c(
    person(
      given = "Ian", 
      family = "Lyttle", 
      email = "ian.lyttle@se.com", 
      role = c("aut", "cre"), 
      comment = c(ORCID = "0000-0001-9962-4849")
    ),
    person("Vega/Vega-Lite Developers", role = c("aut")),
    person(
      given = "Alicia", 
      family = "Schep",
      email = "aschep@gmail.com",
      role = c("ctb"),
      comment = c(ORCID = "orcid.org/0000-0002-3915-0618")
    ),
    person(
      given = "Stuart", 
      family = "Lee", 
      email = "stuart.andrew.lee@gmail.com", 
      role = c("ctb")
    ),
    person("Kanit", "Wongsuphasawat", comment = "Vega/Vega-Lite library", role = c("ctb")),
    person("Dominik", "Moritz", comment = "Vega/Vega-Lite library", role = c("ctb")),
    person("Arvind", "Satyanarayan", comment = "Vega/Vega-Lite library", role = c("ctb")),
    person("Jeffrey", "Heer", comment = "Vega/Vega-Lite library", role = c("ctb")),
    person("Mike", "Bostock", comment = "D3 library", role = c("ctb"))
  )
c(
	person('Miquel', 'De Cáceres', role=c('aut','cre'),
	email='miquelcaceres@gmail.com'))
person("Miguel", "Alvarez", email="kamapu78@gmail.com",
		role=c("aut","cre"), comment=c(ORCID="0000-0003-1500-1834"))
c( 
  person(given = "Sergio", family = "Ibarra-Espinosa",
         role = c("aut", "cre"),
         email = "zergioibarra@gmail.com",
         comment = c(ORCID = "0000-0002-3162-1905")),
         person(given="Joao", family="Bazzo", 
         role="ctb", 
         email="joao.bazzo@gmail.com",
         comment = c(ORCID = "0000-0003-4536-5006")))
c(
    person(
      given = "Ian", 
      family = "Lyttle", 
      email = "ian.lyttle@se.com", 
      role = c("aut", "cre"), 
      comment = c(ORCID = "0000-0001-9962-4849")
    ),
    person(family = "Schneider Electric", role = c("cph"))
  )
c(
    person("Jared", "Huling", , "jaredhuling@gmail.com", c("aut", "cre"), comment = c(ORCID = "0000-0003-0670-4845")),
    person("Muxuan", "Liang", , , c("ctb")),
    person("Yixuan", "Qiu", , , c("cph")),
    person("Gael", "Guennebaud", , , c("cph")),
    person("Ray", "Gardner", , , c("cph")),
    person("Jitse", "Niesen", , , c("cph"))
    )
c(person("Zehao", "Xu", email = "z267xu@uwaterloo.ca", role = c("aut", "cre")), person("R. Wayne", "Oldford", role = "aut"), person("Martin", "Lysy", role = "ctb"))
c(person("Laura", "Bakala",
             email = "erdaradun.gaztea@gmail.com",
             role = c("cre", "aut", "cph")))
person(given = "Jordan D.", family = "Dworkin",  
  email = "jdwor@mail.med.upenn.edu", role = c("aut", "cre"))
c(
    person("Julia", "Silge", , "julia.silge@rstudio.com", role = c("cre", "aut"),
           comment = c(ORCID = "0000-0002-3671-836X")),
    person("RStudio", role = c("cph", "fnd"))
  )
c(person("Andrew", "Sajo-Castelli", email="asajo@usb.ve", role = c("aut", "cre")), person("Desiree", "Villalta", role = "ctb"),person("Lelys", "Bravo", role = "ctb"))
c(
    person("Brodie", "Gaslam", email="brodie.gaslam@yahoo.com",
    role=c("aut", "cre")),
    person(family="Paxdiablo", role="cph", comment="Hash
    table implementation in src/pfhash.h"),
    person(family="R Core Team",
    email="R-core@r-project.org", role="cph",
    comment="Used/adapted several code snippets from R sources, see src/misc-alike.c and src/valname.c"
    ))
c(
              person(c("Muhammad", "Yaseen"),  email = "myaseen208@gmail.com", role = c("aut", "cre"))
            , person(c("Luc",  "Duchateau"),   email = "Luc.Duchateau@ugent.be", role = "ctb")
            , person(c("Paul",  "Janssen"),    email = "paul.janssen@uhasselt.be", role = "ctb")
            , person(c("John", "Rowlands"),    email = " ", role = "ctb")
            )
c(person("Marie", "Davidian", role = "aut"),
             person("A.A.", "Tsiatis", role = "aut"),
             person("Shannon", "Holloway", role = c("aut", "cre"),
                     email = "sthollow@ncsu.edu"))
c(
    person("Rafael", "Henkin", email = "r.henkin@qmul.ac.uk", role = c("cre", "aut"), comment = c(ORCID = "0000-0002-5511-5230")),
    person("Mike", "Bostock", role = c("cph"), comment = "D3.js library, https://d3js.org")
    )
c(
		    person(given="Andre", family="Schuetzenmeister", email="andre.schuetzenmeister@roche.com", role = c("cre", "aut")),
			person(given="Florian", family="Dufey", email="florian.dufey@roche.com", role = c("aut")),
			person(given="Andrea", family="Geistanger", email="andrea.geistanger@roche.com", role = c("ctb"))
		    )
c(person("Sarah", "Goslee", role = c("aut", "cre"),
                     email = "Sarah.Goslee@ars.usda.gov"),
              person("Heather", "Gall", role = "aut"),
			  person("Tamie", "Veith", role = "aut"))
c(person("Thomas", "Yee", role = c("aut", "cre"),
                    email = "t.yee@auckland.ac.nz"),
             person("Cleve", "Moler", role = "ctb",
                    comment = "author of several LINPACK routines"))
c(person("Thomas", "Yee", role = c("aut", "cre", "cph"),
                    email = "t.yee@auckland.ac.nz"),
             person("James", "Gray", role = "dtc"))
c(person("Victor", "Miranda", role = c("aut", "cre", 
           "cph"),
            email = "victor.miranda@aut.ac.nz"),
            person("Thomas", "Yee", role = c( "ctb", "ths", 
            "cph"),
            email = "t.yee@auckland.ac.nz"))
c(
    person(given = "Ali",family = "Mostafa Anwar",role = c("aut", "cre"),email = "ali.mo.anwar@std.agr.cu.edu.eg"),
    person(given = "Mohamed",family = "Soudy",email = "MohmedSoudy2009@gmail.com", role = "aut"))
c(
    person("Ruida", "Zhong", email = "zrd2017@163.com", role = c("aut", "ctb")),
    person("Dongdong", "Kong", role = c("aut", "cre"), 
        email = "kongdd.sysu@gmail.com", comment = c(ORCID = "0000-0003-1836-8172")),
    person("Xiaohong", "Chen", email = "eescxh@mail.sysu.edu.cn", role = c("aut", "ctb")),
    person("Zhaoli", "Wang", email = "wangzhl@scut.edu.cn", role = c("aut", "ctb")),
    person("Chengguang", "Lai", email = "laichg@scut.edu.cn", role = c("aut", "ctb")),
    person("Joseph J", "Hamman", role = c("ctb"), comment = "Contributor and maintainer of VIC source code"),
    person("Keith", "Cherkauer", role = c("ctb"))
    )
person('Boris', 'Hejblum', email = 'boris.hejblum@u-bordeaux.fr', role = c('cre', 'aut'))
c(person(given = "Justin",
           family = "Cally",
           role = c("aut", "cre"),
           email = "justin.g.cally@gmail.com",
           comment = c(ORCID = "0000-0003-4584-2220")),
      person(given = "Rachel",
           family = "Swain",
           role = c("ctb"),
           email = "rachelswain1@gmail.com"),
      person(given = "Andy",
             family = "Teucher",
             role = c("aut"),
             email = "andy.teucher@gov.bc.ca",
             comment = c(ORCID = "0000-0002-7840-692X", "bcdata author")),
      person(given = "Sam",
             family = "Albers",
             role = c("aut"),
             email = "sam.albers@gov.bc.ca",
             comment = c(ORCID = "0000-0002-9270-7884", "bcdata author")),
      person(given = "Stephanie",
             family = "Hazlitt",
             role = c("aut"),
             email = "stephanie.hazlitt@gov.bc.ca",
             comment = c(ORCID = "0000-0002-3161-2304", "bcdata author")),
      person(given = "Province of British Columbia",
             role = "cph",
             comment = "bcdata copyright"))
person("Anh", "Le", email = "anh.le@duke.edu", role = c("aut", "cre"))
c(
        person("Juergen", "Niedballa", email = "niedballa@izw-berlin.de", role = c("aut", "cre"),
                comment = c(ORCID = "0000-0002-9187-2116")),
        person("An The Truong", "Nguyen", role = "ctb")
        )
c(
      person("David", "Ranzolin", email = "daranzolin@gmail.com", role=c("aut", "cre", "ctb")),
      person("Joachim", "Gassen", role=c("aut"))
      )
c(
           person(given = "Cristiane",
                  family = "Taniguti",
                  role = c("aut", "cre"),
                  email = "chtaniguti@tamu.edu"),
           person(given = "Gabriel",
                  family = "Gesteira",
                  role = c("aut")),
           person(given = "Jeekin",
                  family = "Lau",
                  role = c("ctb")),
           person(given = "Guilherme",
                  family = "Pereira",
                  role = c("ctb")),
           person(given = "David",
                  family = "Byrne",
                  role = c("ctb")),
           person(given = "Zhao-Bang",
                  family = "Zeng",
                  role = c("ctb")),   
           person(given = "Oscar",
                  family = "Riera-Lizarazu",
                  role = c("ctb")),
           person(given = "Marcelo",
                  family = "Mollinari",
                  role = c("aut"),
                  email = "mmollin@ncsu.edu")
           )
c(
  person(
    given = "Thomas",
    family = "Thelen",
    role = c("aut", "cre"),
    email = "thelen@nceas.ucsb.edu"
  ),
  person(
    given = "Gerardo",
    family = "Aldana",
    role = c("aut"),
    email = "gvaldana@ucsb.edu"
  ),
  person(
    given = "Marcus",
    family = "Thomson",
    role = c("aut"),
    email = "zizroc@nceas.ucsb.edu"
  ),
  person(
    given = "Toni",
    family = "Gonzalez",
    role = c("aut"),
    email = "tonigonzalez@ucsb.edu"
  ))
c(
  person("Matthias", "Templ", email = "matthias.templ@gmail.com", role = c("aut","cre")),
  person("Alexander", "Kowarik", email = "alexander.kowarik@statistik.gv.at", role = c("aut"), comment=c(ORCID="0000-0001-8598-4130")),
  person("Andreas", "Alfons", role = c("aut")),
  person("Gregor", "de Cillia", email = "gregor.decillia@statistik.gv.at", role = c("aut")),
  person("Bernd", "Prantner", role = c("ctb")),
  person("Wolfgang", "Rannetbauer", role = c("aut")))
c(person(given = "Brian D.",
             family = "Williamson",
             role = c("aut", "cre"),
             email = "bwillia2@fredhutch.org",
             comment = c(ORCID = "0000-0002-7024-548X")),
      person(given = "Jean",
             family = "Feng",
             role = "ctb"),
      person(given = "Noah",
             family = "Simon",
             role = "ths",
             comment = c(ORCID = "0000-0002-8985-2474")),
      person(given = "Marco",
             family = "Carone",
             role = "ths",
             comment = c(ORCID = "0000-0003-2106-0953")))
c(person(given = "Alex",
           family = "Mourer",
           role = "aut",
           email = "mourer.alex@gmail.com"),
    person(given = "Marie",
           family = "Chavent",
           role = c("aut", "ths"),
           email = "marie.chavent@u-bordeaux.fr"),
    person(given = "Madalina",
           family = "Olteanu",
           role = c("aut", "ths", "cre"),
           email = "madalina.olteanu@dauphine.psl.eu"))
c(
    person("Thomas", "Nagler",, "mail@tnagler.com", role = c("aut", "cre")),
    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("Tobias", "Erhardt",, "tobias.erhardt@tum.de", role = "aut"),
    person("Carlos", "Almeida",, role = "ctb"),
    person("Aleksey", "Min",, role = c("ctb", "ths")),
    person("Claudia", "Czado",, role = c("ctb", "ths")),
    person("Mathias", "Hofmann",, role = "ctb"),
    person("Matthias", "Killiches",, role = "ctb"),
    person("Harry", "Joe",, role = "ctb"),
    person("Thibault", "Vatter",, role = "ctb")
    )
c(
        person("Thomas", "Nagler",, "mail@tnagler.com", role = c("aut", "cre")),
        person("Dani", "Kraus",,, role = c("ctb"))
        )
c(person("Yasser", "Gonzalez-Fernandez",
                    role = c("aut", "cre"),
                    email = "ygonzalezfernandez@gmail.com"),
             person("Marta", "Soto",
                    role = "aut",
                    email = "mrosa@icimaf.cu"),
             person("Joris", "Meys",
                    role = "ctb",
                    email = "Joris.Meys@UGent.be"))
c(person("Daniel", "Adler", email = "dadler@uni-goettingen.de", role = c("aut", "cph")),
           person("S. Thomas", "Kelly", email = "tomkellygenetics@gmail.com", role = c("aut", "cre")),
           person("Tom M.", "Elliott", email = "tom.elliott@auckland.ac.nz", role = c("ctb")))
c(
  person("Brandon", "Greenwell", 
          email = "greenwell.brandon@gmail.com",
          role = c("aut", "cre"), 
          comment = c(ORCID = "0000-0002-8120-0084")),
  person("Brad", "Boehmke",
         email = "bradleyboehmke@gmail.com",
         role = c("aut"),
         comment = c(ORCID = "0000-0002-3611-8516")),
  person("Bernie", "Gray",
         email = "bfgray3@gmail.com",
         role = c("aut"),
         comment = c(ORCID = "0000-0001-9190-6032"))
  )
c(
      person("Simon", "Garnier", email = "garnier@njit.edu", role = c("aut", "cre")),
      person("Noam", "Ross", email = "noam.ross@gmail.com", role = c("ctb", "cph")),
      person("Bob", "Rudis", email = "bob@rud.is", role = c("ctb", "cph")),
      person("Marco", "Sciaini", email = "sciaini.marco@gmail.com", role = c("ctb", "cph")),
      person("Antônio Pedro", "Camargo", role = c("ctb", "cph")),
      person("Cédric", "Scherer", email = "scherer@izw-berlin.de", role = c("ctb", "cph"))
  )
person(given="Blas M.", family="Benito", email="blasbenito@gmail.com", role=c("aut", "cre"))
c(person("Carl", "Boettiger", 
                  email = "cboettig@gmail.com", 
                  role = c("aut", "cre", "cph"),
                  comment = c(ORCID = "0000-0002-1642-628X")),
             person("Bryce", "Mecum", 
                    role = "ctb", 
                    email = "brycemecum@gmail.com",
                    comment = c(ORCID = "0000-0002-0381-3766")))
person("Kang", "Yu", email = "kang.yu@outlook.com",
  role = c("aut", "cre"))
c(person(given = "Christopher",
             family = "DeMartini",
             role = c("aut", "cre"),
             email = "chris.demartini.npm@gmail.com"),
      person(given = "Jaime",
             family = "Tanner",
             role = c("aut"),
             email = "jaimeatanner@gmail.com"),
      person(given = "Stephanie",
             family = "Modica",
             role = c("aut"),
             email = "steph.modica.npm@gmail"),
      person(given = "Frank",
             family = "Elavsky",
             role = c("aut"),
             email = "frankelavsky@gmail.com"),
      person(given = "Wojtek",
             family = "Kostelecki",
             role = c("ctb"),
             email = "wojtek.kostelecki@gmail.com"),
      person(given = "Visa Data Experience Team",
             role = c("aut", "fnd")),
      person(given = "Visa, Inc.",
               role = c("cph")))
c(person("Marcio A. Diniz", 
                    role = c("aut", "cre","cph"),
                    email = "marcio.diniz@cshs.org",
                    comment = c(ORCID = "0000-0002-2427-7843")),
              person("Michael Luu", 
                     role = "aut",
                     email = "michael.luu@cshs.org",
                     comment = c(ORCID = "0000-0002-7968-7313")))
person("Peter", "Biber", email = "castor.fiber@gmx.de", 
                  role = c("aut", "cre"),
                  comment = c(ORCID = "0000-0002-9700-8708"))
c(
  person("Nicholas", "Tierney", role = c("aut", "cre"),
      email = "nicholas.tierney@gmail.com", 
      comment = c(ORCID = "https://orcid.org/0000-0003-1460-8722")),
  person("Sean", "Hughes", role = "rev", 
      comment =c(ORCID = "https://orcid.org/0000-0002-9409-9405",
                 "Sean Hughes reviewed the package for rOpenSci, 
                  see https://github.com/ropensci/onboarding/issues/87")),
  person("Mara", "Averick", role = "rev", 
      comment = "Mara Averick reviewed the package for rOpenSci, see 
      https://github.com/ropensci/onboarding/issues/87"),
  person("Stuart", "Lee", role = c("ctb")),
  person("Earo", "Wang", role = c("ctb")),
  person("Nic", "Crane", role = c("ctb"))
      )
c(
    person("Nastasia", "Fouret", , "nastasia.fouret@gmail.com", c("aut", "cph")),
    person( "Mederic", "Descoins", , "mederic.descoins@chu-reunion.fr", role=, c("aut", "cph")),
    person("Elodie", "Garnier", , "e.garnier30@gmail.com", c("aut", "cre", "cph")),
    person( "CEPOI - EA 7388", role="cph" )
    )
c(
        person("Russell", "Goebel", email = "rgoebel@bu.edu", role = "aut"),
        person("Robert", "Bowen", email = "robertbowen.bham@gmail.com", role = c("aut", "cre")),
        person("Beth Ann", "Brackett", email = "eliannkb@gmail.com", role = "ctb"),
        person("Kimihiro", "Noguchi", email = "Kimihiro.noguchi@wwu.edu", role = "aut"),
        person("Dylan", "Way", email = "wayd2@wwu.edu", role="aut")
    )
c(
    person(family = "Almende B.V. and Contributors", role = c("aut", "cph"),
       comment = "vis.js library in htmlwidgets/lib, https://visjs.org/, https://github.com/visjs/vis-network"),
    person("Benoit", "Thieurmel", role = c("aut", "cre"),
       comment = "R interface", email = "benoit.thieurmel@datastorm.fr"),
    person(given = "Titouan", family = "Robert", 
    email = "titouan.robert@datastorm.fr", role = c("aut", "ctb"))
    )
c(
   person("Diego", "Saldana", , "diego.saldana@roche.com", "aut"),
   person("Charlotta", "Fruechtenicht", , "charlotta.fruechtenicht@roche.com", "aut"),
   person("Mark", "Baillie", , "bailliem@gmail.com", c("aut", "cre")),
   person("Marc", "Vandemeulebroecke", , "marc.vandemeulebroecke@novartis.com", "aut"),
   person("Thanos", "Siadimas", , "thanos.siadimas@roche.com", "aut"),
   person("Pawel", "Kawski", , "pawel.kawski@contractors.roche.com>", "aut"),
   person("Steven", "Haesendonckx", , "shaesen2@its.jnj.com", "aut"),
   person("James", "Black", , "james.black.jb2@roche.com", "aut"),
   person("Pelagia", "Alexandra Papadopoulou", , "PPapadop@its.jnj.com", "aut"),
   person("Tim", "Treis", , "tim.treis@outlook.de", "aut"),
   person("Rebecca", "Albrecht", , "rebecca.albrecht.dietsch@gmail.com", "aut"))
c(
    person("Sabine", "Schilling", email = "sabineschilling@gmx.ch", role = c("cre","aut","cph")),
    person("Peter", "Kauf", email = "peter.kauf@zhaw.ch", role = "ctb"))
c(
    person("Sandro", "Raabe", , "sa.ra.online@posteo.de", role = c("aut", "cre"))
    )
c(person("Ashwini", "Venkatasubramaniam", role = c("aut", "cre"), email = "ashwinikv01@gmail.com"), person("Julian", "Wolfson", role = c("aut", "ctb"), email = "julianw@umn.edu"))
c(
    person("Ivan", "Marin-Franch", email="imarinfr@optocom.es", role = c("cre", "aut", "rev", "cph")),
    person("William H", "Swanson", role = c("fnd", "dtc")),
    person("Michael", "Wall", role = c("fnd", "dtc")),
    person("Andrew", "Turpin", role = c("ctb")),
    person("Paul H", "Artes", role = c("ctb", "dtc")),
    person("Cord", "Huchzermeyer", role = c("ctb")),
    person("Giovanni", "Montesano", role = c("ctb")),
    person("Mitchell W", "Dul", role = c("dtc")))
c(
    person("James", "Balamuta", 
           role = c("aut", "cph", "cre"), 
	         email = "james.balamuta@gmail.com", 
           comment = c(ORCID = "0000-0003-2826-8458")
	         )
	  )
person(given = "Javier",
           family = "Portela",
           role = c("aut", "cre"),
           email = "javipgm@gmail.com")
c(person("Rafael", "Henkin",
                    email = "r.henkin@qmul.ac.uk", 
                    role = c("aut", "cre")))
person("Ender", "Celik", email = "celik.p.ender@gmail.com",
                  role = c("aut", "cre"))
c(
  person("Mitchell", "O'Hara-Wild", role=c("aut", "cre"), email = "mail@mitchelloharawild.com", comment=c(ORCID = "0000-0001-6729-7695")),
  person("Rob", "Hyndman", email="Rob.Hyndman@monash.edu", role=c("aut"), comment = c(ORCID = "0000-0002-2140-5352")),
  person("Yihui", "Xie", role = c("ctb"), comment = c(ORCID = "0000-0003-0645-5666")),
  person("Albert", "Krewinkel", role = c("cph"), comment = c("Multiple bibliographies lua filter")),
  person("JooYoung", "Seo", role="ctb", comment = c(ORCID = "0000-0002-4064-6012")))
c(
    person(given = "Alan", family = "Inglis", email = "alan.inglis@mu.ie", role = c("aut", "cre")),
    person(given = "Andrew", family = "Parnell", email = "andrew.parnell@mu.ie", role = "aut"),
    person(given = "Catherine", family = "Hurley", email = "catherine.hurley@mu.ie", role = "aut"))
c(person("Anna", "Kozak", email = "anna1993kozak@gmail.com", role = c("aut", "cre")),
            person("Przemyslaw", "Biecek", role = c("aut", "ths")))
c(
    person("Ignacio", "Martinez", email = "ignacio@protonmail.com", role = c("aut", "cre")),
    person("Linus", "Marco", email = "lmarco163@gmail.com", role = "aut"),
    person("Dan", "Thal", email = "DThal@mathematica-mpr.com", role = "aut"))
c(
    person("Dmitriy", "Sorokin", role = c("aut", "cre"),
           email = "dementiy@yandex.ru"),
    person("Anton", "Antonov", role = "ctb",
           email = "tonytonov@gmail.com"))
c(
            person("Philipp", "Wittenberg", 
            email = "pwitten@hsu-hh.de", 
            role = c("aut", "cre"),
            comment = c(ORCID = "0000-0001-7151-8243")),
            person("Sven", "Knoth", 
            email = "<Sven.Knoth@gmx.de>", 
            role = c("aut", "ths"),
            comment = c(ORCID = "0000-0002-9666-5554")))
c(person("Seongwon", "Ryu", email = "seongwon6154@gmail.com", role = c("aut", "cre")),
            person("Bohui", "Lee", email = "leebh@pusan.ac.kr", role = c("aut")),
            person("Yongseok", "Choi", email = "yschoi@pusan.ac.kr", role = c("ctb")))
c(person("James JW", "Hucklesby", email = "jhuc964@aucklanduni.ac.nz",
                  role = c("aut", "cre"), comment = c(ORCID = "0000-0003-1591-6823")),
            person("Peter S", "Russell", email = "prus023@aucklanduni.ac.nz",
                  role = c("aut"), comment = c(ORCID = "0000-0002-7059-6618")),
            person("Anthony RJ", "Phillips", email = "a.phillips@auckland.ac.nz",
                  role = c("aut"), comment = c(ORCID = "0000-0001-6143-5866")),
            person("Catherine E", "Angel", email = "c.angel@auckland.ac.nz",
                  role = c("aut"), comment = c(ORCID = "0000-0003-1737-8539"))
            )
c(person("Jean-François", "Rey", role = c("cre","aut"), email = "jf.rey.public@gmail.com"))
person("Saturnino", "Luz", email = "luzs@acm.org", role = c("aut", "cre"))
c(person("Katriona", "Goldmann", role = c("aut", "cre"),
                       email = "k.goldmann@qmul.ac.uk",
                       comment = c(ORCID = "0000-0002-9073-6323")),
                person("Myles", "Lewis", role = c("aut"),
                comment = c(ORCID = "0000-0001-9365-5345")))
person("Viktor", "Bozhinov", email = "mail@viktor-bozhinov.de",
  role = c("aut", "cre"))
c(person(given = "Hannah L.",
             family = "Owens",
             role = c("aut", "cre"),
             email = "hannah.owens@gmail.com",
             comment = c(ORCID = "0000-0003-0071-1745")),
      person(given = "Carsten", 
             family = "Rahbek",,,
             role="aut",
             comment = c(ORCID = "0000-0003-4585-0300")))
c(
  person("Alexander", "Kowarik", email = "alexander.kowarik@statistik.gv.at", role = c("aut","cre"), comment=c(ORCID="0000-0001-8598-4130")),
  person("Bernhard", "Meindl", email = "bernhard.meindl@statistik.gv.at", role = c("aut")),
  person("Malaver", "Vojvodic", email = "mmala027@uottawa.ca", role = c("aut")),
  person("Mike", "Bostock", role = c("aut", "cph"), comment = "d3.js library, http://d3js.org"),
  person("Franck", "Lebeau", role = c("aut", "cph"), comment = "d3-voronoi-treemap library, https://github.com/Kcnarf/d3-voronoi-treemap"))
c(person("Lansdon", "Page", email = "lpage@qualtacon.com", role = c("aut", "cre", "mdc")),
    person("Qualta Consulting, LLC.", email = "support@qualtacon.com", role = "cph")
    )
c(
    person("Carlo", "Pacioni", email="C.Pacioni@Murdoch.edu.au", role=c("aut", "cre")),
    person(c("Florian", "W."), "Mayer", email="Florian.Mayer@dpaw.wa.gov.au", role="aut"))
c(
    person("Bryan", "Gertzel",
           role = c("aut", "cre"),
           email = "bryan.gertzel@anu.edu.au"),
    person("Robert", "Ackland",
           email = "robert.ackland@anu.edu.au",
           role = "ctb",
           comment = c(ORCID = "0000-0002-0008-1766")),
    person("Francisca", "Borquez",
           email = "francisca.borquez@anu.edu.au",
           role = "ctb"))
c(
    person("Bob", "Rudis", email = "bob@rud.is", role = c("aut", "cre"), 
           comment = c(ORCID = "0000-0001-5670-2640")),
      person("ProPublica", comment = "Cartogram SVGs used to make the plot data.frames", role = c("cph")),
      person("GovTrack", comment = "Cartogram SVG used to make the plot data.frame", role = c("cph"))
    )
c(person(given = "Deck Technologies",
             role = c("cph", "fnd"),
             email = "info@deck.tools"),
      person(given = "Amanda",
             family = "Dobbyn",
             role = c("aut", "cre"),
             email = "amanda@deck.tools"),
      person(given = "Max",
             family = "Wood",
             role = "aut",
             email = "max@deck.tools"),
      person(given = "Alyssa",
             family = "Frazee",
             role = "aut",
             email = "alyssa@deck.tools"))
c(person("Tyler","Littlefield",email="tylerlittlefield@hey.com",role=c("aut","cre"),comment="Creator of Shiny Wrapper"),
             person("Vaibhav","Tandon",role=c("ctb","cph"),comment="Author of included CSS code"),
             person("Danube", "Huynhle",role=c("ctb")))
c(person("Angel", "Garcia de la Garza", email = "agarciadlg@gmail.com", role = c("aut", "cre")),
             person("Simon", "Vandekar", email= "simonv@mail.med.upenn.edu", role="aut"),
             person("David", "Roalf", email= "roalf@mail.med.upenn.edu", role="aut"),
             person("Kosha", "Ruparel", email= "kruparel@mail.med.upenn.edu", role="aut"),
             person("Ruben", "Gur", email= "gur@mail.med.upenn.edu", role="aut"),
             person("Raquel", "Gur", email= "raquel@mail.med.upenn.edu", role="aut"),
             person("Theodore", "Satterthwaite", email= "sattertt@mail.med.upenn.edu", role="aut"),
             person("R. Taki", "Shinohara", email= "rshi@mail.med.upenn.edu", role="aut")
             )
person(given = "Bastien",
           family = "Lecigne",
           role = c("aut", "cre"),
           email = "lecignebastien@gmail.com",
           comment = c(ORCID = "https://orcid.org/0000-0002-1496-202X"))
c(person("Emily Chisholm",  role = c("aut", "cre","cph"), email = "vprrcontact@gmail.com"), person("Kevin Sorochan", "Dev", role = "aut"), person("Catherine Johnson", "Contributer", role = "aut"))
c(person("Ruslan", "Soldatov", email = "ruslansoldatov@gmail.com", role = c("aut")), person("Peter", "Kharchenko", email = "peter_kharchenko@hms.harvard.edu", role = "aut"), person("Viktor", "Petukhov", email = "viktor.s.petukhov@ya.ru", role = c("aut")), person("Evan", "Biederstedt", email="evan.biederstedt@gmail.com", role=c("cre", "aut")))
c(person(given = "Jim",
             family = "Hester",
             role = "aut",
             email = "jim.hester@rstudio.com",
             comment = c(ORCID = "0000-0002-2739-7082")),
      person(given = "Hadley",
             family = "Wickham",
             role = "aut",
             email = "hadley@rstudio.com",
             comment = c(ORCID = "0000-0003-4757-117X")),
      person(given = "Jennifer",
             family = "Bryan",
             role = c("aut", "cre"),
             email = "jenny@rstudio.com",
             comment = c(ORCID = "0000-0002-6983-2759")),
      person(given = "https://github.com/mandreyel/",
             role = "cph",
             comment = "mio library"),
      person(given = "Jukka",
             family = "Jylänki",
             role = "cph",
             comment = "grisu3 implementation"),
      person(given = "Mikkel",
             family = "Jørgensen",
             role = "cph",
             comment = "grisu3 implementation"),
      person(given = "RStudio",
             role = c("cph", "fnd")))
c(person(given = "Daniel",
           family = "Camacho",
           role = c("aut", "cre"),
           email = "2016711@student.uma.pt",
           comment = c(ORCID = "0000-0002-4980-9391")),
    person(given = "Ana",
           family = "Abreu",
           role = c("aut", "ths"),
           email = "abreu@staff.uma.pt",
           comment = c(ORCID = "0000-0002-6155-8492")))
c(
    person("Karl", "Rohe", , "karlrohe@stat.wisc.edu", role = "aut"),
    person("Muzhe", "Zeng", , "mzeng6@wisc.edu", role = "aut"),
    person("Alex", "Hayes", , "alexpghayes@gmail.com", role = c("aut", "cre", "cph"),
           comment = c(ORCID = "0000-0002-4985-5160")),
    person("Fan", "Chen", role = "aut")
  )
person('Ashley', 'Baldry', email = 'arbaldry91@gmail.com', role = c('aut', 'cre'))
c(person("Robin", "Genuer", email = "Robin.Genuer@u-bordeaux.fr", role = c("aut", "cre")),
             person("Jean-Michel", "Poggi", role = "aut"),
             person("Christine", "Tuleau-Malot", role = "aut"))
c(person(given = "Nick", family = "Huntington-Klein", role = c("aut","cre"), email = "nhuntington-klein@seattleu.edu"))
c(
      person(
        "Evan","You"
        , role = c("aut", "cph")
        , comment = "vue library in lib, https://vuejs.org/; see BACKERS for full list of Patreon backers"
      ),
      person(
        "Kent", "Russell"
        , role = c("aut", "cre")
        , comment = "R interface"
        , email = "kent.russell@timelyportfolio.com"
      )
    )
c(
    person("Troy", "Hill", role = c("aut", "cre"), email = "Hill.Troy@gmail.com", comment = c(ORCID = "0000-0003-2980-4099")),
    person("Shimon", "Anisfeld", role = "ctb")
    )
c(
    person("Hannes", "Kazianka", email = "hkazianka@gmail.com", role = c("cre","aut")),
    person("Anna", "Morgenbesser", email = "anna.morgenbesser@oenb.at", role = "aut"),
    person("Thomas", "Nowak", email = "thomas.nowak@oenb.at", role = "aut"))
c(person("Vincent", "Porretta", role = c("aut", "cre"),
		     email = "vincentporretta@gmail.com"),
	      person("Aki-Juhani", "Kyröläinen", role = "aut"),
	      person("Jacolien", "van Rij", role = "ctb"),
		  person("Juhani", "Järvikivi", role = "ctb"))
person(given = "Josue M.", family = "Polanco-Martinez", role = c("aut", "cre"), email = "josue.m.polanco@gmail.com", comment = c(ORCID = "0000-0001-7164-0185"))
person("Cory", "McCartan", role = c("aut", "cre"),
           email = "cmccartan@g.harvard.edu")
c(
    person("Michael W.", "Kearney", ,
      email = "kearneymw@missouri.edu", role = c("aut", "cre"),
      comment = c(ORCID = "0000-0002-0730-4694")),
    person("Lingshu", "Hu", ,
      role = c("ctb"),
      comment = c(ORCID = "0000-0003-0304-882X")))
c(
      person("Bob", "Rudis", email = "bob@rud.is", role = c("aut", "cre")),
      person("Dave", "Gandy", role = "aut", comment = "Font Awesome")
    )
c(
    person(given = "John",
           family = "Coene",
           role = c("aut", "cre"),
           email = "jcoenep@gmail.com"),
    person(given = "Jinhwan",
           family = "Kim",
           role = "ctb",
           email = 'hwanistic@gmail.com'),
    person(given = "Victor",
           family = "Granda",
           role = c("ctb"),
           email = "victorgrandagarcia@gmail.com",
           comment = c(ORCID = "0000-0002-0469-1991")))
c(
        person("Tyler", "Rinker", email = "tyler.rinker@gmail.com", role = c("aut", "cre")),
        person("Josh", "O'Brien", role = "ctb"), 
        person("Ananda", "Mahto", role = "ctb"), 
        person("Matthew", "Sigal", role = "ctb"),
        person("Jonathan", "Carroll", role = "ctb"),
        person("Scott", "Westenberger", role = "ctb")  
        )
c(
  person("Jamie M.", "Kass", email = "jamie.kass@oist.jp", role = "aut"),
  person("Gonzalo E.", "Pinilla-Buitrago", email = "gpinillabuitrago@gradcenter.cuny.edu", role = c("aut", "cre")),
	person("Bruno", "Vilela", email = "bvilela@wustl.edu", role = "aut"),
	person("Matthew E.", "Aiello-Lammens", email = "matt.lammens@gmail.com", role = "aut"),
	person("Robert", "Muscarella", email = "bob.muscarella@gmail.com", role = "aut"),
	person("Cory", "Merow", email = "corymerow@gmail.com", role = "aut"),
	person("Robert P.", "Anderson", email = "randerson@ccny.cuny.edu", role = "aut"))
c(person("Sébastien", "Déjean",
    email = "sebastien.dejean@math.univ-toulouse.fr", role = c("aut", "cre")),
    person("Harold", "Duruflé",
    email = "harold.durufle@inrae.fr", role = "aut"),
    person("Aurélie", "Mercadié",
    email = "aurelie.mercadie@inrae.fr", role = "aut"))
person("Emil", "Hvitfeldt", email = "emilhhvitfeldt@gmail.com", role = c("aut", "cre"))
c(
    person("Andreas", "Scheidegger", email="andreas.scheidegger@eawag.ch", role="aut"),
    person("Rachel", "Barrett", email=NULL, role="aut"),
    person("Christian", "Foerster", email="christian.foerster@eawag.ch", role="cre")
  )
c( person("Bob", "Rudis", email =
      "bob@rud.is", role = c("aut", "cre"), comment =
      c(ORCID = "0000-0001-5670-2640")) )
c(person("Marcelo", "Araya-Salas", role = c("aut", "cre"), email = "marcelo.araya@ucr.ac.cr", comment = c(ORCID = "0000-0003-3594-619X")), person("Grace", "Smith-Vidaurre", role = c("aut"), email = "gsmithvi@gmail.com"))
c(
    person("Ernest", "Guevarra", email = "ernestgmd@gmail.com", role =  c("aut", "cre")),
    person("Valid International", "", email = "office@validinternational.org", role = "cph"),
    person("Water and Sanitation for the Urban Poor", "", email = "", role = "cph")
  )
person(given = "Rohnin",
           family = "Randles",
           role = c("aut", "cre"),
           email = "rrandles@princeton.edu")
c(person(given = "Ze",
           family = "Jiang",
           role = c("aut", "cre"),
           email = "ze.jiang@unsw.edu.au",
           comment = c(ORCID = "0000-0002-3472-0829")),
      person(given = "Md. Mamunur",
           family = "Rashid",
           role = c("aut"),
           comment = c(ORCID = "0000-0002-0315-9055")),
      person(given = "Ashish",
           family = "Sharma",
           role = c("aut"),
           comment = c(ORCID = "0000-0002-6758-0519")),
      person(given = "Fiona",
           family = "Johnson",
           role = c("aut"),
           comment = c(ORCID = "0000-0001-5708-1807")))
person(given = "James P.", family = "Howard, II",
                          email = "jh@jameshoward.us", role = c("aut", "cre"))
c(person("Richard", "Johansen", email = "richard.a.johansen@erdc.dren.mil", 
    role = c("aut", "cre"), comment = c(ORCID = "0000-0003-4287-9677")),
    person("Jakub", "Nowosad", email="nowosad.jakub@gmail.com",
    role = c("aut"), comment = c(ORCID = "0000-0002-1057-3721")),
    person("Molly", "Reif", email="Molly.K.Reif@usace.army.mil",
    role = c("aut"), comment = c(ORCID = "0000-0003-4207-1459")),
    person("Erich", "Emery", email="Erich.B.Emery@usace.army.mil",
    role = c("aut")),
    person("U.S. Army Corps of Engineers", role = c("fnd")))
person("Sadie", "Gill", email = "sgill@flowwest.com", role = c("cre", "aut"))
c(person("Stuart", "Barber", role=c("aut"),
	email="stuart@maths.leeds.ac.uk"),
        person("Guy", "Nason", role=c("cre", "ctb"),
        email="g.nason@imperial.ac.uk"))
c(person("Ranjit Kumar", "Paul", role = c("aut","cre"), email = "ranjitstat@gmail.com"),
    person("Md", "Yeasin", role = "aut", email = "yeasin.iasri@gmail.com"))
person(given = "Javier",
           family = "Fernandez-Macho",
           role = c("aut", "cre"),
           email = "javier.fernandezmacho@ehu.es",
           comment = c(ORCID = "0000-0002-5970-4382"))
c(person('Philippe', 'Marchand', email = 'marchand.philippe@gmail.com',
                  role = c('aut', 'cre')),
             person('David', 'Gill', role = 'aut'))
c(person(given = "Jenna",
             family = "Hershberger",
             role = c("aut", "cre"),
             email = "jmh579@cornell.edu",
             comment = c(ORCID = "0000-0002-3147-6867")),
      person(given = "Michael",
             family = "Gore",
             role = "ths"),
      person(given = "NSF BREAD IOS-1543958",
             role = "fnd"))
c(
  person("Raphaël", "Jauslin", email = "raphael.jauslin@unine.ch",role = c("aut", "cre"),
    comment = c(ORCID = "0000-0003-1088-3356")
    ),
  person("Yves", "Tillé",role = c("aut"),
    comment = c(ORCID = "0000-0003-0904-5523"))
  )
c(person("Guy", "Nason", role=c("aut", "cre"),
        email="g.nason@imperial.ac.uk"))
c(
    person("Tobias", "Schoch", role = c("aut", "cre"),
        email = "tobias.schoch@gmail.com",
        comment = c(ORCID = "0000-0002-1640-3395")),
    person("R-core", role = c("cph"), email = "R-core@R-project.org",
        comment = "plot.wbaconlm derives from plot.lm"))
c(person(given = "Jesse",
             family = "Piburn",
             role = c("aut", "cre"),
             email = "piburnjo@ornl.gov",
             comment = c(ORCID = "0000-0003-4967-7912")),
      person(given = "UT-Battelle, LLC",
             role = "cph"))
c(person(given = c("Guy", "J."),
             family = "Abel",
             role = c("aut", "cre", "ctb"),
             email = "g.j.abel@gmail.com",
             comment = c(ORCID = "0000-0002-4893-5687")),
      person(given = "Samir",
             family = "K.C.",
             role = "ctb",
             comment = c(ORCID = "0000-0002-5213-9181")),
      person(given = "Michaela",
             family = "Potancokova",
             role = "ctb"),
      person(given = "Claudia",
             family = "Reiter",
             role = "ctb",
             comment = c(ORCID = "0000-0002-1485-3851")),
      person(given = "Andrea",
             family = "Tamburini",
             role = "ctb"),
      person(given = "Dilek",
             family = "Yildiz",
             role = "ctb",
             comment = c(ORCID = "0000-0001-6192-0634")))
c(person("Majid", "Nabipoor", email = "nabipoor@ualberta.ca", role=c("aut", "cre")), 
           person("Cynthia", "Westerhout", email="cindy.westerhout@ualberta.ca", role=c("aut")),
           person("Jeffrey", "Bakal", email="jbakal@ualberta.ca", role=c("aut")))
c(person("Emmanuel", "Sikali", role="pdr", email = "Emmanuel.Sikali@ed.gov"),
             person("Paul", "Bailey", role=c("aut", "cre"), email="pbailey@air.org"),
             person("Ahmad", "Emad", role="aut"),
             person("Eric", "Buehler", role="ctb"))
c(
    person("Vincent", "Arel-Bundock",
           email = "vincent.arel-bundock@umontreal.ca",
           role = c("aut", "cre"),
           comment = c(ORCID = "0000-0003-2042-7063")),
    person("Etienne", "Bacher",
           email = "etienne.bacher@protonmail.com",
           role = "ctb"))
person("Thomas", "Nagler", email = "mail@tnagler.com", role = c("aut", "cre"))
c(person(c('Jeffrey', 'O'), 'Hanson',
    email='jeffrey.hanson@uqconnect.edu.au', role = c('aut', 'cre')))
c(
  person(given = "Yacouba", family = "Boubacar Maïnassara",
         email = "yacouba.boubacar_mainassara@univ-fcomte.fr",
         role = "aut",
       comment = c(ORCID = "0000-0002-8604-5407")),
  person(given = "Julien Yves", family = "Rolland",
       email = "julien.rolland@univ-fcomte.fr",
       role = c("aut", "cre"),
       comment = c(ORCID = "0000-0002-0960-6688")),
  person(given = "Coraline", family = "Parguey",
         role = "ctb"),
  person(given = "Vincent", family = "Mouillot",
         role = "ctb")
  )
c(person("Peter", "de Looff", role = c("aut", "cre"),
                     email = "peterdelooff@gmail.com"),
              person("Remko", "Duursma", role = "aut"),
              person("Saskia", "Koldijk", role = "aut"),
              person("Kees", "de Schepper", role = "aut"),
              person("Matthijs", "Noordzij", role = "ctb"),
              person("Natasha", "Jaques", role = "ctb"),
              person("Sara", "Taylor", role = "ctb"))
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")))
person(given="Greg", 
                family="Hunt", 
                email = "greg@firmansyah.com", 
                role = c("aut", "cre", "cph"))
c(person("Eduard", "Szöcs", role = "aut"),
    person("Robert", "Allaway", role = "ctb"),    
    person("Daniel", "Muench", role = "ctb"),
    person("Johannes", "Ranke", role = "ctb"),
    person("Andreas", "Scharmüller", role = "ctb"),        
    person("Eric R", "Scott", role = "ctb"),
    person("Jan", "Stanstrup", role = "ctb"),
    person("João Vitor", "F Cavalcante", role = "ctb"),
    person("Gordon", "Getzinger", role = "ctb"),
    person("Tamás", "Stirling", email = "stirling.tamas@gmail.com", role = c("ctb", "cre")))
c(
    person("Dale","Barr",
           email = "dalejbarr@protonmail.com",
	   role = c("aut", "cre")),
    person("Lisa", "DeBruine",
           email = "lisa.debruine@glasgow.ac.uk",
           role = "aut"))
c(person(given = "Gábor",
             family = "Csárdi",
             role = c("aut", "cre"),
             email = "csardi.gabor@gmail.com"),
      person(family = "RStudio, Pbc.",
             role = "cph"),
      person(family = "Civetweb contributors",
             role = "ctb",
             comment = "see inst/credits/ciwetweb.md"),
      person(family = "Redoc contributors",
             role = "ctb",
             comment = "see inst/credits/redoc.md"),
      person(given = "L. Peter",
             family = "Deutsch",
             role = "ctb",
             comment = "src/md5.h"),
      person(given = "Martin",
             family = "Purschke",
             role = "ctb",
             comment = "src/md5.h"),
      person(family = "Aladdin Enterprises",
             role = "cph",
             comment = "src/md5.h"),
      person(given = "Maëlle",
             family = "Salmon",
             role = "ctb",
             email = "maelle.salmon@yahoo.se",
             comment = c(ORCID = "0000-0002-2815-0399")))
c(
        person("Jing", "Wang", email = "jingwang.uestc@gmail.com", role = "aut"),
        person("Yuxing", "Liao", email = "yuxingliao@gmail.com", role = c("aut", "cre")),
        person("Eric", "Jaehnig", email = "Eric.Jaehnig@bcm.edu", role = c("ctb")),
        person("Zhiao", "Shi", email = "Zhiao.Shi@bcm.edu", role = c("ctb")),
        person("Quanhu", "Sheng", email = "shengqh@gmail.com", role = c("ctb"))
        )
c(person("Richard", "Barnes", role = c("aut", "cre"), email = "rijard.barnes@gmail.com"))
c(
    person("Scott", "Chamberlain", role = c("aut", "cre"), email =
    "myrmecocystus+r@gmail.com", comment = c(ORCID="0000-0003-1444-9135")),
    person("Aaron", "Wolen", role = "ctb",
    comment = c(ORCID="0000-0003-2542-2202")),
    person("rOpenSci", role = "fnd", comment = "https://ropensci.org")
    )
c(person("Zhiyong", "Zhang", role = c("aut", "cre"),
                     email = "johnnyzhz@gmail.com"),
              person("Yujiao", "Mai", role = "aut"),
              person("Miao", "Yang", role = "ctb",
                     email = "Miao.Yang.100@nd.edu"))
c(person("Keon-Woong", "Moon", role = c("aut", "cre"),
                     email = "cardiomoon@gmail.com"),
           person("Tommaso","Martino",role=c("ctb"),
           email="todoslogos@gmail.com"))
c(
	person("Florian S.", "Schaffner", email = "florian.schaffner@outlook.com", role = c("aut", "cre"), comment = c(ORCID = "0000-0003-3352-6191")),
	person("Charles", "Crabtree", email = "ccrabtr@umich.edu", role = c("ctb")))
c(
    person("Winston", "Chang", email = "winston@rstudio.com", role = c("aut", "cre")),
    person("Yihui", "Xie", role = "ctb"),
    person("Francois", "Guillem", role = "ctb"),
    person("Barret", "Schloerke", role = "ctb"),
    person("Nicolas", "Perriault", role = "ctb", comment = "The CasperJS library")
    )
c(
    person("Winston", "Chang", role = c("aut", "cre"), email = "winston@rstudio.com"),
    person("Joe", "Cheng", role = "aut", email = "joe@rstudio.com"),
    person("Alan", "Dipert", role = "aut"),
    person("Barbara", "Borges", role = "aut"),
    person(family = "RStudio", role = "cph"),
    person("Peter", "Thorson", role = c("ctb", "cph"), comment = "WebSocket++ library"),
    person("René", "Nyffenegger", role = c("ctb", "cph"), comment = "Base 64 library"),
    person("Micael", "Hildenborg", role = c("ctb", "cph"), comment = "SHA1 library"),
    person(family = "Aladdin Enterprises", role = "cph", comment = "MD5 library"),
    person("Bjoern", "Hoehrmann", role = c("ctb", "cph"), comment = "UTF8 Validation library"))
c(
 person("Ivo", "Wengraf", email = "Ivo.Wengraf@RACFoundation.org", role = "cre"),
 person("Will", "Bowditch", , "will.bowditch@dft.gov.uk", role = "aut"))
c(person("Thomas J.", "Leeper",
                    email = "thosjleeper@gmail.com",
                    role = c("aut"), 
                    comment = c(ORCID = "0000-0003-4097-6326")),
             person("Jodi", "Beggs",
                    email = "jodi@alum.mit.edu",
                    role = c("cre", "aut"))      
             )
person(given = "Ram",
           family = "Kripa",
           role = c("aut", "cre"),
           email = "ram.m.kripa@berkeley.edu")
c(
            person("Harith", "Farooq",
                    role = c("aut", "cre"),
                    email = "harithmorgadinho@gmail.com",
                    comment = c(ORCID = "0000-0001-9031-2785")),
            person("Josué", "Azevedo",
                    role = "aut"),
            person("Francesco", "Belluardo",
                    role = "aut"),
            person("Cristóvão", "Nanvonamuquitxo",
                    role = "aut"),
            person("Dom", "Bennett",
                    role = "aut",
                    email = "dominic.john.bennett@gmail.com",
                    comment = c(ORCID = "0000-0003-2722-1359")),
            person("Jason", "Moat",
                    role = "aut"),
            person("Amadeu", "Soares",
                    role = "aut"),
            person("Søren", "Faurby",
                    role = "aut"),
            person("Alexandre", "Antonelli",
                    role = "aut",
                    comment = c(ORCID = "0000-0003-1842-9297")))
c(person('Saiem', 'Gilani', email = 'saiem.gilani@gmail.com',role = c('aut','cre')),
             person('Geoff','Hutchinson', email = 'geoffery.hutchinson@gmail.com', role = c('aut')))
c(
          person("Clayton", "Filho", email = "clayton.fraga.ufes@gmail.com", 
            role = c("aut")),
          person("Thiago", "Martins", email = "thiago_valentim1@hotmail.com", 
            role = c("aut", "cre")))
person(given="Chanseok",
                    family="Park",
                    role = c("aut", "cre"),
                    email="statpnu@gmail.com",
                    comment = c(ORCID = "https://orcid.org/0000-0002-2208-3498"))
c(person("David", "Silkworth", email = "djsilk@openreliability.org", role = "aut"), person("Jurgen", "Symynck", role = "aut"), person("Jacob", "Ormerod", email = "jake@openreliability.org", role = "cre"), person("OpenReliability.org", role = "cph"))
c(person("Tim-Gunnar", "Hensel", 
      email = "tim-gunnar.hensel@tu-berlin.de", 
      role = c("aut", "cre")),
      person("David", "Barkemeyer",
      email = "david.barkemeyer@campus.tu-berlin.de",
      role = "aut")
    )
person("Matthias", "Studer", email = "matthias.studer@unige.ch", role = c("aut", "cre"))
c(person(given = "Cyrill",
             family = "Scheidegger",
             role = c("aut", "cre"),
             email = "cyrill.scheidegger@stat.math.ethz.ch"),
      person(given = "Julia",
             family = "Hoerrmann",
             role = "ths"),
      person(given = "Peter",
             family = "Buehlmann",
             role = "ths"),
      person(given = "Jonas",
             family = "Peters",
             role = c("ctb", "cph"),
             comment = "The code in 'trainFunctions.R' is copied 
             (with small modifications) from the R package
             'GeneralisedCovarianceMeasure' by Jonas Peters and Rajen D. Shah"),
      person(given = "Rajen D.",
             family = "Shah",
             role = c("ctb", "cph"),
             comment = "The code in 'trainFunctions.R' is copied 
             (with small modifications) from the R package 
             'GeneralisedCovarianceMeasure' by Jonas Peters and Rajen D. Shah"))
c(person("Aristidis K.", "Nikoloulopoulos", role = c("aut","cre"), email = "a.nikoloulopoulos@uea.ac.uk"), person("Harry", "Joe", role = c("aut"), email = "harry.joe@ubc.ca"))
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("Scott", "Chamberlain", role = c("aut", "cre"),
    email = "myrmecocystus@gmail.com",
    comment = c(ORCID="0000-0003-1444-9135")),
    person("Os", "Keyes", role = "aut", comment = "author of code in src/")
    )
person("Vincenzo", "Candila", email="candilav@gmail.com", role=c("aut", "cre"))
c(person("Emmanuel", "Sikali", role="pdr", email = "Emmanuel.Sikali@ed.gov"),
             person("Paul", "Bailey", role=c("aut", "cre"), email="pbailey@air.org"),
             person("Claire", "Kelley", role="aut"),
             person("Trang", "Nguyen", role="aut"),
             person("Huade", "Huo", role="aut"),
             person("Eric", "Buehler", role="ctb"),
             person("Christian", "Kjeldsen", role="ctb"))
c(person("Karthik", "Ram", role = c("aut", "cre"),
    email = "karthik.ram@gmail.com", comment = c(ORCID = "0000-0002-0233-1757")),
    person("Hadley", "Wickham", role = "aut",
    email = "h.wickham@gmail.com"),
    person("Clark", "Richards", role = "ctb",
    email = "crichards@whoi.edu"),
    person("Aaron", "Baggett", role = "ctb",
    email = "aaronbaggett@gmail.com"))
c(person("Paul", "McKeigue",
                    email="paul.mckeigue@ed.ac.uk", role=c("aut"),
                    comment=c(ORCID="0000-0002-5217-1034")),
             person("Marco", "Colombo",
                    email="mar.colombo13@gmail.com", role=c("ctb", "cre"),
                    comment=c(ORCID="0000-0001-6672-0623")))
c(
  person("In Song", "Kim", email = "insong@mit.edu", role = c("aut", "cre")),
  person("Kosuke", "Imai", email = "imai@Harvard.edu", role = c("aut"))
  )
person("Iaroslav", "Domin", email = "ya.domin@gmail.com", role = c("aut", "cre"))
c(
        person(given = "Jonathan", family = "Sidi", email =
        "yonicd@gmail.com", role = c("aut", "cre"), comment = c(ORCID =
        "0000-0002-4222-1819")), 
        person("Colin", "Fay", email = "contact@colinfay.me", 
        role = c("ctb"), comment=c(ORCID="0000-0001-7343-1846")),
        person("Kirill", "M\u00fcller", role = c("aut"), 
        email = "krlmlr+r@mailbox.org", 
        comment = c(ORCID = "0000-0002-1416-3412"))
        )
person("Giulio Valentino", "Dalla Riva", email = "me@gvdallariva.net", role = c("aut", "cre"))
c(person("Qiusheng", "Wu", email = "giswqs@gmail.com", role = c("aut")),
             person("Andrew", "Brown", email = "brown.andrewg@gmail.com", role = c("ctb", "cre")))
c(
    person("Jorge", "Lopez Perez", ,"jorge@loperez.com", c("aut", "cre", "cph")),
    person("Jinook", "Jeong", ,"jinookjeong@gmail.com", c("ctb"))
    )
c(
    person(given = c("Eric", "C."), family = "Anderson", email = "eric.anderson@noaa.gov", role = c("aut", "cre"))
  )
c(person(given = "Gábor",
                    family = "Csárdi", 
                    email = "csardi.gabor@gmail.com", 
                    role = c("aut", "cre")),
            person(given = "Maëlle",
                   family = "Salmon",
                    role = "ctb"))
person("Paul", "West", role=c("aut", "cre"), email="fp8049@gmail.com")
c(person("Bijan", "Seyednasrollah", role = c("aut", "cre","cph"), email = "bijan.s.nasr@gmail.com"),
            person("Tim", "Rademacher", role = c("aut", "cph", "wdc"), email = "rademacher.tim@gmail.com"),
            person("David", "Basler", role = c("aut", "cph", "wdc"), email = "david.j.basler@gmail.com"))
c(
    person("Bhaskar", "Karambelkar", email = "bhaskarvk@gmail.com", role = c("aut", "cre")),
    person("Yihui", "Xie", role = c("ctb")),
    person("NPR Visual Team", role = c("ctb", "cph"), comment = "pym.js JavaScript library"),
    person("Bjørn Klinggaard", role = c("ctb", "cph"), comment = "bLazy.js JavaScript library")
    )
c(person("David", "Robinson", email = "admiral.david@gmail.com", role = c("aut", "cre")),
             person("Kanishka", "Misra", role = "ctb"),
             person("Julia", "Silge", role = "ctb", email = "julia.silge@gmail.com"))
c(person("Raphael", "Hartmann", email = "raphael.hartmann@protonmail.com", role = c("aut", "cre")),
             person("Karl C.", "Klauer", email="christoph.klauer@psychologie.uni-freiburg.de", role=c("cph", "aut", "ctb", "ths")),
             person("Steven G.", "Johnson", email="stevenj@math.mit.edu", role=c("ctb")),
             person("Jean M.", "Linhart", email="jlinhart@stata.com", role=c("ctb")),
             person("Brian", "Gough", role=c("ctb")),
             person("Gerard", "Jungman", role=c("ctb")),
             person("Rudolf", "Schuerer", role=c("ctb")),
             person("Przemyslaw", "Sliwa", role=c("ctb")),
             person("Jason H.", "Stover", role=c("ctb")))
c(
    person(given = "Ramiro", family = "Magno",
      email = "ramiro.magno@gmail.com",
      role = c("aut", "cre"),
      comment = c(ORCID = "0000-0001-5226-3441"))
    )
c(
    person("Mikhail", "Popov", email = "mikhail@wikimedia.org",
           role = c("aut", "cre"), comment = "@bearloga on Twitter"),
    person("Wikimedia Foundation", role = "cph")
    )
c(
            person("Thomas", "Shafee", role = c("aut", "cre"), email = "T.Shafee@latrobe.edu.au", comment = c(ORCID = "0000-0002-2298-7593")),
            person("Os", "Keyes", role = "aut", comment = c(ORCID = "0000-0001-5196-609X")),
            person("Serena", "Signorelli", role = "aut"),
            person("Alex", "Lum", role = "ctb", comment = c(ORCID = "0000-0002-9295-9053")),
            person("Christian", "Graul", role = "ctb"),
            person("Mikhail", "Popov", role = "ctb", comment = c(ORCID = "0000-0003-0145-8948"))
           )
c(person(given = "Keith",
           family = "McNulty",
           role = c("aut", "cre"),
           email = "keith.mcnulty@gmail.com",
           comment = c(ORCID = "0000-0002-2332-1654")),
    person(given = "Petr",
           family = "Kajzar",
           role = c("ctb"),
           email = "petr.kajzar@centrum.cz"))
c(
    person("Jemma", "Stachelek", email = "stachel2@msu.edu", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-5924-2464"))
    )
c(person("Avner", "Bar-Hen", role = c("aut", "cre"),
    email = "Avner.Bar-Hen@mi.parisdescartes.fr"),
    person("Louise", "Baschet", role = "ctb"),
    person("Francois-Xavier", "Jollois", role = "ctb"),
    person("Jeremie", "Riou", role = "ctb")
    )
c(
      person(
        "Peter", "Meissner", role = c("aut", "cre"),
        email = "retep.meissner@gmail.com"
      ),
	     person("", "R Core Team", role = "cph")
    )
c(
    person("Scott", "Chamberlain", role = c("aut", "cre"),
      email = "myrmecocystus+r@gmail.com"),
    person("Ethan", "Welty", role = "aut")
  )
c( 
    person("Modesto", "Escobar", email = "modesto@usal.es", role = c("aut", "cph","cre"), comment = c(ORCID = "0000-0003-2072-6071")),
    person("Ángel", "Zazo", email = "angelzazo@usal.es", role = c("aut")), 
    person("Carlos", "Prieto", email = "cprietos@usal.es", role = c("aut"), comment = c(ORCID = "0000-0001-8178-9768")),
    person("David", "Barrios", email = "metal@usal.es", role = "aut"),
    person("Cristina", "Calvo", email = "cristinacalvolopez@usal.es", role = "aut")
    )
c(person(given = "Dion",
           family = "Kwan",
           role = c("aut","cre"),
           email = "dkzk96@yahoo.com.sg"))
person(
  given = c("William", "Michael"),
  family = "Landau",  
  email = "will.landau@gmail.com",
  role = c("aut", "cre"))
person(given="Jed", 
                  family="Long",
                  role=c("aut","cre"),
                  email="jed.long@uwo.ca", 
                  comment = c(ORCID = "0000-0002-2815-0399"))
c(
    person(given = "Megha", 
           family = "Joshi", 
           email = "megha.j456@gmail.com", 
           role = c("aut", "cre"),
           comment = c(ORCID = "0000-0001-7936-076X")),
    person(given = "James", 
           family = "Pustejovsky", 
           email = "jepusto@gmail.com", 
           role = c("aut"), 
           comment = c(ORCID = "0000-0003-0591-9465")),
    person(given = "Sangdon", 
           family = "Lim", 
           email = "sangdonlim@utexas.edu", 
           role = "ctb",
           comment = c(ORCID = "0000-0002-2988-014X")),
    person(given = "Pierce", 
           family = "Cappelli", 
           email = "pierce.cappelli@utexas.edu", 
           role = "ctb"))
c(person(given = "Bradley",
             family = "Rafferty",
             role = c("aut", "cre"),
             email = "brjrafferty@gmail.com"),
      person(given = "Daniel",
             family = "Chung",
             role = "aut",
             email = "dshchung@gmail.com"))
c(
    person("Hendrik", "Schultheis", email = "hendrik.schultheis@mpi-bn.mpg.de", role = c("aut", "cre")),
    person("Jens", "Preussner", email = "jens.preussner@mpi-bn.mpg.de", role = "aut"),
    person("Looso", "Mario", email = "mario.looso@mpi-bn.mpg.de", role = "aut"))
c(person(given = "Kirill",
             family = "Müller",
             role = c("aut", "cre"),
             email = "krlmlr+r@mailbox.org",
             comment = c(ORCID = "0000-0002-1416-3412")),
      person(given = "R Consortium",
             role = "fnd"),
      person(given = "Ian Lance",
             family = "Taylor",
             role = "aut",
             comment = "Bundled libbacktrace library"),
      person(given = "Free Software Foundation",
             role = "cph",
             comment = "Bundled libbacktrace library"))
c(person("Paul","Rabie",role=c("aut")),person("Daniel","Riser-Espinoza", email = "despinoza@west-inc.com",role=c("aut", "cre")),person("Trent","McDonald",role=c("ctb")))
c(person(given = "Novica",
             family = "Nakov",
             role = c("aut", "cre"),
             email = "nnovica@gmail.com"),
      person(given = "Windsor.ai",
             role = "cph", 
             email = "admin@windsor.ai"))
c( 
    person("Katarzyna", "Machowska", , "K.Machowska@stat.gov.pl", role = c("aut")),
    person("Jarosław", "Napora", , "J.Napora@stat.gov.pl", role = c("aut")),
    person("Sebastian", "Wójcik", , "S.Wojcik@stat.gov.pl", role = c("aut", "cre"), comment = c(ORCID = "0000-0003-2425-9626")))
c(person("Ilaria", "Prosdocimi", 
                    email = "prosdocimi.ilaria@gmail.com",
                    role = c("aut", "cre"),
                    comment = c(ORCID = "https://orcid.org/0000-0001-8565-094X")),
             person("Luke", "Shaw", 
                     role = c("aut"),
                     comment = "Luke developped the code to handle the missing and gap periods for Peaks over threshold records."))
