, 
  engineers, and policy makers to base teaching exercises as well as for 
  more applied use to model complex eco-hydrological interactions.
Figures, data sets and examples from the book "A practical guide to ecological modelling - using R as a simulation platform" by Karline Soetaert and Peter MJ Herman (2009). Springer.
  All figures from chapter x can be generated by "demo(chapx)", where x = 1 to 11. 
  The R-scripts of the model examples discussed in the book are in subdirectory "examples", ordered per chapter.
  Solutions to model projects are in the same subdirectories.
Tools for general properties including price, quantity, elasticity, convexity, marginal revenue and manifold of various economics demand systems including Linear, Translog, CES, LES and CREMR.
Estimating individual-level covariate-outcome associations 
 using aggregate data ("ecological inference") or a combination of 
 aggregate and individual-level data ("hierarchical related regression").
Provides an R interface to the EcoData Retriever
    (http://ecodataretriever.org/) via the EcoData Retriever's
    command line interface. The EcoData Retriever automates the
    tasks of finding, downloading, and cleaning ecological datasets,
    and then stores them in a local database.
Classes and methods for implementing aquatic ecosystem models,
  for running these models, and for visualizing their results.
Given a site by species interaction matrix, users can make inferences about species interactions by performance hypothesis comparing test statistics against a null distribution. The current package provides algorithms and metrics for niche-overlap, body size ratios and species co-occurrence. Users can also integrate their own algorithms and metrics within these frameworks or completely novel null models. Detailed explanations about the underlying assumptions of null model analysis in ecology can be found at http://ecosimr.org.
R interface to the Embedded COnic Solver (ECOS), an efficient
	     and robust C library for convex problems. Conic and equality
	     constraints can be specified in addition to integer and
	     boolean variable constraints for mixed-integer problems. This
	     R interface is inspired by the python interface and has
	     similar calling conventions.
Implements stochastic simulations of community assembly (ecological
    diversification) using customizable ecospace frameworks (functional trait
    spaces). Provides a wrapper to calculate common ecological disparity and
    functional ecology statistical dynamics as a function of species richness. Functions are written so they will work in a
    parallel-computing environment.
Collection of R functions and data sets for the support of spatial ecology analyses with a focus on pre-, core and post- modelling analyses of species distribution, niche quantification and community assembly. Written by current and former members and collaborators of the ecospat group of Antoine Guisan, Department of Ecology and Evolution (DEE) & Institute of Earth Surface Dynamics (IDYST), University of Lausanne, Switzerland.
Implementation of the EPA's Ecological Exposure Research Division (EERD) tools (discontinued in 1999) for Probit and Trimmed Spearman-Karber Analysis.
    Probit and Spearman-Karber methods from Finney's book "Probit analysis a statistical treatment of the sigmoid response curve" with options for most accurate results or identical results to the book.
    Probit and all the tables from Finney's book (code-generated, not copied) with the generating functions included.
    Control correction: Abbott, Schneider-Orelli, Henderson-Tilton, Sun-Shepard.
    Toxicity scales: Horsfall-Barratt, Archer, Gauhl-Stover, Fullerton-Olsen, etc.
EcoTroph is an approach and software for modelling marine and freshwater ecosystems. It is articulated entirely around trophic levels. EcoTroph's key displays are bivariate plots, with trophic levels as the abscissa, and biomass flows or related quantities as ordinates. Thus, trophic ecosystem functioning can be modelled as a continuous flow of biomass surging up the food web, from lower to higher trophic levels, due to predation and ontogenic processes. Such an approach, wherein species as such disappear, may be viewed as the ultimate stage in the use of the trophic level metric for ecosystem modelling, providing a simplified but potentially useful caricature of ecosystem functioning and impacts of fishing. This version contains catch trophic spectrum analysis (CTSA) function and corrected versions of the mf.diagnosis and create.ETmain functions.
Functions for evaluating and visualizing
  ecological assessment procedures for surface waters
  containing physical, chemical and biological assessments
  in the form of value functions.
Computer simulations of classical ecological models as a
    learning resource.
Implements various procedures for finding 
	     multiple change-points. Two methods make use of dynamic 
	     programming and probabilistic pruning, with no distributional 
	     assumptions other than the existence of certain absolute 
	     moments in one method. Hierarchical and exact search methods 
	     are included. All methods return the set of estimated change-
	     points as well as other summary information.
Provides a powerful framework for evolutionary
    computing in R. The user can easily construct powerful evolutionary algorithms
    for tackling both single- and multi-objective problems by plugging in different
    predefined evolutionary building blocks, e. g., operators for mutation,
    recombination and selection with just a few lines of code. Your problem
    cannot be easily solved with a standard EA which works on real-valued vectors,
    permutations or binary strings? No problem, 'ecr' has been developed with that in
    mind. Extending the framework with own operators is also possible. Additionally
    there are various comfort functions, like monitoring, logging and more.
Functions useful for exploratory data analysis
    using random forests which can be used to compute multivariate partial
    dependence, observation, class, and variable-wise marginal and joint permutation
    importance as well as observation-specific measures of distance 
    (supervised or unsupervised). All of the aforementioned functions are
    accompanied by 'ggplot2' plotting functions.
This package provides a unified approach for Economic
        Design of Control Charts. The main purpose of this package is
        to find out the optimal parameters to minimize the ECH
        (Expected Cost per Hour) of the process.
Detection of edge points in images based on the difference
        of two asymmetric M-kernel estimators. Linear and circular
        regression clustering based on redescending M-estimators.
        Detection of linear edges in images.
Functions for exploratory and descriptive analysis of event based
    data. Can be used to import and export Xes-files, the IEEE eXtensible Event
    Stream standard (see <http://standards.ieee.org/findstds/standard/1849-2016.html> for more info). Provides methods for describing and selecting process data,
    and for preparing event log data for process mining.
This package will allow to connect with email server through Internet Message Access Protocol (IMAP) and extract header information e.g. from, to, cc, subject, date and time. User will supply their email address and password along with other options. Initially this package is developed only for Gmail. To run the functions from this package user have to have IMAP enabled Gmail account.
An implementation of maximum entropy sampling for spatial
  data is provided. An exact branch-and-bound algorithm as well as greedy and
  dual greedy heuristics are included.
Import physiologic data stored in
    the European Data Format (EDF and EDF+) into R.
    Both EDF and EDF+ files are supported. Discontinuous
    EDF+ files are not yet supported.
Functions for reading in data sets of prey and predator isotopic measurements and producing estimates for discrimination factors.
Reads European Data Format files EDF and EDF+, see http://www.edfplus.info,
    BioSemi Data Format files BDF, see http://www.biosemi.com/faq/file_format.htm,
    and BDF+ files, see http://www.teuniz.net/edfbrowser/bdfplus%20format%20description.html.
    The files are read in two steps: first the header is read
    and then the signals (using the header object as a parameter).
Easily creating empirical distribution functions from data: 'dfun', 'pfun',
    'qfun' and 'rfun'.
In the USA, firms file different forms with the U.S. Securities and
    Exchange Commission (SEC) through EDGAR (Electronic Data Gathering, Analysis,
    and Retrieval system). EDGAR automated system collects all the different
    necessary filings and make it publicly available. Secondly, it validates
    collected filings, then perform indexing and accepting of these submitted
    forms. Investors, regulators, and researchers often require these forms for
    various purposes. This package helps in data gathering, management, and 
	visualization in this regard. It downloads SEC EDGAR quarterly master
    indexes, daily master indexes, and filings from SEC.org site and perform 
    sentiment analysis of these filing.
Generates interactive circle plots with the nodes around the
    circumference and linkages between the connected nodes using hierarchical
    edge bundling via the D3 JavaScript library. See <http://d3js.org/> for more
    information on D3.
Facilitates basic spatial edge correction to point pattern data.
Extends edgeR functionality by improving on exactTest using an unconditional exact test of negative binomial means.
Package EDISON (Estimation of Directed Interactions from
    Sequences Of Non-homogeneous gene expression) runs an MCMC
    simulation to reconstruct networks from time series data, using
    a non-homogeneous, time-varying dynamic Bayesian network.
    Networks segments and changepoints are inferred concurrently,
    and information sharing priors provide a reduction of the
    inference uncertainty.
An integrated editing and imputation method for continuous microdata under linear constraints is implemented. It relies on a Bayesian nonparametric hierarchical modeling approach as described in Kim et al. (2015) <DOI:10.1080/01621459.2015.1040881>. In this approach, the joint distribution of the data is estimated by a flexible joint probability model. The generated edit-imputed data are guaranteed to satisfy all imposed edit rules, whose types include ratio edits, balance edits and range restrictions.
Facilitates reading and manipulating (multivariate) data restrictions
    (edit rules) on numerical and categorical data. Rules can be defined with common R syntax
    and parsed to an internal (matrix-like format). Rules can be manipulated with
    variable elimination and value substitution methods, allowing for feasibility checks
    and more. Data can be tested against the rules and erroneous fields can be found based
    on Fellegi and Holt's generalized principle. Rules dependencies can be visualized with 
    using the igraph package.