c(person(given = "Ying",
             family = "Cui",
             role = c("aut", "cre"),
             email = "cuiyingbeicheng@gmail.com"),
             person(given = "Bo",
             family = "Huang",
             role = "aut"))
c(
  person("Mike", "Meredith", role=c("aut", "cre"), email="mike@mmeredith.net"),
  person("Jason", "Bryer", role="ctb", comment="showShinyApp"),
  person("John", "Kruschke", role="ctb"),
  person("Brian", "Neelon", role="ctb", comment="Bnormal"),
  person("Michael", "Schaub", role="ctb", comment="ch2mArray"),
  person("R Core Team", role="ctb", comment="stats::AIC code adapted for AICc"))
c(
              person("Tavpritesh", "Sethi", role = c("aut", "cre"), email = "tavpriteshsethi@iiitd.ac.in"),
              person("Shubham", "Maheshwari", role = c("aut"), email = "shubham14101@iiitd.ac.in")             
            )
c(person(given = "Jim",
             family = "Hester",
             role = "aut"),
      person(given = "Lionel",
             family = "Henry",
             role = c("aut", "cre"),
             email = "lionel@rstudio.com"),
      person(given = "Kirill",
             family = "Müller",
             role = "aut",
             email = "krlmlr+r@mailbox.org"),
      person(given = "Kevin",
             family = "Ushey",
             role = "aut",
             email = "kevinushey@gmail.com"),
      person(given = "Hadley",
             family = "Wickham",
             role = "aut",
             email = "hadley@rstudio.com"),
      person(given = "Winston",
             family = "Chang",
             role = "aut"),
      person(given = "Jennifer",
             family = "Bryan",
             role = "ctb"),
      person(given = "Richard",
             family = "Cotton",
             role = "ctb"),
      person(given = "RStudio",
             role = c("cph", "fnd")))
c(
  person("Sebastian", "Daberdaku", email = "sebastian.daberdaku@unipd.it", role = c("aut", "cre")),
  person("Erica", "Tavazzi", email = "erica.tavazzi@phd.unipd.it", role = "aut"),
  person("Systems Biology and Bioinformatics Group", "http://sysbiobig.dei.unipd.it/", role="cph"))
person("You", "Li", email = "You.Li2@ed.ac.uk",
  role = c("aut", "cre"))
person(given = "Dewey",
           family = "Dunnington",
           role = c("aut", "cre"),
           email = "dewey@fishandwhistle.net",
           comment = c(ORCID = "0000-0002-9415-4582"))
person("Will", "Frierson", email = "will.frierson@gmail.com",
  role = c("aut", "cre"))
c(
    person("Arne C. Bathke", role = "aut"),
    person("Edgar Brunner", role = "aut"),
    person("Martin Happ", role = c("aut", "cre"),
                      email = "martin.happ@aon.at",
                      comment = c(ORCID = "0000-0003-0009-2665")),
    person("Frank Konietschke", role = "aut")
  )
c(
    person("Alex", "Hallam", , "alexhallam6.28@gmail.com", role = c("aut", "cre")),
    person("R. Cody", "Heimberger", role="ctb"))
person("Samuel I.", "Berchuck", email = "sib2@duke.edu", role = c("aut", "cre"), comment = c(ORCID = "0000-0001-5705-3144"))
person(given = "Michael",
           family = "Bertolacci",
           role = c("aut", "cre", "cph"),
           email = "m.bertolacci@gmail.com",
           comment = c(ORCID = "0000-0003-0317-5941"))
c(
    person("Andreas", "Weidenhiller", email = "a.weidenhiller@holzforschung.at", role = c("cre", "aut"),
        comment = c(ORCID = "0000-0003-0938-2159")),
    person("Anton", "Wegscheider", email = "a.wegscheider@holzforschung.at", role = c("aut"))
    )
c(
    person("Justin M.", "Shea", role = c("aut", "cre"), email = "jshea@roosevelt.edu"),
    person("Kennth H.", "Brown", role = c("ctb"), email = "KennethBrown@MissouriState.edu"))
c(
    person(
      given = c("Caspar", "J."),
      family = "van Lissa",
      email = "c.j.vanlissa@uu.nl",
      role = c("aut", "cre"),
      comment = c(ORCID = "0000-0002-0808-5024")
    ),
    person(
      "Aaron",
      "Peikert",
      role = c("aut"),
      comment = c(ORCID = "0000-0001-7813-818X")
    ),
    person(
      c("Andreas", "M."),
      "Brandmaier",
      role = "aut",
      comment = c(ORCID = "0000-0001-8765-6982")
    )
    )
c(
    person('Jan', 'Wijffels', role = c('aut', 'cre', 'cph'), email = 'jwijffels@bnosac.be', comment = "R wrapper"), 
    person('BNOSAC', role = 'cph', comment = "R wrapper"),
    person('Max', 'Fomichev', role = c('ctb', 'cph'), comment = "Code in src/word2vec"))
c(
    person("Mika", "Braginsky", email = "mika.br@gmail.com", role = c("aut", "cre")),
    person("Daniel", "Yurovsky", email = "yurovsky@stanford.edu", role = "ctb"),
    person("Michael", "Frank", email = "mcfrank@stanford.edu", role = "ctb"),
    person("Danielle", "Kellier", email = "dkellier@stanford.edu", role = "ctb")
    )
c(
  person("David", "Smith", email = "david.alex.smith@gmail.com", role = c("aut", "cre")),
  person("Gethin", "Davies", email = "grd.davies@gmail.com", role = "ctb")
  )
c(person("Ingo", "Feinerer", role = "aut"),
             person("Kurt", "Hornik", role = c("aut", "cre"),
                    email = "Kurt.Hornik@R-project.org",
		    comment = c(ORCID = "0000-0003-4198-9911")),
             person("Mike", "Wallace", role = c("ctb", "cph"),
                    comment = "Jawbone Java WordNet API library"))
c(person("Michael","Scholz", email = "michael.scholz@th-deg.de", role = c("cre", "aut")),
            person("Thomas","Woehner", email = "Thomas.Woehner@eah-jena.de", role = "aut"),
            person("Ralf","Peters", email = "ralf.peters@wiwi.uni-halle.de", role = "ctb"))
c(
    person(given = "Jonathan",
           family = "Bratt",
           role = c("aut"),
           email = "jonathan.bratt@macmillan.com",
           comment = c(ORCID = "0000-0003-2859-0076")),
    person(given = "Jon",
           family = "Harmon",
           role = c("aut", "cre"),
           email = "jonthegeek@gmail.com",
           comment = c(ORCID = "0000-0003-4781-4346")),
    person(given = "Bedford Freeman & Worth Pub Grp LLC DBA Macmillan Learning", 
           role = c("cph")),
    person(given = "Google, Inc", 
           role = c("cph"), comment = "original BERT vocabularies")
    )
person(given = "Michael", family = "Friendly", role=c("aut", "cre"), 
    email="friendly@yorku.ca", comment=c(ORCID="0000-0002-3237-0941"))
person(given = "Nadir",
           family = "Latif",
           role = c("aut", "cre"),
           email = "pakjiddat@gmail.com",
           comment = c(ORCID = "0000-0002-7543-7405"))
person(given = "Conor",
           family = "Neilson",
           role = c("aut", "cre"),
           email = "condwanaland@gmail.com")
person(given = "Mark",
           family = "Rieke",
           role = c("aut", "cre"),
           email = "markjrieke@gmail.com")
c(
    person("John", "Blischak", role = c("aut", "cre"),
           email = "jdblischak@gmail.com",
           comment = c(ORCID = "0000-0003-2634-9879")),
    person("Peter", "Carbonetto", role = c("aut"),
           comment = c(ORCID = "0000-0003-1144-6780")),
    person("Matthew", "Stephens", role = c("aut"),
           comment = c(ORCID = "0000-0001-5397-9257")),
    person("Luke", "Zappia", role = c("ctb"),
           comment = "Instructions for hosting with GitLab"),
    person("Pierre", "Formont", role = c("ctb"),
           comment = "Support for hosting with Shiny Server"),
    person("Tim", "Trice", role = c("ctb"),
           comment = "Instructions for sharing common code"),
    person("Jiaxiang", "Li", role = c("ctb"),
           comment = "Function wflow_toc() to create table of contents"),
    person("Michael J.", "Kane", role = c("ctb"),
           email = "michael.kane@yale.edu",
           comment = c(ORCID = "0000-0003-1899-6662",
                       "Option suppress_report")),
    person("Anh", "Tran", role = c("ctb"),
           comment = "Updated RStudio Project Template"),
    person("Sydney", "Purdue", role = c("ctb"),
           comment = "Improved wflow_start() error handling"),
    person("Giorgio", "Comai", role = c("ctb"),
           comment = "Added argument only_published to wflow_toc()"))
c(person(given = "Vikram B.",
             family = "Baliga", 
             role = c("aut", "cre"),
             email = "vbaliga87@gmail.com",
             comment = c(ORCID = "0000-0002-9367-8974")),
      person(given = "Shreeram", 
             family = "Senthivasan",
             role = "aut",
             email = "shreeramsenthi@gmail.com",
             comment = c(ORCID = "0000-0002-7118-9547")),
      person(given = "Julia",
             family = "Romanowska",
             role = "rev",
             email = "Julia.Romanowska@uib.no",
             comment = "Julia reviewed the package for rOpenSci
             , see <https://github.com/ropensci/software-review/issues/326/>"),
      person(given = "Eric",
             family = "Brown", 
             role = "rev",
             email = "eb@ericebrown.com",
             comment = "Eric reviewed the package for rOpenSci
             , see <https://github.com/ropensci/software-review/issues/326/>"))
c(person("David", "Carslaw", role = c("aut", "cre"), email =
    "david.carslaw@york.ac.uk"))
c( person("Jan", "Holstein" ,  
              email ="janmholstein@gmail.com",  
              role=c("aut","cre", "cph"))     
              )
person("Christopher", "Baker", email = "chriscrewbaker@gmail.com",
  role = c("aut", "cre"))
c(person("Gleb", "Beliakov", role = c("aut", "cre"),
		email = "gleb@deakin.edu.au"),
		person("Daniela", "Calderon", role = "aut"))
c(
    person(given = "Martin", family = "Chan", role = c("aut", "cre"), email = "martin.chan@microsoft.com"),
    person(given = "Carlos", family = "Morales", role = "aut", email = "carlos.morales@microsoft.com"),
    person(given = "Mark", family = "Powers", role = "ctb", email = "mark.powers@microsoft.com"),
    person(given = "Ainize", family = "Cidoncha", role = "ctb", email = "ainize.cidoncha@microsoft.com"),
    person(given = "Rosamary", family = "Ochoa Vargas", role = "ctb", email = "rosamary.ochoa@microsoft.com"),
    person(given = "Tannaz", family = "Sattari", role = "ctb", email = "tannaz.sattari@microsoft.com"),
    person(given = "Lucas", family = "Hogner", role = "ctb", email = "lucas.hogner@microsoft.com"),
    person(given = "Jasminder", family = "Thind", role = "ctb", email = "jasminder.thind@microsoft.com"),
    person(given = "Simone", family = "Liebal", role = "ctb", email = "simone.liebal@microsoft.com"),
	person(given = "Aleksey", family = "Ashikhmin", role = "ctb", email = "alashi@microsoft.com"),
    person(given = "Ellen", family = "Trinklein", role = "ctb"),
    person(given = "Microsoft Corporation", role = "cph")
	)
c(
  person("Alan", "Jassby", role = c("aut")),
  person("James", "Cloern", role = c("ctb")),
  person("Joseph", "Stachelek", role = c("ctb", "cre"), email = "stachel2@msu.edu")
  )
person(given = "Andreas",
           family = "Busjahn",
           role = c("cre", "aut"),
           email = "andreas@busjahn.net",
           comment = c(ORCID = "0000-0001-9650-6919"))
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")))
person("Wolfgang", "Raffelsberger", email = "w.raffelsberger@gmail.com", role = c("aut", "cre"))
c(
    person("Xi", "Liu", email = "xiliu@ucsb.edu", role = c("aut", "cre")),
    person("Matthew", "Coleman", email = "matthewbcoleman@ucsb.edu", role = "aut"),
    person("Alexander", "Petersen", email = "petersen@pstat.ucsb.edu", role = "aut"))
c(
    person("Jeroen", "Ooms", ,"jeroen@berkeley.edu", role = c("aut", "cre"),
      comment = c(ORCID = "0000-0002-4035-0289")),
    person("John McNamara", role = "cph",
      comment = "Author of libxlsxwriter (see AUTHORS and COPYRIGHT files for details)"))
c(person(given="Patrick", family="Mair", email="mair@fas.harvard.edu", role=c("cre","aut")),
             person(given="Rand", family="Wilcox", email="rwilcox@usc.edu", role=c("aut")),
             person(given="Indrajeet", family="Patil", email="patilindrajeet.science@gmail.com", role=c("ctb")))
person(given = "Marcus W.", family = "Beck",
    role = c("aut","cre"),
    email = "mbeck@tbep.org")
person("Wolfgang", "Raffelsberger", email = "w.raffelsberger@unistra.fr", role = c("aut", "cre"))
c(
  person("Kabir", "Khanna", email = "kabirkhanna@gmail.com", role = c("aut", "cre")),
  person("Kosuke", "Imai", email = "imai@harvard.edu", role = c("aut"))
  )
c(person(given = "Javier", family = "Roca-Pardinas", 
           email = "roca@uvigo.es", role = c("aut", "cre")),
           person(given = "Maria Xose", family = "Rodriguez-Alvarez",
              email = "mxrodriguez@bcamath.org", role = "aut"),
           person(given = "Stefan", family = "Sperlich",
              email = "stefan.sperlich@unige.dot.ch", role = "aut"),
           person(give = "Alan", family = "Miller (FORTRAN code lsq.f90: weighted least-squares module)", role = "ctb"))
person("Stephen", "Lee", 
        email = "smlee.981@gmail.com", role = c("aut", "cre")
    )
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("Xianglong", "Liang", email = "xianglongliang@gmail.com", role = c("aut", "cre")),
            person("Hokeun", "Sun", email = "hsun@pusan.ac.kr", role = c("ctb")))
c(person(given = "Qinghan",
             family = "Meng",
             email = "qinghan.meng@gmail.com",
             role="aut"),
      person(given = "He",
             family = "Zhao",
             email = "Simon.Yansen.Zhao@gmail.com",
             role = c("aut", "cre"),
             comment = c(ORCID = "0000-0001-5763-9743")),
      person(given = c("Graham", "J."),
             family = "Williams",
             email = "graham.williams@togaware.com",
             role = "aut",
             comment = c(ORCID = "0000-0001-7041-4127")),
      person(given = "Junchao",
             family = "Lv",
             email = "ljc0711@gmail.com",
             role = "aut"),
      person(given = "Baoxun",
             family = "Xu",
             email = "76276198@qq.com",
             role = "aut"),
      person(given = c("Joshua", "Zhexue"),
             family = "Huang",
             email = "zx.huang@szu.edu.cn",
             role = "aut",
             comment = c(ORCID = "0000-0002-6797-2571")))
c(
    person("Daniel C.", "Reuman", email = "reuman@ku.edu", role = c("aut", "cre")),
    person("Thomas L.", "Anderson", email="anderstl@gmail.com", role=c("aut")),
    person("Jonathan A.", "Walter", email = "jaw3es@virginia.edu", role=c("aut")),
    person("Lei", "Zhao", email = "lei.zhao@cau.edu.cn", role=c("aut")),
    person("Lawrence W.", "Sheppard", email="lwsheppard@ku.edu", role=c("aut"))
    )
person(given = "zhengyu",
           family = "wu",
           role = c("aut", "cre","ctb"),
           email = "zhengyuwu888@163.com")
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("Stéphane", "Guerrier", email = "stef.guerrier@gmail.com", role = c("aut","cre")),
    person("James", "Balamuta", role = "aut"),
    person("Justin", "Lee", role = "aut"),
    person("Roberto", "Molinari", role = "aut"),
    person("Yuming", "Zhang", role = "aut"),
    person("Nathanael", "Claussen", role = "ctb"),
    person("Haotian", "Xu", role = "ctb"),
    person("Lionel", "Voirol", role = "ctb")
    )
c(
    person(given="Xiaodong", family="Luo", email = "Xiaodong.Luo@sanofi.com", role =c("aut", "cre")),
    person(given="Junshan", family="Qiu",  role = "ctb"),
    person(given="Steven", family="Bai",  role = "ctb"),
    person(given="Hong", family="Tian",  role = "ctb"),
    person(given="Mike", family="Mikailov",  role = "ctb"),
    person("Sanofi", role = "cph"))
c(person("Nicolas", "Turenne", role = "aut", email = "nturenne@u-pem.fr"),
    person("Tien T.","Phan", role = c("aut","cre"),  email = "phantien84@gmail.com"))
c(
    person("Heike", "Hofmann", role = c("aut", "cre", "cph"),
            email = "hofmann@iastate.edu",
            comment = c(ORCID = "0000-0001-6216-5183")),
    person("Susan", "Vanderplas", email = "srvander@iastate.edu", 
            role = "aut",
            comment = c(ORCID = "0000-0002-3803-0972")),                      
    person("Ganesh", "Krishnan", role = c("aut"),
            email = "ganeshk@iastate.edu"),
    person("Eric", "Hare", email = "eric@oaiti.org", role = "aut",
            comment = c(ORCID="0000-0002-4277-3146"))
            )
person("Garrick", "Aden-Buie", , "garrick@adenbuie.com", role = c("aut", "cre"),
           comment = c(ORCID = "0000-0002-7111-0077"))
c(person("Hadi", "Esfandyari", email = "esfandyari.hadi@gmail.com", role = c("aut", "cre")),person("Anders", "Christian Sørensen", email = "achristian.sørensen@mbg.au.dk",role = c("aut")))
c(
            person("Kei","Nakagawa",email="kei.nak.0315@gmail.com",role=c("aut","cre"),comment = c(ORCID = "0000-0001-5046-8128")),
            person("Mitsuyoshi","Imamura",email="ic140tg528@gmail.com",role=c("aut"))
          )
c(person("Gert","Janssenswillen",email = "gert.janssenswillen@uhasselt.be", role = c("aut","cre")),
 person("Benoît","Depaire",email = "benoit.depaire@uhasselt.be", role = "ctb"))
c(
  person("Yihui", "Xie", role = c("aut", "cre", "cph"), email = "xie@yihui.name", comment = c(ORCID = "0000-0003-0645-5666")),
  person("Wush", "Wu", role = "ctb"),
  person("Daijiang", "Li", role = "ctb"),
  person("Xianying", "Tan", role = "ctb"),
  person("Salim", "Brüggemann", role = "ctb", email = "salim-b@pm.me", comment = c(ORCID = "0000-0002-5329-5987")),
  person("Christophe", "Dervieux", role = "ctb"),
  person()
  )
c(
  person("Chengjun", "Hou", role = c("aut", "cre"), email = "chengjun.hou@gmail.com"),
  person("Abhishek", "Bishoyi", role = c("aut"), email = "abhishek.bishoyi@gmail.com")
  )
c(
  person("Tianqi", "Chen", role = c("aut"),
         email = "tianqi.tchen@gmail.com"),
  person("Tong", "He", role = c("aut"),
         email = "hetong007@gmail.com"),
  person("Michael", "Benesty", role = c("aut"),
         email = "michael@benesty.fr"),
  person("Vadim", "Khotilovich", role = c("aut"),
         email = "khotilovich@gmail.com"),
  person("Yuan", "Tang", role = c("aut"),
         email = "terrytangyuan@gmail.com",
         comment = c(ORCID = "0000-0001-5243-233X")),
  person("Hyunsu", "Cho", role = c("aut"),
         email = "chohyu01@cs.washington.edu"),
  person("Kailong", "Chen", role = c("aut")),
  person("Rory", "Mitchell", role = c("aut")),
  person("Ignacio", "Cano", role = c("aut")),
  person("Tianyi", "Zhou", role = c("aut")),
  person("Mu", "Li", role = c("aut")),
  person("Junyuan", "Xie", role = c("aut")),
  person("Min", "Lin", role = c("aut")),
  person("Yifeng", "Geng", role = c("aut")),
  person("Yutian", "Li", role = c("aut")),
  person("Jiaming", "Yuan", role = c("aut", "cre"),
         email = "jm.yuan@outlook.com"),
  person("XGBoost contributors", role = c("cph"),
         comment = "base XGBoost implementation")
  )
c(person(given = "Andrew",
             family = "Stein",
             role = c("aut", "cre"),
             email = "andy.stein@gmail.com"),
      person(given = "Alison",
             family = "Margolskee",
             role = "aut",
             email = "alison.margolskee@gmail.com"),
      person(given = "Fariba",
             family = "Khanshan",
             role = "aut",
             email = "faribashan@gmail.com"),
      person(given = "Konstantin",
             family = "Krismer",
             role = "aut",
             email = "krismer@mit.edu",
             comment = c(ORCID = "0000-0001-8994-3416")),
      person(given = "Matthew",
             family = "Fidler",
             role = "ctb",
             email = "matthew.fidler@gmail.com",
             comment = c(ORCID = "0000-0001-8538-6691")),
       person("Novartis Pharma AG", role = c("cph","fnd")))
c(person("Robert Myles ", "McDonnell", email = "robertmylesmcdonnell@gmail.com", role = c("aut", "cre")), 
           person("Colin", "Fay", email = "contact@colinfay.me", role = c("ctb")),
           person("Randall", "Munroe", role = "cph"))
c(person("Mirai Solutions GmbH", role = "aut",
                    email = "xlconnect@mirai-solutions.com"),
             person("Martin", "Studer", role = "cre",
                    email = "martin.studer@mirai-solutions.com"),
             person("The Apache Software Foundation", role = c("ctb", "cph"),
                    comment = "Apache POI"),
             person("Graph Builder", role = c("ctb", "cph"),
                   comment = "Curvesapi Java library"),
             person("Brett Woolridge", role = c("ctb", "cph"),
                   comment = "SparseBitSet Java library"))
c(
    person(given = "Wei",
           family = "Xu",
           role = c("aut"),
           email = "Wei.Xu@uhnresearch.ca"),
    person(given = "Meiling",
           family = "Hao",
           role = c("aut"),
           email = "Meiling.Hao@uhnresearch.ca"),
    person(given = "Yi",
           family = "Zhu",
           role = c("cre"),
           email = "yizhu87@gmail.com"))
c(person("Samet", "Sokel", , "a_s@eskisehir.edu.tr", role = c("aut", "cre"),
           comment = c(ORCID = "0000-0002-4429-5125")),
      person("Yavuz", "Acar", , "yavuz.acar@boun.edu.tr", role = c("aut","rev")))
c(person(given = "Adrian",
             family = "Dragulescu",
             role = c("aut"),
             email = "adrian.dragulescu@gmail.com"),
      person(given = "Cole",
             family = "Arendt",
             role = c("aut", "cre"),
             email = "cole.arendt@outlook.com")
             )
person("Gwang-Jin", "Kim", email = "gwang.jin.kim.phd@gmail.com",
  role = c("aut", "cre"))
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("Chuan", "Hong", role="aut"),
            person("Chongliang", "Luo", role="aut"),
            person("Jiayi", "Tong", role=c("aut", "cre"), 
                    email="Jiayi.Tong@pennmedicine.upenn.edu"),
            person("Rui", "Duan", role="ctb"),
            person("Haitao", "Chu", role="ctb"),
			      person("Yulun", "Liu", role="ctb"),
            person("Yong", "Chen", role=c("aut"),
                     email ="ychen123@upenn.edu"))
c(
    person("Xiaobei", "Zhao", email="xiaobei@binf.ku.dk",
    role=c("aut", "cre", "cph"))
    )
c(person("CRAN Team", role = c('ctb', 'cre'),
                    email = "CRAN@r-project.org",
                    comment = "de facto maintainer since 2013"),
             person("Duncan", "Temple Lang", role = c("aut"),
                    email = "duncan@r-project.org",
                    comment = c(ORCID = "0000-0003-0159-1546")),
	    person("Tomas", "Kalibera", role = "ctb"))
c(person(given = "Hadley",
             family = "Wickham",
             role = c("aut", "cre"),
             email = "hadley@rstudio.com"),
      person(given = "Jim",
             family = "Hester",
             role = "aut"),
      person(given = "Jeroen",
             family = "Ooms",
             role = "aut"),
      person(given = "RStudio",
             role = "cph"),
      person(given = "R Foundation",
             role = "ctb",
             comment = "Copy of R-project homepage cached as example"))
c(
    person("Per", "Nyfelt", email = "per@alipsa.se", role = c("cre", "aut")),
    person("Alipsa HB", email = "info@alipsa.se", role = "cph"),
    person("Steven", "Brandt", role = "ctb")
    )
person("Alex", "Zanidean", email = "AZanidean@mhc.ab.ca",
                  role = c("aut","cre"))
c(person("Joris","Meys", email = "Joris.Meys@UGent.be", role
        = c("cre","aut")), person("Michiel", "Stock", email =
        "Michiel.Stock@UGent.be", role = "aut"))
c(person("Karl W", "Broman", role=c("aut", "cre"),
                    email="broman@wisc.edu",
                    comment=c(ORCID = "0000-0002-4914-6671")),
             person("Il-Youp", "Kwak", role="ctb",
                    comment=c(ORCID = "0000-0002-7117-7669")))
c(
    person("Gábor", "Csárdi",, "csardi.gabor@gmail.com", c("aut", "cre")),
    person("Fathi", "Boudra", role = "aut"),
    person("Rex", "Dieter", role = "aut"),
    person("Kevin", "Krammer", role = "aut"),
    person("Jeremy", "White", role = "aut"))
c(person(given = "Ludvig Renbo",
             family = "Olsen",
             role = c("aut", "cre"),
             email = "r-pkgs@ludvigolsen.dk"),
      person(given = "R. Mark",
             family = "Sharp",
             role = c("ctb")))
c(
      person('Benjamin', 'Guiastrennec',
             role  = c('aut', 'cre', 'cph'),
             email = 'guiastrennec@gmail.com'),
      person('Andrew C.', 'Hooker', 
             role  = c('aut', 'cph'),
             email = 'andrew.hooker@farmaci.uu.se'),
      person('Sebastian', 'Ueckert',
             role  = c('aut', 'cph'),
             email = 'sebastian.ueckert@farmaci.uu.se'),
      person('Mike K.', 'Smith', 
             role  = 'ctb',
             email = 'mike.k.smith@pfizer.com'),
      person('Mats O.', 'Karlsson',
             role  = c('aut', 'cph'),
             email = 'mats.karlsson@farmaci.uu.se'))
c(person('Justin', 'Wilkins', email = 'justin.wilkins@occams.com',
                  role = c('aut', 'cre', 'cph')),
          person('Matthew','Fidler',
      role = c('aut','cph'), email = 'matthew.fidler@gmail.com'),  
            person('Benjamin', 'Guiastrennec',
             role = c('aut'),
             email = 'guiastrennec@gmail.com'),
      person('Andrew C.', 'Hooker', 
             role = c('aut'),
             email = 'andrew.hooker@farmbio.uu.se'),
      person('Anna', 'Olofsson', role = c('aut', 'cph')),
      person('Sebastian', 'Ueckert',
             role = c('aut'),
             email = 'sebastian.ueckert@farmbio.uu.se'),
      person('Ron', 'Keizer', 
             role = 'aut',
             email = 'ronkeizer@gmail.com'),
      person('Kajsa', 'Harling', role = 'ctb'),
      person('Mike K.', 'Smith', role = 'ctb'),
      person('Elodie', 'Plan', role = 'ctb'),
      person('Mats O.', 'Karlsson',
             role = c('aut', 'cph'),
             email = 'mats.karlsson@farmbio.uu.se'),
      person('Pharmetheus', role = 'ctb'),
      person('Pfizer', role = 'ctb'),
      person('Occams', role = 'ctb'),
      person('Novartis', role = 'ctb'))
c(person("Andrew C.","Hooker",role=c("aut","cre","cph"),email="andrew.hooker@farmaci.uu.se"),
             person("Mats O.","Karlsson",role=c("aut","cph")),
             person("Justin J.","Wilkins",role="aut"),
             person("E. Niclas","Jonsson",role=c("aut","trl","cph")),
             person("Ron","Keizer",role="ctb",comment="functionality for bootstrap of GAM and SCM"))
person("Pablo", "Seibelt", email = "pabloseibelt@sicarul.com",
  role = c("aut", "cre"))
person("Karl", "Holub", email = "karljholub@gmail.com", role = c("aut", "cre"))
c(person("Filipe", "Campelo",
  role = c("aut", "cre", "cph"),
  email = "fcampelo@ci.uc.pt",
  comment = c(ORCID = "0000-0001-6022-9948")),
  person("Konrad","Mayer",
  role = "ctb"))
c(
  person("Garrett", "Weaver", email = "gmweaver.usc@gmail.com", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-9918-8386")),
  person("Juan Pablo", "Lewinger", email = "lewinger@usc.edu", role = c("ctb", "ths"))
  )
c(
  person("Krystian", "Igras", email = "krystian8207@gmail.com", role = c("aut", "cre")),
  person("Przemyslaw", "Biecek", role =  c("aut", "ths")))
c(
  person("Yuri", "Zhukov", email = "zhukov@umich.edu", role = c("aut","cre")),
  person("Christian", "Davenport", email = "christiandavenport@mac.com", role = c("aut")),
  person("Nadiya", "Kostyuk", email = "nadiya@umich.edu", role = c("aut")))
c(person("David B.", "Dahl", role="aut"),
             person("David", "Scott", role=c("aut","cre"),
               email="d.scott@auckland.ac.nz"),
             person("Charles", "Roosen", role="aut"),
             person("Arni", "Magnusson", role="aut"),
             person("Jonathan", "Swinton", role="aut"),
             person("Ajay", "Shah", role="ctb"),
             person("Arne", "Henningsen", role="ctb"),
             person("Benno", "Puetz", role="ctb"),
             person("Bernhard", "Pfaff", role="ctb"),
             person("Claudio", "Agostinelli", role="ctb"),
             person("Claudius", "Loehnert", role="ctb"),
             person("David", "Mitchell", role="ctb"),
             person("David", "Whiting", role="ctb"),
             person("Fernando da", "Rosa", role="ctb"),
             person("Guido", "Gay", role="ctb"),
             person("Guido", "Schulz", role="ctb"),
             person("Ian", "Fellows", role="ctb"),
             person("Jeff", "Laake", role="ctb"),
             person("John", "Walker", role="ctb"),
             person("Jun", "Yan", role="ctb"),
             person("Liviu", "Andronic", role="ctb"),
             person("Markus", "Loecher", role="ctb"),
             person("Martin", "Gubri", role="ctb"),
             person("Matthieu", "Stigler", role="ctb"),
             person("Robert", "Castelo", role="ctb"),
             person("Seth", "Falcon", role="ctb"),
             person("Stefan", "Edwards", role="ctb"),
             person("Sven", "Garbade", role="ctb"),
             person("Uwe", "Ligges", role="ctb"))
person("Christofer", "Backlin", role = c("aut", "cre"), email = "christofer.backlin@medsci.uu.se")
c(
    person("Paulo", "Somaini", email = "soma@stanford.edu", role = c("aut", "cre","cph")),
    person("Frank", "Wolak", email= "wolak@stanford.edu", role=c("aut")),
    person("Brian", "Chivers", email = "bchivers@stanford.edu", role = c("aut")))
c(
  person(given=c("Jeffrey","A."), family="Ryan", role=c("aut","cph")),
  person(given=c("Joshua","M."), family="Ulrich", role=c("cre","aut"), email="josh.m.ulrich@gmail.com"),
  person(given="Ross", family="Bennett", role="ctb"),
  person(given="Corwin", family="Joy", role="ctb")
  )
person("Willem", "van den Boom", role = c("aut", "cre"), email = "willem@wvdboom.nl")
c(
    person("Miguel", "Alvarez", email="kamapu78@gmail.com",
        role=c("aut","cre"), comment=c(ORCID="0000-0003-1500-1834")),
    person("Bisrat Haile", "Gebrekidan", role=c("ctb")))
c(	person("Henrik", "Baktoft", email = "hba@aqua.dtu.dk", role = c("cre", "aut"), comment=c(ORCID = "0000-0002-3644-4960")),
				person("Karl", "Gjelland", role=c("aut"), comment=c(ORCID = "0000-0003-4036-4207")),
				person("Uffe H.", "Thygesen", role=c("aut"), comment=c(ORCID = "0000-0002-4311-6324")),
				person("Finn", "Økland", role=c("aut"), comment=c(ORCID = "0000-0002-1938-5460"))
			)
c(
    person("Max", "Kuhn", , "max@rstudio.com", role = "aut"),
    person("Davis", "Vaughan", , "davis@rstudio.com", role = c("aut", "cre")),
    person("RStudio", role = "cph")
  )
c(person("David", "Charte", email = "fdavidcl@ugr.es", role = c("aut", "cre"),
    comment = c(ORCID = "0000-0002-4830-9512")), person("Francisco", "Charte", 
    email = "francisco@fcharte.com", role = "aut", comment = c(ORCID = "0000-0002-3083-8942")))
person("Nathaniel", "Phillips", email = "yarrr.book@gmail.com",
                  role = c("aut", "cre"))
person("Tomasz", "Kalinowski", email = "tkalinow@asu.edu",
                  role = c("aut", "cre"))
c(person(given = "Bonsoo",
             family = "Koo",
             role = c("aut"),
             email = "Bonsoo.Koo@monash.edu"),
      person(given = "Nathaniel",
             family = "Tomasetti",
             role = "aut",
             email = "nathaniel.tomasetti@gmail.com"),
      person(given = "Kai-Yang",
             family = "Goh",
             role = "aut",
             email = "kai-yang.goh@monash.edu"),
      person(given = "Yangzhuoran Fin",
             family = "Yang",
             role = c("aut", "cre"),
             email = "yangyangzhuoran@gmail.com",
             comment = c(ORCID = "0000-0002-1232-8017")))
c(
    person("Joe", "Thorley", , "joe@poissonconsulting.ca", c("aut", "cre"), comment = c(ORCID = "0000-0002-7683-4592")),
    person("Hadley", "Wickham", , "hadley@rstudio.com", role = "aut", comment = c(ORCID = "0000-0003-4757-117X"))
  )
person("Antoine", "Soetewey", email = "antoine.soetewey@uclouvain.be",
                  role = c("aut", "cre"))
c(
    person("Xianying", "Tan", , "shrektan@126.com", role = c("aut", "cre"),
           comment = c(ORCID = "0000-0002-6072-3521")),
    person("Hiroaki", "Yutani", , "yutani.ini@gmail.com", role = c("ctb"),
           comment = c(ORCID = "0000-0002-3385-7233", "configure, configure.win, tools/configure.R"))
    )
c(person(given = "Malcolm",
             family = "Barrett",
             role = c("aut", "cre"),
             email = "malcolmbarrett@gmail.com",
             comment = c(ORCID = "0000-0003-0299-5825")),
      person(given = "Richard",
             family = "Iannone",
             role = "aut",
             email = "rich@rstudio.com",
             comment = c(ORCID = "0000-0003-3925-190X")),
      person(given = "RStudio",
             role = c("cph", "fnd")))
c(
    person("Nathan", "Teetor", role = c("aut", "cre"),
      email = "nathanteetor@gmail.com"),
    person(family = "The Bootstrap Authors", role = "cph",
      comment = "Bootstrap library"),
    person(family = "Twitter, Inc", role = "cph",
      comment = "Bootstrap library"),
    person(family = "JS Foundation", role = "cph",
      comment = "jQuery library"),
    person("Federico", "Zivolo", role = c("ctb", "cph"),
      comment = "popper.js library"),
    person("Johann", "Servoire", role = c("ctb", "cph"),
      comment = "bs-custom-file-input library")
    )
c(
    person("Matías", "Castillo Aguilar", , "matcasti@umag.cl", role = c("aut", "cre"),
           comment = c(ORCID = "0000-0001-7291-247X")),
    person("Pablo", "Valdés Badilla", role = "aut"), 
    person("Tomás", "Herrera Valenzuela", role = "aut"), 
    person("Eduardo", "Guzmán Muñoz", role = "aut"), 
    person("Pedro", "Delgado Floody", role = "aut"), 
    person("David Cristóbal", "Andrade", role = "aut"), 
    person("Michele M.", "Moraes", role = "aut"), 
    person("Rosa M. E.", "Arantes", role = "aut"), 
    person("Cristian", "Núñez Espinosa", role = "aut")
    )
c(
    person(given = "JooYoung",
           family = "Seo",
           role = c("aut", "cre"),
           email = "jooyoung@psu.edu",
           comment = c(ORCID = "0000-0002-4064-6012")), 
    person("Soyoung", "Choi", role = "aut", email = "sxc940@psu.edu", comment = c(ORCID = "0000-0002-0998-3352")))
c(person(given = "Dewi",
           family = "Amaliah",
           role = c("aut", "cre"),
           email = "dlamaleeah@gmail.com"),
           person(given = "Di",
           family = "Cook",
           role = c("aut"),
           email = "dicook@monash.edu"),
           person(given = "Emi",
           family = "Tanaka",
           role = c("aut"),
           email = "emi.tanaka@monash.edu"))
person(given = "Fabio", family = "Demarqui", email = "fndemarqui@est.ufmg.br", role = c("aut", "cre"))
c(
    person(given = "Sajad",
        family   = "Tasharofi",
        role     = c("aut", "cph"),
        email    = "sajad.tasharofi@uta.edu"),
    person(given = "Shashank",
        family   = "Kumbhare",
        role     = c("aut", "cre", "cph"),
        email    = "shashank.kumbhare@mavs.uta.edu"),
    person(given = "Bent",
        family   = "Petersen",
        role     = c("aut"),
        email    = "bent.petersen@sund.ku.dk"),
    person(given = "Morteza",
        family   = "Khaledi",
        role     = c("aut"),
        email    = "morteza.khaledi@uta.edu"),
    person(given = "Amir",
        family   = "Shahmoradi",
        role     = c("aut"),
        email    = "a.shahmoradi@uta.edu") )
person(given = "Gjalt-Jorn",
           family = "Peters",
           role = c("aut", "cre"),
           email = "gjalt-jorn@userfriendlyscience.com")
c(person("Scott", "Chamberlain", role = c("aut", "cre"),
    email = "sckott@protonmail.com", comment = c(ORCID = "0000-0003-1444-9135")))
c(person(given = "Javier",
             family = "Palarea-Albaladejo",
             role = c("cre","aut"),
             email = "javier.palarea@udg.edu",
             comment = c(ORCID = "0000-0003-0162-669X")),
      person(given = "Josep Antoni",
             family = "Martin-Fernandez",
             role = c("aut"),
             email = "josepantoni.martin@udg.edu",
             comment = c(ORCID = "0000-0003-2366-1592")))
c(
    person("František", "Bartoš", email = "f.bartos96@gmail.com", role = c("aut", "cre")),
    person("Ulrich", "Schimmack", email = "ulrich.schimmack@utoronto.ca", role = c("aut")))
c(
    person(given = "Nathan", family = "Teetor", email = "nathanteetor@gmail.com", role = c("aut", "cre")),
    person(given = "Paul", family = "Teetor", role = "ctb"))
c(
	person("Olivier M. F.", "Martin", email = "oliviermfmartin@tutanota.com", role = c("aut", "cre")),
    person("Michel", "Ducher", email = "michel.ducher@chu-lyon.fr", role = "aut"))
c(
    person("Quentin", "Geissmann", role = c("aut", "cre"), email = "qgeissmann@gmail.com"),
    person("Luis", "Garcia", role = c("aut"), email = "luis.garcia@uni-muenster.de")
    )
c(
  person("Emmanuel", "Blondel", role = c("aut", "cre"), email = "emmanuel.blondel1@gmail.com", comment = c(ORCID = "0000-0002-5870-5762")),
  person("Julien", "Barde", role = c("ctb"), email = "julien.barde@ird.fr", comment = c(ORCID = "0000-0002-3519-6141")),
  person("Stephen", "Eglen", role = c("ctb"), comment = c(ORCID = "0000-0001-8607-8025")),
  person("Hans", "Van Calster", role = c("ctb"), comment = c(ORCID = "0000-0001-8595-8426")),
  person("Floris", "Vanderhaeghe", role = c("ctb"), comment = c(ORCID = "0000-0002-6378-6229")))
c(person(given = "Marius", family = "Hofert", role = "aut",
                    email = "marius.hofert@uwaterloo.ca"),
             person(given = "Wayne", family = "Oldford", role =  c("aut", "cre"),
                    email = "rwoldford@uwaterloo.ca"))
c(
             person("Dan", "Richard", 
                    email = "djrichar@ualberta.ca", 
                    role = c("aut", "cre")),
             person("Karen", "Buro",
                    email = "BuroK@macewan.ca",
                    role = "ctb"),
             person("Wanhua", "Su",
                    email = "SuW3@macewan.ca",
                    role = "ctb")
            )
c(
			person("Guillaume", "Latombe", email = "latombe.guillaume@gmail.com", role=c("aut","cre")),
			person("Melodie A.", "McGeoch", role=c("aut")),
			person("David A.", "Nipperess", role=c("aut")), 
			person("Cang", "Hui", role=c("aut")))
c(
    person(given = "Yajing", family = "Hao", email = "yahao@health.ucsd.edu", role  = c("aut"), comment = c(ORCID = "0000-0003-1384-4176")),
    person(given = "Shuyang", family = "Zhang", email = "s4zhang@ucsd.edu", role  = "ctb", comment=c(ORCID="0000-0002-8428-1828")),
    person(given = "Junhui", family = "Li", email = "ljh.biostat@gmail.com", role = "cre"),
    person(given = "Guofeng", family = "Zhao", email = "gzhao@health.ucsd.edu", role = "ctb"),
    person(given = "Xiang-Dong", family = "Fu", email = "xdfu@health.ucsd.edu", role = c("cph","fnd"), comment = c(ORCID = "0000-0001-5499-8732"))
		)
c(
    person("Ming", "Yang", email = "mingyang@biostatstudio.com", role = c("aut", "cre")),
    person("Gideon", "Zamba", role = "aut"),
    person("Joseph", "Cavanaugh", role = "aut")
    )
c(person(given = 'Aldo William', family = 'Medina Garay', email = 'aldo.garay@ufpe.br', role = 'aut'),
             person(given = 'Francyelle', family = 'de Lima Medina', email = 'francyelle.medina@ufpe.br', role = 'aut'),
             person(given = 'Tharso Augustus', family = 'Rossiter Araújo Monteiro', email = 'tharso.augustus@ufpe.br', role = c('aut','cre')))
c(person(given = "Shinya",
             family = "Uryu",
             role = c("aut", "cre"),
             email = "suika1127@gmail.com",
             comment = c(ORCID = "0000-0002-0493-6186")),
      person(given = "Hiroaki",
             family = "Yutani",
             role = "ctb",
             comment = c(ORCID = "0000-0002-3385-7233")),
      person(given = "Kazuhiro",
             family = "Maeda",
             role = "ctb"),
      person(given = "Mao",
             family = "Kobayashi",
             role = "ctb"),
      person(given = "Akiru",
             family = "Kato",
             role = "ctb")
             )
c(person("Qihuang", "Zhang", role = c("aut", "cre","cph"),
                      email = "qihuang.zhang@uwaterloo.ca"),
               person("Grace Y.", "Yi", role = c("aut","ths")) )
c(person("Ariel", "Duarte-López", role = c("aut", "cre"),
                    email = "aduarte@ac.upc.edu",
		    comment = c(ORCID = "0000-0002-7432-0344")),
  person("Marta", "Pérez-Casany", role = c("aut"),
                    email = "marta.perez@upc.edu",
		    comment = c(ORCID = "0000-0003-3675-6902")))
person("Leslie", "Huang", email = "lesliehuang@nyu.edu", role = c("aut", "cre"))
c(person(given = "Fadheel Wisnu",
           family = "Utomo",
           role = c("aut", "trl","cre"),
           email = "221709671@stis.ac.id"),
           person(given = "Ika Yuni",
                  family = "Wulansari",
                  role = c("aut","ths"),
                  email = "ikayuni@stis.ac.id"
                  )
        )
person(given = "Sebastian", family = "Gibb",
                  email = "mail@sebastiangibb.de",
                  role = c("aut", "cre"),
		          comment = c(ORCID = "0000-0001-7406-4443"))
c(person(given = "Eric J.",
           family = "Ward",
           role = c("aut", "cre"),
           email = "eric.ward@noaa.gov",
           comment = c(ORCID = "0000-0002-4359-0296")),
      person(given = "Alexander J.",
           family = "Jensen",
           role = c("aut"),
           email = "jensena1@uw.edu",
           comment = c(ORCID = "0000-0002-2911-8884")),
      person(given = "Ryan P.",
           family = "Kelly",
           role = c("aut"),
           email = "rpkelly@uw.edu",
           comment = c(ORCID = "0000-0001-5037-2441")),
      person(given = "Andrew O.",
           family = "Shelton",
           role = c("aut"),
           email = "ole.shelton@noaa.gov",
           comment = c(ORCID = "0000-0002-8045-6141")),
      person(given = "William H.",
           family = "Satterthwaite",
           role = c("aut"),
           email = "will.satterthwaite@noaa.gov",
           comment = c(ORCID = "0000-0002-0436-7390")),
      person(given = "Eric C.",
           family = "Anderson",
           role = c("aut"),
           email = "eric.anderson@noaa.gov",
           comment = c(ORCID = "0000-0003-1326-0840")))
c(
    person("Matthew", "Cornell", role = c("aut", "cre"), email = "cornell@umass.edu"),
    person("Nicholas", "Reich", role = c("aut", "cph"), email = "nick@schoolph.umass.edu"))
c(
	person("Joona" , "Lehtomaki", role = c("aut", "cre"), email = "joona.lehtomaki@gmail.com"))
c(person(given = "Robin",
             family = "Lovelace",
             role = c("aut", "cre"),
             email = "rob00x@gmail.com",
             comment = c(ORCID = "0000-0001-5679-6536")),
      person(given = "Martijn",
             family = "Tennekes",
             role = "aut",
             email = "mtennekes@gmail.com"))
c(person(given = "Achim", family = "Zeileis", role = c("aut", "cre"), email = "Achim.Zeileis@R-project.org",
                    comment = c(ORCID = "0000-0003-0918-3766")),
             person(given = "Gabor", family = "Grothendieck", role = "aut", email = "ggrothendieck@gmail.com"),
             person(given = c("Jeffrey", "A."), family = "Ryan", role = "aut", email = "jeff.a.ryan@gmail.com"),
             person(given = c("Joshua", "M."), family = "Ulrich", role = "ctb", email = "josh.m.ulrich@gmail.com"),
             person(given = "Felix", family = "Andrews", role = "ctb", email = "felix@nfrac.org"))
c(person("Philippe", "Grosjean", role = c("aut", "cre"),
  email = "phgrosjean@sciviews.org"),
  person("Kevin", "Denis", role = "aut",
  email = "kevin.denis@umons.ac.be"),
  person("Guillaume", "Wacquet", role = "aut",
  email = "guillaume.wacquet@umons.ac.be"))
c(
    person(c("Jose","M"), "Pozo",
           role = c("aut", "cre"),
           email = "josmpozo@gmail.com",
           comment = c(ORCID = "0000-0002-0759-3510")),
    person("Angela", "Trentacoste",
           role = c("aut"),
           email = "angela.trentacoste@arch.ox.ac.uk",
           comment = c(ORCID = "0000-0001-7096-5252")),
    person("Ariadna", "Nieto-Espinet",
           role = c("aut"),
           email = "arinietoespinet@gmail.com",
           comment = c(ORCID = "0000-0003-2567-1735")),
    person("Silvia", "Guimarães Chiarelli",
           role = c("aut"),
           email = "biguimaraes@hotmail.com",
           comment = c(ORCID = "0000-0002-3778-3315")),
    person("Silvia", "Valenzuela-Lamas",
           role = c("aut"),
           email = "svalenzuela@imf.csic.es",
           comment = c(ORCID = "0000-0001-9886-0372")))
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"))
person(given = "Andrew",
           family = "Knight",
           role = c("aut", "cre"),
           email = "knightap@wustl.edu")
c(person("Tom", "August", role = c("aut", "cre"),
                    email = "tomaug@ceh.ac.uk"),
             person("Tim", "Lucas", role = "aut", comment = c(ORCID = "0000-0003-4694-8107")),
             person("Nick", "Golding", role = "aut"),
             person("Emiel", "van Loon", role = "ctb"),
             person("Greg", "McInerny", role = "aut"))
c(
    person("Mark", "Myatt", email = "mark@brixtonhealth.com",
           comment = c(ORCID = "0000-0003-1119-1474"),
           role = c("aut", "cph")),
    person("Ernest", "Guevarra", 
           comment = c(ORCID = "0000-0002-4887-4415"), 
           email = "ernestgmd@gmail.com", role = c("aut", "cre")))
person("Kisung", "You", role = c("aut", "cre"),
                     email = "kyou@nd.edu",comment=c(ORCID="0000-0002-8584-459X"))
c(person("Leah F.","South",role=c("aut","cre"),email="leah.south@hdr.qut.edu.au",comment = c(ORCID = "0000-0002-5646-2963")))
c(person("Angelo", "Canty", role = "aut",
email = "cantya@mcmaster.ca"),
person("Brian", "Ripley", role = c("aut", "trl", "cre"),
email = "ripley@stats.ox.ac.uk",
comment = "author of parallel support"))
c(person("Brian", "Ripley", role = c("aut", "cre", "cph"),
email = "ripley@stats.ox.ac.uk"),
person("William", "Venables", role = "cph"))
c(person("Martin","Maechler", role = c("aut","cre"),
email="maechler@stat.math.ethz.ch", comment = c(ORCID = "0000-0002-8685-9910"))
,person("Peter", "Rousseeuw", role="aut", email="peter.rousseeuw@kuleuven.be",
comment = c("Fortran original", ORCID = "0000-0002-3807-5353"))
,person("Anja", "Struyf", role="aut", comment= "S original")
,person("Mia", "Hubert", role="aut", email= "Mia.Hubert@uia.ua.ac.be",
comment = c("S original", ORCID = "0000-0001-6398-4850"))
,person("Kurt", "Hornik", role=c("trl", "ctb"),
email="Kurt.Hornik@R-project.org",
comment=c("port to R; maintenance(1999-2000)", ORCID="0000-0003-4198-9911"))
,person("Matthias", "Studer", role="ctb")
,person("Pierre", "Roudier", role="ctb")
,person("Juan",   "Gonzalez", role="ctb")
,person("Kamil",  "Kozlowski", role="ctb")
,person("Erich",  "Schubert", role="ctb", comment = c("fastpam options for pam()",
ORCID = "0000-0001-9143-4880"))
,person("Keefe",  "Murphy", role="ctb", comment = "volume.ellipsoid({d >= 3})")
)
c(
person("R Core Team",
email = "R-core@R-project.org", role = c("aut", "cph", "cre")),
person("Roger", "Bivand", role = c("ctb", "cph")),
person(c("Vincent", "J."), "Carey", role = c("ctb", "cph")),
person("Saikat", "DebRoy", role = c("ctb", "cph")),
person("Stephen", "Eglen", role = c("ctb", "cph")),
person("Rajarshi", "Guha", role = c("ctb", "cph")),
person("Swetlana", "Herbrandt", role = "ctb"),
person("Nicholas", "Lewin-Koh", role = c("ctb", "cph")),
person("Mark", "Myatt", role = c("ctb", "cph")),
person("Michael", "Nelson", role = "ctb"),
person("Ben", "Pfaff", role = "ctb"),
person("Brian", "Quistorff", role = "ctb"),
person("Frank", "Warmerdam", role = c("ctb", "cph")),
person("Stephen", "Weigand", role = c("ctb", "cph")),
person("Free Software Foundation, Inc.", role = "cph"))
c(person("Matt", "Wand", role = "aut",
email = "Matt.Wand@uts.edu.au"),
person("Cleve", "Moler", role = "ctb",
comment = "LINPACK routines in src/d*"),
person("Brian", "Ripley", role = c("trl", "cre", "ctb"),
email = "ripley@stats.ox.ac.uk",
comment = "R port and updates"))
c(person("Deepayan", "Sarkar", role = c("aut", "cre"),
email = "deepayan.sarkar@r-project.org",
comment = c(ORCID = "0000-0003-4107-1553")),
person("Felix", "Andrews", role = "ctb"),
person("Kevin", "Wright", role = "ctb", comment = "documentation"),
person("Neil", "Klepeis", role = "ctb"),
person("Johan", "Larsson", role = "ctb", comment = "colorkey title"),
person("Paul", "Murrell", role = "ctb", email = "paul@stat.auckland.ac.nz"))
c(person("Brian", "Ripley", role = c("aut", "cre", "cph"),
email = "ripley@stats.ox.ac.uk"),
person("Bill", "Venables", role = "ctb"),
person(c("Douglas", "M."), "Bates", role = "ctb"),
person("Kurt", "Hornik", role = "trl",
comment = "partial port ca 1998"),
person("Albrecht", "Gebhardt", role = "trl",
comment = "partial port ca 1998"),
person("David", "Firth", role = "ctb"))
c(person("Douglas","Bates", role="aut")
, person("Martin","Maechler", role = c("aut","cre"), email="mmaechler+Matrix@gmail.com",
comment = c(ORCID = "0000-0002-8685-9910"))
, person("Mikael", "Jagan", role="aut",
comment = "new 'packedMatrix' class plus subset/diag/.. methods in C")
, person("Timothy A.", "Davis", role="ctb",
comment = c("SuiteSparse and 'cs' C libraries, notably CHOLMOD, AMD;
collaborators listed in
dir(pattern = '^[A-Z]+[.]txt$', full.names=TRUE,
system.file('doc', 'SuiteSparse', package='Matrix'))"))
, person("Jens", "Oehlschlägel", role="ctb", comment="initial nearPD()")
, person("Jason", "Riedy", role="ctb",
comment = c("condest() and onenormest() for octave",
"Copyright: Regents of the University of California"))
, person("R Core Team", role = "ctb", comment="base R matrix implementation")
)
c(person("José", "Pinheiro", role = "aut", comment = "S version"),
person("Douglas", "Bates", role = "aut", comment = "up to 2007"),
person("Saikat", "DebRoy", role = "ctb", comment = "up to 2002"),
person("Deepayan", "Sarkar", role = "ctb", comment = "up to 2005"),
person("EISPACK authors", role = "ctb", comment = "src/rs.f"),
person("Siem", "Heisterkamp", role = "ctb", comment = "Author fixed sigma"),
person("Bert", "Van Willigen",role = "ctb", comment = "Programmer fixed sigma"),
person("Johannes", "Ranke", role = "ctb", comment = "varConstProp()"),
person("R Core Team", email = "R-core@R-project.org",
role = c("aut", "cre")))
c(person("Terry", "Therneau", role = "aut",
email = "therneau@mayo.edu"),
person("Beth", "Atkinson", role = c("aut", "cre"),
email = "atkinson@mayo.edu"),
person("Brian", "Ripley", role = "trl",
email = "ripley@stats.ox.ac.uk",
comment = "producer of the initial R port, maintainer 1999-2017"))
c(person("Brian", "Ripley", role = c("aut", "cre", "cph"),
email = "ripley@stats.ox.ac.uk"),
person("Roger", "Bivand", role = "ctb"),
person("William", "Venables", role = "cph"))
c(person(c("Terry", "M"), "Therneau",
email="therneau.terry@mayo.edu",
role=c("aut", "cre")),
person("Thomas", "Lumley", role=c("ctb", "trl"),
comment="original S->R port and R maintainer until 2009"),
person("Atkinson", "Elizabeth", role="ctb"),
person("Crowson", "Cynthia", role="ctb"))
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("CRAN Team", role = c('ctb', 'cre'),
email = "CRAN@r-project.org",
comment = "de facto maintainer since 2013"),
person("Duncan", "Temple Lang", role = c("aut"),
email = "duncan@r-project.org",
comment = c(ORCID = "0000-0003-0159-1546")),
person("Tomas", "Kalibera", role = "ctb"))
Send an email for <jafiorucci@gmail.com> with title
'forecTheta Bug'
https://github.com/selva86/InformationValue/issues License:
GPL (>= 2)
Computing methodologies ~ Classification and
regression trees, Computing methodologies ~ Supervised learning
by classification, Computing methodologies ~ Massively parallel
and high-performance simulations, Computing methodologies ~
Distributed simulation
'adegenet.package.R' 'datasets.R' 'dist.genlight.R'
'orthobasis.R' 'classes.R' 'constructors.R' 'accessors.R'
'basicMethods.R' 'handling.R' 'auxil.R' 'minorAllele.R'
'setAs.R' 'SNPbin.R' 'strataMethods.R' 'hierarchyMethods.R'
'glHandle.R' 'glFunctions.R' 'glSim.R' 'find.clust.R'
'hybridize.R' 'scale.R' 'fstat.R' 'import.R' 'seqTrack.R'
'chooseCN.R' 'genind2genpop.R' 'loadingplot.R' 'sequences.R'
'gstat.randtest.R' 'makefreq.R' 'colorplot.R' 'monmonier.R'
'spca.R' 'coords.monmonier.R' 'haploGen.R' 'old2new.R'
'global_local_tests.R' 'dapc.R' 'compoplot.R' 'xvalDapc.R'
'haploPop.R' 'PCtest.R' 'dist.genpop.R' 'Hs.R' 'propShared.R'
'export.R' 'HWE.R' 'propTyped.R' 'inbreeding.R' 'glPlot.R'
'gengraph.R' 'simOutbreak.R' 'mutations.R' 'snpposi.R'
'snpzip.R' 'pairDist.R' 'snapclust.R' 'AIC.snapclust.R'
'AICc.snapclust.R' 'BIC.snapclust.R' 'KIC.snapclust.R'
'snapclust.choose.k.R' 'servers.R' 'showmekittens.R'
'spca_randtest.R' 'export_to_mvmapper.R' 'doc_C_routines.R'
'zzz.R'
adeGsenv.R parameter.R utils.R utilstriangle.R genericMethod.R
utilsclass.R panelfunctions.R ADEg.R ADEgS.R utilsADEgS.R
ADEg.C1.R C1.barchart.R C1.curve.R C1.curves.R C1.density.R
C1.gauss.R C1.dotplot.R C1.hist.R C1.interval.R ADEg.S1.R
S1.boxplot.R S1.class.R S1.distri.R S1.label.R S1.match.R
ADEg.S2.R S2.arrow.R S2.class.R S2.corcircle.R S2.density.R
S2.distri.R S2.image.R S2.label.R S2.logo.R S2.match.R
S2.traject.R S2.value.R ADEg.T.R T.image.R T.value.R T.cont.R
ADEg.Tr.R Tr.class.R Tr.label.R Tr.match.R Tr.traject.R
addhist.R addline.R addpoint.R addsegment.R addtext.R
ade4-kplot.R ade4-scatter.R ade4-score.R ade4-plot.R
multiplot.R s.Spatial.R utilskey.R
adoptr.R util.R DataDistribution.R BinomialDistribution.R
NormalDistribution.R StudentDistribution.R Prior.R
PointMassPrior.R ContinuousPrior.R TwoStageDesign.R
GroupSequentialDesign.R OneStageDesign.R Scores.R constraints.R
minimize.R ConditionalPower.R ConditionalSampleSize.R
regularization.R CompositeScore.R MaximumSampleSize.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'
'AFMGaussianMixAnalyser.R' 'AFMImage.R' 'AFMNetworksAnalyser.R'
'AFMPSDAnalyser.R' 'AFMVariogramAnalyser.R'
'AFMImageAnalyser.R' 'AFMReportMaker.R' 'pkgname.R'
'runAFMApp.R'
'ahp_mat.R' 'ahp_indpref.R' 'ahp_pwerror.R' 'ahp_cr.R' 'ahp.R'
'ahp_RI.R' 'ahp_aggjudge.R' 'ahp_aggpref.R' 'ahp_bweight.R'
'ahp_error.R' 'ahp_harker.R' 'ahp_missing.R' 'ahpsurvey.R'
'data.R'
'Alakazam.R' 'AminoAcids.R' 'Classes.R' 'Core.R' 'Data.R'
'Diversity.R' 'Deprecated.R' 'Fastq.R' 'Gene.R' 'Lineage.R'
'RcppExports.R' 'Sequence.R' 'Topology.R'
'Acl.r' 'Client.r' 'Algorithm.r' 'AlgorithmHandler.r'
'DataDirectory.r' 'DataFile.r' 'data_utilities.r'
'AMAPVox.R' 'VersionManager.R' 'AMAPVoxGUI.R' 'Butterfly.R'
'Canopy.R' 'Classes.R' 'ComputeG.R' 'Crop.R' 'FillNA.R'
'Generics.R' 'Getters.R' 'Ground.R' 'Operators.R' 'PlantArea.R'
'PlotVoxelSpace.R' 'ReadVoxelSpace.R' 'Utils-voxel.R'
'WriteVoxelSpace.R'
'AMModels-package.R' 'amData.R' 'amModel.R' 'amModelLib.R'
'catwrap.R' 'classes.R' 'getters.R' 'grepAMModelLib.R'
'insertAMModelLib.R' 'ls.R' 'metaSummary.R' 'methods-amModel.R'
'methods-amModelLib.R' 'modelMgr.R' 'rmModelsData.R'
'simCovariate.R'
'analysisPipelines_package.R' 'core-functions.R'
'core-functions-batch.R' 'core-functions-meta-pipelines.R'
'core-streaming-functions.R' 'r-batch-eda-utilities.R'
'r-helper-utilites-python.R'
'spark-structured-streaming-utilities.R' 'zzz.R'
'anovir-package.R' 'private_functions.R' 'nll_recovery.R'
'nll_recovery_II.R' 'data_descriptions.R' 'main.R'
'play_file.R' 'nll_functions.R' 'nll_frailty_correlated.R'
'anthro.R' 'utils.R' 'assertions.R' 'prevalence.R'
'z-score-helper.R' 'api.R'
'z-score-arm-circumference-for-age.R' 'z-score-bmi-for-age.R'
'z-score-head-circumference-for-age.R'
'z-score-length-for-age.R'
'z-score-subscapular-skinfold-for-age.R'
'z-score-triceps-skinfold-for-age.R' 'z-score-weight-for-age.R'
'z-score-weight-for-lenhei.R' 'z-score.R'
'AntitrustClasses.R' 'BertrandClasses.R'
'BertrandRUMClasses.R' 'BargainingClasses.R' 'AuctionClasses.R'
'VerticalClasses.R' 'CournotClasses.R' 'OwnershipMethods.R'
'SummaryMethods.R' 'ShowMethods.R' 'AuctionCapMethods.R'
'PricesMethods.R' 'CostMethods.R' 'MarginsMethods.R'
'ParamsMethods.R' 'PriceDeltaMethods.R' 'PSMethods.R'
'OutputMethods.R' 'PlotMethods.R' 'HypoMonMethods.R'
'HHIMethods.R' 'UPPMethods.R' 'DiversionMethods.R'
'ElastMethods.R' 'CMCRMethods.R' 'CVMethods.R'
'DiagnosticsMethods.R' 'AIDSFunctions.R' 'Antitrust_Shiny.R'
'AntitrustPackage.R' 'Auction2ndCapFunctions.R'
'Auction2ndLogitFunctions.R' 'BargainingLogitFunctions.R'
'BertrandFunctions.R' 'CESFunctions.R'
'CMCRBertrandFunctions.R' 'CMCRCournotFunctions.R'
'CournotFunctions.R' 'HHIFunctions.R' 'LinearFunctions.R'
'LogitFunctions.R' 'SimFunctions.R' 'VerticalFunctions.R'
'antitrust-deprecated.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'
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
'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'
'nullOrnumeric.R' 'nullOrlist.R' 'nullOrLASOrDatatable.R'
'nullOrDatatable.R' 'aRchiClass.R' 'BranchAngle.R'
'Compute_A0.R' 'Compute_Mf.R' 'DAI.R' 'ForkRate.R'
'LeonardoRatio.R' 'Make_Node.R' 'Make_Path.R' 'PathFraction.R'
'QSM2mesh.R' 'SelectinQSM_3d.R' 'TreeBiomass.R' 'TreeVolume.R'
'Truncate_QSM.R' 'WBEparameters.R' 'WoodSurface.R' 'aRchi.R'
'aRchi2treeQSM.R' 'add_diameter.R' 'add_leaves.R'
'add_non_reconstructed.R' 'add_physiological_ages.R'
'add_pointcloud.R' 'angle3d.R' 'build_aRchi.R' 'clean_QSM.R'
'clean_point_cloud.R' 'cross_prod_3d.R' 'dist2line.R'
'get_QSM.R' 'get_nodes.R' 'get_operations.R' 'get_paths.R'
'get_pointcloud.R' 'plot.R' 'read_QSM.R' 'read_aRchi.R'
'segment_annual_shoots.R' 'simplify_skeleton.R' 'skeletonize.R'
'smooth_skeleton.R' 'write_aRchi.R'
'AllClasses.R' 'AllGenerics.R' 'arkhe-package.R'
'predicates.R' 'check.R' 'clean.R' 'coerce.R' 'conditions.R'
'initialize.R' 'mutators.R' 'show.R' 'subset.R' 'summary.R'
'utilities.R' 'validate.R' 'zzz.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'
'arrowExports.R' 'enums.R' 'arrow-package.R' 'type.R'
'array-data.R' 'arrow-datum.R' 'array.R' 'arrow-tabular.R'
'buffer.R' 'chunked-array.R' 'io.R' 'compression.R' 'scalar.R'
'compute.R' 'config.R' 'csv.R' 'dataset.R' 'dataset-factory.R'
'dataset-format.R' 'dataset-partition.R' 'dataset-scan.R'
'dataset-write.R' 'deprecated.R' 'dictionary.R'
'dplyr-arrange.R' 'dplyr-collect.R' 'dplyr-count.R'
'dplyr-distinct.R' 'dplyr-eval.R' 'dplyr-filter.R'
'dplyr-funcs-conditional.R' 'dplyr-funcs-datetime.R'
'dplyr-funcs-math.R' 'dplyr-funcs-string.R'
'dplyr-funcs-type.R' 'expression.R' 'dplyr-funcs.R'
'dplyr-group-by.R' 'dplyr-join.R' 'dplyr-mutate.R'
'dplyr-select.R' 'dplyr-summarize.R' 'record-batch.R' 'table.R'
'dplyr.R' 'duckdb.R' 'extension.R' 'feather.R' 'field.R'
'filesystem.R' 'flight.R' 'install-arrow.R' 'ipc-stream.R'
'json.R' 'memory-pool.R' 'message.R' 'metadata.R' 'parquet.R'
'python.R' 'query-engine.R' 'record-batch-reader.R'
'record-batch-writer.R' 'reexports-bit64.R'
'reexports-tidyselect.R' 'schema.R' 'util.R'
'AAADefs.R' 'Adult.R' 'itemMatrix.R' 'associations.R'
'transactions.R' 'tidLists.R' 'itemsets.R' 'rules.R'
'DATAFRAME.R' 'Epub.R' 'Groceries.R' 'Income.R' 'LIST.R'
'Matrix.R' 'Mushroom.R' 'SunBai.R' 'abbreviate.R'
'addComplement.R' 'appearance.R' 'apriori.R' 'c.R'
'check_installed.R' 'confint.R' 'control.R' 'coverage.R'
'crossTable.R' 'discretize.R' 'dissimilarity.R' 'duplicated.R'
'eclat.R' 'extract.R' 'fim4r.R' 'hierarchy.R' 'image.R'
'inspect.R' 'interestMeasures.R' 'is.closed.R' 'is.generator.R'
'is.maximal.R' 'is.redundant.R' 'is.significant.R'
'is.superset.R' 'itemCoding.R' 'itemFrequency.R' 'match.R'
'merge.R' 'parameter.R' 'plot.R' 'pmml.R' 'predict.R'
'random.transactions.R' 'read_write.R' 'ruleInduction.R'
'sample.R' 'sets.R' 'setsItemwise.R' 'size.R' 'sort.R'
'subset.R' 'support.R' 'supportingTransactions.R' 'unique.R'
'warm.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' 'bind.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'
'cache_expr.R' 'weaver.R' 'unexported.R'
'advans.R' 'generics.R' 'facet.R' 'conversion.R' 'model.R'
'pk_model.R' 'rendering.R' 'statement.R' 'nm_model.R'
'declaration-creation.R' 'declaration.R' 'algebraics.R'
'assemblerr-package.R' 'ast.R' 'compartment.R'
'conversion-compartment-nm.R' 'input_variable.R'
'conversion-input_variable-nm.R' 'meta.R'
'conversion-meta-nm.R' 'conversion-nm.R' 'observation.R'
'conversion-observation-nm.R' 'parameter.R'
'conversion-parameter-nm.R' 'node-classes.R' 'tasks.R'
'conversion-tasks-nm.R' 'description.R' 'issues.R'
'model-options.R' 'parameter-values.R' 'pk_component.R'
'test-helpers.R' 'util.R' 'variables.R'
'assert-is-date-string.R' 'imports.R' 'assert-is-time.R'
'is-date-string.R' 'workaround.R' 'is-time.R'
'imports.R' 'assert-is-connection.R' 'assert-is-file-size.R'
'assert-is-file.R' 'internal-connection.R' 'is-connection.R'
'is-file-size.R' 'is-file.R'
'imports.R' 'assert-has-terms.R' 'assert-is-empty-model.R'
'has-terms.R' 'is-empty-model.R'
'assert-is-divisible-by.R' 'assert-is-equal-to.R' 'imports.R'
'assert-is-in-range.R' 'assert-is-infinity-nan.R'
'assert-is-real-imaginary.R' 'assert-is-whole-number.R'
'is-divisible-by.R' 'is-equal-to.R' 'is-in-range.R'
'is-infinity-nan.R' 'is-real-imaginary.R' 'is-whole-number.R'
'are-same-size.R' 'assert-are-same-size.R' 'imports.R'
'assert-has-attributes.R' 'assert-has-dims.R'
'assert-has-dupes.R' 'assert-has-names.R' 'assert-has-slot.R'
'assert-is-atomic-recursive-vector.R'
'assert-is-empty-scalar.R' 'assert-is-monotonic.R'
'assert-is-null.R' 'assert-is-unsorted.R' 'has-attributes.R'
'has-dims.R' 'has-dupes.R' 'has-names.R' 'has-slot.R'
'is-atomic-recursive-vector.R' 'is-empty-scalar.R'
'is-monotonic.R' 'is-null.R' 'is-unsorted.R' 'utils.R'
'imports.R' 'assert-is-32-64-bit.R' 'assert-is-current.R'
'assert-is-decimal-point.R' 'assert-is-ide.R'
'assert-is-on-os-path.R' 'assert-is-os.R' 'assert-is-r-mode.R'
'assert-is-r-version.R' 'assert-r-has-capability.R'
'is-32-64-bit.R' 'is-current.R' 'is-decimal-point.R' 'is-ide.R'
'is-on-os-path.R' 'is-os.R' 'is-r-mode.R' 'is-r-version.R'
'locale.R' 'r-has-capability.R'
'imports.R' 'assert-is-a-type.R' 'assert-is-condition.R'
'assert-is-date.R' 'assert-is-formula.R' 'assert-is-function.R'
'assert-is-type-base.R' 'assert-is-type-data.table.R'
'assert-is-type-dplyr.R' 'assert-is-type-grDevices.R'
'assert-is-type-methods.R' 'assert-is-type-stats.R'
'assert-is-type-utils.R' 'is-a-type.R' 'is-condition.R'
'is-date.R' 'is-formula.R' 'is-function.R' 'is-type-base.R'
'is-type-data.table.R' 'is-type-dplyr.R' 'is-type-grDevices.R'
'is-type-methods.R' 'is-type-stats.R' 'is-type-utils.R'
'assert-that.r' 'on-failure.r' 'assertions-file.r'
'assertions-scalar.R' 'assertions.r' 'base.r'
'base-comparison.r' 'base-is.r' 'base-logical.r' 'base-misc.r'
'utils.r' 'validate-that.R'
'AssociationTestWithCorrectPS.R' 'CalculateGapK.R'
'CalculateWAll.R' 'CalExpect.R' 'ChangeX.R' 'CorrMatNRTest.R'
'DR_main.R' 'EigenStrat_main.R' 'FindCNumRandom.R'
'MAX3_main.R' 'MAX3Sign.R' 'MDS.R' 'PCoC_main.R'
'ModifyNormalization.R' 'NMAX3_main.R' 'NPT_main.R'
'RhombusFormula.R' 'ScoreTest.R' 'SimilarityMatrix.R'
'SMT_main.R' 'Str2Num.R' 'TrendTest.R' 'TW_main.R'
'UniformSample.R' 'UniformTest.R' 'WaldTest.R'
'ASCORE.Ord.R' 'ASCORE.R' 'ASSU.Ord.R' 'ASSU.R' 'ASSUW.Ord.R'
'ASSUW.R' 'ASUM.Ord.R' 'ASUM.R' 'AssotesteR-internal.R' 'BST.R'
'CALPHA.R' 'CARV.R' 'CAST.R' 'CMAT.R' 'CMC.R' 'GDBR.R'
'MULTI.R' 'ORWSS.R' 'RARECOVER.R' 'RBT.R' 'RVT1.R' 'RVT2.R'
'RWAS.R' 'SCORE.R' 'SEQSUM.R' 'SKAT.R' 'SSU.R' 'SSUW.R' 'SUM.R'
'TTEST.R' 'UMINP.R' 'VT.R' 'WSS.R' 'WST.R' 'gdbr_IBS.R'
'gdbr_wIBS.R' 'kernel_IBS.R' 'kernel_twowayx.R' 'kernel_wIBS.R'
'my_check.R' 'print.assoctest.R'
'autocog.R' 'known_cog_groups.R' 'add_cog_group.R'
'field_info.R' 'add_cog_group_.R' 'known_layer_cogs.R'
'add_layer_cogs.R' 'add_layer_cogs_.R' 'cog_desc.R'
'cog_spec.R' 'layer_count.R' 'layer_info.R' 'of_type.R'
'plot_class.R' 'plot_cogs.R'
'treeharp.R' 'th_getter-length.R' 'as.matrix.R' 'autoharp.R'
'check_correctness.R' 'check_rmd.R' 'check_runtime.R'
'count_lints.R' 'env_size.R' 'examplify_to_r.R' 'forestharp.R'
'forestharp_helpers.R' 'generate_thumbnails.R'
'join_treeharp.R' 'lang_2_tree.R' 'lang_2_tree_helpers.R'
'log_summary.R' 'lum_local_match.R' 'nlp_related.R'
'populate_soln_env.R' 'render_one.R' 'reset_path.R'
'run_tuner.R' 'to_BFS.R' 'tree_kernel.R' 'tree_routines.R'
'utils-pipe.R' 'utils.R' 'write_html.R' 'zzz.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'
'0aux.R' 'AzureAppInsights-package.R' 'cfg.R' 'demo.R'
'include_snippet.R' 'tracking.R'
'AllClasses.R' 'AllGenerics.R' 'GMT.R' 'GeneSetCollection.R'
'activate.R' 'add.R' 'add_column.R' 'add_relation.R'
'adjacency.R' 'arrange.R' 'basesets-package.R' 'cardinality.R'
'cartesian.R' 'complement.R' 'data_frame.R' 'deactivate.R'
'droplevels.R' 'elements.R' 'filter.R' 'group.R' 'group_by.R'
'head.R' 'incidence.R' 'independent.R' 'operations.R'
'intersection.R' 'length.R' 'list.R' 'move_to.R' 'mutate.R'
'names.R' 'naming.R' 'nested.R' 'obo.R' 'power_set.R' 'print.R'
'pull.R' 'relations.R' 'remove.R' 'remove_column.R' 'rename.R'
'select.R' 'set.R' 'size.R' 'subtract.R' 'tidy-set.R' 'union.R'
'utils-pipe.R' 'zzz.R'
'RcppExports.R' 'class.R' 'generics.R' 'adjust_lambda.R'
'analysis.R' 'basket_test.R' 'baskexact-package.R'
'borrowing.R' 'check.R' 'helper.R' 'monotonicity.R' 'pow.R'
'rejection_probabilities.R' 'toer.R' 'validate.R'
'autoplot.R' 'auto_sarima.R' 'bayes_factor.R'
'bayesforecast-package.R' 'Birth.R' 'Fit.R' 'forecast.R'
'garch.R' 'get_params.R' 'log_lik.R' 'Misc.R' 'model.R'
'naive.R' 'posterior_intervals.R' 'posterior_predict.R' 'ets.R'
'predictive_error.R' 'print.R' 'prior.R' 'report.R' 'Sarima.R'
'summary.R' 'ssm.R' 'stanmodels.R' 'SVM.R' 'varstan.R'
'stan_models.R'
'RcppExports.R' 'bayesPO-package.R' 'prior-class.R'
'initial-class.R' 'model-class.R' 'fit-class.R' 'bayesPO.R'
'covariate_importance-class.R'
analysisOrganizeData.R appendDateFeatures.R baytrends.R
checkRange.R chkParameter.R closeOut.R createResiduals.R data.R
detrended.flow.R detrended.salinity.R expectMaxFunctions.r
findFile.R flwAveragePred.R fmtPval.R gamDiff.R gamPlotCalc.r
gamPlotDisp.R gamPlotDispSeason.R gamTables.R gamTest.r
gamTestSeason.r getUSGSflow.R headers2.R imputeCensored.R
initializeResults.r layerAggregation.R loadData.R loadExcel.R
loadModels.R loadModelsResid.R makeSurvDF.R mergeFlow.R
mergeSalinity.R reAttDF.R saveDF.R seasAdjflow2.R selectData.R
smwrBase_baseDay.R smwrBase_baseDay2decimal.R
smwrBase_dectime.R smwrBase_dectime2Date.R
smwrBase_eventProcessing.R smwrBase_fillMissing.R
smwrBase_na2miss.R supportFunctions.R unSurv.R gdata_nobs.R
zzz.R
'mle2-class.R' 'mle2-methods.R' 'mle.R' 'confint.R'
'predict.R' 'profile.R' 'update.R' 'dists.R' 'IC.R' 'slice.R'
'impsamp.R' 'TMB.R'
'Archive.R' 'ArchiveBest.R' 'Codomain.R' 'Objective.R'
'ObjectiveRFun.R' 'ObjectiveRFunDt.R' 'OptimInstance.R'
'OptimInstanceMultiCrit.R' 'OptimInstanceSingleCrit.R'
'mlr_optimizers.R' 'Optimizer.R' 'OptimizerCmaes.R'
'OptimizerDesignPoints.R' 'OptimizerGenSA.R'
'OptimizerGridSearch.R' 'OptimizerIrace.R' 'OptimizerNLoptr.R'
'OptimizerRandomSearch.R' 'Progressor.R' 'mlr_terminators.R'
'Terminator.R' 'TerminatorClockTime.R' 'TerminatorCombo.R'
'TerminatorEvals.R' 'TerminatorNone.R'
'TerminatorPerfReached.R' 'TerminatorRunTime.R'
'TerminatorStagnation.R' 'TerminatorStagnationBatch.R'
'assertions.R' 'bb_optimize.R' 'bbotk_reflections.R'
'bibentries.R' 'helper.R' 'helper_irace.R' 'nds_selection.R'
'sugar.R' 'zzz.R'
'Bayesian_Bricks.r' 'Categorical_Inference.r'
'Gamma_Inference.r' 'Gaussian_Inference.r'
'Dirichlet_Process.r' 'MCMC.r' 'bbricks-package.R' 'testData.r'
'bcdata-package.R' 'bcdc-web-services.R' 'bcdc_browse.R'
'bcdc_options.R' 'bcdc_search.R' 'cli.R'
'cql-geom-predicates.R' 'cql-translator.R' 'describe-feature.R'
'get_data.R' 'utils-as_tibble.R' 'utils-classes.R'
'utils-collect.R' 'utils-filter.R' 'utils-is.R'
'utils-mutate.R' 'utils-pipe.R' 'utils-select.R'
'utils-show-query.R' 'utils.R' 'zzz.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
'AbbreviationPipe.R' 'bdpar.log.R' 'wrapper.R' 'Bdpar.R'
'BdparOptions.R' 'Connections.R' 'ContractionPipe.R'
'DefaultPipeline.R' 'DynamicPipeline.R' 'ExtractorEml.R'
'ExtractorFactory.R' 'ExtractorSms.R' 'ExtractorTwtid.R'
'ExtractorYtbid.R' 'File2Pipe.R' 'FindEmojiPipe.R'
'FindEmoticonPipe.R' 'FindHashtagPipe.R' 'FindUrlPipe.R'
'FindUserNamePipe.R' 'GenericPipe.R' 'GenericPipeline.R'
'GuessDatePipe.R' 'GuessLanguagePipe.R' 'Instance.R'
'InterjectionPipe.R' 'MeasureLengthPipe.R' 'ResourceHandler.R'
'SlangPipe.R' 'StopWordPipe.R' 'StoreFileExtPipe.R'
'TargetAssigningPipe.R' 'TeeCSVPipe.R' 'ToLowerCasePipe.R'
'bdpar.Options.R' 'bdparData.R' 'eml.R' 'operator-pipe.R'
'runPipeline.R' 'zzz.R'
bdsmatrix.R gchol.R gchol.bdsmatrix.R as.matrix.bdsmatrix.R
bdsBlock.R bdsI.R bdsmatrix.ibd.R bdsmatrix.reconcile.R
diag.bdsmatrix.R listbdsmatrix.R multiply.bdsmatrix.R
solve.bdsmatrix.R solve.gchol.R solve.gchol.bdsmatrix.R
backsolve.R
'RcppExports.R' 'order.R' 'utils.R' 'benchmark.R'
'benchr-package.R' 'boxplot.R' 'mean.R' 'plot.R' 'units.R'
'summary.R' 'print.R' 'zzz.R'
'class_utils.R' 'bggAPI.R' 'bggAnalytics-package.R'
'bggCollection.R' 'bggGames.R' 'bggSearch.R' 'bgg_tools.R'
'custom_fetches.R' 'data.R' 'h_index.R' 'html_utils.R'
'params_utils.R' 'postprocessing.R' 'squeeze.R' 'utils.R'
'zzz.R'
'bggum-package.R' 'RcppExports.R' 'ggumProbability.R'
'tune_proposals.R' 'tune_temps.R' 'ggumMCMC.R' 'ggumMC3.R'
'ggum_simulation.R' 'color_palettes.R' 'irf.R' 'icc.R'
'summary.R' 'post_process.R'
'auxil.R' 'bigGP.R' 'collectDistribute.R'
'distributedComputation.R' 'krigeProblem.R'
'RcppExports.R' 'data_frame.r' 'data_matrix.r' 'family.r'
'map_reg.r'
'RcppExports.R' 'bigrquery.R' 'bq-auth.R' 'bq-dataset.R'
'bq-download.R' 'bq-field.R' 'bq-job.R' 'bq-param.R'
'bq-parse.R' 'bq-perform.R' 'bq-project.R' 'bq-projects.R'
'bq-query.R' 'bq-refs.R' 'bq-request.R' 'bq-table.R'
'bq-test.R' 'camelCase.R' 'dbi-driver.R' 'dbi-connection.R'
'dbi-result.R' 'dplyr.R' 'gs-object.R' 'old-auth.R'
'old-dataset.R' 'old-id.R' 'old-job-extract.R'
'old-job-query.R' 'old-job-upload.R' 'old-job.R'
'old-project.R' 'old-projects.R' 'old-query.R' 'old-table.R'
'old-tabledata.R' 'utils.R' 'zzz.R'
'AUC.R' 'FBM-attach.R' 'crochet.R' 'FBM.R' 'FBM-code256.R'
'FBM-copy.R' 'RcppExports.R' 'SVD.R' 'apply-parallelize.R'
'biglasso.R' 'bigstatsr-package.R' 'colstats.R'
'crossprodSelf.R' 'mult-mat.R' 'mult-vec.R' 'pcor.R' 'plot.R'
'predict.R' 'randomSVD.R' 'read-write.R' 'scaling.R'
'summary.R' 'tcrossprodSelf.R' 'transpose.R' 'univLinReg.R'
'univLogReg.R' 'utils-assert.R' 'utils.R' 'zzz.R'
'BIOMOD_ConvertOldRun.R' 'BIOMOD_EnsembleForecasting.R'
'BIOMOD_EnsembleModeling.R' 'BIOMOD_FormatingData.R'
'BIOMOD_LoadModels.R' 'BIOMOD_Modeling.R'
'BIOMOD_ModelingOptions.R' 'BIOMOD_Projection.R'
'BIOMOD_RangeSize.R' 'BIOMOD_cv.R' 'BIOMOD_presenceonly.R'
'BIOMOD_tuning.R' 'BinaryTransformation.R' 'Biomod.Models_RE.R'
'BiomodClass.R' 'CVnnet.R' 'CleverCut.R' 'CustomIndexMaker.R'
'Evaluate.models.R' 'FilteringTransformation.R'
'Prepare.Maxent.WorkDir.R' 'ProbDensFunc.R' 'Projection.R'
'Rescaler4.R' 'SampleMat2.R' 'biomod2_models-class.R'
'deprecated_functions.R' 'functionkeep.R' 'level.plot.R'
'makeFormula.R' 'models_scores_graph.R' 'multiple.plot.r'
'pseudo.abs.R' 'response.plot.R' 'sample.factor.levels.R'
'scope.R' 'scopeExpSyst.R' 'sre.R' 'testnull.R'
'transform.outputs.R' 'update_objects.R'
'variables_importance.R' 'zzz.R'
'R-bizdays-package.r' 'calendar.R' 'adjust.date.R'
'bizdays.options.R' 'bizseq.R' 'is.bizday.R' 'offset.R'
'bizdiff.R' 'bizdays.R' 'create-calendars.R'
'calendar-export.R' 'getdate.R' 'getbizdays.R'
'load-buildin-calendars.R' 'zzz.R'
'clean_model.R' 'generics.R' 'utils.R' 'variable.R' 'model.R'
'convert_df.R' 'convert_type.R' 'get_model.R'
'read_custom_types.R' 'read_data.R' 'read_data_laf.R'
'read_fwf_blaise.R' 'read_model.R' 'variable_custom.R'
'variable_date.R' 'variable_dummy.R' 'variable_enum.R'
'variable_integer.R' 'variable_real.R' 'variable_string.R'
'write_data.R' 'write_datamodel.R' 'write_fwf_blaise.R'
'write_fwf_blaise_with_model.R'
'blandr.data.preparation.r' 'blandr.dataset.fibre.r'
'blandr.dataset.sbp.r' 'blandr.dataset.o2sats.r'
'blandr.dataset.pefr.r' 'blandr.dataset.load.r'
'blandr.plot.ggplot.r' 'blandr.plot.rplot.r'
'blandr.plot.limits.r' 'blandr.statistics.r' 'blandr.draw.r'
'blandr.output.text.r' 'blandr.display.and.draw.r'
'blandr.display.and.plot.r' 'blandr.method.comparison.r'
'blandr.output.report.r' 'blandr.plot.normality.r'
'blandr.plot.qq.r' 'blandr.plot.r' 'jamovibaanalysis.b.R'
'jamovibaanalysis.h.R' 'jamovibaplothistogram.b.R'
'jamovibaplothistogram.h.R' 'jamovibaplotqq.b.R'
'jamovibaplotqq.h.R' 'jamovibastats.b.R' 'jamovibastats.h.R'
test_statistics.R methods.R Student.R blindrecalc.R
ChiSquare.R ChiSquare_helper.R FarringtonManning.R
FarringtonManning_helper.R RcppExports.R
'coclusterStrategy.R' 'RCocluster.R' 'optionclasses.R'
'cocluster.R' 'coclusterBinary.R' 'coclusterCategorical.R'
'coclusterContingency.R' 'coclusterContinuous.R' 'onattach.R'
'as.blockmatrix.matrix.R' 'as.matrix.blockmatrix.R'
'blockmatrix.R' 'dim.blockmatrix.R' 'is.zero.blockmatrix.R'
'length.blockmatrix.R' 'matmult.blockmatrix.R'
'methods.blockmatrix.R' 'names.blockmatrix.R'
'ncol.blockmatrix.R' 'ncol_elements.blockmatrix.R'
'nrow.blockmatrix.R' 'nrow_elements.blockmatrix.R'
'solve.blockmatrix.R' 't.blockmatrix.R' 'value.blockmatrix.R'
'value.replacement.blockmatrix.R' 'zbracket[.blockmatrix.R'
'zbracket[.replacement.blockmatrix.R'
'BMAmevt-package.R' 'Laplace.evt.r' 'MCpriorIntFun.nl.r'
'MCpriorIntFun.pb.r' 'MCpriorIntFun.r' 'add.frame.r'
'transf.to.equi.r' 'cons.angular.dat.r' 'ddirimix.grid.r'
'ddirimix.grid1D.r' 'ddirimix.r' 'dgridplot.R' 'diagnose.r'
'discretize.R' 'dnestlog.grid.r' 'dnestlog.r'
'dpairbeta.grid.r' 'dpairbeta.r' 'emp_density.r'
'excessProb.condit.dm.r' 'excessProb.nl.r' 'excessProb.pb.r'
'lAccept.ratio.r' 'marginal.lkl.nl.r' 'marginal.lkl.pb.r'
'marginal.lkl.r' 'maxLikelihood.r' 'posterior.predictive.nl.r'
'posterior.predictive.pb.r' 'posterior.predictive3D.r'
'posteriorDistr.bma.r' 'posteriorMCMC.nl.r'
'posteriorMCMC.pb.r' 'posteriorMCMC.r' 'posteriorMean.r'
'posteriorWeights.r' 'prior.nl.r' 'prior.pb.r' 'proposal.nl.r'
'proposal.pb.r' 'rdirimix.r' 'rect.integrate.r' 'rnestlog.r'
'rpairbeta.r' 'rstable.posit.R' 'scores3D.r' 'transf.to.rect.r'
'wrapper.R' 'zzz.r'
'bmkconverge.R' 'bmk.R' 'bmksensitive.R' 'bmksummary.R'
'HBconverg1.R' 'HDistNoSize.R' 'HDistSize.R' 'HWconverg1.R'
BME.R BMTME.R BMTME_Package.R BMORS.R BMORSEnv.R cholesky.R
datasets.R detectCore.R RandomPartition.R RcppExports.R utils.R
'glm_stats.R' 'brainGraph_GLM.R' 'glm_methods.R' 'NBS.R'
'analysis_random_graphs.R' 'atlas.R' 'auc.R' 'boot_global.R'
'brainGraph_mediate.R' 'centr_lev.R' 'communicability.R'
'contract_brainGraph.R' 'corr_matrix.R' 'count_edges.R'
'create_graphs.R' 'create_mats.R' 'data.R' 'data_tables.R'
'distances.R' 'edge_asymmetry.R' 'get_resid.R' 'glm_design.R'
'glm_fit.R' 'glm_randomise.R' 'graph_efficiency.R' 'hubs.R'
'import.R' 'individ_contrib.R' 'list.R' 'method_helpers.R'
'mtpc.R' 'methods.R' 'permute_group.R' 'plot_brainGraph.R'
'plot_brainGraph_gui.R' 'plot_brainGraph_multi.R'
'plot_global.R' 'plot_group_means.R' 'plot_rich_norm.R'
'plot_vertex_measures.R' 'random_graphs.R' 'rich_club.R'
'robustness.R' 's_core.R' 'set_brainGraph_attributes.R'
'small_world.R' 'spatial_dist.R' 'update_brainGraph_gui.R'
'utils.R' 'utils_matrix.R' 'vertex_roles.R' 'vulnerability.R'
'write_brainnet.R' 'zzz.R'
'brickr.R' 'bricks-from-mosaic.R' 'bricks-from-tables.R'
'build-bricks-rgl.R' 'build-instructions.R' 'build-mosaic.R'
'collect-bricks.R' 'colors-and-themes.R' 'data.R'
'geom-brick-rect.R' 'image-to-mosaic.R'
'image-to-prep-mosaic.R' 'piece-count.R' 'utils-pipe.R'
'feedbacks.R' 'useful-items.R' 'tabs.R' 'render-functions.R'
'cards.R' 'dashboardSidebar.R' 'dashboardBody.R'
'dashboardFooter.R' 'dashboardControlbar.R' 'dashboardHeader.R'
'dashboardPage.R' 'aliases.R' 'auto-color.R'
'bs4Dash-package.r' 'bs4DashGallery.R' 'deps.R' 'grid.R'
'inputs.R' 'skinSelector.R' 'utils.R'
'BSL-package.R' 'RcppExports.R' 's4-MODEL.R' 's4-BSL.R'
'bsl.R' 'cell.R' 'combinePlotsBSL.R' 'covWarton.R'
'estimateLoglike.R' 'estimateWhiteningMatrix.R'
'gaussianRankCorr.R' 'gaussianSynLike.R'
'gaussianSynLikeGhuryeOlkin.R' 'imports.R' 'kernelCDF.R'
'logitTransform.R' 'ma2.R' 'mgnk.R' 'myMiniProgressBar.R'
's4-PENALTY.R' 'selectPenalty.R' 'semiparaKernelEstimate.R'
'sliceGammaMean.R' 'sliceGammaVariance.R' 'synLikeMisspec.R'
'toad.R'
'bootswatch.R' 'bs-current-theme.R' 'bs-dependencies.R'
'bs-global.R' 'bs-remove.R' 'bs-theme-layers.R' 'utils.R'
'bs-theme-preview.R' 'bs-theme-update.R' 'bs-theme.R'
'deprecated.R' 'files.R' 'imports.R' 'nav-items.R'
'nav-update.R' 'navs-legacy.R' 'navs.R' 'onLoad.R' 'page.R'
'precompiled.R' 'print.R' 'shiny-devmode.R' 'utils-shiny.R'
'utils-tags.R' 'version-default.R' 'versions.R'
'data.R' 'BTYD.R' 'bgbb.R' 'bgnbd.R' 'pnbd.R' 'dc.R' 'spend.R'
'0-onLoad.R' '1-setGeneric.R' 'BuyseMultComp.R' 'BuyseTTEM.R'
'BuyseTest-Peron.R' 'BuyseTest-check.R' 'BuyseTest-inference.R'
'BuyseTest-initialization.R' 'BuyseTest-package.R'
'BuyseTest-print.R' 'BuyseTest.R' 'BuyseTest.options.R'
'PairScore.R' 'RcppExports.R' 'S4-BuysePower.R'
'S4-BuysePower-summary.R' 'S4-BuysePower-show.R'
'S4-BuyseTest.R' 'S4-BuyseTest-coef.R' 'S4-BuyseTest-confint.R'
'S4-BuyseTest-get.R' 'S4-BuyseTest-sensitivity.R'
'S4-BuyseTest-summary.R' 'S4-BuyseTest-show.R'
'S4-BuyseTest.options.R' 'as.data.table.performance.R' 'auc.R'
'autoplot.sensitivity.R' 'brier.R' 'constStrata.R'
'discreteRoot.R' 'iid.prodlim.R' 'normexp.R' 'performance.R'
'performanceResample.R' 'powerBuyseTest.R' 'predict.logit.R'
'simBuyseTest.R' 'simCompetingRisks.R' 'valid.R'
'RcppExports.R' 'add_priors.R' 'add_priors.bvarmodel.R'
'add_priors.bvecmodel.R' 'add_priors.dfmodel.R' 'bvar.R'
'bvar_fill_helper.R' 'bvarpost.R' 'bvartools.R' 'bvec.R'
'bvec_to_bvar.R' 'bvecpost.R' 'data.R' 'dfm.R' 'dfmpost.R'
'draw_posterior.R' 'draw_posterior.bvarmodel.R'
'draw_posterior.bvecmodel.R' 'draw_posterior.dfmodel.R'
'fevd.R' 'fevd.bvar.R' 'gen_dfm.R' 'gen_var.R' 'gen_vec.R'
'get_regressor_names.R' 'inclusion_prior.R' 'irf.R'
'irf.bvar.R' 'minnesota_prior.R' 'plot.bvar.R'
'plot.bvarfevd.R' 'plot.bvarirf.R' 'plot.bvarprd.R'
'plot.bvec.R' 'plot.dfm.R' 'predict.bvar.R' 'summary.bvar.R'
'print.summary.bvar.R' 'summary.bvec.R' 'print.summary.bvec.R'
'ssvs_prior.R' 'summary.bvarlist.R' 'summary.dfm.R'
'thin.bvar.R' 'thin.bvarlist.R' 'thin.bvec.R' 'thin.dfm.R'
'tvpribbon.R' 'zzz.R'
'global.R' 'utilities.R' 'check.R' 'generic.R' 'seed.R'
'distribution.R' 'dataset_config.R' 'time_entry.R' 'occasion.R'
'occasions.R' 'treatment_iov.R' 'treatment_iovs.R'
'dose_adaptation.R' 'dose_adaptations.R' 'treatment_entry.R'
'treatment.R' 'observations.R' 'observations_set.R'
'covariate.R' 'covariates.R' 'bootstrap.R' 'protocol.R' 'arm.R'
'arms.R' 'event.R' 'events.R' 'scenario.R' 'scenarios.R'
'simulation_engine.R' 'dataset.R' 'parameter_uncertainty.R'
'event_logic.R' 'simulation_progress.R' 'dataset_summary.R'
'simulate_preprocess.R' 'simulate.R' 'results_processing.R'
'default_plot.R'
'global.R' 'data.R' 'utilities.R' 'special_operators.R'
'check.R' 'generic.R' 'generic_element_list.R'
'generic_element_position.R' 'generic_list.R' 'pattern.R'
'model_statement.R' 'model_statements.R'
'model_unknown_statement.R' 'model_comment.R'
'model_line_break.R' 'model_equation.R' 'model_ode.R'
'model_if_statement.R' 'compartment.R' 'compartment_property.R'
'compartment_properties.R' 'compartment_bioavailability.R'
'compartment_lag_time.R' 'compartment_infusion_duration.R'
'compartment_infusion_rate.R' 'compartment_initial_condition.R'
'compartments.R' 'parameter.R' 'parameters.R' 'code_record.R'
'code_records.R' 'model_parser.R' 'campsis_model.R'
'rxode_model.R' 'rxode_conversion.R' 'mrgsolve_model.R'
'mrgsolve_conversion.R'
Cascade-package.R global.R micro_array.R network.R
micro_array-network.R micropredict.R datasets.R
'align.R' 'utils.R' 'background.R' 'border.R' 'cascadess.R'
'display.R' 'eval-style.R' 'flex.R' 'float.R' 'font.R' 'gap.R'
'height.R' 'margin.R' 'overflow.R' 'padding.R' 'position.R'
'reexports.R' 'responsive.R' 'shadow.R' 'text.R' 'utils-docs.R'
'visible.R' 'width.R' 'zzz.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
'category.R' 'ceg.R' 'event_tree.R' 'staged_tree.R'
'ceg_model.R' 'cont_table.R' 'data_documentation.R'
'model_search_algorithm.R'
'exhaustive_model_search_algorithm.R' 'dinamic_programming.R'
'distribution_of_probability.R' 'dirchlet_distribution.R'
'dirchlet_mpnl_distribution.R'
'heuristic_model_search_algorithm.R' 'lib_funtions.R'
'multinomial_distribution.R' 'oahc.R'
'posterior_distribution.R' 'prior_distribution.R'
'stratified_event_tree.R' 'stratified_staged_tree.R'
'stratified_ceg_model.R' 'variable.R'
'CENFA.R' 'GLcenfa-class.R' 'GLcenfa.R' 'GLdeparture-class.R'
'GLdeparture.R' 'RcppExports.R' 'brStick.R' 'calc.R'
'climdat.fut.R' 'climdat.hist.R' 'cnfa-class.R' 'cnfa.R'
'covij.R' 'departure-class.R' 'departure.R' 'enfa-class.R'
'enfa.R' 'exposure_map.R' 'overlay.R' 'parCov.R' 'parScale.R'
'vulnerability-class.R' 'predict.R' 'scatter.R'
'sensitivity_map.R' 'slots.R' 'stretchPlot.R' 'tree-data.R'
'vulnerability.R' 'vulnerability_map.R'
'AssertCollection.R' 'allMissing.R' 'anyInfinite.R'
'anyMissing.R' 'anyNaN.R' 'asInteger.R' 'assert.R' 'helper.R'
'makeExpectation.R' 'makeTest.R' 'makeAssertion.R'
'checkAccess.R' 'checkArray.R' 'checkAtomic.R'
'checkAtomicVector.R' 'checkCharacter.R' 'checkChoice.R'
'checkClass.R' 'checkComplex.R' 'checkCount.R'
'checkDataFrame.R' 'checkDataTable.R' 'checkDate.R'
'checkDirectoryExists.R' 'checkDisjunct.R' 'checkDouble.R'
'checkEnvironment.R' 'checkFALSE.R' 'checkFactor.R'
'checkFileExists.R' 'checkFlag.R' 'checkFormula.R'
'checkFunction.R' 'checkInt.R' 'checkInteger.R'
'checkIntegerish.R' 'checkList.R' 'checkLogical.R'
'checkMatrix.R' 'checkMultiClass.R' 'checkNamed.R'
'checkNames.R' 'checkNull.R' 'checkNumber.R' 'checkNumeric.R'
'checkOS.R' 'checkPOSIXct.R' 'checkPathForOutput.R' 'checkR6.R'
'checkRaw.R' 'checkScalar.R' 'checkScalarNA.R'
'checkSetEqual.R' 'checkString.R' 'checkSubset.R' 'checkTRUE.R'
'checkTibble.R' 'checkVector.R' 'coalesce.R' 'isIntegerish.R'
'matchArg.R' 'qassert.R' 'qassertr.R' 'vname.R' 'wfwl.R'
'zzz.R'
closed.R shortcuts.R Meinshausen.R regionmethod.R DAGmethod.R
DAGstructure.R structuredHolm.R
'acs.R' 'choropleth.R' 'admin1.R' 'admin1_region.R'
'choroplethr_animate.R' 'choroplethr_wdi.R' 'country.R' 'usa.R'
'county.R' 'county_zoom.R' 'data.R'
'get_congress_116_party_data.R'
'get_congressional_demographics.R' 'get_county_demographics.R'
'get_state_demograhpics.R' 'get_tract_demographics.R'
'startup_messages.R' 'state.R' 'tract.R' 'utils.R'
zzz.R classes.R data.R colours.R reconstruction.R utility.R
platemodel.R fetching.R info.R XArray-base.R XArray-combine.R
XArray-xattrib.R XArray-subset.R XArray-apply.R
RasterArray-base.R RasterArray-cast.R RasterArray-combine.R
RasterArray-groupgen.R RasterArray-raster.R
RasterArray-subset.R RasterArray-xattrib.R SpatialStack-base.R
SpatialStack-sp.R SpatialStack-subset.R SpatialArray-base.R
SpatialArray-sp.R SpatialArray-cast.R SpatialArray-combine.R
SpatialArray-subset.R inter.R plotting.R
'ZZZ.R' 'auxiliary.R' 'classes.R' 'constructors.R'
'deprecated.R' 'methods.R' 'RcppExports.R'
'CIplot.R' 'CIplot.default.R' 'CIplot.htest.R'
'CIplot.TukeyHSD.R' 'CIplot.glht.R' 'CIplot.glm.R'
'CIplot.ORci.R' 'CIplot.posthocTGH.R' 'ORci.R' 'print.ORci.R'
'await.R' 'civis_future.R' 'civis_ml.R' 'civis_ml_plot.R'
'civis_ml_utils.R' 'civis_ml_workflows.R' 'client_base.R'
'generate_client.R' 'generated_client.R' 'io.R' 'logs.R'
'pagination_helpers.R' 'reports.R' 'rstudio.R' 'scripts.R'
'tables.R' 'utils.R' 'zzz.R'
'allGenerics.R' 'allClasses.R' 'accessor-methods.R'
'get_classification.R' 'internals.R' 'query.R' 'show-method.R'
'zzz.R'
'utils.R' 'internals.R' 'checks.R' 'cleanr-package.R'
'options.R' 'throw.R' 'wrappers.R' 'zzz.R'
'algoComp.R' 'numExpRealData.R' 'numExpSimData.R' 'fitClere.R'
'Clere-class.R' 'sClere-class.R' 'fitPacs.R' 'Pacs-class.R'
'clere-package.R'
'RcppExports.R' 'clevr.R' 'measures_clusterings.R'
'transformations.R' 'measures_pairs.R'
'RcppExports.R' 'clhs-internal.R' 'clhs-data.frame.R'
'clhs-package.R' 'clhs-raster.R' 'utils.R' 'clhs.R' 'clhs-sf.R'
'clhs-sp.R' 'plot.R' 'similarity.R'
'dataFrame.R' 'cfStation.R' 'cfData.R' 'cfDataList.R'
'cfData-plotMethods.R' 'cfDatatype.R' 'cfQuery.R' 'cfUser.R'
'findStations.R'
'ClimInd.R' 'ClimIndNews.R' 'custom_functions.R' 'data.R'
'ffdi.R' 'fwi1D.R' 'penman_fao_dia.R' 'nesterovIndex.R'
'macArthurFFDI.R' 'kbdindex.R' 'indecis_indices_functions.R'
'indecis_indices.R' 'indecis.R'
'cluspred.R' 'RcppExports.R' 'Singleblock_algo_NP.R'
'Singleblock_algo_Param.R' 'Singleblock_prediction.R' 'tool.R'
'TwoSteps_algo.R' 'Twosteps_computelogPDFwithZ.R'
'TwoSteps_Mstep.R'
'clusvis.R' 'estimation.R' 'smartinit.R' 'RcppExports.R'
'plot.R' 'clusvismixmod.R' 'clusvisvarsellcm.R' 'modessearch.R'
'RcppExports.R' 'cache.R' 'coala.R' 'model.R' 'feature.R'
'feature_growth.R' 'feature_ignore_singletons.R'
'feature_migration.R' 'feature_mutation.R' 'feature_outgroup.R'
'feature_pop_merge.R' 'feature_recombination.R'
'feature_sample.R' 'feature_selection.R'
'feature_size_change.R' 'feature_sumstats.R'
'feature_unphased.R' 'interface_abc.R' 'locus.R'
'model_build.R' 'model_check.R' 'model_examples.R'
'model_getters.R' 'model_print.R' 'model_scale.R'
'model_simulate.R' 'onLoad.R' 'parameter.R' 'parameter_prior.R'
'parameter_variation.R' 'parameter_zero_inflation.R'
'search_executable.R' 'segsites.R' 'simulation_tasks.R'
'simulator_class.R' 'simulator_ms.R' 'simulator_msms.R'
'simulator_scrm.R' 'simulator_seqgen.R' 'sumstat.R'
'sumstat_dna.R' 'sumstat_file.R' 'sumstat_four_gamete.R'
'sumstat_ihh.R' 'sumstat_jsfs.R' 'sumstat_mcmf.R'
'sumstat_nucleotide_div.R' 'sumstat_omega.R'
'sumstat_seg_sites.R' 'sumstat_sfs.R' 'sumstat_tajimas_d.R'
'sumstat_trees.R' 'tools.R'
'CFR_estimation.R' 'dic.fit.mcmc.R' 'dic.fit.R'
'get.obs.type.R' 'S4stuff.R' 'sampleSizeSimulation.R'
'coarseDataTools-package.R'
'0helper.r' 'as.htest.r' 'cocor-package.R'
'cocor.dep.groups.nonoverlap.r' 'cocor.dep.groups.overlap.r'
'cocor.indep.groups.r' 'cocor.r' 'get.cocor.input.r'
'get.cocor.results.r'
classes.R librarySymbols.R functionHandlers.R codeDepends.R
sectionDepends.R sweave.R xml.R jss.R frags.R codeTypes.R gc.R
graph.R parallel.R deps.R separateBlocks.R callGraph.R isPlot.R
isOutput.R refScript.R sideEffects.R highlightCode.R
freeVariables.R convenienceFuns.R
'check.R' 'claudia.R' 'coloc-package.R' 'plot.R' 'private.R'
'sensitivity.R' 'split.R' 'susie.R' 'testdata.R' 'zzz.R'
'colorpatch.R' 'colorpatch_methods.R' 'colorpatch_ggplot.R'
'colorpatch_impl.R' 'data.R'
'adjacent.R' 'anagrams.R' 'analogous.R' 'col2HSV.R'
'complementary.R' 'opposite.R' 'pals.R' 'pizza.R'
'sequential.R' 'setColors.R' 'splitComp.R' 'square.R'
'tetradic.R' 'triadic.R' 'wheel.R'
'eplot.R' 'addxaxis.R' 'compactr-package.R' 'mm.R' 'aplot.R'
'Comparator.R' 'CppSeqComparator.R' 'PairwiseMatrix.R'
'SequenceComparator.R' 'StringComparator.R' 'BinaryComp.R'
'NumericComparator.R' 'Chebyshev.R' 'Constant.R'
'Levenshtein.R' 'DamerauLevenshtein.R' 'Minkowski.R'
'Euclidean.R' 'FuzzyTokenSet.R' 'Hamming.R' 'InVocabulary.R'
'Jaro.R' 'JaroWinkler.R' 'LCS.R' 'Lookup.R' 'Manhattan.R'
'TokenComparator.R' 'MongeElkan.R' 'OSA.R' 'RcppExports.R'
'generalized_mean.R' 'strcompr-package.R' 'util.R'
'commonAUC.R' 'segLength.R' 'popFunc.R' 'uniqueCombinations.R'
'createDF.R' 'deldup.R' 'CAUCkernel.R' 'cnvData.R' 'compFunc.R'
'dataChecks.R' 'test.qt.R' 'test.bt.R' 'pv_func.R' 'concur.R'
'MultiFramer.R' 'SingleFramer.R' 'binaryVisualiseR.R'
'dummycoder.R' 'globals.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'
'RcppExports.R' 'ReadConQuestLibrary.R'
'ReadConQuestRout_createDF.R' 'ReadConQuestState.R'
'ReadConQuestState_createDF.R' 'conquestr.R' 'conquestrFunc.R'
'generateHelpers.R' 'infoHelpers.R' 'itanalHelpers.R'
'plotGeneral.R' 'plotRout.R' 'pvHelpers.R' 'residHelpers.R'
'thrstThrsh.R' 'cleaningHelpers.R'
'contoureR-package.R' 'RcppExports.R' 'RcppExports-Doc.R'
'convexHull.R' 'delaunayMesh.R' 'contourLines.R'
'orderPoints.R'
'zooStep.R' 'IPCWStep.R' 'VStep.R' 'VStep2.R' 'verifyInputs.R'
'ctCoxMSM.R' 'ctSFTM.R' 'ctcData.R' 'print.ctc.R'
'aggregation-matrix.R' 'aggregation-matrix-default.R'
'aggregation-matrix-membership-difference.R' 'factor.R'
'factors-of-interest.R' 'option-factor-availability.R'
'option-resources.R' 'option.R' 'option-portfolio.R'
'project-criterion.R' 'project-criteria.R' 'project.R'
'project-portfolio.R' 'coppe-cosenza.R' 'coppe-cosenzaR.R'
AllClass.R Classes.R AllGeneric.R Auxiliaries.R aux-acopula.R
asymCopula.R mixCopula.R rotCopula.R Copula.R special-func.R
amhCopula.R claytonCopula.R frankCopula.R cop_objects.R
nacopula.R dC-dc.R amhExpr.R An.R archmCopula.R cCopula.R
claytonExpr.R ellipCopula.R empCopula.R empPsi.R acR.R
estimation.R evCopula.R evTests.R exchTests.R fgmCopula.R
fitCopula.R fitLambda.R fitMvdc.R fixedPar.R frankExpr.R
galambosCopula.R galambosExpr-math.R galambosExpr.R
ggraph-tools.R pairsRosenblatt.R prob.R gofTrafos.R
gofEVTests.R gofCopula.R graphics.R gumbelCopula.R gumbelExpr.R
huslerReissCopula.R huslerReissExpr.R indepCopula.R fhCopula.R
lowfhCopula.R upfhCopula.R indepTests.R joeCopula.R K.R
logseries.R mvdc.R margCopula.R matrix_tools.R normalCopula.R
opower.R plackettCopula.R plackettExpr.R moCopula.R rstable1.R
safeUroot.R schlatherCopula.R stable.R timing.R tCopula.R
tawnCopula.R tawnExpr.R tevCopula.R varianceReduction.R
wrapper.R xvCopula.R zzz.R
EDA.R edaSeed.R edaSelect.R edaOptimize.R edaReport.R
edaReplace.R edaTerminate.R edaRun.R margins.R problems.R
edaIndepRuns.R edaCriticalPopSize.R CEDA.R VEDA.R
'coranking-package.R' 'coranking.R' 'coranking_internals.R'
'lcmc.R' 'criteria.R' 'image.R' 'rankmatrix.R' 'submatrix.R'
'r_nx.R'
'utils.R' 'generics.R' 'ConvexCombCopula.R'
'empiricalCopula.R' 'Cort.R' 'CortForest.R' 'RcppExports.R'
'cbCopula.R' 'cbkmCopula.R' 'cort-package.R' 'data.R'
'Countr-package.R' 'RcppExports.R' 'anc.R' 'coefnames.R'
'convCount_loglik.R' 'convCount_moments.R' 'convCount_probs.R'
'dWeibull.R' 'dWeibullgamma.R' 'data.R' 'renewal_IV.R'
'renewal_tools.R' 'renewal_cstr.R' 'tools.R'
'renewal_methods.R'
'sepindex.R' 'couples.R' 'blocks.R' 'covablocks.R'
'covastat.R' 'covaprop.R' 'covastatM.R' 'read.STdata.R'
'vv_13.R'
'add_sub.R' 'align_plots.R' 'as_grob.R' 'as_gtable.R'
'axis_canvas.R' 'cowplot.R' 'draw.R' 'get_plot_component.R'
'get_axes.R' 'get_titles.R' 'get_legend.R' 'get_panel.R'
'gtable.R' 'key_glyph.R' 'plot_grid.R' 'save.R'
'set_null_device.R' 'setup.R' 'stamp.R' 'themes.R'
'utils_ggplot2.R'
'CPsurv-package.R' 'bootbiascorrect.R' 'cpest.R'
'cpsurv-data.R' 'cpsurv.R' 'km.sim.survtimes.R' 'methods.R'
'neg.loglik.WeibExp.R' 'sim.survdata.R'
'aaa-rstudio-detect.R' 'aaaa-rematch2.R'
'aab-num-ansi-colors.R' 'aac-num-ansi-colors.R' 'ansi-256.r'
'ansi-palette.R' 'combine.r' 'string.r' 'utils.r'
'crayon-package.r' 'disposable.r' 'has_ansi.r' 'has_color.r'
'link.R' 'styles.r' 'machinery.r' 'parts.r' 'print.r'
'style-var.r' 'show.r' 'string_operations.r'
'Schedule.R' 'utils-pipe.R' 'criticalpath.R'
'cpt_calculate_critical_path.R' 'cpt_schedule_status.R'
'cpt_topological_organization.R' 'cpt_utils.R' 'sch_activity.R'
'sch_relation.R' 'sch_schedule.R'
'sch_topological_indicators.R'
'classes.R' 'crmn-package.R' 'misc.R' 'norm.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'
'crqa.R' 'crqa_helpers.R' 'drpfromts.R' 'lorenzattractor.R'
'mdDelay.R' 'mdFnn.R' 'optimizeParam.R' 'piecewiseRQA.R'
'plotRP.R' 'simts.R' 'spdiags.R' 'wincrqa.R' 'windowdrp.R'
'utils.R' 'curry.R' 'partial.R' 'scaffold.R' 'tail_curry.R'
'accessors.R' 'aggregate.R' 'bwplot.R' 'cost.R' 'cvFit.R'
'cvFolds.R' 'cvReshape.R' 'cvSelect.R' 'cvTool.R' 'cvTuning.R'
'densityplot.R' 'dotplot.R' 'plot.R' 'print.R' 'repCV.R'
'selectBest.R' 'subset.R' 'summary.R' 'utils.R' 'xyplot.R'
'CVXR.R' 'data.R' 'globals.R' 'generics.R' 'interface.R'
'canonical.R' 'expressions.R' 'constant.R' 'variable.R'
'lin_ops.R' 'atoms.R' 'affine.R' 'problem.R' 'constraints.R'
'elementwise.R' 'coeff_extractor.R' 'reductions.R'
'reduction_solvers.R' 'complex2real.R' 'conic_solvers.R'
'eliminate_pwl.R' 'dcp2cone.R' 'dgp2dcp.R' 'qp2quad_form.R'
'qp_solvers.R' 'utilities.R' 'solver_utilities.R'
'transforms.R' 'exports.R' 'rcppUtils.R' 'R6List.R'
'ProblemData-R6.R' 'LinOp-R6.R' 'LinOpVector-R6.R'
'RcppExports.R' 'CVXcanon-R6.R' 'Deque.R' 'canonInterface.R'
'CorpusData.R' 'corpus.R' 'cwb.R' 'cwbtools.R' 'directories.R'
'encoding.R' 'ner.R' 'p_attribute.R' 'pkg.R' 'registry_file.R'
's_attribute.R' 'vrt.R' 'zenodo.R'
'cyl_cop_class.R' 'Ccond.R' 'aaaglobal.R' 'correlation.R'
'cyl_cubsec.R' 'cyl_quadsec.R' 'cyl_rect_combine.R'
'cyl_rot_combine.R' 'cyl_vonmises.R' 'cylcop-package.R'
'density.R' 'fit_cop_corr.R' 'fit_cop_mle.R' 'fit_margin.R'
'mixedvonmises.R' 'opt_auto.R' 'plotting_functions.R'
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'test-sql.R' 'test-meta.R' 'test-transaction.R'
'test-compliance.R' 'test-stress.R' 'tweaks.R' 'utf8.R'
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'dbplyr.R' 'explain.R' 'ident.R' 'lazy-ops.R' 'memdb.R'
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'test-frame.R' 'testthat.R' 'translate-sql.R' 'utils-format.R'
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'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'
'dplyr_verbs.R' 'guard.R' 'modifier.R' 'modify.R' 'validate.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
'as.data.frame.mcSimulation.R' 'chance_event.R'
'compound_figure.R' 'paramtnormci_fit.R'
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'rmvnorm90ci_exact.R' 'estimate.R' 'mcSimulation.R'
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'individualEvpiSimulation.R' 'estimate_read_csv_old.R'
'decisionSupport.R' 'decisionSupport-package.R' 'discount.R'
'empirical_EVPI.R' 'global_variables.R' 'gompertz_yield.R'
'hist.mcSimulation.R' 'make_CPT.R' 'multi_EVPI.R'
'plainNames2data.frameNames.R' 'plot_cashflow.R'
'plot_distributions.R' 'plot_evpi.R' 'plot_pls.R'
'plsr.mcSimulation.R' 'print.mcSimuation.R'
'print.summary.mcSimulation.R' 'random_state.R' 'sample_CPT.R'
'sample_simple_CPT.R' 'scenario_mc.R' 'summary.mcSimulation.R'
'temp_situations.R' 'vv.R'
'assertions.R' 'authors-at-r.R' 'built.R' 'classes.R'
'collate.R' 'constants.R' 'deps.R' 'desc-package.R'
'description.R' 'encoding.R' 'latex.R' 'non-oo-api.R'
'package-archives.R' 'read.R' 'remotes.R' 'str.R'
'syntax_checks.R' 'urls.R' 'utils.R' 'validate.R' 'version.R'
'capt.R' 'options.R' 'pager.R' 'check.R' 'finalizer.R'
'misc.R' 'html.R' 'styles.R' 's4.R' 'core.R' 'diff.R' 'get.R'
'guides.R' 'hunks.R' 'layout.R' 'myerssimple.R' 'rdiff.R'
'rds.R' 'set.R' 'subset.R' 'summmary.R' 'system.R' 'text.R'
'tochar.R' 'trim.R' 'word.R'
'utils.R' 'bernstein_polynomials.R' 'privacy_params.R'
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'exponential_mechanism.R' 'numeric_mechanism.R'
'gaussian_mechanism.R' 'laplace_mechanism.R'
'sensitivity_sampler.R'
'AllClasses.R' 'AllGenerics.R' 'digitalDLSorteR.R'
'dnnModel.R' 'evalMetrics.R' 'loadData.R' 'simBulk.R'
'simSingleCell.R' 'utils.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'
'AllClasses.R' 'AllGenerics.R' 'bootstrap.R' 'ca.R'
'dimensio-package.R' 'helpers.R' 'jackknife.R' 'joint.R'
'mutators.R' 'pca.R' 'plot.R' 'show.R' 'subset.R' 'summary.R'
'svd.R' 'zzz.R'
'dimRedMethod-class.R' 'misc.R' 'dimRedData-class.R'
'dimRedResult-class.R' 'autoencoder.R' 'dataSets.R' 'diffmap.R'
'dimRed.R' 'drr.R' 'embed.R' 'fastica.R' 'get_info.R'
'graph_embed.R' 'hlle.R' 'isomap.R' 'kpca.R' 'l1pca.R' 'leim.R'
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'pca.R' 'plot.R' 'quality.R' 'rotate.R' 'soe.R' 'tsne.R'
'umap.R'
'accessors.R' 'arithmetic.R' 'date_xx.R' 'date_y.R'
'date_ym.R' 'date_yq.R' 'date_yw.R' 'dint-package.R'
'extract.r' 'first_of.R' 'format.R' 'increment.R' 'parser.R'
'predicates.R' 'utils-sfmisc.R' 'zoo-compat.R'
'scale_date_xx.R' 'seq.R' 'utils.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
'bflSmooth.R' 'data.R' 'disaggR.R' 'utils.R' 'in.R'
's4register.R' 'twoStepsBenchmark.R' 'methods.R' 'plot.R'
'praislm.R' 'reView.R' 'threeRuleSmooth.R'
'DIscBIO-classes.R' 'DIscBIO-generic-ClassVectoringDT.R'
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'DIscBIO-generic-clustheatmap.R' 'DIscBIO-generic-comptSNE.R'
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'DIscBIO-generic-plotSymbolstSNE.R'
'DIscBIO-generic-plottSNE.R'
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'Jaccard.R' 'NetAnalysis.R' 'Networking.R' 'PPI.R'
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'customConverters.R' 'datasets.R'
'internal-functions-samr-adapted.R' 'internal-functions.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'
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'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'
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'data.R' 'diseq.R' 'equation_base.R' 'system_base.R'
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'diseq_basic.R' 'diseq_deterministic_adjustment.R'
'diseq_directional.R' 'diseq_stochastic_adjustment.R'
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'gradient_stochastic_adjustment.R' 'hessian_basic.R'
'hessian_directional.R' 'likelihood_basic.R'
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'likelihood_directional.R' 'likelihood_equilibrium.R'
'likelihood_stochastic_adjustment.R' 'market_fit.R'
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"distrr.R" "invented_data.R" "compat-lazyeval.R"
"dplyr_new_wrappers.R" "gibutils.R" "jointfuns.R"
"Fhat_conditional.R" "distrr_funs.R" "dcc_new.R" "wq_df.R"
"dcc6_fixed.R"
'capture-requests.R' 'connection.R' 'dbExistsTable.R'
'dbListTables-Fields.R' 'driver-specific-connections.R'
'dbQueries-Results.R' 'dbMisc.R' 'mock-paths.R' 'dittodb-env.R'
'expect-sql.R' 'mock-db.R' 'nycflights13-sql.R' 'paths.R'
'quote.R' 'redact.R' 'use-dittodb.R' 'utils.R'
'headers.R' 'diversity.R' 'div_conf_colour.R'
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'print.paygap.R' 'summary.paygap.R'
AllClass.R AllGenerics.R AllMethod.R austFilter.R
bouts_helpers.R bouts.R calibrate.R detDive.R detPhase.R
distSpeed.R diveStats.R oneDiveStats.R plotTDR.R plotZOC.R
readLocs.R readTDR.R runquantile.R speedStats.R stampDive.R
zoc.R diveMove-deprecated.R diveMove-defunct.R zzz.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'
'double_ml.R' 'double_ml_data.R' 'double_ml_iivm.R'
'double_ml_irm.R' 'double_ml_pliv.R' 'double_ml_plr.R'
'helper.R' 'datasets.R' 'zzz.R'
'VEplot_type2.R' 'VEcal.R' 'postProcess.R' 'CoxReg.R'
'RcppExports.R' 'VEplot_type1.R' 'postInterval.R' 'postBeta.R'
'infBeta.R' 'postProcessDove.R' 'scores.R' 'loc.R' 'vcFit.R'
'verifyInputs.R' 'dove.R' 'dove2.R' 'doveData.R' 'iterations.R'
'plot.DOVE.R' 'postVE.R' 'print.DOVE.R' 'vaccine.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'
'Data.R' 'Dowser.R' 'Clones.R' 'Classes.R' 'Plotting.R'
'Germlines.R' 'Statistics.R' 'TreeFunctions.R'
'utils_definitions.R' 'utils_globals.R' 'utils_names.R'
'utils_style-network.R' 'utils_ui-choices.R'
'fct_prepare-med-data.R' 'fct_build-legend.R'
'fct_build-linear-models.R' 'fct_build-network.R'
'fct_build-shiny-tables.R' 'fct_calculate-network-info.R'
'fct_export-network.R' 'fct_style-network.R' 'fct_timeline.R'
'mod_choose-color-palette.R'
'mod_choose-custom-element-colors.R' 'app_config.R'
'app_server.R' 'app_ui.R' 'run_app.R'
'util_fun.R' 'time_util_fun.R' 'color_fun.R' 'data.R'
'data_fun.R' 'text_fun.R' 'time_fun.R' 'theme_fun.R'
'plot_fun.R' 'start.R'
'DSObject.R' 'hidden.R' 'DSConnection.R' 'DSDriver.R'
'DSI-package.R' 'DSLoginBuilder.R' 'DSResult.R'
'datashield.aggregate.R' 'datashield.assign.R'
'datashield.connections.R' 'datashield.errors.R'
'datashield.list.R' 'datashield.login.R' 'datashield.logout.R'
'datashield.status.R' 'datashield.symbol.R'
'datashield.workspace.R' 'rd.R' 'utils.R'
'DSLiteDriver.R' 'DSLiteConnection.R' 'DSLiteResult.R'
'DSLiteServer.R' 'data.cnsim.R' 'data.dasim.R'
'data.discordant.R' 'data.survival.R' 'data.testing.dataset.R'
'defaultDSConfiguration.R' 'getDSLiteData.R' 'setupCNSIMTest.R'
'setupDASIMTest.R' 'setupDATASETTest.R' 'setupDISCORDANTTest.R'
'setupDSLiteServer.R' 'setupSURVIVALTest.R'
'ArmadilloDriver.R' 'ArmadilloConnection.R' 'ArmadilloOAuth.R'
'ArmadilloResult.R' 'DSMolgenisArmadillo-package.R' 'utils.R'
'DSOpal-package.R' 'OpalDriver.R' 'OpalConnection.R'
'OpalResult.R' 'datashield.aggregate.r' 'datashield.assign.r'
'datashield.command.r' 'datashield.method.r'
'datashield.symbol.r' 'datashield.workspace.r' 'logindata.R'
'utils.R'
'Class.Constructors.R' 'Coverage.Grid.R' 'Transect.R'
'Region.R' 'generic.functions.R' 'Survey.Design.R'
'Line.Transect.Design.R' 'Line.Transect.R'
'Point.Transect.Design.R' 'Point.Transect.R'
'Segment.Transect.Design.R' 'Segment.Transect.R'
'calc.region.width.R' 'calculate.effort.R'
'calculate.trackline.pl.R' 'calculate.trackline.segl.R'
'calculate.trackline.zz.R' 'calculate.trackline.zzcom.R'
'check.design.R' 'check.line.design.R' 'check.point.design.R'
'check.shape.R' 'dssd-package.R' 'generate.eqspace.zigzags.R'
'generate.parallel.lines.R' 'generate.random.points.R'
'generate.segmented.grid.R' 'generate.systematic.points.R'
'get.intersection.points.R' 'line.coords.as.dataframe.R'
'point.coords.as.dataframe.R' 'run.coverage.R'
'write.transects.R'
'DDF.Data.R' 'generic.functions.R' 'DDF.Analysis.R'
'DS.Analysis.R' 'Survey.Design.R' 'PT.Design.R'
'PT.Nested.Design.R' 'PT.Systematic.Design.R'
'PT.Random.Design.R' 'LT.Design.R' 'LT.User.Specified.Design.R'
'LT.EqSpace.ZZ.Design.R' 'LT.EqAngle.ZZ.Design.R'
'LT.Random.Design.R' 'LT.Systematic.Design.R' 'Density.R'
'Population.Description.R' 'Region.R' 'Region.Table.R'
'Sample.Table.R' 'Obs.Table.R' 'Single.Obs.DDF.Data.R'
'Transect.R' 'Line.Transect.R' 'Detectability.R' 'Population.R'
'LT.Survey.Results.R' 'Survey.Results.R' 'DSM.Analysis.R'
'Simulation.R' 'Class.Constructors.R' 'Design.Summary.R'
'LT.SegmentedGrid.Design.R' 'LT.SegmentedTrack.Design.R'
'Survey.R' 'LT.Survey.R' 'Point.Transect.R' 'PT.Survey.R'
'Population.Summary.R' 'Simulation.Summary.R'
'Single.Obs.LT.Survey.R' 'Single.Obs.PT.Survey.R'
'accumulate.PP.results.R' 'accumulate.warnings.R'
'add.covariate.values.R' 'add.dist.error.R' 'add.miss.dists.R'
'add.summary.results.R' 'calc.area.R'
'calc.poss.detect.dists.lines.R'
'calc.poss.detect.dists.lines.largeN.R'
'calc.poss.detect.dists.points.R' 'calculate.fitted.R'
'calculate.scale.param.R' 'check.LinkID.order.R'
'check.covariates.R' 'check.intersection.R' 'check.shapefile.R'
'check.sim.setup.R' 'coords.from.shapefile.R' 'create.bins.R'
'create.results.arrays.R' 'data.for.distance.R'
'description.summary.R' 'dssim.update.R'
'extract.spat.poly.coords.R' 'generate.pop.D.R'
'generate.pop.N.R' 'get.ave.density.R' 'get.bound.box.R'
'get.line.sampler.info.R' 'get.point.sampler.info.R'
'get.shapefile.names.R' 'get.surface.constant.R'
'get.surface.gam.R' 'hn.detect.R' 'hr.detect.R' 'in.polygons.R'
'is.gap.R' 'message.handler.R' 'modify.strata.for.analysis.R'
'rename.duplicates.R' 'rtpois.R' 'save.sim.results.R'
'setcov.R' 'simulate.detections.R' 'single.simulation.loop.R'
'store.ddf.results.R' 'store.dht.results.R'
'VerifyCriticalValue.R' 'VerifyData.R' 'VerifyERT.R'
'VerifyModels.R' 'VerifyRandomSplit.R' 'VerifyReplace.R'
'VerifySampleSize.R' 'VerifySplitRule.R' 'VerifySurvivalTime.R'
'VerifyTieMethod.R' 'VerifyTimePoints.R' 'VerifyTxName.R'
'VerifyUniformSplit.R' 'VerifyUsePrevTime.R'
'class_CriticalValue.R' 'class_CriticalValueMean.R'
'class_CriticalValueSurvival.R' 'class_TreeConditions.R'
'class_TreeType.R' 'criticalValue.R' 'class_TimeInfo.R'
'class_Parameters.R' 'predictSurvTree.R' 'class_SurvRF.R'
'survRF.R' 'shiftMat.R' 'class_Optimal.R' 'class_DTRSurvStep.R'
'class_DTRSurv.R' 'dtrSurv.R'
'CENTROIDS-dba.R' 'CENTROIDS-pam.R' 'CENTROIDS-sdtw-cent.R'
'CENTROIDS-shape-extraction.R' 'CLUSTERING-all-cent2.R'
'CLUSTERING-compare-clusterings.R'
'CLUSTERING-cvi-evaluators.R' 'CLUSTERING-ddist2.R'
'CLUSTERING-partitional-fuzzy.R'
'CLUSTERING-repeat-clustering.R' 'CLUSTERING-tadpole.R'
'CLUSTERING-tsclust-controls.R' 'CLUSTERING-tsclust.R'
'DISTANCES-dtw-basic.R' 'DISTANCES-dtw-lb.R' 'DISTANCES-dtw2.R'
'DISTANCES-gak.R' 'DISTANCES-lb-improved.R'
'DISTANCES-lb-keogh.R' 'DISTANCES-sbd.R' 'DISTANCES-sdtw.R'
'GENERICS-cvi.R' 'S4-Distmat.R' 'S4-PairTracker.R'
'S4-SparseDistmat.R' 'S4-tsclustFamily.R'
'S4-TSClusters-classes.R' 'S4-TSClusters-methods.R'
'SHINY-interactive-clustering.R' 'SHINY-ssdtwclust.R'
'SHINY-utils.R' 'UTILS-as-methods.R' 'UTILS-compute-envelope.R'
'UTILS-data.R' 'UTILS-expressions.R' 'UTILS-globals-internal.R'
'UTILS-nccc.R' 'UTILS-reinterpolate.R' 'UTILS-rng.R'
'UTILS-tslist.R' 'UTILS-utils.R' 'UTILS-zscore.R' 'pkg.R'
'class-crossValidation.R' 'class-twdtwRaster.R'
'class-twdtwAssessment.R' 'class-twdtwTimeSeries.R'
'class-twdtwMatches.R' 'createPatterns.R' 'data.R' 'dtw.R'
'dwtSat.R' 'getInternals.R' 'getTimeSeries.R' 'linearWeight.R'
'logisticWeight.R' 'methods.R' 'miscellaneous.R' 'plot.R'
'plotAccuracy.R' 'plotAdjustedArea.R' 'plotAlignments.R'
'plotArea.R' 'plotChanges.R' 'plotClassification.R'
'plotCostMatrix.R' 'plotDistance.R' 'plotMapSamples.R'
'plotMaps.R' 'plotMatches.R' 'plotPaths.R' 'plotPatterns.R'
'plotTimeSeries.R' 'resampleTimeSeries.R' 'subset.R' 'twdtw.R'
'twdtwApply.R' 'twdtwAssess.R' 'twdtwClassify.R'
'twdtwCrossValidate.R' 'twdtwDist.R' 'twdtwXtable.R'
'twdtw_reduce_time.R' 'zzz.R'
'util.R' 'assert.R' 'tree.R' 'names.R' 'assign.R'
'utils.R' 'dupree.R' 'dupree_classes.R'
'dupree_data_validity.R' 'dupree_code_enumeration.R'
'dups-class.R'
'milestone_palette.R' 'add_milestone_coloring.R'
'add_cell_coloring.R' 'add_density_coloring.R' 'data.R'
'dummy_proofing.R' 'expect_ggplot.R' 'is_colour_vector.R'
'linearise_cells.R' 'mix_colors.R' 'optimize_order.R'
'package.R' 'plot_dendro.R' 'project_waypoints.R'
'plot_dimred.R' 'plot_edge_flips.R' 'plot_graph.R'
'plot_heatmap.R' 'plot_linearised_comparison.R' 'plot_onedim.R'
'plot_strip.R' 'plot_topology.R' 'theme_clean.R'
'dynrData.R' 'dynrRecipe.R' 'dynrModelInternal.R'
'dynrModel.R' 'dynrCook.R' 'dynrPlot.R' 'dynrFuncAddress.R'
'dynrMi.R' 'dynrTaste.R' 'dynrVersion.R' 'dataDoc.R'
'dynrGetDerivs.R' 'dynrPredict.R'
'EBMAforecast.R' 'forecastData.R' 'EBMApredict.R'
'RcppExports.R' 'calibrateEnsemble.R' 'compareModels.R'
'document-data.R' 'fitEnsembleLogit.R' 'fitEnsembleNormal.R'
'global.R' 'makeForecastData.R' 'predictLogit.R'
'predictNormal.R' 'summary_plot.R' 'print_show.R'
'utility-functions.R'
'adjacency.R' 'as.igraph.R' 'editset.R' 'editarray.R'
'editmatrix.R' 'as.matrix.R' 'backtracker.R' 'blocks.R' 'c.R'
'cateditmatrix.R' 'checkDatamodel.R' 'checkRows.R' 'contains.R'
'disjunct.R' 'duplicated.R' 'echelon.R' 'editAttr.R'
'editarrayAttr.R' 'editfile.R' 'editmatrixAttr.R'
'editrules-data.R' 'eliminate.R' 'errorLocalizer.R'
'errorLocalizer_mip.R' 'errorLocation.R' 'expandEdits.R'
'generateEdits.R' 'getH.R' 'getUpperBounds.R' 'getVars.R'
'is.R' 'isFeasible.R' 'isObviouslyInfeasible.R'
'isObviouslyRedundant.R' 'isSubset.R' 'list2env.R'
'localizeErrors.R' 'mip.R' 'parseCat.R' 'parseEdits.R'
'parseMix.R' 'parseNum.R' 'perturbWeights.R' 'pkg.R' 'plot.R'
'plot_errorLocation.R' 'print.R' 'reduce.R' 'removeRedundant.R'
'softEdits.R' 'str.R' 'subsetting.R' 'substValue.R' 'summary.R'
'violatedEdits.R' 'writeELAsMip.R' 'zzz.R'
'EDMeasure-package.R' 'cmdm_functions.R' 'pmdd.R' 'mdd.R'
'cmdm_test.R' 'mdc.R' 'mddm.R' 'mdm.R' 'mdm_ica_functions.R'
'mdm_ica.R' 'mdm_test.R' 'pmdc.R'
'BatchCorrection.R' 'ELISAplate.R' 'ELISAtools_IO.R'
'ELISAtools.R' 'Regression.R'
'harrival.R' 'hspec.R' 'hmoment.R' 'hllf.R' 'hfit.R'
'utilities.R' 'hgfit.R' 'hreal.R' 'hsim.R' 'script.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'
'constants.R' 'assert.R' 'diagram.R' 'eplusr.R' 'utils.R'
'impl.R' 'parse.R' 'impl-epw.R' 'impl-idd.R' 'impl-idf.R'
'idf.R' 'idd.R' 'epw.R' 'err.R' 'format.R' 'geom.R' 'group.R'
'iddobj.R' 'idfobj-sch.R' 'impl-idfobj.R' 'idfobj.R'
'impl-geom.R' 'impl-iddobj.R' 'impl-idfobj-sch.R' 'impl-sql.R'
'impl-viewer.R' 'install.R' 'job.R' 'options.R' 'param.R'
'rdd.R' 'reload.R' 'run.R' 'sql.R' 'transition.R' 'units.R'
'validate.R' 'viewer.R' 'zzz.R'
'ci_connorm.r' 'epsiwal.r' 'pconnorm.r' 'ptruncnorm.r'
'utils.r'
'coord.R' 'utils.R' 'epwshiftr-package.R' 'esgf.R' 'morph.R'
'netcdf.R'
'eqs2lavaan.R' 'eqsCorr.R' 'eqsCov.R' 'eqsDesc.R'
'out2lavaan.R' 'plotCov.R'
'MipRules.R' 'addnoise.R' 'categorical.R' 'conditional.R'
'dnf.R' 'errorlocalizer.R' 'errorlocate-package.r'
'errorlocation.R' 'expand_weights.R' 'expr_manip.R'
'inspect_mip.R' 'linear.R' 'locate-errors.R' 'log_extract.R'
'mip_lpsolve.R' 'mip_rule.R' 'replace-errors.R' 'soft-rule.R'
'utils.R' 'values.R'
calEdgeCorScore.R ESEA.Main.R PlotGlobEdgeCorProfile.R
PlotPathwayGraph.R PlotRunEnrichment.R SavePathway2File.R
getEnvironmentData.R GetExampleData.R GetEdgesBackgrandData.R
GetPathwayEdgeData.R
'AAA.R' 'eppMatrix.R' 'SpatialPointsBreeding.R'
'DirichletPolygons.R' 'epp.R' 'eppSimDat.R' 'expp-package.R'
'neighborsDataFrame.R'
'cluster_utilities.R' 'decathlon2.R' 'print.factoextra.R'
'get_hmfa.R' 'fviz_hmfa.R' 'get_mfa.R' 'fviz_mfa.R'
'deprecated.R' 'fviz_add.R' 'eigenvalue.R' 'utilities.R'
'dist.R' 'fviz_dend.R' 'hcut.R' 'get_pca.R' 'fviz_cluster.R'
'eclust.R' 'facto_summarize.R' 'fviz.R' 'fviz_ca.R'
'fviz_contrib.R' 'fviz_cos2.R' 'fviz_ellipses.R' 'fviz_famd.R'
'get_mca.R' 'fviz_mca.R' 'fviz_mclust.R' 'fviz_nbclust.R'
'fviz_pca.R' 'fviz_silhouette.R' 'get_ca.R'
'get_clust_tendency.R' 'get_famd.R' 'hkmeans.R' 'housetasks.R'
'multishapes.R' 'poison.R' 'zzz.R'
'constants.R' 'fansi-package.R' 'internal.R' 'load.R' 'misc.R'
'nchar.R' 'strwrap.R' 'strtrim.R' 'strsplit.R' 'substr2.R'
'trimws.R' 'tohtml.R' 'unhandled.R' 'normalize.R' 'sgr.R'
'FastImputation.R' 'TrainFastImputation.R' 'UnfactorColumns.R'
'BoundNormalizedVariable.R' 'NormalizeBoundedVariable.R'
'CovarianceWithMissing.R' 'FI_train.R' 'FI_test.R' 'FI_true.R'
'utilities.R' 'fastqc.R' 'fastqc_install.R' 'qc_aggregate.R'
'qc_plot.R' 'qc_plot_collection.R' 'qc_problems.R' 'qc_read.R'
'qc_read_collection.R' 'qc_report.R' 'qc_unzip.R'
'design.R' 'fhir_url.R' 'fhir_bundle.R' 'fhir_bundle_list.R'
'download_resources.R' 'fhir_body.R' 'fhir_xpath_expression.R'
'fhir_columns.R' 'fhir_style.R' 'fhir_resource_type.R'
'fhir_table_description.R' 'fhir_design.R' 'fhir_table_list.R'
'flatten_resources.R' 'miscellaneous.R' 'multiple_entries.R'
'loggers.R' 'aaa.R' 'HandlerStack.R' 'Fire.R' 'FutureStack.R'
'fake_request.R' 'fiery-package.R'
filehash.R filehash-DB1.R filehash-RDS.R coerce.R dump.R
hash.R queue.R stack.R zzz.R
"fixedincome-internal.R" "utils.R" "term-class.R"
"daycount-class.R" "compounding-class.R"
"interpolation-class.R" "spotrate-class.R"
"spotratecurve-class.R" "spotratecurve-interpolation.R"
"forwardrate-class.R" "compound-method.R" "hidden.R" "zzz.R"
flamingos-package.R RcppExports.R utils.R kmeans.R
mkStochastic.R FData.R ParamMixHMM.R ParamMixHMMR.R
ParamMixRHLP.R StatMixHMM.R StatMixHMMR.R StatMixRHLP.R
ModelMixHMMR.R ModelMixHMM.R ModelMixRHLP.R emMixHMM.R
emMixHMMR.R emMixRHLP.R cemMixRHLP.R data-toydataset.R
'RcppExports.R' 'birds_documentation.R'
'constant_muscle_mass.R' 'constant_specific_power.R'
'constant_specific_work.R' 'control.R' 'method_2.R'
'method_1.R' 'input_match.R' 'lookup_table2.R'
'misc_functions.R' 'flight_simulation.R' 'flying.R' 'migrate.R'
'zzz.R'
'FlyingR.R' 'RcppExports.R' 'birds_documentation.R'
'constant_muscle_mass.R' 'constant_specific_power.R'
'constant_specific_work.R' 'control.R' 'method_2.R'
'method_1.R' 'input_match.R' 'lookup_table2.R'
'misc_functions.R' 'flight_simulation.R' 'migrate.R'
'stopover_mass_calculator.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'
'callCombine.R' 'foreach.R' 'do.R' 'foreach-ext.R'
'foreach-pkg.R' 'getDoPar.R' 'getDoSeq.R' 'getsyms.R' 'iter.R'
'nextElem.R' 'onLoad.R' 'setDoPar.R' 'setDoSeq.R' 'times.R'
'utils.R'
'fill_gaps.R' 'create_windows.R' 'create_skeleton.R'
'combine_forecasts.R' 'lagged_df.R' 'return_error.R'
'return_hyper.R' 'train_model.R' 'data_seatbelts.R'
'data_buoy.R' 'data_buoy_gaps.R' 'zzz.R'
'RcppExports.R' 'acfARp.R' 'f.R' 'forecastSNSTS-package.R'
'measure-of-accuracy.R' 'models.R'
classes_definitions.R classes_constructors.R accessors.R
simufreqD.R simupopD.R AuxFunc.R changepop.R PE.R likelihood.R
likestim.R mincontri.R A2.simu.R A3.simu.R A4.simu.R
mastermix.R N2Exact.R N2error.R simMixSNP.R wrapdataL.R
simPCR2.R simPCR2TK.R PV.R Hbsimu.R LR.R LRmixTK.R
'as.character.formula.R' 'env.R' 'get.vars.R' 'invert.R'
'is.one.sided.R' 'is.two.sided.R' 'lhs.vars.R' 'op.type.R'
'parts.R' 'parts.lhs.get.R' 'parts.lhs.set.R' 'parts.op.get.R'
'parts.op.set.R' 'parts.rhs.get.R' 'parts.rhs.set.R'
'rhs.vars.R' 'split.terms.R' 'terms.R' 'toggle.sign.R' 'zzz.R'
'RcppExports.R' 'autoplot.emfrail.R' 'ca_test_cph.R'
'ca_test_fit.R' 'em_fit.R' 'emfrail_aux.R' 'emfrail.R'
'emfrail_arguments.R' 'emfrail_methods.R' 'fast_Estep.R'
'frailtyEM.R' 'plot.emfrail.R' 'predict.emfrail.R'
'print.emfrail.R' 'print.emfrail_summary.R' 'summary.emfrail.R'
'distances.R' 'expressions.R' 'franc.R' 'ngrams.R'
'normalize.R' 'script.R' 'speakers.R' 'trigrams.R'
alphafrontier.2d.r alphafrontier.3d.r alphascore.r
ordermscore.r ordermscore.boot.r ordermfrontier.2d.r ROCscore.r
'fsr_base.R' 'builder_functions.R' 'fsi_functions.R'
'utility_functions.R' 'spa_functions.R'
'functClust-package.R' 'data.R' 'constants.R' 'stats.R'
'tools.R' 'labelling.R' 'calibrating.R' 'predicting.R'
'clustering.R' 'validating.R' 'validating_loo.R'
'validating_jack.R' 'fclust.R' 'plot_fclust.R' 'test_fclust.R'
'boot_fclust.R' 'fexport.R'
'funDataClass.R' 'coerce.R' 'funDataMethods.R' 'get_set.R'
'names.R' 'plotMethods.R' 'simulation.R' 'str.R' 'subset.R'
'summary.R' 'zzz.R'
'0_funGp_Doc.R' '0_show_Doc.R' '8_outilsCode.R'
'3_ant_admin.R' '2_fgpKern_Class.R' '2_fgpProj_Class.R'
'1_fgpm_Class.R' '1_Xfgpm_Class.R' '3_ant_search.R'
'3_training_F.R' '3_training_S.R' '3_training_SF.R'
'4_prediction_F.R' '4_prediction_S.R' '4_prediction_SF.R'
'5_simulation_F.R' '5_simulation_S.R' '5_simulation_SF.R'
'6_updating.R' '7_blackBoxFunctions.R' '7_checkingFunctions.R'
'7_correlFunctions.R' '7_dimRedFunctions.R'
'7_distanceFunctions.R' '7_plottingFunctions.R'
'8_outilsStats.R'
'options.R' 'appender.R' 'constants.R' 'layout.R' 'logger.R'
'scat.R' 'futile.logger-package.R'
'class-FuzzyData.R' 'class-PairwiseComparisonMatrix.R'
'class-FuzzyPairwiseComparisonMatrix.R' 'class-FuzzyWeights.R'
'class-Weights.R' 'function-bindFuzzyData.R'
'function-buildFuzzyPairwiseComparisonMatrix.R'
'function-buildPairwiseComparisonMatrix.R'
'function-calculateAHP.R' 'function-calculateWeights.R'
'function-compare.R' 'function-consistencyIndex.R'
'function-consistencyRatio.R' 'function-defuzzify.R'
'function-getFuzzyNumbers.R' 'function-getFuzzyScale.R'
'function-insertInto.R' 'function-internal-optimization.R'
'function-internal-weights.R' 'function-print.R'
'function-strictConsistency.R' 'function-textRepresentation.R'
'function-weakConsistency.R' 'package-FuzzyAHP.R'
'Bool-generics.R' 'utils.R' 'Segment-generics.R'
'GaApiRequest.R' 'meta.R' 'globaldata.R' 'Expr-generics.R'
'operand-classes.R' 'comparator-classes.R' 'var-classes.R'
'expr-classes.R' 'segment-classes.R' 'Bool-methods.R'
'Comparator-generics.R' 'comparator-coerce.R'
'Comparator-methods.R' 'TableFilter-generics.R'
'table-filter-classes.R' 'var-list-classes.R' 'query-classes.R'
'Query-generics.R' 'Creds-methods.R' 'Date-generics.R'
'date-coerce.R' 'ga-api-classes.R' 'management-api-classes.R'
'Date-methods.R' 'operand-coerce.R' 'Operand-generics.R'
'Operand-methods.R' 'Var-list-generics.R' 'Var-generics.R'
'var-list-coerce.R' 'var-coerce.R' 'Var-methods.R'
'expr-coerce.R' 'Expr-methods.R' 'GaGetCoreReport.R'
'GaView-generics.R' 'view-coerce.R' 'GaView-methods.R'
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'toCControlList.R' 'validData.R'
parse_text.R sim_numbers.R isNA.R mintersect.R pad.R args.R
history.R cdf2qf.R S4utils.R pseudoInverse.R
'CA.R' 'CR.R' 'DTM.R' 'ExportedFuncs.R' 'GDINA.R' 'GDI.R'
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'autoGDINA.R' 'bootSE.R' 'coef.R' 'dif.R' 'ecpe.R' 'simGDINA.R'
'itemfit.R' 'modelcomp.R' 'extract.R' 'frac20.R'
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'plotIRF.GDINA.R' 's3GDINA.R' 'print.GDINA.R' 'score.R'
'sim10GDINA.R' 'sim10MCDINA.R' 'sim10MCDINA2.R'
'sim20seqGDINA.R' 'sim21seqDINA.R' 'sim30DINA.R' 'sim30GDINA.R'
'sim30pGDINA.R' 'startGDINA.R' 'structuralparm.R'
'summary.GDINA.R' 'utils.R' 'zzz.R'
'anohe.R' 'arrayize.R' 'blobbogram.R' 'template.R' 'code.R'
'data.R' 'deviance.R' 'forest.R' 'solveLP.R' 'inits.R'
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'minimum.diameter.spanning.tree.R' 'mtc.data.studyrow.R'
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'rank.probability.R' 'regression.R' 'relative.effect.R'
'relative.effect.table.R'
'BCdata-class.R' 'BCdata-class-methods.R'
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'text_params.R' 'geom_textpath.R' 'geom_labelpath.R'
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'geom_textdensity2d.R' 'geom_texthline.R' 'geom_textsf.R'
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'text_helpers.R' 'text_placement.R' 'trig_helpers.R'
'gestalt.R' 'utils.R' 'closure.R' 'compose.R' 'constant.R'
'partial.R' 'fn.R' 'context.R' 'posure.R'
'annotate_textp.r' 'coord_proj.r' 'formatters.r' 'fortify.r'
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'geom_xspline.r' 'geom_xspline2.r' 'stat-stepribbon.r'
'ggalt-package.r' 'grob_absolute.r' 'guide_axis.r'
'stateface.r' 'utils.r' 'zzz.r'
'aaa.R' 'anim_save.R' 'animate.R' 'animation_store.R'
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'layer_type.R' 'match_shapes.R' 'plot-build.R' 'post_process.R'
'renderers.R' 'scene.R' 'shadow-.R' 'shadow-mark.R'
'shadow-null.R' 'shadow-trail.R' 'shadow-wake.R' 'transformr.R'
'transition-.R' 'transition-components.R' 'transition-events.R'
'transition-filter.R' 'transition-manual.R'
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'transition-time.R' 'transition_reveal.R' 'transmute-.R'
'transmute-enter.R' 'transmute-exit.R' 'transmuters.R'
'tween_before_stat.R' 'view-.R' 'view-follow.R' 'view-static.R'
'view-step.R' 'view-step-manual.R' 'view-zoom.R'
'view-zoom-manual.R' 'zzz.R'
'asymmetrise.R' 'asymmetrise_stats.R' 'geom_asymmat.R'
'ggasym.R' 'scale_continuous_asym.R' 'scale_fill_gradient.R'
'utils.R'
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"abstract_stat.R" "abstract_stat_slabinterval.R"
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"distributions.R" "draw_key_slabinterval.R" "geom.R"
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"geom_lineribbon.R" "geom_pointinterval.R" "geom_slab.R"
"lkjcorr_marginal.R" "parse_dist.R" "point_interval.R"
"position_dodgejust.R" "rd.R" "rd_slabinterval.R"
"rd_lineribbon.R" "scale_colour_ramp.R" "scale_.R" "stat.R"
"stat_slabinterval.R" "stat_dotsinterval.R"
"stat_pointinterval.R" "stat_interval.R" "stat_lineribbon.R"
"student_t.R" "testthat.R" "theme_ggdist.R"
"tidy_format_translators.R" "deprecated.R"
'RcppExports.R' 'aaa.R' 'shape.R' 'arc_bar.R' 'arc.R'
'autodensity.R' 'autohistogram.R' 'autopoint.R' 'bezier.R'
'bspline.R' 'bspline_closed.R' 'circle.R' 'diagonal.R'
'diagonal_wide.R' 'ellipse.R' 'facet_grid_paginate.R'
'facet_matrix.R' 'facet_row.R' 'facet_stereo.R'
'facet_wrap_paginate.R' 'facet_zoom.R' 'ggforce_package.R'
'ggproto-classes.R' 'interpolate.R' 'link.R' 'mark_circle.R'
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'parallel_sets.R' 'position-jitternormal.R' 'position_auto.R'
'position_floatstack.R' 'regon.R' 'scale-depth.R'
'scale-unit.R' 'sina.R' 'spiro.R' 'themes.R' 'trans.R'
'trans_linear.R' 'utilities.R' 'voronoi.R' 'zzz.R'
'MIpop-doc.R' 'formula2aes.R' 'gf_aux.R' 'gf_dist.R'
'layer_factory.R' 'gf_function2d.R' 'gf_functions.R'
'gf_plot.R' 'ggstance.R' 'ggridges.R' 'ggstrings.R'
'newplots.R' 'reexports.R' 'scales.R' 'utils.R' 'relabel.R'
'vdiffr.R' 'zzz.R'
'conveniences.R' 'element_part_rect.R' 'facet_grid2.R'
'facet_wrap2.R' 'facet_manual.R' 'facet_nested.R'
'facet_nested_wrap.R' 'facetted_pos_scales.R'
'force_panelsize.R' 'geom_pointpath.R' 'geom_polygonraster.R'
'geom_rectrug.R' 'geom_text_aimed.R' 'ggh4x-package.R'
'ggh4x_extensions.R' 'guide_axis_logticks.R'
'guide_axis_manual.R' 'guide_axis_minor.R'
'guide_axis_nested.R' 'guide_axis_truncated.R'
'guide_axis_utils.R' 'guide_dendrogram.R'
'guide_stringlegend.R' 'help_secondary.R'
'position_disjoint_ranges.R' 'position_lineartrans.R'
'scale_dendrogram.R' 'scale_listed.R' 'scale_multi.R'
'stat_difference.R' 'stat_funxy.R' 'stat_rle.R' 'stat_roll.R'
'stat_theodensity.R' 'strip_vanilla.R' 'strip_themed.R'
'strip_nested.R' 'themes.R' 'utils.R' 'utils_grid.R'
'utils_gtable.R'
'utilities.R' 'geom_half_dotplot.R' 'geom_half_boxplot.R'
'geom_half_point.R' 'geom_half_point_panel.R'
'geom_half_violin.R' 'stat-half-bindot.R' 'stat-half-point.R'
'stat-half-ydensity.R' 'ggproto-classes.R'
'data.R' 'functions.R' 'ggmcmc.R' 'ggs.R' 'ggs_Rhat.R'
'ggs_autocorrelation.R' 'ggs_caterpillar.R'
'ggs_compare_partial.R' 'ggs_crosscorrelation.R'
'ggs_density.R' 'ggs_effective.R' 'ggs_geweke.R'
'ggs_diagnostics.R' 'ggs_grb.R' 'ggs_histogram.R' 'ggs_pairs.R'
'ggs_pcp.R' 'ggs_ppmean.R' 'ggs_ppsd.R' 'ggs_rocplot.R'
'ggs_running.R' 'ggs_separation.R' 'ggs_traceplot.R'
'globals.R' 'help.R'
'utilities.R' 'fortify-igraph.R' 'fortify-network.R'
'geom-nodes.R' 'geom-edges.R' 'ggnetwork.R'
'pacman-package.R' 'compute_points_eaten.R'
'compute_pacman_coord.R' 'compute_ghost_status.R'
'compute_ghost_coord.R' 'animate_pacman.R'
'ggparliament_package.R' 'data.R' 'draw_majoritythreshold.R'
'helper_funcs.R' 'parliament_data.R' 'draw_partylabels.R'
'draw_totalseats.R' 'geom_parliament_seats.R'
'geom_emphasize_parliamentarians.R' 'geom_parliament_bar.R'
'geom_highlight_government.R' 'geom_overhang_seats.R'
'theme_ggparliament.R'
'aaa-ggplot2-compat-plyr.R' 'aaa-ggplot2-ggplot-global.R'
'aaa-ggplot2-performance.R' 'aaa-ggplot2-scale-manual.R'
'aaa-ggplot2-utilities-grid.R' 'aaa-ggplot2-utilities.R'
'aab-utils.R' 'geom-.R' 'geom-rect.R' 'geom-bar.R'
'geom-bin2d.R' 'geom-boxplot.R' 'geom-col.R' 'geom-crossbar.R'
'geom-ribbon.R' 'geom-density.R' 'geom-polygon.R' 'geom-map.R'
'geom-sf.R' 'geom-tile.R' 'geom-violin.R' 'ggpattern-defunct.R'
'ggpattern-deprecated.R' 'ggpattern-package.R' 'pattern.R'
'polygon_df.R' 'scale-pattern-alpha.R' 'scale-pattern-brewer.R'
'scale-pattern-colour.R' 'scale-pattern-gradient.R'
'scale-pattern-grey.R' 'scale-pattern-hue.R'
'scale-pattern-linetype.R' 'scale-pattern-shape.R'
'scale-pattern-size.R' 'scale-pattern-viridis.R'
'scale-pattern.R' 'zxx.r' 'zzz.R'
'facet_share.R' 'utilities.R' 'geom_arcbar.R' 'geom_bartext.R'
'geom_boxjitter.R' 'geom_circle.R' 'geom_confmat.R'
'geom_parliament.R' 'geom_tshighlight.R' 'ggproto-classes.R'
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'stat-confmat.R' 'stat-parliament.R'
'utilities_color.R' 'utilities_base.R' 'desc_statby.R'
'utilities.R' 'add_summary.R' 'annotate_figure.R' 'as_ggplot.R'
'axis_scale.R' 'background_image.R' 'bgcolor.R' 'border.R'
'compare_means.R' 'create_aes.R' 'diff_express.R' 'facet.R'
'font.R' 'gene_citation.R' 'geom_bracket.R' 'geom_exec.R'
'get_breaks.R' 'get_legend.R' 'get_palette.R' 'ggadd.R'
'ggarrange.R' 'ggballoonplot.R' 'ggpar.R' 'ggbarplot.R'
'ggboxplot.R' 'ggdensity.R' 'ggpie.R' 'ggdonutchart.R'
'stat_conf_ellipse.R' 'stat_chull.R' 'ggdotchart.R'
'ggdotplot.R' 'ggecdf.R' 'ggerrorplot.R' 'ggexport.R'
'gghistogram.R' 'ggline.R' 'ggmaplot.R' 'ggpaired.R'
'ggparagraph.R' 'ggpubr_args.R' 'ggqqplot.R'
'utilities_label.R' 'stat_cor.R' 'stat_stars.R' 'ggscatter.R'
'ggscatterhist.R' 'ggstripchart.R' 'ggsummarystats.R'
'ggtext.R' 'ggtexttable.R' 'ggviolin.R' 'gradient_color.R'
'grids.R' 'reexports.R' 'rotate.R' 'rotate_axis_text.R'
'rremove.R' 'set_palette.R' 'show_line_types.R'
'show_point_shapes.R' 'stat_central_tendency.R'
'stat_compare_means.R' 'stat_mean.R'
'stat_overlay_normal_density.R' 'stat_pvalue_manual.R'
'stat_regline_equation.R' 'text_grob.R' 'theme_pubr.R'
'theme_transparent.R' 'utils-geom-signif.R' 'utils-pipe.R'
'data.R' 'ggridges.R' 'geoms.R' 'geomsv.R' 'geoms-gradient.R'
'geom-density-line.R' 'position.R' 'scale-cyclical.R'
'scale-point.R' 'scale-vline.R' 'stats.R' 'theme.R'
'utils_ggplot2.R' 'utils.R'
'geom-glowpath.r' 'geom-glowpoint.r' 'geom-shadowpath.r'
'geom-shadowpoint.r' 'internal-doc.r' 'scale-shadow.r'
'ggsom.R' 'ggsom_aes.R' 'ggsom_entropy.R' 'ggsom_plot.R'
'ggsom_utils.R' 'zzz.R'
'flip-aes.R' 'geom-barh.R' 'legend-draw.R' 'geom-boxploth.R'
'geom-colh.R' 'geom-crossbarh.R' 'geom-errorbarh.R'
'geom-histogramh.R' 'geom-linerangeh.R' 'geom-pointrangeh.R'
'geom-violinh.R' 'ggstance.R' 'position-dodgev.R'
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'calc-mahalanobis-distance.R' 'calc-kde2d-weighted.R'
'doc-data.R' 'doc-theme-convenience.R' 'depreciated.R'
'labels-new.R' 'labels-percent.R' 'legend-draw-tern.R'
'ggtern-constructor.R' 'gg-internal.R'
'modifications-gridExtra.R' 'onLoad.R' 'plot.R' 'plot-build.R'
'plot-construction.R' 'position-.R' 'position-nudge-tern.R'
'position-jitter-tern.R' 'save.R' 'scales-tern.R'
'strip-unapproved.R' 'tern-limits.R' 'theme.R'
'theme-arrowlength.R' 'theme-clockwise.R' 'theme-defaults.R'
'theme-elements.R' 'theme-gridsontop.R' 'theme-bordersontop.R'
'theme-legend-position.R' 'theme-latex.R' 'theme-mesh.R'
'theme-noarrows.R' 'theme-nomask.R' 'theme-novar-tern.R'
'theme-rotate.R' 'theme-showgrid.R' 'theme-showlabels.R'
'theme-ticks.R' 'theme-showtitles.R' 'theme-ticksoutside.R'
'theme-zoom.R' 'utilities.R' 'utilities-help.R'
'geom-density-tern.R' 'stat-density-tern.R' 'geom-mask.R'
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'stat-confidence-tern.R' 'geom-errorbarX.R'
'geom-smooth-tern.R' 'stat-smooth-tern.R' 'geom-mean-ellipse.R'
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'geom-crosshair-tern.R' 'geom-point-swap.R' 'geom-hex-tern.R'
'stat-hex-tern.R' 'annotation-tern.R'
'annotation-raster-tern.R' 'geom-text-viewport.R'
'geom-label-viewport.R' 'geom-polygon-closed.R'
'geom-tri-tern.R' 'stat-tri-tern.R'
GiniDistance-package.R RcppExports.R gmd.R gCov.R gCor.R
dCov.R dCor.R Kgmd.R KgCov.R KgCor.R KdCov.R KdCor.R
ConfidenceInterval.R PermutationTest.R utils.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'
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'punch_card.R' 'push.R' 'reference.R' 'reflog.R' 'refspec.R'
'remote.R' 'repository.R' 'reset.R' 'revparse.R' 'sha.R'
'signature.R' 'stash.R' 'status.R' 'tag.R' 'time.R' 'tree.R'
'when.R'
'clean_data_path.R' 'datahash.R' 'git2rdata_package.R'
'write_vc.R' 'is_git2rdata.R' 'is_git2rmeta.R' 'list_data.R'
'meta.R' 'prune.R' 'read_vc.R' 'recent_commit.R' 'reexport.R'
'relabel.R' 'rename_variable.R' 'upgrade_data.R' 'utils.R'
'verify_vc.R'
'RcppExports.R' 'allClasses.R' 'cut.dataset.R'
'discretize.link.R' 'generic_cutpoints.R'
'generic_discretize.R' 'glmdisc.R' 'initialization.R'
'method_cutpoints.R' 'method_discretize.R' 'method_plot.R'
'method_predict.R' 'methods_disc.R' 'normalizedGini.R'
'semDiscretization.R'
'glmnetUtils.r' 'cvGlmnetFormula.r' 'cvaGlmnetFormula.r'
'glmnetFormula.r' 'makeModelComponents.r'
generics.R glpk_ptrClass.R glpk.R glpkAPI.R zzz.R
'analysis.R' 'calcPower.R' 'graphMCP.R' 'ci.R' 'closure.R'
'convertFromOldClassDefinition.R' 'doRUnitTests.R'
'exampleGraphs.R' 'gACT-internal.R' 'gMCP.R' 'gMCP.extended.R'
'gPAD.R' 'generateBounds.R' 'generatePvals.R' 'generateTest.R'
'generateWeights.R' 'graph2latex.R' 'graphTest.R' 'helperGUI.R'
'matrix2graph.R' 'misc.R' 'modifyGraphs.R' 'onLoad.R'
'plotCI.R' 'powerPlot.R' 'rqmvnorm.R' 'sampSize.R'
'startGUIs.R' 'subVariables.R'
'Anamorphosis.R' 'preparations.R' 'gstatCompatibility.R'
'gmAnisotropy.R' 'compositionsCompatibility.R' 'variograms.R'
'gmSpatialMethodParameters.R' 'abstractClasses.R' 'accuracy.R'
'closeup.R' 'data.R' 'exploratools.R' 'genDiag.R' 'geostats.R'
'gmDataFrameStack.R' 'gmSimulation.R' 'gmSpatialModel.R'
'gmValidationStrategy.R' 'grids.R' 'mask.R' 'spSupport.R'
'uncorrelationTest.R' 'zzz.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'
'helpers.R' 'means.R' 'weights.R' 'contributions.R'
'price-indexes.R' 'geks.R' 'operators.R' 'offset-prices.R'
'outliers.R'
'AAA-utils.R' 'R6--aaa-utils.R' 'S3--aaa-setup.R'
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'R6-6.1-executing-requests.R' 'R6-Schema.R' 'R6z-from-json.R'
'graphql_json.R' 'R6-3.2-directives.R' 'gqlr_schema.R'
'R6-4-introspection.R' 'R6-6.2-executing-operations.R'
'R6-6.3-executing-selection-sets.R' 'R6-6.4-executing-fields.R'
'R6-7-response.R' 'R6-ErrorList.R' 'R6-ObjectHelpers.R'
'R6-Result.R' 'R6-VariableValdationHelper.R'
'S3-3.1.2.3-validation-object-type.R' 'S3-str.R'
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'zzz.R'
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'distance.R' 'export.R' 'extract.R' 'ggGS.R' 'graphdf.R'
'habitatConnectivityEngine.R' 'patchFilter.R' 'plot.R'
'point.R' 'theme_grainscape.R' 'threshold.R' 'zzz.R'
'granovaGG-package.R' 'granovagg.1w-helpers.R'
'theme-defaults.R' 'shared-functions.R' 'granovagg.1w.R'
'granovagg.contr.R' 'granovagg.ds.R'
'RcppExports.R' 'tools_misc.R' 'models_classes.R'
'fit_classes.R' 'tools_cleanpath.R' 'alg_genetic.R'
'alg_hybrid.R' 'alg_classes.R' 'alg_multistart.R' 'data.R'
'greed.R' 'model_combinedmodels.R' 'model_dclbm.R'
'model_dcsbm.R' 'model_diaggmm.R' 'model_gmm.R' 'model_lca.R'
'model_mom.R' 'model_mor.R' 'model_multsbm.R' 'model_sbm.R'
'tools_generator.R' 'tools_ploting.R'
'GREMLINS_package.R' 'BMPOO.R' 'defineNetwork.R'
'multipartiteBM.R' 'VEMPOOVersion.R'
'multipartiteBMFixedModel.R' 'initializeVEM.R' 'plotMBM.R'
'searchVKPOOVersion.R' 'utils.R' 'compLikICL.R'
'extractClustersMBM.R' 'rMBM.R' 'comparClassif.R'
'MPEcoNetwork.R' 'predictMBM.R'
'package.R' 'utils.R' 'greta_mcmc_list.R' 'tf_functions.R'
'overloaded.R' 'node_class.R' 'node_types.R' 'variable.R'
'probability_distributions.R' 'mixture.R' 'joint.R'
'unknowns_class.R' 'greta_array_class.R' 'as_data.R'
'distribution.R' 'operators.R' 'functions.R' 'transforms.R'
'structures.R' 'extract_replace_combine.R' 'dag_class.R'
'greta_model_class.R' 'progress_bar.R' 'inference_class.R'
'samplers.R' 'optimisers.R' 'inference.R'
'install_tensorflow.R' 'calculate.R' 'callbacks.R' 'simulate.R'
'chol2symm.R' 'install_greta_deps.R' 'conda_greta_env.R'
'greta_stash.R' 'greta_create_conda_env.R'
'greta_install_miniconda.R' 'greta_install_python_deps.R'
'new_install_process.R' 'reinstallers.R' 'checkers.R'
'test_if_forked_cluster.R' 'testthat-helpers.R' 'zzz.R'
'internals.R'
'create.R' 'grnn-package.r' 'guess.r' 'kernel.R' 'learn.R'
'smooth.R'
'MSPE.R' 'cps.R' 'gen_informative_sample.R' 'gpdpgrow.R'
'gmrfdpgrow.R' 'gp_car_fit_compare_facet.R' 'gp_cluster_plot.R'
'gp_informative_compare_plot.R' 'help.R' 'plot_cluster.R'
'predict_plot.R'
'negativeExponential.R' 'monomolecular.R' 'mitcherlich.R'
'gompertz.R' 'logistic.R' 'chapmanRichards.R' 'richard.R'
'vonBertalanffy.R' 'weibull.R' 'loglogistic.R' 'mmf.R'
'schnute.R' 'stannard.R' 'brody.R' 'growthmodels.R'
'blumberg.R'
'aaa_growthmodel-class.R' 'aab_growthmodel-constructor.R'
'aac_classes.R' 'aad_set_generics.R' 'all_easylinear.R'
'all_growthmodels.R' 'all_splines.R' 'antibiotic.R'
'bactgrowth.R' 'cost.R' 'extcoef_logistic.R' 'extract.R'
'fit_easylinear.R' 'fit_growthmodel.R' 'fit_spline.R'