Perform Dynamic Model Averaging with grid search as in Dangl and Halling (2012) <doi:10.1016/j.jfineco.2012.04.003> using parallel computing.
The library contains R-functions to estimate the effective
        dimension reduction space in 'multi-index' regression models.
This package comes to illustrate the articles "A graphical
        tool for selecting the number of slices and the dimension of
        the model in SIR and SAVE approaches" and "Comparison of sliced
        inverse regression approaches for underdetermined cases"
Provides convenience functions and pre-programmed Stan models
    related to item response theory. Its purpose is to make fitting
    common item response theory models using Stan easy.
Data from the 2013 Australian Federal Election (House of 
    Representatives) and the 2011 Australian Census. Includes tools for 
    visualizing and analysing the data. This package incorporates 
    data that is copyright Commonwealth of Australia (Australian 
    Electoral Commission) 2016.
Package with tools for classification of electroencephalography (EEG) data. Feature extraction techniques such as Fourier Transform and Continuous Wavelet Transform (CWT) are available. Support Vector Machines (SVM) can be used to classify the extracted features. An algorithm using Analysis of Variance (ANOVA), False Discovery Rate (FDR), and SVM is available to feature selection. Additionally, the package contains functions to plot data and features.
Analysis and visualization tools for electroencephalography (EEG) data. Includes functions for plotting (a) EEG caps, (b) single- and multi-channel EEG time courses, and (c) EEG spatial maps. Also includes smoothing and Independent Component Analysis functions for EEG data analysis, and a function for simulating event-related potential EEG data.
Contains the example EEG data used in the package eegkit. Also contains code for easily creating larger EEG datasets from the EEG Database on the UCI Machine Learning Repository.
Compute the extended empirical log likelihood ratio (Tsao & Wu, 2014) for the mean and parameters defined by estimating equations.
Read raw EEM data and prepares them for further analysis.
Provides various tools to calculate EEM fluorescence metrics and
    preprocess EEM for PARAFAC analysis.
Collection of convenience functions to make working with
    administrative records easier and more consistent. Includes functions to
    clean strings, identify cut points, and quickly combine shapefiles and
    data frames for plotting. Also includes three example data sets of 
    administrative education records for learning how to process records with 
    errors.
Enhanced False Discovery Rate (EFDR) is a tool to detect anomalies
    in an image. The image is first transformed into the wavelet domain in
    order to decorrelate any noise components, following which the coefficients
    at each resolution are standardised. Statistical tests (in a multiple
    hypothesis testing setting) are then carried out to find the anomalies. The
    power of EFDR exceeds that of standard FDR, which would carry out tests on
    every wavelet coefficient: EFDR choose which wavelets to test based on a
    criterion described in Shen et al. (2002). The package also provides
    elementary tools to interpolate spatially irregular data onto a grid of the
    required size. The work is based on Shen, X., Huang, H.-C., and Cressie, N.
    'Nonparametric hypothesis testing for a spatial signal.' Journal of the
    American Statistical Association 97.460 (2002): 1122-1140.
Use structural equation modeling to estimate average and
    conditional effects of a treatment variable on an outcome variable, taking into
    account multiple continuous and categorical covariates.
Graphical and tabular effect displays, e.g., of interactions, for 
  various statistical models with linear predictors.
Functions to test for changes of a response to a stimulus grouping relative 
  to a background or baseline response.
Notice: The package EffectStars2 provides a more up-to-date implementation of effect stars! EffectStars provides functions to visualize regression models with categorical response. The effects of the variables are plotted with star plots in order to allow for an optical impression of the fitted model.
Provides functions for the method of effect star. Effect stars can be used to visualize estimates of parameters corresponding to different groups, for example in multinomial logit models. Beside the main function 'effectstars' there exist methods for special objects, for example for 'vglm' objects from the 'VGAM' package.
In personalized medicine, one wants to know, for a given patient and his or her outcome for a predictor (pre-treatment variable), how likely it is that a treatment will be more beneficial than an alternative treatment. This package allows for the quantification of the predictive causal association (i.e., the association between the predictor variable and the individual causal effect of the treatment) and related metrics. Part of this software has been developed using funding provided from the European Union's 7th Framework Programme for research, technological development and demonstration under Grant Agreement no 602552.
Fitting a causal loglinear model and calculating the causal effects for a causal loglinear model with the multiplicative interaction or without the multiplicative interaction, obtaining the natural direct, indirect and the total effect. It calculates also the cell effect, which is a new interaction effect.
A collection of functions to compute the standardized 
  effect sizes for experiments (Cohen d, Hedges g, Cliff delta, Vargha-Delaney A). 
  The computation algorithms have been optimized to allow efficient computation even 
  with very large data sets.
The European Eddy Fluxes Database Cluster distributes fluxes of different Green House Gases measured mainly using the eddy covariance technique acquired in sites involved in EU projects but also single sites in Europe, Africa and others continents that decided to share their measurements in the database (cit. http://gaia.agraria.unitus.it ). The package provides two functions to load and row-wise bind CSV files distributed by the database. Currently only L3 and L4 (L=Level), half-hourly and daily (aggregation) files are supported.
Provides a function to check the
  importance of a feature based on a dependent classification
  variable. An ensemble of feature selection methods
  is used to determine the normalized importance value of
  all features. Combining these methods in one function
  (building the cumulative importance values) provides a 
  stable feature selection tool. This selection
  can also be viewed in a barplot using the barplot_fs() function
  and proved using the evaluation function efs_eval().
Functions for assigning Clarke or Parkes (Consensus) error grid
    zones to blood glucose values, and for plotting both types of error grids.
An easy-to-use implementation of the Engle-Granger
  two-step procedure for identifying pairs of cointegrated series.  It is geared towards 
  the analysis of pairs of securities.  Summary and plot functions are provided, 
  and the package is able to fetch closing prices of securities from Yahoo.
  A variety of unit root tests are supported, and an improved unit root test is included.
An implementation of hierarchical models
    for faecal egg count data to assess anthelmintic
    efficacy. Bayesian inference is done via MCMC sampling using Stan.
A small tool for Social Network Analysis, dealing with
        ego-centric network measures, including Burt's effective size
        and aggregate constraint and an import code suitable for a
        large number of adjacency matrices. A free web application is
        also available on http://www.egonet.associazionerospo.org
Statistics and graphics for streamflow history,
    water quality trends, and the statistical modeling algorithm: Weighted
    Regressions on Time, Discharge, and Season (WRTDS).
Collection of functions to evaluate uncertainty of results from
    water quality analysis using the Weighted Regressions on Time Discharge and
    Season (WRTDS) method. This package is an add-on to the EGRET package that
    performs the WRTDS analysis.
Sampling of risk sets in Cox regression, selections in 
        the Lexis diagram, bootstrapping. Parametric proportional 
        hazards fitting with left truncation and right censoring for 
        common families of distributions, piecewise constant hazards, 
        and discrete models. AFT regression for left truncated and 
        right censored data. Binary and Poisson regression for 
        clustered data, fixed and random effects with bootstrapping.
Extended and enhanced hierarchical logistic regression models (called Huisman-Olff-Fresco in biology, see Huisman eta l. 1993 JVS) models. Response curves along one-dimensional gradients including no response, monotone, plateau, unimodal and bimodal models.
Software accompanying Gary King's book: A Solution to the Ecological Inference Problem. (1997). Princeton University Press. ISBN 978-0691012407.
An R wrapper to allow the user to query categories and Series IDs, and import data, from the EIA's API.
Compares estimates from three ecological inferences routines, based on King et. al.'s approach.
Solves the ``inverse eigenvalue problem'' which is to
        generate a real-valued matrix that has the specified real
        eigenvalue spectrum.  It can generate infinitely many dense
        matrices, symmetric or asymmetric, with the given set of
        eigenvalues.  Algorithm can also generate stochastic and doubly
        stochastic matrices.
This package estimates the parameters of a model for
        symmetric relational data (e.g., the above-diagonal part of a
        square matrix), using a model-based eigenvalue decomposition
        and regression. Missing data is accomodated, and a posterior
        mean for missing data is calculated under the assumption that
        the data are missing at random. The marginal distribution of
        the relational data can be arbitrary, and is fit with an
        ordered probit specification.
Computes confidence intervals for the proportion explained by the first 1,2,k principal components, and computes confidence intervals for each eigenvalue. Both computations are done via nonparametric bootstrap.
Implementation of Efficient Inference of Local Ancestry 
	     using fused quantile regression and k-means classifier
Provides methods for analyzing RxC ecological contingency
        tables using the extreme case analysis, ecological regression,
        and Multinomial-Dirichlet ecological inference models.  Also
        provides tools for manipulating higher-dimension data objects.
EIVE performs a compact genetic algorithm search to reduce errors-in-variables bias in linear regression.
This package allows to use the hybrid Multinomial-Dirichlet-Model
    of Ecological Inference for estimating inner Cells of RxC-Tables. This was
    already implemented in the eiPack-package. eiwild-package now has the
    possibility to use individual level data to support the aggregate level
    data and using different Hyperpriori-Distributions.