'grow_baranyi.R' 'grow_exponential.R' 'grow_genlogistic.R'
'grow_gompertz.R' 'grow_gompertz2.R' 'grow_huang.R'
'grow_logistic.R' 'grow_richards.R' 'grow_twostep.R'
'growthrates-package.R' 'lm_window.R' 'methods.R'
'multisplit.R' 'names.R' 'parse_formula.R'
'parse_formula_nonlin.R' 'plot.R' 'predict.R' 'utilities.R'
'as_data_frame.R' 'build_data.R' 'compile_scss.R'
'data_color.R' 'datasets.R' 'dt__.R' 'dt_body.R' 'dt_boxhead.R'
'dt_cols_merge.R' 'dt_data.R' 'dt_footnotes.R' 'dt_formats.R'
'dt_groups_rows.R' 'dt_has_built.R' 'dt_heading.R'
'dt_locale.R' 'dt_options.R' 'dt_row_groups.R'
'dt_source_notes.R' 'dt_spanners.R' 'dt_stub_df.R'
'dt_stubhead.R' 'dt_styles.R' 'dt_summary.R' 'dt_transforms.R'
'export.R' 'format_data.R' 'gt-package.R' 'gt.R' 'gt_preview.R'
'helpers.R' 'image.R' 'info_tables.R' 'knitr-utils.R'
'location_methods.R' 'modify_columns.R' 'modify_rows.R'
'tab_create_modify.R' 'opts.R' 'print.R' 'reexports.R'
'render_as_html.R' 'resolver.R' 'shiny.R' 'summary_rows.R'
'text_transform.R' 'utils.R' 'utils_formatters.R'
'utils_general_str_formatting.R' 'utils_pipe.R'
'utils_render_common.R' 'utils_render_footnotes.R'
'utils_render_html.R' 'utils_render_latex.R'
'utils_render_rtf.R' 'zzz.R'
'new-data-frame.r' 'add-grob.r' 'add-rows-cols.r'
'add-space.r' 'grid.r' 'gtable-layouts.r' 'gtable-package.R'
'gtable.r' 'rbind-cbind.r' 'utils.r' 'trim.r' 'filter.r'
'align.r' 'padding.r' 'z.r'
'gtfsio_error.R' 'assert_gtfs.R' 'assert_inputs.R' 'checks.R'
'export_gtfs.R' 'get_gtfs_standards.R' 'gtfs_methods.R'
'gtfs_subset.R' 'gtfsio.R' 'import_gtfs.R' 'new_gtfs.R'
'data.R' 'utils.R' 'define_variance_wrapper.R'
'variance_function.R' 'define_statistic_wrapper.R'
'standard_statistic_wrapper.R' 'qvar.R'
'browser.R' 'class-associations.R' 'class-studies.R'
'class-traits.R' 'class-variants.R' 'data.R' 'ebi_server.R'
'generics.R' 'get_associations.R' 'get_metadata.R'
'get_studies.R' 'get_traits.R' 'get_variants.R'
'gwasrapidd-package.R' 'id_mapping.R' 'list_joins.R'
'missing.R' 'parse-associations.R' 'parse-studies.R'
'parse-traits.R' 'parse-utils.R' 'parse-variants.R'
'post-studies.R' 'post-traits.R' 'post-variants.R'
'recursive_apply.R' 'request.R' 's4-utils.R' 'sure.R' 'tests.R'
'utils-pipe.R' 'utils.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'
'List.R' 'gWidgets2tcltk-package.R' 'misc.R' 'tcltk-misc.R'
'dnd.R' 'GComponent.R' 'GContainer.R' 'GWidget.R' 'dialogs.R'
'gmenu.R' 'gaction.R' 'gbutton.R' 'gcalendar.R' 'gcheckbox.R'
'gtable.R' 'gcheckboxgroup.R' 'gcombobox.R' 'tablelist.R'
'gdf.R' 'gedit.R' 'gexpandgroup.R' 'gfile.R' 'gformlayout.R'
'ggroup.R' 'gframe.R' 'gimage.R' 'glabel.R' 'glayout.R'
'gnotebook.R' 'gpanedgroup.R' 'gprogressbar.R' 'gradio.R'
'gseparator.R' 'gslider.R' 'gspinbutton.R' 'gstackwidget.R'
'gstatusbar.R' 'gtext.R' 'gtimer.R' 'gtoolbar.R' 'gtree.R'
'gvarbrowser.R' 'gwindow.R' 'icons.R' 'tk2tip.R' 'zzz.R'
'aggregator.R' 'astfun.R' 'automl.R' 'classes.R' 'config.R'
'connection.R' 'constants.R' 'datasets.R' 'logging.R'
'communication.R' 'kvstore.R' 'frame.R' 'targetencoder.R'
'import.R' 'isolationforest.R' 'extendedisolationforest.R'
'parse.R' 'export.R' 'edicts.R' 'models.R' 'coxph.R'
'coxphutils.R' 'kmeans.R' 'gam.R' 'anovaglm.R' 'gbm.R'
'generic.R' 'glm.R' 'glrm.R' 'modelselection.R' 'pca.R' 'svd.R'
'psvm.R' 'deeplearning.R' 'stackedensemble.R' 'xgboost.R'
'randomforest.R' 'naivebayes.R' 'word2vec.R' 'w2vutils.R'
'locate.R' 'grid.R' 'segment.R' 'predict.R' 'tf-idf.R'
'rulefit.R' 'explain.R' 'permutation_varimp.R' 'infogram.R'
'zzz.R' 'upliftrandomforest.R'
'HDIofMCMC.R' 'preprocess_funcs.R' 'stanmodels.R' 'settings.R'
'hBayesDM_model.R' 'alt_delta.R' 'alt_gamma.R'
'bandit2arm_delta.R' 'bandit4arm2_kalman_filter.R'
'bandit4arm_2par_lapse.R' 'bandit4arm_4par.R'
'bandit4arm_lapse.R' 'bandit4arm_lapse_decay.R'
'bandit4arm_singleA_lapse.R' 'bart_ewmv.R' 'bart_par4.R'
'cgt_cm.R' 'choiceRT_ddm.R' 'choiceRT_ddm_single.R'
'choiceRT_lba.R' 'choiceRT_lba_single.R' 'cra_exp.R'
'cra_linear.R' 'dbdm_prob_weight.R' 'dd_cs.R' 'dd_cs_single.R'
'dd_exp.R' 'dd_hyperbolic.R' 'dd_hyperbolic_single.R'
'estimate_mode.R' 'extract_ic.R' 'gng_m1.R' 'gng_m2.R'
'gng_m3.R' 'gng_m4.R' 'hBayesDM.R' 'igt_orl.R'
'igt_pvl_decay.R' 'igt_pvl_delta.R' 'igt_vpp.R' 'multiplot.R'
'peer_ocu.R' 'plot.hBayesDM.R' 'plotDist.R' 'plotHDI.R'
'plotInd.R' 'printFit.R' 'prl_ewa.R' 'prl_fictitious.R'
'prl_fictitious_multipleB.R' 'prl_fictitious_rp.R'
'prl_fictitious_rp_woa.R' 'prl_fictitious_woa.R' 'prl_rp.R'
'prl_rp_multipleB.R' 'pst_Q.R' 'pst_gainloss_Q.R' 'ra_noLA.R'
'ra_noRA.R' 'ra_prospect.R' 'rdt_happiness.R' 'rhat.R'
'task2AFC_sdt.R' 'ts_par4.R' 'ts_par6.R' 'ts_par7.R'
'ug_bayes.R' 'ug_delta.R' 'wcs_sql.R' 'zzz.R'
'AllClass.R' 'AllGeneric.R' 'AllMethod.R' 'clustControl.R'
'trainControl.R' 'hmmvbClust.R' 'hmmvbTrain.R'
'vbSearchControl.R' 'package-HDclust.R' 'RcppExports.R'
'sim3.R' 'sim2.R' 'hmmvbBIC.R' 'hmmvbFindModes.R'
'clustModes.R'
'Common_functions.R' 'Compound.R' 'H5constants.R'
'Helper_functions.R' 'Misc.R' 'R6Classes.R' 'R6Classes_H5A.R'
'R6Classes_H5D.R' 'R6Classes_H5File.R' 'R6Classes_H5Group.R'
'R6Classes_H5P.R' 'R6Classes_H5R.R' 'R6Classes_H5S.R'
'R6Classes_H5T.R' 'adapt_during_onLoad.R' 'convert.R'
'factor_ext.R' 'globalVariables.R' 'h5errorHandling.R'
'h5wrapper.R' 'hdf5r.R' 'high_level_UI.R' 'open_objs.R' 'zzz.R'
'documentation_page.R' 'clusters.R' 'permutations.R'
'gaussian_multi.R' 'wmw_multi.R' 'wmw_fonc.R' 'scan_fonc.R'
'scan_multi.R' 'plot_map.R' 'RcppExports.R'
'dfree_index_multi.R' 'pointwise_dfree.R'
'pointwise_wmw_multi.R' 'pointwise_wmw_uni.R'
'transform_data.R' 'data_fmulti_data.R' 'data_funi_data.R'
'data_multi_data.R' 'data_map_sites.R' 'posteriori_filtering.R'
'non_overlap.R' 'scan_uni.R' 'wmw_uni.R' 'dfree.R'
'finalizing_scan_procedure.R' 'HDSpatialScan_plot.R'
'HDSpatialScan_Classes.R' 'HDSpatialScan_plotCurves.R'
'HDSpatialScan_plotSummary.R' 'HDSpatialScan_print.R'
'HDSpatialScan_summary.R' 'InitScan.R' 'SpatialScan.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'
'index_div.R' 'hill_div.R' 'div_profile.R'
'div_profile_plot.R' 'div_test.R' 'div_test_plot.R'
'depth_cov.R' 'div_part.R' 'alpha_div.R' 'gamma_div.R'
'beta_dis.R' 'pair_dis.R' 'pair_dis_plot.R' 'UqN.R' 'CqN.R'
'VqN.R' 'SqN.R' 'match_data.R' 'depth_filt.R' 'copy_filt.R'
'to.incidence.R' 'tss.R' 'is.nested.R' 'tree_depth.R' 'data.R'
'add_shadow_2d.R' 'matrix_to_raster.R' 'write_raster.R'
'utils.R' 'hillshader.R' 'maungawhau.R' 'maungawhau_hr.R'
utils.R simHIMA.R hima.R survHIMA.R microHIMA.R onAttach.R
HIMA-package.R
'For_Rccp_int.R' 'All_classes.R' 'RcppExports.R' 'Utility.R'
'Met_HTS.R' 'Met_MatH.R' 'Met_distributionH.R' 'Fuzzy_cmeans.R'
'H_time_series.R' 'HistDAWass-package.R' 'Kohonen_maps.R'
'principal_components.R' 'regression.R'
'unsuperv_classification.R' 'Plotting_with_ggplot.R'
'00first.R' 'allClasses.R' 'history.R' 'tracking.R' 'zzz.R'
'saveload.R' 'prov.R'
'diagnostic_functions.R' 'group_level_residual_functions.R'
'identification.R' 'plot_functions.R' 'adjust_formula_lmList.R'
'case_delete.R' 'LSresids.R' 'HLMdiag-deprecated.R'
'HLMdiag-package.R' 'hlm_augment.R' 'hlm_influence.R'
'hlm_resid.R' 'help.R' 'influence_functions.R'
'utility_functions.R' 'rotate_ranefs.R' 'autism.R' 'ahd.R'
'radon.R' 'pull_resid.R' 'residual_functions.R'
'HLMdiag-defunct.R'
RcppExports.R helper_functions.R HMB-class.R
SummaryHMB-class.R ghmb.R gtsmb.R hmb.R tsmb.R
'age2int.R' 'HFDutils.R' 'HMDutils.R' 'readHFD.R' 'readHMD.R'
'userInput.R'
'colors.R' 'html_dependency.R' 'html_escape.R' 'html_print.R'
'images.R' 'known_tags.R' 'selector.R' 'shim.R' 'tag_query.R'
'utils.R' 'tags.R' 'template.R'
'RcppExports.R' 'httpuv.R' 'random_port.R' 'server.R'
'static_paths.R' 'utils.R'
'validate.R' 'hyperspec-class.R' 'unittest.R' 'paste.row.R'
'Arith.R' 'Compare.R' 'DollarNames.R' 'Math.R' 'chk.hy.R'
'read.txt.wide.R' 'read.txt.long.R' 'options.R' 'wl.R'
'fileio.optional.R' 'NEW-functions.R' 'Summary.R' 'aggregate.R'
'all.equal.R' 'apply.R' 'as.data.frame.R' 'barbiturates.R'
'bind.R' 'call.list.R' 'chondro.R' 'colMeans.R' 'collapse.R'
'count_lines.R' 'cov.R' 'decomposition.R' 'deprecated.R'
'dim.R' 'dimnames.R' 'droplevels.R' 'empty.R' 'wl2i.R'
'extract.R' 'factor2num.R' 'fix_spc_colnames.R' 'flu.R'
'getbynames.R' 'regexps.R' 'guesswavelength.R' 'paracetamol.R'
'laser.R' 'hyperspec-package.R' 'initialize.R' 'labels.R'
'plotmap.R' 'levelplot.R' 'makeraster.R' 'map.identify.R'
'map.sel.poly.R' 'mark.dendrogram.R' 'mark.peak.R'
'matlab.palette.R' 'mean_sd.R' 'merge.R' 'mvtnorm.R'
'normalize01.R' 'orderwl.R' 'pearson.dist.R' 'plot.R' 'plotc.R'
'plotmat.R' 'plotspc.R' 'plotvoronoi.R' 'qplot.R'
'qplotmixmap.R' 'quantile.R' 'rbind.fill.R' 'read.ENVI.R'
'read.ENVI.HySpex.R' 'read.ENVI.Nicolet.R' 'read.txt.Witec.R'
'read.asc.Andor.R' 'read.asc.PerkinElmer.R' 'read.ini.R'
'read.jdx.R' 'read.mat.Cytospec.R' 'read.mat.Witec.R'
'read.spc.Kaiser.R' 'read.spc.R' 'read.spc.Shimadzu.R'
'read.spe.R' 'read.txt.Horiba.R' 'read.txt.Renishaw.R'
'read.txt.Shimadzu.R' 'replace.R' 'sample.R' 'scale.R' 'seq.R'
'show.R' 'spc.NA.approx.R' 'spc.bin.R' 'spc.fit.poly.R'
'spc.identify.R' 'spc.loess.R' 'spc.rubberband.R'
'spc.spline.R' 'split.R' 'split.string.R' 'splitdots.R'
'subset.R' 'sweep.R' 'trellis.factor.key.R' 'units.R'
'vandermonde.R' 'wc.R' 'wleval.R' 'write.txt.long.R'
'write.txt.wide.R' 'y-pastenames.R' 'zzz.R'
'i2dashboard.R' 'AllGenerics.R' 'assemble.R' 'components.R'
'colormap.R' 'get_set.R' 'i2dash-pkg.R' 'pages.R' 'reexports.R'
'sidebar.R' 'vis_objects.R'
CrossValidation.R IBCF.R IBCF.MTME.R IBCFYear.R methods.R
Td2M.R Title.R Wheat_IBCF.R Year_IBCF.R
'myOptim.R' 'minObj.R' 'baseInfo.R' 'CaseCohort_Obj.R'
'CaseCohort_gr.R' 'CaseCohort_fn.R' 'class_ICODS.R'
'CaseCohort_class.R' 'bernstein.R' 'CaseCohortIC.R'
'CaseCohort_data.R' 'ODSDesign_Obj.R' 'ODSDesign_class.R'
'ODSDesignIC.R' 'ODSDesign_data.R' 'ODSDesign_fn.R'
'ODSDesign_gr.R' 'testInputData.R'
zzz.R data.R utils-conversion.R utils-spherical.R
utils-vectors.R grid-build.R grid-lookup.R grid-move.R
grid-subset.R grid-attributes.R grid-graphs.R grid-sp-lines.R
grid-sp-polygons.R grid-resample.R data-gridlayer-basic.R
data-gridlayer-attributes.R data-gridlayer-groupgen.R
data-gridlayer-subset.R data-facelayer-basic.R
data-facelayer-graphs.R data-facelayer-resample.R plot-legend.R
plot-2d-grid.R plot-2d-facelayer.R plot-rgl-util.R
plot-rgl-grid.R plot-rgl-facelayer.R plot-rgl-sp3d.R
'VEplot.R' 'VEcal.R' 'postProcess.R' 'EMmeth.R'
'RcppExports.R' 'verifyInputs.R' 'idove.R' 'idoveData.R'
'intCens.R' 'plot.iDOVE.R' 'print.iDOVE.R'
'adjacency.R' 'auto.R' 'assortativity.R' 'attributes.R'
'basic.R' 'bipartite.R' 'centrality.R' 'centralization.R'
'cliques.R' 'cocitation.R' 'cohesive.blocks.R' 'coloring.R'
'community.R' 'components.R' 'console.R' 'conversion.R'
'data_frame.R' 'decomposition.R' 'degseq.R' 'demo.R'
'efficiency.R' 'embedding.R' 'epi.R' 'eulerian.R' 'fit.R'
'flow.R' 'foreign.R' 'games.R' 'glet.R' 'hrg.R'
'igraph-package.R' 'incidence.R' 'indexing.R' 'interface.R'
'iterators.R' 'layout.R' 'layout_drl.R' 'lazyeval.R' 'make.R'
'minimum.spanning.tree.R' 'motifs.R' 'nexus.R' 'operators.R'
'other.R' 'package.R' 'palette.R' 'par.R' 'paths.R' 'plot.R'
'plot.common.R' 'plot.shapes.R' 'pp.R' 'print.R' 'printr.R'
'random_walk.R' 'rewire.R' 'scan.R' 'scg.R' 'sgm.R'
'similarity.R' 'simple.R' 'sir.R' 'socnet.R' 'sparsedf.R'
'structural.properties.R' 'structure.info.R' 'test.R'
'tkplot.R' 'topology.R' 'trees.R' 'triangles.R' 'utils.R'
'uuid.R' 'versions.R' 'weakref.R' 'zzz-deprecate.R'
'AllClasses.R' 'AllGenerics.R' 'annotations.R' 'axes.R'
'axis_labels.R' 'axis_titles.R' 'barplot.R' 'clustering.R'
'clusters.R' 'colorbars.R' 'colors.R' 'components.R'
'dendogram.R' 'generic_subplots.R' 'groups.R' 'main_heatmap.R'
'iheatmap.R' 'iheatmapr.R' 'layout.R' 'list_utils.R' 'shiny.R'
'shiny_test.R' 'signal.R' 'subplot.R' 'summary.R' 'to_widget.R'
'utils.R'
'utils.R' 'complexity.R' 'contrast.R' 'imagefluency-package.R'
'imagefluencyApp.R' 'self-similarity.R' 'simplicity.R'
'symmetry.R' 'typicality.R'
'MainFunction.R' 'Diagnostics.R' 'FullConditionals.R'
'Gibbs_FA.R' 'Gibbs_IFA.R' 'Gibbs_IMFA.R' 'Gibbs_IMIFA.R'
'Gibbs_MFA.R' 'Gibbs_MIFA.R' 'Gibbs_OMFA.R' 'Gibbs_OMIFA.R'
'IMIFA.R' 'PlottingFunctions.R' 'SimulateData.R' 'data.R'
'RcppExports.R' 'class_imputeMulti.R' 'data-tract2221.R'
'data_dep_prior_multi.R' 'imputeMulti-package.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'
'0_inlabru_envir.R' 'bru.covariate.R' 'bru.gof.R'
'bru.inference.R' 'bru.integration.R' 'bru.spatial.R'
'covariate.R' 'data.Poisson1_1D.R' 'data.Poisson2_1D.R'
'data.Poisson3_1D.R' 'data.gorillas.R' 'data.mexdolphin.R'
'data.mrsea.R' 'data.seals.R' 'data.shrimp.R'
'data.toygroups.R' 'deltaIC.R' 'deprecated.R' 'dsdata.R'
'dsmdata.R' 'dsmdata.tools.R' 'effect.R' 'environment.R'
'fmesher_crs.R' 'fmesher_utils.R' 'ggplot.R' 'inla.R'
'inlabru.R' 'integration.R' 'local_testthat.R' 'mesh.R'
'model.R' 'nlinla.R' 'plotsample.R' 'prediction.R' 'rgl.R'
'sampling.R' 'shapefile.R' 'spatstat.R' 'spde.R' 'stack.R'
'track_plotting.R' 'transformation.R' 'utils.R'
'ConnectionWeights.R' 'Convert_keras.R' 'Convert_neuralnet.R'
'Convert_torch.R' 'Converter.R' 'DeepLift.R' 'GradienBased.R'
'InterpretingLayer.R' 'InterpretingMethod.R' 'LRP.R'
'Layer_conv1d.R' 'Layer_conv2d.R' 'Layer_dense.R'
'Layer_other.R' 'Layer_pooling.R' 'innsight.R' 'utils.R'
'getFollowers.R' 'getFollows.R' 'getUser.R' 'getUserMedia.R'
'instaOAuth.R' 'searchInstagram.R' 'utils.R' 'getTagCount.R'
'getComments.R' 'getLikes.R' 'getPopular.R' 'getLocation.R'
read.emeld.R read.lift.R interlineaR-package.R read.toolbox.R
read.pangloss.R dataset.R lift.specifications.R common.R
'RcppExports.R' 'data.R' 'generics.R' 'gstat-extensions.R'
'intKrigeDriver.R' 'intgrd-class.R' 'intsp-class.R' 'pkgname.R'
'supportFunctions.R' 'unloads.R'
'RcppExports.R' 'class.R' 'Survi.R' 'assessment.R' 'bootSe.R'
'coef.R' 'cox_cure.R' 'cox_cure_net.R' 'iCoxph.R' 'intsurv.R'
'misc.R' 'prep_model.R' 'print.R' 'show.R' 'simData4cure.R'
'simData4iCoxph.R' 'summary.R'
's4-generics.R' 'DoubleCenter.R' 'NormalizedLaplacian.R'
'PerturbedLaplacian.R' 'RegularizedLaplacian.R'
'invertiforms-package.R' 'utils.R'
'parallelization.R' 'check.stopping.R' 'clustering.mode.R'
'clustering.R' 'data.R' 'export.groups.R' 'get.node.info.R'
'ipcaps-package.R' 'process.each.node.R' 'output.template.R'
'save.html.R' 'save.eigenplots.html.R'
'save.plots.label.html.R' 'save.plots.cluster.html.R'
'save.plots.R' 'postprocess.R' 'preprocess.R' 'ipcaps.R'
'top.discriminator.R'
Class-Metric.R BSSUtils.R ISPAQUtils.R basicStatsMetric.R
correlationMetric.R crossCorrelationMetric.R
dailyDCOffsetMetric.R DCOffsetTimesMetric.R gapsMetric.R
PSDMetric.R SNRMetric.R spikesMetric.R STALTAMetric.R
stateOfHealthMetric.R upDownTimesMetric.R
transferFunctionMetric.R maxRangeMetric.R
sampleRateChannelMetric.R sampleRateRespMetric.R
Class-Trace.R Class-Stream.R Class-IrisClient.R
mseedWrappers.R Utils.R spectralUtils.R
'class_unions.r' 'logging.r' 'comm_manager.r' 'compat.r'
'completion.r' 'environment_runtime.r' 'environment_shadow.r'
'options.r' 'execution.r' 'handlers.r' 'help.r' 'installspec.r'
'utils.r' 'kernel.r' 'main.r' 'onload.r'
'RcppExports.R' 'ability_estimation.R' 'bilog.R' 'cat_sim.R'
'cat_sim_helper_functions.R' 'item-class.R'
'item-class-methods.R' 'itempool-class.R'
'itempool-class-methods.R' 'dif.R' 'response-class.R'
'generate_objects.R' 'info.R' 'ipd.R' 'irt.R' 'item_analysis.R'
'item_fit.R' 'kernel_smoothing.R' 'response_set-class.R'
'max_score.R' 'mean.R' 'misc.R' 'person_fit.R'
'plot_distractor_icc.R' 'plot_icc.R' 'plot_itempool.R'
'plot_info.R' 'plot_item.R' 'plot_ks.R' 'plot_resp_loglik.R'
'prob.R' 'resp_lik.R' 'resp_loglik.R'
'response-class-methods.R' 'response_set-class-methods.R'
'rsss.R' 'sim_resp.R' 'testlet-class-methods.R' 'var.R' 'zzz.R'
'ability.R' 'estimate.R' 'itemfit.R' 'nonpar.R' 'readin.R'
'scale.R' 'tracelines.R' 'utils.R' 'irtoys-package.R'
'interaction.R'
'IsotopeRModelsGroups.R' 'IsotopeRModelsNoGroups.R'
'IsotopeRgui.R' 'Plot_jags.R' 'IsotopeRModelsNoGroupsNoInd.R'
'ISOweek.R' 'ISOweekday.R' 'date2ISOweek.R' 'ISOweek2date.R'
'ISOweek-internal.R'
'ISQG.R' 'Mating.R' 'R6Classes.R' 'Functions.R' 'Trait.R'
'RcppExports.R' 'Hooks.R'
'daily2fraction-method.R' 'fraction2daily-method.R'
'jubilee-adj-fault-line-method.R' 'jubilee-calc-cape-method.R'
'jubilee-package.R' 'jubilee-class.R' 'jubilee-constructor.R'
'jubilee-eqty-ols-method.R' 'jubilee-forward-rtn-method.R'
'jubilee-fred-data-method.R' 'jubilee-locate-file.R'
'jubilee-macro-cost-method.R' 'jubilee-macro-fit-method.R'
'jubilee-macro-predict-method.R' 'jubilee-mcsapply-method.R'
'jubilee-ols-method.R' 'jubilee-optimal-tb3ms-method.R'
'jubilee-predict-method.R' 'jubilee-read-fred-file.R'
'jubilee-repo-class.R' 'jubilee-repo-config.R'
'jubilee-repo-constructor.R' 'jubilee-std-fault-line-method.R'
'jubilee-yield-inversion-method.R' 'tri-wave-class.R'
'tri-wave-constructor.R' 'tri-wave-model.R'
'AllClasses.R' 'AllGenerics.R' 'aoristic.R' 'apportion.R'
'coerce.R' 'event_date.R' 'event_plot.R' 'event_resample.R'
'fit.R' 'kairos-package.R' 'mcd.R' 'mutators.R' 'plot.R'
'reexport.R' 'subset.R' 'utilities.R' 'validate.R' 'zzz.R'
'pathway.r' 'GWASdata.r' 'data.R' 'get_network_matrix.r'
'kernel.r' 'lkmt.r'
'CovFormulas.R' 'allGenerics.R' 'checkGrad.R' 'covComp.R'
'covMan.R' 'covQual.R' 'q1CompSymm.R' 'q1Symm.R' 'q1LowRank.R'
'covQualNested.R' 'covQualOrd.R' 'covRadial.R' 'covTS.R'
'covTP.R' 'covANOVA.R' 'covZZAll.R' 'gp.R' 'kFuns.R'
'kernelNorm.R' 'kernels1d_Call.R' 'logLikFuns.R' 'methodGLS.R'
'methodMLE.R' 'miscUtils.R' 'prinKrige.R' 'q1Diag.R'
'simulate_gp.R' 'warpFuns.R'
'api.R' 'assertions.R' 'backend-class.R' 'backend-env.R'
'backend-file.R' 'backend-macos.R' 'backend-secret-service.R'
'backend-wincred.R' 'default_backend.R' 'package.R' 'pass.R'
'utils.R'
'anomalize.R' 'colour.R' 'compare.R' 'convert.R' 'ggplot2.R'
'khroma-package.R' 'plot.R' 'scale_colour_crameri.R'
'scale_colour_okabeito.R' 'scale_colour_science.R'
'scale_colour_tol.R'
'DispersalModel.R' 'KinPairData.R' 'dispersal_model.R'
'KinPairSimulation.R' 'app.R' 'app_ports.R'
'axial_helper_functions.R' 'axials_standard.R' 'data.R'
'export_functions.R' 'import_kinpairs.R'
'kindisperse-package.R' 'sample_kindist.R' 'simgraph_data.R'
'simgraph_graph.R' 'simulate_kindist_composite.R'
'simulate_kindist_custom.R' 'simulate_kindist_simple.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_LaTeX_report.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'
global.R clusterLongData.R parKml.R parChoice.R kml.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-conversion.R' 'utils-rd2html.R'
'utils-sweave.R' 'utils-upload.R' 'utils-vignettes.R' 'zzz.R'
'01_class_01_kRp.text.R' '02_method_filterByClass.R'
'koRpus-internal.R' '00_environment.R' '01_class_02_kRp.TTR.R'
'01_class_03_kRp.corp.freq.R' '01_class_04_kRp.lang.R'
'01_class_05_kRp.readability.R'
'01_class_81_kRp.connection_union.R'
'02_method_get_set_kRp.text.R'
'01_class_90_deprecated_classes.R' '02_method_cTest.R'
'02_method_clozeDelete.R' '02_method_correct.R'
'02_method_docTermMatrix.R' '02_method_freq.analysis.R'
'02_method_hyphen.R' '02_method_jumbleWords.R'
'02_method_lex.div.R' '02_method_pasteText.R'
'02_method_plot.kRp.text.R' '02_method_query.R'
'02_method_read.corp.custom.R' '02_method_readTagged.R'
'02_method_readability.R' '02_method_show.kRp.lang.R'
'02_method_show.kRp.TTR.R' '02_method_show.kRp.corp.freq.R'
'02_method_show.kRp.readability.R' '02_method_show.kRp.text.R'
'02_method_split_by_doc_id.R' '02_method_summary.kRp.lang.R'
'02_method_summary.kRp.TTR.R'
'02_method_summary.kRp.readability.R'
'02_method_summary.kRp.text.R' '02_method_textTransform.R'
'02_method_tokenize.R' '02_method_treetag.R'
'02_method_types_tokens.R' 'available.koRpus.lang.R'
'get.kRp.env.R' 'guess.lang.R' 'install.koRpus.lang.R'
'kRp.POS.tags.R' 'kRp.cluster.R'
'koRpus-internal.freq.analysis.R' 'koRpus-internal.import.R'
'koRpus-internal.lexdiv.formulae.R'
'koRpus-internal.rdb.formulae.R'
'koRpus-internal.rdb.params.grades.R'
'koRpus-internal.read.corp.custom.R' 'koRpus-package.R'
'lex.div.num.R' 'read.BAWL.R' 'read.corp.LCC.R'
'read.corp.celex.R' 'readability.num.R' 'segment.optimizer.R'
'set.kRp.env.R' 'set.lang.support.R' 'textFeatures.R'
'wrapper_functions_lex.div.R' 'wrapper_functions_readability.R'
'composerr.R' 'imports.R' 'utilities.R' 'lama_dictionary.R'
'lama_merge.R' 'lama_mutate.R' 'lama_read.R' 'lama_select.R'
'lama_rename.R' 'lama_translate.R' 'lama_translate_all.R'
'lama_write.R' 'lappli.R'
'generics.R' 'laf.R' 'laf_column.R' 'meta.R' 'open.R'
'read_dm_blaise.R' 'stats.R' 'textutils.R' 'types.R'
'utility.R'
'Math-Functions.R' 'RcppExports.R' 'graphics.R' 'AgriLand.R'
'Class-LandsepiParams.R' 'GPKGTools.R' 'tools.R'
'Methods-LandsepiParams.R' 'demo_landsepi.R' 'landsepi.R'
'output.R' 'runShiny.R' 'simul_landsepi.R'
'assert.R' 'compute.R' 'data.R' 'formula.R' 'generics.R'
'latrend.R' 'make.R' 'matrix.R' 'method.R' 'methodMatrix.R'
'methodAKMedoids.R' 'methodCrimCV.R' 'methodCustom.R'
'methodDtwclust.R' 'trajectories.R' 'model.R' 'modelCustom.R'
'methodFeature.R' 'methodFlexmix.R' 'methodFlexmixGBTM.R'
'methodFunFEM.R' 'methodLMKM.R' 'methodGCKM.R' 'methodKML.R'
'methodLcmmGMM.R' 'methodLcmmGBTM.R' 'methodLongclust.R'
'methodMclustLLPA.R' 'methodMixAK_GLMM.R' 'methodMixTVEM.R'
'methodMixtoolsGMM.R' 'methodMixtoolsNPRM.R' 'methodRandom.R'
'methodStratify.R' 'methods.R' 'metricsInternal.R'
'metricsExternal.R' 'model-evaluation.R' 'model-summary.R'
'model-transform.R' 'modelApprox.R' 'modelCrimCV.R'
'modelDtwclust.R' 'modelFeature.R' 'modelFlexmix.R'
'modelFunFEM.R' 'modelKML.R' 'modelPartition.R' 'modelLMKM.R'
'modelLcmmGMM.R' 'modelLcmmGBTM.R' 'modelLongclust.R'
'modelMclustLLPA.R' 'modelMixAK_GLMM.R' 'modelMixAK_GLMMlist.R'
'modelMixTVEM.R' 'modelMixtoolsGMM.R' 'modelMixtoolsRM.R'
'modelStratify.R' 'modelWeightedPartition.R' 'models.R'
'random.R' 'timing.R' 'verbose.R' 'zzz.R'
'SFSQLConnection.R' 'SFSQLDriver.R' 'SFSQLResult.R'
'connect.R' 'lazysf-package.R' 'lazysf.R' 'utils-pipe.R'
'zzz.R'
ArrayUtils.R MCMCenv_refClass.R CR_Support.R Lcm_CR_fn.R
Lcm_CR_Strat_fn.R
'ldhmm-calc_stats_from_obs.R' 'ldhmm-numericOrNull-class.R'
'ldhmm-package.R' 'ldhmm-class.R' 'ldhmm-conditional_prob.R'
'ldhmm-constructor.R' 'ldhmm-decode_stats_history.R'
'ldhmm-decoding.R' 'ldhmm-forecast_prob.R'
'ldhmm-forecast_state.R' 'ldhmm-forecast_volatility.R'
'ldhmm-fred_data.R' 'ldhmm-gamma_init.R' 'ldhmm-ld_stats.R'
'ldhmm-log_forward.R' 'ldhmm-mle.R' 'ldhmm-mllk.R'
'ldhmm-n2w.R' 'ldhmm-oxford_man_index_list.R'
'ldhmm-oxford_man_plot_obs.R'
'ldhmm-oxford_man_realized_data.R' 'ldhmm-oxford_man_ts.R'
'ldhmm-plot_spx_vix_obs.R' 'ldhmm-pseudo_residuals.R'
'ldhmm-read_sample_object.R' 'ldhmm-simulate_abs_acf.R'
'ldhmm-simulate_state_transition.R' 'ldhmm-sma.R'
'ldhmm-state_ld.R' 'ldhmm-state_pdf.R' 'ldhmm-ts_abs_acf.R'
'ldhmm-ts_log_rtn.R' 'ldhmm-viterbi.R' 'ldhmm-w2n.R'
'data.R' 'misc.R' 'unit.R' 'layer.R' 'network.R' 'leabRa.R'
'NS_par.R' 'RcppExports.R' 'calc_M1.R' 'calc_Q.R'
'calc_growth.R' 'calc_indicators.R' 'calc_mature.R'
'param_setup.R' 'run_LeMans.R' 'calc_output.R' 'calc_phi.R'
'calc_prefs.R' 'calc_ration_growthfac.R' 'calc_recruits.R'
'calc_suit_vect.R' 'get_Catch.R' 'get_SSB.R' 'get_indicators.R'
'other.R' 'plot_SSB.R' 'plot_indicators.R'
'ggplot2.r' 'lemon-plot.r' 'axis-annotation.r' 'brackets.R'
'scale-view.r' 'scale-expansion.r' 'coord-cartesian.r'
'coord-flex.r' 'coord-capped.r' 'dot.r' 'facet-rep-lab.r'
'facet-wrap.r' 'geom-pointline.r' 'lemon_print.r'
'geom-siderange.r' 'grob_utils.r' 'gtable_show-.r' 'legends.r'
'lemon.r' 'scale-symmetric.r'
'verbs.R' 'lens.R' 'array-lenses.R' 'base-lenses.R' 'utils.R'
'dataframe-lenses.R' 'utils-pipe.R'
'available_data.R' 'cliches.R' 'common_names.R'
'constraining_loughran_mcdonald.R' 'freq_first_names.R'
'freq_last_names.R' 'function_words.R' 'grady_augmented.R'
'hash_emoticons.R' 'hash_grady_pos.R' 'hash_internet_slang.R'
'hash_lemmas.R' 'hash_nrc_emotion.R' 'hash_sentiment_emojis.R'
'hash_sentiment_huliu.R' 'utils.R' 'hash_sentiment_jockers.R'
'hash_sentiment_jockers_rinker.R'
'hash_sentiment_loughran_mcdonald.R' 'hash_sentiment_nrc.R'
'hash_sentiment_senticnet.R' 'hash_sentiment_sentiword.R'
'hash_sentiment_slangsd.R' 'hash_sentiment_socal_google.R'
'hash_valence_shifters.R' 'key_contractions.R'
'key_corporate_social_responsibility.R' 'key_grade.R'
'key_ratings.R' 'key_regressive_imagery.R' 'lexicon-package.R'
'modal_loughran_mcdonald.R' 'nrc_emotions.R'
'pos_action_verb.R' 'pos_df_irregular_nouns.R'
'pos_df_pronouns.R' 'pos_interjections.R' 'pos_preposition.R'
'profanity_alvarez.R' 'profanity_arr_bad.R'
'profanity_banned.R' 'profanity_racist.R'
'profanity_zac_anger.R' 'sw_dolch.R' 'sw_fry_100.R'
'sw_fry_1000.R' 'sw_fry_200.R' 'sw_fry_25.R' 'sw_jockers.R'
'sw_loughran_mcdonald.R' 'sw_lucene.R' 'sw_mallet.R'
'sw_python.R'
'Filterable.R' 'utils-sfmisc.R' 'utils.R' 'Appender.R'
'Filter.R' 'log_levels.R' 'LogEvent.R' 'Layout.R' 'Logger.R'
'basic_config.R' 'default_functions.R' 'event_list.R'
'get_logger.R' 'lgr-package.R' 'logger_config.R'
'logger_index.R' 'logger_tree.R' 'read_json_lines.R'
'simple_logging.R' 'string_repr.R' 'use_logger.R'
'utils-formatting.R' 'utils-logging.R' 'utils-rd.R'
'utils-rotor.R'
'HelperFunctions.R' 'InsuranceParameters.R'
'ProfitParticipation_Functions.R' 'ProfitParticipation.R'
'InsuranceTarif.R' 'InsuranceContract.R'
'addDataTableWorksheet.R' 'contractGrid.R'
'exportInsuranceContract_xlsx.R' 'showVmGlgExamples.R'
'exportInsuranceContractExample.R'
'EX.R' 'global_var.R' 'Person.R' 'experiments.R' 'lifelogr.R'
'shinyApp.R' 'viz_daily.R' 'viz_intraday.R' 'viz_sleep.R'
'linkspotterComplete.R' 'clusterVariables.R'
'linkspotterDurationEstimator.R' 'linkspotterGraph.R'
'linkspotterGraphOnMatrix.R' 'linkspotterOnFile.R'
'linkspotterUI.R' 'corCouplesToMatrix.R' 'matrixToCorCouples.R'
'maxNMI.R' 'BeEFdiscretization_numfact.R'
'BeEFdiscretization_numnum.R' 'multiBivariateCorrelation.R'
'NormalizedMI.R' 'createShinyAppFolder.R' 'EFdiscretization.R'
'non_informative_var.R'
'T_and_F_symbol_linter.R' 'utils.R' 'aaa.R' 'actions.R'
'addins.R' 'assignment_linter.R' 'cache.R'
'closed_curly_linter.R' 'commas_linter.R' 'comment_linters.R'
'comments.R' 'cyclocomp_linter.R' 'declared_functions.R'
'object_name_linters.R' 'deprecated.R' 'equals_na_lintr.R'
'exclude.R' 'expect_lint.R' 'extract.R'
'extraction_operator_linter.R' 'function_left_parentheses.R'
'get_source_expressions.R' 'ids_with_token.R'
'implicit_integer_linter.R' 'infix_spaces_linter.R'
'line_length_linter.R' 'lint.R' 'methods.R' 'namespace.R'
'no_tab_linter.R' 'object_usage_linter.R' 'open_curly_linter.R'
'paren_brace_linter.R' 'path_linters.R'
'pipe_continuation_linter.R' 'semicolon_terminator_linter.R'
'seq_linter.R' 'settings.R' 'single_quotes_linter.R'
'spaces_inside_linter.R' 'spaces_left_parentheses_linter.R'
'trailing_blank_lines_linter.R' 'trailing_whitespace_linter.R'
'tree-utils.R' 'undesirable_function_linter.R'
'undesirable_operator_linter.R'
'unneeded_concatenation_linter.R' 'with_id.R' 'zzz.R'
'anova_contrasts.R' 'contest.R' 'contrast_utils.R'
'data_documentation.R' 'drop1.R' 'estimability.R' 'legacy.R'
'lmer.R' 'lmerTest.R' 'lmer_anova.R' 'lmer_summary.R'
'ls_means.R' 'ranova.R' 'step.R' 'terms_utils.R' 'utils.R'
'0-onload.R' 'LMMstar-package.R' 'LMMstar.options.R' 'anova.R'
'autoplot.R' 'baselineAdjustment.R' 'coef.R'
'confint.anova_lmm.R' 'confint.lmm.R' 'df.R' 'doc-data.R'
'dummy.coef.R' 'emmeans.R' 'estimate.R' 'fitted.R' 'formula.R'
'getCoef.R' 'getVarCov.R' 'iid.R' 'information.R' 'levels.R'
'lmm.R' 'logLik.R' 'mlmm.R' 'model.frame.R' 'model.matrix.R'
'model.tables.R' 'moments.R' 'multcomp.R' 'nobs.R' 'plot.R'
'precompute.R' 'predict.R' 'print.anova_lmm.R' 'print.lmm.R'
'rbind.anova_lmm.R' 'reparametrize.R' 'residuals.R'
'sampleRem.R' 'score.R' 'structure-calc_Omega.R'
'structure-calc_d2Omega.R' 'structure-calc_dOmega.R'
'structure-initialization.R' 'structure-skeleton.R'
'structure.R' 'summarize.R' 'summary.anova_lmm.R'
'summary.lmm.R' 'terms.R' 'transformSummaryTable.R' 'utils.R'
'vcov.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'
lslxData-class.R lslxData-initialize-method.R
lslxModel-class.R lslxModel-initialize-method.R
lslxFitting-class.R lslxFitting-initialize-method.R
prelslx-class.R prelslx-initialize-method.R
prelslx-prefit-method.R lslx-class.R lslx-print-method.R
lslx-set-coefficient-method.R lslx-set-block-method.R
lslx-set-directed-method.R lslx-set-heterogeneity-method.R
lslx-set-undirected-method.R lslx-set-data-method.R
RcppExports.R lslx-fit-method.R lslx-get-method.R
lslx-extract-method.R lslx-test-method.R
lslx-summarize-method.R lslx-plot-method.R
lslx-validate-method.R lslx-s3-interface.R polyhedral.R
utility-function.R
PowerPoisson.R RcppExports.R Plot.R Print.R table_f.R
tableToData.R
'ltmle-package.R' 'GeneralUtilities.R' 'ltmle.R' 'ltmle_sg.R'
'BinaryToCensoring.R' 'DeterministicFunctions.R' 'zzz.R'
'Dates.r' 'POSIXt.r' 'util.r' 'parse.r' 'timespans.r'
'intervals.r' 'difftimes.r' 'durations.r' 'periods.r'
'accessors-date.R' 'accessors-day.r' 'accessors-dst.r'
'accessors-hour.r' 'accessors-minute.r' 'accessors-month.r'
'accessors-quarter.r' 'accessors-second.r' 'accessors-tz.r'
'accessors-week.r' 'accessors-year.r' 'am-pm.r' 'time-zones.r'
'numeric.r' 'coercion.r' 'constants.r' 'cpp11-wrappers.R'
'cpp11.R' 'cyclic_encoding.r' 'data.r' 'decimal-dates.r'
'deprecated.r' 'format_ISO8601.r' 'guess.r' 'hidden.r'
'instants.r' 'leap-years.r' 'ops-addition.r' 'ops-compare.r'
'ops-division.r' 'ops-integer-division.r' 'ops-m+.r'
'ops-modulo.r' 'ops-multiplication.r' 'ops-subtraction.r'
'package.r' 'pretty.r' 'round.r' 'stamp.r' 'tzdir.R' 'update.r'
'vctrs.R' 'zzz.R'
'class-CategoryLabel.R' 'class-ObsLulcRasterStack.R'
'class-PredictiveModelList.R' 'class-ExpVarRasterList.R'
'class-NeighbRasterStack.R' 'class-Model.R'
'class-ThreeMapComparison.R' 'AgreementBudget.R'
'ExpVarRasterList.R' 'FigureOfMerit.R' 'Model.R'
'NeighbRasterStack.R' 'ObsLulcRasterStack.R'
'class-PerformanceList.R' 'PerformanceList.R'
'class-PredictionList.R' 'PredictionList.R'
'ThreeMapComparison.R' 'allocate.R' 'allow.R' 'allowNeighb.R'
'approxExtrapDemand.R' 'as.data.frame.R'
'c.PredictiveModelList.R' 'index.R' 'coerce.R' 'compareAUC.R'
'crossTabulate.R' 'data.R' 'getPredictiveModelInputData.R'
'length.R' 'lulcc-package.R' 'models.R' 'names.R' 'partition.R'
'performance.rocr.R' 'plot.AgreementBudget.R'
'plot.FigureOfMerit.R' 'plot.Performance.R' 'plot.R'
'predict.R' 'resample.R' 'show.R' 'subset.R' 'summary.R'
'total.R'
'accelerator.R' 'as_dataloader.R' 'utils.R' 'callbacks.R'
'callbacks-interrupt.R' 'callbacks-monitor-metrics.R'
'callbacks-profile.R' 'context.R' 'lr-finder.R' 'metrics.R'
'metrics-auc.R' 'module-plot.R' 'module-print.R' 'module.R'
'reexports.R' 'serialization.R'
init.R RcppExports.R geohash.R split.R subdivide.R valid.R
transform.R bounding_circle.R bearing.R snap_to_grid.R
startpoint.R twkb.R perimeter.R clockwise.R geod.R wkt.R
wrap_x.R
'import.R' 'module_class.R' 'module_functions.R'
'administered_functions.R' 'datasets.R' 'examinee_class.R'
'examinee_updaters.R' 'extensions.R'
'module_structure_operators.R' 'package.R' 'sim_functions.R'
'plot_functions.R' 'print_functions.R' 'prior_functions.R'
'routing_functions.R' 'show_functions.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'
'classes.R' 'MLControl.R' 'MLInput.R' 'MLMetric.R' 'MLModel.R'
'MLOptimization.R' 'ML_AdaBagModel.R' 'ML_AdaBoostModel.R'
'ML_BARTMachineModel.R' 'ML_BARTModel.R' 'ML_BlackBoostModel.R'
'ML_C50Model.R' 'ML_CForestModel.R' 'ML_CoxModel.R'
'ML_EarthModel.R' 'ML_FDAModel.R' 'ML_GAMBoostModel.R'
'ML_GBMModel.R' 'ML_GLMBoostModel.R' 'ML_GLMModel.R'
'ML_GLMNetModel.R' 'ML_KNNModel.R' 'ML_LARSModel.R'
'ML_LDAModel.R' 'ML_LMModel.R' 'ML_MDAModel.R' 'ML_NNetModel.R'
'ML_NaiveBayesModel.R' 'ML_ParsnipModel.R' 'ML_PLSModel.R'
'ML_POLRModel.R' 'ML_QDAModel.R' 'ML_RFSRCModel.R'
'ML_RPartModel.R' 'ML_RandomForestModel.R' 'ML_RangerModel.R'
'ML_SVMModel.R' 'ML_StackedModel.R' 'ML_SuperModel.R'
'ML_SurvRegModel.R' 'ML_TreeModel.R' 'ML_XGBModel.R'
'MachineShop-package.R' 'ModelFrame.R' 'ModelRecipe.R'
'ModelSpecification.R' 'TrainedInputs.R' 'TrainedModels.R'
'TrainingParams.R' 'append.R' 'calibration.R' 'case_comps.R'
'coerce.R' 'combine.R' 'conditions.R' 'confusion.R' 'convert.R'
'data.R' 'dependence.R' 'diff.R' 'expand.R' 'extract.R' 'fit.R'
'grid.R' 'metricinfo.R' 'metrics.R' 'metrics_factor.R'
'metrics_numeric.R' 'modelinfo.R' 'models.R' 'performance.R'
'performance_curve.R' 'plot.R' 'predict.R' 'predictors.R'
'print.R' 'recipe_roles.R' 'reexports.R' 'resample.R'
'response.R' 'rfe.R' 'settings.R' 'step_kmeans.R'
'step_kmedoids.R' 'step_lincomp.R' 'step_sbf.R' 'step_spca.R'
'summary.R' 'survival.R' 'utils.R' 'varimp.R'
'AllClass.r' 'bind.r' 'blockrep.r' 'coerce.r' 'data.r' 'det.r'
'diag.r' 'eigen.r' 'elwise.r' 'madness_pkg.r' 'matwise.r'
'max.r' 'norm.r' 'numderiv.r' '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' 'mads-package.R'
manifestoR-package.r manifestoR-defunct.R globals.R
pipe_helpers.R cache.R db_api.R corpus.R manifesto.R codes.R
scaling_general.R scaling_rile.R scaling_functions.R
issue_attention.R nicheness.R clarity.R scaling_bootstrap.R
dataset.R codebook.R dedication.R
'comparisons.R' 'complete_levels.R' 'datagrid.R'
'find_variable_class.R' 'get_coef.R' 'get_contrasts.R'
'get_contrasts_character.R' 'get_contrasts_factor.R'
'get_contrasts_logical.R' 'get_contrasts_numeric.R'
'get_dydx.R' 'get_gradient.R' 'get_group_names.R' 'get_hdi.R'
'get_jacobian.R' 'get_predict.R' 'get_vcov.R'
'marginaleffects.R' 'marginalmeans.R' 'mean_or_mode.R'
'set_coef.R' 'methods_MASS.R' 'methods_aod.R'
'methods_betareg.R' 'methods_bife.R' 'sanity_model.R'
'methods_nnet.R' 'methods_brglm2.R' 'methods_brms.R'
'methods_crch.R' 'methods_fixest.R' 'methods_glmx.R'
'methods_lme4.R' 'methods_mclogit.R' 'methods_mgcv.R'
'methods_ordinal.R' 'methods_plm.R' 'methods_pscl.R'
'methods_quantreg.R' 'methods_robust.R' 'methods_robustlmm.R'
'methods_rstanarm.R' 'methods_sampleSelection.R'
'methods_scam.R' 'methods_stats.R' 'methods_survival.R'
'package.R' 'plot.R' 'plot_cap.R' 'plot_cme.R' 'poorman.R'
'posteriordraws.R' 'predictions.R' 'sanity.R' 'sanity_dots.R'
'sanity_type.R' 'standard_errors_delta.R' 'summary.R' 'tidy.R'
'type_dictionary.R' 'utils.R' 'warn_once.R'
'gregt.R' 'varMase.R' 'GREG.R' 'gregElasticNett.R'
'gregElasticNet.R' 'gregTree.R' 'gregtreet.R' 'htt.R'
'horvitzThompson.R' 'logisticGregElasticNett.R'
'logisticGregt.R' 'postStratt.R' 'postStrat.R'
'ratioEstimatort.R' 'ratioEstimator.R'
'decagon_maze.r' 'dodecagon_maze.r' 'eq_triangle_maze.r'
'hexaflake_maze.r' 'hexagon_maze.r' 'holey_line.r'
'holey_path.r' 'iso_trapezoid_maze.r' 'koch_maze.r'
'mazealls.r' 'octagon_maze.r' 'parallelogram_maze.r'
'sierpinski_carpet_maze.r' 'sierpinski_maze.r'
'sierpinski_trapezoid_maze.r' 'utils.r'
'mbsts.forecast.R' 'mbsts_class.R' 'mbsts_function.R'
'package.R' 'para.est.R' 'plot_comp.R' 'plot_prob.R'
'simdata.R' 'tsc.setting.R'
'AuditorFitters.R' 'MCBoost.R' 'MCBoostSurv.R'
'PipelineMCBoost.R' 'PipeOpLearnerPred.R' 'PipeOpMCBoost.R'
'PipeOpMCBoostSurv.R' 'Predictor.R' 'ProbRange.R'
'ProbRange2D.R' 'helpers.R' 'zzz.R'
'MCMCsim.R' 'MH.R' 'tabMatrix.R' 'MatrixUtils.R'
'RcppExports.R' 'TMVN_sampler.R' 'aux_closures.R' 'cholesky.R'
'conjugate_gradients.R' 'family.R' 'formulas.R' 'kronprod.R'
'mc_block.R' 'mc_gen.R' 'mc_gl.R' 'mc_mec.R' 'mc_reg.R'
'mc_vfac.R' 'mc_vreg.R' 'mcmcsae.R' 'model_eval.R'
'modelmatrix.R' 'models.R' 'opts.R' 'parallel.R' 'plots.R'
'prediction.R' 'priors.R' 'random.R' 'samplers.R' 'sbc.R'
'sparse_template.R' 'utils.R'
mc.R mcompanion.R utils_Jordan.R mat.R sim.R class_MC.R
class_MF.R class_Jordan.R chains_smc.R class_SMC.R
class_mcSpec.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"
meteorits-package.R RcppExports.R logsumexp.R utils.R
sampleUnivNMoE.R sampleUnivSNMoE.R sampleUnivStMoE.R
sampleUnivTMoE.R ParamSNMoE.R ParamStMoE.R ParamTMoE.R
ParamNMoE.R StatSNMoE.R StatStMoE.R StatTMoE.R StatNMoE.R
ModelSNMoE.R ModelStMoE.R ModelTMoE.R ModelNMoE.R emSNMoE.R
emStMoE.R emTMoE.R emNMoE.R data-tempanomalies.R
'param.R' 'checkinputs.R' 'rmodel.R' 'data.R' 'MHMM.R'
'results.R' 'plot.R' 'smartinit.R' 'viterbi.R' 'MHMMpackage.R'
'RcppExports.R'
'applyTraitTradeOffs.R' 'assignNAsToMFGs.R'
'assignStrainTraits.R' 'checkResInfo.R' 'checkSolution.R'
'checkStoichiom.R' 'combineGrowthLimFuncDefault.R'
'combinePathsFuncDefault.R' 'convertFlowsToMoles.R'
'convertStatesToMoles.R' 'createDF.R' 'data.R'
'derivsDefault.R' 'entryRateFuncDefault.R'
'extraGrowthLimFuncDefault.R' 'getAllResources.R'
'getGroupName.R' 'getKeyRes.R' 'getMolarStoichiom.R'
'getMolarYields.R' 'getNonBoostFrac.R' 'getNumPaths.R'
'getPHcorners.R' 'getStrainPHcorners.R'
'getStrainParamsFromFile.R' 'getVNPlotObject.R' 'getValues.R'
'growthLimFuncDefault.R' 'makeInflowFromSoln.R'
'makeNetworkMatrices.R' 'makeParamMatrixG.R'
'makeParamMatrixS.R' 'massBalanceFuncDefault.R'
'meanTraitFunc.R' 'microPop-package.R' 'microPopModel.R'
'networkDFfromMPinput.R' 'networkDFfromMPoutput.R'
'pHFuncDefault.R' 'pHLimFuncDefault.R' 'pHcentreOfMass.R'
'plotMicrobes.R' 'plotResources.R' 'plotTraitChange.R'
'productionFuncDefault.R' 'quickPlot.R' 'quickPlot1.R'
'quickPlot2.R' 'removalRateFuncDefault.R' 'uptakeFuncDefault.R'
'rateFuncsDefault.R' 'replaceListItems.R' 'reshapeFlowMat.R'
'runMicroPopExample.R' 'subsetFunc.R' 'sumConcOverStrains.R'
'sumFlowOverStrains.R' 'sumFlowsOverPaths.R'
'waterUptakeRatio.R'
'RcppExports.R' 'aaaGenerics.R' 'appFunctions.R'
'constraintFunctions.R' 'fastRankSchemes.R' 'internalHelpers.R'
'objectPkModelParent.R' 'objectSetOfSchemes.R'
'objectPkModel.R' 'objectPkModelRange.R'
'objectSetOfTimePoints.R' 'pkFunctions.R' 'schemeStatistics.R'
'rankScheme.R' 'rankTimePoints.R' 'schemeGenerator.R'
'timePointGeneration.R'
'deriv.R' 'imidasreg.R' 'lagspec.R' 'midas_nlpr.R'
'midas_r_methods.R' 'midas_nlpr_methods.R' 'midas_qr_methods.R'
'midas_sp.R' 'midas_sp_methods.R' 'midaslag.R' 'midasqr.R'
'midasr-package.R' 'midasreg.R' 'modsel.R' 'nonparametric.R'
'simulate.R' 'tests.R'
'gini.R' 'migration.indices.R' 'crude.R' 'others.R' 'acv.R'
'graph.R'
Corrp2miRfile.R EnrichmentScore.R S2N.R MirSEA.R MsReport.R
GetExampleData.R getEnvironmentData.R GetMiRTargetData.R
GetPathwayData.R PlotCorrelation.R PlotHeatMap.R
PlotRunEnrichment.R
'utils_missSBM.R' 'R6Class-networkSampling.R'
'R6Class-networkSampling_fit.R' 'R6Class-simpleSBM_fit.R'
'R6Class-missSBM_fit.R' 'R6Class-missSBM_collection.R'
'R6Class-networkSampler.R' 'R6Class-partlyObservedNetwork.R'
'RcppExports.R' 'er_network.R' 'estimateMissSBM.R'
'frenchblog2007.R' 'kmeans.R' 'missSBM-package.R'
'observeNetwork.R' 'war.R'
'ClusterAlgo.R' 'ClusterAlgoPredict.R' 'ClusterInit.R'
'ClusterStrategy.R' 'IClusterModel.R' 'ClusterModelNames.R'
'global.R' 'ClusterCategorical.R' 'ClusterDiagGaussian.R'
'ClusterGamma.R' 'ClusterMixedData.R' 'ClusterPlot.R'
'ClusterPoisson.R' 'IClusterPredict.R' 'ClusterPredict.R'
'LearnAlgo.R' 'Learners.R' 'MixAll.R' 'kmmNames.R' 'kmm.R'
'kmmMixedData.R' 'missingValues.R'
raggedCoef.R raggedCoefS.R mixComp.R mixAR.R mixARcalc.R
mixutil.R dist.R predict.R mix_se.R obs_info_matrix.R
samp_functions.R bayes_mixAR.R label_switch.R Choose_pk.R
marg_loglik.R mixSARfit.R mixARreg.R fit_mixARreg.R
raggedCoefV.R mixVAR.R fit_mixVAR.R cond_loglikV.R mixVAR_sim.R
tsdiag.R mixAR_diag.R devel.R em0.R emGaussian.R emgen.R
00marmath.R exmodels.R mixARsim.R
'mixmapTest.R' 'mixmapPI.R' 'MixMAPclass.R' 'plot.MixMAP.R'
'summary.MixMAP.R'
'helpers.R' 'MizerParams-class.R' 'MizerSim-class.R'
'reproduction.R' 'saveParams.R' 'species_params.R'
'setColours.R' 'setInteraction.R' 'setPredKernel.R'
'setSearchVolume.R' 'setMaxIntakeRate.R' 'setMetabolicRate.R'
'setMetadata.R' 'setExtMort.R' 'setReproduction.R'
'setResource.R' 'setFishing.R' 'setInitialValues.R'
'setBevertonHolt.R' 'upgrade.R' 'selectivity_funcs.R'
'pred_kernel_funcs.R' 'resource_dynamics.R' 'project.R'
'mizer-package.R' 'project_methods.R' 'rate_functions.R'
'summary_methods.R' 'plots.R' 'plotBiomassObservedVsModel.R'
'plotYieldObservedVsModel.R' 'animateSpectra.R'
'newMultispeciesParams.R' 'wrapper_functions.R'
'newSingleSpeciesParams.R' 'steady.R' 'test-helpers.R'
'extension.R' 'data.R' 'RcppExports.R' 'deprecated.R'
'get_initial_n.R' 'compareParams.R' 'customFunction.R'
'manipulate_species.R' 'calibrate.R' 'match.R'
'cli.R' 'databricks-utils.R' 'globals.R' 'imports.R'
'install.R' 'logging.R' 'mlflow-package.R' 'model-crate.R'
'model-python.R' 'model.R' 'model-utils.R' 'model-h2o.R'
'model-keras.R' 'model-registry.R' 'model-serve.R'
'model-swagger.R' 'model-xgboost.R' 'project-param.R'
'project-run.R' 'project-source.R' 'python.R'
'tracking-client.R' 'tracking-experiments.R'
'tracking-observer.R' 'tracking-globals.R' 'tracking-rest.R'
'tracking-runs.R' 'tracking-server.R' 'tracking-ui.R'
'tracking-utils.R'
'LearnerClust.R' 'LearnerClustAffinityPropagation.R'
'LearnerClustAgnes.R' 'LearnerClustCMeans.R'
'LearnerClustCobweb.R' 'LearnerClustDBSCAN.R'
'LearnerClustDiana.R' 'LearnerClustEM.R' 'LearnerClustFanny.R'
'LearnerClustFarthestFirst.R' 'LearnerClustFeatureless.R'
'LearnerClustHclust.R' 'LearnerClustKKMeans.R'
'LearnerClustKMeans.R' 'LearnerClustMeanShift.R'
'LearnerClustMiniBatchKMeans.R' 'LearnerClustPAM.R'
'LearnerClustSimpleKMeans.R' 'LearnerClustXMeans.R'
'MeasureClust.R' 'measures.R' 'MeasureClustInternal.R'
'PredictionClust.R' 'PredictionDataClust.R' 'TaskClust.R'
'TaskClust_usarrest.R' 'as_prediction_clust.R'
'as_task_clust.R' 'helper.R' 'zzz.R'
'Filter.R' 'mlr_filters.R' 'FilterAUC.R' 'FilterAnova.R'
'FilterCMIM.R' 'FilterCarScore.R' 'FilterCorrelation.R'
'FilterDISR.R' 'FilterFindCorrelation.R' 'FilterImportance.R'
'FilterInformationGain.R' 'FilterJMI.R' 'FilterJMIM.R'
'FilterKruskalTest.R' 'FilterMIM.R' 'FilterMRMR.R'
'FilterNJMIM.R' 'FilterPerformance.R' 'FilterPermutation.R'
'FilterRelief.R' 'FilterSelectedFeatures.R' 'FilterVariance.R'
'bibentries.R' 'flt.R' 'helper.R' 'reexports.R' 'zzz.R'
'assertions.R' 'AutoFSelector.R' 'ArchiveFSelect.R'
'ObjectiveFSelect.R' 'helper.R' 'mlr_fselectors.R'
'auto_fselector.R' 'extract_inner_fselect_archives.R'
'extract_inner_fselect_results.R' 'fselect.R'
'fselect_nested.R' 'FSelector.R' 'FSelectorFromOptimizer.R'
'FSelectorExhaustiveSearch.R' 'FSelectorRFE.R'
'FSelectorRandomSearch.R' 'FSelectorSequential.R'
'FSelectorShadowVariableSearch.R' 'FSelectorDesignPoints.R'
'FSelectorGeneticSearch.R' 'FSelectInstanceMultiCrit.R'
'FSelectInstanceSingleCrit.R' 'reexports.R' 'sugar.R'
'bibentries.R' 'zzz.R'
'TunerHyperband.R' 'TunerSuccessiveHalving.R'
'OptimizerHyperband.R' 'OptimizerSuccessiveHalving.R'
'bibentries.R' 'helper.R' 'zzz.R'
'assertions.R' 'bibentries.R' 'measures.R' 'binary_auc.R'
'binary_bbrier.R' 'binary_dor.R' 'binary_fbeta.R'
'binary_fdr.R' 'binary_fn.R' 'binary_fnr.R' 'binary_fomr.R'
'binary_fp.R' 'binary_fpr.R' 'binary_mcc.R' 'binary_npv.R'
'binary_ppv.R' 'binary_prauc.R' 'binary_tn.R' 'binary_tnr.R'
'binary_tp.R' 'binary_tpr.R' 'classif_acc.R' 'classif_auc.R'
'classif_bacc.R' 'classif_ce.R' 'classif_logloss.R'
'classif_mbrier.R' 'confusion_matrix.R' 'helper.R'
'regr_bias.R' 'regr_ktau.R' 'regr_mae.R' 'regr_mape.R'
'regr_maxae.R' 'regr_maxse.R' 'regr_medae.R' 'regr_medse.R'
'regr_mse.R' 'regr_msle.R' 'regr_pbias.R' 'regr_rae.R'
'regr_rmse.R' 'regr_rmsle.R' 'regr_rrse.R' 'regr_rse.R'
'regr_rsq.R' 'regr_sae.R' 'regr_smape.R' 'regr_srho.R'
'regr_sse.R' 'roxygen.R' 'similarity_jaccard.R'
'similarity_phi.R' 'zzz.R'
'Graph.R' 'GraphLearner.R' 'mlr_pipeops.R' 'multiplicity.R'
'utils.R' 'PipeOp.R' 'PipeOpEnsemble.R' 'LearnerAvg.R'
'NO_OP.R' 'PipeOpTaskPreproc.R' 'PipeOpBoxCox.R'
'PipeOpBranch.R' 'PipeOpChunk.R' 'PipeOpClassBalancing.R'
'PipeOpClassWeights.R' 'PipeOpClassifAvg.R' 'PipeOpColApply.R'
'PipeOpColRoles.R' 'PipeOpCollapseFactors.R' 'PipeOpCopy.R'
'PipeOpDateFeatures.R' 'PipeOpEncode.R' 'PipeOpEncodeImpact.R'
'PipeOpEncodeLmer.R' 'PipeOpFeatureUnion.R' 'PipeOpFilter.R'
'PipeOpFixFactors.R' 'PipeOpHistBin.R' 'PipeOpICA.R'
'PipeOpImpute.R' 'PipeOpImputeConstant.R' 'PipeOpImputeHist.R'
'PipeOpImputeLearner.R' 'PipeOpImputeMean.R'
'PipeOpImputeMedian.R' 'PipeOpImputeMode.R' 'PipeOpImputeOOR.R'
'PipeOpImputeSample.R' 'PipeOpKernelPCA.R' 'PipeOpLearner.R'
'PipeOpLearnerCV.R' 'PipeOpMissingIndicators.R'
'PipeOpModelMatrix.R' 'PipeOpMultiplicity.R' 'PipeOpMutate.R'
'PipeOpNMF.R' 'PipeOpNOP.R' 'PipeOpOVR.R' 'PipeOpPCA.R'
'PipeOpProxy.R' 'PipeOpQuantileBin.R'
'PipeOpRandomProjection.R' 'PipeOpRandomResponse.R'
'PipeOpRegrAvg.R' 'PipeOpRemoveConstants.R'
'PipeOpRenameColumns.R' 'PipeOpScale.R' 'PipeOpScaleMaxAbs.R'
'PipeOpScaleRange.R' 'PipeOpSelect.R' 'PipeOpSmote.R'
'PipeOpSpatialSign.R' 'PipeOpSubsample.R'
'PipeOpTextVectorizer.R' 'PipeOpThreshold.R' 'PipeOpTrafo.R'
'PipeOpTuneThreshold.R' 'PipeOpUnbranch.R' 'PipeOpVtreat.R'
'PipeOpYeoJohnson.R' 'Selector.R' 'assert_graph.R'
'bibentries.R' 'greplicate.R' 'gunion.R' 'mlr_graphs.R'
'operators.R' 'pipeline_bagging.R' 'pipeline_branch.R'
'pipeline_greplicate.R' 'pipeline_ovr.R' 'pipeline_robustify.R'
'pipeline_stacking.R' 'pipeline_targettrafo.R' 'po.R' 'ppl.R'
'reexports.R' 'typecheck.R' 'zzz.R'
'LearnerDens.R' 'LearnerDensHistogram.R' 'LearnerDensKDE.R'
'LearnerRegrGaussian.R' 'LearnerSurv.R' 'LearnerSurvCoxPH.R'
'LearnerSurvKaplan.R' 'LearnerSurvRpart.R' 'MeasureDens.R'
'MeasureDensLogloss.R' 'MeasureRegrLogloss.R' 'MeasureSurv.R'
'MeasureSurvAUC.R' 'MeasureSurvCalibrationAlpha.R'
'MeasureSurvCalibrationBeta.R' 'MeasureSurvChamblessAUC.R'
'MeasureSurvCindex.R' 'MeasureSurvDCalibration.R'
'MeasureSurvGraf.R' 'MeasureSurvHungAUC.R'
'MeasureSurvIntLogloss.R' 'MeasureSurvLogloss.R'
'MeasureSurvMAE.R' 'MeasureSurvMSE.R' 'MeasureSurvNagelkR2.R'
'MeasureSurvOQuigleyR2.R' 'MeasureSurvRCLL.R'
'MeasureSurvRMSE.R' 'MeasureSurvSchmid.R'
'MeasureSurvSongAUC.R' 'MeasureSurvSongTNR.R'
'MeasureSurvSongTPR.R' 'MeasureSurvUnoAUC.R'
'MeasureSurvUnoTNR.R' 'MeasureSurvUnoTPR.R' 'MeasureSurvXuR2.R'
'PipeOpCrankCompositor.R' 'PipeOpDistrCompositor.R'
'PipeOpTransformer.R' 'PipeOpPredTransformer.R'
'PipeOpPredRegrSurv.R' 'PipeOpPredSurvRegr.R'
'PipeOpProbregrCompositor.R' 'PipeOpSurvAvg.R'
'PipeOpTaskRegrSurv.R' 'PipeOpTaskSurvRegr.R'
'PipeOpTaskTransformer.R' 'PredictionDataDens.R'
'PredictionDataSurv.R' 'PredictionDens.R' 'PredictionSurv.R'
'RcppExports.R' 'TaskDens.R' 'TaskDens_X.R'
'TaskGeneratorSimdens.R' 'TaskGeneratorSimsurv.R' 'TaskSurv.R'
'TaskSurv_X.R' 'as_prediction_dens.R' 'as_prediction_surv.R'
'as_task_dens.R' 'as_task_surv.R' 'assertions.R' 'bibentries.R'
'cindex.R' 'data.R' 'helpers.R' 'histogram.R'
'integrated_scores.R' 'mlr3proba-package.R' 'partition.R'
'pecs.R' 'pipelines.R' 'plot.R' 'surv_measures.R'
'surv_return.R' 'zzz.R'
'ArchiveTuning.R' 'AutoTuner.R' 'ObjectiveTuning.R'
'mlr_tuners.R' 'Tuner.R' 'TunerCmaes.R' 'TunerDesignPoints.R'
'TunerFromOptimizer.R' 'TunerGenSA.R' 'TunerGridSearch.R'
'TunerIrace.R' 'TunerNLoptr.R' 'TunerRandomSearch.R'
'TuningInstanceMulticrit.R' 'TuningInstanceSingleCrit.R'
'as_search_space.R' 'assertions.R' 'auto_tuner.R'
'bibentries.R' 'extract_inner_tuning_archives.R'
'extract_inner_tuning_results.R' 'helper.R' 'reexport.R'
'sugar.R' 'tune.R' 'tune_nested.R' 'zzz.R'
'mlr_tuning_spaces.R' 'TuningSpace.R' 'bibentries.R'
'helper.R' 'sugar.R' 'tuning_spaces_default.R'
'tuning_spaces_rbv2.R' 'zzz.R'
'BenchmarkResult.R' 'Filter.R' 'LearnerClassifCVGlmnet.R'
'LearnerClassifGlmnet.R' 'LearnerClassifRpart.R'
'LearnerClustHierarchical.R' 'LearnerRegrCVGlmnet.R'
'LearnerRegrGlmnet.R' 'LearnerRegrRpart.R'
'OptimInstanceSingleCrit.R' 'Prediction.R'
'PredictionClassif.R' 'PredictionClust.R' 'PredictionRegr.R'
'PredictionSurv.R' 'ResampleResult.R' 'Task.R' 'TaskClassif.R'
'TaskClust.R' 'TaskDens.R' 'TaskRegr.R' 'TaskSurv.R'
'TuningInstanceSingleCrit.R' 'as_precrec.R' 'bibentries.R'
'helper.R' 'plot_learner_prediction.R' 'reexports.R'
'theme-mlr3.R' 'zzz.R'
'CPOHelp.R' 'fauxCPOConstructor.R' 'auxiliary.R'
'ParamSetSugar.R' 'callInterface.R' 'FormatCheck.R' 'callCPO.R'
'properties.R' 'parameters.R' 'listCPO.R' 'makeCPO.R'
'CPO_applyFun.R' 'CPO_asNumeric.R' 'operators.R' 'NULLCPO.R'
'CPO_meta.R' 'CPO_cbind.R' 'CPO_collapseFact.R'
'CPO_dropConstants.R' 'CPO_dropMostlyConstants.R'
'CPO_encode.R' 'CPO_filterFeatures.R' 'CPO_fixFactors.R'
'CPO_ica.R' 'CPO_impute.R' 'CPO_makeCols.R'
'CPO_missingIndicators.R' 'CPO_modelMatrix.R' 'CPO_pca.R'
'CPO_quantileBinNumerics.R' 'CPO_regrResiduals.R'
'CPO_responseFromSE.R' 'CPO_scale.R' 'CPO_scaleMaxAbs.R'
'CPO_scaleRange.R' 'CPO_select.R' 'CPO_smote.R'
'CPO_spatialSign.R' 'CPO_subsample.R' 'CPO_wrap.R'
'RetrafoState.R' 'attributes.R' 'auxhelp.R'
'composeProperties.R' 'doublecaret.R' 'inverter.R' 'learner.R'
'makeCPOHelp.R' 'print.R' 'zzz.R'
'utils.R' 'CapsuledMlr3Learner.R' 'ParamHelpersParamSet.R'
'optimize.R' 'paramset.R' 'TunerInterMBO.R' 'surrogates.R'
'zzz.R'
'apiTools.R' 'apiManager.R' 'mlxComputeRCleaner.R'
'mlxComputeRLibraryBuilder.R' 'addFieldsFromHeader.R'
'catplotmlx.R' 'commentModel.R' 'convertmlx.R' 'exposure.R'
'generateModelFromPkModel.R' 'ggplotmlx.R' 'hformat.R'
'hgdata.R' 'inlineDataFrame.R' 'inlineModel.R' 'isfield.R'
'kmplotmlx.R' 'mlxplore.R' 'monolix2simulx.R' 'pkmodel.R'
'prctilemlx.R' 'processing_monolix.R' 'processing_setting.R'
'processing_target.R' 'readVector.R' 'readdatamlx.R'
'shinymlx.R' 'simpopmlx.R' 'simulR.R' 'simulx.R' 'statmlx.R'
'stoolsmlx.R' 'toolsmlx.R' 'translateIOV.R' 'translateIOVind.R'
'uuid.R' 'writeDatamlx.R'
'RcppExports.R' 'extract.R' 'file-dim.R' 'mmapchar.R'
'mmapcharr-package.r' 'utils.R'
algoCat.R algoCont.R algoMixed.R MNARclust.R RcppExports.R
sampler.R tools.R
'methodObj.R' 'methodObjPredict.R' 'methodObjSolver.R'
'methodObjSolverFormula.R' 'modelObj.R' 'modelObjFormula.R'
'methodObjSolverXY.R' 'modelObjXY.R' 'buildModelObj.R'
'modelObjFit.R' 'warnMsg.R'
'coef_rename.R' 'convenience.R' 'datasummary.R'
'datasummary_balance.R' 'datasummary_correlation.R'
'datasummary_crosstab.R' 'datasummary_df.R'
'datasummary_extract.R' 'datasummary_functions.R'
'datasummary_skim.R' 'dvnames.R' 'escape.R' 'factory.R'
'factory_dataframe.R' 'factory_flextable.R' 'factory_gt.R'
'factory_huxtable.R' 'factory_kableExtra.R'
'format_estimates.R' 'format_gof.R' 'get_estimates.R'
'get_gof.R' 'get_vcov.R' 'glance_custom.R' 'gof_map.R'
'methods_did.R' 'methods_estimatr.R' 'methods_fixest.R'
'methods_lfe.R' 'methods_stats.R' 'modelplot.R'
'modelsummary.R' 'modelsummary_list.R' 'modelsummary_wide.R'
'poorman.R' 'reexport.R' 'sanitize_gof_map.R'
'sanitize_group.R' 'sanitize_models.R' 'sanitize_output.R'
'sanitize_vcov.R' 'sanity_checks.R' 'settings.R' 'stars.R'
'supported_models.R' 'themes.R' 'tidy_custom.R' 'utils_pad.R'
'utils_print.R' 'utils_replace.R' 'utils_rounding.R'
'utils_stats.R' 'utils_warn.R'
addItem.R findItem.R delItem.R addRow.R findRow.R checkArray.R
checkCTechSTu.R getCTechSTM1u.R getCTechSTM2u.R getCharSTM1u.R
getCharSTM2u.R findOptimalBasisM1FromScratch.R
findOptimalBasisM2FromScratch.R compContourM1u.R
compContourM2u.R evalContour.R
'amodule.R' 'NAMESPACE.R' 'getSearchPath.R' 'class.R'
'depend.R' 'export.R' 'expose.R' 'extend.R' 'import.R'
'module-class.R' 'module-coercion.R' 'module-helper.R'
'module.R' 'use.R' 'testModule.R' 'base-override.R'
AllClasses.R AllGenerics.R alapl.R bms_ortho.R cil.R cox.R
derivatives_nlps.R distribs.R dmom.R emomLM.R gam.R greedyGLM.R
infocriteria.R initParameters.R msPriorSpec.R modelsearch.R
modelSelection.R modelSelectionGLM.R mombf.R normaliwish.R
normmix.R nlpMarginal.R pmomLM.R pmomPM.R postMode.R
pplProbit.R ppmodel.R rmom.R RcppExports.R testfunction.R
zellnerLM.R
'allClasses.R' 'validity.R' 'momentData.R'
'momentModel-methods.R' 'momentModel.R'
'momentWeights-methods.R' 'gmmfit-methods.R'
'specTest-methods.R' 'summary-methods.R' 'rModel-methods.R'
'hypothesisTest-methods.R' 'sysMomentModel.R'
'sysMomentModel-methods.R' 'rsysMomentModel-methods.R'
'sgmmfit-methods.R' 'gmm4.R' 'gel.R' 'gelfit-methods.R'
'gel4.R'
'BBCVEM.R' 'MoMPCA.R' 'benchmarks.R' 'data.R'
'init_functions.R' 'mmpcaClustcontrol_class.R'
'mmpcaClust_class.R' 'mmpca_clust.R'
'mmpca_clust_modelselection.R' 'plot.R' 'simulate_BBC.R'
'utility_functions.R' 'utils-pipe.R'
'normalization.R' 'downsizing.R' 'querries.R' 'filtering.R'
'visualization.R' 'analyses.R' 'data.R' 'mapreduce.R'
'utils.R' 'A_NAMESPACE.R' 'computeMu.R' 'multiRun.R'
'optimParams.R' 'plot.R' 'sampleIO.R'
'DocumentData.R' 'mortalityTable.R' 'mortalityTable.period.R'
'mortalityTable.ageShift.R' 'ageShift.R'
'mortalityTable.joined.R' 'mortalityTable.mixed.R' 'ages.R'
'baseTable.R' 'baseYear.R' 'fillAges.R' 'pensionTable.R'
'commutationNumbers.R' 'mortalityTable.improvementFactors.R'
'mortalityTable.trendProjection.R' 'deathProbabilities.R'
'getCohortTable.R' 'getOmega.R' 'getPeriodTable.R'
'lifeTable.R' 'makeQxDataFrame.R' 'mortalityComparisonTable.R'
'mortalityImprovement.R' 'mortalityTable.MakehamGompertz.R'
'mortalityTable.Weibull.R' 'mortalityTable.deMoivre.R'
'periodDeathProbabilities.R' 'mortalityTable.jointLives.R'
'utilityFunctions.R' 'mortalityTable.observed.R'
'mortalityTables.list.R' 'mortalityTables.load.R'
'plot.mortalityTable.R' 'plotMortalityTableComparisons.R'
'plotMortalityTables.R' 'plotMortalityTrend.R' 'setLoading.R'
'setModification.R' 'undampenTrend.R'
'whittaker.mortalityTable.R'
'customnsga2.R' 'datagen.R' 'ecrshims.R' 'evalmosmafs.R'
'filtermat.R' 'objective.R' 'utils.R' 'operators.R'
'plotting.R' 'selectorcpo.R' 'terminators.R' 'zzz.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'
'utils.R' 'parseq.R' 'sens.R' 'AAA.R' 'lsa.R' 'sens_each.R'
'sens_grid.R' 'sens_plot.R' 'sens_run.R' 'seq.R'
'RcppExports.R' 'utils.R' 'package.R' 'generics.R'
'class_tgrid.R' 'class_numericlist.R' 'class_matlist.R'
'class_ev.R' 'class_derived.R' 'class_mrgmod.R'
'class_mrgsims.R' 'Aaaa.R' 'annot.R' 'chain.R' 'class_build.R'
'class_evd.R' 'events.R' 'class_rx.R' 'compile.R' 'data_set.R'
'datasets.R' 'env.R' 'funset.R' 'handle_spec_block.R'
'idata_set.R' 'init.R' 'inven.R' 'knobs.R' 'matlist.R'
'matrix.R' 'mcode.R' 'model_include.R' 'modlib.R' 'modspec.R'
'mread.R' 'mrgindata.R' 'mrgsim_q.R' 'mrgsims.R' 'mrgsolve.R'
'nm-mode.R' 'nmxml.R' 'param.R' 'print.R' 'r_to_cpp.R'
'realize_addl.R' 'relabel.R' 'render.R' 'update.R'
'workflows.R'
mstherm.R classes.R normalization.R modeling.R plot.R
analysis.R export.R
'api-authentication.R' 'api-cubes.R' 'api-datasets.R'
'api-projects.R' 'api-reports.R' 'api-misc.R' 'connection.R'
'cube.R' 'dataset.R' 'report.R' 'utils-model.R'
'utils-formjson.R' 'utils-parser.R' 'utils-encoder.R'
'utils-errors.R' 'utils-filter.R' 'utils-files.R' 'utils-gui.R'
'utils-import.R' 'utils-helpers.R' 'utils-export.R' 'server.R'
'app.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'
'AllGeneric.R' 'Rn_Gauss.R' 'Rn_floorNorm.R' 'domainChecks.R'
'misc.R' 'multIntTestFunc.R' 'standardSimplex_Dirichlet.R'
'standardSimplex_exp_sum.R' 'unitBall_normGauss.R'
'unitBall_polynomial.R' 'unitCube_cos2.R' 'unitCube_floor.R'
'unitSphere_innerProduct1.R' 'unitSphere_polynomial.R'
'Data_Source.R' 'get_general_contribution.R'
'get_proportion.R' 'get_internal_data.R' 'M_within.R'
'mutual.R' 'get_contribution.R' 'M_value.R' 'M.R' 'globals.R'
'prepare_data.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
bootstrap.R evolmodelest.R matrixexps.R phylgls.R
sdecovariancephyl.R wrappers.R ci.R fitch.mvsl.R
calls_to_matrixcalc.R matrixparametrizations.R PhyloSDEestim.R
sdemoments.R estimBM.R getESS.R modelparams.R phyltree_paths.R
simulVasicekprocphyl.R estimGLSGC.R loglik.R
modelparamssummary.R precalcs.R simulVasicekproc.R
estimMAXLIK.R make.states.mvsl.R modelparamstransform.R
regimes.R trees2slouch.mvsl.R simulclustphyl.R
OUphylregression.R sdemeanphyl.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'
All.R summary.R cen.R km.R reg.R mle.R ken.R ros.R icmle.R
'add-cols.R' 'add-n-prop-miss.R' 'cast-shadows.R'
'data-common-na-numbers.R' 'data-common-na-strings.R'
'data-oceanbuoys.R' 'data-pedestrian.R' 'data-riskfactors.R'
'legend-draw.R' 'geom-miss-point.R' 'geom2plotly.R'
'gg-miss-case-cumsum.R' 'gg-miss-case.R' 'gg-miss-fct.R'
'gg-miss-span.R' 'gg-miss-upset.R' 'gg-miss-var-cumsum.R'
'gg-miss-var.R' 'gg-miss-which.R' 'helpers.R' 'impute-median.R'
'impute_below.R' 'impute_mean.R' 'label-miss.R' 'mcar-test.R'
'miss-complete-x-pct-prop.R' 'miss-prop-pct-summary.R'
'miss-scan-count.R' 'miss-x-cumsum.R' 'miss-x-run.R'
'miss-x-span.R' 'miss-x-summary.R' 'miss-x-table.R'
'n-prop-miss-complete-rows.R' 'n-prop-miss-complete.R'
'n-var-miss.R' 'nabular.R' 'naniar-ggproto.R'
'naniar-package.R' 'prop-pct-var-case-miss-complete.R'
'replace-to-na.R' 'replace-with-na.R'
'scoped-replace-with-na.R' 'shade.R' 'shadow-recode.R'
'shadow-shifters.R' 'shadows.R' 'stat-miss-point.R' 'utils.R'
'where-na.R'
'nanotime.R' 'nanoival.R' 'nanoduration.R' 'nanoperiod.R'
'RcppExports.R'
'alphashape3d.R' 'amiralandmarks-io.R' 'amiramesh-io.R'
'cmtk-reformat.R' 'cmtk.R' 'cmtk_geometry.R' 'cmtk_io.R'
'cmtkreg.R' 'coordinates.R' 'dist3D_Segment_to_Segment.R'
'neuron.R' 'dotprops.R' 'graph-nodes.R' 'hxsurf.R' 'im3d.R'
'nat-data.R' 'nat-package.R' 'ndigest.R' 'neuron-io-amira.R'
'neuron-io-fiji.R' 'neuron-io-neuroml.R' 'neuron-io.R'
'neuron-plot.R' 'neuronlist.R' 'neuronlist_interactive_3d.R'
'neuronlist_sets.R' 'neuronlistfh.R' 'ngraph.R' 'nrrd-io.R'
'pop3d.R' 'potential_synapses.R' 'reglist.R' 'seglist.R'
'summary.R' 'utils.R' 'vaa3draw-io.R' 'vtk-io.R' 'xform.R'
'xformimage.R' 'xformpoints.R' 'zzz.R'
'nbc4va.R' 'nbc4va_data.R' 'nbc4va_validation.R'
'nbc4va_internal.R' 'nbc4va_main.R' 'nbc4va_extra.R'
'nbc4va_utility.R' 'nbc4va_wrapper.R'
'FindBestTransform.R' 'all_classes.R' 'all_generics.R'
'control.R' 'copiedFromOtherPackages.R' 'generateCountData.R'
'getShrinkedDispersions.R' 'helper_functions.R'
'normalize_counts.R' 'package_and_supplementary.R'
'plot.nblda.R' 'predict.nblda.R' 'trainNBLDA.R' 'zzz_methods.R'
'distancematrix.R' 'nonbimatch.R' 'assign.grp.R'
'fill.missing.R' 'gendistance.R' 'make.phantoms.R'
'nbpmatching-package.R' 'qom.R' 'quantile.R' 'subsetMatches.R'
'utils.R'
'RcppExports.R' 'imports.r' 'graph_data.r' 'adjmat.r' 'bass.r'
'bootnet.r' 'data.r' 'diffnet-c.R' 'diffnet-class.r'
'diffnet-indexing.r' 'diffnet-methods.r' 'egonets.R'
'formula.r' 'igraph.r' 'infect_suscept.r' 'mentor.r' 'misc.r'
'moran.r' 'netmatch.r' 'network.r' 'options.R' 'package-doc.r'
'plot_diffnet2.r' 'rewire.r' 'random_graph.R' 'rdiffnet.r'
'read_write_foreign.r' 'select_egoalter.R' 'spatial.R'
'stats.R' 'struct_equiv.R' 'struct_test.R'
'survey_to_diffnet.R'
'Agent-classes.R' 'NetLogoR-package.R'
'worldNLR-classes-methods.R' 'agentMatrix-Class-methods.R'
'agentset-functions.R' 'function-arguments.R' 'helpers.R'
'patch-functions.R' 'plot.R' 'world-functions.R' 'quickPlot.R'
'spades-functions.R' 'turtle-functions.R'
'0_helper_net.r' 'NetOrigin.r' 'compute_mu_lambda.R'
'origin_helper.r' 'origin_methods.r' 'distance.r' 'data.r'
'data_handling.r' 'initial_condition_sib_model.R'
'robustness.r' 'stochastic_sib_model.R'
'access.R' 'as.edgelist.R' 'assignment.R' 'coercion.R'
'constructors.R' 'dataframe.R' 'fileio.R' 'layout.R' 'misc.R'
'network-package.R' 'operators.R' 'plot.R' 'printsum.R' 'zzz.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'
'FilteringF.r' 'gridfunction.r' 'GridP.r' 'LikeF.r' 'LikeF2.r'
'ngssm.bayes.r' 'ngssm.mle.r' 'NumFail.r' 'PlotF.r'
'Prediction.r' 'PriorF.r' 'ProdXtChi.r' 'SmoothingF.r'
'TTime.r' 'ngssm.mle.p.r' 'predict.ngssm.mle.p.r'
'ngssm.bayes.p.r' 'predict.ngssm.bayes.p.r'
'summary.ngssm.bayes.r' 'summary.ngssm.mle.r'
'left_xl.R' 'len_xl.R' 'linkScrapeR.R' 'mid_xl.R'
'nhs_data_elements.R' 'scrapeR.R' 'tableR.R'
'nhs_table_findeR.R' 'right_xl.R' 'openSafely_listR.R'
'xpathTextR.R'
'init.R' 'describeNiLeDAM.R' 'describeSrilanka.R' 'ages.R'
'calculate.R' 'oneAgeTest.R' 'ageTests.R' 'tests.R'
'ages-oneAgeTest-ageTests.R' 'liveNILEDAM.R'
config.R all_utils.R options.R distributions_inputList.R
distributions_processInputList.R
distributions_implementations.R BUGS_BUGSdecl.R BUGS_contexts.R
BUGS_nimbleGraph.R BUGS_modelDef.R BUGS_model.R
BUGS_graphNodeMaps.R BUGS_readBUGS.R BUGS_macros.R
BUGS_testBUGS.R BUGS_getDependencies.R BUGS_utils.R
BUGS_mathCompatibility.R externalCalls.R genCpp_exprClass.R
genCpp_operatorLists.R genCpp_RparseTree2exprClasses.R
genCpp_initSizes.R genCpp_buildIntermediates.R
genCpp_processSpecificCalls.R genCpp_sizeProcessing.R
genCpp_toEigenize.R genCpp_insertAssertions.R genCpp_maps.R
genCpp_liftMaps.R genCpp_eigenization.R genCpp_addDebugMarks.R
genCpp_generateCpp.R RCfunction_core.R RCfunction_compile.R
nimbleFunction_util.R nimbleFunction_core.R
nimbleFunction_nodeFunction.R nimbleFunction_nodeFunctionNew.R
nimbleFunction_Rexecution.R nimbleFunction_compile.R
nimbleFunction_keywordProcessing.R nimbleList_core.R
types_util.R types_symbolTable.R types_modelValues.R
types_modelValuesAccessor.R types_modelVariableAccessor.R
types_nimbleFunctionList.R types_nodeFxnVector.R
types_numericLists.R cppDefs_utils.R cppDefs_variables.R
cppDefs_core.R cppDefs_namedObjects.R cppDefs_ADtools.R
cppDefs_BUGSmodel.R cppDefs_RCfunction.R
cppDefs_nimbleFunction.R cppDefs_nimbleList.R
cppDefs_modelValues.R cppDefs_cppProject.R
cppDefs_outputCppFromRparseTree.R cppInterfaces_utils.R
cppInterfaces_models.R cppInterfaces_modelValues.R
cppInterfaces_nimbleFunctions.R cppInterfaces_otherTypes.R
nimbleProject.R initializeModel.R CAR.R MCMC_utils.R
MCMC_configuration.R MCMC_build.R MCMC_run.R MCMC_samplers.R
MCMC_conjugacy.R MCMC_autoBlock.R MCMC_RJ.R MCMC_WAIC.R
MCEM_build.R crossValidation.R BNP_distributions.R
BNP_samplers.R NF_utils.R miscFunctions.R makevars.R
setNimbleInternalFunctions.R registration.R nimble-package.r
zzz.R
utils.R dBetaBinom.R dCJS.R dDynOcc.R dDHMM.R dHMM.R dOcc.R
dNmixture.R zzz.R
calcWindowSizes.R getWindowIndex.R
integrateIntensityLocal_normal.R integrateIntensityLocal_exp.R
integrateIntensity_normal.R integrateIntensity_exp.R
stratRejectionSampler_normal.R stratRejectionSampler_exp.R
dDispersal_exp.R dHabitatMask.R dbernppAC.R
dbernppACmovement_normal.R dbernppACmovement_exp.R
dbernppDetection_normal.R dbernppLocalACmovement_normal.R
dbernppLocalACmovement_exp.R dbernppLocalDetection_normal.R
dbinomLocal_normal.R dbinom_sparseLocalSCR.R dbinom_vector.R
dnormalizer.R dpoisLocal_normal.R dpoisppAC.R
dpoisppDetection_normal.R dpoisppLocalDetection_normal.R
getLocalObjects.R getLocalTraps.R getMidPointNodes.R
getWindowCoords.R getSparseY.R localTrapCalculations.R
makeConstantNimbleFunction.R marginalVoidProbIntegrand.R
marginalVoidProbNumIntegration.R scaleCoordsToHabitatGrid.R
zzz.R
AuxiliaryFilter.R BootstrapFilter.R EnKFilter.R IF2Filter.R
LiuWestFilter.R MCMCSamplers.R resamplers.R utils.R zzz.R
'logLik1.R' 'nlmeU-package.R' 'Pwr.R' 'simulateY.R' 'varia.R'
'colorcode.R' 'options.R' 'grid.R' 'atracks.R' 'aheatmap.R'
'algorithmic.R' 'nmf-package.R' 'rmatrix.R' 'utils.R'
'versions.R' 'NMF-class.R' 'transforms.R' 'Bioc-layer.R'
'NMFstd-class.R' 'NMFOffset-class.R' 'heatmaps.R'
'NMFns-class.R' 'nmfModel.R' 'fixed-terms.R' 'NMFfit-class.R'
'NMFSet-class.R' 'NMFStrategy-class.R' 'registry.R'
'NMFSeed-class.R' 'NMFStrategyFunction-class.R'
'NMFStrategyIterative-class.R' 'NMFplots.R'
'registry-algorithms.R' 'algorithms-base.R' 'algorithms-lnmf.R'
'algorithms-lsnmf.R' 'algorithms-pe-nmf.R' 'algorithms-siNMF.R'
'algorithms-snmf.R' 'data.R' 'extractFeatures.R' 'parallel.R'
'registry-seed.R' 'nmf.R' 'rnmf.R' 'run.R' 'seed-base.R'
'seed-ica.R' 'seed-nndsvd.R' 'setNMFClass.R' 'simulation.R'
'tests.R'
'NMproject-options.R' 'NMproject-package.R' 'Vectorize.R'
'addin-apps.R' 'apply_manual_edit.R' 'basic-ctl-manipulation.R'
'utils.R' 'basic_methods.R' 'boot.R' 'nm_object.R' 'cache.R'
'covariate-explore.R' 'cran_note_handle.R' 'data_filter.R'
'decision.R' 'deprecated.R' 'derived-data-prep.R'
'developer-funs.R' 'directory-management.R' 'dollar_input.R'
'find-nonmem.R' 'import-code.R' 'init_funs.R' 'input_data.R'
'job_stats.R' 'low-level-ctl-handling-funs.R'
'make_OCC_every_dose.R' 'make_project.R' 'manual-edit.R'
'monitoring.R' 'nm-gettersetters.R' 'nm_diff.R'
'nm_read_table.R' 'nm_render.R' 'nm_tran.R' 'nm_tree.R'
'nmsave.R' 'ofv.R' 'output_table.R' 'param-manipulate.R'
'plot_iter.R' 'post-run-summaries.R' 'prompt_overwrite.R'
'psn_style_scm.R' 'read_ext.R' 'rmd_to_vignette.R' 'run_nm.R'
'shiny-apps.R' 'show-file.R' 'sim-diagnostics.R'
'snippet_setup.R' 'stepwise-covariate.R' 'subroutine.R'
'system-hooks.R' 'text.R' 'update_parameters.R'
'with_temp_git_config.R' 'zzz.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
'utils.R' 'dplyr_integration.R' 'noctua.R' 'Driver.R'
'Connection.R' 'DataTypes.R' 'File_Parser.R' 'Options.R'
'fetch_utils.R' 'Result.R' 'Table.R' 'View.R'
'athena_low_api.R' 'column_parser.R' 'sql_translate_utils.R'
'sql_translate_env.R' 'zzz.R'
'nofrills.R' 'closure.R' 'exprs.R' 'fn.R' 'as-fn.R'
'make-fn-aware.R' 'curry.R'
'NpdeSimData.R' 'NpdeData.R' 'aaa_generics.R'
'NpdeData-methods.R' 'NpdeRes.R' 'NpdeRes-methods.R'
'NpdeObject.R' 'NpdeObject-methods.R' 'compute_distribution.R'
'compute_npde.R' 'compute_pd.R' 'compute_ploq.R' 'mainNpde.R'
'npde.R' 'npdeControl.R' 'plotNpde-auxDistPlot.R'
'plotNpde-auxScatter.R' 'plotNpde-auxScatterPlot.R'
'plotNpde-binningPI.R' 'plotNpde-covplot.R'
'plotNpde-distributionPlot.R' 'plotNpde-methods.R'
'plotNpde-plotFunctions.R' 'plotNpde-scatterplot.R'
npsCommon.R npsInvalid.R npsMain.R npsNecessary.R npsValid.R
RNested.R
'arg.R' 'caller.R' 'quo.R' 'dots.R' 'getpromise.R'
'conversions.R' 'format.R' 'force.R' 'shortcut.R' 'missing.R'
'util.R' 'CNPJ.R' 'CPF.R' 'RENAVAN.R' 'numbersBR.R'
'alignment.R' 'as_factor.R' 'constants.R' 'f_12_hour.R'
'utils.R' 'f_abbreviation.R' 'f_affirm.R' 'f_affix.R' 'f_bin.R'
'f_comma.R' 'f_data.R' 'f_date.R' 'f_degree.R' 'f_denom.R'
'f_dollar.R' 'f_list.R' 'f_logical.R' 'f_month.R' 'f_num.R'
'f_ordinal.R' 'f_pad_zero.R' 'f_parenthesis.R' 'f_percent.R'
'f_pval.R' 'f_quarter.R' 'f_replace.R' 'f_sign.R' 'f_state.R'