Empirical likelihood (EL) inference for two-sample problems. The following statistics are included: the difference of two-sample means, smooth Huber estimators, quantile (qdiff) and cumulative distribution functions (ddiff), probability-probability (P-P) and quantile-quantile (Q-Q) plots as well as receiver operating characteristic (ROC) curves.
Performs some enhanced variable selection algorithms 
  based on the least absolute shrinkage and selection operator for regression model.
Connect to 'Elasticsearch', a 'NoSQL' database built on the 'Java'
    Virtual Machine. Interacts with the 'Elasticsearch' 'HTTP' 'API'
    (<https://www.elastic.co/products/elasticsearch>), including functions for
    setting connection details to 'Elasticsearch' instances, loading bulk data,
    searching for documents with both 'HTTP' query variables and 'JSON' based body
    requests. In addition, 'elastic' provides functions for interacting with 'APIs'
    for 'indices', documents, nodes, clusters, an interface to the cat 'API', and
    more.
Description: Uses k-fold cross-validation and elastic-net regularization to estimate the
              Ising model on binary data. Produces 3D plots of the cost function as a function
              of the tuning parameter in addition to the optimal network structure.
This package provides functions for fitting the entire
        solution path of the Elastic-Net and also provides functions
        for estimating sparse Principal Components. The Lasso solution
        paths can be computed by the same function. First version:
        2005-10.
A lightweight R interface to 'Elasticsearch' - a NoSQL search-engine and 
    column store database (see <https://www.elastic.co/products/elasticsearch> for more 
    information). This package implements a simple Domain-Specific Language (DSL) for indexing, 
    deleting, querying, sorting and aggregating data using 'Elasticsearch'.
This is a bizzare collection of functions written to do
        various sorts of statistical election audits.  There are also
        functions to generate simulated voting data, and simulated
        "truth" so as to do simulations to check charactaristics of
        these methods.
An extension of the elec package intended for use on
        election audits using stratified random samples.  Includes
        functions to obtain conservative and exact p-values, and
        functions that give sample sizes that may make election audits
        more efficient.
Offers a set of functions that automatically downloads and aggregates
    election data from Brazil, directly from the Superior Electoral Court website.
    Among others, there are data available on local and federal elections for all positions
    (city councillor, mayor, state deputy, federal deputy, governor, and president)
    disaggregated by state of the Federation, city, zone, and polling stations.
    In addition, data on the verification (blank votes, null votes, abstention) and the
    candidates' background are also available.
Aims to facilitate the reduction of elemental microchemistry data from solid-phase LA-ICPMS analysis (laser ablation inductive coupled plasma mass spectrometry). The elementR package provides a reactive and user friendly interface for conducting all steps needed for an optimal data reduction while leaving maximum control for user.
Useful when reading the book above mentioned, in the
        documentation referred to as `the book'.
Provides R access to election results data. Wraps elex (https://github.com/newsdev/elex/), a Python package and command line tool for fetching and parsing Associated Press election results.
A tool to draw samples from a Empirical Likelihood Bayesian posterior
   of parameters using Hamiltonian Monte Carlo.
Correlation chart of two set (x and y) of data.  
  Using Quantiles.  Visualize the effect of factor.
This package contains various routines for drawing
        ellipses and ellipse-like confidence regions, implementing the
        plots described in Murdoch and Chow (1996), A graphical display
        of large correlation matrices, The American Statistician 50,
        178-180. There are also routines implementing the profile plots
        described in Bates and Watts (1988), Nonlinear Regression
        Analysis and its Applications.
A suite of elliptic and related functions including Weierstrass and
 Jacobi forms.  Also includes various tools for manipulating and
 visualizing complex functions.
Training and predict functions for SLFN ( Single
        Hidden-layer Feedforward Neural Networks ) using the ELM
        algorithm. ELM algorithm differs from the traditional
        gradient-based algorithms for very short training times ( it
        doesn't need any iterative tuning, this makes learning time
        very fast ) and there is no need to set any other parameters
        like learning rate, momentum, epochs, etc.
Training and prediction functions are provided for the Extreme Learning Machine algorithm (ELM). The ELM use a Single Hidden Layer Feedforward Neural Network (SLFN) with random generated weights and no gradient-based backpropagation. The training time is very short and the online version allows to update the model using small chunk of the training set at each iteration. The only parameter to tune is the hidden layer size and the learning function.
Allows calculating global scores for characteristics of visual stimuli. Stimuli are presented as sequence of pairwise comparisons ('contests'), during each of which a rater expresses  preference for one stimulus over the other. The algorithm for calculating global scores is based on Elo rating, which updates individual scores after each single pairwise contest. Elo rating is widely used to rank chess players according to their performance. Its core feature is that dyadic contests with expected outcomes lead to smaller changes of participants' scores than outcomes that were unexpected. As such, Elo rating is an efficient tool to rate individual stimuli when a large number of such stimuli are paired against each other in the context of experiments where the goal is to rank stimuli according to some characteristic of interest.
Calculate Elo ratings as means to describe animal dominance hierarchies
elrm implements a Markov Chain Monte Carlo algorithm to
        approximate exact conditional inference for logistic regression
        models. Exact conditional inference is based on the
        distribution of the sufficient statistics for the parameters of
        interest given the sufficient statistics for the remaining
        nuisance parameters. Using model formula notation, users
        specify a logistic model and model terms of interest for exact
        inference.
Flexible implementation of Elston-Stewart algorithm
Empirical likelihood ratio tests for the Yang and Prentice (short/long term hazards ratio) models. 
             Empirical likelihood tests within a Cox model, for parameters defined via 
			 both baseline hazard function and regression parameters.
We propose both a clear analysis strategy and a selection of tools to investigate microarray gene expression data. The most usual and relevant existing R functions were discussed, validated and gathered in an easy-to-use R package (EMA) devoted to gene expression microarray analysis. These functions were improved for ease of use, enhanced visualisation and better interpretation of results.
Unsupervised, multivariate, binary clustering for meaningful annotation of data, taking into account the uncertainty in the data. A specific constructor for trajectory movement analysis yields behavioural annotation of trajectories based on estimated local measures of velocity and turning angle, eventually with solar position covariate as a daytime indicator, ("Expectation-Maximization Binary Clustering for Behavioural Annotation").
Tools to analyze the embryo growth and the sexualisation thermal reaction norms.
random walk Metropolis, Metropolis Hasting, parallel tempering, evolutionary Monte Carlo, temperature ladder construction and placement
EM algorithms and several efficient
        initialization methods for model-based clustering of finite
        mixture Gaussian distribution with unstructured dispersion
        in both of unsupervised and semi-supervised learning.
This package carries out empirical mode decomposition and Hilbert spectral
        analysis. For usage of EMD, see Kim and Oh, 2009 (Kim, D and Oh, H.-S. (2009) EMD: A Package for Empirical 
        Mode Decomposition and Hilbert Spectrum, The R Journal, 1, 40-46).
Auxiliary functions and data sets for "Ecological Models and Data", a book presenting maximum likelihood estimation and related topics for ecologists (ISBN 978-0-691-12522-0).
Functions that support estimating, assessing and mapping regional disaggregated indicators. So far, estimation methods comprise the model-based approach Empirical Best Prediction (see "Small area estimation of poverty indicators" by Molina and Rao (2010)<doi:10.1002/cjs.10051>), as well as their precision estimates. The assessment of the used model is supported by a summary and diagnostic plots. For a suitable presentation of estimates, map plots can be easily created. Furthermore, results can easily be exported to excel.
Package providing calculation of Earth Mover's Distance
        (EMD).
Provides basic distribution functions for a mixture model of a Gaussian and exponential distribution.
A toolbox for designing and evaluating predictive models with
    resampling methods. The aim of this package is to provide a simple and
    efficient general framework for working with any type of prediction
    problem, be it classification, regression or survival analysis, that is
    easy to extend and adapt to your specific setting. Some commonly used
    methods for classification, regression and survival analysis are included.
Various Expectation-Maximization (EM) algorithms are implemented for item response theory
				(IRT) models. The current implementation includes IRT models for binary and ordinal
				responses, along with dynamic and hierarchical IRT models with binary responses. The
				latter two models are derived and implemented using variational EM.  Subsequent edits
				also include variational network and text scaling models.
The EmiStatR provides a fast and parallelised calculator to estimate combined wastewater emissions. 
    It supports the planning and design of urban drainage systems, without the requirement of 
    extensive simulation tools. The EmiStatR package implements modular R methods. This enables 
    to add new functionalities through the R framework. Furthermore, EmiStatR was implemented 
    with an interactive user interface with sliders and input data exploration.
Log-linear modeling is a popular method for the analysis of contingency table data. When the table is sparse, the data can fall on the boundary of the convex support, and we say that "the MLE does not exist" in the sense that some parameters cannot be estimated. However, an extended MLE always exists, and a subset of the original parameters will be estimable. The 'eMLEloglin' package determines which sampling zeros contribute to the non-existence of the MLE. These problematic zero cells can be removed from the contingency table and the model can then be fit (as far as is possible) using the glm() function.