'f_text_bar.R' 'f_title.R' 'f_weekday.R' 'f_wrap.R' 'f_year.R'
'fv_num_percent.R' 'fv_percent.R' 'fv_percent_diff.R'
'fv_percent_lead.R' 'fv_runs.R' 'glue-reexports.R'
'highlight_cells.R' 'numform-package.R' 'round.R'
'time_digits.R'
'idfocean.R' 'idfeez.R' 'data.R' 'dotplot.R' 'idfland.R'
'idfcode.R'
'odbc.R' 'Driver.R' 'Connection.R' 'DataTypes.R'
'RcppExports.R' 'Result.R' 'Table.R' 'Viewer.R' 'db.R'
'hidden.R' 'utils.R' 'zzz.R'
'data.r' 'harsm.r' 'hensm.r' 'linodds.r' 'ohenery.r'
'RcppExports.R' 'rsm.r' 'utils.r'
'OmnibusFisher_iteration_GM.R' 'OmnibusFisher_iteration_GMR.R'
'OmnibusFisher_iteration_GR.R' 'OmnibusFisher_iteration_MR.R'
'OmnibusFisher_outer.R' 'example_data.R'
'abstract-model-impl.R' 'helper.R'
'linear-optimization-model-impl.R'
'linear-optimization-model-linear-constraints.R'
'linear-optimization-model-linear-functions.R' 'model-api.R'
'milp-impl.R' 'milp-linearopt-variables.R' 'ompr-package.R'
'solution-api.R' 'solution-impl.R'
'argument_types.R' 'authentication.R' 'zzz.R'
'capabilities-mapping.R' 'process_graph_building.R'
'utilities.R' 'client.R' 'debugging.R' 'jobs.R' 'services.R'
'user_defined_processes.R' 'coerce-functions.R'
'collection-functions.R' 'collections.R' 'ops.R'
'predefined_processes.R' 'print-functions.R' 'sample_data.R'
'server_metadata.R' 'udf.R' 'user.R' 'viewer.R'
'OpenLand-package.R' 'rasters_input.R' 'demolandscape.R'
'contingencyTable.R' 'data.R' 'generalfunctions.R'
'intensityClass.R' 'generic_method.R' 'intensityAnalysis.R'
'plotMethods.R' 'otherplots.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' 'MxSquareBracket.R' 'MxEval.R'
'MxRename.R' 'MxPath.R' 'MxObjectiveMetaData.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'
'MxErrorHandling.R' 'MxDetectCores.R' 'MxSaturatedModel.R'
'omxBrownie.R' 'omxConstrainThresholds.R' 'omxGetNPSOL.R'
'MxFitFunctionR.R' 'MxRObjective.R'
'MxExpectationHiddenMarkov.R' 'MxExpectationMixture.R'
'MxExpectationStateSpace.R' 'MxExpectationBA81.R'
'MxFitFunctionGREML.R' 'MxExpectationGREML.R' 'MxMI.R'
'MxFactorScores.R' 'MxRobustSE.R' 'MxAvailableOptimizers.R'
'MxTryHard.R' 'MxSE.R' 'MxAutoStart.R' 'MxRetro.R'
'MxPenalty.R' 'MxMMI.R' 'omxReadGRMBin.R' 'MxPredict.R' 'zzz.R'
'bertin.r' 'calc.r' 'data-openrepgrid.r' 'dev-functions.r'
'distance.R' 'double-entry.R' 'export.r' 'gmMain.r' 'import.r'
'measures.r' 'onair.r' 'openrepgrid.r' 'repgrid.r'
'repgrid-basicops.r' 'repgrid-constructs.r'
'repgrid-elements.r' 'repgrid-output.r' 'repgrid-plots.r'
'repgrid-ratings.r' 'rgl-3d.r' 'settings.r' 'utils-import.r'
'utils.r' 'zzz.r'
'CommentClass.R' 'HyperlinkClass.R' 'RcppExports.R'
'class_definitions.R' 'StyleClass.R' 'WorkbookClass.R'
'asserts.R' 'baseXML.R' 'borderFunctions.R' 'build_workbook.R'
'chartsheet_class.R' 'conditional_formatting.R'
'data-fontSizeLookupTables.R' 'helperFunctions.R'
'loadWorkbook.R' 'onUnload.R' 'openXL.R' 'openxlsx-package.R'
'openxlsx.R' 'openxlsxCoerce.R' 'readWorkbook.R'
'sheet_data_class.R' 'utils.R' 'workbook_column_widths.R'
'workbook_read_workbook.R' 'workbook_write_data.R'
'worksheet_class.R' 'wrappers.R' 'writeData.R'
'writeDataTable.R' 'writexlsx.R' 'zzz.R'
OPI-package.r opi.r compassClient.r displayClient.r
daydreamClient.r dbTocd.r fourTwo.r full_threshold.r
imoClient.r kowaAP7000Client.r mocs.r octopus600.r
octopus900Client.r pix2deg.r phoneVR.r simDisplay.r simG.r
simH.r simH_RT.r simNo.r simYes.r zest.r data-RtDbUnits.r
data-RtSigmaUnits.r opiKineticStimulus.r opiStaticStimulus.r
opiTemporalStimulus.r
'internal.R' 'pproto.R' 'Parameter-proto.R'
'ArrayParameter-proto.R' 'MiscParameter-proto.R'
'Parameters-proto.R' 'ScalarParameter-proto.R' 'parameters.R'
'waiver.R' 'ProjectModifier-proto.R' 'Constraint-proto.R'
'Collection-proto.R' 'Decision-proto.R' 'Id.R'
'Objective-proto.R' 'OptimizationProblem-proto.R'
'OptimizationProblem-methods.R' 'ProjectProblem-proto.R'
'RcppExports.R' 'Solver-proto.R' 'Target-proto.R'
'Weight-proto.R' 'action_names.R' 'add_absolute_targets.R'
'add_binary_decisions.R' 'add_default_solver.R'
'add_feature_weights.R' 'add_gurobi_solver.R'
'add_heuristic_solver.R' 'add_locked_in_constraints.R'
'add_locked_out_constraints.R' 'add_lpsolveapi_solver.R'
'add_lpsymphony_solver.R' 'tbl_df.R' 'add_manual_targets.R'
'add_manual_locked_constraints.R'
'add_max_phylo_div_objective.R' 'star_phylogeny.R'
'add_max_richness_objective.R'
'add_max_targets_met_objective.R' 'add_min_set_objective.R'
'add_random_solver.R' 'add_relative_targets.R'
'add_rsymphony_solver.R' 'branch_matrix.R' 'compile.R'
'constraints.R' 'data.R' 'decisions.R' 'feature_names.R'
'magrittr-operators.R' 'misc.R' 'new_optimization_problem.R'
'number_of_actions.R' 'number_of_features.R'
'number_of_projects.R' 'objectives.R' 'package.R'
'solution_statistics.R' 'plot.R' 'plot_feature_persistence.R'
'plot_phylo_persistence.R' 'predefined_optimization_problem.R'
'print.R' 'problem.R' 'project_cost_effectiveness.R'
'project_names.R' 'rake_phylogeny.R' 'replacement_costs.R'
'show.R' 'simulate_ppp_data.R' 'simulate_ptm_data.R' 'solve.R'
'solvers.R' 'targets.R' 'weights.R' 'zzz.R'
orphaned-Code.R optCluster-Classes.R optCluster-Methods.R
optCluster-Internal.R optCluster-Functions.R
'optim.functions.R' 'vectorize.R' 'sampling.R'
'single.objective.R' 'functions.R'
'allocate_wave.R' 'sample_strata.R' 'wave.R' 'phase.R'
'multiwave.R' 'merge_samples.R' 'optimum_allocation.R'
'get_data.R' 'apply_multiwave.R' 'matwgt_sim.R'
'merge_strata.R' 'multiwave_diagram.R' 'new_multiwave.R'
'optimall_shiny.R' 'split_strata.R' 'summary.multiwave.R'
Package.R import_package.R scaledT.R ClassUnions.R
ClassFitNormalDist.R ClassFitLogNormalDist.R
ClassFitGammaDist.R ClassFitStudentDist.R
ClassFitLogisticDist.R ClassFitUserDefinedDist.R
ClassUndefined.R ClassNormalDist.R ClassLogNormalDist.R
ClassGammaDist.R ClassStudentDist.R ClassLogisticDist.R
ClassUnionsDist.R ClassCompoundDist.R ClassTrtSelOptThresh.R
ClassTrtSelRelUtility.R ClassDiagOptThresh.R
ClassDiagRelUtility.R cdf.R gradient.R hessian.R ARS.R
samplePosteriorDist.R global.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' 'scores.R' 'solver.R' 'strata.R'
'stratumStructure.R' 'subDivStrat.R' 'summary.ism.R'
'summary.optmatch.R' 'utilities.R' 'zzz.R'
'zzzDistanceSpecification.R'
workspace.R file.R dict.R ore.R match.R es.R zzz.R
'auditTrail.R' 'niftiS4.R' 'analyzeS4.R' 'afniS4.R' 'is.R'
'nifti_assign.R' 'coerce.R' 'writeS4.R' 'convert_anlz.R'
'convert_nifti.R' 'cal_img.R' 'drop_img_dim.R' 'hotmetal.R'
'miscellaneous.R' 'plot.R' 'slice.R' 'slice_overlay.R'
'blend.R' 'readS4.R' 'remove.R' 'tim_colors.R' 'transform.R'
'wrappers.R' 'onefile.R' 'voxdim.R' 'anlz_Operators.R'
'Operators.R' 'zero_trans.R' 'aux_file.R' 'cal_max.R'
'cal_min.R' 'descrip.R' 'glmax.R' 'glmin.R' 'pixdim.R'
'qform_code.R' 'scl_inter.R' 'scl_slope.R' 'sform_code.R'
'vox_offset.R' 'bitpix.R' 'data_type.R' 'datatype.R'
'db_name.R' 'dim_.R' 'dim_info.R' 'extender.R' 'extents.R'
'img_data.R' 'intent_code.R' 'intent_name.R' 'intent_p1.R'
'intent_p2.R' 'intent_p3.R' 'magic.R' 'qoffset_x.R'
'qoffset_y.R' 'qoffset_z.R' 'quatern_b.R' 'quatern_c.R'
'quatern_d.R' 'quaternion.R' 'regular.R' 'session_error.R'
'sizeof_hdr.R' 'slice_code.R' 'slice_duration.R' 'slice_end.R'
'slice_start.R' 'srow_x.R' 'srow_y.R' 'srow_z.R' 'toffset.R'
'xyzt_units.R' 'cal_units.R' 'compressed.R' 'dim_un0.R'
'exp_date.R' 'exp_time.R' 'field_skip.R' 'funused1.R'
'funused2.R' 'funused3.R' 'generated.R' 'hist_un0.R'
'hkey_un0.R' 'niftiImage_class.R' 'nii2oro.R' 'oro2nii.R'
'omax.R' 'omin.R' 'orient.R' 'origin.R' 'patient_id.R'
'scannum.R' 'smax.R' 'smin.R' 'start_field.R' 'unused1.R'
'verified.R' 'views.R' 'vols_added.R' 'vox_units.R' 'voxres.R'
'img_length.R' 'zzz.R' 'zzz_niftiImage.R'
'osm-descriptors.R' 'source.R' 'osmar-plotting.R'
'as-osmar-elements.R' 'as-osmar.R' 'as-osm.R' 'as-sp.R' 'get.R'
'osmar-subsetting.R' 'osmar-finding.R' 'osmar.R' 'source-api.R'
'source-file.R' 'source-osmosis.R' 'as-osmar-sp.R'
'as-igraph.R'
'RcppExports.R' 'osqp-package.R' 'solve.R' 'osqp.R' 'params.R'
'anolis.R' 'print.R' 'package.R' 'ouchtree.R' 'ape2ouch.R'
'bimac.R' 'bootstrap.R' 'update.R' 'simulate.R' 'summary.R'
'logLik.R' 'coef.R' 'rmvnorm.R' 'glssoln.R' 'brown.R' 'plot.R'
'hansen.R' 'paint.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'
data.R PaleoPopModel.R PaleoPopResults.R PaleoRegion.R
paleopop_simulator.R paleopop.R region_subset.R
'helpers.R' 'labels.R' 'summarize.R' 'plot.R' 'prettify.R'
'toLatex.R'
'Condition.R' 'Design.R' 'NoDefault.R' 'Param.R' 'ParamDbl.R'
'ParamFct.R' 'ParamInt.R' 'ParamLgl.R' 'ParamSet.R'
'ParamSetCollection.R' 'ParamUty.R' 'Sampler.R' 'Sampler1D.R'
'SamplerHierarchical.R' 'SamplerJointIndep.R' 'SamplerUnif.R'
'asserts.R' 'default_values.R' 'helper.R' 'domain.R'
'generate_design_grid.R' 'generate_design_lhs.R'
'generate_design_random.R' 'ps.R' 'reexports.R' 'to_tune.R'
'zzz.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
'arrow.R' 'draw.R' 'latent.R' 'manifest.R' 'wall.R'
Patterns-package.R global.R datasets.R micro_array.R network.R
micro_array-network.R micropredict.R
'athena_service.R' 'athena_interfaces.R' 'athena_operations.R'
'cloudsearch_service.R' 'cloudsearch_interfaces.R'
'cloudsearch_operations.R' 'cloudsearchdomain_service.R'
'cloudsearchdomain_interfaces.R'
'cloudsearchdomain_operations.R' 'datapipeline_service.R'
'datapipeline_interfaces.R' 'datapipeline_operations.R'
'elasticsearchservice_service.R'
'elasticsearchservice_interfaces.R'
'elasticsearchservice_operations.R' 'emr_service.R'
'emr_interfaces.R' 'emr_operations.R' 'firehose_service.R'
'firehose_interfaces.R' 'firehose_operations.R'
'glue_service.R' 'glue_interfaces.R' 'glue_operations.R'
'kafka_service.R' 'kafka_interfaces.R' 'kafka_operations.R'
'kinesis_service.R' 'kinesis_interfaces.R'
'kinesis_operations.R' 'kinesisanalytics_service.R'
'kinesisanalytics_interfaces.R' 'kinesisanalytics_operations.R'
'kinesisanalyticsv2_service.R'
'kinesisanalyticsv2_interfaces.R'
'kinesisanalyticsv2_operations.R' 'mturk_service.R'
'mturk_interfaces.R' 'mturk_operations.R'
'quicksight_service.R' 'quicksight_interfaces.R'
'quicksight_operations.R'
'eventbridge_service.R' 'eventbridge_interfaces.R'
'eventbridge_operations.R' 'mq_service.R' 'mq_interfaces.R'
'mq_operations.R' 'sfn_service.R' 'sfn_interfaces.R'
'sfn_operations.R' 'sns_service.R' 'sns_interfaces.R'
'sns_operations.R' 'sqs_service.R' 'sqs_interfaces.R'
'sqs_operations.R' 'swf_service.R' 'swf_interfaces.R'
'swf_operations.R'
'struct.R' 'handlers.R' 'iniutil.R' 'dateutil.R'
'credential_sso.R' 'credential_sts.R' 'url.R' 'net.R'
'credential_providers.R' 'credentials.R' 'client.R' 'config.R'
'convert.R' 'service.R' 'custom_dynamodb.R' 'custom_rds.R'
'xmlutil.R' 'util.R' 'stream.R' 'custom_s3.R' 'error.R'
'handlers_core.R' 'handlers_ec2query.R' 'handlers_jsonrpc.R'
'handlers_query.R' 'handlers_rest.R' 'handlers_restjson.R'
'handlers_restxml.R' 'idempotency.R' 'jsonutil.R' 'populate.R'
'populateutil.R' 'tags.R' 'queryutil.R' 'request.R'
'signer_v4.R' 'signer_s3.R' 'signer_s3v4.R' 'signer_v2.R'
'time.R'
'batch_service.R' 'batch_interfaces.R' 'batch_operations.R'
'ec2_service.R' 'ec2_interfaces.R' 'ec2_operations.R'
'ec2instanceconnect_service.R'
'ec2instanceconnect_interfaces.R'
'ec2instanceconnect_operations.R' 'ecr_service.R'
'ecr_interfaces.R' 'ecr_operations.R' 'ecs_service.R'
'ecs_interfaces.R' 'ecs_operations.R' 'eks_service.R'
'eks_interfaces.R' 'eks_operations.R'
'elasticbeanstalk_service.R' 'elasticbeanstalk_interfaces.R'
'elasticbeanstalk_operations.R' 'lambda_service.R'
'lambda_interfaces.R' 'lambda_operations.R'
'lightsail_service.R' 'lightsail_interfaces.R'
'lightsail_operations.R'
'serverlessapplicationrepository_service.R'
'serverlessapplicationrepository_interfaces.R'
'serverlessapplicationrepository_operations.R'
'budgets_service.R' 'budgets_interfaces.R'
'budgets_operations.R' 'costandusagereportservice_service.R'
'costandusagereportservice_interfaces.R'
'costandusagereportservice_operations.R'
'costexplorer_service.R' 'costexplorer_interfaces.R'
'costexplorer_operations.R'
'marketplacecommerceanalytics_service.R'
'marketplacecommerceanalytics_interfaces.R'
'marketplacecommerceanalytics_operations.R'
'marketplaceentitlementservice_service.R'
'marketplaceentitlementservice_interfaces.R'
'marketplaceentitlementservice_operations.R'
'marketplacemetering_service.R'
'marketplacemetering_interfaces.R'
'marketplacemetering_operations.R' 'pricing_service.R'
'pricing_interfaces.R' 'pricing_operations.R'
'connect_service.R' 'connect_interfaces.R'
'connect_operations.R' 'pinpoint_service.R'
'pinpoint_interfaces.R' 'pinpoint_operations.R'
'pinpointemail_service.R' 'pinpointemail_interfaces.R'
'pinpointemail_operations.R' 'pinpointsmsvoice_service.R'
'pinpointsmsvoice_interfaces.R' 'pinpointsmsvoice_operations.R'
'ses_service.R' 'ses_interfaces.R' 'ses_operations.R'
'dax_service.R' 'dax_interfaces.R' 'dax_operations.R'
'docdb_service.R' 'docdb_interfaces.R' 'docdb_operations.R'
'dynamodb_service.R' 'dynamodb_interfaces.R'
'dynamodb_operations.R' 'dynamodbstreams_service.R'
'dynamodbstreams_interfaces.R' 'dynamodbstreams_operations.R'
'elasticache_service.R' 'elasticache_interfaces.R'
'elasticache_operations.R' 'neptune_service.R'
'neptune_interfaces.R' 'neptune_operations.R' 'rds_service.R'
'rds_operations.R' 'rds_custom.R' 'rds_interfaces.R'
'rdsdataservice_service.R' 'rdsdataservice_interfaces.R'
'rdsdataservice_operations.R' 'redshift_service.R'
'redshift_interfaces.R' 'redshift_operations.R'
'simpledb_service.R' 'simpledb_interfaces.R'
'simpledb_operations.R'
'cloud9_service.R' 'cloud9_interfaces.R' 'cloud9_operations.R'
'codebuild_service.R' 'codebuild_interfaces.R'
'codebuild_operations.R' 'codecommit_service.R'
'codecommit_interfaces.R' 'codecommit_operations.R'
'codedeploy_service.R' 'codedeploy_interfaces.R'
'codedeploy_operations.R' 'codepipeline_service.R'
'codepipeline_interfaces.R' 'codepipeline_operations.R'
'codestar_service.R' 'codestar_interfaces.R'
'codestar_operations.R' 'xray_service.R' 'xray_interfaces.R'
'xray_operations.R'
'appstream_service.R' 'appstream_interfaces.R'
'appstream_operations.R' 'workdocs_service.R'
'workdocs_interfaces.R' 'workdocs_operations.R'
'worklink_service.R' 'worklink_interfaces.R'
'worklink_operations.R' 'workspaces_service.R'
'workspaces_interfaces.R' 'workspaces_operations.R'
'comprehend_service.R' 'comprehend_interfaces.R'
'comprehend_operations.R' 'comprehendmedical_service.R'
'comprehendmedical_interfaces.R'
'comprehendmedical_operations.R'
'lexmodelbuildingservice_service.R'
'lexmodelbuildingservice_interfaces.R'
'lexmodelbuildingservice_operations.R'
'lexruntimeservice_service.R' 'lexruntimeservice_interfaces.R'
'lexruntimeservice_operations.R' 'machinelearning_service.R'
'machinelearning_interfaces.R' 'machinelearning_operations.R'
'personalize_service.R' 'personalize_interfaces.R'
'personalize_operations.R' 'personalizeevents_service.R'
'personalizeevents_interfaces.R'
'personalizeevents_operations.R' 'personalizeruntime_service.R'
'personalizeruntime_interfaces.R'
'personalizeruntime_operations.R' 'polly_service.R'
'polly_interfaces.R' 'polly_operations.R'
'rekognition_service.R' 'rekognition_interfaces.R'
'rekognition_operations.R' 'sagemaker_service.R'
'sagemaker_interfaces.R' 'sagemaker_operations.R'
'sagemakerruntime_service.R' 'sagemakerruntime_interfaces.R'
'sagemakerruntime_operations.R' 'textract_service.R'
'textract_interfaces.R' 'textract_operations.R'
'transcribeservice_service.R' 'transcribeservice_interfaces.R'
'transcribeservice_operations.R' 'translate_service.R'
'translate_interfaces.R' 'translate_operations.R'
'applicationautoscaling_service.R'
'applicationautoscaling_interfaces.R'
'applicationautoscaling_operations.R'
'applicationinsights_service.R'
'applicationinsights_interfaces.R'
'applicationinsights_operations.R' 'autoscaling_service.R'
'autoscaling_interfaces.R' 'autoscaling_operations.R'
'autoscalingplans_service.R' 'autoscalingplans_interfaces.R'
'autoscalingplans_operations.R' 'cloudformation_service.R'
'cloudformation_interfaces.R' 'cloudformation_operations.R'
'cloudtrail_service.R' 'cloudtrail_interfaces.R'
'cloudtrail_operations.R' 'cloudwatch_service.R'
'cloudwatch_interfaces.R' 'cloudwatch_operations.R'
'cloudwatchevents_service.R' 'cloudwatchevents_interfaces.R'
'cloudwatchevents_operations.R' 'cloudwatchlogs_service.R'
'cloudwatchlogs_interfaces.R' 'cloudwatchlogs_operations.R'
'configservice_service.R' 'configservice_interfaces.R'
'configservice_operations.R' 'health_service.R'
'health_interfaces.R' 'health_operations.R'
'licensemanager_service.R' 'licensemanager_interfaces.R'
'licensemanager_operations.R' 'opsworks_service.R'
'opsworks_interfaces.R' 'opsworks_operations.R'
'opsworkscm_service.R' 'opsworkscm_interfaces.R'
'opsworkscm_operations.R' 'organizations_service.R'
'organizations_interfaces.R' 'organizations_operations.R'
'pi_service.R' 'pi_interfaces.R' 'pi_operations.R'
'resourcegroups_service.R' 'resourcegroups_interfaces.R'
'resourcegroups_operations.R'
'resourcegroupstaggingapi_service.R'
'resourcegroupstaggingapi_interfaces.R'
'resourcegroupstaggingapi_operations.R'
'servicecatalog_service.R' 'servicecatalog_interfaces.R'
'servicecatalog_operations.R' 'servicequotas_service.R'
'servicequotas_interfaces.R' 'servicequotas_operations.R'
'ssm_service.R' 'ssm_interfaces.R' 'ssm_operations.R'
'support_service.R' 'support_interfaces.R'
'support_operations.R'
'apigateway_service.R' 'apigateway_interfaces.R'
'apigateway_operations.R' 'apigatewaymanagementapi_service.R'
'apigatewaymanagementapi_interfaces.R'
'apigatewaymanagementapi_operations.R' 'apigatewayv2_service.R'
'apigatewayv2_interfaces.R' 'apigatewayv2_operations.R'
'appmesh_service.R' 'appmesh_interfaces.R'
'appmesh_operations.R' 'cloudfront_service.R'
'cloudfront_interfaces.R' 'cloudfront_operations.R'
'directconnect_service.R' 'directconnect_interfaces.R'
'directconnect_operations.R' 'elb_service.R' 'elb_interfaces.R'
'elb_operations.R' 'elbv2_service.R' 'elbv2_interfaces.R'
'elbv2_operations.R' 'globalaccelerator_service.R'
'globalaccelerator_interfaces.R'
'globalaccelerator_operations.R' 'route53_service.R'
'route53_interfaces.R' 'route53_operations.R'
'route53domains_service.R' 'route53domains_interfaces.R'
'route53domains_operations.R' 'route53resolver_service.R'
'route53resolver_interfaces.R' 'route53resolver_operations.R'
'servicediscovery_service.R' 'servicediscovery_interfaces.R'
'servicediscovery_operations.R'
'acm_service.R' 'acm_interfaces.R' 'acm_operations.R'
'acmpca_service.R' 'acmpca_interfaces.R' 'acmpca_operations.R'
'clouddirectory_service.R' 'clouddirectory_interfaces.R'
'clouddirectory_operations.R' 'cloudhsm_service.R'
'cloudhsm_interfaces.R' 'cloudhsm_operations.R'
'cloudhsmv2_service.R' 'cloudhsmv2_interfaces.R'
'cloudhsmv2_operations.R' 'cognitoidentity_service.R'
'cognitoidentity_interfaces.R' 'cognitoidentity_operations.R'
'cognitoidentityprovider_service.R'
'cognitoidentityprovider_interfaces.R'
'cognitoidentityprovider_operations.R' 'cognitosync_service.R'
'cognitosync_interfaces.R' 'cognitosync_operations.R'
'directoryservice_service.R' 'directoryservice_interfaces.R'
'directoryservice_operations.R' 'fms_service.R'
'fms_interfaces.R' 'fms_operations.R' 'guardduty_service.R'
'guardduty_interfaces.R' 'guardduty_operations.R'
'iam_service.R' 'iam_interfaces.R' 'iam_operations.R'
'inspector_service.R' 'inspector_interfaces.R'
'inspector_operations.R' 'kms_service.R' 'kms_interfaces.R'
'kms_operations.R' 'macie_service.R' 'macie_interfaces.R'
'macie_operations.R' 'ram_service.R' 'ram_interfaces.R'
'ram_operations.R' 'secretsmanager_service.R'
'secretsmanager_interfaces.R' 'secretsmanager_operations.R'
'securityhub_service.R' 'securityhub_interfaces.R'
'securityhub_operations.R' 'shield_service.R'
'shield_interfaces.R' 'shield_operations.R' 'sts_service.R'
'sts_interfaces.R' 'sts_operations.R' 'waf_service.R'
'waf_interfaces.R' 'waf_operations.R' 'wafregional_service.R'
'wafregional_interfaces.R' 'wafregional_operations.R'
'backup_service.R' 'backup_interfaces.R' 'backup_operations.R'
'dlm_service.R' 'dlm_interfaces.R' 'dlm_operations.R'
'efs_service.R' 'efs_interfaces.R' 'efs_operations.R'
'fsx_service.R' 'fsx_interfaces.R' 'fsx_operations.R'
'glacier_service.R' 'glacier_interfaces.R'
'glacier_operations.R' 's3_service.R' 's3_operations.R'
's3_custom.R' 's3_interfaces.R' 's3control_service.R'
's3control_interfaces.R' 's3control_operations.R'
'storagegateway_service.R' 'storagegateway_interfaces.R'
'storagegateway_operations.R'
ABCdist.R ABCmessage.R ABC.R ABCupdate.R draw.R
from_geiger_2_0_6_2_diversification.R getTipSample.R rates.R
simsde.R simtree.R
'matrix.p.sig.R' 'pcps.R' 'pcps.sig.R' 'FUN.ADONIS.R'
'FUN.GLM.R' 'FUN.GLS.marginal.R' 'FUN.GLS.sequential.R'
'FUN.LME.marginal.R' 'FUN.LME.sequential.R' 'FUN.MANTEL.R'
'FUN.RDA.R' 'check.formula.R' 'define.clade.R'
'matrix.p.null.R' 'mutate.names.matrix.p.null.R'
'organize.pcps.R' 'pcoa.sig.R' 'pcps.curve.R'
'pcpc.curve.calc.R' 'plot.pcps.R' 'plot.pcpscurve.R'
'print.pcoasig.R' 'print.pcps.R' 'print.pcpscurve.R'
'print.pcpssig.R' 'print.summarypcoasig.R'
'print.summarypcps.R' 'scores.pcps.R' 'select.pcpsmethod.R'
'self.belonging.R' 'summary.pcoasig.R' 'summary.pcps.R'
'summary.pcpscurve.R' 'wcmdscale.org.R'
utils.R test1.r PeriodicCalc.R pcstat.R pc00smallutil.r
pc02filters.r pc03simu.r acfsums.R pcls.R pcarma_model.R
pcarma_acf.R generics.R autocovariances.R classCycle.R
pcFilterClasses.R PeriodicClasses.R cyclic.R
FittedPeriodicModels.R fitPM.R pcTest.R PeriodicVector.R sim.R
optimcore.R trig.R
'as_p.R' 'as_d.R' 'as_q.R' 'as_r.R' 'assertions.R'
'form-compare.R' 'form-other.R' 'form_regrid.R'
'form_resupport.R' 'form_retype.R' 'form_tails.R'
'form_trans.R' 'group-generics.R' 'meta.R' 'print.R' 'new_p.R'
'new_d.R' 'new_q.R' 'new_r.R' 'pdqr-package.R' 'plot.R'
'region.R' 'summ-other.R' 'summ_center.R' 'summ_classmetric.R'
'summ_distance.R' 'summ_entropy.R' 'summ_hdr.R'
'summ_interval.R' 'summ_moment.R' 'summ_order.R' 'summ_pval.R'
'summ_roc.R' 'summ_separation.R' 'summ_spread.R' 'utils-as.R'
'utils-form.R' 'utils-new.R' 'utils-summ.R' 'utils.R' 'x_tbl.R'
'zzz.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
'dLogLikelihood.R' 'importDataSet.R' 'normalDistribution.R'
'pguCorrValidator.R' 'pguCorrelator.R' 'pguDMwR.R' 'pguFile.R'
'pguData.R' 'pguFilter.R' 'pguLimitsOfQuantification.R'
'pguImporter.R' 'pguStatus.R' 'pguReporter.R' 'pguExporter.R'
'pguValidator.R' 'pguImputation.R' 'pguOutlierDetection.R'
'pguOutliers.R' 'pguMissingsCharacterizer.R' 'pguMissings.R'
'pguNormalizer.R' 'sLogLikelihood.R' 'pguNormDist.R'
'pguModel.R' 'pguTransformator.R' 'pguOptimizer.R'
'pguExplorer.R' 'pguDelegate.R' 'transposeTibble.R' 'pguIMP.R'
'pguRegressor.R'
'oldclasses-class.R' 'internal-constructors.R'
'phylo4-methods.R' 'RcppExports.R' 'checkdata.R'
'phylo4-class.R' 'getNode-methods.R' 'formatData.R'
'phylo4d-class.R' 'phylo4d-methods.R' 'MRCA-methods.R'
'addData-methods.R' 'ancestors.R' 'phylo4-accessors.R'
'root-methods.R' 'nodeId-methods.R' 'edgeLength-methods.R'
'setAs-methods.R' 'extractTree.R' 'labels-methods.R'
'multiphylo4-class.R' 'pdata.R' 'phylo4d-accessors.R'
'phylobase-package.R' 'phylobase.options.R' 'phylomats-class.R'
'print-methods.R' 'readNCL.R' 'reorder-methods.R'
'shortestPath-methods.R' 'subset-methods.R' 'summary-methods.R'
'tbind.R' 'tdata-methods.R' 'treePlot.R' 'treestruc.R' 'zzz.R'
'load.tree.R' 'as_tree.R' 'get.descendants.R'
'nodes.branches.R' 'phylocanvas.R'
'utility.R' 'tokenize.R' 'newick.R' 'nexus.R' 'format.R'
'mbattrs.R'
'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'
'assert_that.R' 'conditions.R' 'comma_list.R' 'find_scope.R'
'infix.R' 'skip_scope.R' 'suppress.R' 'translate.R'
'bibtex.R' 'colors.R' 'data.R' 'namespace.R' 'utils.R'
'devutils.R' 'files.R' 'graphics.R' 'options.R' 'logging.R'
'unitTests.R' 'is.R' 'knitr.R' 'registry.R' 'package-extra.R'
'package.R' 'packages.R' 'project.R' 'rd.R' 'user.R'
'vignette.R' 'with.r'
'PLNfit-class.R' 'PLN.R' 'PLNLDA.R' 'PLNLDAfit-S3methods.R'
'PLNLDAfit-class.R' 'PLNPCA.R' 'PLNPCAfamily-S3methods.R'
'PLNfamily-class.R' 'PLNPCAfamily-class.R'
'PLNPCAfit-S3methods.R' 'PLNPCAfit-class.R'
'PLNfamily-S3methods.R' 'PLNfit-S3methods.R' 'PLNmixture.R'
'PLNmixturefamily-S3methods.R' 'PLNmixturefamily-class.R'
'PLNmixturefit-S3methods.R' 'PLNmixturefit-class.R'
'PLNmodels-package.R' 'PLNnetwork.R'
'PLNnetworkfamily-S3methods.R' 'PLNnetworkfamily-class.R'
'PLNnetworkfit-S3methods.R' 'PLNnetworkfit-class.R'
'RcppExports.R' 'deprecated.R' 'import_utils.R' 'mollusk.R'
'oaks.R' 'plot_utils.R' 'trichoptera.R' 'utils-pipe.R'
'utils.R' 'zzz.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'
'async.R' 'content-types.R' 'default-handlers.R' 'hookable.R'
'shared-secret-filter.R' 'parser-cookie.R' 'parse-body.R'
'parse-query.R' 'plumber.R' 'deprecated-R6.R' 'deprecated.R'
'digital-ocean.R' 'find-port.R' 'globals.R' 'includes.R'
'json.R' 'new-rstudio-project.R' 'openapi-spec.R'
'openapi-types.R' 'options_plumber.R' 'paths.R' 'plumb-block.R'
'plumb-globals.R' 'plumb.R' 'plumber-response.R'
'plumber-static.R' 'plumber-step.R' 'pr.R' 'pr_set.R'
'serializer.R' 'session-cookie.R' 'ui.R' 'utf8.R'
'utils-pipe.R' 'utils.R' 'validate_api_spec.R' 'zzz.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'
'GenericClass.R' 'Region.R' 'GenericModel.R' 'SpatialModel.R'
'DispersalFriction.R' 'GenerativeTemplate.R'
'DispersalTemplate.R' 'Generator.R' 'DispersalGenerator.R'
'GenericManager.R' 'LatinHypercubeSampler.R' 'ModelSimulator.R'
'SimulationModel.R' 'PopulationModel.R' 'SimulationResults.R'
'PopulationResults.R' 'ResultsManager.R' 'SimulationManager.R'
'SimulatorReference.R' 'SpatialCorrelation.R' 'Validator.R'
'data.R' 'poems.R' 'population_density.R'
'population_dispersal.R' 'population_env_stoch.R'
'population_results.R' 'population_transformation.R'
'population_transitions.R' 'population_simulator.R'
'A_Kernel.R' 'A_BurdenKernel.R' 'A_LinearKernel.R'
'A_PolyKernel.R' 'B_PvMethod.R' 'B_PvMethod_Davies.R'
'B_PvMethod_Liu.R' 'C_BinomialTrait.R' 'C_GaussianTrait.R'
'D_NullResult.R' 'calcLocalKernel.R' 'distanceMatrix.R'
'mainCode.R' 'point.R' 'pvResamp.R'
'polmineR.R' 'S4classes.R' 'p_attributes.R' 'textstat.R'
'bundle.R' 'corpus.R' 'count.R' 'partition.R'
'partition_bundle.R' 'ngrams.R' 'features.R' 'context.R'
'TermDocumentMatrix.R' 'annotations.R' 'as.VCorpus.R'
'as.markdown.R' 'kwic.R' 'decode.R' 'cooccurrences.R'
'as.sparseMatrix.R' 'as.speeches.R' 'blapply.R' 'coerce.R'
'hits.R' 'cpos.R' 'dispersion.R' 'dotplot.R' 'encoding.R'
'enrich.R' 'format.R' 'highlight.R' 'html.R' 'info.R' 'means.R'
'noise.R' 'opencpu.R' 'phrases.R' 'polmineR-defunct.R'
'ranges.R' 'regions.R' 'read.R' 'registry.R' 'reindex.R'
'renamed.R' 'restore.R' 's_attributes.R' 'size.R' 'split.R'
'stats.R' 'templates.R' 'terms.R' 'token_stream.R' 'tooltips.R'
'trim.R' 'type.R' 'use.R' 'utils.R' 'view.R' 'weigh.R' 'zzz.R'
'AAA_check_installed.R' 'AAA_imports.R' 'AAA_pomdp-package.R'
'POMDP.R' 'MDP.R' 'Maze.R' 'Tiger.R' 'colors.R'
'optimal_action.R' 'plot_belief_space.R' 'plot_policy_graph.R'
'plot_value_function.R' 'policy.R' 'print.text.R'
'read_write_POMDP.R' 'read_write_pomdp_solve.R' 'reward.R'
'round_stochchastic.R' 'sample_belief_space.R' 'solve_MDP.R'
'solve_POMDP.R' 'solve_SARSOP.R' 'transition_matrix.R'
'update_belief.R' 'visNetwork.R'
'pstop.R' 'undefined.R' 'package.R' 'csnippet.R' 'pomp_fun.R'
'parameter_trans.R' 'covariate_table.R' 'skeleton_spec.R'
'rprocess_spec.R' 'safecall.R' 'pomp_class.R' 'load.R'
'workhorses.R' 'continue.R' 'summary.R' 'prior_spec.R'
'dmeasure_spec.R' 'dprocess_spec.R' 'rmeasure_spec.R'
'rinit_spec.R' 'templates.R' 'builder.R' 'pomp.R' 'probe.R'
'abc.R' 'accumulators.R' 'kalman.R' 'pfilter.R' 'wpfilter.R'
'proposals.R' 'pmcmc.R' 'mif2.R' 'listie.R' 'simulate.R'
'spect.R' 'plot.R' 'bsmc2.R' 'as_data_frame.R' 'as_pomp.R'
'authors.R' 'bake.R' 'basic_components.R' 'basic_probes.R'
'betabinom.R' 'blowflies.R' 'bsflu.R' 'bsplines.R'
'childhood.R' 'coef.R' 'concat.R' 'cond_logLik.R' 'covmat.R'
'dacca.R' 'design.R' 'distributions.R' 'ebola.R'
'eff_sample_size.R' 'elementary_algorithms.R' 'emeasure_spec.R'
'estimation_algorithms.R' 'extract.R' 'filter_mean.R'
'filter_traj.R' 'flow.R' 'forecast.R' 'gompertz.R' 'kf.R'
'probe_match.R' 'spect_match.R' 'nlf.R' 'trajectory.R'
'traj_match.R' 'objfun.R' 'loglik.R' 'logmeanexp.R' 'lookup.R'
'mcap.R' 'melt.R' 'obs.R' 'ou2.R' 'parmat.R' 'parus.R' 'pipe.R'
'pomp_examp.R' 'pred_mean.R' 'pred_var.R' 'show.R' 'print.R'
'profile_design.R' 'resample.R' 'ricker.R' 'runif_design.R'
'rw2.R' 'sannbox.R' 'saved_states.R' 'sir.R' 'slice_design.R'
'sobol_design.R' 'spy.R' 'states.R' 'time.R' 'timezero.R'
'traces.R' 'transformations.R' 'userdata.R' 'verhulst.R'
'vmeasure_spec.R' 'window.R'
'utils.R' 'scheduler.R' 'pool.R' 'pool-methods.R' 'object.R'
'DBI.R' 'DBI-connection-interpolate.R' 'DBI-connection-quote.R'
'DBI-connection-sql.R' 'DBI-connection-transaction.R'
'DBI-connection.R' 'DBI-driver.R' 'DBI-object-deprecated.R'
'DBI-object.R' 'DBI-pool.R' 'dbplyr.R' 'zzz-hooks.R'
'ACAT.R' 'CKAT.R' 'POSTmData.R' 'distanceMatrix.R'
'getKernel.R' 'glmFit.R' 'lmFit.R' 'p.adjust.POST.R'
'plot.POST.R' 'post.R' 'print.POST.R'
'aaa_all_classes.R' 'all_generics.R' 'bootstrap.R'
'bootstrap_p.R' 'checks.R' 'compare_distributions.R'
'conlnorm.R' 'data_help_files.R' 'def_conexp.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'
'Backend.R' 'Basis.R' 'Model.R' 'GgmModel.R' 'Interpolation.R'
'Spline.R' 'StepThree.R' 'StepTwo.R' 'Statistic.R'
'PowerStatistic.R' 'StatisticFactory.R' 'ModelFactory.R'
'StepOne.R' 'Range.R' 'Method.R' 'OsqpSolver.R'
'QuadprogSolver.R' 'Solver.R' 'SolverFactory.R' 'Validation.R'
'constants.R' 'exports.R' 'helpers.R' 'logo.R'
'powerly-package.R'
'prais_winsten.R' 'print.prais.R' 'summary.prais.R'
'print.summary.prais.R' 'pw_transform.R' 'vcovHC.R' 'vcovPC.R'
'zzz.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'
'GKGamma.R' 'PResidData.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' 'krige.prevR.r' 'kde.prevR.r'
'Noptim.r' 'theme_prevR.r' 'direct.label_prevR.r'
'quick.prevR.r'
'RcppExports.R' 'bias_correct_curve.R' 'deprecated.R'
'package.R' 'periodic_lowess.R' 'smoother_functions.R'
'principal_curve.R' 'start_circle.R'
'block.R' 'cor.R' 'explained-variance.R' 'get-blocks.R'
'thresholding.R' 'scale.R' 'utils.R' 'pla.R'
'prinvars-package.R'
'RcppExports.R' 'internal.R' 'data-class.R' 'writeOutputs.R'
'optimizationProblem-class.R' 'presolve.R' 'evalBlm.R'
'problem_modifier.R' 'evalBudget.R' 'evalTarget.R'
'getActions.R' 'getConnectivityPenalty.R' 'getCost.R'
'getModelInfo.R' 'getPerformance.R' 'getPotentialBenefit.R'
'getSolutionBenefit.R' 'inputData.R' 'package.R'
'portfolio-class.R' 'print.R' 'problem.R' 'show.R' 'simData.R'
'solution-class.R' 'solve.R' 'utils-pipe.R' 'zzz.R'
'internals.R' 'procmod_frame.R' 'multivariate.R' 'procmod.R'
'covls.R' 'corls_test.R' 'procuste.R' 'simulate.R'
'api.R' 'compat-purrr.R' 'pprof-from-ds.R' 'rprof-read.R'
'pprof-read.R' 'pprof-to-ds.R' 'rprof-write.R' 'pprof-write.R'
'profile-package.R' 'proto.R' 'rprof-from-ds.R' 'rprof-to-ds.R'
'utils.R'
'set_generics.R' 'class-projects_author.R'
'class-projects_stage.R' 'new.R' 'edit.R' 'email_authors.R'
'file_management.R' 'getters.R' 'header.R'
'metadata_manipulation.R' 'projects.R' 'reproducibility.R'
'setup.R' 'update.R' 'utilities.R' 'utils-pipe.R'
'validation.R' 'zzz.R'
'add.config.R' 'preinstalled.readers.R' 'add.extension.R'
'addins.R' 'arff.reader.R' 'get.project.R' 'cache.R'
'cache.name.R' 'cache.project.R' 'clean.variable.name.R'
'clear.R' 'clear.cache.R' 'translate.dcf.R' 'config.R'
'create.project.R' 'create.template.R' 'csv.reader.R'
'csv2.reader.R' 'db.reader.R' 'dbf.reader.R' 'epiinfo.reader.R'
'feather.reader.R' 'file.reader.R' 'help.R' 'list.data.R'
'load.project.R' 'migrate.project.R' 'migrate.template.R'
'mp3.reader.R' 'mtp.reader.R' 'octave.reader.R' 'ppm.reader.R'
'project.config.R' 'r.reader.R' 'rdata.reader.R' 'rds.reader.R'
'reload.project.R' 'require.package.R' 'run.project.R'
'show.project.R' 'spss.reader.R' 'sql.reader.R'
'stata.reader.R' 'stopifnotproject.R' 'stub.tests.R'
'systat.reader.R' 'test.project.R' 'tsv.reader.R'
'url.reader.R' 'wsv.reader.R' 'xls.reader.R' 'xlsx.reader.R'
'xport.reader.R'
'import.R' 'configPROsetta.R' 'post_functions.R'
'core_functions.R' 'datasets.R' 'example.R'
'helper_functions.R' 'linking_functions.R' 'plot_functions.R'
'runshiny.R'
'data-00-selection-from-PD.r' 'data-01-consolidation.r'
'data-02-merging.r' 'data-03-analysis.r' 'zzz.R'
aa2mass.R centroid.R comet.R deisotoper.R de_novo.R findNN.R
fragmentIon.R genMod.R iTRAQ2GroupAnalysis.R lcmsmap.R mascot.R
mdp.R parentIonMass.R peakplot.R pepxml.R pgImporter.R pgLFQ.R
pp.R psm.R queue.R PTM_MarkerFinder.R ssrc.R zzz.R
RcppExports.R protViz-package.R
registry.R database.R dist.R similarities.R dissimilarities.R
util.R seal.R
'annotatePeptides.R' 'readjustWindow.R' 'cdsw.R'
'computeFDR.R' 'createDecoyDB.R' 'create_fgcz_fasta_db.R'
'greedy.R' 'hello.R' 'loadContaminantsFasta.R'
'prepareMatrix.R' 'readFasta.R' 'removeSignalPeptides.R'
'reverseSeq.R' 'writeFasta.R' 'zzz.R'
'add_cov.R' 'add_time.R' 'add_mcmc.R' 'simu_cov.R'
'match_cov.R' 'simu_time.R' 'run_mcmc.R' 'get_summary.R'
'utils.R'
'MetSSS.R' 'MetSSS_model.R' 'methods.R' 'misc.R' 'classes.R'
'composite.R' 'onLoad.R' 'predictions.R'
'RcppExports.R' 'aaa-classes.R' 'aaa-generics.R' 'data.R'
'deprec-functions.R' 'helper-functions.R' 'onUnload.R'
'psychtm-package.R' 'psychtm.R' 'sldax-methods.R' 'utils.R'
'data-anderson2010.R' 'data-baskerville2012.R'
'data-cuddy2018.R' 'data-dang2018.R' 'data-motyl2017.R'
'densities-helpers.R' 'densities-mpsnorm.R'
'densities-phnorm.R' 'densities-psnorm.R' 'densities-snorm.R'
'ma.R' 'generics.R' 'publipha-package.R' 'stanmodels.R'
'tools.R' 'utility.R' 'zzz.R'
'Abstracts-class.R' 'HGNC-class.R' 'Yearwise.R' 'Genewise.R'
'combineabs.R' 'gene_atomization.R' 'Find_conclusion.R'
'getabs.R' 'getabsT.R' 'gethgnc.R' 'ready.R' 'readabs.R'
'removeabs.R' 'searchabsL.R' 'searchabsT.R' 'sendabs.R'
'subabs.R' 'cleanabs.R' 'word_atomizations.R' 'SentenceToken.R'
'contextSearch.R' 'uniprotfun.R' 'local_uniprotfun.R'
'tdm_for_lsa.R' 'printabs.R' 'pubtator_function.R'
'cos_sim_calc.R' 'cos_sim_calc_boot.R' 'wordscluster.R'
'whichcluster.R' 'wordsclusterview.R' 'find_intro_conc_html.R'
'cluster_words.R' 'get_original_term.R' 'get_original_term2.R'
'input_for_find_intro_conc_html.R' 'xmlreadabs.R'
'xmlword_atomizations.R' 'xmlgene_atomizations.R'
'pubtator_result_list_to_table.R' 'genes_BWI.R' 'BWI.R'
'currentabs_fn.R' 'previousabs_fn.R' 'altnamesfun.R'
'subsetabs.R' 'prevsymbol_fn.R' 'alias_fn.R' 'get_NMids.R'
'get_PMCIDS.R' 'get_PMCtable.R' 'get_Sequences.R'
'Give_Sentences_PMC.R' 'head_abbrev.R' 'names_fn.R'
'official_fn.R' 'pmids_to_abstracts.R' 'get_gene_sentences.R'
'Give_Sentences.R' 'get_MedlinePlus.R' 'co_occurrence_fn.R'
'space_quasher.R' 'readabsnew.R' 'word_associations.R'
'get_DOIs.R' 'additional_info.R' 'new_xmlreadabs.R'
'pubtator_function_JSON.R' 'xmlgene_atomizations_new.R'
'co_occurrence_advance.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'
'one_sided.R' 'ppplot.R' 'qqconf-package.R' 'qqplot.R'
'RcppExports.R' 'two_sided.R'
'data.R' 'geom_qq_band.R' 'qqplotr.R' 'runShinyExample.R'
'stat_pp_band.R' 'stat_pp_line.R' 'stat_pp_point.R'
'stat_qq_line.R' 'stat_qq_band.R' 'stat_qq_point.R'
'state.R' 'sqgate.R' 'ccqgate.R' 'cnotgate.R' 'cqgate.R'
'export2qiskit.R' 'measure.R' 'phase_estimation.R'
'plot-qstate.R' 'qft.R' 'qsimulatR-package.R' 'swapgate.R'
'CppLcpFinder-class.R' 'CppNode-class.R' 'CppQuadtree-class.R'
'classes.R' 'generics.R' 'as_data_frame.R' 'as_raster.R'
'as_vector.R' 'copy.R' 'data_documentation.R' 'extent.R'
'extract.R' 'get_neighbors.R' 'lcp.R' 'n_cells.R'
'plot_LcpFinder.R' 'plot_Quadtree.R' 'projection.R'
'qtree-exports.R' 'quadtree-package.R' 'quadtree.R'
'read_write.R' 'set_values.R' 'summary_LcpFinder.R'
'summary_Quadtree.R' 'transform_values.R'
'ValAR.R' 'bic_d.R' 'llqrcv.R' 'llqr.R' 'cqs.R'
'RcppExports.R' 'meta.R' 'quanteda-documentation.R' 'aaa.R'
'bootstrap_dfm.R' 'casechange-functions.R' 'char_select.R'
'convert.R' 'corpus-addsummary-metadata.R' 'corpus-methods.R'
'corpus.R' 'corpus_group.R' 'corpus_reshape.R'
'corpus_sample.R' 'corpus_segment.R' 'corpus_subset.R'
'corpus_trim.R' 'data-documentation.R' 'dfm-classes.R'
'dfm-methods.R' 'dfm-print.R' 'dfm-subsetting.R' 'dfm.R'
'dfm_compress.R' 'dfm_group.R' 'dfm_lookup.R' 'dfm_match.R'
'dfm_replace.R' 'dfm_sample.R' 'dfm_select.R' 'dfm_sort.R'
'dfm_subset.R' 'dfm_trim.R' 'dfm_weight.R' 'dictionaries.R'
'dimnames.R' 'fcm-classes.R' 'docnames.R' 'docvars.R'
'fcm-methods.R' 'fcm-print.R' 'fcm-subsetting.R' 'fcm.R'
'fcm_select.R' 'index.R' 'kwic.R' 'message.R' 'nfunctions.R'
'object-builder.R' 'object2fixed.R' 'pattern2fixed.R'
'phrases.R' 'quanteda_options.R' 'readtext-methods.R'
'spacyr-methods.R' 'stopwords.R' 'summary.R' 'textmodel.R'
'textplot.R' 'texts.R' 'textstat.R' 'tokenizers.R'
'tokens-methods.R' 'tokens.R' 'tokens_chunk.R'
'tokens_compound.R' 'tokens_group.R' 'tokens_lookup.R'
'tokens_ngrams.R' 'tokens_replace.R' 'tokens_sample.R'
'tokens_segment.R' 'tokens_select.R' 'tokens_split.R'
'tokens_subset.R' 'utils.R' 'validator.R' 'wordstem.R' 'zzz.R'
'RcppExports.R' 'quanteda.textmodels-package.R'
'data-documentation.R' 'textmodel-methods.R'
'textmodel_affinity.R' 'textmodel_ca.R' 'textmodel_lsa.R'
'textmodel_lr.R' 'textmodel_nb.R' 'textmodel_svm.R'
'textmodel_svmlin.R' 'textmodel_wordfish.R'
'textmodel_wordscores.R' 'textplot_influence.R' 'utils.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'
'compat.R' 'agg_scalar.R' 'check_expressions.R'
'column_references.R' 'common.R' 'extract_alias.R'
'parse_clauses.R' 'parse_expression.R' 'parse_join.R'
'translations.R' 'process_translations.R' 'parse_query.R'
'parse_table_reference.R' 'replace.R' 'secure.R'
'split_query.R' 'squish_sql.R' 'translate.R' 'unpipe.R'
'unqualify.R' 'wrap_bangs.R'
'environment.R' 'plotting-classes.R' 'plotting-colours.R'
'plotting-helpers.R' 'plotting-other.R' 'plotting.R'
'quickPlot-classes.R' 'quickPlot-package.R' 'zzz.R'
'ancestry_categories.R' 'class-cohorts.R'
'class-performance_metrics.R' 'class-publications.R'
'class-releases.R' 'class-sample_sets.R' 'class-scores.R'
'class-trait_categories.R' 'class-traits.R' 'clear_cache.R'
'contains_question_mark.R' 'count.R' 'drop_metadata_cols.R'
'first_non_na.R' 'generics.R' 'get.R'
'get_ancestry_categories.R' 'get_cohorts.R' 'get_column.R'
'get_performance_metrics.R' 'get_publications.R'
'get_releases.R' 'get_sample_sets.R' 'get_scores.R'
'get_trait_categories.R' 'get_traits.R' 'id_mapping.R'
'is_json_empty.R' 'is_paginated.R' 'is_pgs_id.R'
'is_pubmed_id.R' 'messages.R' 'n_pages.R' 'nr_to_na.R'
'offsets.R' 'open_in_dbsnp.R' 'open_in_pgs_catalog.R'
'open_in_pubmed.R' 'parse-ancestry_categories.R'
'parse-cohorts.R' 'parse-performance_metrics.R'
'parse-publications.R' 'parse-releases.R' 'parse-sample_sets.R'
'parse-scores.R' 'parse-trait_categories.R' 'parse-traits.R'
'read_file_column_names.R' 'read_pgs_scoring_file.R'
'relocate_metadata_cols.R' 'remap_id.R' 'request.R'
's4-utils.R' 'stages.R' 'sure.R' 'unwrap_cohort.R'
'unwrap_demographics.R' 'unwrap_efotrait.R' 'unwrap_interval.R'
'unwrap_publication.R' 'unwrap_sample.R' 'utils-pipe.R'
'warnings.R' 'write_xlsx.R'
'location.R' 'generics.R' 'element.R' 'slide.R'
'presentation.R' 'methods.R' 'utils.R' 'zzz.R'
'Data.R' 'rabhit.R' 'internal_functions.R' 'functions.R'
'graphic_functions.R' 'zzz.R'
'amChartsAPI.R' 'base_data.R' 'base_startupMessage.R'
'chart_amBarplot.R' 'chart_amBoxplot.R' 'chart_amBullet.R'
'chart_amCandlestick.R' 'chart_amDataset.R'
'chart_amFloatingBar.R' 'chart_amFunnel.R' 'chart_amGauge.R'
'chart_amHist.R' 'chart_amMekko.R' 'chart_amOHLC.R'
'chart_amPie.R' 'chart_amPlot.R' 'chart_amRadar.R'
'chart_amTimeSeries.R' 'chart_amWaterfall.R' 'chart_amWind.R'
'utils_sharedGenerics.R' 'class_AmObject.R' 'class_AxisBase.R'
'class_ValueAxis.R' 'utils_basicClassUnions.R'
'class_TrendLine.R' 'class_Title.R' 'class_AmChart.R'
'class_StockPanel.R' 'class_StockEvent.R'
'class_PeriodSelector.R' 'class_Label.R' 'class_Guide.R'
'class_GaugeAxis.R' 'class_GaugeBand.R' 'class_GaugeArrow.R'
'class_DataSet.R' 'class_ChartScrollbar.R'
'class_ChartCursor.R' 'class_AmLegend.R' 'class_AmGraph.R'
'class_AmBalloon.R' 'classUnions.R'
'class_AmChart_constructors.R' 'class_CategoryAxis.R'
'class_AmChart_setters.R' 'class_AmChart_shinyUtils.R'
'class_AmStockChart.R' 'class_AmStockChart_setters.R'
'class_AxisBase_setters.R' 'class_ChartCursor_setters.R'
'class_ChartScrollbar_setters.R' 'class_DataSetSelector.R'
'class_DataSet_setters.R' 'class_GaugeArrow_setters.R'
'class_GaugeAxis_setters.R' 'class_Guide_setters.R'
'class_StockEvent_setters.R' 'class_StockPanel_setters.R'
'class_TrendLine_setters.R' 'shiny_examples.R'
'shiny_modules_export.R' 'shiny_modules_timeSeries.R'
'union_AmCharts.R' 'utils.R' 'utils_amOptions.R'
'utils_amTests.R'
'getDesign.R' 'randPar.R' 'abcdPar.R' 'randSeq.R' 'abcdSeq.R'
'survEndp.R' 'getExpectation.R' 'expEndp.R' 'normEndp.R'
'endpoint.R' 'util.R' 'getStat.R' 'power.R' 'imbalance.R'
'corGuess.R' 'logRankDistribution.R' 'doublyF.R' 'testDec.R'
'doublyT.R' 'chronBias.R' 'selBias.R' 'bias.R' 'issue.R'
'assess.R' 'bbcdPar.R' 'bbcdSeq.R' 'ebcPar.R' 'bsdPar.R'
'bsdSeq.R' 'chenPar.R' 'chenSeq.R' 'chronBiasStepT.R'
'combinedBias.R' 'compare.R' 'crPar.R' 'crSeq.R'
'createParam.R' 'derFunc.R' 'desFunc.R' 'getDesFunc.R'
'derringerLs.R' 'derringerRs.R' 'derringerTs.R' 'desScores.R'
'desirability.R' 'ebcSeq.R' 'evaluate.R' 'gbcdPar.R'
'gbcdSeq.R' 'hadaPar.R' 'hadaSeq.R' 'mpPar.R' 'mpSeq.R'
'pbrPar.R' 'pbrSeq.R' 'probUnDes.R' 'rtbdSeq.R' 'rpbrSeq.R'
'randomBlockSeq.R' 'randomizeROverview.R' 'randomizeRPackage.R'
'rarPar.R' 'rarSeq.R' 'rpbrPar.R' 'tbdPar.R' 'rtbdPar.R'
'saveAssess.R' 'saveRand.R' 'tbdSeq.R' 'udPar.R' 'udSeq.R'
'AQSAPI_helperfunctions.R' 'bysite.R' 'bycounty.R' 'bystate.R'
'byma.R' 'bypqao.R' 'bycbsa.R' 'bybox.R'
'RAQSAPIlistfunctions.R' 'setupfunctions.R' 'zzz.R'
'RAQSAPI-package.R'
'CvM.stat.R' 'H.cdf.R' 'randomization.test.R' 'group.action.R'
'PT.Khmaladze.fit.R' 'PT.Khmaladze.MultTest.R'
'PTQTE.Khmaladze.fit.R' 'RDperm.R' 'RPT.R' 'lee2008.R'
'plot.RDperm.R' 'prepivot.ks.permtest.R'
'summary.PT.Khmaladze.MultTest.R' 'summary.PT.Khmaladze.fit.R'
'summary.PTQTE.Khmaladze.fit.R' 'summary.RDperm.R'
'summary.RPT.R' 'summary.prepivot.ks.permtest.R'
'utils.R' 'dplyr_integration.R' 'RAthena.R' 'Driver.R'
'Connection.R' 'DataTypes.R' 'File_Parser.R' 'Options.R'
'fetch_utils.R' 'Result.R' 'View.R' 'athena_low_api.R'
'column_parser.R' 'install.R' 'sql_translate_utils.R'
'sql_translate_env.R' 'table.R' 'zzz.R'
'rb3-package.R' 'util.R' 'transmute.R' 'fields.R' 'handlers.R'
'marketdata.R' 'download-data.R' 'file.R' 'convert_to.R'
'scraper-cdi.R' 'scraper-futures.R' 'scraper-yc.R'
'scraper-cotahist.R' 'addin-show-templates.R'
'addin-display-template.R' 'readers.R' 'downloaders.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'
'knots.R' 'GBFObj.R' 'semiDefinite.R' 'miPrior.R'
'MIpriorInv.R' 'checkInputs.R' 'checkDB.R' 'checkNW.R'
'checkSpatialData.R' 'generatingBasis.R' 'getArea.R'
'wMatrix.R' 'choleskyInvW.R' 'obledCruetinBasis.R' 'OCbasis.R'
'QPriorObj.R' 'QPriorObj_MI.R' 'QPriorObj_Wishart.R'
'biSquare.R' 'cage.R' 'countyExampleData.R' 'yOpt.R'
'hMatrix.R' 'dCCAGE.R' 'gibbs.R' 'verifyQPrior.R'
'verifyffdir.R' 'verifyCovariates.R' 'verifyNumericVector.R'
'inputPrep.R' 'summaryInfo.R' 'map.R' 'minCAGE.R' 'minCAGESH.R'
'oceanExampleData.R' 'optimalDCAGE.R' 'optRegion.R'
'plot.rcage.R' 'radial.R' 'region.R' 'wendland.R'
'windExampleData.R' 'yCageSource.R' 'yFinest.R'
'init.R' 'build.R' 'buildFPGrowth.R' 'classification.R'
'fpgrowth.R' 'pruning.R' 'utils.R'
'zzz.R' 'rChoiceDialogs-package.R' 'rFileChooser.R'
'rListChooser.R' 'rMiscFunctions.R'
'RcppExports.R' 'RClickhouse.R' 'ClickhouseDriver.R'
'ClickhouseConnection.R' 'ClickhouseResult.R'
'dbplyr-helpers.R' 'dplyr.R' 'zzz.R'
'aaa-package.r' 'geom-stepribbon.r' 'guiparts-aaa.r'
'guiparts-checkboxes.r' 'guiparts-radioboxes.r'
'guiparts-textfield.r' 'guiparts-textfields.r'
'guiparts-toolbox.r' 'guiparts-variableboxes.r' 'data-aaa.r'
'plot-aaa.r' 'plot-factorise.r' 'plot-gcont.r' 'plot-gbox.r'
'plot-gdiscbar.r' 'plot-gdist.r' 'plot-gkm.r' 'plot-ghist.r'
'plot-gline.r' 'plot-gpie.r' 'plot-gqq.r' 'plot-gscat.r'
'plot-gscatmat.r' 'theme-natrisk.r' 'theme-natrisk21.r'
'theme-natriskbg.r' 'theme-simple.r' 'theme-wsj2.r'
'util-gettextKmg2.r' 'util-ggsaveKmg2.r' 'util-lastcom.r'
'util-nonFactors.r' 'zzz.r'
'CompadreDB.R' 'CompadreDB-Methods.R'
'CompadreDB-Subsetting.R' 'CompadreDB-Tidyverse.R'
'CompadreMat.R' 'ClassUnionMethods.R' 'cdb_check_species.R'
'cdb_build_cdb.R' 'cdb_collapse.R' 'cdb_compare.R'
'cdb_fetch.R' 'cdb_flag.R' 'cdb_flatten.R' 'cdb_id.R'
'cdb_id_stages.R' 'cdb_id_studies.R' 'cdb_mean_matF.R'
'cdb_metadata.R' 'cdb_rbind.R' 'cdb_unflatten.R' 'cdb_unnest.R'
'data.R' 'mpm_mean.R' 'mpm_methods.R' 'string_representation.R'
'zzz.R'
'RcppCWB_package.R' 'cl.R' 'cqp.R' 'cwb.R' 'checks.R'
'count.R' 'RcppExports.R' 'decode.R' 'cbow.R' 'region_matrix.R'
'misc.R' 'zzz.R' 'xml.R'
aclassesEnums.R bitClasses.R xbits.R base64.R binary.S
classes.S curl.S curlAuthConstants.R curlEnums.R curlError.R
curlInfo.S dynamic.R form.S getFormParams.R getURLContent.R
header.R http.R httpError.R httpErrors.R iconv.R info.S mime.R
multi.S options.S scp.R support.S upload.R urlExists.R zclone.R
zzz.R
'kernelwts.R' 'DCdensity.R' 'IKbandwidth.R' 'RDestimate.R'
'plot.RD.R' 'print.RD.R' 'rdd-package.R' 'summary.RD.R'
'attr_check.R' 'bw_ik09.R' 'bw_ik12.R' 'data.R' 'wt_kern.R'
'dc_test.R' 'treat_assign.R' 'wt_kern_bivariate.R' 'mfrd_est.R'
'var_center.R' 'rd_est.R' 'mrd_est.R' 'mrd_impute.R'
'mrd_power.R' 'mrd_sens_bw.R' 'mrd_sens_cutoff.R' 'plot.mfrd.R'
'predict.rd.R' 'plot.rd.R' 'print.mfrd.R' 'print.rd.R'
'rd_impute.R' 'rd_power.R' 'rd_sens_bw.R' 'rd_sens_cutoff.R'
'rd_type.R' 'rddapp-package.R' 'sens_plot.R' 'shiny_run.R'
'summary.mfrd.R' 'summary.mrd.R' 'summary.mrdi.R'
'summary.mrdp.R' 'summary.rd.R' 'summary.rdp.R'
'ActivityGroupService.R' 'ActivityService.R'
'AdExclusionRuleService.R' 'AdRuleService.R'
'AdjustmentService.R' 'AudienceSegmentService.R'
'BaseRateService.R' 'CdnConfigurationService.R'
'CmsMetadataService.R' 'CompanyService.R' 'ContactService.R'
'ContentBundleService.R' 'ContentService.R' 'CreativeService.R'
'CreativeSetService.R' 'CreativeTemplateService.R'
'CreativeWrapperService.R' 'CustomFieldService.R'
'CustomTargetingService.R' 'DaiAuthenticationKeyService.R'
'ExchangeRateService.R' 'ForecastService.R'
'InventoryService.R' 'LabelService.R'
'LineItemCreativeAssociationService.R' 'LineItemService.R'
'LineItemTemplateService.R' 'LiveStreamEventService.R'
'MobileApplicationService.R' 'NativeStyleService.R'
'NetworkService.R' 'OrderService.R' 'PackageService.R'
'PlacementService.R' 'PremiumRateService.R'
'ProductPackageItemService.R' 'ProductPackageService.R'
'ProductService.R' 'ProductTemplateService.R'
'ProposalLineItemService.R' 'ProposalService.R'
'PublisherQueryLanguageService.R' 'RateCardService.R'
'ReconciliationLineItemReportService.R'
'ReconciliationOrderReportService.R'
'ReconciliationReportRowService.R'
'ReconciliationReportService.R' 'ReportService.R'
'SuggestedAdUnitService.R' 'TargetingPresetService.R'
'TeamService.R' 'UserService.R' 'UserTeamAssociationService.R'
'WorkflowRequestService.R' 'dfp_auth.R'
'dfp_options_settings.R' 'dfp_service_endpoints.R'
'dfp_utils.R' 'dfp_utils_xml.R' 'rdfp.R'
'ancestral_nodes.R' 'binary.R' 'chainsaw.R'
'check_partition.R' 'check_phypartition.R' 'check_similarity.R'
'class-distance.R' 'class-metacommunity.R' 'class-powermean.R'
'class-relativeentropy.R' 'class-similarity.R'
'descendant_tips.R' 'similarity.R' 'dist2sim.R' 'distance.R'
'metadiv.R' 'subdiv.R' 'metacommunity.R'
'diversity-components.R' 'diversity-measures.R' 'gen2dist.r'
'geneid.R' 'genevec.R' 'hs_parameters.R' 'inddiv.R'
'phy2branch.R' 'phy2dist.R' 'phy_abundance.R' 'phy_struct.R'
'power_mean.R' 'powermean.R' 'rdiversity-package.R'
'relativeentropy.R' 'repartition.R' 'smatrix.R' 'summarise.R'
'tax2dist.R' 'taxfac.R' 'taxid.R' 'taxmask.R' 'taxvec.R'
'tbar.R' 'zmatrix.R'
'RDML.types.R' 'RDML.R' 'RDML.AsDendrogram.R' 'RDML.AsTable.R'
'RDML.GetFData.R' 'RDML.Merge.R' 'RDML.SetFData.R'
'RDML.init.R' 'functional_wrappers.R' 'rdmlEdit.R'
'CDS.F.R' 'C_region.F.R' 'DNA.treatment.R' 'D_loop.F.R'
'D_segment.F.R' 'F_UTR.F.R' 'Feature.sep.R' 'J_segment.F.R'
'N_region.F.R' 'Reference.sep.R' 'Reorganize.report.R'
'STS.F.R' 'S_region.F.R' 'T_UTR.F.R' 'V_region.F.R'
'V_segment.F.R' 'assembly_gap.F.R' 'centromere.F.R' 'exon.F.R'
'extract.gb.R' 'gap.F.R' 'gene.F.R' 'iDNA.F.R' 'intron.F.R'
'mRNA.F.R' 'mat_peptide.F.R' 'misc_RNA.F.R' 'misc_binding.F.R'
'misc_difference.F.R' 'misc_feature.F.R' 'misc_recomb.F.R'
'misc_structure.F.R' 'mobile_element.F.R' 'modified_base.F.R'
'ncRNA.F.R' 'old_sequence.F.R' 'operon.F.R' 'oriT.F.R'
'polyA_site.F.R' 'precursor_RNA.F.R' 'prim_transcript.F.R'
'primer_bind.F.R' 'propeptide.F.R' 'protein_bind.F.R'
'rRNA.F.R' 'read.gb.R' 'regulatory.F.R' 'rep_origin.F.R'
'repeat_region.F.R' 'sig_peptide.F.R' 'source.F.R'
'stem_loop.F.R' 'tRNA.F.R' 'telomere.F.R' 'tmRNA.F.R'
'transit_peptide.F.R' 'unsure.F.R' 'variation.F.R'
'export-base.R' 'export-datetimes.R' 'export-numbers.R'
'export-unicode.R' 'imports.R' 'regex-package.R'
'alternation.R' 'regex-methods.R' 'backreferences.R'
'capture.R' 'internal.R' 'grouping-and-repetition.R'
'constants.R' 'class-groups.R' 'concatenation.R'
'compound-constants.R' 'escape_special.R' 'lookaround.R'
'misc.R' 'mode-modifiers.R'
'imports.R' 'unicode-classes.R' 'unicode-constants.R'
'unicode-general-category-classes.R'
'unicode-general-category-constants.R' 'unicode-operators.R'
register-S3-classes.r RLBigData-classes.r RLResult-class.r
accessor-methods.r evt.r classify.r classifySupv-methods.r
genSamples.r strcmp.r compare.r getPairs.r summary.r
em-methods.r internals.r em.r mygllm.r epilink-methods.r
phonetics.r onAttach.r getPairs-methods.r serialization.r
tools.r stochastic.r
'RcppExports.R' 'class.R' 'Recur.R' 'Survr.R' 'aic.R'
'baseline.R' 'coef.R' 'data.R' 'mcf-generic.R' 'mcf-formula.R'
'mcf-rateReg.R' 'mcfDiff.R' 'misc.R' 'plot.R' 'rateReg.R'
'reda.R' 'show.R' 'simEvent.R' 'summary.R' 'zzz.R'
'CheckVal.R' 'DataFunctions.R' 'aEddy.R' 'EddyGapfilling.R'
'EddyPartitioning.R' 'EddyPlotting.R'
'EddyUStarFilterChangePointDetection.R' 'EddyUStarFilterDP.R'
'Example.R' 'FileHandling.R' 'FileHandlingFormats.R'
'GeoFunctions.R' 'LRC_base.R' 'LRC_logisticSigmoid.R'
'LRC_nonrectangular.R' 'LRC_rectangular.R'
'PartitioningLasslop10.R' 'PartitioningLasslop10Nighttime.R'
'RcppExports.R' 'estimate_vpd_from_dew.R' 'imports.R'
'logitnorm.R' 'variableNames.R' 'zzzDebugCode.R'
'redland.R' 'World.R' 'Node.R' 'Statement.R' 'Storage.R'
'Model.R' 'Parser.R' 'Query.R' 'QueryResults.R' 'Serializer.R'
'mergeNamespace_roclet.R' 'redland-package.R' 'util.R'
'Omegas.R' 'af.R' 'af_old.R' 'amc.R' 'ccb.fpc.R'
'create.prep.func.R' 'coefficients.pfr.R' 'dt_basis.R'
'irreg2mat.R' 'fbps.R' 'fgam.R' 'fosr.R' 'fosr.perm.R'
'fosr.perm.fit.R' 'fosr.perm.test.R' 'fosr.vs.R' 'fosr2s.R'
'fpc.R' 'fpca2s.R' 'fpca.sc.R' 'fpca.face.R' 'fpca.ssvd.R'
'fpcr.R' 'fpcr.setup.R' 'lf.R' 'lf_old.R' 'lf.vd.R' 'lofocv.R'
'lpeer.R' 'lpfr.R' 'quadWeights.R' 'lw.test.R' 'osplinepen2d.R'
'parse.predict.pfr.R' 'peer.R' 'peer_old.R' 'pffr-ff.R'
'pffr-ffpc.R' 'pffr-methods.R' 'pffr-pcre.R' 'pffr-robust.R'
'pffr-sff.R' 'pffr-utilities.R' 'pffr.R' 'pfr.R' 'pfr_old.R'
'pi_basis.R' 'plot.fosr.R' 'plot.fosr.perm.R' 'plot.fosr.vs.R'
'plot.fpcr.R' 'plot.lpeer.R' 'plot.peer.R' 'plot.pfr.R'
'poridge.R' 'postprocess.pfr.R' 'predict.fgam.R'
'predict.fosr.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' 'mfpca.face.R'
'face.Cov.mfpca.R' 'fpca.lfda.R' 'predict.fbps.R'
'select_knots.R'
'utils.R' 'SL-class.R' 'XL-class.R' 'QS-class.R'
'Claims-class.R'
'cite_easystats.R' 'format_algorithm.R' 'format_citation.R'
'format_formula.R' 'format_model.R' 'utils_combine_tables.R'
'report.lm.R' 'report.MixMod.R' 'report_text.R' 'report.R'
'report.htest.R' 'report.aov.R' 'report.bayesfactor_models.R'
'report.lme4.R' 'report.stanreg.R' 'report.brmsfit.R'
'report.character.R' 'report.compare_performance.R'
'report.data.frame.R' 'report.default.R' 'report.factor.R'
'report.glm.R' 'report.glmmTMB.R' 'report.ivreg.R'
'report.lavaan.R' 'report.lme.R' 'report.numeric.R'
'report.sessionInfo.R' 'report.survreg.R'
'report.test_performance.R' 'report.zeroinfl.R'
'report_effectsize.R' 'report_info.R' 'report_intercept.R'
'report_misc.R' 'report_model.R' 'report_parameters.R'
'report_participants.R' 'report_performance.R'
'report_priors.R' 'report_random.R' 'report_sample.R'
'report_statistics.R' 'report_table.R' 'utils_error_message.R'
'utils_grouped_df.R'
'generics.r' 'options.r' 'package.r' 'repr_datatable.r'
'repr_datetime.r' 'utils.r' 'repr_list.r' 'repr_vector.r'
'repr_factor.r' 'repr_function.r'
'repr_help_files_with_topic.r' 'repr_htmlwidget.r'
'repr_matrix_df.r' 'repr_packageIQR.r' 'repr_plotly.r'
'repr_recordedplot.r' 'repr_spatial.r' 'repr_ts.r'
'repr_vega.r'
'DBI.R' 'cache-helpers.R' 'cache-internals.R' 'cache-tools.R'
'robustDigest.R' 'cache.R' 'checksums.R' 'cloud.R' 'cloudOld.R'
'convertPaths.R' 'copy.R' 'download.R' 'gis.R' 'git.R'
'helpers.R' 'objectSize.R' 'options.R' 'packages.R' 'pipe.R'
'postProcess.R' 'preProcess.R' 'prepInputs.R' 'reexports.R'
'reproducible-deprecated.R' 'reproducible-package.R' 'search.R'
'spatialObjects-class.R' 'zzz.R'
CRUDProtocol-class.R CacheInfo-class.R Credentials-class.R
HTTP-class.R Media-class.R MediaCache-class.R RestUri-class.R
RestContainer-class.R test_restfulr_package.R utils.R
'utils.R' 'eutil-error.R' 'eutil.R' 'ecitmatch.R'
'parse-params.R' 'efetch.R' 'egquery.R' 'einfo.R' 'elink.R'
'epost.R' 'esearch.R' 'espell.R' 'esummary.R' 'parse-docsum.R'
'reutils-package.R' 'zzz.R'
'aaa.R' 'utils.R' 'escape.R' 'capture.R' 'character_class.R'
'counts.R' 'lookarounds.R' 'match.R' 'or.R' 'rex-mode.R'
'rex.R' 'shortcuts.R' 'wildcards.R' 'zzz.R'
'R_preprocessing.R' 'RcppExports.R' 'forestry.R'
'adaptive_forestry.R' 'backwards_compatible.R'
'compute_rf_lp.R' 'neighborhood_imputation.R' 'plottree.R'
rfPermute_package.R dataSets.R internals.R rfPermute.R
summary.R importance.R combineRP.R classPriors.R
confusionMatrix.R casePredictions.R pctCorrect.R plotInbag.R
plotImpPreds.R plotNull.R plotPredictedProbs.R plotProximity.R
plotTrace.R plotVotes.R balancedSampsize.R cleanRFdata.R
init.R rgeoda.R sf_geoda.R RcppExports.R read_geoda.R
weights.R utils.R lisa.R clustering.R
AAA.R options.R rgrass.R bin_link.R rast_link.R vect_link.R
vect_link_ng.R initGRASS.R xml1.R
'RcppExports.R' 'baserate.R' 'baserateCT.R' 'baserateSet.R'
'calcKappa.R' 'calcRho.R' 'calculations.R' 'checkBRPKcombo.R'
'codeSet.R' 'contingencyTable.R' 'contingencyToSet.R'
'createRandomSet.R' 'createSimulatedCodeSet.R' 'genPKcombo.R'
'genPcombo.R' 'generateKPs.R' 'getBootPvalue.R' 'getHandSet.R'
'getHandSetIndices.R' 'getR.R' 'getTestSet.R' 'kappa.R'
'kappaCT.R' 'kappaSet.R' 'prset.R' 'rho.R' 'rho.file.R'
'rhoCT.R' 'rhoK.R' 'rhoMin.R' 'rhoR.R' 'rhoSet.R' 'utils.R'
'zzz.R'
'HRT.R' 'avHRT.R' 'HRTList.R' 'Testdata-description.R'
'constants.R' 'conversion_to_HRT.R' 'roll.R'
'comp_util.R' 'init_txt.R' 'init_pal.R' 'init_prob.R'
'comp_prob_prob.R' 'init_freq.R' 'comp_min_N.R' 'init_num.R'
'init_prob_num.R' 'init_freq_num.R' 'comp_freq_freq.R'
'comp_prob_freq.R' 'comp_xxxx_prob.R' 'comp_popu.R'
'comp_accu.R' 'plot_util.R' 'plot_area.R' 'plot_tab.R'
'plot_prism.R' 'plot_fnet.R' 'plot_bar.R' 'plot_icons.R'
'plot_curve.R' 'plot_plane.R' 'plot_tree.R' 'plot_mosaic.R'
'data.R' 'read_data.R' 'riskyr_class.R' 'start_riskyr.R'
readJSON.R asVars.R isValid.R json.R simpleHandler.R stream.R
'KorAPConnection.R' 'KorAPCorpusStats.R'
'RKorAPClient-package.R' 'KorAPQuery.R' 'association-scores.R'
'ci.R' 'collocationAnalysis.R' 'collocationScoreQuery.R'
'hc_add_onclick_korap_search.R' 'hc_freq_by_year_ci.R' 'misc.R'
'reexports.R'
RLumModel-package.R RcppExports.R calc_signal.R
calc_concentrations.R create_DRT.sequence.R
create_SAR.sequence.R extract_pars.R
model_LuminescenceSignals.R read_SEQ2R.R set_pars.R
simulate_CW_OSL.R simulate_heating.R simulate_illumination.R
simulate_irradiation.R simulate_LM_OSL.R simulate_pause.R
simulate_RF.R simulate_RF_and_heating.R simulate_TL.R
translate_sequence.R trace_ParameterStateEvolution.R
'app_RLum.R' 'addin.R' 'chooser.R' 'jscolor.R' 'tooltip.R'
'popover.R' 'RLumShiny.R' 'module_aboutTab.R'
'module_exportTab.R' 'module_printCode.R' 'zzz.R'
czek_matrix.R dot_functions.R plot.czek_matrix.R
register_seriate_ga.R RMaCzek.R seriate_ga.R Um_factor.R
utils_czek_matrix.R read_maczek.R
'MariaDBConnection.R' 'MariaDBDriver.R' 'MariaDBResult.R'
'RMariaDB-package.R' 'RcppExports.R' 'coerce.R'
'compatRowNames.R' 'connect.R'
'dbAppendTable_MariaDBConnection.R'
'dbBegin_MariaDBConnection.R' 'dbBind_MariaDBResult.R'
'dbClearResult_MariaDBResult.R' 'dbColumnInfo_MariaDBResult.R'
'dbCommit_MariaDBConnection.R' 'dbConnect_MariaDBDriver.R'
'dbDataType_MariaDBConnection.R' 'dbDataType_MariaDBDriver.R'
'dbDisconnect_MariaDBConnection.R'
'dbExistsTable_MariaDBConnection_character.R'
'dbFetch_MariaDBResult.R' 'dbGetInfo_MariaDBConnection.R'
'dbGetInfo_MariaDBDriver.R' 'dbGetRowCount_MariaDBResult.R'
'dbGetRowsAffected_MariaDBResult.R'
'dbGetStatement_MariaDBResult.R'
'dbHasCompleted_MariaDBResult.R'
'dbIsValid_MariaDBConnection.R' 'dbIsValid_MariaDBDriver.R'
'dbIsValid_MariaDBResult.R'
'dbListObjects_MariaDBConnection_ANY.R'
'dbListTables_MariaDBConnection.R'
'dbQuoteIdentifier_MariaDBConnection_Id.R'
'dbQuoteIdentifier_MariaDBConnection_SQL.R'
'dbQuoteIdentifier_MariaDBConnection_character.R'
'dbQuoteLiteral_MariaDBConnection.R'
'dbQuoteString_MariaDBConnection_SQL.R'
'dbQuoteString_MariaDBConnection_character.R'
'dbReadTable_MariaDBConnection_character.R'
'dbRemoveTable_MariaDBConnection_character.R'
'dbRollback_MariaDBConnection.R'
'dbSendQuery_MariaDBConnection_character.R'
'dbSendStatement_MariaDBConnection_character.R'
'dbUnloadDriver_MariaDBDriver.R'
'dbUnquoteIdentifier_MariaDBConnection_SQL.R'
'dbWriteTable_MariaDBConnection_character_character.R'
'dbWriteTable_MariaDBConnection_character_data.frame.R'
'default.R' 'export.R' 'names.R' 'query.R' 'quote.R'
'show_MariaDBConnection.R' 'sqlData_MariaDBConnection.R'
'table.R' 'transaction.R' 'utils.R' 'zzz.R'
rmgarch-extrafun.R rmgarch-var.R rmgarch-functions.R
rmgarch-classes.R rmgarch-ica.R rmgarch-series.R
rmgarch-mmean.R gogarch-classes.R gogarch-distributions.R
gogarch-main.R gogarch-methods.R rdcc-classes.R rdcc-main.R
rdcc-likelihoods.R rdcc-plots.R fdcc-likelihoods.R fdcc-main.R
rdcc-methods.R rdcc-mdistributions.R rdcc-postestimation.R
rdcc-solver.R copula-classes.R copula-distributions.R
copula-likelihoods.R copula-solver.R copula-fn.R
copula-transformations.R copula-main.R copula-postestimation.R
copula-methods.R rmgarch-tests.R rmgarch-scenario.R zzz.R
'CI.R' 'STDERR.R' 'group.UCL.R' 'group.CI.R' 'group.STDERR.R'
'lr.glover.R' 'multiplot.R' 'panel.circle.R' 'rounder.R'
'rsi.R' 'summarySE.R'
'A_NAMESPACE.R' 'global.R' 'Model.R' 'MultinomialModel.R'
'GaussianModel.R' 'CompositeModel.R' 'Parameter.R'
'MultinomialParameter.R' 'GaussianParameter.R'
'CompositeParameter.R' 'MixmodResults.R' 'Mixmod.R'
'Strategy.R' 'MixmodCluster.R' 'MixmodDAResults.R'
'MixmodLearn.R' 'MixmodPredict.R' 'MixmodXml.R' 'Rmixmod.R'
'zzz.R'
'associate.R' 'crowding_distance.R' 'data.R'
'generate_reference_points.R' 'geneticoperator.R'
'get_fixed_rowsum_integer_matrix.R' 'miscfun.R' 'niching.R'
'non_dominated_fronts.R' 'numberOrNAOrMatrix-class.R'
'nsga-class.R' 'nsga.R' 'nsga2-class.R' 'nsga2.R'
'nsga3-class.R' 'nsga3.R' 'nsgaControl.R'
'reference_point_multi_layer.R' 'rmoo.R'
'performance_metrics.R' 'plotting.R' 'sharing.R'
'update_points.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'
'auth.R' 'helpers.R' 'nassqs.R' 'params.R' 'request.R'
'wrappers.R'
'S4-utils.R' 'classes.R' 'add_basic_meta.R' 'add_characters.R'
'add_meta.R' 'add_namespaces.R' 'nexmlTree.R' 'add_trees.R'
'character_classes.R' 'concatenate_nexml.R' 'constructors.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'
'nexml_add.R' 'nexml_get.R' 'nexml_methods.R' 'nexml_publish.R'
'nexml_read.R' 'nexml_validate.R' 'nexml_write.R'
'prefixed-uris.R' 'simmap.R' 'taxize_nexml.R' 'tbl_df.R'
'utils.R'
robis.R util.R area.R checklist.R dataset.R dna.R map.R mof.R
node.R occurrence.R taxon.R
'ghq.R' 'psiFunc2.R' 'AllClass.R' 'rlmer.R' 'accessors.R'
'fromLme4.R' 'DAS-scale.R' 'fit.effects.R' 'helpers.R'
'AllGeneric.R' 'lmer.R' 'mutators.R' 'plot.R' 'emmeans.R'
'LR.R' 'multishift.R' 'M.MSH.sampler.R' 'CFTP.R'
'ROCFTP.MMS.R'
'ROCket.R' 'generics.R' 'auc.R' 'rkt_ecdf.R' 'rkt_prep.R'
'rkt_roc.R' 'roc_methods.R' 'utils.R' 'zzz.R'
AllGenerics.R AllClasses.R show-methods.R rococo.R
rococo.test-methods.R gauss.cor.R gauss.cor.test-methods.R
'ode_generics.R' 'ODESolver.R' 'ODE.R' 'AbstractODESolver.R'
'ODEAdaptiveSolver.R' 'DormandPrince45.R' 'Euler.R'
'EulerRichardson.R' 'ODESolverFactory.R' 'RK4.R' 'RK45.R'
'Verlet.R' 'rODE-package.r' 'utils.R'
'complete_cases.R' 'content_checks.R' 'file_checks.R'
'ip_checks.R' 'type_checks.R' 'operators.R' 'os_checks.R'
'paste_functions.R' 'shorthand.R' 'utils.R'
'Q4-class.R' 'RcppExports.R' 'preliminary.R' 'SO3-class.R'
'bayes.R' 'data.R' 'distributions.R' 'estimators.R'
'grid-search.R' 'help.R' 'kappa.R' 'plot.R' 'primitives.R'
'regions.R' 'robust.R' 'rotations-package.R' 'zzz.R'
'RcppExports.R' 'f_core_constants.R' 'f_core_utilities.R'
'f_core_assertions.R' 'f_analysis_utilities.R'
'f_parameter_set_utilities.R' 'class_core_parameter_set.R'
'class_core_plot_settings.R' 'f_analysis_base.R'
'class_analysis_dataset.R' 'f_core_plot.R' 'class_design.R'
'class_analysis_stage_results.R' 'class_analysis_results.R'
'class_time.R' 'class_design_set.R' 'f_design_utilities.R'
'class_design_plan.R' 'class_design_power_and_asn.R'
'class_event_probabilities.R' 'f_simulation_utilities.R'
'f_simulation_base_survival.R' 'class_simulation_results.R'
'class_summary.R' 'data.R' 'f_analysis_base_means.R'
'f_analysis_base_rates.R' 'f_analysis_base_survival.R'
'f_analysis_enrichment.R' 'f_analysis_enrichment_means.R'
'f_analysis_enrichment_rates.R'
'f_analysis_enrichment_survival.R' 'f_analysis_multiarm.R'
'f_analysis_multiarm_means.R' 'f_analysis_multiarm_rates.R'
'f_analysis_multiarm_survival.R' 'f_core_output_formats.R'
'f_design_fisher_combination_test.R'
'f_design_group_sequential.R'
'f_design_sample_size_calculator.R' 'f_simulation_base_means.R'
'f_simulation_base_rates.R' 'f_simulation_enrichment.R'
'f_simulation_enrichment_means.R'
'f_simulation_enrichment_rates.R'
'f_simulation_enrichment_survival.R' 'f_simulation_multiarm.R'
'f_simulation_multiarm_means.R' 'f_simulation_multiarm_rates.R'
'f_simulation_multiarm_survival.R' 'parameter_descriptions.R'
'pkgname.R'
'init.R' 'classes.R' 'fit.R' 'drm.R' 'nrm.R' 'mcm.R' 'grm.R'
'LSAT.R' 'sample.R' 'dataframe.R' 'diagnose.R' 'science.R'
'kct.R' 'openmx.R' 'flexmirt.R' 'util.R' 'lmp.R' 'grmp.R'
'gpcmp.R' 'RcppExports.R'
'Rpolyhedra-package.R' 'polyhedra-lib.R' 'ledger-lib.R'
'db-lib.R' 'env-lib.R' 'package-lib.R' 'serialization-lib.R'
'public-lib.R' 'test-lib.R' 'zzz.R'
'PqDriver.R' 'PqConnection.R' 'PqResult.R' 'RPostgres-pkg.R'
'RcppExports.R' 'Redshift.R' 'dbAppendTable_PqConnection.R'
'dbBegin_PqConnection.R' 'dbBind_PqResult.R'
'dbClearResult_PqResult.R' 'dbColumnInfo_PqResult.R'
'dbCommit_PqConnection.R' 'dbConnect_PqDriver.R'
'dbConnect_RedshiftDriver.R' 'dbDataType_PqConnection.R'
'dbDataType_PqDriver.R' 'dbDisconnect_PqConnection.R'
'dbExistsTable_PqConnection_Id.R'
'dbExistsTable_PqConnection_character.R' 'dbFetch_PqResult.R'
'dbGetInfo_PqConnection.R' 'dbGetInfo_PqDriver.R'
'dbGetRowCount_PqResult.R' 'dbGetRowsAffected_PqResult.R'
'dbGetStatement_PqResult.R' 'dbHasCompleted_PqResult.R'
'dbIsValid_PqConnection.R' 'dbIsValid_PqDriver.R'
'dbIsValid_PqResult.R' 'dbListFields_PqConnection_Id.R'
'dbListFields_PqConnection_character.R'
'dbListObjects_PqConnection_ANY.R'
'dbListTables_PqConnection.R'
'dbQuoteIdentifier_PqConnection_Id.R'
'dbQuoteIdentifier_PqConnection_SQL.R'
'dbQuoteIdentifier_PqConnection_character.R'
'dbQuoteLiteral_PqConnection.R'
'dbQuoteString_PqConnection_SQL.R'
'dbQuoteString_PqConnection_character.R'
'dbReadTable_PqConnection_character.R'
'dbRemoveTable_PqConnection_character.R'
'dbRollback_PqConnection.R' 'dbSendQuery_PqConnection.R'
'dbUnloadDriver_PqDriver.R'
'dbUnquoteIdentifier_PqConnection_SQL.R'
'dbWriteTable_PqConnection_character_data.frame.R' 'default.R'
'export.R' 'names.R' 'quote.R' 'show_PqConnection.R'
'sqlData_PqConnection.R' 'tables.R' 'transactions.R' 'utils.R'
S4R.R zzz.R PostgreSQLSupport.R dbObjectId.R PostgreSQL.R
'rPref.r' 'RcppExports.R' 'pref-classes.r' 'base-pref.r'
'base-pref-macros.r' 'complex-pref.r' 'general-pref.r'
'pref-eval.r' 'show-pref.r' 'visualize.r' 'pred-succ.r'
'PrestoDriver.R' 'Presto.R' 'utility_functions.R'
'PrestoSession.R' 'PrestoConnection.R' 'PrestoCursor.R'
'PrestoResult.R' 'RcppExports.R' 'src.presto.R'
'copy.to.src.presto.R' 'db.data.type.PrestoConnection.R'
'dbplyr_compatible.R' 'db.explain.PrestoConnection.R'
'db.query.rows.PrestoConnection.R' 'request_headers.R'
'dbHasCompleted.R' 'dbClearResult.R' 'dbConnect.R'
'dbDataType.R' 'dbDisconnect.R' 'dbSendQuery.R'
'json.tabular.to.data.frame.R' 'extract.data.R'
'parse.response.R' 'dbFetch.R' 'dbGetQuery.R' 'dbListTables.R'
'dbExistsTable.R' 'dbGetInfo.R' 'dbGetRowCount.R'
'dbGetStatement.R' 'dbIsValid.R' 'dbListFields.R'
'dbUnloadDriver.R' 'description_from_info.R'
'db_desc.PrestoConnection.R' 'db_query_fields.R' 'fetch.R'
'help.R' 'sql_escape_date.R' 'sql_escape_datetime.R'
'sql_translate_env.R' 'tbl.src.presto.R'
'attach.R' 's4_object.R' 'functions.R' 'functions_helpers.R'