The evolutionary model-based multiresponse approach (EMMA)
        is a novel methodology to process optimisation and product
        improvement. The approach is suitable to contexts in which the
        experimental cost and/or time limit the number of implementable
        trials.
End-member modelling analysis of grain-size data.
This package includes functions to read and write to an
        EMME/2 databank
For forming contrasts in the mixed effects for mixtures of linear mixed models fitted to the gene profiles.
EM algorithm for Mixture of Multivariate Skew Normal/T Distributions
Functions to fit finite mixture of unrestricted multivariate skew t (FM-uMST) model, random sample generation, discriminant analysis, 2D and 3D contour plots
Fit models of modularity to morphological landmarks. Perform model 
  selection on results. Fit models with a single within-module correlation or
  with separate within-module correlations fitted to each module.
The main functions are 'emmreml', and 'emmremlMultiKernel'. 'emmreml' solves a mixed model with known covariance structure using the 'EMMA' algorithm.  'emmremlMultiKernel' is a wrapper for 'emmreml' to handle multiple random components with known covariance structures. The function 'emmremlMultivariate' solves a multivariate gaussian mixed model with known covariance structure using the 'ECM' algorithm.
Collection of building blocks for the design and analysis
        of evolutionary multiobjective optimization algorithms.
An implementation of using emoji font in both base and 'ggplot2' graphics.
Statistical tools for environmental and ecological surveys.
    Simulation-based power and precision analysis; detection probabilities from
    different survey designs; visual fast count estimation.
Fixation and saccade detection in eye movement recordings. This package implements a dispersion-based algorithm (I-DT) proposed by Salvucci & Goldberg (2000) which detects fixation duration and position.
Functions for estimating EMP (Expected Maximum Profit Measure) in Credit Risk Scoring and Customer Churn Prediction, according to Verbraken et al (2013, 2014).
Routines for performing empirical calibration of observational
    study estimates. By using a set of negative control hypotheses we can
    estimate the empirical null distribution of a particular observational
    study setup. This empirical null distribution can be used to compute a
    calibrated p-value, which reflects the probability of observing an
    estimated effect size when the null hypothesis is true taking both random
    and systematic error into account.
Auxiliary functions for the DESeq2 package to simulate read counts according to the null hypothesis (i.e., with empirical sample size factors, per-gene total counts and dispersions, but without effects of predictor variables) and to compute the empirical false discovery rate.
Empirical likelihood ratio tests for means/quantiles/hazards
 	from possibly censored and/or truncated data. Now does regression too.
	This version contains some C code.
Calculates the p-value for a mean-type hypothesis (or multiple mean-type hypotheses) based on two samples with censored data.
The analysis of variance table including the expected mean squares (EMS) for various types of experimental design is provided. When some variables are random effects or we use special experimental design such as nested design, repeated-measures design, or split-plot design, it is not easy to find the appropriate test, especially denominator for F-statistic which depends on EMS.
Background correction of spectral like data. Handles variations in
  scaling, polynomial baselines, interferents, constituents and replicate variation.
  Parameters for corrections are stored for further analysis, and spectra are corrected
  accordingly.
The package provides functions to carry out a
        Goodness-of-fit test for discrete multivariate data. It is
        tested if a given observation is likely to have occurred under
        the assumption of an ab-initio model. A p-value can be
        calculated using different distance measures between observed
        and expected frequencies. A Monte Carlo method is provided to
        make the package capable of solving high-dimensional problems.
This package allows one to estimate the output of a computer program,
 as a function of the input parameters, without actually running it.
 The computer program is assumed to be a Gaussian process, whose
 parameters are estimated using Bayesian techniques that give a PDF of
 expected program output.  This PDF is conditional on a ``training set''
 of runs, each consisting of a point in parameter space and the model
 output at that point.  The emphasis is on complex codes that take
 weeks or months to run, and that have a large number of undetermined
 input parameters; many climate prediction models fall into this
 class.  The emulator essentially determines Bayesian posterior
 estimates of the PDF of the output of a model, conditioned on results
 from previous runs and a user-specified prior linear model.  A 
 working example is given in the help page for function `interpolant()',
 which should be the first point of reference.
Provides the next iteration of the EMU Speech 
    Database Management System (EMU-SDMS) with database management, data 
    extraction, data preparation and data visualization facilities.
Contains logic for the data-driven optimization of annotations via minimization of the entropy of variable group members over discrete variable clusters.
Provides algorithms for the analysis of ecological networks.
Likelihood-based approaches to estimate linear regression parameters and treatment effects in the presence of endogeneity. Specifically, this package includes James Heckman's classical simultaneous equation models-the sample selection model for outcome selection bias and hybrid model with structural shift for endogenous treatment. For more information, see the seminal paper of Heckman (1978) <DOI:10.3386/w0177> in which the details of these models are provided. This package accommodates repeated measures on subjects with a working independence approach. The hybrid model further accommodates treatment effect modification.
endogMNP is an R package that fits a Bayesian multinomial
        probit model with endogenous selection, which is sometimes
        called an endogenous switching model.  This can be used to
        model discrete choice data when respondents select themselves
        into one of several groups.  This package is based on the MNP
        package by Kosuke Imai and David A. van Dyk.  This package
        modifies their code.
This R package implements the statistical model proposed by Bullock, Imai, and Shapiro (2011; Political Analysis) to analyze endorsement experiments.  Endorsement experiments are a survey methodology for eliciting truthful responses to sensitive questions.  This methodology is helpful when measuring support for socially sensitive political actors such as militant groups.  The model is fitted with the Markov chain Monte Carlo algorithm and produces the output containing draws from the posterior distribution.
Computes the expectation of the number of transmissions and receptions considering an End-to-End transport model with limited number of retransmissions per packet. It provides theoretical results and also estimated values based on Monte Carlo simulations.
E-statistics (energy) tests and statistics for multivariate and univariate inference,
             including distance correlation, one-sample, two-sample, and multi-sample tests for
             comparing multivariate distributions, are implemented. Measuring and testing
             multivariate independence based on distance correlation, partial distance correlation,
             multivariate goodness-of-fit tests, clustering based on energy distance, testing for
             multivariate normality, distance components (disco) for non-parametric analysis of
             structured data, and other energy statistics/methods are implemented.
Data published by the United States Federal Energy Regulatory Commission including
    electric company financial data, natural gas company financial data, 
    hydropower plant data, liquified natural gas plant data, oil company financial data
    natural gas company financial data, and natural gas storage field data.
Allow numbers to be presented in an English language
        version, one, two, three, ...
Datasets from Nelson, Coffin and Copeland "Introductory
        Statistics for Engineering Experimentation" (Elsevier, 2003)
        with sample code.
Analyzing English & European soccer results data from 1871-2016.
The company 'Enigma' (https://enigma.io) holds many public 'datasets' from
    governments, companies, universities, and organizations. 'Enigma' provides an
    'API' for data, 'metadata', and statistics on each of the 'datasets'. 'enigma' is
    a client to interact with the 'Enigma' 'API', including getting the data
    and 'metadata' for 'datasets' in 'Enigma', as well as collecting statistics on
    'datasets'. In addition, you can download a 'gzipped' 'csv' file of a 'dataset'
    if you want the whole 'dataset'. An 'API' key from 'Enigma' is required to use
    'enigma'.
A set of tools for the analysis of ecological niche of species and calculation of habitat suitability maps.
Automatically partitions data into evaluation bins, executes ecological niche models across a range of settings, and calculates a variety of evaluation statistics.  Current version only implements ENMs with Maxent (Phillips et al. 2006).
The ENmisc package contains utility function for different
        purposes: mtapply and mlapply (multivariate version of tapply
        and lapply), wtd.boxplot (a boxplot with weights), and a visual
        interface to restructuring mosaic plots.
An algorithmic framework for measuring feature importance, outlier detection, model applicability domain evaluation, and ensemble predictive modeling with (sparse) partial least squares regressions.
An R package for joint statistical modelling of ChIP-seq data, accounting for technical/biological replicates, multiple conditions and different ChIP efficiencies of the individual experiments.
These programs are used for calculating enrichment
        factors, drawing enrichment curves to evaluate virtual
        screening approaches.
Provides the "enrich" method to enrich list-like R objects with new, relevant components. The current version has methods for enriching objects of class 'family', 'link-glm' and 'glm'. The resulting objects preserve their class, so all methods associated with them still apply. The package can also be used to produce customisable source code templates for the structured implementation of methods to compute new components.
Extensible S4 classes and methods for batch training of regression and classification algorithms such as Random Forest, Gradient Boosting Machine, Neural Network, Support Vector Machines, K-Nearest Neighbors, Penalized Regression (L1/L2), and Bayesian Additive Regression Trees. These algorithms constitute a set of 'base learners', which can subsequently be combined together to form ensemble predictions. This package provides cross-validation wrappers to allow for downstream application of ensemble integration techniques, including best-error selection. All base learner estimation objects are retained, allowing for repeated prediction calls without the need for re-training. For large problems, an option is provided to save estimation objects to disk, along with prediction methods that utilize these objects. This allows users to train and predict with large ensembles of base learners without being constrained by system RAM.
Bayesian Model Averaging to create probabilistic forecasts
        from ensemble forecasts and weather observations.
Extends the base classes and methods of EnsembleBase package for cross-validation-based integration of base learners. Default implementation calculates average of repeated CV errors, and selects the base learner / configuration with minimum average error. The package takes advantage of the file method provided in EnsembleBase package for writing estimation objects to disk in order to circumvent RAM bottleneck. Special save and load methods are provided to allow estimation objects to be saved to permanent files on disk, and to be loaded again into temporary files in a later R session. The package can be extended, e.g. by adding variants of the current implementation.
Ensemble Model Output Statistics to create probabilistic
        forecasts from ensemble forecasts and weather observations.
Extends the base classes and methods of 'EnsembleBase' package for Principal-Components-Regression-based (PCR) integration of base learners. Default implementation uses cross-validation error to choose the optimal number of PC components for the final predictor. The package takes advantage of the file method provided in 'EnsembleBase' package for writing estimation objects to disk in order to circumvent RAM bottleneck. Special save and load methods are provided to allow estimation objects to be saved to permanent files on disk, and to be loaded again into temporary files in a later R session. Users and developers can extend the package by extending the generic methods and classes provided in 'EnsembleBase' package as well as this package.
Extending the base classes and methods of EnsembleBase package for Penalized-Regression-based (Ridge and Lasso) integration of base learners. Default implementation uses cross-validation error to choose the optimal lambda (shrinkage parameter) for the final predictor. The package takes advantage of the file method provided in EnsembleBase package for writing estimation objects to disk in order to circumvent RAM bottleneck. Special save and load methods are provided to allow estimation objects to be saved to permanent files on disk, and to be loaded again into temporary files in a later R session. Users and developers can extend the package by extending the generic methods and classes provided in EnsembleBase package as well as this package.
Functions to use ensembles of several machine learning models
    specified in caret package.
Add simple runtime contracts to R values. These ensure that values
    fulfil certain conditions and will raise appropriate errors if they do not.
Measurement and partitioning of diversity, based on Tsallis entropy.
This package implements various estimators of entropy, such
  as the shrinkage estimator by Hausser and Strimmer, the maximum likelihood 
  and the Millow-Madow estimator, various Bayesian estimators, and the 
  Chao-Shen estimator.  It also offers an R interface to the NSB estimator.
  Furthermore, it provides functions for estimating Kullback-Leibler divergence,
  chi-squared, mutual information, and chi-squared statistic of independence.
  In addition there are functions for discretizing continuous random variables.
Contains methods for the estimation of Shannon's entropy, variants of Renyi's entropy, mutual information, Kullback-Leibler divergence, and generalized Simpson's indices. The estimators used have a bias that decays exponentially fast.
Rows of two matrices are compared for Shannon entropy,
     coefficient of variation, and expression. P-values can be requested for all metrics.
Tools for automatic model selection and diagnostics for Climate and Environmental data.  In particular the envcpt() function does automatic model selection between a variety of trend, changepoint and autocorrelation models.  The envcpt() function should be your first port of call.
A collection of R functions of common use in
   the Kostas Lab for microbial ecology.
Fast and very memory-efficient calculation of isotope patterns,
    subsequent convolution to theoretical envelopes (profiles) plus valley
    detection and centroidization or intensoid calculation. Batch processing,
    resolution interpolation, wrapper, adduct calculations and molecular
    formula parsing.
Sequential partitioning, clustering and peak detection of
    centroided LC-MS mass spectrometry data (.mzXML). Interactive result and raw
    data plot.
Generation of bioclimatic rasters that will be particularly 
	useful for species distribution modeling.
Functions and datasets to support the book by Nhu D Le and James V Zidek, Springer (2006).
Envelope methodology and aster modeling are combined to provide users with precise estimation of expected Darwinian fitness.
A plot overlying the niche of multiple species is obtained: 1) to determine the niche conditions which favor a higher species richness, 2) to create a box plot with the range of environmental variables of the species, 3) to obtain a list of species in an area of the niche selected by the user and, 4) to estimate niche overlap among the species.
Graphical and statistical analyses of environmental data, with 
  focus on analyzing chemical concentrations and physical parameters, usually in 
  the context of mandated environmental monitoring.  Major environmental 
  statistical methods found in the literature and regulatory guidance documents, 
  with extensive help that explains what these methods do, how to use them, 
  and where to find them in the literature.  Numerous built-in data sets from 
  regulatory guidance documents and environmental statistics literature.  Includes 
  scripts reproducing analyses presented in the book "EnvStats:  An R Package for 
  Environmental Statistics" (Millard, 2013, Springer).
A collection of nice plotting functions directly from a
        data.frame with limited customisation possibilities.
Analyzing censored variables usually requires the use of optimization algorithms. This package provides an alternative algebraic approach to the task of determining the expected value of a random censored variable with a known censoring point. Likewise this approach allows for the determination of the censoring point if the expected value is known. These results are derived under the assumption that the variable follows an Epanechnikov kernel distribution with known mean and range prior to censoring. Statistical functions related to the uncensored Epanechnikov distribution are also provided by this package.
Reads water network simulation data in Epanet's text-based
    '.inp' and '.rpt' formats into R. Also reads results from Epanet-msx.
    Provides basic summary information and plots. 
    The README file has a quick introduction. See http://www2.epa.gov/
    water-research/epanet for more information on the Epanet software for modeling
    hydraulic and water quality behavior of water piping systems.
The main functions compute the expectation propagation approximation of a Bayesian probit/logit models with Gaussian prior. More information can be found in Chopin and Ridgway (2015). More models and priors should follow.
Functions for demographic and epidemiological analysis in
  the Lexis diagram, i.e. register and cohort follow-up data, in
  particular representation, manipulation and simulation of multistate 
  data - the Lexis suite of functions, which includes interfaces to
  'mstate', 'etm' and 'cmprsk' packages. 
  Also contains functions for Age-Period-Cohort and Lee-Carter
  modeling and a function for interval censored data and some useful
  functions for tabulation and plotting, as well some epidemiological
  datasets.
This package contains elementary tools for analysis of
        common epidemiological problems, ranging from sample size
        estimation, through 2x2 contingency table analysis and basic
        measures of agreement (kappa, sensitivity/specificity).
        Appropriate print and summary statements are also written to
        facilitate interpretation wherever possible.  Source code is
        commented throughout to facilitate modification.  The target
        audience includes advanced undergraduate and graduate students
        in epidemiology or biostatistics courses, and clinical
        researchers.
Hierarchical Bayesian models for use in disease 
		freedom and disease prevalence studies, designed 
		with epidemiological applications in mind. The models 
		themselves are in the spirit of those presented in Branscum 
		et al. (2006) (see package documentation for full 
		reference). The helper functions and methods were designed 
		to make implementation and processing of the complex model 
		output relatively simple in application.
Routines for epidemiological contact tracing
    and visualisation of network of contacts.
Package for data exploration and result presentation.
             Full 'epicalc' package with data management functions is available 
             at the author's repository.
Mathematical models of infectious diseases in humans and animals.
    Both, deterministic and stochastic models can be simulated and plotted.
This package provides tools to quantify transmissibility
        throughout an epidemic from the analysis of time series of
        incidence.
Provides flexible model fitting used in epidemiological data
    analysis by a unified model specification, along with some data manipulation
    functions. This package covers fitting of variety models including Cox
    regression models, linear regression models, Poisson regression models, logistic
    models and others whose likelihood is expressed in negative binomial, gamma and
    Weibull distributions.
Tools for simulating mathematical models of infectious disease. 
    Epidemic model classes include deterministic compartmental models, stochastic 
    agent-based models, and stochastic network models. Network models use the
    robust statistical methods of exponential-family random graph models (ERGMs) 
    from the Statnet suite of software packages in R. Standard templates for epidemic 
    modeling include SI, SIR, and SIS disease types. EpiModel features 
    an easy API for extending these templates to address novel scientific research aims.
A collection of epidemic/network-related tools. Simulates transmission of diseases through contact networks. Performs Bayesian inference on network and epidemic parameters, given epidemic data.
Tools for the analysis of epidemiological data. Contains functions for directly and indirectly adjusting measures of disease frequency, quantifying measures of association on the basis of single or multiple strata of count data presented in a contingency table, and computing confidence intervals around incidence risk and incidence rate estimates. Miscellaneous functions for use in meta-analysis, diagnostic test interpretation, and sample size calculations.
Basic sensitivity analysis of the observed relative risks adjusting
    for unmeasured confounding and misclassification of the
    exposure/outcome, or both. It follows the bias analysis methods and
    examples from the book by Lash T.L, Fox M.P, and Fink A.K.
    "Applying Quantitative Bias Analysis to Epidemiologic Data",
    ('Springer', 2009).
A collection of R functions supporting the text book
  Modern Epidemiology, Second Edition, by Kenneth J.Rothman and Sander Greenland.
  ISBN 13: 978-0781755641  See <http://www.krothman.org/> for more information.