'functions_websocket.R' 'globals.R' 'rRofex.R'
utility_functions.R reduce_rank_regression.R pca.R cva.R lda.R
rrr.R data.R
'gridtools.R' 'RSAGA-core.R' 'RSAGA-modules.R' 'RSAGA-utils.R'
'RSAGA-package.R' 'landslides.R'
'add2rtoi.R' 'api.R' 'connections.R' 'data.R' 'extent_crs.R'
'mosaic_fun_SYN.R' 'mosaic_fun_ls.R' 'mosaic_fun_mod.R'
'mosaic_fun_sen2.R' 'mosaic_generic.R' 'package_tools.R'
'records.R' 'rtoi.R' 'plot.R' 'rsat.R' 'rsat_cloud_mask.R'
'rsat_derive.R' 'rsat_download.R' 'rsat_list_data.R'
'rsat_mosaic.R' 'rsat_preview.R' 'search_mod.R' 'search_ls.R'
'search_sen.R' 'rsat_search.R' 'rsat_smoothing_images.R'
'testing_function.R' 'variables.R'
'EAPConfig.R' 'SimResult.R' 'configClasses.R' 'launchApp.R'
'runSim.R' 'scoreMethodConfig.R' 'shinyAppServer.R'
'shinyAppUI.R' 'utilFunctions.R' 'zzz.R'
'RSelenium.R' 'errorHandler.R' 'remoteDriver.R' 'rsDriver.R'
'selKeys-data.R' 'util.R' 'webElement.R'
mlxConnectors.R bootstrap.R buildmlx.R buildVar.R buildAll.R
confintmlx.R correlationModelSelection.R
covariateModelSelection.R covariateSearch.R
errorModelSelection.R llp.R newConnectors.R setSettings.R
testmlx.R zzz.R RsmlxTools.R pkbuild.R pkpopini.R
whichPKmodel.R writeDataSmlx.R
'RcppExports.R' 'SQLiteConnection.R'
'SQLKeywords_SQLiteConnection.R' 'SQLiteDriver.R' 'SQLite.R'
'SQLiteResult.R' 'coerce.R' 'compatRowNames.R' 'copy.R'
'datasetsDb.R' 'dbAppendTable_SQLiteConnection.R'
'dbBeginTransaction.R' 'dbBegin_SQLiteConnection.R'
'dbBind_SQLiteResult.R' 'dbClearResult_SQLiteResult.R'
'dbColumnInfo_SQLiteResult.R' 'dbCommit_SQLiteConnection.R'
'dbConnect_SQLiteConnection.R' 'dbConnect_SQLiteDriver.R'
'dbDataType_SQLiteConnection.R' 'dbDataType_SQLiteDriver.R'
'dbDisconnect_SQLiteConnection.R'
'dbExistsTable_SQLiteConnection_character.R'
'dbFetch_SQLiteResult.R' 'dbGetException_SQLiteConnection.R'
'dbGetInfo_SQLiteConnection.R' 'dbGetInfo_SQLiteDriver.R'
'dbGetPreparedQuery.R'
'dbGetPreparedQuery_SQLiteConnection_character_data.frame.R'
'dbGetRowCount_SQLiteResult.R'
'dbGetRowsAffected_SQLiteResult.R'
'dbGetStatement_SQLiteResult.R' 'dbHasCompleted_SQLiteResult.R'
'dbIsValid_SQLiteConnection.R' 'dbIsValid_SQLiteDriver.R'
'dbIsValid_SQLiteResult.R' 'dbListResults_SQLiteConnection.R'
'dbListTables_SQLiteConnection.R'
'dbQuoteIdentifier_SQLiteConnection_SQL.R'
'dbQuoteIdentifier_SQLiteConnection_character.R'
'dbReadTable_SQLiteConnection_character.R'
'dbRemoveTable_SQLiteConnection_character.R'
'dbRollback_SQLiteConnection.R' 'dbSendPreparedQuery.R'
'dbSendPreparedQuery_SQLiteConnection_character_data.frame.R'
'dbSendQuery_SQLiteConnection_character.R'
'dbUnloadDriver_SQLiteDriver.R'
'dbUnquoteIdentifier_SQLiteConnection_SQL.R'
'dbWriteTable_SQLiteConnection_character_character.R'
'dbWriteTable_SQLiteConnection_character_data.frame.R'
'db_bind.R' 'deprecated.R' 'export.R' 'fetch_SQLiteResult.R'
'initExtension.R' 'initRegExp.R'
'isSQLKeyword_SQLiteConnection_character.R'
'make.db.names_SQLiteConnection_character.R' 'names.R'
'pkgconfig.R' 'show_SQLiteConnection.R'
'sqlData_SQLiteConnection.R' 'table.R' 'transactions.R'
'utils.R' 'zzz.R'
catplotmlx.R ggplotmlx.R kmplotmlx.R prctilemlx.R simulxR.R
exposure.R shinymlx.R statmlx.R stoolsmlx.R simpop.R
smlx-checks.R smlx-tools.R smlx-data.R smlx-project.R
writeData.R apiTools.R apiManager.R utils.R data.R zzz.R
'Generics.R' 'Classifier.R' 'CrossValidation.R'
'LeastSquaresClassifier.R' 'EMLeastSquaresClassifier.R'
'NormalBasedClassifier.R' 'LinearDiscriminantClassifier.R'
'EMLinearDiscriminantClassifier.R' 'NearestMeanClassifier.R'
'EMNearestMeanClassifier.R' 'LogisticRegression.R'
'EntropyRegularizedLogisticRegression.R' 'Evaluate.R'
'GRFClassifier.R' 'GenerateSSLData.R' 'HelperFunctions.R'
'ICLeastSquaresClassifier.R' 'ICLinearDiscriminantClassifier.R'
'KernelLeastSquaresClassifier.R'
'KernelICLeastSquaresClassifier.R'
'LaplacianKernelLeastSquaresClassifier.R' 'LaplacianSVM.R'
'LearningCurve.R' 'LinearSVM.R' 'LogisticLossClassifier.R'
'MCLinearDiscriminantClassifier.R' 'MCNearestMeanClassifier.R'
'MCPLDA.R' 'MajorityClassClassifier.R' 'Measures.R'
'Plotting.R' 'QuadraticDiscriminantClassifier.R' 'RSSL.R'
'RcppExports.R' 'S4VM.R' 'SVM.R' 'SelfLearning.R' 'TSVM.R'
'USMLeastSquaresClassifier.R' 'WellSVM.R' 'scaleMatrix.R'
'svmd.R' 'svmlin.R' 'testdata-data.R'
'utilities.R' 'add_significance.R' 'adjust_pvalue.R'
'factorial_design.R' 'utilities_two_sample_test.R'
'anova_summary.R' 'anova_test.R' 'as_cor_mat.R' 'binom_test.R'
'box_m.R' 'chisq_test.R' 'cochran_qtest.R' 'cohens_d.R'
'cor_as_symbols.R' 'replace_triangle.R' 'pull_triangle.R'
'cor_mark_significant.R' 'cor_mat.R' 'cor_plot.R'
'cor_reorder.R' 'cor_reshape.R' 'cor_select.R' 'cor_test.R'
'counts_to_cases.R' 'cramer_v.R' 'df.R' 'doo.R' 't_test.R'
'dunn_test.R' 'emmeans_test.R' 'eta_squared.R' 'factors.R'
'fisher_test.R' 'freq_table.R' 'friedman_test.R'
'friedman_effsize.R' 'games_howell_test.R' 'get_comparisons.R'
'get_manova_table.R' 'get_mode.R' 'get_pvalue_position.R'
'get_summary_stats.R' 'get_test_label.R' 'kruskal_effesize.R'
'kruskal_test.R' 'levene_test.R' 'mahalanobis_distance.R'
'make_clean_names.R' 'mcnemar_test.R' 'multinom_test.R'
'outliers.R' 'p_value.R' 'prop_test.R' 'prop_trend_test.R'
'reexports.R' 'remove_ns.R' 'sample_n_by.R' 'shapiro_test.R'
'sign_test.R' 'tukey_hsd.R' 'utils-manova.R' 'utils-pipe.R'
'welch_anova_test.R' 'wilcox_effsize.R' 'wilcox_test.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'
'AAAClassDefinitions.R' 'addPackage.R' 'addRow.R' 'addon.R'
'autogentags.R' 'backup.R' 'breakpoint.R' 'command.R'
'datasheet.R' 'datasheetRaster.R' 'dateModified.R' 'delete.R'
'dependency.R' 'deprecated.R' 'description.R' 'disableAddon.R'
'enableAddon.R' 'filepath.R' 'ignoreDependencies.R' 'info.R'
'internalHelpers.R' 'name.R' 'scenarioId.R' 'projectId.R'
'sqlStatement.R' 'scenario.R' 'project.R' 'ssimLibrary.R'
'session.R' 'internalWrappers.R' 'mergeDependencies.R'
'owner.R' 'package.R' 'parentId.R' 'print.R' 'printCmd.R'
'readOnly.R' 'removePackage.R' 'rsyncrosim.R' 'run.R'
'runLog.R' 'saveDatasheet.R' 'silent.R' 'ssimEnvironment.R'
'ssimUpdate.R' 'updatePackage.R' 'version.R'
'00tabletrees.R' 'Viewer.R' 'argument_conventions.R'
'as_html.R' 'utils.R' 'colby_constructors.R'
'compare_rtables.R' 'deprecated.R' 'format_rcell.R' 'indent.R'
'make_subset_expr.R' 'simple_analysis.R' 'split_funs.R'
'summary.R' 'tree_accessors.R' 'tt_afun_utils.R'
'tt_compare_tables.R' 'tt_compatibility.R' 'tt_dotabulation.R'
'tt_paginate.R' 'tt_pos_and_access.R' 'tt_showmethods.R'
'tt_sort.R' 'tt_test_afuns.R' 'tt_toString.R' 'tt_export.R'
'index_footnotes.R' 'tt_from_df.R' 'zzz_constants.R'
'guessFileFormat.R' 'makeFileFormat.R' 'rtapeAdd.R'
'rtape_apply.R' 'rtapeAsList.R' 'rtapeFilter.R' 'rtapeLapply.R'
'rtapeRerecord.R' 'shared.R'
'TdClient.R' 'bulk_import.R' 'database.R' 'job.R' 'table.R'
'td.R'
'rtematres.api.base.R' 'rtematres.api.convenience.R'
'rtematres.api.helpers.R' 'rtematres.options.R'
'rtematres-package.R' 'rtematres.helpers.R' 'zzz.R'
'LdFlags.R' 'RcppExports.R' 'TRNG.Engine.R' 'TRNG.Random.R'
'TRNGkind.R' 'TRNGseed.R' 'TRNGjump.R' 'TRNGsplit.R'
'TRNG.Random.seed.R' 'TRNG.Version.R' 'currentEngine.R'
'defaultKind.R' 'inline.R' 'rTRNG-package.R' 'rbinom_trng.R'
'rlnorm_trng.R' 'rnorm_trng.R' 'rpois_trng.R' 'runif_trng.R'
'zzz.R'
rugarch-imports.R rugarch-cwrappers.R rugarch-solvers.R
rugarch-lossfn.R rugarch-distributions.R rugarch-kappa.R
rugarch-helperfn.R rugarch-numderiv.R rugarch-series.R
rugarch-startpars.R rugarch-tests.R rugarch-armafor.R
rugarch-graphs.R rugarch-classes.R rugarch-sgarch.R
rugarch-figarch.R rugarch-csgarch.R rugarch-fgarch.R
rugarch-egarch.R rugarch-gjrgarch.R rugarch-aparch.R
rugarch-igarch.R rugarch-mcsgarch.R rugarch-realgarch.R
rugarch-multi.R rugarch-plots.R rugarch-rolling.R
rugarch-uncertainty.R rugarch-bootstrap.R rugarch-methods.R
rugarch-benchmarks.R arfima-classes.R arfima-multi.R
arfima-main.R arfima-methods.R rugarch-cv.R zzz.R
'AllClass.R' 'AllGeneric.R' 'methods-CramerLundberg.R'
'methods-CramerLundbergCapitalInjections.R'
'methods-SparreAndersen.R'
'methods-SparreAndersenCapitalInjections.R' 'plot_path.R'
'ruin_probability.R' 'zzz.R'
unuran_distr.R unuran_cont.R unuran_discr.R unuran_cmv.R
Runuran.R universal.R distributions.R deprecated.R options.R
utils.R zzz.R
'RcppExports.R' 'RUVIII_C.R' 'RUVIII_C_Varying.R' 'data.R'
'roxygen.R' 'threads.R'
'Construct.R' 'Plot.R' 'SetOperations.R' 'RVenn.R'
'Enrichment.R' 'Pairs.R' 'Heatmap.R'
'methods_rxylib.R' 'rxylib.R' 'RcppExports.R' 'read_xyData.R'
'convert_xy2TKA.R'
'CalcAddon.R' 'CalcEAD.R' 'CalcPFE.R' 'CalcRC.R'
'ExampleBasisVol.R' 'ExampleComm.R' 'ExampleCredit.R'
'ExampleFX.R' 'ExampleIRD.R' 'ExampleIRDCommMargined.R'
'ExampleIRDCredit.R' 'HandleBasisVol.R' 'LoadSupervisoryData.R'
'runExampleCalcs.R' 'CalculateFactorMult.R'
'CreateTradeGraph.R' 'GroupCommTrades.R' 'GroupCreditTrades.R'
'GroupEquityTrades.R' 'GroupFXTrades.R' 'GroupIRDTrades.R'
'GroupTrades.R' 'SACCRCalculator.R' 'SingleTradeAddon.R'
'onLoad.R' 'DetermineCCRMethodology.R' 'GroupOtherTrades.R'
'cobraInit.R' 'cobraPhaseI.R' 'cobraPhaseII.R' 'debugModel.R'
'defaultDebugRBF.R' 'defaultRI.R' 'defaultSAC.R' 'defaultTR.R'
'defaultCA.R' 'drawSurrogate3d.R' 'evalReal.R' 'fnArchive.R'
'getPredY.R' 'initialHjkb.R' 'innerFuncs.R' 'isres2.R'
'modifyEquCons.R' 'modelSelection.R' 'multiRunPlot.R'
'multiRunPlot_2.R' 'multiCOBRA.R' 'nmkb2.R' 'RbfInter.R'
'repairChootinan.R' 'repairInfeasRI2.R' 'SACOBRA.R'
'startCobra.R' 'trainSurrogates.R' 'trustRegion.R'
'updateSaveCobra.R' 'Gproblems.R'
'cumulants.r' 'dnbeta.r' 'dneta.r' 'dnf.r' 'dnt.r' 'kprime.r'
'lambdap.r' 'moments.r' 'prodchisqpow.r' 'proddnf.r'
'prodnormal.r' 'runExample.r' 'sadists.r' 'sumchisqpow.r'
'sumlogchisq.r' 'upsilon.r' 'utils.r'
'aaa_generics.R' 'SaemixData.R' 'SaemixModel.R' 'SaemixRes.R'
'SaemixObject.R' 'backward.R' 'compute_LL.R' 'forward.R'
'func_FIM.R' 'func_aux.R' 'func_compare.R' 'func_distcond.R'
'func_estimParam.R' 'func_npde.R' 'func_plots.R'
'func_simulations.R' 'func_stepwise.R' 'main.R' 'main_estep.R'
'main_initialiseMainAlgo.R' 'main_mstep.R' 'stepwise.R' 'zzz.R'
'NAMESPACE.R' 'RcppExports.R' 'bootstrap.R' 'check.R'
'correlation.R' 'fit.R' 'fixedPoint.R' 'helper.R' 'makeXY.R'
'mat.R' 'matModels.R' 'mse.R' 'plot.R' 'print.R'
'psiFunctions.R' 'rfh.R' 'robustEstimationEquations.R'
'robustObjectiveFunctions.R' 'testData.R' 'update.R'
'variance.R'
'RcppExports.R' 'location-class.R' 'internal-classes.R'
'samc-class.R' 'absorption.R' 'check.R' 'cond_passage.R'
'data.R' 'visitation.R' 'dispersal.R' 'distribution.R'
'generics.R' 'internal-functions.R' 'locate.R' 'map.R'
'mortality.R' 'pairwise.R' 'samc.R' 'survival.R' 'zzz.R'
zzz.R transparentColorBase.R initRandomSeed.R boltDimensions.R
bboxCheck.R bboxToPoly.R bboxSum.R checkStemDimensions.R
defStemEnv.R transfMatrix.R spCircle.R sampleLogs.R
sampleTrees.R sampSurfClassUnions.R chainsawSliver.R
StemClass.R StemContainerClass.R TractClass.R
TractConstructors.R downLog.R downLogs.R standingTree.R
standingTrees.R ArealSamplingClass.R
ArealSamplingConstructors.R InclusionZoneClass.R
InclusionZoneConstructors.R izContainer.R
izContainerConstructors.R InclusionZoneGridClass.R
InclusionZoneGridConstructors.R izGridConstruct.R mirage.R
sampSurfClass.R sampSurfConstructors.R hist.R plotStem.R
plotStemContainer.R plotArealSampling.R plotInclusionZone.R
plotInclusionZoneGrid.R plotTract.R plotizContainer.R
plotSampSurf.R showArealSampling.R showInclusionZone.R
showInclusionZoneGrid.R showStem.R showTract.R showSampSurf.R
summaryStem.R summaryStemContainer.R summaryArealSampling.R
summaryInclusionZone.R summaryIZContainer.R
summaryInclusionZoneGrid.R summaryTract.R summarySampSurf.R
setAsStem.R setAsizContainer.R bbox.R perimeter.R area.R
heapIZ.R gridCellEnhance.R plot3D.R clipStemsToTract.R
monteClass.R monteConstructors.R summaryMonte.R showMonte.R
histMonte.R horizontalLineSampling.R criticalHeightSampling.R
antitheticCHS.R taperInterpolate.R getID.R segmentVolume.R
MonteCarloContainer.R MonteCarloClass.R
MonteCarloConstructors.R MonteCarloProxy.R showMonteCarlo.R
summaryMonteCarlo.R plotMonteCarlo.R
horizontalPointMCSampling.R smithPlot.R
samurais-package.R RcppExports.R utils.R dynamicProg.R
fitPWRFisher.R mkStochastic.R hmmProcess.R MData.R ParamHMMR.R
ParamMHMMR.R ParamMRHLP.R ParamPWR.R ParamRHLP.R StatHMMR.R
StatMHMMR.R StatMRHLP.R StatPWR.R StatRHLP.R ModelHMMR.R
ModelMHMMR.R ModelMRHLP.R ModelPWR.R ModelRHLP.R emHMMR.R
emMHMMR.R emMRHLP.R emRHLP.R selectHMMR.R selectMHMMR.R
selectMRHLP.R selectRHLP.R data-multivrealdataset.R
data-multivtoydataset.R data-univrealdataset.R
data-univtoydataset.R
'data.R' 'getters.R' 'helpers.R' 'imports.R' 'metrics.R'
'sfn_data_classes.R' 'sfn_data_generics.R' 'sfn_data_methods.R'
'sfn_dplyr.R' 'visualizations.R'
RcppExports.R utils.R generics.R filterClasses.R
modelClasses.R sarima.R autocovariances.R armacalc.R fit.R
wrapKFAS.R arma_Q0dotdotstats.R Kalman.R fitTools.R
periodogram.R predict.Sarima.R zzz.R
satin-class.R read.cmems.R read.ghrsst.R read.nasaoc.R
read.osunpp.R fmainPos.R imageScale.R plot.satin.R
satinPalette.R quiver.R verticalProfiles.R satinDataframe.R
pixelate.R climatology.R crop.R extractPts.R isolines.R
satinMean.R anomaly.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'
'R6Class-SBM.R' 'R6Class-BipartiteSBM.R'
'R6Class-BipartiteSBM_fit.R' 'R6Class-MultipartiteSBM.R'
'R6Class-MultipartiteSBM_fit.R' 'R6Class-MultiplexSBM_fit.R'
'R6Class-SimpleSBM.R' 'R6Class-SimpleSBM_fit.R' 'RcppExports.R'
'defineSBM.R' 'estimate.R' 'fungusTreeNetwork.R'
'multipartiteEcologicalNetwork.R' 'plotAlluvial.R'
'plotMyMatrix.R' 'plotMyMultipartiteMatrix.R'
'plotMyMultiplexMatrix.R' 'sample.R' 'sbm-package.R'
'utils-pipe.R' 'utils.R' 'utils_plot.R' 'war.R'
'data.R' 'interaction_analysis.R' 'objects.R' 'utils_LRI.R'
'utils_cci.R' 'utils_filtering.R' 'utils_interactiveNetworks.R'
'utils_ora.R' 'utils_permutation.R' 'utils_preprocessing.R'
'utils_validation.R'
'imports.R' 'biomarker.R' 'SHUM.R' 'EHUM.R' 'ULBA.R' 'SCOR.R'
'SCOR-package.R' 'optimized_HUM.R' 'youden_points.R'
'YoudenBoxPlot.R'
'allClasses.R' 'allMethods.R' 'augmentation.R'
'check_consistency.R' 'chi2.R' 'chiM.R' 'cut.dataset.R'
'data.R' 'discretize.cutp.R' 'echi2.R' 'fuzzyAugmentation.R'
'generate_data.R' 'get_cutp.R' 'mdlp.R' 'methodsDisc.R'
'modChi2.R' 'model_f.R' 'normalizedGini.R' 'parcelling.R'
'reclassification.R' 'runDemo.R' 'scoringTools.R' 'topdown.R'
'twins.R'
'agpop.R' 'agsrs.R' 'agstrat.R' 'anthrop.R' 'anthsrs.R'
'anthuneq.R' 'audit.R' 'books.R' 'certify.R' 'coots.R'
'counties.R' 'divorce.R' 'golfsrs.R' 'htpop.R' 'htsrs.R'
'htstrat.R' 'journal.R' 'lahiri.design.R' 'measles.R' 'ncvs.R'
'nybight.R' 'otters.R' 'ozone.R' 'samples.R' 'seals.R'
'selectrs.R' 'statepop.R' 'statepps.R' 'syc.R' 'teachers.R'
'teachmi.R' 'teachnr.R' 'winter.R'
'0classes.r' 'addGhostVars.R' 'addNoise.r' 'aux_functions.r'
'createNewID.R' 'dataGen.r' 'dataSets.R' 'dRisk.R' 'dRiskRMD.R'
'dUtility.R' 'freqCalc.r' 'globalRecode.R' 'groupAndRename.R'
'GUIfunctions.R' 'indivRisk.R' 'LocalRecProg.R' 'localSupp.R'
'localSuppression.R' 'mdav.R' 'measure_risk.R' 'methods.r'
'microaggregation.R' 'modRisk.R'
'muargus_compatibility_functions.R' 'mvTopCoding.R'
'plotFunctions.R' 'plotMicro.R' 'pram.R' 'rankSwap.R'
'RcppExports.R' 'report.R' 'riskyCells.R' 'sdcMicro-package.R'
'shuffle.R' 'suda2.R' 'timeEstimation.R' 'topBotCoding.R'
'valTable.R' 'zzz.R' 'printFunctions.R' 'mafast.R' 'maG.R'
'sdcApp.R' 'show_sdcMicroObj.R'
'ANN-class.R' 'BRT-class.R' 'Maxent-class.R' 'Maxnet-class.R'
'RF-class.R' 'RcppExports.R' 'SWD-class.R' 'SDMmodel-class.R'
'SDMmodel2MaxEnt.R' 'SDMmodelCV-class.R' 'SDMtune-class.R'
'SDMtune-package.R' 'addSamplesToBg.R' 'aicc.R' 'auc.R'
'chart-utils.R' 'checkMaxentInstallation.R' 'confMatrix.R'
'convertFolds.R' 'corVar.R' 'doJk.R' 'getTunableArgs.R'
'gridSearch.R' 'maxentTh.R' 'maxentVarImp.R' 'mergeSWD.R'
'modelReport.R' 'optimizeModel.R' 'plotCor.R' 'plotJk.R'
'plotPA.R' 'plotPred.R' 'plotROC.R' 'plotResponse.R'
'plotVarImp.R' 'predict-ANN.R' 'predict-BRT.R'
'predict-Maxent.R' 'predict-RF.R' 'predict-SDMmodel.R'
'predict-SDMmodelCV.R' 'prepareSWD.R' 'randomFolds.R'
'randomSearch.R' 'reduceVar.R' 'swd2csv.R' 'thinData.R'
'thresholds.R' 'train.R' 'trainANN.R' 'trainBRT.R'
'trainMaxent.R' 'trainMaxnet.R' 'trainRF.R' 'trainValTest.R'
'tss.R' 'utils.R' 'varImp.R' 'varSel.R' 'virtualSp.R' 'zzz.R'
seismicRoll.R RcppExports.R findOutliers.R roll_hampel.R
roll_mean.R roll_median.R roll_sd.R roll_stalta.R roll_range.R
'calc.effects.R' 'coefs.sensmed.R' 'data.R'
'mediationmethods.R' 'sensmediation.R'
'package-sentiment_ai.R' 'init_and_install.R' 'sentiment.R'
'choose_model.R' 'data-default_data.R' 'data-example_data.R'
'object-sentiment_env.R' 'matrix_helpers.R' 'constants.R'
'create_error_text.R' 'utils-data-table.R' 'globals.R'
'local_from_reticulate.R'
'AAA_check_installed.R' 'AAA_color_palette.R' 'AAA_map.R'
'AAA_parameters.R' 'AAA_registry_criterion.R'
'AAA_registry_seriate.R' 'AAA_seriation-package.R'
'Chameleon.R' 'Irish.R' 'Munsingen.R' 'Psych24.R'
'SupremeCourt.R' 'Townships.R' 'VAT.R' 'Wood.R' 'Zoo.R' 'bea.R'
'bertinplot.R' 'criterion.R' 'criterion.array.R'
'criterion.dist.R' 'criterion.matrix.R' 'dissplot.R'
'get_order.R' 'ggVAT.R' 'ggbertinplot.R' 'ggdissplot.R'
'hmap.R' 'gghmap.R' 'pimage.R' 'ggpimage.R' 'grid_helpers.R'
'hclust_greedy.R' 'lines_and_ordered_data.R' 'permute.R'
'register_DendSer.R' 'register_GA.R' 'register_optics.R'
'register_tsne.R' 'register_umap.R' 'reorder.hclust.R'
'robinson.R' 'ser_dist.R' 'ser_permutation.R'
'ser_permutation_vector.R' 'ser_permutation_vector2matrix.R'
'seriate.R' 'seriate.array.R' 'seriate.data.frame.R'
'seriate.dist.R' 'seriate.matrix.R'
'seriate_ARSA_Branch-Bound.R' 'seriate_BEA.R' 'seriate_HC.R'
'seriate_MDS.R' 'seriate_PCA.R' 'seriate_QAP.R' 'seriate_R2E.R'
'seriate_SA.R' 'seriate_SPIN.R' 'seriate_TSP.R' 'seriate_VAT.R'
'seriate_heatmap.R' 'seriate_spectral.R' 'uniscale.R'
'Properties.R' 'RcppExports.R' 'Set.R' 'SetWrapper.R'
'SetWrapper_ComplementSet.R' 'SetWrapper_ExponentSet.R'
'SetWrapper_PowersetSet.R' 'SetWrapper_ProductSet.R'
'SetWrapper_UnionSet.R' 'Set_Complex.R' 'Set_ConditionalSet.R'
'Set_FuzzySet.R' 'Set_FuzzySet_FuzzyMultiset.R'
'Set_FuzzySet_FuzzyTuple.R' 'Set_Interval.R' 'setSymbol.R'
'Set_Interval_SpecialSet.R' 'Set_LogicalSet.R' 'Set_Logicals.R'
'Set_Multiset.R' 'Set_Tuple.R' 'Set_Universal.R'
'Set_UniversalSet.R' 'asFuzzySet.R' 'asInterval.R' 'asSet.R'
'helpers.R' 'assertions.R' 'atomic_coercions.R'
'listSpecialSets.R' 'operation_cleaner.R'
'operation_powerset.R' 'operation_setcomplement.R'
'operation_setintersect.R' 'operation_setpower.R'
'operation_setproduct.R' 'operation_setsymdiff.R'
'operation_setunion.R' 'operators.R' 'set6-deprecated.R'
'set6-package.R' 'set6.news.R' 'useUnicode.R' 'zzz.R'
'RcppExports.R' 'reexports.R' 'generics.R' 'clustering.R'
'visualization.R' 'convenience.R' 'data.R'
'differential_expression.R' 'dimensional_reduction.R'
'integration.R' 'mixscape.R' 'objects.R' 'preprocessing.R'
'tree.R' 'utilities.R' 'zzz.R'
'RcppExports.R' 'zzz.R' 'generics.R' 'graph.R' 'assay.R'
'centroids.R' 'command.R' 'data.R' 'default.R' 'jackstraw.R'
'dimreduc.R' 'segmentation.R' 'keymixin.R' 'molecules.R'
'spatial.R' 'fov.R' 'logmap.R' 'neighbor.R' 'seurat.R'
'utils.R'
'RcppExports.R' 'init.R' 'crs.R' 'bbox.R' 'read.R' 'db.R'
'sfc.R' 'sfg.R' 'sf.R' 'bind.R' 'wkb.R' 'wkt.R' 'plot.R'
'geom-measures.R' 'geom-predicates.R' 'geom-transformers.R'
'transform.R' 'proj.R' 'sp.R' 'grid.R' 'arith.R' 'tidyverse.R'
'tidyverse-vctrs.R' 'cast_sfg.R' 'cast_sfc.R' 'graticule.R'
'datasets.R' 'aggregate.R' 'agr.R' 'maps.R' 'join.R' 'sample.R'
'valid.R' 'collection_extract.R' 'jitter.R' 'sgbp.R'
'spatstat.R' 'stars.R' 'crop.R' 'gdal_utils.R' 'nearest.R'
'normalize.R' 'defunct.R' 'z_range.R' 'm_range.R'
'shift_longitude.R' 'make_grid.R' 's2.R' 'terra.R'
'geos-overlayng.R'
sftime.R init.R join.R plot.R st_cast.R st_geometry.R
st_time.R tidyverse.R bind.R crop.R geom-transformers.R
'sgo.R' 'sgo_area.R' 'sgo_bng.R' 'sgo_distance.R' 'sgo_laea.R'
'sgo_ngr.R' 'sgo_points.R' 'sgo_set_gcs.R' 'sgo_wgs84.R'
'sgo_transform.R'
'SharpeR.r' 'data.r' 'utils.r' 'distributions.r' 'sr.r'
'estimation.r' 'sr_bias.r' 'tests.r' 'unified.r'
'Shazam.R' 'Core.R' 'RegionDefinitions.R' 'Baseline.R'
'DistToNearest.R' 'MutationDefinitions.R' 'MutationProfiling.R'
'RegionsExtend.R' 'Shmulate.R' 'TargetingModels.R'
'globals.R' 'app-state.R' 'app_template.R' 'bind-cache.R'
'bind-event.R' 'bookmark-state-local.R' 'bookmark-state.R'
'bootstrap-deprecated.R' 'bootstrap-layout.R' 'conditions.R'
'map.R' 'utils.R' 'bootstrap.R' 'cache-utils.R' 'deprecated.R'
'devmode.R' 'diagnose.R' 'fileupload.R' 'graph.R' 'reactives.R'
'reactive-domains.R' 'history.R' 'hooks.R' 'html-deps.R'
'image-interact-opts.R' 'image-interact.R' 'imageutils.R'
'input-action.R' 'input-checkbox.R' 'input-checkboxgroup.R'
'input-date.R' 'input-daterange.R' 'input-file.R'
'input-numeric.R' 'input-password.R' 'input-radiobuttons.R'
'input-select.R' 'input-slider.R' 'input-submit.R'
'input-text.R' 'input-textarea.R' 'input-utils.R'
'insert-tab.R' 'insert-ui.R' 'jqueryui.R' 'knitr.R'
'middleware-shiny.R' 'middleware.R' 'timer.R' 'shiny.R'
'mock-session.R' 'modal.R' 'modules.R' 'notifications.R'
'priorityqueue.R' 'progress.R' 'react.R' 'reexports.R'
'render-cached-plot.R' 'render-plot.R' 'render-table.R'
'run-url.R' 'runapp.R' 'serializers.R'
'server-input-handlers.R' 'server-resource-paths.R' 'server.R'
'shiny-options.R' 'shiny-package.R' 'shinyapp.R' 'shinyui.R'
'shinywrappers.R' 'showcase.R' 'snapshot.R' 'tar.R'
'test-export.R' 'test-server.R' 'test.R' 'update-input.R'
'utils-lang.R' 'version_bs_date_picker.R'
'version_ion_range_slider.R' 'version_jquery.R'
'version_selectize.R' 'version_strftime.R' 'viewer.R'
'aaa.R' 'filechoose.R' 'dirchoose.R' 'filesave.R'
'shinyFiles-package.R'
'enum.R' 'data.R' 'analysis.R' 'detect.R' 'auth.R'
'make_report.R' 'plotting.R' 'shiny-recorder.R' 'url.R'
'util.R' 'util-pipe.R'
'archive.R' 'display.R' 'format.R' 'imports.R' 'utils.R'
'metareactive.R' 'observe.R' 'globals.R' 'output-code.R'
'print.R' 'render.R' 'report.R' 'utils-format.R' 'zzz.R'
'R6-helper.R' 'app-driver-chromote.R' 'app-driver-dir.R'
'app-driver-expect-download.R' 'app-driver-expect-js.R'
'app-driver-expect-screenshot.R'
'app-driver-expect-unique-names.R' 'app-driver-expect-values.R'
'app-driver-get-log.R' 'app-driver-initialize.R'
'app-driver-log-message.R' 'app-driver-message.R'
'app-driver-node.R' 'app-driver-set-inputs.R'
'app-driver-start.R' 'app-driver-stop.R'
'app-driver-upload-file.R' 'app-driver-variant.R'
'app-driver-wait.R' 'app-driver-window.R' 'app-driver.R'
'chromote-methods.R' 'expect-snapshot.R' 'expr-recurse.R'
'httr.R' 'migrate.R' 'missing-value.R' 'utils.R' 'platform.R'
'record-test-unique-name.R' 'record-test.R' 'rstudio.R'
'save-app.R' 'shiny-browser.R' 'shinytest2-logs.R' 'test-app.R'
'use.R'
'AbandonedCheckout.R' 'AccessScope.R' 'Announcement.R'
'ApplicationCharge.R' 'ApplicationCredit.R' 'Article.R'
'Asset.R' 'Blog.R' 'CarrierService.R' 'Checkout.R' 'Collect.R'
'CollectionListing.R' 'Comment.R' 'Country.R'
'CustomCollection.R' 'Customer.R' 'CustomerAddress.R'
'CustomerSavedSearch.R' 'DiscountCode.R' 'DraftOrder.R'
'Event.R' 'Fulfillment.R' 'FulfillmentEvent.R'
'FulfillmentService.R' 'GiftCard.R' 'GraphQL.R'
'InventoryItem.R' 'InventoryLevel.R' 'Location.R'
'MarketingEvent.R' 'MetaField.R' 'Order.R' 'OrderRisks.R'
'Page.R' 'Policy.R' 'PriceRule.R' 'Product.R' 'ProductImage.R'
'ProductListing.R' 'ProductVariant.R' 'Province.R'
'RecurringApplicationCharge.R' 'Redirect.R' 'Refund.R'
'Report.R' 'ResourceFeedback.R' 'ScriptTag.R' 'ShippingZone.R'
'Shop.R' 'Webhook.R' 'User.R' 'UsageCharge.R' 'Transaction.R'
'Theme.R' 'SmartCollection.R' 'private.R' 'ShopifyShop.R'
'shopifyr-package.R'
'categorize.R' 'mainFunctions.R' 'multipleFitFunction.R'
'sigmoidalFitFunctions.R' 'doublesigmoidalFitFunctions.R'
'normalizationFunction.R' 'sicegar.R' 'dataInputCheck.R'
'parameterCalculation.R' 'figureGeneration.R'
'sig.R' 'list_sigs.R' 'as.R' 'is.R' 'sig_report.R'
'indexing.R' 'utils.R' 'write_sigs.R'
'convergence.r' 'doc.r' 'estimationfunctions.r'
'generate.eq.r' 'grfunctions.r' 'plot.sigProb.r'
'predict.sigfit.r' 'print.sigfit.r' 'sigint.r'
'summary.sigfit.r' 'toLatex.sigfit.r'
'RcppExports.R' 'data.R' 'differential_accessibility.R'
'generics.R' 'dimension_reduction.R' 'footprinting.R'
'fragments.R' 'genomeinfodb-methods.R' 'granges-methods.R'
'heatmaps.R' 'iranges-methods.R' 'links.R' 'mito.R' 'motifs.R'
'objects.R' 'peaks.R' 'preprocessing.R' 'quantification.R'
'region-enrichment.R' 'utilities.R' 'visualization.R' 'zzz.R'
'C-generator.R' 'check_arguments.R' 'init.R' 'valid.R'
'classes.R' 'SimInf_model.R' 'SEIR.R' 'SIR.R' 'SISe.R'
'SISe3.R' 'SISe3_sp.R' 'SISe_sp.R' 'SimInf.R' 'SimInf_events.R'
'run.R' 'density_ratio.R' 'abc.R' 'abc_support.R' 'degree.R'
'distance.R' 'distributions.R' 'match_compartments.R'
'mparse.R' 'n.R' 'openmp.R' 'package_skeleton.R' 'plot.R'
'prevalence.R' 'print.R' 'punchcard.R' 'trajectory.R'
'0classes.R' 'addKnownMargins.R' 'addWeights.r' 'calibPop.R'
'calibSample.R' 'calibVars.R' 'contingencyWt.R' 'correctHeap.R'
'fitGPD.R' 'getBreaks.R' 'getCat.R' 'ipu.r' 'ipu2.r' 'meanWt.R'
'printFunctions.R' 'quantileWt.R' 'sampHH.R' 'RcppExports.R'
'silcTools.R' 'silcTools2.R' 'simAnnealingDT.R'
'simCategorical.R' 'simComponents.R' 'simContinuous.R'
'simEUSILC.R' 'simGPD.R' 'simInitSpatial.R' 'simple_dis.R'
'simPop-package.R' 'simRelation.R' 'simStructure.R'
'spBwplot.R' 'spBwplotStats.R' 'spCdf.R' 'spCdfplot.R'
'spMosaic.R' 'spPredict.R' 'spSample.R' 'spTable.R'
'specifyInput.R' 'sprague.R' 'tableWt.R' 'utility.R' 'utils.R'
'whipple.R' 'dataSets.R' 'zzz.R'
'windowed_sindy.R' 'sindy.R' 'features.R'
'finite_differences.R' 'finite_difference.R'
'deprecated.R' 'dplyr.R' 'stats.R' 'skim_with.R'
'get_skimmers.R' 'reshape.R' 'sfl.R' 'skim.R' 'skim_obj.R'
'skim_print.R' 'skimr-package.R' 'summary.R' 'utils.R'
'vctrs.R'
'block.R' 'conditions.R' 'hop-common.R' 'hop-index-common.R'
'hop-index.R' 'hop-index2.R' 'hop.R' 'hop2.R' 'names.R'
'phop-index.R' 'phop.R' 'slide-index2.R' 'pslide-index.R'
'slide-period2.R' 'pslide-period.R' 'slide2.R' 'pslide.R'
'segment-tree.R' 'slide-common.R' 'slide-index-common.R'
'slide-index.R' 'slide-period-common.R' 'slide-period.R'
'slide.R' 'slider-package.R' 'summary-index.R'
'summary-slide.R' 'utils.R' 'zzz.R'
'slm-main.R' 'slm.R' 'generative.R' 'auxiliary-fun.R'
'slm-method.R' 'data.R'
'alpha.fun.R' 'b.com.est.R' 'coef.sma.R' 'com.ci.R'
'defineAxis.R' 'elev.com.R' 'elev.test.R' 'fitted.sma.R'
'huber.M.R' 'line.cis.R' 'logLik_sma.R' 'lr.b.com.R' 'ma.R'
'makeLogMinor.R' 'meas.est.R' 'multcompmatrix.R' 'nicePlot.R'
'plot.sma.R' 'predict.sma.R' 'print.sma.R' 'residuals.sma.R'
'robust.factor.R' 'seqLog.R' 'shift.com.R' 'slope.com.R'
'slope.test.R' 'sma.R' 'summary.sma.R'
'customerAcquisition.R' 'acquisitionRetention.R'
'customerChurn.R' 'customerWinBack.R'
'customerRetentionDemographics.R'
'customerRetentionLifetimeDuration.R'
'customerRetentionTransactions.R'
'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'
'Class-Host.R' 'Class-ViralPop.R' 'Methods-Host.R'
'Methods-ViralPop.R' 'Methods-time.R' 'SMITIDstruct.R' 'demo.R'
'diversity.R' 'index.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'
solvebio.R annotation.R application.R beacon.R beacon_set.R
dataset.R dataset_commit.R dataset_export.R dataset_field.R
dataset_import.R dataset_migration.R dataset_template.R
global_search.R object.R saved_query.R shiny.R task.R user.R
utils.R vault.R zzz.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
'Plots.R' 'module-dependencies-class.R' 'misc-methods.R'
'environment.R' 'helpers.R' 'simList-class.R' 'cache.R'
'check.R' 'priority.R' 'checkpoint.R' 'code-checking.R'
'convertToPackage.R' 'copy.R' 'debugging.R' 'downloadData.R'
'simulation-parseModule.R' 'simulation-simInit.R' 'load.R'
'memory-leaks.R' 'memory.R' 'modActiveBinding.R'
'module-define.R' 'module-dependencies-methods.R'
'module-repository.R' 'module-template.R' 'moduleCoverage.R'
'moduleMetadata.R' 'objectSynonyms.R' 'options.R' 'times.R'
'simList-accessors.R' 'plotting-diagrams.R' 'plotting.R'
'progress.R' 'project-template.R' 'reexports.R' 'restart.R'
'save.R' 'saveLoadSimList.R' 'simulation-spades.R'
'spades-classes.R' 'spades-core-deprecated.R'
'spades-core-package.R' 'suppliedElsewhere.R' 'zzz.R'
'RcppExports.R' 'heading.R' 'SELES.R'
'distanceFromEachPoint.R' 'environment.R' 'helpers.R'
'initialize.R' 'mapReduce.R' 'mergeRaster.R' 'movement.R'
'neighbourhood.R' 'numerical-comparisons.R' 'probability.R'
'resample.R' 'rings.R' 'spades-tools-deprecated.R'
'spades-tools-package.R' 'splitRaster.R' 'spread.R' 'spread2.R'
'spread3.R' 'studyArea.R' 'zzz.R'
'geocode_cadastral.R' 'cadastral_references-data.R'
'to_number.R' 'cantidades-data.R' 'to_words.R' 'utils-pipe.R'
'spanish.R'
'imports.R' 'globals.R' 'sdf_utils.R' 'asof_join.R' 'init.R'
'window_exprs.R' 'summarizers.R' 'ols_regression.R'
'reexports.R' 'utils.R'
'call_all_functions.R' 'cov_estimation_tri.R'
'cov_estimation_tri_constr.R' 'cov_estimation_whole.R'
'cov_estimation_tri_constr_nest.R' 'fpc_estimation.R'
'fpc_famm_estimation.R' 'get_cross_products.R'
'mean_estimation.R' 'tri_constraint_constructor.R'
'useful_functions.R' 'zzz.R'
'spatPomp_class.R' 'abf.R' 'abfir.R' 'get_covariate_names.R'
'as_data_frame.R' 'as_spatPomp.R' 'bm.R' 'bpfilter.R'
'city_data_UK.R' 'skeleton_spec.R' 'safecall.R'
'rprocess_spec.R' 'pstop.R' 'undefined.R' 'spatPomp.R'
'dunit_measure.R' 'enkf.R' 'eunit_measure.R' 'gbm.R' 'girf.R'
'iter_filter.R' 'ienkf.R' 'igirf.R' 'iubf.R' 'loglik.R'
'lorenz.R' 'mcap.R' 'measles.R' 'measlesUK.R' 'munit_measure.R'
'package.R' 'pipe.R' 'plot.R' 'print.R' 'runit_measure.R'
'simulate.R' 'spatPomp_Csnippet.R' 'spatPomp_workhorses.R'
'unit_names.R' 'vec_dmeasure.R' 'vec_rmeasure.R'
'vunit_measure.R'
'CUSUMlib.R' 'model.R' 'main.R' 'CUSUM.R' 'EWMAlib.R' 'EWMA.R'
'data.R' 'lm.R' 'logreg.R' 'shewhart.R'
class_Stratification.R class_CompactStratification.R
class_CompactStratificationEqualArea.R
class_CompactStratificationPriorPoints.R
class_SamplingPattern.R class_SamplingPatternPurposive.R
class_SamplingPatternCentroids.R
class_SamplingPatternPriorPoints.R
class_SamplingPatternRandom.R
class_SamplingPatternRandomSamplingUnits.R
class_SamplingPatternRandomComposite.R class_Statistic.R
class_SpatialMean.R class_SamplingVariance.R
class_StandardError.R class_SpatialVariance.R
class_SpatialCumulativeDistributionFunction.R
generic_estimate.R generic_getArea.R generic_getRelativeArea.R
generic_getAttributes.R generic_getCellSize.R
generic_getCentroid.R generic_getSampleSize.R
generic_getNumberOfCells.R generic_getNumberOfStrata.R
generic_getObjectiveFunctionValue.R generic_stratify.R
generic_plot.R methods_estimate.R
method_getArea-CompactStratification.R
method_getRelativeArea-CompactStratification.R
method_getCellSize-CompactStratification.R
method_getCellSize-SpatialPixels.R
method_getCentroid-CompactStratification.R
method_getNumberOfCells-CompactStratification.R
method_getNumberOfStrata-CompactStratification.R
method_getObjectiveFunctionValue-CompactStratification.R
method_getSampleSize-SamplingPattern.R
method_getSampleSize-SamplingPatternRandomComposite.R
method_initialize-CompactStratification.R
method_initialize-CompactStratificationPriorPoints.R
method_stratify-SpatialGrid.R method_stratify-SpatialPixels.R
method_stratify-SpatialPolygons.R methods_plot.R
methods_spsample.R methods_setAs.R method_show-Stratification.R
method_show-SamplingPattern.R method_show-Statistic.R onLoad.R
onAttach.R
misc-spd.R classes-spd.R methods-GPD.R methods-spdFit.R
methods-spdDistribution.R methods-spdPlots.R
'spectacles-package.R' 'AAA-Classes.R' 'Spectra-methods.R'
'SpectraDataFrame-methods.R' 'ZZZ.R' 'aggregate_spectra.R'
'australia.R' 'big_head.R' 'kenstone.R' 'performance.R'
'plot-methods.R' 'preprocessing.R' 'setters.R' 'splice.R'
'internalHelpers.R' 'allGenericFunctions.R'
'objectSpectraInTime.R' 'objectProcessTimes.R'
'objectLinking.R' 'alignmentFunctions.R'
'dataManagementTools.R' 'defaults.R' 'readSPC.R'
'saveSpectraInTime.R' 'spectralAnalysis.R'
'spectralIntegration.R' 'spectralNMF.R'
'spectralPreprocessing.R' 'spectralVisualization.R'
'subsetting.R'
'create_neig.r' 'extract_extrema.r' 'extrema_irr.r'
'mean_enveloppe.r' 'spemd.r'
'spflow-package.R' 'utils_classes.R'
'class_0_generics-and-maybe-classes.R'
'class_sp-network-pair.R' 'class_sp-network-nodes.R'
'class_sp-network-meta.R' 'class_sp-network-multi.R'
'class_spflow-model-meta.R' 'class_spflow-model-children.R'
'example_data.R' 'fit-spflow-mcmc.R' 'fit-spflow-mle.R'
'fit-spflow-mle_hessian.R' 'fit-spflow-ols.R'
'fit-spflow-s2sls.R' 'spflow-control.R' 'spflow-formula.R'
'spflow-likelihood.R' 'spflow-logdet.R'
'spflow-model-estimation.R' 'spflow-model-matrix_1.R'
'spflow-model-matrix_2-transformations.R'
'spflow-model-matrix_3-spatial-lags.R'
'spflow-model-moments_1.R' 'spflow-model-moments_2-blocks.R'
'spflow-simulations.R' 'spflow.R' 'utils.R' 'utils_error.R'
'utils_formula.R' 'utils_fp.R' 'utils_math.R' 'utils_matrix.R'
'utils_strings.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
'data.R' 'stats.R' 'hnn.R' 'local.R' 'sca.R' 'moransi.R'
'zzz.R'
'SDM.R' 'Algorithm.SDM.R' 'Ensemble.SDM.R' 'Env.R'
'Occurrences.R' 'PA.select.R' 'SSDM.R' 'Stacked.SDM.R'
'accuracy.R' 'checkargs.R' 'data.values.R' 'ensemble.R'
'modelling.R' 'ensemble_modelling.R' 'optim.thresh.R'
'evaluate.R' 'evaluate.axes.R' 'get_PA.R' 'get_model.R' 'gui.R'
'load_model.R' 'load_occ.R' 'load_var.R' 'mapDiversity.R'
'plot.model.R' 'project.R' 'save.model.R' 'stack_modelling.R'
'stacking.R' 'update.stack.R' 'zzz.R'
cte.R ExternalPackageInterface.R ToolsFct.R Interpolation.R
RegularInverse.R stableCF.R CFbasedMoment.R WeightingMatrix.R
eCFfirstZero.R tSchemes.R MultiDimIntegral.R InitialGuess.R
KoutParamsEstim.R MLParamsEstim.R GMMParamsEstim.R
CgmmParamsEstim.R OutputFileManip.R CheckPoint.R BestT_Class.R
Estim_Class.R Estim.R Simulation.R BestT.R
'ConnectionODBC.R' 'RequeteODBC.R' 'RequeteODBCwhere.R'
'RequeteODBCwheredate.R' 'utilities.R' 'stacomirtools.R'
'stacomirtools-package.R''ConnectionDB.R' 'RequeteDB.R'
'RequeteDBwhere.R' 'RequeteDBwheredate.R' 'zzz.R'
'SolverOptions.R' 'addNamesToList.R' 'checkICs.R'
'checkSolution.R' 'deathFunction.R' 'emigrationFunction.R'
'developmentFunction.R' 'immigrationFunction.R'
'durationFunction.R' 'reproductionFunction.R'
'defaultRateFunctions.R' 'derivDede.R' 'derivPBS.R'
'genericPlot.R' 'initConditions.R' 'namingVariables.R'
'plotStrains.R' 'popModel.R' 'rateFuncCheck.R' 'rateFunction.R'
'runStagePopExample.R' 'stagePop-package.R' 'sumStrains.R'
'vecDivideFunc.R' 'vecToMatList.R'
'init.R' 'stars.R' 'read.R' 'sf.R' 'dimensions.R' 'values.R'
'plot.R' 'tidyverse.R' 'transform.R' 'ops.R' 'write.R'
'raster.R' 'sp.R' 'spacetime.R' 'ncdf.R' 'proxy.R' 'factors.R'
'rasterize.R' 'subset.R' 'warp.R' 'aggregate.R' 'xts.R'
'intervals.R' 'geom.R' 'mosaic.R' 'spatstat.R'
'OpenStreetMap.R' 'sample.R' 'extract.R' 'datasets.R'
'RcppExports.R' 'starvz_data.R' 'phase1.R' 'phase1_outlier.R'
'phase1_parse_csv.R' 'phase2.R' 'phase2_aggregation.R'
'phase2_applications.R' 'phase2_atree.R' 'phase2_config.R'
'phase2_events.R' 'phase2_gaps.R' 'phase2_imbalance.R'
'phase2_kchart.R' 'phase2_lackready.R' 'phase2_memory.R'
'phase2_metrics.R' 'phase2_mpi.R' 'phase2_node_summary.R'
'phase2_pmtool.R' 'phase2_states_chart.R' 'phase2_themes.R'
'phase2_time_integration.R' 'phase2_util.R'
'phase2_var_chart.R' 'phase2_var_panels.R' 'read_functions.R'
'write_functions.R'
'aaa.R' 'geom-rrect.r' 'geom-rtile.R' 'geom-statebins.r'
'gutil.R' 'statebins-package.R' 'statebins.R'
'theme-statebin.R' 'util.R'
'zzz.R' 'utility.R' 'stutil.R' 'stwin.R' 'stsubpop.R'
'stmodelKM.R' 'stmodelCI.R' 'stmodelGLM.R' 'stmodel.R'
'steppes.R' 'stepp.R'
'init.R' 'sticky.R' 'append.R' 'extract1.R' 'print.R'
'subset.R' 'utils.R' 'zzz.R'
'artif_spo.R' 'chull_poly.R' 'compare_areas.R' 'data.R'
'date_checker.R' 'extract_coords.R' 'gtp.R' 'make_grids.R'
'p_prob.R' 'poly_tester.R' 'walker.R' 'psim_artif.R'
'psim_real.R' 'space_restriction.R' 'stm.R' 'stp_learner.R'
'helpers.R' 'algorithms_rna.R' 'algorithms_upper.R' 'opt.R'
'pop.R' 'stratallo-package.R' 'variance.R'
'filterStream.R' 'parseTweets.R' 'sampleStream.R'
'userStream.R' 'streamR-package.R' 'readTweets.R'
'addins.R' 'communicate.R' 'compat-dplyr.R' 'compat-tidyr.R'
'detect-alignment-utils.R' 'detect-alignment.R'
'environments.R' 'expr-is.R' 'indent.R' 'initialize.R' 'io.R'
'nest.R' 'nested-to-tree.R' 'parse.R' 'reindent.R'
'token-define.R' 'relevel.R' 'roxygen-examples-add-remove.R'
'roxygen-examples-find.R' 'roxygen-examples-parse.R'
'roxygen-examples.R' 'rules-indention.R' 'rules-line-breaks.R'
'rules-spaces.R' 'rules-tokens.R' 'serialize.R'
'set-assert-args.R' 'style-guides.R' 'styler.R'
'stylerignore.R' 'testing-mocks.R' 'testing-public-api.R'
'ui-caching.R' 'testing.R' 'token-create.R' 'transform-block.R'
'transform-code.R' 'transform-files.R' 'ui-styling.R'
'unindent.R' 'utils-cache.R' 'utils-files.R'
'utils-navigate-nest.R' 'utils-strings.R' 'utils.R'
'vertical.R' 'visit.R' 'zzz.R'
fdr.est.R getBackground.R getEdgeLabel.R getEdgeLty.R
getInteGraphList.R GetK2riData.R getLayout.R getLocSubGraph.R
getOneNodePath.R getSymbolFromGene.R identifyGraph.R
initializeK2ri.R plotGraph.R printGraph.R updateOrgEnvir.R
mytriangle.R
ExpProcess.r getBackgroundLnc.r getEdgeLabel.r getEdgeLty.r
getEnvironmentData.r getInteGraphList.r getInteUMGraph.r
getKGeneFromGene.r getLayout.r getLncGenePairs.r
getExampleData.r getLocSubGraphLnc.r getNodeLabel.r
getOneNodePath.r getSymbolFromGene.r identifyLncGraphW.r
plotAnnGraph.r plotGraphL.r printGraphW.r
'RcppExports.R' 'predict.surbayes.R' 'sur_sample_powerprior.R'
'sur_sample_dmc.R' 'sur_sample.R' 'surbayes-package.R'
'confIntArea.R' 'nrAtRisk.R' 'oneGroupOneEvent.R'
'survCurve.R' 'survPlot.R'
'BMT.R' 'BRCAOV.survInfo.R' 'add_ggsurvplot.R' 'utilities.R'
'surv_summary.R' 'ggsurvtable.R' 'surv_pvalue.R'
'ggsurvplot_df.R' 'ggsurvplot_core.R' 'ggsurvplot_add_all.R'
'ggsurvplot_list.R' 'ggsurvplot_group_by.R' 'ggsurvplot.R'
'arrange_ggsurvplots.R' 'ggadjustedcurves.R'
'ggcompetingrisks.R' 'ggcoxdiagnostics.R' 'ggcoxfunctional.R'
'ggcoxzph.R' 'ggflexsurvplot.R' 'ggforest.R' 'ggsurvevents.R'
'ggsurvplot_combine.R' 'ggsurvplot_facet.R' 'ggsurvtheme.R'
'ggurvplot_arguments.R' 'myeloma.R' 'pairwise_survdiff.R'
'surv_cutpoint.R' 'surv_group_by.R' 'surv_fit.R'
'surv_median.R'
'ten.R' 'nc.R' 'sf.R' 'ci.R' 'autoplotTAP.R' 'autoplotTen.R'
'print.R' 'asWide.R' 'COV.R' 'predict.R' 'comp.R' 'cutp.R'
'gastric.R' 'gof.R' 'onAttach.R' 'profLik.R' 'rsq.R'
'survMisc_package.R' 'xtable.R'
generics.R validmodelorg.R validoptsol.R validreactId.R
validreact.R reactClass.R validreactId_Exch.R validsysBiolAlg.R
addAlgorithm.R addExchReact.R addReact.R addSolver.R
blockedReact.R bracket_pairs.R ceilValues.R changeBounds.R
changeGPR.R changeObjFunc.R checkAlgorithm.R
checkDefaultMethod.R checkEmptyField.R checkReactId.R
check_brackets.R createReactionString.R deadEndMetabolite.R
doInRound.R doubleFluxDel.R doubleGeneDel.R doubleReact.R
editEnvir.R findExchReact.R floorValues.R fluxVar.R geneDel.R
geneDeletion.R generateFluxdels.R generateModKey.R generateWT.R
getsybilenv.R makeLPcompatible.R mod2irrev.R modelorg2ExPA.R
modelorg2text.R modelorg2tsv.R multiDel.R oneFluxDel.R
oneGeneDel.R onlyChangeGPR.R onlyCheckGPR.R optObj_basicfunc.R
optObj_lpSolveAPIcompat.R optimizer.R parseBoolean.R phpp.R
ppProcessing.R prepareSubSysMatrix.R printLogComment.R
printNamedList.R progress.R promptSysBiolAlg.R recodeMatrix.R
readTEXTmod.R readTSVmod.R reassignFwBwMatch.R rmReact.R
robAna.R settings.R singletonMetabolite.R sybilStack.R ypd.R
zzz.R modelorgClass.R modelorg_irrevClass.R optObj_pointer.R
optObjClass.R optObj_clpAPIClass.R optObj_cplexAPIClass.R
optObj_glpkAPIClass.R optObj_lpSolveAPIClass.R
sybilErrorClass.R ppProcClass.R netFluxClass.R
fluxDistributionClass.R reactIdClass.R reactId_ExchClass.R
optsolClass.R optsol_blockedReactClass.R
optsol_optimizeProbClass.R optsol_fluxVarClass.R
optsol_fluxdelClass.R optsol_robAnaClass.R optsol_phppClass.R
optsol_genedelClass.R checksolClass.R summaryOptsolClass.R
sysBiolAlgClass.R sysBiolAlg_fbaClass.R
sysBiolAlg_fbaEasyConstraintClass.R sysBiolAlg_fvClass.R
sysBiolAlg_lmomaClass.R sysBiolAlg_momaClass.R
sysBiolAlg_mtfClass.R sysBiolAlg_mtfEasyConstraintClass.R
sysBiolAlg_roomClass.R sybilLogClass.R upgradeModelorg.R
mergeReact2Modelorg.R
'00_environment.R' '01_class_01_kRp.hyph.pat.R'
'01_class_02_kRp.hyphen.R' '02_method_correct.R'
'02_method_hyphen.R' '02_method_kRp.hyphen.R'
'02_method_show.kRp.hyphen.R' '02_method_summary.kRp.hyphen.R'
'available.sylly.lang.R' 'get.sylly.env.R'
'install.sylly.lang.R' 'manage.hyph.pat.R' 'read.hyph.pat.R'
'set.hyph.support.R' 'set.sylly.env.R' 'sylly-internal.R'
'sylly-internal_langpack_generator.R' 'sylly-package.R'
'RcppExports.R' 'basic-getinfo.R' 'classes.R' 'basic.R'
'codegen.R' 'double_visitor.R' 'dxdt.R' 'function_symbol.R'
'knitr.R' 'lambdify.R' 'language_conversion.R' 'matrix.R'
'misc.R' 'ops.R' 'solve.R' 'summary.R' 'symbolic_array.R'
'symengine.R' 'symengine_info.R' 'utils-subset.R' 'vector.R'
'zzz.R'
'CollectNames.R' 'ProgressBAR.R' 'belonging.R' 'cent.norm.R'
'cor.matrix.R' 'cor.mantel.R' 'cor.matrix.partial.R'
'cor.matrix2.R' 'cor.procrustes.R' 'matmult.syncsa.R'
'matrix.p.R' 'matrix.t.R' 'matrix.x.R' 'optimal.R'
'organize.syncsa.R' 'part.cor.R' 'pca.R' 'permut.row.matrix.R'
'permut.vector.R' 'plot.pcasyncsa.R' 'print.optimal.R'
'syncsa.R' 'print.syncsa.R' 'pro.matrix.R'
'pro.matrix.partial.R' 'pro.matrix2.R' 'procrustes.syncsa.R'
'procrustes.partial.R' 'rao.diversity.R' 'startup.R'
'var.dummy.R' 'var.type.R'
'DELIMITERS-chain.R' 'DELIMITERS-end_expr.R'
'DELIMITERS-start_expr.R' 'R6-ExprBuilder.R'
'R6-EagerExprBuilder.R' 'UTILS-frame_append.R' 'UTILS-misc.R'
'UTILS-nest_expr.R' 'UTILS-tidyselect.R' 'VERBS-joins.R'
'VERBS-anti_join.R' 'VERBS-arrange.R' 'VERBS-distinct.R'
'VERBS-filter.R' 'VERBS-filter_on.R' 'VERBS-filter_sd.R'
'VERBS-full_join.R' 'VERBS-group_by.R' 'VERBS-inner_join.R'
'VERBS-key_by.R' 'VERBS-left_join.R' 'VERBS-max_by.R'
'VERBS-min_by.R' 'pkg.R' 'VERBS-mutate.R' 'VERBS-mutate_join.R'
'VERBS-mutate_sd.R' 'VERBS-order_by.R' 'VERBS-right_join.R'
'VERBS-select.R' 'VERBS-semi_join.R' 'VERBS-summarize.R'
'VERBS-transmute.R' 'VERBS-transmute_sd.R' 'VERBS-where.R'
'constraints.checkUnique.R' 'constraints.checkRequired.R'
'constraints.checkPattern.R' 'constraints.checkMinLength.R'
'constraints.checkMinimum.R' 'constraints.checkMaxLength.R'
'constraints.checkMaximum.R' 'constraints.checkEnum.R'
'constraints.R' 'types.castArray.R' 'types.castYearmonth.R'
'types.castYear.R' 'types.castTime.R' 'types.castString.R'
'types.castObject.R' 'types.castNumber.R' 'types.castList.R'
'types.castInteger.R' 'types.castGeopoint.R'
'types.castGeojson.R' 'types.castDuration.R'
'types.castDatetime.R' 'types.castDate.R' 'types.castBoolean.R'
'types.castAny.R' 'types.R' 'field.R' 'helpers.R' 'infer.R'
'is.valid.R' 'tableschemaerror.R' 'profile.R' 'readable.R'
'readable.array.R' 'readable.connection.R' 'schema.R' 'table.R'
'tableschema.r.R' 'validate.R' 'writable.R'
'AllClasses.R' 'AllGenerics.R' 'coerce.R' 'deprecate.R'
'ggplot2.R' 'index_diversity.R' 'index_heterogeneity.R'
'index_rarefaction.R' 'index_richness.R' 'index_similarity.R'
'index_turnover.R' 'mutators.R' 'permute.R' 'plot_bertin.R'
'plot_diversity.R' 'plot_ford.R' 'plot_heatmap.R' 'plot_rank.R'
'plot_spot.R' 'reexport.R' 'refine_ca.R' 'seriate_average.R'
'seriate_rank.R' 'show.R' 'statistics.R' 'subset.R'
'tabula-package.R' 'test_diversity.R' 'utilities.R'
'validate.R' 'zzz.R'
'Other.R' 'helpFunctions.R' 'EstimationBR.R'
'EstimationGumbel.R' 'EstimationML.R' 'empbetacop.R'
'compile-cell.R' 'cell-hmisc.R' 'cell-lancet.R' 'cell-nejm.R'
'compile-clmm2.R' 'compile-operators.R' 'compile-post.R'
'parser.R' 'compile.R' 'compile-rms.R' 'compile-typing.R'
'helper-format.R' 'hmisc-cut2.R' 'hmisc-lm.fit.qr.bare.R'
'hmisc-impute.R' 'hmisc-biVar.R' 'iify.R' 'render-csv.R'
'render-html5.R' 'render-index.R' 'render-latex-map.R'
'render-latex.R' 'render-rmd.R' 'render-rtf.R'
'render-summary.R' 'smd.R' 'transform-hmisc.R'
'transform-lancet.R' 'transform-nejm.R' 'transform-proc-tab.R'
'transform-smd.R'
'imports.R' 'internal.R' 'deprecated-functions.R'
'replace_x.R' 'dissect_name.R' 'clean_strings.R'
'taxlist-class.R' 'clean.R' 'as.list.R' 'taxon_views.R'
'count_taxa.R' 'taxon_names.R' 'taxon_relations.R'
'taxon_traits.R' 'levels.R' 'accepted_name.R' 'get_children.R'
'merge_taxa.R' 'Extract.R' 'subset.R' 'backup_object.R'
'summary.R' 'df2taxlist.R' 'tv2taxlist.R' 'tnrs.R'
'tax2traits.R' 'match_names.R' 'print_name.R' 'indented_list.R'
'Easplist-data.R' 'taxlist-package.R'
'defaultSC.R' 'defaultOpts.R' 'makeTdmRandomSeed.r'
'printTDMclassifier.r' 'printTDMregressor.r' 'tdmBigLoop.r'
'tdmClassify.r' 'tdmClassifyLoop.r' 'tdmDefaultsFill.r'
'tdmDispatchTuner.r' 'tdmEnvTMakeNew.r' 'tdmGeneralUtils.r'
'tdmGraphicUtils.r' 'tdmMapDesign.r' 'tdmMetacostRf.r'
'tdmModelingUtils.r' 'tdmOptsDefaults.r' 'tdmParaBootstrap.r'
'tdmPreprocUtils.r' 'tdmReadAndSplit.r' 'tdmReadDataset.r'
'tdmRegress.r' 'tdmRegressLoop.r' 'tdmROCR.r' 'tdmStartSpot2.r'
'tdmStartOther.r' 'tdmTuneIt.r' 'unbiasedRun.r'
'chk-filter.R' 'chk.R' 'comparison.R' 'deprec-chk.R'
'deprec-expect.R' 'deprec-reporter.R' 'expect-make.R'
'expect-chk.R' 'expect-conditional.R' 'expect-data.R'
'expect-datacomp.R' 'expect-exclusive.R' 'expect-generic.R'
'expect-labels.R' 'expect-proportion.R' 'expect-unique.R'
'expectation.R' 'reporter-excel.R' 'reporter-zzz.R'
'testdat-package.R' 'utils.R' 'zzz.R'
'RcppExports.R' 'import.R' 'extensions.R' 'item_class.R'
'item_functions.R' 'loading_functions.R' 'static_class.R'
'shadow_class.R' 'item_pool_operators.R'
'item_attrib_operators.R' 'st_attrib_operators.R'
'constraints_operators.R' 'static_functions.R'
'shadow_functions.R' 'bayes_functions.R'
'constraint_functions.R' 'cpp_documents.R' 'datasets.R'
'eligibility_functions.R' 'exposure_control_functions.R'
'solver_functions.R' 'helper_functions.R'
'item_pool_cluster_operators.R' 'other_functions.R'
'partitioning_class.r' 'partitioning_functions.r'
'plot_functions.R' 'summary_class.R' 'print_functions.R'
'runshiny.R' 'shadowtest_functions.R' 'summary_functions.R'
'show_functions.R' 'test_operators.R' 'theta_functions.R'
'xdata_functions.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'
'AIC.evm.R' 'Dcond.R' 'Profile_likelihood_HT_unc.R'
'Profile_likelihood_cd_nm_joint_D_KT.R'
'Profile_likelihood_cd_nm_joint_D_KT_neg.R' 'RcppExports.R'
'bootmex.R' 'gpd.sandwich.R' 'gpd.info.R' 'texmexFamily.R'
'cgpd.R' 'chi.R' 'coef.evmSim.R' 'coefficients.evm.R'
'coefficients.migpd.R' 'constructObject.R' 'copulas.R' 'cv.R'
'degp3.R' 'dgev.R' 'dgpd.R' 'efficient.closures.R' 'egp3.R'
'egp3RangeFit.R' 'endPoint.R' 'estimate_HT.R'
'estimate_HT_KPT_joint_posneg_nm.R' 'evm.R' 'evm.simSetSeed.R'
'evmBoot.R' 'evmFit.R' 'evmSim.R' 'extremalIndex.R' 'gev.R'
'ggplot.bayes.R' 'ggplot.boot.R' 'ggplot.cluster.R'
'ggplot.evm.R' 'ggplot.mex.R' 'ggplot.migpd.R'
'ggplot.predict.R' 'ggplot.thresh.R' 'glo.R'
'globalVariables.R' 'gpd.R' 'gpdProfileLikRetLevels.R'
'gpdRangeFit.R' 'gumbel.R' 'hist.evm.R' 'initial_posneg.R'
'jointExceedanceCurves.R' 'mex.R' 'mexDependence.R'
'mexDependenceLowLevelFunctions.R' 'mexMonteCarlo.R'
'mexRangeFit.R' 'mexTransform.R' 'migpd.R' 'migpdCoefs.R'
'mrl.R' 'mspearman.R' 'multivariate.cauchy.R' 'pegp3.R'
'pgev.R' 'pgpd.R' 'plot.bootmex.R' 'plot.evm.R' 'plot.evmSim.R'
'plot.mex.R' 'plot.mexPrediction.R' 'plot.migpd.R'
'plot.predict.link.evm.R' 'plotrl.evm.R' 'ppevm.R'
'predict.evm.R' 'predict.mex.R' 'predictWorkers.R'
'print.bootmex.R' 'print.evm.R' 'print.evmSim.R'
'print.mexDependence.R' 'print.mexPrediction.R' 'print.migpd.R'
'profile_minmax_joint_posneg_KT.R' 'qegp3.R' 'qgev.R' 'qgpd.R'
'qgpd2.R' 'qqevm.R' 'rMaxAR.R' 'regp3.R' 'residuals.evm.R'
'revTransform.R' 'rgev.R' 'rgpd.R' 'roots.R' 'simulate.R'
'sombrero-internal.R' 'summary.evm.R' 'summary.evmSim.R'
'summary.migpd.R' 'summary.predict.mex.R' 'texmex-package.R'
'texmexWorkers.R' 'thinAndBurn.evm.sim.R' 'u2gpd.R'
'weibull.info.R' 'weibull.R'
'add_comma_space.R' 'add_missing_endmark.R' 'utils.R'
'replace_html.R' 'check_text_logicals.R' 'check_text.R'
'drop_element.R' 'drop_row.R' 'fgsub.R' 'filter_element.R'
'filter_row.R' 'glue-reexports.R' 'has_endmark.R'
'make_plural.R' 'match_tokens.R' 'mgsub.R'
'replace_contraction.R' 'replace_date.R' 'replace_email.R'
'replace_emoji.R' 'replace_emoticon.R' 'replace_grade.R'
'replace_hash.R' 'replace_incomplete.R'
'replace_internet_slang.R' 'replace_kerning.R'
'replace_money.R' 'replace_names.R' 'replace_non_ascii.R'
'replace_number.R' 'replace_ordinal.R' 'replace_rating.R'
'replace_symbol.R' 'replace_tag.R' 'replace_time.R'
'replace_to.R' 'replace_tokens.R' 'replace_url.R'
'replace_white.R' 'replace_word_elongation.R' 'strip.R'
'sub_holder.R' 'swap.R' 'textclean-package.R'
'cache_info.R' 'dataset_ag_news.R' 'dataset_dbpedia.R'
'dataset_imdb.R' 'dataset_sentence_polarity.R' 'dataset_trec.R'
'embedding_glove.R' 'lexicon_nrc_vad.R' 'lexicon_nrc_eil.R'
'lexicon_nrc.R' 'lexicon_bing.R' 'lexicon_loughran.R'
'lexicon_afinn.R' 'download_functions.R' 'info.R'
'load_dataset.R' 'printer.R' 'process_functions.R'
'textdata-package.R'
'Tier-class.R' 'IntervalTier-class.R'
'IntervalTier-accessors.R' 'IntervalTier-constructor.R'
'IntervalTier-utilities.R' 'PointTier-class.R'
'PointTier-accessors.R' 'PointTier-constructor.R'
'PointTier-utilities.R' 'TextGrid-class.R'
'TextGrid-accessors.R' 'TextGrid-constructor.R'
'TextGrid-utilities.R' 'Tier-accessors.R' 'Tier-utilities.R'
'finders.R' 'textgRid.R'
'bind_list.R' 'bind_table.R' 'bind_vector.R' 'change_index.R'
'cluster_matrix.R' 'column_to_rownames.R' 'combine.R'
'duration.R' 'flatten.R' 'from_to.R' 'grab_index.R'
'grab_match.R' 'mtabulate.R' 'split_index.R' 'split_match.R'
'split_match_regex_to_transcript.R' 'split_portion.R'
'split_run.R' 'split_sentence.R' 'split_sentence_token.R'
'split_speaker.R' 'split_token.R' 'split_transcript.R'
'split_word.R' 'textshape-package.R' 'tidy_colo_dtm.R'
'utils.R' 'tidy_dtm.R' 'tidy_list.R' 'tidy_matrix.R'
'tidy_table.R' 'tidy_vector.R' 'unique_pairs.R' 'unnest_text.R'
'TheiaAuth.R' 'TheiaTile.R' 'TheiaCollection.R' 'TheiaQuery.R'
'theiaR.R' 'utils.R'
'auto.R' 'base.R' 'cache.R' 'gfonts.R' 'ggplot.R' 'globals.R'
'hooks.R' 'imports.R' 'knitr.R' 'lattice.R' 'onLoad.R'
'thematic-save-plot.R' 'thematic.R' 'utils.R'
'view-shinytest.R'
'thrift.R' 'transport.R' 'rpc.R' 'parser.R'
'protocol_binary.R' 'server.R' 'transport_buffered.R'
'transport_memory.R' 'transport_socket.R' 'utils.R'
"ggdist-curve_interval.R" "ggdist-cut_cdf_qi.R"
"ggdist-geom_slabinterval.R" "ggdist-geom_dotsinterval.R"
"ggdist-geom_interval.R" "ggdist-geom_lineribbon.R"
"ggdist-geom_pointinterval.R" "ggdist-lkjcorr_marginal.R"
"ggdist-parse_dist.R" "ggdist-scales.R"
"ggdist-stat_slabinterval.R" "ggdist-stat_dist_slabinterval.R"
"ggdist-stat_sample_slabinterval.R"
"ggdist-stat_dotsinterval.R" "ggdist-stat_pointinterval.R"
"ggdist-stat_interval.R" "ggdist-stat_lineribbon.R"
"ggdist-student_t.R" "ggdist-theme_ggdist.R"
"ggdist-tidy_format_translators.R" "tidybayes-package.R"
"add_draws.R" "combine_chains.R" "compare_levels.R"
"compose_data.R" "density_bins.R" "emmeans_comparison.R"
"epred_draws.R" "epred_rvars.R" "flip_aes.R" "gather_draws.R"
"gather_emmeans_draws.R" "gather_pairs.R" "gather_rvars.R"
"gather_variables.R" "get_variables.R" "global_variables.R"
"linpred_draws.R" "linpred_rvars.R" "nest_rvars.R" "onAttach.R"
"point_interval.R" "predict_curve.R" "predicted_draws.R"
"predicted_rvars.R" "recover_types.R" "residual_draws.R"
"sample_draws.R" "spread_draws.R" "spread_rvars.R"
"summarise_draws.R" "testthat.R" "tidy_draws.R"
"tidybayes-models.R" "ungather_draws.R" "unspread_draws.R"
"util.R" "x_at_y.R" "deprecated.R"
'compat.R' 'query.R' 'join.R' 'quote.R' 'remove.R' 'replace.R'
'show_dplyr.R' 'unscope.R'
'infl.R' 'augment.R' 'aipw.R' 'prob_aipw.R' 'kaplanMeier.R'
'prob_ipw.R' 'verifyInputs.R' 'catProbs.R' 'ipw.R'
'print.catProbsObj.R' 'print.tLagPropOdds.R' 'tLagData.R'
'tLagPropOdds.R'
'01_class_01_kRp.corpus.R'
'02_method_01_kRp.corpus-class_readability.R'
'02_method_02_kRp.corpus-class_hyphen.R'
'02_method_03_kRp.corpus-class_lex.div.R'
'02_method_04_kRp.corpus-class_read.corp.custom.R'
'02_method_05_kRp.corpus-class_freq.analysis.R'
'02_method_06_kRp.corpus-class_summary.R'
'02_method_07_kRp.corpus-class_correct.R'
'02_method_08_kRp.corpus-class_query.R'
'02_method_09_kRp.corpus-class_filterByClass.R'
'02_method_10_kRp.corpus-class_jumbleWords.R'
'02_method_11_kRp.corpus-class_clozeDelete.R'
'02_method_12_kRp.corpus-class_cTest.R'
'02_method_13_kRp.corpus-class_textTransform.R'
'02_method_14_kRp.corpus-class_docTermMatrix.R'
'02_method_15_kRp.corpus-class_split_by_doc_id.R'
'02_method_20_kRp.corpus_get_set_is.R'
'02_method_21_kRp.corpus-class_show.R' 'corpus_files.R'
'deprecated.R' 'kRpSource.R' 'readCorpus.R'
'tm.plugin.koRpus-internal.R' 'tm.plugin.koRpus-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'
'R7.R' 'RcppExports.R' 'tensor.R' 'autograd.R' 'backends.R'
'call_torch_function.R' 'codegen-utils.R' 'compat-purrr.R'
'compilation_unit.R' 'conditions.R' 'contrib.R'
'creation-ops.R' 'cuda.R' 'device.R' 'dimname_list.R' 'utils.R'
'distributions-constraints.R' 'distributions-utils.R'
'distributions-exp-family.R' 'distributions.R'
'distributions-bernoulli.R' 'distributions-categorical.R'
'distributions-gamma.R' 'distributions-chi2.R'
'distributions-mixture_same_family.R'
'distributions-multivariate_normal.R' 'distributions-normal.R'
'distributions-poisson.R' 'dtype.R' 'gen-method.R'
'gen-namespace-docs.R' 'gen-namespace-examples.R'
'gen-namespace.R' 'generator.R' 'help.R' 'indexing.R'
'install.R' 'ivalue.R' 'jit-compile.R' 'lantern_load.R'
'lantern_sync.R' 'layout.R' 'linalg.R' 'memory_format.R'
'utils-data.R' 'nn.R' 'nn-activation.R' 'nn-batchnorm.R'
'nn-conv.R' 'nn-distance.R' 'nn-dropout.R' 'nn-flatten.R'
'nn-init.R' 'nn-linear.R' 'nn-loss.R' 'nn-normalization.R'
'nn-pooling.R' 'nn-rnn.R' 'nn-sparse.R' 'nn-upsampling.R'
'nn-utils-clip-grad.R' 'nn-utils-rnn.R' 'nn-utils.R'
'nn_adaptive.R' 'nnf-activation.R' 'nnf-batchnorm.R'
'nnf-conv.R' 'nnf-distance.R' 'nnf-dropout.R' 'nnf-embedding.R'
'nnf-fold.R' 'nnf-instancenorm.R' 'nnf-linear.R' 'nnf-loss.R'
'nnf-normalization.R' 'nnf-padding.R' 'nnf-pixelshuffle.R'
'nnf-pooling.R' 'nnf-upsampling.R' 'nnf-vision.R' 'operators.R'
'optim.R' 'optim-adadelta.R' 'optim-adagrad.R' 'optim-adam.R'
'optim-asgd.R' 'optim-lbfgs.R' 'optim-lr_scheduler.R'
'optim-rmsprop.R' 'optim-rprop.R' 'optim-sgd.R' 'package.R'
'qscheme.R' 'quantization.R' 'reduction.R' 'save.R' 'scalar.R'
'script_module.R' 'stack.R' 'storage.R' 'tensor_options.R'
'threads.R' 'trace.R' 'type-info.R' 'utils-data-collate.R'
'utils-data-dataloader.R' 'utils-data-enum.R'
'utils-data-fetcher.R' 'utils-data-sampler.R' 'utils-pipe.R'
'variable_list.R' 'with-indices.R' 'wrapers.R'
'TariffClasses.R' 'ps-methods.R' 'HypoMonMethods.R'
'QuotaClasses.R' 'summary-methods.R' 'bargaining_tariff.R'
'SimFunctions.R' 'TariffCournot-methods.R'
'TariffMonComRUM-methods.R' 'auction2nd_tariff.R'
'bertrand_quota.R' 'bertrand_tariff.R' 'cournot_tariff.R'
'initialize-methods.R' 'monopolistic_competition_tariff.R'
'trade-deprecated.R' 'trade_shiny.R'
'AngularDistance.R' 'Future.R' 'Swap.R' 'Vol.R' 'Option.R'
'Trade.R' 'IRD.R' 'Bond.R' 'CSA.R' 'Chebyshev_distance.R'
'Collateral.R' 'Commodity.R' 'Credit.R' 'CrossSampleEntropy.R'
'Curve.R' 'DynamicBeta.R' 'Equity.R' 'FX.R' 'GetTradeDetails.R'
'HashTable.R' 'InformationAdjustedBeta.R'
'InformationAdjustedCorr.R' 'NormXASampEn.R' 'Other.R'
'ParseTrades.R' 'SampleEntropy.R' 'SelectDerivatives.R'
'VariationOfInformation.R' 'onLoad.R'
Class-Tracks.R Tracks-methods.R generalize.R stcube.R stplot.R
difftrack.R compare-methods.R rtracks.R Trackstat.R
'calc-methods.R' 'check_methods.R' 'cnvrt-methods.R'
'gen-methods.R' 'get-nd-methods.R' 'get-nds-methods.R'
'get-spcl-methods.R' 'manip-methods.R' 'ndlst-methods.R'
'ndmtrx-methods.R' 'node-declaration.R' 'read-write-methods.R'
'server-methods.R' 'set-methods.R' 'treeman-declaration.R'
'treemen-declaration.R' 'update-methods.R' 'viz-methods.R'
'zzz.R'
RcppExports.R metrics.R medTree.R treespace.R findGroves.R
plotGroves.R plotTreeDiff.R servers.R transmissionTrees.R
data.R makeCollapsedTree.R relatedTreeDist.R simulateIndTree.R
tipsMRCAdepths.R treeConcordance.R
'dtriangle.R' 'ltriangle.r' 'ptriangle.r' 'qtriangle.R'
'rtriangle.r'
'constants.r' 'utils.r' 'class.r' 'download.r' 'read10x.r'
'readTrust.r' 'refIMGT.r' 'test.r'
'basic_objects.R' 'armacopula.R' 'dvinecopula.R'
'dvinecopula2.R' 'fitting_basic.R' 'margins.R' 'full_models.R'
'vtransforms.R' 'fitting_vtscopula.R' 'helper_vtarma.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'
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
'RcppExports.R' 'submodel.R' 'response.R' 'inputs.R' 'fit.R'
'posterior_predict.R' 'posterior_linpred.R' 'fitted.R' 'gof.R'
'occu.R' 'colext.R' 'missing.R' 'distsamp.R' 'fitlist.R'
'occuRN.R' 'mb_chisq.R' 'multinomPois.R' 'occuTTD.R' 'pcount.R'
'plot_effects.R' 'plot_posteriors.R' 'predict.R' 'priors.R'
'ranef.R' 'residuals.R' 'spatial.R' 'stanmodels.R'
'test-helpers.R' 'ubms-package.R' 'ubmsFit-methods.R'
'ubmsFitList-methods.R' 'umf.R' 'utils.R'
'color_def_1.R' 'color_def_2.R' 'color_util.R' 'color_fun.R'
'plot_util.R' 'plot_box.R' 'plot_box_calls.R' 'plot_text.R'
'plot_text_calls.R' 'plot_themes.R' 'plot_kn.R' 'start_unikn.R'
'simRedCard.R' 'getLineup.R' 'formation.R' 'overtime.R'
'penaltyGoalsProb.R' 'summary.R' 'unitedRPackage.R'
'unitedRoverview.R' 'unitedSimClass.R' 'unitedSimResults.R'
'unitedSimOne.R' 'unitedSim.R'
'asciiml.R' 'capture.R' 'is.R' 'global.R' 'change.R'
'class_unions.R' 'list.R' 'conditions.R' 'item.R' 'deparse.R'
'text.R' 'item.sub.R' 'section.R' 'test.R' 'unitizer.R'
'exec.R' 'prompt.R' 'browse.struct.R' 'browse.R' 'demo.R'
'diff.R' 'faux_prompt.R' 'get.R' 'heal.R' 'load.R' 'ls.R'
'misc.R' 'search.R' 'options.R' 'onload.R' 'parse.R' 'rename.R'
'repairenvs.R' 'result.R' 'shims.R' 'size.R' 'state.R'
'state.compare.R' 'traceback.R' 'translate.R' 'unitize.R'
'unitize.core.R' 'unitizer-package.R' 'unitizer.add.R'
'upgrade.R'
'classes.R' 'unmarkedEstimate.R' 'mapInfo.R' 'unmarkedFrame.R'
'unmarkedFit.R' 'utils.R' 'getDesign.R' 'colext.R' 'distsamp.R'
'multinomPois.R' 'occu.R' 'occuRN.R' 'occuMulti.R' 'pcount.R'
'gmultmix.R' 'pcountOpen.R' 'gdistsamp.R' 'unmarkedFitList.R'
'unmarkedLinComb.R' 'ranef.R' 'boot.R' 'occuFP.R' 'gpcount.R'
'occuPEN.R' 'pcount.spHDS.R' 'occuMS.R' 'occuTTD.R'
'distsampOpen.R' 'multmixOpen.R' 'unmarkedCrossVal.R' 'piFun.R'
'vif.R' 'makePiFun.R' 'posteriorSamples.R' 'nmixTTD.R'
'gdistremoval.R' 'mixedModelTools.R' 'power.R' 'simulate.R'
'predict.R' 'RcppExports.R'
'utils.R' 'pipe.R' 'rawrd.R' 'formulas.R' 'checklist.R'
'components.R' 'call.R' 'firmly.R' 'validate.R' 'scope.R'
'checkers.R' 'valaddin.R'
'rule.R' 'sugar.R' 'validate_pkg.R' 'parse.R'
'expressionset.R' 'indicator.R' 'validator.R' 'confrontation.R'
'compare.R' 'factory.R' 'genericrules.R' 'lumberjack.R'
'plot.R' 'retailers.R' 'run_validation.R' 'sdmx.R' 'syntax.R'
'utils.R' 'yaml.R'
'aggregate.R' 'tbl_validation.R' 'as-data-frame.R' 'compute.R'
'confront.R' 'confront_tbl.R' 'confront_tbl_sparse.R'
'is_record_based.R' 'rule_works_on_tbl.R' 'show_query.R'
'summary.R' 'tbl.R' 'validatedb-package.R' 'values.R'
'plot_valse.R' 'main.R' 'selectVariables.R'
'constructionModelesLassoRank.R'
'constructionModelesLassoMLE.R' 'computeGridLambda.R'
'initSmallEM.R' 'EMGrank.R' 'EMGLLF.R' 'generateXY.R'
'A_NAMESPACE.R' 'util.R'
'model_gen.R' 'refit.R' 'varband_cv.R' 'varband_path.R'
'RcppExports.R' 'misc.R' 'utils.R'
'CheckInputs.R' 'data.R' 'model.R' 'param.R' 'results.R'
'ICLexact.R' 'DesignOutput.R' 'Summary.R' 'Print.R'
'VarSelLCM.R' 'RcppExports.R' 'Plot.R' 'Imputation.R'
'withoutmixture.R' 'VarSelShiny.R' 'ARI.R' 'Extractors.R'
'Predict.R'
Class-Cartesian2DCoordinateTransformation.R
Cartesian2DCoordinateTransformation-methods.R
Class-SimilarityTransformation.R Class-AffineTransformation.R
SimilarityTransformation-methods.R
AffineTransformation-methods.R
'imports.R' 'NULLing.R' 'coverconvert-class.R'
'vegtable-class.R' 'shaker-class.R' 'transform.R'
'cover_trans.R' 'clean.R' 'as.list.R' 'merge_taxa.R'
'add_releves.R' 'header.R' 'Extract.R' 'veg_relation.R'
'vegtable_stat.R' 'df2vegtable.R' 'used_synonyms.R' 'subset.R'
'names.R' 'tv2vegtable.R' 'crosstable.R' 'aggregate.R'
'write_juice.R' 'vegtable2kml.R' 'layers2samples.R'
'make_cocktail.R' 'summary.R' 'match_names.R' 'count_taxa.R'
'trait_stats.R' 'update_det.R' 'aspect_con-data.R'
'braun_blanquet-data.R' 'dune_veg-data.R' 'Kenya_veg-data.R'
'Wetlands-data.R' 'vegtable-deprecated.R'
'entryFit.R' 'gFunctions.R' 'estimateb.R' 'estimateu.R'
'ulbound.R' 'pre_dNYu.R' 'pre_dNYb.R' 'esttheta.R'
'plot.VEwaning.R' 'print.VEwaning.R' 'psiFit.R' 'unblindFit.R'
've.R' 'veWaning.R' 'veWaningData.R'
'RcppExports.R' 'censorFit.R' 'confirmIntegerLike.R' 'pzero.R'
'survFunc.R' 'entryFit.R' 'esttheta.R' 'fitCensor.R' 'psiFit.R'
'unblindFit.R' 'fitERPsi.R' 'gFunc_v.R'
'plot.VEwaningVariant.R' 'print.VEwaningVariant.R' 'variant.R'
'variantData.R' 've.R' 'verifyPhase.R' 'verifyModels.R'
'verifyInputs.R' 'veWaningVariant.R'
'globals.R' 'build-query.R' 'check_geoserver.R'
'cql-predicates.R' 'cql-translate.R' 'filter.R' 'select.R'
'listFeatures.R' 'polygonFormat.R' 'utils-collect.R'
'utils-classes.R' 'utils-head.R' 'utils-metadata.R'
'utils-options.R' 'utils-pipe.R' 'utils-show_query.R'
'metadata.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
'visa.R' 'Spectra-class.R' 'cm.nsr.R' 'cm.sr.R'
'data-specdb.R' 'data-specdf.R' 'ggplot-method.R'
'ggplot.lmfit.R' 'ggspectra.R' 'ndvi2.R' 'spectra.R' 'sr.R'
'wavelength.R'
'dataPrep.R' 'vitality.4p.R' 'vitality.6p.R' 'vitality.k.R'
'vitality.ku.R' 'vitality.utils.R' 'data_documentation.R'
'density.R' 'mortality_rate.R' 'package_documentation.R'
'auth.R' 'board.R' 'database.R' 'friends.R' 'groups.R'
'igraph_gefx_exporter.R' 'likes.R' 'messages.R'
'mongo_connection.R' 'network.R' 'newsfeed.R' 'queries.R'
'status.R' 'search.R' 'users.R' 'utils.R' 'wall.R' 'vkR.R'
'vmr.R' 'package.R' 'virtualbox.R' 'vagrantcloudAPI.R'
'vagrant.R' 'vmr-methods.R'
'options.R' 'plot_forest.R' 'plot_hazard.R'
'plot_parametric.R' 'util.R' 'vsd-package.R' 'vsd.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'
'data.R' 'nam.R' 'plot.R' 'wcep_core.R' 'wcep.R'
'internal.R' 'package.R' 'read_sf_n.R' 'st_erase_overlaps.R'
'st_repair_geometry.R' 'wdpa_clean.R' 'wdpa_dissolve.R'
'wdpa_url.R' 'wdpa_latest_version.R' 'wdpa_fetch.R'
'wdpa_read.R' 'zzz.R'
'aaa.R' 'collate.R' 'compat-defer.R' 'connection.R' 'db.R'
'defer.R' 'wrap.R' 'local_.R' 'with_.R' 'devices.R' 'dir.R'
'env.R' 'file.R' 'language.R' 'libpaths.R' 'locale.R'
'makevars.R' 'namespace.R' 'options.R' 'par.R' 'path.R' 'rng.R'
'seed.R' 'sink.R' 'tempfile.R' 'timezone.R' 'torture.R'
'utils.R' 'with.R'
'00_class_01_XiMpLe.node.R' '00_class_02_XiMpLe.doc.R'
'00_class_03_XiMpLe.validity.R' '01_method_01_pasteXML.R'
'XiMpLe-internal.R' '01_method_02_node.R' '01_method_03_show.R'
'01_method_04_validXML.R' '01_method_05_XMLgenerators.R'
'XMLNode.R' 'XMLTree.R' 'XMLValidity.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.R XMLClasses.R
applyDOM.R assignChild.R catalog.R createNode.R dynSupports.R
error.R flatTree.R nodeAccessors.R parseDTD.R schema.R
summary.R tangle.R toString.R 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
'S4.R' 'as_list.R' 'xml_parse.R' 'as_xml_document.R'
'classes.R' 'init.R' 'paths.R' 'utils.R' 'xml_attr.R'
'xml_children.R' 'xml_find.R' 'xml_modify.R' 'xml_name.R'
'xml_namespaces.R' 'xml_path.R' 'xml_schema.R'
'xml_serialize.R' 'xml_structure.R' 'xml_text.R' 'xml_type.R'
'xml_url.R' 'xml_write.R' 'zzz.R'
'xmlr.R' 'utils.R' 'AbstractClass.R' 'Content.R' 'Document.R'
'Text.R' 'Element.R' 'Stack.R' 'DomBuilder.R' 'Parser.R'
'xmlImporter.R' 'xmlConverter.R'
'Class_linearFilter.R' 'all_generics.R' 'Class_permtest.R'
'Class_tskrr.R' 'Class_tskrrHeterogeneous.R'
'Class_tskrrHomogeneous.R' 'Class_tskrrImpute.R'
'Class_tskrrImputeHeterogeneous.R'
'Class_tskrrImputeHomogeneous.R' 'Class_tskrrTune.R'
'Class_tskrrTuneHeterogeneous.R' 'Class_tskrrTuneHomogeneous.R'
'as_tuned.R' 'create_grid.R' 'data_drugtarget.R'
'data_proteinInteraction.R' 'dim.R' 'eigen2hat.R' 'fitted.R'
'get_loo_fun.R' 'getlooInternal.R' 'getters_linearFilter.R'
'getters_permtest.R' 'getters_tskrr.R' 'getters_tskrrImpute.R'
'getters_tskrrTune.R' 'hat.R' 'impute_tskrr.R'
'impute_tskrr.fit.R' 'internal_helpers.R' 'is_symmetric.R'
'labels.R' 'linear_filter.R' 'loo.R' 'looInternal.R' 'loss.R'
'loss_functions.R' 'match_labels.R' 'permtest.R' 'plot.tskrr.R'
'plot_grid.R' 'predict.R' 'prepare_lambdas.R' 'residuals.R'
'test_input.R' 'test_symmetry.R' 'tskrr.R' 'tskrr.fit.R'
'tune.R' 'update.R' 'valid_dimensions.R' 'valid_labels.R'
'weights.R' 'xnet-package.R'
'CalcNGR.R' 'CalcPD.R' 'CalcSimulatedExposure.R' 'CalcVA.R'
'GenerateTimeGrid.R' 'calcCVACapital.R' 'calcDefCapital.R'
'calcEADRegulatory.R' 'calcEffectiveMaturity.R' 'calcKVA.R'
'xVACalculator.R' 'xVACalculatorExample.R' 'onLoad.R'
Wilfried Thuiller <wilfried.thuiller@ujf-grenoble.fr>, Damien
Georges <damien.georges2@gmail.com>, Maya Gueguen
<maya.gueguen@univ-grenoble-alpes.fr>
Marie-Laure Delignette-Muller
<marielaure.delignettemuller@vetagro-sup.fr> or Christophe
Dutang <christophe.dutang@ensimag.fr>
Marie Chion <marie.chion@protonmail.fr>, Frederic Bertrand
<frederic.bertrand@utt.fr>
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The portion of numerical integration is based on Cubature,
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(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/).
See the file COPYRIGHTS for various embedded Eigen library
copyright details
Copyright Notice GENERAL DISCLAIMER This program is free
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All files are copyright Institut National de la Sante Et de
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Some functions have been adapted from Matlab code written by
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ANN library is copyright University of Maryland and Sunil
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copyright Samuel Kemp 2005-9 and Gregory Jefferis 2009-2013.
See file COPYRIGHT for details
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reserved. jQuery is licensed under the MIT license. Copyright
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The fortran routines for the function HW.original are from
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are derived from original Francesco Laio Matlab code (non
published material). The fortran routines for the function
'bestlm' are from Alan Miller and were obtained from his R
package 'leaps'.
2006-2011, Bank of Canada. 2012-2016, Paul Gilbert
New Zealand Institute of Language, Brain and Behaviour,
University of Canterbury
OPUS Miner algorithm and C++ implementation Copyright (C)
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Dempster.
See the file COPYRIGHTS for various pagoda2 copyright
details
Pokemon, pokedex and all pokemon names are trademarks of
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rights reserved.
See 'pbdSLAP/inst/ScaLAPACK_LICENSE.txt' for the files in
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See files AUTHORS, COPYING, and COPYING.LESSER in
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'src/zmq_src/' which are under GPL-3.
Copyright (C) 2019 University of Liège (Belgium).
See the file COPYRIGHT for copyright details on some of the
functions and algorithms used.
See phyclust/inst/Documents/ for files in src/msdir/,
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Implementation of the binary heap is by Hewlett-Packard
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Inc.(1996-1999). Modifications to the code by Johnathan Wells
(2002)
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Copyright (C) Hossein Kamalzadeh and Michael Hahsler.
pomdp-solve is Copyright (C) Anthony R. Cassandra; LASPack
is Copyright (C) Tomas Skalicky; lp-solve is Copyright (C)
Michel Berkelaar, Kjell Eikland, Peter Notebaert; all other
code is Copyright (C) Michael Hahsler.
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Holyoke College
See the file COPYRIGHTS for copyright details on some of the
functions.
University of Oxford + file inst/COPYRIGHTS
This package is Copyright (C) 2015-2020 Weierstrass
Institute for Applied Analysis and Stochastics.
This package includes code from the 'zstd' library owned by
Facebook, Inc. and created by Yann Collet; the 'lz4' library
created and owned by Yann Collet; xxHash library created and
owned by Yann Collet; and code derived from the 'Blosc' library
created and owned by Francesc Alted.
Code for Brent's method for finding the root of a univariate
function was taken from R 2.15.1 (Copyright 1999, 2001 The R
Core Team)
Code for Brent's method for univariate function optimization
was taken from R 3.2.2 (Copyright 1995, 1996 Robert Gentleman
and Ross Ihaka, Copyright 2003-2004 The R Foundation, Copyright
1998-2014 The R Core Team).
Incorporates code modified from tqDist
<doi:10.1093/bioinformatics/btu157>.
See QZ/inst/LAPACK_LICENSE.txt for the files in src/qz/.
Source data from ICRP 107 copyright 2008 Eckerman and Endo.
See the file license.txt file for details.
Gleb Beliakov. The 'gsl_randist' random number generation
library is copyright to James Theiler and Brian Gough.
ANN library is copyright University of Maryland and Sunil
Arya and David Mount. See file COPYRIGHT for details.
Original python appdirs module copyright (c) 2010
ActiveState Software Inc. R port copyright Hadley Wickham,
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United States Environmental Protection Agency
(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 Matching. Functions of receiver-operating
characteristics curve analyses were derived from pROC package.
Many dialog functions in EZR were modified from the
corresponding functions in Rcmdr package and
RcmdrPlugin.survivalT package.
For the copyrights for the 'Corpus Workbench' (CWB) and
acknowledgement of authorship, see file COPYRIGHTS.
See the file COPYRIGHTS for various Eigen copyright details
Data is freely available for research use when acknowledged
with the following reference: Improving Recommendation Lists
Through Topic Diversification, Cai-Nicolas Ziegler, Sean M.
McNee, Joseph A. Konstan, Georg Lausen; Proceedings of the 14th
International World Wide Web Conference (WWW '05), May 10-14,
2005, Chiba, Japan. To appear.
Freely available for research use when acknowledged with the
following reference: Eigentaste: A Constant Time Collaborative
Filtering Algorithm. Ken Goldberg, Theresa Roeder, Dhruv Gupta,
and Chris Perkins. Information Retrieval, 4(2), 133-151. July
2001.
(C) 2015 Coherent Logic Limited; ALL RIGHTS RESERVED.
See /inst/LAPACK_LICENSE.txt for code in /src.
Gleb Beliakov. The 'lpsolve' library and its parts are
copyright to various holders, including Kjell Eikland, Michel
Berkelaar, University of Florida, National Institute of
Standards and Technology, Free Software Foundation, Inc.
MaxMind, Inc. for the underlying libmaxmminddb library,
IP2Location.com for libip2location, and the package authors for
all other content.
(C) 2018 Coherent Logic Limited; ALL RIGHTS RESERVED.
Code for the wavelet transform is based on Brandon
Whitcher's work. See file COPYRIGHT for details
Copyright (c) 2010-2018 LabKey Corporation
The package includes the source code of matio written by
Christopher Hulbert (http://sourceforge.net/projects/matio/)
(License: Simplified BSD). The matio io routines have been
adopted to use R printing and error routines.
Authors, and Mixmod use Eigen3 C++ library
Inria - Université de Lille - CNRS; Patrick Wieschollek,
Tobias Wood & the respective contributors for
CppOptimizationLibrary
Code is Copyright (C) Jan Wijffels and BNOSAC
Institut national de recherche pour l’agriculture,
l’alimentation et l’environnement (INRAE)
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.
Institut National de la Recherche pout l’Agriculture,
l’Alimentation et l'Environnment (INRAE)
(c) 2018-2019 Benjamin Christoffersen, except dchud.f and
dchdd.f. They are originally from LINPACK and are in the public
domain in the United States. They are modified by Madeleine
Thompson.
Authors listed above, PostgreSQL Global Development Group,
and The Regents of the University of California
Original SNNS software Copyright (C) 1990-1995 SNNS Group,
IPVR, Univ. Stuttgart, FRG; 1996-1998 SNNS Group, WSI, Univ.
Tuebingen, FRG. R interface Copyright (C) DiCITS Lab, Sci2s
group, DECSAI, University of Granada.
This software is in the public domain because it contains
materials that originally came from the United States
Geological Survey (USGS), an agency of the United States
Department of Interior. For more information, see the official
USGS copyright policy at
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for the STK++ library
see files COPYRIGHTS for detailed information
This software is in the public domain because it contains
materials that originally came from the U.S. Bureau of
Reclamation, an agency of the U.S. Department of Interior.
(c) 2010-2021 Madeleine Thompson, except dchud.f and
dchdd.f, which are from LINPACK and are in the public domain in
the United States.
(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), SQLite (Public Domain), Zstandard (MIT license). For
tabix, we removed standard IO related functions, e.g. printf,
fprintf ; also changed its un-safe pointer arithmetics. For
zstandard, we removed compiler (clang, MSVC) specific
preprocessing flags.
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.
OSTN transformation data included in this module remains
Crown Copyright (C) 2016 under the terms of the BSD licence:
(c) Copyright and database rights Ordnance Survey Limited 2016,
(c) Crown copyright and database rights Land & Property
Services 2016 and/or (c) Ordnance Survey Ireland, 2016. All
rights reserved.
Dr Martin Porter (2001) and Richard Boulton (2004, 2005) for
the 'libstemmer' C library, and Milan Bouchet-Valat (2013) for
the R package contents.
See the file COPYRIGHTS for copyright, authorship and
license details
MOA code in inst/java/moa.jar is Copyright (C) The
University of Waikato and distributed under the Apache License,
version 2.0. All other code is Copyright (C) Matthew Bolanos,
John Forrest and Michael Hahsler
Copyright for the bundled 'PCRE2' library is held by
University of Cambridge, Zoltan Herczeg and Tilera Coporation
(Stack-less Just-In-Time compiler); Copyright for the bundled
'xxHash' code is held by Yann Collet.
The R package bundles the 'SymEngine' library source and its
subcomponents under 'src/upstream' directory. See file
COPYRIGHTS for retained copyright notices as a relicensing and
redistribution requirement.
Underlying C++ code copyright 2018 Chris Whidden.
Copyright 2012. The Regents of the University of California
(Regents). All Rights Reserved.
Tomba technology web service LLC, and the package authors
for all other content.
Jonker-Volgenant Linear Assignment Problem implementation by
Roy Jonker modified by Yong Yang and Yi Cao.
Incorporates C/C++ code from Morphy Phylogenetic Library by
Martin Brazeau <https://github.com/mbrazeau/MorphyLib> (GPL3)
Incorporates C/C++ code from 'ape' by Emmanuel Paradis
<doi:10.1093/bioinformatics/bty633>
2007-2011 Bank of Canada.  2012-2015 Paul Gilbert.
2007-2011 Bank of Canada.  2012-2017 Paul Gilbert.
2007-2011 Bank of Canada.  2012-2016 Paul Gilbert.
All code is Copyright (C) Michael Hahsler and Kurt Hornik.
2019 Conservation Science Partners, Inc.
This software is in the public domain because it contains
materials that originally came from the United States
Geological Survey (USGS), an agency of the United States
Department of Interior (DOI). For more information, see the
official USGS copyright policy at
https://www.usgs.gov/information-policies-and-instructions/copyrights-and-credits#copyright
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
2009-2020 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.
Original GPL-2 C++ code by Naoki Yoshinaga (see
http://www.tkl.iis.u-tokyo.ac.jp/~ynaga/yakmo/)
(C) 2022 John W. Emerson and Walton Green
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.
Given a protein multiple sequence alignment, it is daunting task to assess the effects of substitutions along sequence length. 'aaSEA' package is intended to help researchers to rapidly analyse property changes caused by single, multiple and correlated amino acid substitutions in proteins. Methods for identification of co-evolving positions from multiple sequence alignment are as described in :  Pelé et al., (2017) <doi:10.4172/2379-1764.1000250>.
Compute approach bias scores using different scoring algorithms,
    compute bootstrapped and exact split-half reliability estimates,
    and compute confidence intervals for individual participant scores.
A set of Shiny apps for effective communication and understanding in statistics. The current version includes properties of normal distribution, properties of sampling distribution, one-sample z and t tests, two samples independent (unpaired) t test and analysis of variance.
Strings are abbreviated to at least "minlength" characters, such that they remain unique 
    (if they were). The abbreviations should be recognisable.
Get text from images of text using Abbyy Cloud Optical Character
    Recognition (OCR) API. Easily OCR images, barcodes, forms, documents with
    machine readable zones, e.g. passports. Get the results in a variety of formats
    including plain text and XML. To learn more about the Abbyy OCR API, see 
    <http://ocrsdk.com/>.
Implements several ABC algorithms for
        performing parameter estimation, model selection, and goodness-of-fit.
        Cross-validation tools are also available for measuring the
        accuracy of ABC estimates, and to calculate the
        misclassification probabilities of different models.
Contains data which are used by functions of the 'abc' package.
It aims to identify candidate genes that are “differentially
    methylated” between cases and controls. It applies Student’s t-test and delta beta analysis to
    identify candidate genes containing multiple “CpG sites”.
Estimate parameters of accumulated damage (load duration) models based on failure time data under a Bayesian framework, using Approximate Bayesian Computation (ABC). Assess long-term reliability under stochastic load profiles. Yang, Zidek, and Wong (2019) <doi:10.1080/00401706.2018.1512900>.
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>.
Multi-category angle-based large-margin classifiers.
    See Zhang and Liu (2014) <doi:10.1093/biomet/asu017> for details.
An implementation of Karaboga (2005) Artificial Bee Colony
    Optimization algorithm <http://mf.erciyes.edu.tr/abc/pub/tr06_2005.pdf>.
    This (working) version is a Work-in-progress, which is
    why it has been implemented using pure R code. This was developed upon the basic
    version programmed in C and distributed at the algorithm's official website.
Tests the goodness of fit of a distribution of offspring to the Normal, Poisson, and Gamma distribution and estimates the proportional paternity of the second male (P2) based on the best fit distribution.
Performs Approximate Bayesian Computation (ABC) model choice and parameter inference via random forests.
  Pudlo P., Marin J.-M., Estoup A., Cornuet J.-M., Gautier M. and Robert C. P. (2016) <doi:10.1093/bioinformatics/btv684>.
  Estoup A., Raynal L., Verdu P. and Marin J.-M. <http://journal-sfds.fr/article/view/709>.
  Raynal L., Marin J.-M., Pudlo P., Ribatet M., Robert C. P. and Estoup A. (2019) <doi:10.1093/bioinformatics/bty867>.
Offers methods to perform asymptotically bias-corrected regularized linear discriminant analysis (ABC_RLDA) for cost-sensitive binary classification. The bias-correction is an estimate of the bias term added to regularized discriminant analysis (RLDA) that minimizes the overall risk. The default magnitude of misclassification costs are equal and set to 0.5; however, the package also offers the options to set them to some predetermined values or, alternatively, take them as hyperparameters to tune.
    A. Zollanvari, M. Abdirash, A. Dadlani and B. Abibullaev (2019) <doi:10.1109/LSP.2019.2918485>.
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).
A collection of measures for measuring ecological diversity.
  Ecological diversity comes in two flavors: alpha diversity measures the
  diversity within a single site or sample, and beta diversity measures the
  diversity across two sites or samples. This package overlaps considerably
  with other R packages such as 'vegan', 'gUniFrac', 'betapart', and 'fossil'.
  We also include a wide range of functions that are implemented in software
  outside the R ecosystem, such as 'scipy', 'Mothur', and 'scikit-bio'.  The
  implementations here are designed to be basic and clear to the reader.
Performs augmented backward elimination and checks the stability of the obtained model. Augmented backward elimination combines significance or information based criteria with the change in estimate to either select the optimal model for prediction purposes or to serve as a tool to obtain a practically sound, highly interpretable model. More details can be found in Dunkler et al. (2014) <doi:10.1371/journal.pone.0113677>.
Extremely efficient toolkit for solving the best subset selection problem <arXiv:2110.09697>. This package is its R interface. The package implements and generalizes algorithms designed in <doi:10.1073/pnas.2014241117> that exploits a novel sequencing-and-splicing technique to guarantee exact support recovery and globally optimal solution in polynomial times for linear model. It also supports best subset selection for logistic regression, Poisson regression, Cox proportional hazard model, Gamma regression, multiple-response regression, multinomial logistic regression, ordinal regression, (sequential) principal component analysis, and robust principal component analysis. The other valuable features such as the best subset of group selection <arXiv:2104.12576> and sure independence screening <doi:10.1111/j.1467-9868.2008.00674.x> are also provided.
Loads ABF2 files containing gap-free data from electrophysiological recordings, as created by Axon Instruments/Molecular Devices software such as pClamp 10.
Implements a Bayesian adaptive graphical lasso data-augmented block Gibbs sampler. The sampler simulates the posterior distribution of precision matrices of a Gaussian Graphical Model. This sampler was adapted from the original MATLAB routine proposed in Wang (2012) <doi:10.1214/12-BA729>.
Easy to use functions to visualize marker data
    from biparental populations. Useful for both analyzing and
    presenting genotypes in the ABH format.
Combine multidimensional arrays into a single array.
  This is a generalization of 'cbind' and 'rbind'.  Works with
  vectors, matrices, and higher-dimensional arrays.  Also
  provides functions 'adrop', 'asub', and 'afill' for manipulating,
  extracting and replacing data in arrays.
The Brazilian Jurimetrics Association (ABJ in Portuguese, see
    <https://abj.org.br/> for more information) is a non-profit
    organization which aims to investigate and promote the use of
    statistics and probability in the study of Law and its institutions.
    This package implements general purpose tools used by ABJ, such as
    functions for sampling and basic manipulation of Brazilian lawsuits
    identification number. It also implements functions for text cleaning,
    such as accentuation removal.
Bayesian network analysis is a form of probabilistic graphical models which derives from empirical data a directed acyclic graph, DAG, describing the dependency structure between random variables. 
 An additive Bayesian network model consists of a form of a DAG where each node comprises a generalized linear model, GLM. Additive Bayesian network models are equivalent to Bayesian multivariate regression using graphical modelling, they generalises the usual multivariable regression, GLM, to multiple dependent variables. 
 'abn' provides routines to help determine optimal Bayesian network models for a given data set, where these models are used to identify statistical dependencies in messy, complex data. The additive formulation of these models is equivalent to multivariate generalised linear modelling (including mixed models with iid random effects). 
 The usual term to describe this model selection process is structure discovery. 
 The core functionality is concerned with model selection - determining the most robust empirical model of data from interdependent variables. Laplace approximations are used to estimate goodness of fit metrics and model parameters, and wrappers are also included to the INLA package which can be obtained from <https://www.r-inla.org>. 
 The computing library JAGS <https://mcmc-jags.sourceforge.io> is used to simulate 'abn'-like data. 
 A comprehensive set of documented case studies, numerical accuracy/quality assurance exercises, and additional documentation are available from the 'abn' website <http://r-bayesian-networks.org>.
Contains the functions to implement the methodology and considerations laid out by Marks et al. in the manuscript Measuring Abnormality in High Dimensional Spaces: Applications in Biomechanical Gait Analysis. As of 2/27/2018 this paper has been submitted and is under scientific review. Using high-dimensional datasets to measure a subject’s overall level of abnormality as compared to a reference population is often needed in outcomes research. Utilizing applications in instrumented gait analysis, that article demonstrates how using data that is inherently non-independent to measure overall abnormality may bias results. A methodology is introduced to address this bias to accurately measure overall abnormality in high dimensional spaces. While this methodology is in line with previous literature, it differs in two major ways. Advantageously, it can be applied to datasets in which the number of observations is less than the number of features/variables, and it can be abstracted to practically any number of domains or dimensions. After applying the proposed methodology to the original data, the researcher is left with a set of uncorrelated variables (i.e. principal components) with which overall abnormality can be measured without bias. Different considerations are discussed in that article in deciding the appropriate number of principal components to keep and the aggregate distance measure to utilize.
Performs angle-based outlier detection on a given dataframe. Three methods are available, a full but slow implementation using all the data that has cubic complexity, a fully randomized one which is way more efficient and another using k-nearest neighbours. These algorithms are specially well suited for high dimensional data outlier detection.
An implementation of the Abnormal Blood Profile Score (ABPS,
    part of the Athlete Biological Passport program of the World Anti-Doping
    Agency), which combines several blood parameters into a single score in
    order to detect blood doping (Sottas et al. (2006)
    <doi:10.2202/1557-4679.1011>). The package also contains functions to
    calculate other scores used in anti-doping programs, such as the
    OFF-score (Gore et al. (2003) <http://www.haematologica.org/content/88/3/333>),
    as well as example data.
Provides functions to convert origin-destination data,
    represented as straight 'desire lines' in the 'sf' Simple Features
    class system, into JSON files that can be directly imported into
    A/B Street <https://www.abstreet.org>, a free and open source tool
    for simulating urban transport systems and scenarios of change
    <doi:10.1007/s10109-020-00342-2>.
An R-Shiny application to create visual abstracts for original research. A variety of user defined options and formatting are included.
Provides functions for Bayesian A/B testing including prior elicitation
    options based on Kass and Vaidyanathan (1992) <doi:10.1111/j.2517-6161.1992.tb01868.x>. 
    Gronau, Raj K. N., & Wagenmakers (2021) <doi:10.18637/jss.v100.i17>.
Fit and predict with the high-dimensional principal fitted
        components model.  This model is described by Cook, Forzani, and Rothman (2012)
	<doi:10.1214/11-AOS962>.
Infers directional conservative causal core (gene) networks. It is an advanced version of the algorithm C3NET by providing directional network. Gokmen Altay (2018) <doi:10.1101/271031>, bioRxiv.
Offers an interactive function for the detection of breakpoints in series.
Package to query the Twitter Academic Research Product Track,
    providing access to full-archive search and other v2 API endpoints. Functions
    are written with academic research in mind. They provide flexibility in how 
    the user wishes to store collected data, and encourage regular storage of data
    to mitigate loss when collecting large volumes of tweets. They also provide
    workarounds to manage and reshape the format in which data is provided on
    the client side.
Processes accelerometer data from uni-axial and tri-axial devices,
    and generates data summaries. Also includes functions to plot, analyze, and
    simulate accelerometer data.
It performs Canonical Correlation Analysis and provides inferential guaranties on the correlation components. The p-values are computed following the resampling method developed in Winkler, A. M., Renaud, O., Smith, S. M., & Nichols, T. E. (2020). Permutation inference for canonical correlation analysis. NeuroImage, <doi:10.1016/j.neuroimage.2020.117065>. Furthermore, it provides plotting tools to visualize the results.
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.
Allows clinicians to predict the rate and severity of future acute exacerbation in Chronic Obstructive Pulmonary Disease (COPD) patients, based on the clinical prediction model published in Adibi et al. (2020) <doi:10.1016/S2213-2600(19)30397-2>.
Provides functionality for creating and
	evaluating acceptance sampling plans. Sampling plans can be single,
	double or multiple.
Provides a simple method to improve the accessibility of
    'rmarkdown' documents. The package provides functions for creating or
    modifying 'rmarkdown' documents, resolving known errors and alerts that
    result in accessibility issues for screen reader users.
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.
Tracking accrual in clinical trials is important for trial success. 
    If accrual is too slow, the trial will take too long and be too expensive. If
    accrual is much faster than expected, time sensitive tasks such as the writing 
    of statistical analysis plans might need to be rushed. 'accrualPlot' provides 
    functions to aid the tracking of accrual and predict when a trial will reach 
    it's intended sample size.
Implementation of sparse linear discriminant analysis, which is a supervised
    classification method for multiple classes. Various novel optimization approaches to
    this problem are implemented including alternating direction method of multipliers (ADMM),
    proximal gradient (PG) and accelerated proximal gradient (APG) (See Atkins et al. <arXiv:1705.07194>).
    Functions for performing cross validation are also supplied along with basic prediction
    and plotting functions.
    Sparse zero variance discriminant analysis (SZVD) is also included in the package
    (See Ames and Hong, <arXiv:1401.5492>). See the github wiki for a more extended description.
An isotope natural abundance correction algorithm that
  is needed especially for high resolution mass spectrometers. Supports
  correction for 13C, 2H and 15N. Su X, Lu W and Rabinowitz J (2017)
  <doi:10.1021/acs.analchem.7b00396>.
Categorical data analysis with complete or missing responses
Features tools for exploring congruent phylogenetic birth-death models. It can construct the pulled speciation- and net-diversification rates from a reference model. Given alternative speciation- or extinction rates, it can construct new models that are congruent with the reference model. Functionality is included to sample new rate functions, and to visualize the distribution of one congruence class. See also Louca & Pennell (2020) <doi:10.1038/s41586-020-2176-1>.
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.
The ACE file format is used in genomics to store contigs from sequencing machines.
  This tools converts it into FASTQ format. Both formats contain the 
  sequence characters and their
  corresponding quality information. Unlike the FASTQ file, the ace file stores the
  quality values numerically.
  The conversion algorithm uses the standard Sanger formula. The package facilitates insertion
  into pipelines, and content inspection.
Optimal Bayesian experimental design using the approximate coordinate exchange (ACE) algorithm. See <doi:10.18637/jss.v095.i13>.
Wraps the 'Ace' editor in a HTML widget. The 'Ace' editor has support for many languages. It can be opened in the viewer pane of 'RStudio', and this provides a second source editor.
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.
'ACE' (Advanced Cohort Engine) is a powerful tool that allows constructing cohorts of patients 
             extremely quickly and efficiently. This package is designed to interface directly with an 
             instance of 'ACE' search engine and facilitates API queries and data dumps. Prerequisite
             is a good knowledge of the temporal language to be able to efficiently construct a query.
             More information available at <https://shahlab.stanford.edu/start>.
Estimate the dynamic behaviour of the variance components in the classical twin models with respect to age using B-splines and P-splines.
Non-robust and robust computations of the sample autocovariance (ACOVF) and sample autocorrelation functions (ACF) of univariate and multivariate processes. The methodology consists in reversing the diagonalization procedure involving the periodogram or the cross-periodogram and the Fourier transform vectors, and, thus, obtaining the ACOVF or the ACF as discussed in Fuller (1995) <doi:10.1002/9780470316917>. The robust version is obtained by fitting robust M-regressors to obtain the M-periodogram or M-cross-periodogram as discussed in Reisen et al. (2017) <doi:10.1016/j.jspi.2017.02.008>.
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.
Access and manage the application programming interface (API) of the Armed Conflict Location & Event Data Project (ACLED) at <https://acleddata.com/>. The package makes it easy to retrieve a user-defined sample (or all of the available data) of ACLED, enabling a seamless integration of regular data updates into the research work flow. It requires a minimal number of dependencies. See the package's README file for a note on replicability when drawing on ACLED data. When using this package, you acknowledge that you have read ACLED's terms and conditions of use, and that you agree with their attribution requirements.
A summarization method to estimate allele-specific copy number signals for Affymetrix SNP microarrays using non-negative matrix factorization (NMF).
Provides SNP array data from different types of
    copy-number regions. These regions were identified manually by the authors
    of the package and may be used to generate realistic data sets with known
    truth.
Archimax copulas are mixture of Archimedean and EV copulas. The package provides definitions of several parametric families of generator and dependence function, computes CDF and PDF, estimates parameters, tests for goodness of fit, generates random sample and checks copula properties for custom constructs. In 2-dimensional case explicit formulas for density are used, in the contrary to higher dimensions when all derivatives are linearly approximated. Several non-archimax families (normal, FGM, Plackett) are provided as well.
Make acoustic cues to use with the R packages 'ndl' or 'ndl2'. The package implements functions used
              in the PLoS ONE paper:
              Denis Arnold, Fabian Tomaschek, Konstantin Sering, Florence Lopez, and R. Harald Baayen (2017).
              Words from spontaneous conversational speech can be recognized with human-like accuracy by 
              an error-driven learning algorithm that discriminates between meanings straight from smart 
              acoustic features, bypassing the phoneme as recognition unit.  PLoS ONE 12(4):e0174623
              <doi:10.1371/journal.pone.0174623>
              More details can be found in the paper and the supplement.
              'ndl' is available on CRAN. 'ndl2' is available by request from <konstantin.sering@uni-tuebingen.de>.
Analysis of count data exhibiting autoregressive properties, using the Autoregressive Conditional Poisson model (ACP(p,q)) proposed by Heinen (2003).
A tool for generating acronyms and initialisms from arbitrary text input.
Construct minimum-cost regular spanning subgraph as part of a
    non-parametric two-sample test for equality of distribution.
Functions for testing affine hypotheses on the regression coefficient vector in regression models with autocorrelated errors.
Provides a general toolkit for downloading, managing,
  analyzing, and presenting data from the U.S. Census
  (<https://www.census.gov/data/developers/data-sets.html>), including
  SF1 (Decennial short-form), SF3 (Decennial long-form), and the
  American Community Survey (ACS).  Confidence intervals provided with
  ACS data are converted to standard errors to be bundled with
  estimates in complex acs objects.  Package provides new methods to
  conduct standard operations on acs objects and present/plot data in
  statistically appropriate ways.
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.
The Aligned Corpus Toolkit (act) is designed for linguists that work with time aligned transcription data. It offers functions to import and export various annotation file formats ('ELAN' .eaf, 'EXMARaLDA .exb and 'Praat' .TextGrid files), create print transcripts in the style of conversation analysis, search transcripts (span searches across multiple annotations, search in normalized annotations, make concordances etc.), export and re-import search results (.csv and 'Excel' .xlsx format), create cuts for the search results (print transcripts, audio/video cuts using 'FFmpeg' and video sub titles in 'Subrib title' .srt format), modify the data in a corpus (search/replace, delete, filter etc.), interact with 'Praat' using 'Praat'-scripts, and exchange data with the 'rPraat' package. The package is itself written in R and may be expanded by other users.
Cluster analysis for cognitive diagnosis based on the Asymptotic Classification Theory (Chiu, Douglas & Li, 2009; <doi:10.1007/s11336-009-9125-0>). Given the sample statistic of sum-scores, cluster analysis techniques can be used to classify examinees into latent classes based on their attribute patterns. In addition to the algorithms used to classify data, three labeling approaches are proposed to label clusters so that examinees' attribute profiles can be obtained.
Circadian rhythms are rhythms that oscillate about every 24 h, which has been observed in multiple physiological processes including core body temperature, hormone secretion, heart rate, blood pressure, and many others. Measuring circadian rhythm with wearables is based on a principle that there is increased movement during wake periods and reduced movement during sleep periods, and has been shown to be reliable and valid. This package can be used to extract nonparametric circadian metrics like intradaily variability (IV), interdaily stability (IS), and relative amplitude (RA); and parametric cosinor model and extended cosinor model coefficient. Details can be found in Junrui Di et al (2019) <doi:10.1007/s12561-019-09236-4>.
Designed for studies where animals tagged with acoustic tags are expected
    to move through receiver arrays. This package combines the advantages of automatic sorting and checking 
    of animal movements with the possibility for user intervention on tags that deviate from expected 
    behaviour. The three analysis functions (explore(), migration() and residency()) 
    allow the users to analyse their data in a systematic way, making it easy to compare results from 
    different studies.
    CJS calculations are based on Perry et al. (2012) <https://www.researchgate.net/publication/256443823_Using_mark-recapture_models_to_estimate_survival_from_telemetry_data>.
Recent studies haven shown that, on top of total daily active/sedentary volumes, the time 
  accumulation strategies provide more sensitive information. This package provides functions to extract 
  commonly used fragmentation metrics to quantify such time accumulation strategies based on minute level 
  actigraphy-measured activity counts data.
This contains helpful functions for parsing, managing, plotting, and
    visualizing activities, most often from GPX (GPS Exchange Format) files
    recorded by GPS devices. It allows easy parsing of the source files into
    standard R data formats, along with functions to compute derived data for
    the activity, and to plot the activity in a variety of ways.
A mutation analysis tool that discovers cancer driver genes with frequent mutations in protein signalling sites such as post-translational modifications (phosphorylation, ubiquitination, etc). The Poisson generalised linear regression model identifies genes where cancer mutations in signalling sites are more frequent than expected from the sequence of the entire gene. Integration of mutations with signalling information helps find new driver genes and propose candidate mechanisms to known drivers. Reference: Systematic analysis of somatic mutations in phosphorylation signaling predicts novel cancer drivers. Juri Reimand and Gary D Bader. Molecular Systems Biology (2013) 9:637 <doi:10.1038/msb.2012.68>.
A method for finding an enrichment of cancer simple somatic mutations (SNVs and Indels) in functional elements across the human genome. 'ActiveDriverWGS' detects coding and noncoding driver elements using whole genome sequencing data. The method is part of the following publication: Candidate Cancer Driver Mutations in Distal Regulatory Elements and Long-Range Chromatin Interaction Networks. Molecular Cell (2020) <doi:10.1016/j.molcel.2019.12.027>.
The active subspace method is a sensitivity analysis technique that finds important linear combinations of input variables for a simulator. This package provides functions allowing estimation of the active subspace without gradient information using Gaussian processes as well as sequential experimental design tools to minimize the amount of data required to do so. Implements Wycoff et al. (2019) <arXiv:1907.11572>.
Framework for analysing multiple omics datasets in the context of molecular pathways, biological processes and other types of gene sets. The package uses p-value merging to combine gene- or protein-level signals, followed by ranked hypergeometric tests to determine enriched pathways and processes. This approach allows researchers to interpret a series of omics datasets in the context of known biology and gene function, and discover associations that are only apparent when several datasets are combined. The package is part of the following publication: Integrative Pathway Enrichment Analysis of Multivariate Omics Data. Paczkowska M^, Barenboim J^, Sintupisut N, Fox NS, Zhu H, Abd-Rabbo D, Mee MW, Boutros PC, PCAWG Drivers and Functional Interpretation Working Group; Reimand J, PCAWG Consortium. Nature Communications (2020) <doi:10.1038/s41467-019-13983-9>.
Provides functions to express clock time data relative to 
    anchor points (typically solar); fit kernel density functions to animal 
    activity time data; plot activity distributions; quantify overall 
    levels of activity; statistically compare activity metrics through 
    bootstrapping; evaluate variation in linear variables with time (or 
    other circular variables).
ActiLife software generates activity counts from data collected by Actigraph accelerometers <https://s3.amazonaws.com/actigraphcorp.com/wp-content/uploads/2017/11/26205758/ActiGraph-White-Paper_What-is-a-Count_.pdf>.
  Actigraph is one of the most common research-grade accelerometers. There is considerable research
  validating and developing algorithms for human activity using ActiLife counts. Unfortunately,
  ActiLife counts are proprietary and difficult to implement if researchers use different accelerometer brands.
  The code  creates ActiLife counts from raw acceleration data for different accelerometer brands and it is developed
  based on the study done by Brond and others (2017) <doi:10.1249/MSS.0000000000001344>.
Bayesian parametric generalized circular mixed effect mixture models (GCMMs) for estimating animal activity patterns from camera trap data and other nested data structures using 'JAGS', including automatic Bayesian k-cluster selection and random circular intercepts for nested data. The GCMM function automatically selects the number of components for the mixture model (supporting up to 4 mixture components) based on a Bayesian linear finite normal mixture model and fits a Bayesian parametric circular mixed effect  mixture model with one or two random effects as random circular intercepts with a a von Mises or wrapped Cauchy distribution. Provides graphs of the combined mixture model or separate mixture components. Functionality is provided to allow quantitative  comparisons between model parameters. See Campbell et al. (in press) It's time to expand our analyses of animal activity;  Campbell et al. (in press) Temporal and microspatial niche partitioning; Campbell et al. (in press) A novel approach to comparing animal activity patterns. News, updates, and tutorials will be available on www.atlasgoldenwolf.org/stats and www.github.com/LizADCampbell .
Reads raw 'accelerometry' from 'GT3X+' data and 
    plain table data to calculate Activity Index from 'Bai et al.' (2016)
    <doi:10.1371/journal.pone.0160644>.  The Activity Index refers to the 
    square root of the second-level average variance of the three
    'accelerometry' axes.
Contains functions to generate pre-defined summary statistics from activPAL
    events files <http:www.palt.com>.  The package also contains functions to produce informative 
    graphics that visualise physical activity behaviour and trends.  This includes
    generating graphs that align physical activity behaviour with additional time based 
    observations described by other data sets, such as sleep diaries and continuous glucose 
    monitoring data.
Read in activity measurements from standard file formats
  used by circadian rhythm researchers, currently only 'ClockLab' format,
  and process and plot the data.  The central type of plot is the actogram,
  as first described by in "Activity and distribution of certain wild mice 
  in relation to biotic communities" by MS Johnson (1926) <doi:10.2307/1373575>.
Functions and data sets for actuarial science:
  modeling of loss distributions; risk theory and ruin theory;
  simulation of compound models, discrete mixtures and compound
  hierarchical models; credibility theory. Support for many additional
  probability distributions to model insurance loss size and
  frequency: 23 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.
Actuarial reports are prepared for the last day of a specific 
    period, such as a month, a quarter or a year. Actuarial models assume that 
    certain events happen at the beginning or end of periods. The package 
    contains functions to easily refer to the first or last (working) day 
    within a specific period relative to a base date to facilitate actuarial 
    reporting and to compare results.
This code provides a simple method for representing a visual scene as it may be seen by an animal with less acute vision. When using (or for more information), please cite the original publication.
Create, upload and run 'Acumos' R models.
  'Acumos' (<https://www.acumos.org>) is a platform and open source framework intended to make it easy to build,
  share, and deploy AI apps. 'Acumos' is part of the 'LF AI Foundation', an umbrella
  organization within 'The Linux Foundation'. With this package, user can create a
  component, and push it to an 'Acumos' platform.
Package 'ACV' (short for Affine Cross-Validation) offers an improved time-series cross-validation loss estimator which utilizes both in-sample and out-of-sample forecasting performance via a carefully constructed affine weighting scheme. Under the assumption of stationarity, the estimator is the best linear unbiased estimator of the out-of-sample loss. Besides that, the package also offers improved versions of Diebold-Mariano and Ibragimov-Muller tests of equal predictive ability which deliver more power relative to their conventional counterparts. For more information, see the accompanying article Stanek (2021) <doi:10.2139/ssrn.3996166>.
Functions for calculating the acute chronic workload ratio using three 
            different methods: exponentially weighted moving average (EWMA), rolling 
            average coupled (RAC) and rolling averaged uncoupled (RAU). Examples of this 
            methods can be found in Williams et al. (2017) <doi:10.1136/bjsports-2016-096589>
            for EWMA and Windt & Gabbet (2018) for RAC and RAU <doi: 10.1136/bjsports-2017-098925>.
Performs discrete, real, and gentle boost under both exponential and 
             logistic loss on a given data set.  The package ada provides a straightforward, 
             well-documented, and broad boosting routine for classification, ideally suited 
             for small to moderate-sized data sets.
It implements Freund and Schapire's Adaboost.M1 algorithm and Breiman's Bagging
	algorithm using classification trees as individual classifiers. Once these classifiers have been
	trained, they can be used to predict on new data. Also, cross validation estimation of the error can
	be done. Since version 2.0 the function margins() is available to calculate the margins for these
	classifiers. Also a higher flexibility is achieved giving access to the rpart.control() argument
	of 'rpart'. Four important new features were introduced on version 3.0, AdaBoost-SAMME (Zhu 
	et al., 2009) is implemented and a new function errorevol() shows the error of the ensembles as
	a function of the number of iterations. In addition, the ensembles can be pruned using the option 
	'newmfinal' in the predict.bagging() and predict.boosting() functions and the posterior probability of
	each class for observations can be obtained. Version 3.1 modifies the relative importance measure
	to take into account the gain of the Gini index given by a variable in each tree and the weights of 
	these trees. Version 4.0 includes the margin-based ordered aggregation for Bagging pruning (Guo
	and Boukir, 2013) and a function to auto prune the 'rpart' tree. Moreover, three new plots are also 
	available importanceplot(), plot.errorevol() and plot.margins(). Version 4.1 allows to predict on 
	unlabeled data. Version 4.2 includes the parallel computation option for some of the functions.
Estimate the causal treatment effect for subjects that can adhere 
    to one or both of the treatments. Given longitudinal data with missing 
    observations, consistent causal effects are calculated. Unobserved potential
    outcomes are estimated through direct integration as described in: 
    Qu et al., (2019) <doi:10.1080/19466315.2019.1700157> and 
    Zhang et. al., (2021) <doi:10.1080/19466315.2021.1891965>.
The R package 'adagio' will provide methods and algorithms for
    discrete optimization, e.g. knapsack and subset sum procedures,
	derivative-free Nelder-Mead and Hooke-Jeeves minimization, and
	some (evolutionary) global optimization functions.
Huber-type estimation for mean, covariance and (regularized) regression. For all the methods, the robustification parameter tau is chosen by a tuning-free principle.
Collection of several algorithms to obtain archetypoids with small and large databases, and with both classical 
	multivariate data and functional data (univariate and multivariate). Some of these algorithms also allow to detect 
	anomalies (outliers). Please see Vinue and Epifanio (2020) <doi:10.1007/s11634-020-00412-9>.
The Genetic Algorithm (GA) is a type of optimization method of Evolutionary Algorithms. It uses the biologically inspired operators such as mutation, crossover, selection and replacement.Because of their global search and robustness abilities, GAs have been widely utilized in machine learning, expert systems, data science, engineering, life sciences and many other areas of research and business. However, the regular GAs need the techniques to improve their efficiency in computing time and performance in finding global optimum using some adaptation and hybridization strategies. The adaptive GAs (AGA) increase the convergence speed and success of regular GAs by setting the parameters crossover and mutation probabilities dynamically. The hybrid GAs combine the exploration strength of a stochastic GAs with the exact convergence ability of any type of deterministic local search algorithms such as simulated-annealing, in addition to other nature-inspired algorithms such as ant colony optimization, particle swarm optimization etc. The package 'adana' includes a rich working environment with its many functions that make possible to build and work regular GA, adaptive GA, hybrid GA and hybrid adaptive GA for any kind of optimization problems. Cebeci, Z. (2021, ISBN: 9786254397448).
For distributions whose probability density functions are log-concave, the adaptive rejection sampling algorithm can be used to build envelope functions for sampling. For others, we can use the modified adaptive rejection sampling algorithm, the concave-convex adaptive rejection sampling algorithm and the adaptive slice sampling algorithm. So we designed an R package mainly including 4 functions: rARS(), rMARS(), rCCARS() and rASS(). These functions can realize sampling based on the algorithms above.
A collection of several pharmacovigilance signal detection methods based on adaptive lasso. Additional lasso-based and propensity score-based signal detection approaches are also supplied.
Infer the code style (which style rules are followed and which ones are 
    not) from one package and use it to check another. This makes it easier to
    find and correct the most important problems first.
Simulate clinical trials for diagnostic test devices and evaluate 
    the operating characteristics under an adaptive design with futility
    assessment determined via the posterior predictive probabilities.
Fits semiparametric additive regression models with spatially adaptive penalized splines and computes simultaneous confidence bands and associated specification (lack-of-fit) tests. Simultaneous confidence bands cover the entire curve with a prescribed level of confidence and allow us to assess the estimation uncertainty for the whole curve. In contrast to pointwise confidence bands, they permit statements about the statistical significance of certain features (e.g. bumps) in the underlying curve.The method allows for handling of spatially heterogeneous functions and their derivatives as well as heteroscedasticity in the data. See Wiesenfarth et al. (2012) <doi:10.1080/01621459.2012.682809>.
Multimodal distributions can be modelled as a mixture of components. The model is derived using the Pareto Density Estimation (PDE) for an estimation of the pdf. PDE has been designed in particular to identify groups/classes in a dataset. Precise limits for the classes can be calculated using the theorem of Bayes. Verification of the model is possible by QQ plot, Chi-squared test and Kolmogorov-Smirnov test. The package is based on the publication of Ultsch, A., Thrun, M.C., Hansen-Goos, O., Lotsch, J. (2015)  <DOI:10.3390/ijms161025897>.
Implements adaptive gPCA, as described in: Fukuyama, J. (2017)
    <arXiv:1702.00501>. The package also includes functionality for applying
    the method to 'phyloseq' objects so that the method can be easily applied
    to microbiome data and a 'shiny' app for interactive visualization.
Implements Figueiredo EM algorithm for adaptive sparsity (Jeffreys prior) (see Figueiredo, M.A.T.; , "Adaptive sparseness for supervised learning," Pattern Analysis and Machine Intelligence, IEEE Transactions on , vol.25, no.9, pp. 1150- 1159, Sept. 2003) and Wong algorithm for adaptively sparse gaussian geometric models (see Wong, Eleanor, Suyash Awate, and P. Thomas Fletcher. "Adaptive Sparsity in Gaussian Graphical Models." In Proceedings of the 30th International Conference on Machine Learning (ICML-13), pp. 311-319. 2013.)
Implements adaptive tau leaping to approximate the
        trajectory of a continuous-time stochastic process as
        described by Cao et al. (2007) The Journal of Chemical Physics
        <doi:10.1063/1.2745299> (aka. the Gillespie stochastic
        simulation algorithm).  This package is based upon work
        supported by NSF DBI-0906041 and NIH K99-GM104158 to Philip
        Johnson and NIH R01-AI049334 to Rustom Antia.
Enables sampling from arbitrary distributions if the log density is known up to a constant; a common situation in the context of Bayesian inference. The implemented sampling algorithm was proposed by Vihola (2012) <DOI:10.1007/s11222-011-9269-5> and achieves often a high efficiency by tuning the proposal distributions to a user defined acceptance rate.
Implementation of adaptive p-value thresholding (AdaPT), including both a framework that allows the user to specify any 
  algorithm to learn local false discovery rate and a pool of convenient functions that implement specific 
  algorithms. See Lei, Lihua and Fithian, William (2016) <arXiv:1609.06035>.
Package that simulates adaptive clinical trials using adaptive
    stopping, adaptive arm dropping, and/or adaptive randomisation.
    Developed as part of the INCEPT (Intensive Care Platform Trial) project
    (<https://incept.dk/>), which is primarily supported by a grant
    from Sygeforsikringen "danmark" (<https://www.sygeforsikring.dk/>).
Tools to construct (or add to) cell-type signature matrices using flow sorted or single cell samples and deconvolve bulk gene expression data.
    Useful for assessing the quality of single cell RNAseq experiments, estimating the accuracy of signature matrices, and determining cell-type spillover. 
    Please cite: Danziger SA et al. (2019) ADAPTS: Automated Deconvolution Augmentation of Profiles for Tissue Specific cells <doi:10.1371/journal.pone.0224693>.
The functions defined in this program serve for implementing adaptive
    two-stage tests. Currently, four tests are included: Bauer and Koehne (1994),
    Lehmacher and Wassmer (1999), Vandemeulebroecke (2006), and the horizontal conditional
    error function. User-defined tests can also be implemented. Reference: Vandemeulebroecke,
    An investigation of two-stage tests, Statistica Sinica 2006.
Implements the adaptive sampling procedure, a framework for both positive unlabeled learning and learning with class label noise. Yang, P., Ormerod, J., Liu, W., Ma, C., Zomaya, A., Yang, J. (2018) <doi:10.1109/TCYB.2018.2816984>.
Implements the adaptive smoothing spline estimator for the function-on-function linear regression model described in Centofanti et al. (2020) <arXiv:2011.12036>.
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.
Create 'ggplot2' themes and color palettes.
Functions to fit the binomial and multinomial additive hazard models and to estimate the contribution of diseases/conditions to the disability prevalence, as proposed by Nusselder and Looman (2004) and extended by Yokota et al (2017).
Contains tools to fit the additive hazards model to data from a cohort,
    random sampling, two-phase Bernoulli sampling and two-phase finite population sampling,
    as well as calibration tool to incorporate phase I auxiliary information into the
    two-phase data model fitting.  This package provides regression parameter estimates and
    their model-based and robust standard errors. It also offers tools to make prediction of
    individual specific hazards.
Provide addins for 'RStudio'.
  It currently contains 3 addins. The first to add a shortcut for the double pipe. The second is to add a shortcut for the same operator. And the third to simplify the creation of vectors from texts pasted from the computer transfer area.
Browse through a continuously updated list of existing RStudio 
    addins and install/uninstall their corresponding packages.
'RStudio' allows to show and navigate for the outline of a 
    R Markdown file, but not for R Markdown projects with multiple 
    files. For this reason, I have developed several 'RStudio' addins capable 
    of show project outline. Each addin is specialized in showing projects 
    of different types: R Markdown project, 'bookdown' package project 
    and 'LaTeX' project. There is a configuration file that allows you 
    to customize additional searches.
Fit Generalized Additive Models (GAM) using 'mgcv' with 'parsnip'/'tidymodels'
    via 'additive' <doi:10.5281/zenodo.5884867>. 'tidymodels' is a collection of
    packages for machine learning; see Kuhn and Wickham (2020) <https://www.tidymodels.org>).
    The technical details of 'mgcv' are described in Wood (2017)
    <doi:10.1201/9781315370279>.
Provides functions for calculating efficiency with two types of additive Data Envelopment Analysis models: (i) Generalized Efficiency Measures: unweighted additive model (Cooper et al., 2007 <doi:10.1007/978-0-387-45283-8>), Range Adjusted Measure (Cooper et al., 1999, <doi:10.1023/A:1007701304281>), Bounded Adjusted Measure (Cooper et al., 2011 <doi:10.1007/s11123-010-0190-2>), Measure of Inefficiency Proportions (Cooper et al., 1999 <doi:10.1023/A:1007701304281>), and the Lovell-Pastor Measure (Lovell and Pastor, 1995 <doi:10.1016/0167-6377(95)00044-5>); and (ii) the Slacks-Based Measure (Tone, 2001 <doi:10.1016/S0377-2217(99)00407-5>). The functions provide several options: (i) constant and variable returns to scale; (ii) fixed (non-controllable) inputs and/or outputs; (iii) bounding the slacks so that unrealistically large slack values are avoided; and (iv) calculating the efficiency of specific Decision-Making Units (DMUs), rather than of the whole sample. Package additiveDEA also provides a function for reducing computation time when datasets are large.
Implementation of the Tukey, Mandel, Johnson-Graybill, LBI, Tusell
    and modified Tukey non-additivity tests.
Methods for fitting identity-link GLMs and GAMs to discrete data,
    using EM-type algorithms with more stable convergence properties than standard methods.
Modifies trellis objects by adding horizontal and/or vertical
 reference lines or shaded regions that provide visual scaling information. 
 This is mostly useful in multi-panel plots that use the relation = 'free'
 option in their 'scales' argument list.
Accelerated destructive degradation tests (ADDT) are often used to collect necessary data for assessing the long-term properties of polymeric materials. Based on the collected data, a thermal index (TI) is estimated. The TI can be useful for material rating and comparison. This package implements the traditional method based on the least-squares method, the parametric method based on maximum likelihood estimation, and the semiparametric method based on spline methods, and the corresponding methods for estimating TI for polymeric materials. The traditional approach is a two-step approach that is currently used in industrial standards, while the parametric method is widely used in the statistical literature. The semiparametric method is newly developed. Both the parametric and semiparametric approaches allow one to do statistical inference such as quantifying uncertainties in estimation, hypothesis testing, and predictions. Publicly available datasets are provided illustrations. More details can be found in Jin et al. (2017).
Tools for multivariate data analysis. Several methods are provided for the analysis (i.e., ordination) of one-table (e.g., principal component analysis, correspondence analysis), two-table (e.g., coinertia analysis, redundancy analysis), three-table (e.g., RLQ analysis) and K-table (e.g., STATIS, multiple coinertia analysis). The philosophy of the package is described in Dray and Dufour (2007) <doi:10.18637/jss.v022.i04>.
A Tcl/Tk GUI for some basic functions in the 'ade4' package.
The meaning of adea is "alternate DEA". This package is devoted to provide the alternative method of DEA described in the paper entitled "Stepwise Selection of Variables in DEA Using Contribution Load", by F. Fernandez-Palacin, M. A. Lopez-Sanchez and M. Munoz-Marquez. Pesquisa Operacional 38 (1), pg. 1-24, 2018. <doi:10.1590/0101-7438.2018.038.01.0031>.
    A full functional on-line and interactive version is available at
    <https://knuth.uca.es/shiny/DEA/>.
Toolset for the exploration of genetic and genomic
    data. Adegenet provides formal (S4) classes for storing and handling
    various genetic data, including genetic markers with varying ploidy
    and hierarchical population structure ('genind' class), alleles counts
    by populations ('genpop'), and genome-wide SNP data ('genlight'). It
    also implements original multivariate methods (DAPC, sPCA), graphics,
    statistical tests, simulation tools, distance and similarity measures,
    and several spatial methods. A range of both empirical and simulated
    datasets is also provided to illustrate various methods.
Graphical functionalities for the representation of multivariate data. It is a complete re-implementation of the functions available in the 'ade4' package.
A collection of tools for the estimation of animals home range.
A collection of tools for the analysis of habitat selection.
A collection of tools for the analysis of animal movements.
A collection of tools to deal with raster maps.
Multivariate tools to analyze comparative data, i.e. a phylogeny
    and some traits measured for each taxa.
Contains a 'shiny' application called AdEPro (Animation of Adverse Event Profiles) which (audio-)visualizes adverse events occurring in clinical trials. As this data is usually considered sensitive, this tool is provided as a stand-alone application that can be launched from any local machine on which the data is stored.
Designed for optimal use in performing fast, 
    accurate walking strides segmentation from high-density 
    data collected from a wearable accelerometer worn 
    during continuous walking activity.
Created to host raw accelerometry data sets and their derivatives which are used in the corresponding 'adept' package.
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.
Data sets used in Cayuela and De la Cruz (2022, ISBN:978-84-8476-705-3).
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). 
    Several approaches are described in the review Dray et al (2012)
    <doi:10.1890/11-1183.1>.
Amiga Disk Files (ADF) are virtual
    representations of 3.5 inch floppy disks for the
    Commodore Amiga. Most disk drives from other systems
    (including modern drives) are not able to read these
    disks. To be able to emulate this system, the ADF
    format was created. This package enables you to read
    ADF files and import and export files from and to such
    virtual DOS-formatted disks.
Anderson-Darling GoF test with p-value calculation based on Marsaglia's 2004 paper "Evaluating the Anderson-Darling Distribution"
A (mildly) opinionated set of functions to help assess medication adherence for researchers working with medication claims data.
    Medication adherence analyses have several complex steps that are often convoluted and can be time-intensive. The focus is to create a 
    set of functions using "tidy principles" geared towards transparency, speed, and flexibility while working with adherence metrics. All functions perform exactly one task 
    with an intuitive name so that a researcher can handle details (often achieved with vectorized solutions) while we handle non-vectorized tasks common to most 
    adherence calculations such as adjusting fill dates and determining episodes of care. The methodologies in referenced in this package come from
    Canfield SL, et al (2019) "Navigating the Wild West of Medication Adherence Reporting in Specialty Pharmacy" <doi:10.18553/jmcp.2019.25.10.1073>.
Computation of adherence to medications from Electronic Health care 
    Data and visualization of individual medication histories and adherence 
    patterns. The package implements a set of S3 classes and
    functions consistent with current adherence guidelines and definitions. 
    It allows the computation of different measures of
    adherence (as defined in the literature, but also several original ones), 
    their publication-quality plotting,
    the estimation of event duration and time to initiation,
    the interactive exploration of patient medication history and 
    the real-time estimation of adherence given various parameter settings.
    It scales from very small datasets stored in flat CSV files to very large 
    databases and from single-thread processing on mid-range consumer
    laptops to parallel processing on large heterogeneous computing clusters.
    It exposes a standardized interface allowing it to be used from other
    programming languages and platforms, such as Python.
Interactive graphical user interface (GUI) for the package 
    'AdhereR', allowing the user to access different data sources, to explore 
    the patterns of medication use therein, and the computation of various  
    measures of adherence. It is implemented using Shiny and HTML/CSS/JavaScript.
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>.
Functions, data sets and examples for the calculation of various indices of biodiversity including species, functional and phylogenetic diversity. Part of the indices are expressed in terms of equivalent numbers of species. The package also provides ways to partition biodiversity across spatial or temporal scales (alpha, beta, gamma diversities). In addition to the quantification of biodiversity, ordination approaches are available which rely on diversity indices and allow the detailed identification of species, functional or phylogenetic differences between communities.
Implements a constrained version of hierarchical agglomerative 
    clustering, in which each observation is associated to a position, and 
    only adjacent clusters can be merged. Typical application fields in 
    bioinformatics include Genome-Wide Association Studies or Hi-C data 
    analysis, where the similarity between items is a decreasing function of 
    their genomic distance. Taking advantage of this feature, the implemented 
    algorithm is time and memory efficient. This algorithm is described in 
    Ambroise et al (2019) <https://almob.biomedcentral.com/articles/10.1186/s13015-019-0157-4>.
For a binary classification the adjusted sensitivity and specificity are measured for a given fixed threshold. If the threshold for either sensitivity or specificity is not given, the crossing point between the sensitivity and specificity curves are returned.
Functions in this package fit a stratified Cox proportional hazards and a proportional subdistribution hazards model by extending Zhang et al., (2007) <doi: 10.1016/j.cmpb.2007.07.010> 
  and Zhang et al., (2011) <doi: 10.1016/j.cmpb.2010.07.005> respectively to clustered right-censored data. The functions also provide the estimates of the cumulative baseline hazard along with their standard errors. Furthermore, the adjusted survival and cumulative incidence probabilities are also provided along with their standard errors. Finally, the estimate of cumulative incidence and survival probabilities given a vector of covariates along with their standard errors are also provided.
Adjusts output of 'cranlogs' package to account for 'CRAN'-wide daily automated downloads and re-downloads caused by package updates.
Package for the access and distribution of Long-term lake datasets from lakes in the 
    Adirondack Park, northern New York state. Includes a wide variety of physical, chemical, and 
    biological parameters from 28 lakes. Data are from multiple collection organizations and have 
    been harmonized in both time and space for ease of reuse.
Adaptive wavelet lifting transforms for signal denoising using optimal local neighbourhood regression, from Nunes et al. (2006) <doi:10.1007/s11222-006-6560-y>.
A toolbox for programming Clinical Data Standards Interchange Consortium
    (CDISC) compliant Analysis Data Model (ADaM) datasets in R. ADaM datasets are a
    mandatory part of any New Drug or Biologics License Application submitted to the
    United States Food and Drug Administration (FDA). Analysis derivations are
    implemented in accordance with the "Analysis Data Model Implementation Guide"
    (CDISC Analysis Data Model Team, 2021, <https://www.cdisc.org/standards/foundational/adam/adamig-v1-3-release-package>).
A set of Study Data Tabulation Model (SDTM) datasets from the Clinical
    Data Interchange Standards Consortium (CDISC) pilot project used for testing
    and developing Analysis Data Model (ADaM) derivations inside the 'admiral'
    package.
Contains functions used across packages 'DDIwR', 'QCA' and 'venn'.
    Interprets and translates, factorizes and negates SOP - Sum of Products
    expressions, for both binary and multi-value crisp sets, and extracts
    information (set names, set values) from those expressions. Other functions
    perform various other checks if possibly numeric (even if all numbers reside
    in a character vector) and coerce to numeric, or check if the numbers are
    whole. It also offers, among many others, a highly versatile recoding
    routine and a more flexible alternative to the base function 'with()'.
    Some of the functions in this package use related functions from package
    'QCA'. Users are encouraged to install that package despite not being listed
    in the Imports field, due to circular dependency issues.
Provides functions to perform the fitting of an adaptive mixture
    of Student-t distributions to a target density through its kernel function as described in
    Ardia et al. (2009) <doi:10.18637/jss.v029.i03>. The
    mixture approximation can then be used as the importance density in importance
    sampling or as the candidate density in the Metropolis-Hastings algorithm to
    obtain quantities of interest for the target density itself.
Implements several methods to estimate the unknown quantities related 
    to two-component admixture models, where the two components can belong to any
    distribution (note that in the case of multinomial mixtures, the two components
    must belong to the same family). Estimation methods depend on the assumptions 
    made on the unknown component density (see Bordes and Vandekerkhove (2010) <doi:10.3103/S1066530710010023>; 
    Patra and Sen (2016) <doi:10.1111/rssb.12148>); Milhaud, Pommeret, Salhi and Vandekerkhove 
    (2021) <doi:10.1016/j.jspi.2021.05.010>). In practice, one can estimate both the 
    mixture weight and the unknown component density in a wide variety of frameworks.
    On top of that, hypothesis tests can be performed in one and two-samples contexts 
    to test the unknown component density. Finally, clustering of unknown
    mixture components is also feasible in a K-samples setting.
An interface for performing all stages of 'ADMIXTOOLS' analyses
    (<https://reich.hms.harvard.edu/software>) entirely from R. Wrapper functions
    (D, f4, f3, etc.) completely automate the generation of intermediate
    configuration files, run 'ADMIXTOOLS' programs on the command-line, and
    parse output files to extract values of interest. This allows users to focus
    on the analysis itself instead of worrying about low-level technical details.
    A set of complementary functions for processing and filtering of data in
    the 'EIGENSTRAT' format is also provided.
Provides algorithms to solve popular optimization problems in statistics such as regression or denoising based on Alternating Direction Method of Multipliers (ADMM).
    See Boyd et al (2010) <doi:10.1561/2200000016> for complete introduction to the method.
Solves the problem of identifying the densest submatrix in a given or sampled binary matrix, Bombina et al. (2019) <arXiv:1904.03272>.