Produce one-dimensional density estimates using 
 exponential epi-splines. The user may incorporate soft information, by 
 imposing constraints that (i) require unimodality; (ii) require that the 
 density be monotone non-increase or non-decreasing; (iii) put upper bounds
 on first or second moments; (iv) bound the density's values at mesh points;
 (v) require that the estimate be continuous or continuously differentiable;
 and more.
An efficient multi-core package to reconstruct an underlying network of
             genomic signatures of high-dimensional epistatic selection from 
             partially observed genotype data. The phenotype that we consider is viability. 
			 The network captures the conditional dependent short- and long-range linkage 
			 disequilibrium structure of genomes and thus reveals aberrant marker-marker 
			 associations that are due to epistatic selection. We target on high-dimensional
			 genotype data where number of variables (markers) is larger than number of 
			 sample sizes (p >> n). The computations is memory-optimized using the sparse 
			 matrix output.
EpiTools: R Package for Epidemiologic Data and Graphics
Users can easily derive the calendar dates from epidemiological weeks, and vice versa.
Aim: Adjust the graphical parameters to create nicer
    longitudinal series plots. The default set of graphical parameters is very
    general, and can be improved upon when we are interested in plotting data
    points observed over time. Functions facilitate plotting those kind of
    series, univariate plots, bivariate plots (with vertical axis on both left
    and right hand sides), multivariate plots and plots which allow to examine
    whether a new observation is 'unusual' via construction and visualization
    of prediction intervals around it.
Estimates sparse matrices A or G using fast lasso regression from mRNA transcript levels Y and CNA profiles U. Two models are provided, EPoC A where
      AY + U + R = 0
  and EPoC G where
      Y = GU + E,
  the matrices R and E are so far treated as noise. For details see the reference and the manual page of `lassoshooting'.
The package performs analysis of polynomial regression in simple designs with quantitative treatments
Computation of the EQL for a given family of variance
        functions, Saddlepoint-approximations and related auxiliary
        functions (e.g. Hermite polynomials)
Transitioning from EQS to R for structural equation modeling (SEM)
    is made easier with a set of functions to convert .out files into R code.
    The EQS output can be converted into lavaan syntax and run in the R
    environment.  Other functions parse descriptive statistics and the covariance matrix
    from an EQS .out file.  A heat map plot of a covariance matrix is also
    included.
Analysis of experimental crosses to identify genes (called
        quantitative trait loci, QTLs) contributing to variation in
        quantitative traits.
Contains methods for observed-score linking
    and equating under the single-group, equivalent-groups,
    and nonequivalent-groups with anchor test(s) designs.
    Equating types include identity, mean, linear, general
    linear, equipercentile, circle-arc, and composites of
    these. Equating methods include synthetic, nominal
    weights, Tucker, Levine observed score, Levine true
    score, Braun/Holland, frequency estimation, and chained
    equating. Plotting and summary methods, and methods for
    multivariate presmoothing and bootstrap error estimation
    are also provided.
Computation of direct, chain and average (bisector) equating coefficients with standard errors using Item Response Theory (IRT) methods for dichotomous items. Test scoring can be performed by true score equating and observed score equating methods.
Provides statistical tests and graphics for assessing tests
        of equivalence.  Such tests have similarity as the alternative
	hypothesis instead of the null.  Sample data sets are included.
Automated compound deconvolution, alignment across samples, and identification of metabolites by spectral library matching in Gas Chromatography - Mass spectrometry (GC-MS) untargeted metabolomics. Outputs a table with compound names, matching scores and the integrated area of the compound for each sample.
Expectile regression is a nice tool for estimating the conditional expectiles of a response variable given a set of covariates. This package implements a regression tree based gradient boosting estimator for nonparametric multiple expectile regression.
Functions, datasets, and sample codes related to the book of 'Empirical Research in Economics: Growing up with R' by Dr. Changyou Sun are included. Marginal effects for binary or ordered choice models can be calculated. Static and dynamic Almost Ideal Demand System (AIDS) models can be estimated. A typical event analysis in finance can be conducted with several functions included.
An integrated set of tools to analyze and simulate networks based on exponential-family random graph models (ERGM). "ergm" is a part of the "statnet" suite of packages for network analysis.
A set of extensions for the 'ergm' package to fit weighted networks whose edge weights are counts.
Utilities for managing egocentrically sampled network data and a wrapper around the 'ergm' package to facilitate ERGM inference and simulation from such data.
Integrates graphlet statistics based model terms for use in exponential-family random graph models ('ergm') as part of the 'statnet' suite of packages.
Data for use with the Sage Introduction to Exponential
        Random Graph Modeling text by Jenine K. Harris.  Network data
        set consists of 1283 local health departments and the
        communication links among them along with several attributes.
A set of extensions for the 'ergm' package to fit weighted networks whose edge weights are ranks.
A template package to demonstrate the use of user-specified statistics for use in "ergm" models as part of the "statnet" suite of packages.
Fits Rasch models (RM), linear logistic test models (LLTM), rating scale model (RSM), linear rating scale models (LRSM), partial credit models (PCM), and linear partial credit models (LPCM).  Missing values are allowed in the data matrix.  Additional features are the ML estimation of the person parameters, Andersen's LR-test, item-specific Wald test, Martin-Löf-Test, nonparametric Monte-Carlo Tests, itemfit and personfit statistics including infit and outfit measures, various ICC and related plots, automated stepwise item elimination, simulation module for various binary data matrices.
The functions provided in the package ERP are designed for the significance analysis of ERP data in a linear model framework. The possible procedures are either the collection of FDR or FWER controlling methods available in the generic function p.adjust, the same collection combined with a factor modeling of the time dependence among tests (see Sheu, Perthame, Lee and Causeur, 2016) and the Guthrie and Buchwald (1991) test.
A set of user-friendly functions to aid in organizing, plotting 
    and analyzing event-related potential (ERP) data.  Provides an easy-to-learn 
    method to explore ERP data. Should be useful to those without a background 
    in computer programming, and to those who are new to ERPs (or new to the 
    more advanced ERP software available). Emphasis has been placed on highly 
    automated processes using functions with as few arguments as possible. 
    Expects processed (cleaned) data.
This package is dedicated to the analysis of event-related potentials (ERPs). Event-related potentials are the measured brain responses associated with a specific sensory, cognitive, or motor event and are obtained from electroencephalographic (EEG) signal. The erpR package contains a series of functions for importing ERP data, computing traditional ERP measures, exploratory ERP analyses and plotting.
Build and analyze error intervals for a particular model predictions assuming different distributions for noise in the data.
Provides a function to convert existing R functions into "errorized" versions 
    with added logging and handling functionality when encountering errors or warnings. 
    The errorize function accepts an existing R function as its first argument and 
    returns a R function with the exact same arguments and functionality. However, 
    if an error or warning occurs when running that "errorized" R function, it will save a 
    .Rds file to the current working directory with the relevant objects and information 
    required to immediately recreate the error.
Implementation of the Edge Selection Algorithm
These functions estimate the latent factors of a given matrix, no matter it is high-dimensional or not. It tries to first estimate the number of factors using bi-cross-validation and then estimate the latent factor matrix and the noise variances. For more information about the method, see Art B. Owen and Jingshu Wang 2015 archived article on factor model (http://arxiv.org/abs/1503.03515).
Implementation of the web-based 'Practical Meta-Analysis Effect Size
    Calculator' from David B. Wilson in R. Based on the input, the effect size
    can be returned as standardized mean difference, Hedges' g, correlation
    coefficient r or Fisher's transformation z, odds ratio or log odds effect size.
The package can identify the dysregulated canonical pathways by investigating the changes of biological relationships of pathways in the context of gene expression data. (1) The ESEA package constructs a background set of edges by extracting pathway structure (e.g. interaction, regulation, modification, and binding etc.) from the seven public databases (KEGG; Reactome; Biocarta; NCI; SPIKE; HumanCyc; Panther) and the edge sets of pathways for each of the above databases. (2) The ESEA package can can quantify the change of correlation between genes for each edge based on gene expression data with cases and controls. (3) The ESEA package uses the weighted Kolmogorov-Smirnov statistic to calculate an edge enrichment score (EES), which reflects the degree to which a given pathway is associated the specific phenotype. (4) The ESEA package can provide the visualization of the results.
The package presents a "Scenarios" class containing
        general parameters, risk parameters and projection results.
        Risk parameters are gathered together into a ParamsScenarios
        sub-object. The general process for using this package is to
        set all needed parameters in a Scenarios object, use the
        customPathsGeneration method to proceed to the projection, then
        use xxx_PriceDistribution() methods to get asset prices.
Toolkit for Monte Carlo simulations of financial assets and
    interest rates models, involved in an Economic Scenario Generator (ESG).
    The underlying simulation loops have been implemented in C++.
Functions for classification and group membership probability estimation are given. 
             The issue of non-informative features in the data is addressed by utilizing the ensemble method. 
             A few optimal models are selected in the ensemble from an initially large set of base k-nearest neighbours (KNN) models, generated on subset of features from the training data.
             A two stage assessment  is applied in selection of optimal models for the ensemble in the training function. 
             The prediction functions for classification and class membership probability estimation returns class outcomes and class membership probability estimates for the test data. 
             The package includes measure of classification error and brier score, for classification and probability estimation tasks respectively.
Provides an interface to e-Stat API, the one-stop service for official statistics of the Japanese government.
Estimates item and person parameters for the Samejima's Continuous Response Model (CRM), computes item fit residual statistics, draws empirical 3D item category response curves, draws theoretical 3D item category response curves, and generates data under the CRM for simulation studies.
Our method is a variable selection method to select active components in sparse linear mixed models in order to estimate the heritability. The selection allows us to reduce the size of the data sets which improves the accuracy of the estimations. Our package also provides a confidence interval for the estimated heritability.
Provides tools for determining estimability of linear functions of regression coefficients, 
  and 'epredict' methods that handle non-estimable cases correctly.
This package is intended to speedup the process of
        creating model-comparing tables common in Macroeconomics. The
        function collection stores the estimates of several models and
        formats it to a table of the form estimate starred and std.err.
        below. The default output is LaTeX but output to CSV for later
        editing in a spreadsheet tool is possible as well. It works for
        linear models (lm) and panel models from the "plm"-package
        (plm). Two further implemented functions "descsto" and
        "desctab" enable you to export descriptive statistics of
        data-frames and single variables to LaTeX and CSV.
This package deals with the estimation of the jump rate for piecewise-deterministic Markov processes (PDMPs), from only one observation of the process within a long time. The main functions provide an estimate of this function. The state space may be discrete or continuous. The associated paper has been published in Scandinavian Journal of Statistics and is given in references. Other functions provide a method to simulate random variables from their (conditional) hazard rate, and then to simulate PDMPs.
A table function for descriptive statistics in tabular format, using variables in a data.frame. You can create simple or highly customized tables.
Fitting the space-time Epidemic Type
        Aftershock Sequence (ETAS) model to earthquake catalogs
        using a stochastic declustering approach.
        The ETAS model is a spatio-temporal marked point process
        model and a special case of the Hawcks process.
        The package is based on Fortran code by Jiancang Zhuang
        (available at http://bemlar.ism.ac.jp/zhuang/software.html),
        which is translated into C such that it can be called from R.
Estimation of the components of an ETAS model for earthquake description. Non-parametric background seismicity can be estimated through FLP (Forward Likelihood Predictive), while parametric components are estimated through maximum likelihood. The two estimation steps are alternated until convergence is obtained. For each event the probability of being a background event is estimated and used as a weight for declustering steps. Many options to control the estimation process are present, together with some diagnostic tools. Some descriptive functions for earthquakes catalogs are  present; also plot, print, summary, profile methods are defined for main output (objects of class 'etasclass').
The package allows selecting those treatments of a one-way layout
 being equivalent to a control. Bonferroni adjusted "two one-sided t-tests"
 (TOST) and related simultaneous confidence intervals are given for both
 differences or ratios of means of normally distributed data. For the case of
 equal variances and balanced sample sizes for the treatment groups, the
 single-step procedure of Bofinger and Bofinger (1995) can be chosen. For
 non-normal data, the Wilcoxon test is applied.
A framework for loading medium-sized data from
    the Internet to a local or remote relational database management system.
    This package itself doesn't do much more than provide a toy example and set up
    the method structure. Packages that depend on this package will facilitate the
    construction and maintenance of their respective databases.
Provides functions to facilitate the use of the 'ff' package in
    interaction with big data in 'SQL' databases (e.g. in
    'Oracle', 'MySQL', 'PostgreSQL', 'Hive') by allowing easy importing directly into 'ffdf'
    objects using 'DBI', 'RODBC' and 'RJDBC'. Also contains some basic utility
    functions to do fast left outer join merging based on 'match', factorisation of data and a basic
    function for re-coding vectors.
Matrix of transition probabilities for any time-inhomogeneous multistate model with finite state space
Traditional meta-regression based method has been developed for using meta-analysis data, but it faced the challenge of inconsistent estimates. This package purpose a new statistical method to detect epistasis using incomplete information summary, and have proven it not only successfully let consistency of evidence, but also increase the power compared with traditional method (Detailed tutorial is shown in website).
Computes moments of univariate truncated t distribution.
    There is only one exported function, e_trunct(), which should be seen for details.
Client to interact with the 'etcd' 'key-value' data store
    <https://github.com/coreos/etcd>. Functions included for managing
    directories, keys, nodes, and getting statistics.
An eulerian path is a path in a graph which visits every edge exactly once. This package provides methods to handle eulerian paths or cycles.
If possible, generates exactly area-proportional Euler diagrams,
    or otherwise approximately proportional diagrams using numeric optimization.
    A Euler diagram is a generalization of a Venn diagram, relaxing the
    criterion that all interactions need to be represented.
Calculations for DNA mixtures accounting for possibly inbred pedigrees (simulations with conditioning, LR). Calculation of exact p-values.
An R Client for the Europe PubMed Central RESTful Web Service. It
    gives access to both metadata on life science literature and open access
    full texts. Europe PMC indexes all PubMed content and other literature
    sources including Agricola, a bibliographic database of citations to the
    agricultural literature, or Biological Patents. In addition to bibliographic
    metadata, the client allows users to fetch citations and reference lists.
    Links between life-science literature and other EBI databases, including
    ENA, PDB or ChEMBL are also accessible. No registration or API key is
    required. See the vignettes for usage examples.
Frequentist and Bayesian single arm trial design and sample size software. 
	Designs cover one and two binary endpoints with both single and multi-stage 
	methodology. The research leading to these results has received funding from the 
	European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement 
	number 278742 (Eurosarc).
Tools to download data from the Eurostat database
    <http://ec.europa.eu/eurostat> together with search and manipulation
    utilities.
Goodness-of-fit tests for selection of r in the r-largest order
    statistics (GEVr) model. Goodness-of-fit tests for threshold selection in the
    Generalized Pareto distribution (GPD). Random number generation and density functions
    for the GEVr distribution. Profile likelihood for return level estimation
    using the GEVr and Generalized Pareto distributions. P-value adjustments for
    sequential, multiple testing error control. Non-stationary fitting of GEVr and
    GPD.
Provides functions for evaluating (time series) model
	estimation methods. These facilitate Monte Carlo experiments of repeated
	simulations and estimations. Also provides methods for
	looking at the distribution of the results from these experiments,
	including model roots (which are an equivalence class invariant).
Parsing and evaluation tools that make it easy to recreate the
    command line behaviour of R.
Provides Some of the most important evaluation measures for evaluating a model. Just by giving the real and predicted class, measures such as accuracy, sensitivity, specificity, ppv, npv, fmeasure, mcc and ... will be returned.
Uses data and constants to calculate potential evapotranspiration (PET) and actual evapotranspiration (AET) from 21 different formulations including Penman, Penman-Monteith FAO 56, Priestley-Taylor and Morton formulations.
Different evidential distance-based classifiers, which provide
    outputs in the form of Dempster-Shafer mass functions. The methods are: the
    evidential K-nearest neighbor rule and the evidential neural network.
Various clustering algorithms that produce a credal partition,
    i.e., a set of Dempster-Shafer mass functions representing the membership of objects
    to clusters. The mass functions quantify the cluster-membership uncertainty of the objects.
    The algorithms are: Evidential c-Means (ECM), Relational Evidential c-Means (RECM),
    Constrained Evidential c-Means (CECM), EVCLUS and EK-NNclus.
Package for combining pieces of evidence
Extends simulation, distribution, quantile and density
        functions to univariate and multivariate parametric extreme
        value distributions, and provides fitting functions which
        calculate maximum likelihood estimates for univariate and
        bivariate maxima models, and for univariate and bivariate
        threshold models.
Provides functions for the bayesian analysis of extreme
        value models, using MCMC methods.
Compare lists (from 2 to infinite) and plot the results in a Venn diagram if (N<=4) with regulation details. It allows to produce a complete annotated file, merging the annotations of the compared lists. It is also possible to compute an overlaps table to show the overlaps proportions of all the couples of lists and draw proportional Venn diagrams.
Functions for analysis of rate changes in sequential events.
Stores, manipulates, aggregates and otherwise messes with event
    data from KEDS/TABARI or any other extraction tool with similar output
Implementation of short and long term event study
        methodology
Integrated Sensitivity Analysis of Evidence Factors in Observational Studies.
Functions for extreme value theory, which may be divided
        into the following groups; exploratory data analysis, block
        maxima, peaks over thresholds (univariate and bivariate), point
        processes, gev/gpd distributions.
The usual distribution functions, maximum likelihood inference and
    model diagnostics for univariate stationary extreme value mixture models
    are provided. Kernel density estimation including various boundary
    corrected kernel density estimation methods and a wide choice of kernels,
    with cross-validation likelihood based bandwidth estimator.
    Reasonable consistency with the base functions in the 'evd' package is
    provided, so that users can safely interchange most code.
Comparative analysis of continuous traits influencing discrete states, and utility tools to facilitate comparative analyses.  Implementations of ABBA/BABA type statistics to test for introgression in genomic data. Wright-Fisher, phylogenetic tree, and statistical distribution Shiny interactive simulations for use in teaching.
Provides functions for covariance matrix comparisons, estimation
    of repeatabilities in measurements and matrices, and general evolutionary
    quantitative genetics tools.
An implementation of the evolvability parameters defined in Hansen and Houle (2008).
Evolutionary process simulation using geometric morphometric data. Manipulation of landmark data files (TPS), shape plotting and distances plotting functions.
The EvoPER, Evolutionary Parameter Estimation for 'Repast Simphony'
    Agent-Based framework, provides optimization driven parameter estimation methods
    based on evolutionary computation techniques which could be more efficient and
    require, in some cases, fewer model evaluations than other alternatives
    relaying on experimental design.
Uses maximum likelihood to estimate rates of trait evolution across environmental gradients.
Computes extreme value index (EVI) estimate for heavy tailed models by Mean of order p (MOP) 
	     and peaks over random threshold (PORT) Hill methodologies. 
             Besides, also computes moment, generalised Hill and mixed moment estimates for EVI. 
             Compute high quantile or value-at-risk (VaR) based on above EVI estimates.
Commonly used classification and regression tree methods like the CART algorithm
             are recursive partitioning methods that build the model in a forward stepwise search.
	     Although this approach is known to be an efficient heuristic, the results of recursive
	     tree methods are only locally optimal, as splits are chosen to maximize homogeneity at
	     the next step only. An alternative way to search over the parameter space of trees is
	     to use global optimization methods like evolutionary algorithms. The 'evtree' package
	     implements an evolutionary algorithm for learning globally optimal classification and
	     regression trees in R. CPU and memory-intensive tasks are fully computed in C++ while
	     the 'partykit' package is leveraged to represent the resulting trees in R, providing
	     unified infrastructure for summaries, visualizations, and predictions.
An implementation of a large number of the goodness-of-fit tests for the exponential and Weibull distributions classified into families: the tests based on the empirical distribution function, the tests based on the probability plot, the tests based on the normalized spacings, the tests based on the Laplace transform and the likelihood-based tests.
Performs unconditional exact tests and power calculations for 2x2 contingency tables. Unconditional exact tests are often more powerful than conditional exact tests and asymptotic tests.
Calculates Fisher's exact test, Blaker's exact test, or the exact McNemar's test with appropriate matching confidence intervals. Provides power and sample size calculations. Also gives melded confidence intervals for the binomial case.
Calculates exact tests and confidence intervals for one-sample binomial and one- or two-sample Poisson cases.
This is a package for exact Confidence Intervals for the
        difference between two independent or dependent proportions.
Monte Carlo and MCMC goodness of fit tests for log-linear
        models
Perform exact fixed effect meta analysis for rare events data without the need of artificial continuity correction.
ExactPath implements an algorithm for exact LASSO
        solution. Two methods are provided to print and visualize the
        whole solution paths. Use ?ExactPath to see an introduction.
        Packages ncvreg and lars are required so that their data sets
        can be used in examples.
Computes exact conditional p-values and quantiles using an
 implementation of the Shift-Algorithm by Streitberg & Roehmel.
Automatic generation of exams based on exercises in Markdown or LaTeX format,
	possibly including R code for dynamic generation of exercise elements.
	Exercise types include single- and multiple-choice questions, arithmetic problems,
	string questions, and combinations thereof (cloze). Output formats include
	PDF, HTML, Moodle XML, QTI 1.2 (for OLAT/OpenOLAT), QTI 2.1, Blackboard, ARSnova, and TCExam.
	In addition to fully customizable PDF exams, a standardized PDF format is provided that can be
	printed, scanned, and automatically evaluated.
Tools for constructing confidence regions for exceedance regions
    and contour lines.
Allows access to data in running instance of Microsoft Excel
    (e. g. 'xl[a1] = xl[b2]*3' and so on). Graphics can be transferred with
    'xl[a1] = current.graphics()'. There is an Excel workbook with examples of
    calling R from Excel in the 'doc' folder. It tries to keep things as
    simple as possible - there are no needs in any additional
    installations besides R, only 'VBA' code in the Excel workbook.
    Microsoft Excel is required for this package.
Interactive tools to explore topographic-like data
    sets.  Such data sets take the form of a matrix in which the rows and
    columns provide location/frequency information, and the matrix elements
    contain altitude/response information.  Such data is found in cartography,
    2D spectroscopy and chemometrics.  The functions in this package create
    interactive web pages showing the contoured data, possibly with
    slices from the original matrix parallel to each dimension. The interactive
    behavior is created using the D3.js 'JavaScript' library by Mike Bostock.
Functions that compute probabilistic excursion sets, contour credibility regions, contour avoiding regions, and simultaneous confidence bands for latent Gaussian random processes and fields. The package also contains functions that calculate these quantities for models estimated with the INLA package.
Extracts Exchangeable Image File Format (EXIF) metadata, such as camera make and model, ISO speed and the date-time
             the picture was taken on, from JPEG images. Incorporates the 'easyexif' (https://github.com/mayanklahiri/easyexif)
             library.
Reads EXIF data using ExifTool <http://www.sno.phy.queensu.ca/~phil/exiftool/>
    and returns results as a data frame.
    ExifTool is a platform-independent Perl library plus a command-line
    application for reading, writing and editing meta information in a wide variety
    of files. ExifTool supports many different metadata formats including EXIF,
    GPS, IPTC, XMP, JFIF, GeoTIFF, ICC Profile, Photoshop IRB, FlashPix, AFCP and
    ID3, as well as the maker notes of many digital cameras by Canon, Casio, FLIR,
    FujiFilm, GE, HP, JVC/Victor, Kodak, Leaf, Minolta/Konica-Minolta, Motorola, Nikon,
    Nintendo, Olympus/Epson, Panasonic/Leica, Pentax/Asahi, Phase One, Reconyx, Ricoh,
    Samsung, Sanyo, Sigma/Foveon and Sony.
Calls copy number variants (CNVs) from targeted sequence data, typically exome sequencing experiments designed to identify the genetic basis of Mendelian disorders.
For a given metabolic model with well formed Gene-Protein-Reaction (GPR) associations and an expressionSet with their associated gene expression values, this package converts gene expression values to the FBA boundaries for each reaction based in the boolean rules described in its associated GPR.
Generates feature matrix outputs from R object inputs
    using a variety of expansion functions.  The generated
    feature matrices have applications as inputs
    for a variety of machine learning algorithms.
    The expansion functions are based on coercing the input
    to a matrix, treating the columns as features and
    converting individual columns or combinations into blocks of
    columns.
    Currently these include expansion of columns by
    efficient sparse embedding by vectors of lags,
    quadratic expansion into squares and unique products,
    powers by vectors of degree,
    vectors of orthogonal polynomials functions,
    and block random affine projection transformations (RAPTs).
    The transformations are
    magrittr- and cbind-friendly, and can be used in a
    building block fashion.  For instance, taking the cos() of
    the output of the RAPT transformation generates a
    stationary kernel expansion via Bochner's theorem, and this
    expansion can then be cbind-ed with other features.
    Additionally, there are utilities for replacing features,
    removing rows with NAs,
    creating matrix samples of a given distribution,
    a simple wrapper for LASSO with CV,
    a Freeman-Tukey transform,
    generalizations of the outer function,
    matrix size-preserving discrete difference by row,
    plotting, etc.
Modular implementation of the Differential Evolution algorithm for
    experimenting with different types of operators.
Package for analysis of simple experimental designs (CRD,
        RBD and LSD), experiments in double factorial schemes (in CRD
        and RBD), experiments in a split plot in time schemes (in CRD
        and RBD), experiments in double factorial schemes with an
        additional treatment (in CRD and RBD), experiments in triple
        factorial scheme (in CRD and RBD) and experiments in triple
        factorial schemes with an additional treatment (in CRD and
        RBD), performing the analysis of variance and means comparison
        by fitting regression models until the third power
        (quantitative treatments) or by a multiple comparison test,
        Tukey test, test of Student-Newman-Keuls (SNK), Scott-Knott,
        Duncan test, t test (LSD) and Bonferroni t test (protected LSD)
        - for qualitative treatments.
Pacote destinado a analise de delineamentos experimentais
        simples (DIC, DBC e DQL), experimentos em esquema de fatorial
        duplo (em DIC e DBC), experimentos em esquema de parcelas
        subdivididas no tempo (em DIC e DBC), experimentos em esquema
        de fatorial duplo com um tratamento adicional (em DIC e DBC),
        experimentos em esquema de fatorial triplo (em DIC e DBC) e
        experimentos em esquema de fatorial triplo com um tratamento
        adicional (em DIC e DBC); realizando a analise de variancia e
        comparacao de medias pelo ajuste de modelos de regressao ate o
        terceiro grau (tratamentos quantitativos) ou por testes de
        comparacao multipla: teste de Tukey, teste de
        Student-Newman-Keuls (SNK), teste de Scott-Knott, teste de
        Duncan, teste t (LSD), teste t de Bonferroni (LSD protegido) e
        teste Bootstrap - tratamentos qualitativos.