Fit linear and cox models regularized with net (L1 and Laplacian), elastic-net (L1 and L2) or lasso (L1) penalty, and their adaptive forms, such as adaptive lasso and net adjusting for signs of linked coefficients. In addition, it treats the number of non-zero coefficients as another tuning parameter and simultaneously selects with the regularization parameter. The package uses one-step coordinate descent algorithm and runs extremely fast by taking into account the sparsity structure of coefficients.
Estimates a penalized precision matrix via the alternating direction method of multipliers (ADMM) algorithm. It currently supports a general elastic-net penalty that allows for both ridge and lasso-type penalties as special cases. This package is an alternative to the 'glasso' package.
    See Boyd et al (2010) <doi:10.1561/2200000016> for details regarding the estimation method.
Provides tools to directly model underlying population dynamics using date datasets (radiocarbon and other) with a Continuous Piecewise Linear (CPL) model framework. Various other model types included. Taphonomic loss included optionally as a power function. Model comparison framework using BIC. Package also calibrates 14C samples, generates Summed Probability Distributions (SPD), and performs SPD simulation analysis to generate a Goodness-of-fit test for the best selected model. Details about the method can be found in Timpson A., Barberena R., Thomas M. G., Mendez C., Manning K. (2020) <doi:10.1098/rstb.2019.0723>.
Bayesian inference using the no-U-turn (NUTS) algorithm by 
 Hoffman and Gelman (2014) <https://www.jmlr.org/papers/v15/hoffman14a.html>. 
 Designed for 'AD Model Builder' ('ADMB') models,
 or when R functions for log-density and log-density gradient
 are available, such as 'Template Model Builder'
 models and other special cases. Functionality is similar to 'Stan', 
 and the 'rstan' and 'shinystan' packages are used for diagnostics and 
 inference.
Connect to the 'Adobe Analytics' API v2.0 <https://github.com/AdobeDocs/analytics-2.0-apis>
             which powers 'Analysis Workspace'. The package was developed
             with the analyst in mind, and it will continue to be
             developed with the guiding principles of iterative,
             repeatable, timely analysis.
Optimize one or two-arm, two-stage designs for clinical trials with 
    respect to several pre-implemented objective criteria or implement custom 
    objectives.
    Optimization under uncertainty and conditional (given stage-one outcome) 
    constraints are supported.
    See <doi:10.1002/sim.8291> and <doi:10.18637/jss.v098.i09> for details.
Calculate users prevalence of a product based on the prevalence of triers in the population. The measurement of triers is relatively easy. It is just a question of whether a person tried a product even once in his life or not. On the other hand, The measurement of people who also adopt it as part of their life is more complicated since adopting an innovative product is a subjective view of the individual. Mickey Kislev and Shira Kislev developed a formula to calculate the prevalence of a product's users to overcome this difficulty. The current package assists in calculating the users prevalence of a product based on the prevalence of triers in the population. See for: Kislev, M. M., and S. Kislev (2020) <doi:10.5539/ijms.v12n4p63>.
An implementation of ADPclust clustering procedures (Fast
    Clustering Using Adaptive Density Peak Detection). The work is built and
    improved upon the idea of Rodriguez and Laio (2014)<DOI:10.1126/science.1242072>. 
    ADPclust clusters data by finding density peaks in a density-distance plot 
    generated from local multivariate Gaussian density estimation. It includes 
    an automatic centroids selection and parameter optimization algorithm, which 
    finds the number of clusters and cluster centroids by comparing average 
    silhouettes on a grid of testing clustering results; It also includes a user 
    interactive algorithm that allows the user to manually selects cluster 
    centroids from a two dimensional "density-distance plot". Here is the 
    research article associated with this package: "Wang, Xiao-Feng, and 
    Yifan Xu (2015)<DOI:10.1177/0962280215609948> Fast clustering using adaptive 
    density peak detection." Statistical methods in medical research". url:
    http://smm.sagepub.com/content/early/2015/10/15/0962280215609948.abstract.
This function takes a vector or matrix of data and smooths
    the data with an improved Savitzky Golay transform. The Savitzky-Golay
    method for data smoothing and differentiation calculates convolution
    weights using Gram polynomials that exactly reproduce the results of
    least-squares polynomial regression. Use of the Savitzky-Golay
    method requires specification of both filter length and
    polynomial degree to calculate convolution weights. For maximum
    smoothing of statistical noise in data, polynomials with
    low degrees are desirable, while a high polynomial degree
    is necessary for accurate reproduction of peaks in the data.
    Extension of the least-squares regression formalism with
    statistical testing of additional terms of polynomial degree
    to a heuristically chosen minimum for each data window leads
    to an adaptive-degree polynomial filter (ADPF). Based on noise
    reduction for data that consist of pure noise and on signal
    reproduction for data that is purely signal, ADPF performed
    nearly as well as the optimally chosen fixed-degree
    Savitzky-Golay filter and outperformed sub-optimally chosen
    Savitzky-Golay filters. For synthetic data consisting of noise
    and signal, ADPF outperformed both optimally chosen and
    sub-optimally chosen fixed-degree Savitzky-Golay filters. See Barak, P. (1995) <doi:10.1021/ac00113a006> for more information.
Provides the functions for planning and conducting a
  clinical trial with adaptive sample size determination. Maximal statistical
  efficiency will be exploited even when dramatic or multiple adaptations
  are made. Such a trial consists of adaptive determination of sample size
  at an interim analysis and implementation of frequentist statistical test at the
  interim and final analysis with a prefixed significance level. The required
  assumptions for the stage-wise test statistics are independent and stationary
  increments and normality. Predetermination of adaptation rule is not required.
Perform first- and second-order multi-scale analyses derived from Ripley K-function (Ripley B. D. (1977) <doi:10.1111/j.2517-6161.1977.tb01615.x>), 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.
Interact with Google Ads Data Hub API <https://developers.google.com/ads-data-hub/reference/rest>. The functionality allows to fetch customer details, submit queries to ADH.
Provides different functionalities and calculations used in the world of basketball to analyze the statistics of the players, the statistics of the teams, the statistics of the quintets and the statistics of the plays. For more details of the calculations included in the package can be found in the book Basketball on Paper written by Dean Oliver.
Functions to compute upper Clopper-Pearson confidence limits of early life failure probabilities and required sample sizes of burn-in studies under further available information, e.g. from other products or technologies.
S4 Object Oriented for Advanced Fuzzy Clustering and Fuzzy COnsensus Clustering. Techniques that provided by this package are Fuzzy C-Means, Gustafson Kessel (Babuska Version), Gath-Geva, Sum Voting Consensus, Product Voting Consensus, and Borda Voting Consensus. This package also provide visualization via Biplot and Radar Plot.
This software solves an Advection Bi-Flux Diffusive Problem using the Finite Difference Method FDM. Vasconcellos, J.F.V., Marinho, G.M., Zanni, J.H., 2016, Numerical analysis of an anomalous diffusion with a bimodal flux distribution. <doi:10.1016/j.rimni.2016.05.001>. Silva, L.G., Knupp, D.C., Bevilacqua, L., Galeao, A.C.N.R., Silva Neto, A.J., 2014, Formulation and solution of an Inverse Anomalous Diffusion Problem with Stochastic Techniques. <doi:10.5902/2179460X13184>. In this version, it is possible to include a source as a function depending on space and time, that is, s(x,t).
Interactive 'R' tutorials written using 'learnr' for Field (2016), "An Adventure in Statistics", <ISBN:9781446210451>.
    Topics include general workflow in 'R' and 'Rstudio', the 'R' environment and 'tidyverse', summarizing data, model fitting, central tendency, 
    visualising data using 'ggplot2', inferential statistics and robust estimation, hypothesis testing, the general linear model, comparing means,
    repeated measures designs, factorial designs, multilevel models, growth models, and generalized linear models (logistic regression).
Accurate point and interval estimation methods for multiple linear regression coefficients, under classical normal and independent error assumptions, taking into account variable selection.
The irregularly-spaced data are interpolated onto regular latitude-longitude 
    grids by weighting each station according to its distance and angle from the 
    center of a search radius.
Implements wavelet-based approaches for describing population admixture. Principal Components Analysis (PCA) is used to define the population structure and produce a localized admixture signal for each individual. Wavelet summaries of the PCA output describe variation present in the data and can be related to population-level demographic processes. For more details, see J Sanderson, H Sudoyo, TM Karafet, MF Hammer and MP Cox. 2015. Reconstructing past admixture processes from local genomic ancestry using wavelet transformation. Genetics 200:469-481 <doi:10.1534/genetics.115.176842>.
Allows access to selected services that are part of the 
    'Google Adwords' API <https://developers.google.com/adwords/api/docs/guides/start>.
	'Google Adwords' is an online advertising service by 'Google', that delivers Ads 
	to users. This package offers a authentication process using 'OAUTH2'.  Currently, 
	there are two methods of data of accessing the API, depending on the type of 
	request. One method uses 'SOAP' requests which require building an 'XML' structure 
	and then sent to the API. These are used for the 'ManagedCustomerService' and 
	the 'TargetingIdeaService'. The second method is by building 'AWQL' queries for 
	the reporting side of the 'Google Adwords' API.
Package to incorporate change point analysis in ARIMA forecasting.
We extend existing gene enrichment tests to perform adverse
    event enrichment analysis. Unlike the continuous gene expression data,
    adverse event data are counts. Therefore, adverse event data has many zeros
    and ties. We propose two enrichment tests. One is a modified Fisher's exact
    test based on pre-selected significant adverse events, while the other is
    based on a modified Kolmogorov-Smirnov statistic. We add Covariate
    adjustment to improve the analysis."Adverse event enrichment tests using
    VAERS" Shuoran Li, Lili Zhao (2020) <arXiv:2007.02266>.
Developed for Computing the probability density function,
             cumulative distribution function, random generation, estimating the parameters
 			       of asymmetric exponential power distribution, and robust regression analysis 
 			       with error term that follows asymmetric exponential power distribution.
Functions, data sets, examples, demos, and vignettes for the book
             Christian Kleiber and Achim Zeileis (2008),
	     Applied Econometrics with R, Springer-Verlag, New York.
	     ISBN 978-0-387-77316-2. (See the vignette "AER" for a package overview.)
Different tools for managing databases of airborne particles, elaborating the main calculations and visualization of results. In a first step, data are checked using tools for quality control and all missing gaps are completed. Then, the main parameters of the pollen season are calculated and represented graphically. Multiple graphical tools are available: pollen calendars, phenological plots, time series, tendencies, interactive plots, abundance plots...
Estimate ideal efficiencies of aerosol sampling through sample
    lines. Functions were developed consistent with the approach described in 
    Hogue, Mark; Thompson, Martha; Farfan, Eduardo; Hadlock, Dennis, (2014), 
    "Hand Calculations for Transport of Radioactive Aerosols through Sampling 
    Systems" Health Phys 106, 5, S78-S87, <doi:10.1097/HP.0000000000000092>.
Estimates the attributable fraction in different sampling designs
    adjusted for measured confounders using logistic regression (cross-sectional
    and case-control designs), conditional logistic regression (matched case-control
    design), Cox proportional hazard regression (cohort design with time-to-
    event outcome), gamma-frailty model with a Weibull baseline hazard and instrumental variables analysis.
    An exploration of the AF with a genetic exposure can be found in the package 'AFheritability' Dahlqwist E et al. (2019) <doi:10.1007/s00439-019-02006-8>.
This is an implementation of the Generalized Discrimination Score
    (also known as Two Alternatives Forced Choice Score, 2AFC) for various 
    representations of forecasts and verifying observations. The Generalized 
    Discrimination Score is a generic forecast verification framework which 
    can be applied to any of the following verification contexts: dichotomous, 
    polychotomous (ordinal and nominal), continuous, probabilistic, and ensemble.
    A comprehensive description of the Generalized Discrimination Score, including 
    all equations used in this package, is provided by Mason and Weigel (2009) 
    <doi:10.1175/MWR-D-10-05069.1>.
Estimate diagnosis performance (Sensitivity, Specificity, 
    Positive predictive value, Negative predicted value) of a diagnostic test
    where can not measure the golden standard but can estimate it using the 
    attributable fraction.
Convenience functions for analyzing factorial experiments using ANOVA or
         mixed models. aov_ez(), aov_car(), and aov_4() allow specification of
         between, within (i.e., repeated-measures), or mixed (i.e., split-plot) 
         ANOVAs for data in long format (i.e., one observation per row),
         automatically aggregating multiple observations per individual and cell 
         of the design. mixed() fits mixed models using lme4::lmer() and computes 
         p-values for all fixed effects using either Kenward-Roger or Satterthwaite 
         approximation for degrees of freedom (LMM only), parametric bootstrap 
         (LMMs and GLMMs), or likelihood ratio tests (LMMs and GLMMs). 
         afex_plot() provides a high-level interface for interaction or one-way 
         plots using ggplot2, combining raw data and model estimates. afex uses 
         type 3 sums of squares as default (imitating commercial statistical software).
Tools for raster georeferencing, grid affine transforms, and general raster logic. 
 These functions provide converters between raster specifications, world vector, geotransform, 
 'RasterIO' window, and 'RasterIO window' in 'sf' package list format. There are functions to offset
 a matrix by padding any of four corners (useful for vectorizing neighbourhood operations), and
 helper functions to harvesting user clicks on a graphics device to use for simple georeferencing
 of images.  Methods used are available from <https://en.wikipedia.org/wiki/World_file> and
 <https://gdal.org/user/raster_data_model.html>.
Tools to study sorting patterns in matching markets and to estimate the affinity matrix of both the bipartite one-to-one matching model without frictions and with Transferable Utility by 'Dupuy' and 'Galichon' (2014) <doi:10.1086/677191> and its 'unipartite' variant by 'Ciscato', 'Galichon' and 'Gousse' (2020) <doi:10.1086/704611>. It also contains all the necessary tools to implement the 'saliency' analysis, to run rank tests of the affinity matrix and to build tables and plots summarizing the findings.
Enables to compute the statistical indices of affluence (richness) with bootstrap errors, and inequality and polarization indices. Moreover, gives the possibility of calculation of Medeiros affluence line. In 2.1 version some simple errors are fixed.
The AFfunction() is a function which returns an estimate of the Attributable Fraction (AF) and a plot of the AF as a function of heritability, disease prevalence, size of target group and intervention effect. 
             Since the AF is a function of several factors, a shiny app is used to better illustrate how the relationship between the AF and heritability depends on several other factors. The app is ran by the function runShinyApp().
             For more information see Dahlqwist E et al. (2019) <doi:10.1007/s00439-019-02006-8>.
Provides Atomic Force Microscope images analysis such as Gaussian mixes identification, Power
    Spectral Density, roughness against lengthscale, experimental variogram and variogram models,
    fractal dimension and scale, 2D network analysis. The AFM images can be exported to STL format for 3D
    printing.
Set of functions for analyzing Atomic Force Microscope (AFM) force-distance curves. It allows to obtain the contact and unbinding points, perform the baseline correction, estimate the Young's modulus, fit up to two exponential decay function to a stress-relaxation / creep experiment, obtain adhesion energies. These operations can be done either over a single F-d curve or over a set of F-d curves in batch  mode.
Allows estimation and modelling of flight costs in animal (vertebrate) flight,
    implementing the aerodynamic power model described in Klein Heerenbrink et al.
    (2015) <doi:10.1098/rspa.2014.0952>. Taking inspiration from the program
    'Flight', developed by Colin Pennycuick (Pennycuick (2008) "Modelling the flying
    bird". Amsterdam: Elsevier. ISBN 0-19-857721-4), flight performance is estimated
    based on basic morphological measurements such as body mass, wingspan and wing
    area. 'afpt' can be used to make predictions on how animals should adjust their
    flight behaviour and wingbeat kinematics to varying flight conditions.
Tool is created for regression, prediction and forecast analysis of macroeconomic and credit data.
  The package includes functions from existing R packages adapted for banking sector of Kazakhstan.
  The purpose of the package is to optimize statistical functions for easier interpretation for bank analysts and non-statisticians.
A collection of methods for both the rank-based estimates and least-square estimates to the Accelerated Failure Time (AFT) model. For rank-based estimation, it provides approaches that include the computationally efficient Gehan's weight and the general's weight such as the logrank weight. Details of the rank-based estimation can be found in Chiou et al. (2014) <doi:10.1007/s11222-013-9388-2> and Chiou et al. (2015) <doi:10.1002/sim.6415>. For the least-square estimation, the estimating equation is solved with generalized estimating equations (GEE). Moreover, in multivariate cases, the dependence working correlation structure can be specified in GEE's setting. Details on the least-squares estimation can be found in Chiou et al. (2014) <doi:10.1007/s10985-014-9292-x>.
Functions for Posterior estimates of Accelerated Failure Time(AFT) model with MCMC and Maximum likelihood estimates of AFT model without MCMC for univariate and multivariate analysis in high dimensional gene expression data are available in this 'afthd' package.
            AFT model with Bayesian framework for multivariate in high dimensional data has been proposed by Prabhash et al.(2016)
             <doi:10.21307/stattrans-2016-046>.
Tools for downloading and extracting data from the Copernicus "Agrometeorological indicators 
  from 1979 to present derived from reanalysis"
  <https://cds.climate.copernicus.eu/cdsapp#!/dataset/sis-agrometeorological-indicators?tab=overview> (AgERA5).
Tools for the analysis of growth data: to extract an 
    LMS table from a gamlss object, to calculate the standard 
    deviation scores and its inverse, and to superpose two wormplots 
    from different models. The package contains a some varieties of 
    reference tables, especially for The Netherlands.
Convenience functions for aggregating a data frame or data table.
    Currently mean, sum and variance are supported. For Date variables, the recency
    and duration are supported. There is also support for dummy variables in
    predictive contexts. Code has been completely re-written in data.table for computational speed.
Contains functionality for performing the following methods of p-value aggregation: Fisher's method [Fisher, RA (1932, ISBN: 9780028447308)], the Lancaster method (weighted Fisher's method) [Lancaster, HO (1961, <doi:10.1111/j.1467-842X.1961.tb00058.x>)], and Sidak correction [Sidak, Z (1967, <doi:10.1080/01621459.1967.10482935>)].  Please cite Yi et al., the manuscript corresponding to this package [Yi, L et al., (2017), <doi:10.1101/190199>].
Computation of A (pedigree), G (genomic-base), and H (A corrected
    by G) relationship matrices for diploid and autopolyploid species. Several methods
    are implemented considering additive and non-additive models.
Adaptive Gauss Hermite Quadrature for Bayesian inference.
    The AGHQ method for normalizing posterior distributions
    and making Bayesian inferences based on them. Functions are provided for doing
    quadrature and marginal Laplace approximations, and summary methods are provided
    for making inferences based on the results. 
    See Stringer (2021). "Implementing Adaptive Quadrature for Bayesian Inference: 
    the aghq Package" <arXiv:2101.04468>.
Provides functions to fit Accurate Generalized Linear Model (AGLM) models, visualize them, and predict for new data. AGLM is defined as a regularized GLM which applies a sort of feature transformations using a discretization of numerical features and specific coding methodologies of dummy variables. For more information on AGLM, see Suguru Fujita, Toyoto Tanaka, Kenji Kondo and Hirokazu Iwasawa (2020) <https://www.institutdesactuaires.com/global/gene/link.php?doc_id=16273&fg=1>.
Tools supporting multi-criteria and group decision making,
    including variable number of criteria, by means of
    aggregation operators, spread measures,
    fuzzy logic connectives, fusion functions,
    and preordered sets. Possible applications include,
    but are not limited to, quality management, scientometrics,
    software engineering, etc.
Functionalities to simulate space-time data and to estimate dynamic-spatial panel data models. 
             Estimators implemented are the BCML (Elhorst (2010), <doi:10.1016/j.regsciurbeco.2010.03.003>), the MML (Elhorst (2010) <doi:10.1016/j.regsciurbeco.2010.03.003>) and the INLA Bayesian estimator (Lindgren and Rue, (2015) <doi:10.18637/jss.v063.i19>; Bivand, Gomez-Rubio and Rue, (2015) <doi:10.18637/jss.v063.i20>) 
             adapted to panel data. The package contains functions to replicate the analyses of the scientific article entitled "Agricultural Productivity in Space" (Baldoni and Esposti (under revision)).
Bland-Altman plot and scatter plot with identity line 
             for visualization and point and 
             interval estimates for different metrics related to 
             reproducibility/repeatability/agreement including
             the concordance correlation coefficient, 
             intraclass correlation coefficient,
             within-subject coefficient of variation,
             smallest detectable difference, 
             and mean normalized smallest detectable difference.
A tool for calculating agreement interval of two measurement methods (Jason Liao (2015) <DOI:10.1515/ijb-2014-0030>) and present results in plots with discordance rate and/or clinically meaningful limit to quantify agreement quality.
Original idea was presented in the thesis "A statistical analysis tool for agricultural research" to obtain the degree of Master on science, National Engineering University (UNI), Lima-Peru. Some experimental data for the examples come from the CIP and others research. Agricolae offers extensive functionality on experimental design especially for agricultural and plant breeding experiments, which can also be useful for other purposes. It supports planning of lattice, Alpha, Cyclic, Complete Block, Latin Square, Graeco-Latin Squares, augmented block, factorial, split and strip plot designs. There are also various analysis facilities for experimental data, e.g. treatment comparison procedures and several non-parametric tests comparison, biodiversity indexes and consensus cluster.
Visualization of Design of Experiments from the 'agricolae' package with 'ggplot2' framework
    The user provides an experiment design from the 'agricolae' package, calls the corresponding function and will receive a 
    visualization with 'ggplot2' based functions that are specific for each design. As there are many different designs, each design is tested on its type.
    The output can be modified with standard 'ggplot2' commands or with other packages with 'ggplot2' function extensions.
Datasets from books, papers, and websites related to agriculture.
    Example graphics and analyses are included. Data come from small-plot trials,
    multi-environment trials, uniformity trials, yield monitors, and more.
Functions to calculate Gray Level Co-occurrence Matrix(GLCM), RGB-based Vegetative Index(RGB VI) and Normalized Difference Vegetation Index(NDVI) family image features. GLCM calculations are based on Haralick (1973) <doi:10.1109/TSMC.1973.4309314>.
Example software for the analysis of data from designed 
	experiments, especially agricultural crop experiments. The basics 
	of the analysis of designed experiments are discussed 
	using real examples from agricultural field trials. 
	A range of statistical methods using a range
	of R statistical packages are  exemplified . The experimental
	data is made available as separate data sets for each example
	and the R analysis code is made available as example code.
	The example code can be readily extended, as required.
Spatial modeling of energy balance and actual 
    evapotranspiration using satellite images and meteorological data. 
    Options of satellite are: Landsat-8 (with and without thermal bands), 
    Sentinel-2 and MODIS. Respectively spatial resolutions are 30, 100, 
    10 and 250 meters. User can use data from a single meteorological 
    station or a grid of meteorological stations (using any spatial 
    interpolation method). Silva, Teixeira, and Manzione (2019) <doi:10.1016/j.envsoft.2019.104497>.
Calculates concentration and dispersion in ordered rating scales. It implements various measures of concentration and dispersion to describe what researchers variably call agreement, concentration, consensus, dispersion, or polarization among respondents in ordered data. It also implements other related measures to classify distributions. In addition to a generic city-block based concentration measure and a generic dispersion measure, the package implements various measures, including van der Eijk's (2001) <DOI: 10.1023/A:1010374114305> measure of agreement A, measures of concentration by Leik, Tatsle and Wierman, Blair and Lacy, Kvalseth, Berry and Mielke, Reardon, and Garcia-Montalvo and Reynal-Querol. Furthermore, the package provides an implementation of Galtungs AJUS-system to classify distributions, as well as a function to identify the position of multiple modes.
Collection of functions to compute agroclimatic indices useful
    to zoning areas based on climatic variables and to evaluate the importance
    of temperature and precipitation for individual crops, or in general for 
    agricultural lands.
Performs the analysis of completely randomized experimental designs (CRD), randomized blocks (RBD) and Latin square (LSD), experiments in double and triple factorial scheme (in CRD and RBD), experiments in subdivided plot scheme (in CRD and RBD), subdivided and joint analysis of experiments in CRD and RBD, linear regression analysis, test for two samples. The package performs analysis of variance, ANOVA assumptions and multiple comparison test of means or regression, according to Pimentel-Gomes (2009, ISBN: 978-85-7133-055-9), nonparametric test (Conover, 1999, ISBN: 0471160687), test for two samples, joint analysis of experiments according to Ferreira (2018, ISBN: 978-85-7269-566-4) and generalized linear model (glm) for binomial and Poisson family in CRD and RBD (Carvalho, FJ (2019), <doi:10.14393/ufu.te.2019.1244>). It can also be used to obtain descriptive measures and graphics, in addition to correlations and creative graphics used in agricultural sciences (Agronomy, Zootechnics, Food Science and related areas).
Linear and nonlinear regression analysis common in agricultural science articles (Archontoulis & Miguez (2015). <doi:10.2134/agronj2012.0506>). The package includes polynomial, exponential, gaussian, logistic, logarithmic, segmented, non-parametric models, among others. The functions return the model coefficients and their respective p values, coefficient of determination, root mean square error, AIC, BIC, as well as graphs with the equations automatically.
Statistical procedures to perform stability analysis in plant breeding and to identify stable genotypes under diverse environments. It is possible to calculate coefficient of homeostaticity by Khangildin et al. (1979), variance of specific adaptive ability by Kilchevsky&Khotyleva (1989), weighted homeostaticity index by Martynov (1990), steadiness of stability index by Udachin (1990), superiority measure by Lin&Binn (1988) <doi:10.4141/cjps88-018>, regression on environmental index by Erberhart&Rassel (1966) <doi:10.2135/cropsci1966.0011183X000600010011x>, Tai's (1971) stability parameters <doi:10.2135/cropsci1971.0011183X001100020006x>, stability variance by Shukla (1972) <doi:10.1038/hdy.1972.87>, ecovalence by Wricke (1962), nonparametric stability parameters by Nassar&Huehn (1987) <doi:10.2307/2531947>, Francis&Kannenberg's parameters of stability (1978) <doi:10.4141/cjps78-157>.
In total it has 7 functions, three for calculating machine calibration, which determine application rate (L/ha), nozzle flow (L/min) and amount of product (L or kg) to be added. to the tank with each sprayer filling. Two functions for graphs of the flow distribution of the nozzles (L/min) in the application bar and, of the temporal variability of the meteorological conditions (air temperature, relative humidity of the air and wind speed). Two functions to determine the spray deposit (uL/cm2), through the methodology called spectrophotometry, with the aid of bright blue (Palladini, L.A., Raetano, C.G., Velini, E.D. (2005), <doi:10.1590/S0103-90162005000500005>) or metallic markers (Chaim, A., Castro, V.L.S.S., Correles, F.M., Galvão, J.A.H., Cabral, O.M.R., Nicolella, G. (1999), <doi:10.1590/S0100-204X1999000500003>). The package supports the analysis and representation of information, using a single free software that meets the most diverse areas of activity in application technology.
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
Fast and automatic gradient tree boosting designed
    to avoid manual tuning and cross-validation by utilizing an information
    theoretic approach. This makes the algorithm adaptive to the dataset at
    hand; it is completely automatic, and with minimal worries of overfitting.
    Consequently, the speed-ups relative to state-of-the-art implementations
    can be in the thousands while mathematical and technical knowledge required
    on the user are minimized.
'Align-GVGD' ('A-GVGD') is a method to predict the impact of
    'missense' substitutions based on the properties of amino acid side
    chains and protein multiple sequence alignments
    <doi:10.1136/jmg.2005.033878>. 'A-GVGD' is an extension of the original
    'Grantham' distance to multiple sequence alignments. This package
    provides an alternative R implementation to the web version found on
    <http://agvgd.hci.utah.edu/>.
Computationally efficient procedures for regularized
        estimation with the semiparametric additive hazards regression
        model.
An implementation of the additive heredity model for the mixture-of-mixtures experiments of Shen et al. (2019) in Technometrics <doi:10.1080/00401706.2019.1630010>. The additive heredity model considers an additive structure to inherently connect the major components with the minor components. The additive heredity model has a meaningful interpretation for the estimated model because of the hierarchical and heredity principles applied and the nonnegative garrote technique used for variable selection.
Methods for fitting additive hazards model.
             Perform the maximum likelihood method as well as the traditional Aalen's method for estimating the additive hazards model. 
             For details see Chengyuan Lu(2021) <arXiv:2004.06156>.
Implementation of the Artificial Hydrocarbon Networks for data
    modeling.
Aho-Corasick is an optimal algorithm for finding many
    keywords in a text. It can locate all matches in a text in O(N+M) time; i.e.,
    the time needed scales linearly with the number of keywords (N) and the size of
    the text (M). Compare this to the naive approach which takes O(N*M) time to loop
    through each pattern and scan for it in the text. This implementation builds the
    trie (the generic name of the data structure) and runs the search in a single
    function call. If you want to search multiple texts with the same trie, the
    function will take a list or vector of texts and return a list of matches to
    each text. By default, all 128 ASCII characters are allowed in both the keywords
    and the text. A more efficient trie is possible if the alphabet size can be
    reduced. For example, DNA sequences use at most 19 distinct characters and
    usually only 4; protein sequences use at most 26 distinct characters and usually
    only 20. UTF-8 (Unicode) matching is not currently supported.
The AHP method (Analytic Hierarchy Process) is a multi-criteria decision-making method addressing
             choice and outranking problems. The method enables to perform the analysis of alternatives in each
             type of criterion and then provides a global performance of each alternative in the decision context.
             The main difference of this package is the possibility of evaluating the alternatives using quantitative
             data, by numerical representation, and qualitative data, using the Saaty scale, providing preference
             relation between variables by a pairwise evaluation.
The Analytic Hierarchy Process is a versatile multi-criteria decision-making tool introduced by Saaty (1987) <doi:10.1016/0270-0255(87)90473-8> that allows decision-makers to weigh attributes and evaluate alternatives presented to them.    This package provides a consistent methodology for researchers to reformat data and run analytic hierarchy process in R on data that are formatted using the survey data entry mode. It is optimized for performing the analytic hierarchy process with many decision-makers, and provides tools and options for researchers to aggregate individual preferences and test multiple options. It also allows researchers to quantify, visualize and correct for inconsistency in the decision-maker's comparisons.
Implementation of a hybrid MCDM method build from the AHP (Analytic Hierarchy Process) and TOPSIS-2N (Technique for Order of Preference by Similarity to Ideal Solution - with two normalizations). This method is described in Souza et al. (2018) <doi: 10.1142/S0219622018500207>.
An implementation of probability mass function and sampling algorithms is provided for the attraction Indian buffet distribution (AIBD), originally from Dahl (2016) <https://ww2.amstat.org/meetings/jsm/2016/onlineprogram/ActivityDetails.cfm?SessionID=213038>.
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', 'rjags', and 'jagsUI' classes.  Functions also implement model selection using BIC.  Objects following model selection and multimodel inference can be formatted to LaTeX using 'xtable' methods included in the package.
Performs Box-Cox power transformation for different purposes, graphical approaches, assesses the success of the transformation via tests and plots, computes mean and confidence interval for back transformed data.
Read objects from the AIDA (<http://aida.freehep.org/>) file and make them available as dataframes in R.
The 'AI Fairness 360' <https://aif360.mybluemix.net/> toolkit is an open-source library to help detect 
    and mitigate bias in machine learning models. The AI Fairness 360 R package includes 
    a comprehensive set of metrics for datasets and models to test for biases, 
    explanations for these metrics, and algorithms to mitigate bias in datasets and models.
A collection of Automatic Item Generators used mainly for
    psychological research. This package can generate linear syllogistic reasoning,
    arithmetic and 2D/3D/Double 3D spatial reasoning items. It is recommended for research
    purpose only.
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.
Named after the Irish name for weather, this package contains 
    tidied data from the Irish Meteorological Service's hourly observations for 2017. 
    In all, the data sets include observations from 25 weather stations, and also
    latitude and longitude coordinates for each weather station. Now includes energy 
    generation data for Ireland and Northern Ireland (2017), including Wind Generation data.
The 'AIPW' pacakge implements the augmented inverse probability weighting, a doubly robust estimator, for average causal effect estimation with user-defined stacked machine learning algorithms. To cite the 'AIPW' package, please use: "Yongqi Zhong, Edward H. Kennedy, Lisa M. Bodnar, Ashley I. Naimi (2021, In Press). AIPW: An R Package for Augmented Inverse Probability Weighted Estimation of Average Causal Effects. American Journal of Epidemiology". Visit: <https://yqzhong7.github.io/AIPW/> for more information.
Tools for downloading hourly averages, daily maximums and minimums from each of the 
    pollution, wind, and temperature measuring stations or geographic zones in the Mexico City 
    metro area. The package also includes the locations of each of the stations and zones. See 
    <http://aire.cdmx.gob.mx/> for more information.
Hydrological modelling tools developed at INRAE-Antony (HYCAR Research Unit, France). The package includes several conceptual rainfall-runoff models (GR4H, GR5H, GR4J, GR5J, GR6J, GR2M, GR1A) that can be applied either on a lumped or semi-distributed way. A snow accumulation and melt model (CemaNeige) and the associated functions for the calibration and evaluation of models are also included. Use help(airGR) for package description and references.
Add-on to the 'airGR' package which provides the tools to assimilate observed discharges in daily GR hydrological models. The package consists in two functions allowing to perform the assimilation of observed discharges via the Ensemble Kalman filter or the Particle filter as described in Piazzi et al. (2021) <doi:10.1029/2020WR028390>.
Semi-distributed Precipitation-Runoff Modelling based on
    'airGR' package models integrating human infrastructures and their
    managements.
Add-on package to the 'airGR' package that simplifies its use and is aimed at being used for teaching hydrology. The package provides 1) three functions that allow to complete very simply a hydrological modelling exercise 2) plotting functions to help students to explore observed data and to interpret the results of calibration and simulation of the GR ('Génie rural') models 3) a 'Shiny' graphical interface that allows for displaying the impact of model parameters on hydrographs and models internal variables.
Get information about air quality using 'Airly' <https://airly.eu/> API through R.
Utilities for working with hourly air quality monitoring data
    with a focus on small particulates (PM2.5). A compact data model is 
    structured as a list with two dataframes. A 'meta' dataframe contains 
    spatial and measuring device metadata associated with deployments at known 
    locations. A 'data' dataframe contains a 'datetime' column followed by 
    columns of measurements associated with each "device-deployment".
    Algorithms to calculate NowCast and the associated Air Quality Index (AQI)
    are defined at the US Environmental Projection Agency AirNow program:
    <https://www.airnow.gov/sites/default/files/2020-05/aqi-technical-assistance-document-sept2018.pdf>.
Retrieves open source airport data and provides tools to look up information, translate names into codes and vice-verse, as well as some basic calculation functions for measuring distances. Data is licensed under the Open Database License.
Geographic, use, and property related data on airports.
These dataset contains daily quality air measurements in 
  Spain over a period of 18 years (from 2001 to 2018). The measurements refer to 
  several pollutants. These data are openly published by the Government of Spain.  
  The datasets were originally spread over a number of files and formats. Here, 
  the same information is contained in simple dataframe for convenience of 
  researches, journalists or general public. See the Spanish Government website 
  <http://www.miteco.gob.es/> for more information.
Schema definitions and read, write and validation tools for data 
    formatted in accordance with the AIRR Data Representation schemas defined 
    by the AIRR Community <http://docs.airr-community.org>.
Process and display data from air quality sensors.
    Initial focus is on PM2.5 measurements from sensors produced by 'PurpleAir'
    <https://www2.purpleair.com>.
An evaluation framework for algorithm portfolios using Item Response
    Theory (IRT). We use continuous and polytomous IRT models to evaluate algorithms and introduce
    algorithm characteristics such as stability, effectiveness and anomalousness 
    (Kandanaarachchi, Smith-Miles 2020) <doi:10.13140/RG.2.2.11363.09760>.
Deals with many computations related to the thermodynamics of atmospheric processes. It includes many functions designed to consider the density of air with varying degrees of water vapour in it, saturation pressures and mixing ratios, conversion of moisture indices, computation of atmospheric states of parcels subject to dry or pseudoadiabatic vertical evolutions and atmospheric instability indices that are routinely used for operational weather forecasts or meteorological diagnostics.
A thin wrapper around the 'ajv' JSON validation package for
    JavaScript. See <http://epoberezkin.github.io/ajv/> for details.
A tidy framework for automatic knowledge classification and visualization. Currently, the core functionality of the framework is mainly supported by modularity-based clustering (community detection) in keyword co-occurrence network, and focuses on co-word analysis of bibliometric research. However, the designed functions in 'akc' are general, and could be extended to solve other tasks in text mining as well.
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.
Flagger to detect acute kidney injury (AKI) in a patient dataset.
Several cubic spline interpolation methods of H. Akima for irregular and
  regular gridded data are available through this package, both for the bivariate case
  (irregular data: ACM 761, regular data: ACM 760) and univariate case (ACM 433 and ACM 697).
  Linear interpolation of irregular gridded data is also covered by reusing D. J. Renkas
  triangulation code which is part of Akimas Fortran code. A bilinear interpolator
  for regular grids was also added for comparison with the bicubic interpolator on
  regular grids.
Implements the alternating k-means biclustering algorithm in Fraiman and Li (2020) <arXiv:2009.04550>.
Advances a novel adaptation of longitudinal k-means clustering 
    technique (Genolini et al. (2015) <doi:10.18637/jss.v065.i04>) 
    for grouping trajectories based on the similarities of their 
    long-term trends and determines the optimal solution based 
    on either the average silhouette width (Rousseeuw P. J. 1987) 
    or the Calinski-Harabatz criterion (Calinski and Harabatz (1974) 
    <doi:10.1080/03610927408827101>). Includes functions to extract 
    descriptive statistics and generate a visualisation of the 
    resulting groups, drawing methods from the 'ggplot2' library (Wickham H. (2016) 
    <doi:10.1007/978-3-319-24277-4>). The package also includes a number of 
    other useful functions for exploring and manipulating longitudinal 
    data prior to the clustering process.
Augmented Lagrangian Adaptive Barrier Minimization
        Algorithm for optimizing smooth nonlinear objective functions
        with constraints. Linear or nonlinear equality and inequality
        constraints are allowed.
Provides methods for high-throughput adaptive immune 
    receptor repertoire sequencing (AIRR-Seq; Rep-Seq) analysis. In 
    particular, immunoglobulin (Ig) sequence lineage reconstruction, 
    lineage topology analysis, diversity profiling, amino acid property 
    analysis and gene usage.
    Citations: 
    Gupta and Vander Heiden, et al (2017) <doi:10.1093/bioinformatics/btv359>,
    Stern, Yaari and Vander Heiden, et al (2014) <doi:10.1126/scitranslmed.3008879>.
Penalized variable selection tools for the Cox 
    proportional hazards model with interval censored and possibly 
    left truncated data. It performs variable selection via 
    penalized nonparametric maximum likelihood estimation with an 
    adaptive lasso penalty. The optimal thresholding parameter can be 
    searched by the package based on the profile Bayesian information 
    criterion (BIC). The asymptotic validity of the methodology is 
    established in Li et al. (2019 <doi:10.1177/0962280219856238>). 
    The unpenalized nonparametric maximum likelihood estimation for 
    interval censored and possibly left truncated data is also 
    available.
Perform parallel factor analysis (PARAFAC: Hitchcock, 1927)
	<doi:10.1002/sapm192761164> on fluorescence excitation-emission
	matrices: handle scattering signal and inner filter effect, scale
	the dataset, fit the model; perform split-half validation or
	jack-knifing. Modified approaches such as Whittaker interpolation,
	randomised split-half, and fluorescence and scattering model
	estimation are also available. The package has a low dependency
	footprint and has been tested on a wide range of R versions.
Implements discrete time deterministic and stochastic age-structured population dynamics models described in Erguler and others (2016) <doi:10.1371/journal.pone.0149282> and Erguler and others (2017) <doi:10.1371/journal.pone.0174293>.
It provides the density, distribution function, quantile function, 
             random number generator, likelihood function, moments and Maximum Likelihood estimators for a given sample, all this for
             the three parameter Asymmetric Laplace Distribution defined 
             in Koenker and Machado (1999). This is a special case of the skewed family of distributions
             available in Galarza et.al. (2017) <doi:10.1002/sta4.140> useful for quantile regression.
EM algorithm for estimation of parameters and other methods in a quantile regression.
The goal of the package 'aldvmm' is to fit adjusted limited 
    dependent variable mixture models of health state utilities. Adjusted 
    limited dependent variable mixture models are finite mixtures of normal 
    distributions with an accumulation of density mass at the limits, and a gap 
    between 100% quality of life and the next smaller utility value. The 
    package 'aldvmm' uses the likelihood and expected value functions proposed 
    by Hernandez Alava and Wailoo (2015) <doi:10.1177/1536867X1501500307> using 
    normal component distributions and a multinomial logit model of 
    probabilities of component membership.
Visualizes the main effects of individual predictor variables and their second-order interaction effects in black-box supervised learning models. The package creates either Accumulated Local Effects (ALE) plots and/or Partial Dependence (PD) plots, given a fitted supervised learning model.
Determination of absolute protein quantities is necessary for multiple applications, such as mechanistic modeling of biological systems. Quantitative liquid chromatography tandem mass spectrometry (LC-MS/MS) proteomics can measure relative protein abundance on a system-wide scale. To estimate absolute quantitative information using these relative abundance measurements requires additional information such as heavy-labeled references of known concentration. Multiple methods have been using different references and strategies; some are easily available whereas others require more effort on the users end. Hence, we believe the field might benefit from making some of these methods available under an automated framework, which also facilitates validation of the chosen strategy. We have implemented the most commonly used absolute label-free protein abundance estimation methods for LC-MS/MS modes quantifying on either MS1-, MS2-levels or spectral counts together with validation algorithms to enable automated data analysis and error estimation. Specifically, we used Monte-carlo cross-validation and bootstrapping for model selection and imputation of proteome-wide absolute protein quantity estimation. Our open-source software is written in the statistical programming language R and validated and demonstrated on a synthetic sample.
Allows you to connect to an 'Alfresco' content management repository and interact
  with its contents using simple and intuitive functions.  You will be able to establish a connection session to the 'Alfresco' repository,
  read and upload content and manage folder hierarchies.  For more details on the 'Alfresco' content management repository
  see <https://www.alfresco.com/ecm-software/document-management>.
Provides direct access to the ALFRED (<https://alfred.stlouisfed.org>) and FRED (<https://fred.stlouisfed.org>) databases.
    Its functions return tidy data frames for different releases of the specified time series. 
    Note that this product uses the FRED© API but is not endorsed or certified by the Federal Reserve Bank of St. Louis.
Functions that facilitate the use of accepted taxonomic nomenclature, collection of
	functional trait data, and assignment of functional group classifications to phytoplankton
	species. Possible classifications include Morpho-functional group (MFG; Salmaso et al. 2015 
	<doi:10.1111/fwb.12520>) and CSR (Reynolds 1988; Functional morphology and the 
	adaptive strategies of phytoplankton. In C.D. Sandgren (ed). Growth and reproductive 
	strategies of freshwater phytoplankton, 388-433. Cambridge University Press, New York). 
	Versions 1.3.0 and later no longer include the algae_search() function for querying the 
	algaebase online taxonomic database (www.algaebase.org). Users are advised to verify 
	taxonomic names directly using algaebase and cite the database in resulting publications.
	Note that none of the algaeClassify authors are affiliated with algaebase in any way.
	The algaeClassify package is a product of the GEISHA (Global Evaluation of the Impacts of 
	Storms on freshwater Habitat and Structure of phytoplankton Assemblages), funded by CESAB 
    (Centre for Synthesis and Analysis of Biodiversity) and the USGS John Wesley Powell Center for
	Synthesis and Analysis, with data and other support provided by members of GLEON 
	(Global Lake Ecology Observation Network). This software is preliminary or provisional and
	is subject to revision. It is being provided to meet the need for timely best science. 
	The software has not received final approval by the U.S. Geological Survey (USGS). 
	No warranty, expressed or implied, is made by the USGS or the U.S. Government as to the 
	functionality of the software and related material nor shall the fact of release constitute 
	any such warranty. The software is provided on the condition that neither the USGS nor the U.S. 
	Government shall be held liable for any damages resulting from the authorized or unauthorized 
	use of the software.
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.
Allows the user to implement an address search auto completion menu on 'shiny' text inputs.
    This is done using the 'Algolia Places' 'JavaScript' library. See <https://community.algolia.com/places/>.
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
    <https://algorithmia.com/developers>.
Queries multiple resources authors HGNC (2019) <https://www.genenames.org>, authors limma (2015) <doi:10.1093/nar/gkv007> 
    to find the correspondence between evolving nomenclature of human gene symbols, aliases, previous symbols or synonyms with 
    stable, curated gene entrezID from NCBI database. This allows fast, accurate and up-to-date correspondence
    between human gene expression datasets from various date and platform (e.g: gene symbol: BRCA1 - ID: 672).
Acknowledge all contributors to a project via a
    single function call. The function appends to a 'README' or other
    specified file(s) a table with names of all individuals who
    contributed via code or repository issues.  The package also includes
    several additional functions to extract and quantify contributions to
    any repository.
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.
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 call the full citation. For users with access to the associated journal article, tutorial material is also available as supplementary material to the article describing this software, the citation for which can be called using citation('allelematch').
Simulate the effect of management or demography on allele retention and inbreeding accumulation in bottlenecked populations of animals with overlapping generations.
Methods (Kindt, R. 2021. <doi:10.7717/peerj.11534>) are provided of calibrating and predicting shifts in allele frequencies through redundancy analysis ('vegan::rda()') and generalized additive models ('mgcv::gam()'). Visualization functions for predicted changes in allele frequencies include 'shift.dot.ggplot()', 'shift.pie.ggplot()', 'shift.moon.ggplot()', 'shift.waffle.ggplot()' and 'shift.surf.ggplot()' that are made with input data sets that are prepared by helper functions for each visualization method. Examples in the documentation show how to prepare animated climate change graphics through a time series with the 'gganimate' package. Function 'amova.rda()' shows how Analysis of Molecular Variance can be directly conducted with the results from redundancy analysis.
Estimates and plots effect estimates from models with all possible 
    combinations of a list of variables. It can be used for assessing treatment 
    effects in clinical trials or risk factors in bio-medical and epidemiological 
    research. Like Stata command 'confall' (Wang Z. Stata Journal 2007; 7, Number 2, pp. 183–196), 
    'allestimates' calculates and stores all effect estimates, and plots them against p values or 
    Akaike information criterion (AIC) values. It currently has functions for linear 
    regression: all_lm(), logistic and Poisson regression: all_glm() and all_speedglm(), 
    and Cox proportional hazards regression: all_cox().
We developed a lightweight machine learning tool for RNA profiling of acute lymphoblastic leukemia (ALL), however, it can be used for any problem where multiple classes need to be identified from multi-dimensional data. The methodology is described in Makinen V-P, Rehn J, Breen J, Yeung D, White DL (2022) Multi-cohort transcriptomic subtyping of B-cell acute lymphoblastic leukemia, medRxiv, <doi:10.1101/2022.02.17.22270919>. The classifier contains optimized mean profiles of the classes (centroids) as observed in the training data, and new samples are matched to these centroids using the shortest Euclidean distance. Centroids derived from a dataset of 1,598 ALL patients are included, but users can train the models with their own data as well. The output includes both numerical and visual presentations of the classification results. Samples with mixed features from multiple classes or atypical values are also identified.
Creating alluvial diagrams (also known as parallel sets plots) for multivariate
  and time series-like data.
Provides tools for defining recurrence rules and
    recurrence bundles. Recurrence rules are a programmatic way to define
    a recurring event, like the first Monday of December. Multiple
    recurrence rules can be combined into larger recurrence bundles.
    Together, these provide a system for adjusting and generating
    sequences of dates while simultaneously skipping over dates in a
    recurrence bundle's event set.
A collection of tools that support data splitting, predictive modeling, and model evaluation. 
    A typical function is to split a dataset into a training dataset and a test dataset. 
    Then compare the data distribution of the two datasets.
    Another feature is to support the development of predictive models and to compare the performance of several predictive models, 
    helping to select the best model.
Provides a routine to partial out factors with many levels during the
  optimization of the log-likelihood function of the corresponding generalized linear model (glm).
  The package is based on the algorithm described in Stammann (2018) <arXiv:1707.01815> and is
  restricted to glm's that are based on maximum likelihood estimation and non-linear. It also offers
  an efficient algorithm to recover estimates of the fixed effects in a post-estimation routine and 
  includes robust and multi-way clustered standard errors. Further the package provides analytical 
  bias corrections for binary choice models (logit and probit) derived by Fernandez-Val 
  and Weidner (2016) <doi:10.1016/j.jeconom.2015.12.014> and Hinz, Stammann, and Wanner (2020) 
  <arXiv:2004.12655>.
Provides algorithms for frequency-based pairing of alpha-beta T
  cell receptors.
Computation of the alpha-shape and alpha-convex
        hull of a given sample of points in the plane. The concepts of
        alpha-shape and alpha-convex hull generalize the definition of
        the convex hull of a finite set of points. The programming is
        based on the duality between the Voronoi diagram and Delaunay
        triangulation. The package also includes a function that
        returns the Delaunay mesh of a given sample of points and its
        dual Voronoi diagram in one single object.
Given the parameters of a distribution, the package uses the concept of alpha-outliers by Davies and Gather (1993) to flag outliers in a data set. See Davies, L.; Gather, U. (1993): The identification of multiple outliers, JASA, 88 423, 782-792, <doi:10.1080/01621459.1993.10476339> for details.
A software that implements a method for partitioning genetic trends to
    quantify the sources of genetic gain in breeding programmes.
    The partitioning method is described in Garcia-Cortes et al.
    (2008) <doi:10.1017/S175173110800205X>. The package includes the
    main function AlphaPart for partitioning breeding values and auxiliary
    functions for manipulating data and summarizing, visualizing, and saving
    results.
Implementation in R of the alpha-shape of a finite set of points in the three-dimensional space. The alpha-shape generalizes the convex hull and allows to recover the shape of non-convex and even non-connected sets in 3D, given a random sample of points taken into it. Besides the computation of the alpha-shape, this package provides users with functions to compute the volume of the alpha-shape, identify the connected components and facilitate the three-dimensional graphical visualization of the estimated set.
The successor to the 'AlphaSim' software for breeding program 
  simulation [Faux et al. (2016) <doi:10.3835/plantgenome2016.02.0013>]. 
  Used for stochastic simulations of breeding programs to the level of DNA 
  sequence for every individual. Contained is a wide range of functions for 
  modeling common tasks in a breeding program, such as selection and crossing. 
  These functions allow for constructing simulations of highly complex plant and 
  animal breeding programs via scripting in the R software environment. Such 
  simulations can be used to evaluate overall breeding program performance and 
  conduct research into breeding program design, such as implementation of 
  genomic selection. Included is the 'Markovian Coalescent Simulator' ('MaCS') 
  for fast simulation of biallelic sequences according to a population 
  demographic history [Chen et al. (2009) <doi:10.1101/gr.083634.108>].
Developed to perform the tasks given by the following. 1-computing the probability density function and distribution function of a univariate stable distribution; 2- generating from univariate stable, truncated stable, multivariate elliptically contoured stable, and bivariate strictly stable distributions; 3- estimating the parameters of univariate symmetric stable, skew stable, Cauchy, multivariate elliptically contoured stable, and multivariate strictly stable distributions; 4- estimating
             the parameters of the mixture of symmetric stable and mixture of Cauchy distributions.
Alpha Vantage has free historical financial information. 
    All you need to do is get a free API key at <https://www.alphavantage.co>.
    Then you can use the R interface to retrieve free equity information.
    Refer to the Alpha Vantage website for more information.
Datasets to Accompany S. Weisberg (2014, ISBN: 978-1-118-38608-8), 
 "Applied Linear Regression," 4th edition.  Many data files 
 in this package are included in the `alr3` package as well, so only one of them 
 should be used.
Using of the accelerated line search algorithm  for simultaneously diagonalize a set of symmetric positive definite matrices.
Functions and Data set presented in Applied Linear Statistical Models Fifth Edition (Chapters 1-9 and 16-25), Michael H. Kutner; Christopher J. Nachtsheim; John Neter; William Li, 2005. (ISBN-10: 0071122214, ISBN-13: 978-0071122214) that do not exist in R, are gathered in this package. The whole book will be covered in the next versions.
Functions for interacting directly with the 'ALTADATA' API. With this R package, developers can build applications around the 'ALTADATA' API without having to deal with accessing and managing requests and responses. 'ALTADATA' is a curated data marketplace for more information go to <https://www.altadata.io>.
Interface to 'Altair' <https://altair-viz.github.io>, which itself 
  is a 'Python' interface to 'Vega-Lite' <https://vega.github.io/vega-lite/>.
  This package uses the 'Reticulate' framework 
  <https://rstudio.github.io/reticulate/> to manage the interface between R
  and 'Python'.
Provides a number of functions to access the 
    National Energy Research Laboratory Alternate Fuel Locator API 
    <https://developer.nrel.gov/docs/transportation/alt-fuel-stations-v1/>. 
    The Alternate Fuel Locator shows the location of alternate fuel stations in the United States 
    and Canada. This package also includes the data from the US Department of Energy Alternate Fuel
    database as a data set.
Provides alternative statistical methods for meta-analysis, including:
 - bivariate generalized linear mixed models for synthesizing odds ratios, relative risks,
   and risk differences
   (Chu et al., 2012 <doi:10.1177/0962280210393712>)
 - heterogeneity tests and measures that are robust to outliers
   (Lin et al., 2017 <doi:10.1111/biom.12543>);
 - measures, tests, and visualization tools for publication bias or small-study effects
   (Lin and Chu, 2018 <doi:10.1111/biom.12817>;
    Lin, 2019 <doi:10.1002/jrsm.1340>;
    Lin, 2020 <doi:10.1177/0962280220910172>;
    Shi et al., 2020 <doi:10.1002/jrsm.1415>);
 - meta-analysis of diagnostic tests for synthesizing sensitivities, specificities, etc.
   (Reitsma et al., 2005 <doi:10.1016/j.jclinepi.2005.02.022>;
    Chu and Cole, 2006 <doi:10.1016/j.jclinepi.2006.06.011>);
 - meta-analysis methods for synthesizing proportions
   (Lin and Chu, 2020 <doi:10.1097/ede.0000000000001232>);
 - models for multivariate meta-analysis, measures of inconsistency degrees of freedom
   in Bayesian network meta-analysis, and predictive P-score
   (Lin and Chu, 2018 <doi:10.1002/jrsm.1293>;
    Lin, 2020 <doi:10.1080/10543406.2020.1852247>;
    Rosenberger et al., 2021 <doi:10.1186/s12874-021-01397-5>).
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. For more details, see Seo and Pan (2015) <doi:10.32614/RJ-2015-029>.
Provides alternatives to the normal adjusted R-squared estimator for the estimation of the multiple squared correlation in regression models, 
              as fitted by the lm() function. The alternative estimators are described in Karch (2016) <DOI:10.31234/osf.io/v8dz5>.
Evaluates land suitability for different crops production. 
  The package is based on the Food and Agriculture Organization (FAO) and the 
  International Rice Research Institute (IRRI) methodology for land evaluation. 
  Development of ALUES is inspired by similar tool for land evaluation, Land Use 
  Suitability Evaluation Tool (LUSET). The package uses fuzzy logic approach to evaluate 
  land suitability of a particular area based on inputs such as rainfall, temperature, 
  topography, and soil properties. The membership functions used for fuzzy modeling are 
  the following: Triangular, Trapezoidal and Gaussian. The methods for computing the 
  overall suitability of a particular area are also included, and these are the Minimum, 
  Maximum and Average. Finally, ALUES is a highly optimized library with core algorithms 
  written in C++.
Combination of results for meta-analysis using significance
    and effect size only. P-values and fold-change are combined to obtain
    a global significance on each metabolite. Produces a volcano plot
    summarising the relevant results from meta-analysis. Vote-counting
    reports for metabolites. And explore plot to detect discrepancies
    between studies at a first glance. Methodology is described in the
    Llambrich et al. (2021) <doi:10.1093/bioinformatics/btab591>.
Alternating Manifold Proximal Gradient Method for Sparse PCA uses the Alternating Manifold Proximal 
    Gradient (AManPG) method to find sparse principal components from a data or covariance matrix. Provides
    a novel algorithm for solving the sparse principal component analysis problem which provides
    advantages over existing methods in terms of efficiency and convergence guarantees.
    Chen, S., Ma, S., Xue, L., & Zou, H. (2020) <doi:10.1287/ijoo.2019.0032>.
    Zou, H., Hastie, T., & Tibshirani, R. (2006) <doi:10.1198/106186006X113430>.
    Zou, H., & Xue, L. (2018) <doi:10.1109/JPROC.2018.2846588>.
Tools for Clustering and Principal Component Analysis
        (With robust methods, and parallelized functions).
Getting and parsing data of location geocode/reverse-geocode and administrative regions from 'AutoNavi Maps'<https://lbs.amap.com/api/webservice/summary> API.
Read, manipulate and write voxel spaces. Voxel spaces are
    read from text-based output files of the 'AMAPVox' software. 'AMAPVox'
    is a LiDAR point cloud voxelisation software that aims at estimating
    leaf area through several theoretical/numerical approaches. See more
    in the article Vincent et al. (2017) <doi:10.23708/1AJNMP> and the
    technical note Vincent et al. (2021) <doi:10.23708/1AJNMP>.
Implements distributed transient groundwater modelling. The model
    is based on the groundwater flow equation solved numerically using the 
    finite difference explicit scheme.
Generation of natural looking noise has many application within 
    simulation, procedural generation, and art, to name a few. The 'ambient' 
    package provides an interface to the 'FastNoise' C++ library and allows for
    efficient generation of perlin, simplex, worley, cubic, value, and white 
    noise with optional pertubation in either 2, 3, or 4 (in case of simplex and
    white noise) dimensions.
A web framework inspired by 'express.js' to build
  any web service from multi-page websites to 'RESTful' 
  application programming interfaces.
Accompanies "Designing experiments and 
    analyzing data: A model comparison perspective" (3rd ed.) by 
    Maxwell, Delaney, & Kelley (2018; Routledge). 
    Contains all of the data sets in the book's chapters and 
    end-of-chapter exercises. Information about the book is available at 
    <http://www.DesigningExperiments.com>.
Generate code for use with the Optical Mark Recognition free software Auto Multiple Choice (AMC). More specifically, this package provides functions that use as input the question and answer texts, and output the LaTeX code for AMC.
A tool that "multiply imputes" missing data in a single cross-section
  (such as a survey), from a time series (like variables collected for
  each year in a country), or from a time-series-cross-sectional data
  set (such as collected by years for each of several countries).
  Amelia II implements our bootstrapping-based algorithm that gives
  essentially the same answers as the standard IP or EMis approaches,
  is usually considerably faster than existing approaches and can
  handle many more variables.  Unlike Amelia I and other statistically
  rigorous imputation software, it virtually never crashes (but please
  let us know if you find to the contrary!).  The program also
  generalizes existing approaches by allowing for trends in time series
  across observations within a cross-sectional unit, as well as priors
  that allow experts to incorporate beliefs they have about the values
  of missing cells in their data.  Amelia II also includes useful
  diagnostics of the fit of multiple imputation models.  The program
  works from the R command line or via a graphical user interface that
  does not require users to know R.
Implements anomaly detection as binary classification for cross-sectional data. Uses maximum likelihood estimates and normal probability functions to classify observations as anomalous. The method is presented in the following lecture from the Machine Learning course by Andrew Ng: <https://www.coursera.org/learn/machine-learning/lecture/C8IJp/algorithm/>, and is also described in: Aleksandar Lazarevic, Levent Ertoz, Vipin Kumar, Aysel Ozgur, Jaideep Srivastava (2003) <doi:10.1137/1.9781611972733.3>.
Analysis of dyadic network and relational data using additive and
    multiplicative effects (AME) models. The basic model includes
    regression terms, the covariance structure of the social relations model
    (Warner, Kenny and Stoto (1979) <DOI:10.1037/0022-3514.37.10.1742>, 
    Wong (1982) <DOI:10.2307/2287296>), and multiplicative factor
    models (Hoff(2009) <DOI:10.1007/s10588-008-9040-4>). 
    Several different link functions accommodate different
    relational data structures, including binary/network data, normal
    relational data, zero-inflated positive outcomes using a tobit model, ordinal relational data and data from
    fixed-rank nomination schemes. Several of these link functions are
    discussed in Hoff, Fosdick, Volfovsky and Stovel (2013) 
    <DOI:10.1017/nws.2013.17>. Development of this
    software was supported in part by NIH grant R01HD067509.
This package includes a set of pricing functions for
        American call options. The following cases are covered: Pricing
        of an American call using the standard binomial approximation;
        Hedge parameters for an American call with a standard binomial
        tree; Binomial pricing of an American call with continuous
        payout from the underlying asset; Binomial pricing of an
        American call with an underlying stock that pays proportional
        dividends in discrete time; Pricing of an American call on
        futures using a binomial approximation; Pricing of a currency
        futures American call using a binomial approximation; Pricing
        of a perpetual American call. The user should kindly notice
        that this material is for educational purposes only. The codes
        are not optimized for computational efficiency as they are
        meant to represent standard cases of analytical and numerical
        solution.
Programmatic interface to the 'AmeriFlux' database
    (<https://ameriflux.lbl.gov/>). Provide query, download,
    and data summary tools.
A color palette generator inspired by American politics, with colors ranging from blue on the 
    left to gray in the middle and red on the right. A variety of palettes allow for a range of applications 
    from brief discrete scales (e.g., three colors for Democrats, Independents, and Republicans) to 
    continuous interpolated arrays including dozens of shades graded from blue (left) to red (right). This
    package greatly benefitted from building on the source code (with permission) from Ram and Wickham (2015).
Raw and processed versions of the data from De Cock (2011) <http://ww2.amstat.org/publications/jse> are included in the package.
AMGET allows to simply and rapidly creates highly informative diagnostic plots for ADAPT 5 models. Features include data analysis prior any modeling form either NONMEM or ADAPT shaped dataset, goodness-of-fit plots (GOF), posthoc-fits plots (PHF), parameters distribution plots (PRM) and visual predictive check plots (VPC) based on ADAPT output.
Modern software often poorly support older file formats. This
    package intends to handle many file formats that were native to the
    antiquated Commodore Amiga machine. This package focuses on file types from
    the older Amiga operating systems (<= 3.0). It will read and write specific
    file formats and coerces them into more contemporary data.
This package implements the adaptive mixed lasso (AML) method proposed by Wang et al.(2011).  AML applies adaptive lasso penalty to a large number of predictors,  thus producing a sparse model, while accounting for the population structure in the linear mixed model framework.  The package here is primarily designed for application to genome wide association studies or genomic prediction in plant breeding populations, though it could be applied to other settings of linear mixed models.
Flexible multi-environment trials analysis via MCMC method for Additive Main Effects and Multiplicative Model (AMMI) for continuous data. 
 Biplot with the averages and regions of confidence can be generated. The chains run in parallel on Linux systems and run serially on Windows.
Helps enable adaptive management by codifying knowledge in the
    form of models generated from numerous analyses and data sets. Facilitates
    this process by storing all models and data sets in a single object that can
    be updated and saved, thus tracking changes in knowledge through time. A shiny
    application called AM Model Manager (modelMgr()) enables the use of these
    functions via a GUI.
Provides a function to calculate the concentration of un-ionized ammonia in the total ammonia in aqueous solution using the pH and temperature values.
Automated generation, running, and interpretation of moderated nonlinear factor analysis models for obtaining scores from observed variables, using the method described by Gottfredson and colleagues (2019) <doi:10.1016/j.addbeh.2018.10.031>. This package creates M-plus input files which may be run iteratively to test two different types of covariate effects on items: (1) latent variable impact (both mean and variance); and (2) differential  item functioning. After sequentially testing for all effects, it also creates a final model by including all significant effects after adjusting for multiple comparisons. Finally, the package creates a scoring model which uses the final values of parameter estimates to generate latent variable scores. \n\n This package generates TEMPLATES for M-plus inputs, which can and should be inspected, altered, and run by the user. In addition to being presented without warranty of any kind, the package is provided under the assumption that everyone who uses it is reading, interpreting, understanding, and altering every M-plus input and output file. There is no one right way to implement moderated nonlinear factor analysis, and this package exists solely to save users time as they generate M-plus syntax according to their own judgment.
A testing framework for testing the multivariate point null hypothesis. 
    A testing framework described in Elder et al. (2022) <arXiv:2203.01897> to test the multivariate point null hypothesis.  After the user selects a parameter of interest and defines the assumed data generating mechanism, this information should be encoded in functions for the parameter estimator and its corresponding influence curve. Some parameter and data generating mechanism combinations have codings in this package, and are explained in detail in the article.
A method for automatic detection of peaks in noisy periodic and quasi-periodic signals. This method, called automatic multiscale-based peak detection (AMPD), is based on the calculation and analysis of the local maxima scalogram, a matrix comprising the scale-dependent occurrences of local maxima.
     For further information see  <doi:10.3390/a5040588>.
Predicts antimicrobial peptides 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. The AmpGram model is too large 
    for CRAN and it has to be downloaded separately from the repository:
    <https://github.com/michbur/AmpGramModel>.
A toolkit to predict antimicrobial peptides from protein sequences on a genome-wide scale.
    It incorporates two support vector machine models ("precursor" and "mature") trained on publicly available antimicrobial peptide data using calculated
    physico-chemical and compositional sequence properties described in Meher et al. (2017) <doi:10.1038/srep42362>.
    In order to support genome-wide analyses, these models are designed to accept any type of protein as input
    and calculation of compositional properties has been optimised for high-throughput use. For best results it is important to select the model that accurately 
    represents your sequence type: for full length proteins, it is recommended to use the default "precursor" model. The alternative, "mature", model is best suited
    for mature peptide sequences that represent the final antimicrobial peptide sequence after post-translational processing. For details see Fingerhut et al. (2020) <doi:10.1093/bioinformatics/btaa653>.
    The 'ampir' package is also available via a Shiny based GUI at <https://ampir.marine-omics.net/>.
Three Shiny apps are provided that introduce Harvest Control Rules (HCR) for fisheries management.
    'Introduction to HCRs' provides a simple overview to how HCRs work. Users are able to select their own HCR and
    step through its performance, year by year. Biological variability and estimation uncertainty are introduced.
    'Measuring performance' builds on the previous app and introduces the idea of using performance indicators
    to measure HCR performance.
    'Comparing performance' allows multiple HCRs to be created and tested, and their performance compared so that the
    preferred HCR can be selected.
Increasingly powerful techniques for high-throughput sequencing open the possibility to comprehensively characterize microbial communities, including rare species. However, a still unresolved issue are the substantial error rates in the experimental process generating these sequences. To overcome these limitations we propose an approach, where each sample is split and the same amplification and sequencing protocol is applied to both halves. This procedure should allow to detect likely PCR and sequencing artifacts, and true rare species by comparison of the results of both parts. The AmpliconDuo package, whereas amplicon duo from here on refers to the two amplicon data sets of a split sample, is intended to help interpret the obtained read frequency distribution across split samples, and to filter the false positive reads.
Functions to simplify and standardise antimicrobial resistance (AMR)
    data analysis and to work with microbial and antimicrobial properties by
    using evidence-based methods and reliable reference data such as LPSN
    <doi:10.1099/ijsem.0.004332>.
Manage and analyze animal movement data. The functionality of 'amt' includes methods to calculate home ranges, track statistics (e.g. step lengths, speed, or turning angles), prepare data for fitting habitat selection analyses, and simulation of space-use from fitted step-selection functions.
Predicts amyloid proteins using random forests trained on the
    n-gram encoded peptides. The implemented algorithm can be accessed from
    both the command line and shiny-based GUI.
Is a collection of models to analyze genome scale codon
        data using a Bayesian framework. Provides visualization
        routines and checkpointing for model fittings. Currently
        published models to analyze gene data for selection on codon
        usage based on Ribosome Overhead Cost (ROC) are: ROC (Gilchrist
        et al. (2015) <doi:10.1093/gbe/evv087>), and ROC with phi
        (Wallace & Drummond (2013) <doi:10.1093/molbev/mst051>). In
        addition 'AnaCoDa' contains three currently unpublished models.
        The FONSE (First order approximation On NonSense Error) model
        analyzes gene data for selection on codon usage against of
        nonsense error rates. The PA (PAusing time) and PANSE (PAusing
        time + NonSense Error) models use ribosome footprinting data to
        analyze estimate ribosome pausing times with and without
        nonsense error rate from ribosome footprinting data.
Performs simple and canonical CA (covariates on rows/columns) on a two-way frequency table (with missings) by means of SVD. Different scaling methods (standard, centroid, Benzecri, Goodman) as well as various plots including confidence ellipsoids are provided.
Provides a set of functions for interacting with the 'Digital
    Ocean' API <https://www.digitalocean.com/>, 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.
Enables data scientists to compose pipelines of analysis which consist of data manipulation, exploratory analysis & reporting, as well as modeling steps. Data scientists can use tools of their choice through an R interface, and compose interoperable pipelines between R, Spark, and Python.
    Credits to Mu Sigma for supporting the development of the package.
    Note - To enable pipelines involving Spark tasks, the package uses the 'SparkR' package. 
    The SparkR package needs to be installed to use Spark as an engine within a pipeline. SparkR is distributed natively with Apache Spark and is not distributed on CRAN. The SparkR version needs to directly map to the Spark version (hence the native distribution), and care needs to be taken to ensure that this is configured properly.
    To install SparkR from Github, run the following command if you know the Spark version: 'devtools::install_github('apache/spark@v2.x.x', subdir='R/pkg')'.
    The other option is to install SparkR by running the following terminal commands if Spark has already been installed: '$ export SPARK_HOME=/path/to/spark/directory && cd $SPARK_HOME/R/lib/SparkR/ && R -e "devtools::install('.')"'.
Class with methods to read and execute R commands described as steps in a CSV file.
Easy data analysis and quality checks which are commonly used in data science. It combines the tabular and graphical visualization for easier usability. This package also creates an R Notebook with detailed data exploration with one function call. The notebook can be made interactive.
Functions for normalisation, differentially analysis of microarray data and local False Discovery Rate.
Assigns genetic ancestry to an individual and
    studies relationships between local and global populations.
Performs AnchorRegression proposed by Rothenhäusler et al. 2020. 
    The code is adapted from the original paper repository. (<https://github.com/rothenhaeusler/anchor-regression>)
    The code was developed independently from the authors of the paper.
Andrews curves for visualization of multidimensional data
Storing very large data objects on a local drive, while still making it possible to manipulate the data in an efficient manner.
The goal of 'andurinha' is provide a fast and friendly way to 
    process spectroscopic data. It is intended for processing several spectra of 
    samples with similar composition (tens to hundreds of spectra). It compiles 
    spectroscopy data files, produces standardized and second derivative spectra, 
    finds peaks and allows to select the most significant ones based on the second 
    derivative/absorbance sum spectrum. It also provides functions for graphic 
    evaluation of the outputs.
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.'
Simulate an angler population, sample the simulated population with a user-specified survey times, and calculate metrics from a bus route-type creel survey.
Helper functions for working with Regional Ocean Modeling System 'ROMS' output. See
    <https://www.myroms.org/> for more information about 'ROMS'.
Provides: (1) Tools to infer dominance hierarchies based on calculating Elo scores, but with custom functions to improve estimates in animals with relatively stable dominance ranks. (2) Tools to plot the shape of the dominance hierarchy and estimate the uncertainty of a given data set.
Simple animated versions of basic R plots, using the 'animation'
    package. Includes animated versions of plot, barplot, persp, contour,
    filled.contour, hist, curve, points, lines, text, symbols, segments, and
    arrows.
Synthetic generation of 1-D and 2-D correlated random walks (CRWs) for animal movement with behavioral switching, and particle filter estimation of movement parameters from observed trajectories using Extended Kalman Filter (EKF) model. See Ackerman (2018) <https://digital.library.temple.edu/digital/collection/p245801coll10/id/499150>.
Functions for generating and visualising networks for characterising the physical attributes and spatial organisations of habitat components (i.e. habitat physical configurations). The network generating algorithm first determines the X and Y coordinates of N nodes within a rectangle with a side length of L and an area of A. Then it computes the pair-wise Euclidean distance Dij between node i and j, and then a complete network with 1/Dij as link weights is constructed. Then, the algorithm removes links from the complete network with the probability as shown in the function ahn_prob(). Such link removals can make the network disconnected whereas a connected network is wanted. In such cases, the algorithm rewires one network component to its spatially nearest neighbouring component and repeat doing this until the network is connected again. Finally, it outputs an undirected network (weighted or unweighted, connected or disconnected). This package came with a manuscript on modelling the physical configurations of animal habitats using networks (in preparation).
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.
Utilities for spatial-temporal analysis and visualization of animal (e.g. cattle) tracking data. The core feature is a 'shiny' web application for customized processing of GPS logs, including features for data augmentation (e.g. elevation lookup), data selection, export, plotting, and statistical summaries. A data validation application allows for side-by-side comparison via time series plots and extreme value detection described by J.P. van Brakel <https://stackoverflow.com/questions/22583391/peak-signal-detection-in-realtime-timeseries-data/>.
Provides functions for animations in statistics, covering topics
    in probability theory, mathematical statistics, multivariate statistics,
    non-parametric statistics, sampling survey, linear models, time series,
    computational statistics, data mining and machine learning. These functions
    may be helpful in teaching statistics and data analysis. Also provided in this
    package are a series of functions to save animations to various formats, e.g.
    Flash, 'GIF', HTML pages, 'PDF' and videos. 'PDF' animations can be inserted
    into 'Sweave' / 'knitr' easily.
Functions are provided for defining animated,
 interactive data visualizations in R code, and rendering
 on a web page. The 2018 Journal of Computational and 
 Graphical Statistics paper,
 <doi:10.1080/10618600.2018.1513367>
 describes the concepts implemented.
Functions required to classify subjects within camera trap field data. The package can handle both images and videos. The authors recommend a two-step approach using Microsoft's 'MegaDector' model and then a second model trained on the classes of interest.
Animation of observed trajectories using spline-based interpolation (see for example, Buderman, F. E., Hooten, M. B., Ivan, J. S. and Shenk, T. M. (2016), <doi:10.1111/2041-210X.12465> "A functional model for characterizing long-distance movement behaviour". Methods Ecol Evol). Intended to be used exploratory data analysis, and perhaps for preparation of presentations.
Animate Shiny and R Markdown content when it comes into view using 'animate-css' effects thanks to 'jQuery AniView'.
Computationally efficient method to estimate orthant probabilities of high-dimensional Gaussian vectors. Further implements a function to compute conservative estimates of excursion sets under Gaussian random field priors.
Training of neural networks for classification and regression tasks
    using mini-batch gradient descent. Special features include a function for 
    training autoencoders, which can be used to detect anomalies, and some 
    related plotting functions. Multiple activation functions are supported, 
    including tanh, relu, step and ramp. For the use of the step and ramp 
    activation functions in detecting anomalies using autoencoders, see 
    Hawkins et al. (2002) <doi:10.1007/3-540-46145-0_17>. Furthermore, 
    several loss functions are supported, including robust ones such as Huber 
    and pseudo-Huber loss, as well as L1 and L2 regularization. The possible 
    options for optimization algorithms are RMSprop, Adam and SGD with momentum.
    The package contains a vectorized C++ implementation that facilitates 
    fast training through mini-batch learning.
A 'reticulate' wrapper for the Python package 'anndata'.
    Provides a scalable way of keeping track of data and learned
    annotations.  Used to read from and write to the h5ad file format.
We curated 147 of expression array, from 3 species(human,mouse,rat), 
              3 companies('Affymetrix','Illumina','Agilent'), 
              by aligning the 'Fasta' sequences of all probes of each platform to their corresponding reference genome, 
              and then annotate them to genes.
Extraction of subsequences into FASTA files from GenBank annotations where gene names may vary among accessions. Borstein & O'Meara (2018) <doi:10.7717/peerj.5179>.
Annuity Random Interest Rates proposes different techniques for the approximation of the present and final value of a unitary annuity-due or annuity-immediate considering interest rate as a random variable. Cruz Rambaud et al. (2017) <doi:10.1007/978-3-319-54819-7_16>. Cruz Rambaud et al. (2015) <doi:10.23755/rm.v28i1.25>.
Provides ANOCVA (ANalysis Of Cluster VAriability), a non-parametric statistical test
    to compare clustering structures with applications in functional magnetic resonance imaging
    data (fMRI). The ANOCVA allows us to compare the clustering structure of multiple groups
    simultaneously and also to identify features that contribute to the differential clustering.
Analysis of means (ANOM) as used in technometrical computing. The package takes results from multiple comparisons with the grand mean (obtained with 'multcomp', 'SimComp', 'nparcomp', or 'MCPAN') or corresponding simultaneous confidence intervals as input and produces ANOM decision charts that illustrate which group means deviate significantly from the grand mean.
The 'anomalize' package enables a "tidy" workflow for detecting anomalies in data.
    The main functions are time_decompose(), anomalize(), and time_recompose().
    When combined, it's quite simple to decompose time series, detect anomalies,
    and create bands separating the "normal" data from the anomalous data at scale (i.e. for multiple time series). 
    Time series decomposition is used to remove trend and seasonal components via the time_decompose() function
    and methods include seasonal decomposition of time series by Loess ("stl") and 
    seasonal decomposition by piecewise medians ("twitter"). The anomalize() function implements
    two methods for anomaly detection of residuals including using an inner quartile range ("iqr")
    and generalized extreme studentized deviation ("gesd"). These methods are based on
    those used in the 'forecast' package and the Twitter 'AnomalyDetection' package. 
    Refer to the associated functions for specific references for these methods.
Implements Collective And Point Anomaly (CAPA) <arXiv:1806.01947>, Multi-Variate Collective And Point Anomaly (MVCAPA) <arXiv:1909.01691>, Proportion Adaptive Segment Selection (PASS) <doi:10.1093/biomet/ass059>, and Bayesian Abnormal Region Detector (BARD) <arXiv:1412.5565> methods for the detection of anomalies in time series data. Also includes sequential versions of CAPA and MVCAPA <arXiv:2009.06670>.
An interactive document on  the topic of one-way and two-way analysis of variance using 'rmarkdown' and 'shiny' packages. Runtime examples are provided in the package function as well as at <https://tinyurl.com/ANOVAStatsTool>.
Allows for the computation of a prior predictive p-value to test replication of relevant features of original ANOVA studies. Relevant features are captured in informative hypotheses. The package also allows for the computation of sample sizes for new studies, post-hoc power calculations, and comes with a Shiny application in which all calculations can be conducted as well. The statistical underpinnings are described in Zondervan-Zwijnenburg (2019) <doi:10.31234/osf.io/6myqh>.
An interactive document on  the topic of one-way and two-way analysis of variance using 'rmarkdown' and 'shiny' packages. Runtime examples are provided in the package function as well as at  <https://kartikeyab.shinyapps.io/ANOVAShiny/>.
Epidemiological population dynamics models traditionally define 
    a pathogen's virulence as the increase in the per capita rate of mortality 
    of infected hosts due to infection. This package provides functions 
    allowing virulence to be estimated by maximum likelihood techniques. The 
    approach is based on the analysis of relative survival comparing survival 
    in matching cohorts of infected vs. uninfected hosts (Agnew 2019) 
    <doi:10.1101/530709>.
Anscombe's quartet are a set of four two-variable datasets that 
    have several common summary statistics but which have very different joint 
    distributions.  This becomes apparent when the data are plotted, which 
    illustrates the importance of using graphical displays in Statistics.  This
    package enables the creation of datasets that have identical marginal sample
    means and sample variances, sample correlation, least squares regression 
    coefficients and coefficient of determination.  The user supplies an initial 
    dataset, which is shifted, scaled and rotated in order to achieve target 
    summary statistics.  The general shape of the initial dataset is retained. 
    The target statistics can be supplied directly or calculated based on a 
    user-supplied dataset.  The 'datasauRus' package 
    <https://cran.r-project.org/package=datasauRus> provides further examples 
    of datasets that have markedly different scatter plots but share many 
    sample summary statistics.
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.
Edit an 'Antares' simulation before running it : create new areas, links, thermal
    clusters or binding constraints or edit existing ones. Update 'Antares' general & optimization settings.
    'Antares' is an open source power system generator, more information available here : <https://antares-simulator.org/>.
Process results generated by 'Antares', a powerful open source software developed by
    RTE (Réseau de Transport d’Électricité) to simulate and study electric power systems (more information about
    'Antares' here: <https://github.com/AntaresSimulatorTeam/Antares_Simulator>).
    This package provides functions to create new columns like net load, load factors, upward and
    downward margins or to compute aggregated statistics like economic surpluses
    of consumers, producers and sectors.
Import, manipulate and explore results generated by 'Antares', a 
    powerful open source software developed by RTE (Réseau de Transport d’Électricité) to simulate and study electric power systems
    (more information about 'Antares' here : <https://antares-simulator.org/>).
Visualize results generated by Antares, a powerful open source software
    developed by RTE to simulate and study electric power systems
    (more information about 'Antares' here: <https://github.com/AntaresSimulatorTeam/Antares_Simulator>).
    This package provides functions that create interactive charts to help
    'Antares' users visually explore the results of their simulations.
Provides WHO Child Growth Standards (z-scores) with
             confidence intervals and standard errors around the
             prevalence estimates, taking into account complex sample designs.
             More information on the methods is
             available online:
             <https://www.who.int/tools/child-growth-standards>.
Provides WHO 2007 References for School-age Children and
             Adolescents (5 to 19 years) (z-scores) with
             confidence intervals and standard errors around the
             prevalence estimates, taking into account complex sample designs.
             More information on the methods is
             available online:
             <https://www.who.int/tools/growth-reference-data-for-5to19-years>.
Offers a graphical user interface for the calculation of the mean measure of divergence, with facilities for trait selection and graphical representations <doi:10.1002/ajpa.23336>.
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. Please see Vinue (2017) <doi:10.18637/jss.v077.i06>.
Visualization of antibody titer scores is valuable for examination of vaccination 
    effects. 'AntibodyTiters' visualizes antibody titers of all or selected patients. This 
    package also produces empty excel files in a specified format, in which users can fill in 
    experimental data for visualization. Excel files with toy data can also be produced, so that 
    users can see how it is visualized before obtaining real data. The data should contain 
    titer scores at pre-vaccination, after-1st shot, after-2nd shot, and at least one 
    additional sampling points. Patients with missing values can be included. The first two 
    sampling points (pre-vaccination and after-1st shot) will be plotted discretely, whereas 
    those following will be plotted on a continuous time scale that starts from the day of 
    second shot. Half-life of titer can also be calculated for each pair of sampling points.
The method of anticlustering partitions a pool of elements
    into groups (i.e., anticlusters) with the goal of maximizing
    between-group similarity or within-group heterogeneity. 
    The anticlustering approach thereby reverses the logic
    of cluster analysis that strives for high within-group homogeneity and
    low similarity of the different groups. Computationally,
    anticlustering is accomplished by maximizing instead of minimizing a
    clustering objective function, such as the intra-cluster variance
    (used in k-means clustering) or the sum of pairwise distances within
    clusters.  The function anticlustering() implements exact and
    heuristic anticlustering algorithms as described in Papenberg and Klau
    (2021; <doi:10.1037/met0000301>). The exact algorithms require that
    the GNU linear programming kit
    (<https://www.gnu.org/software/glpk/glpk.html>) is available and the R
    package 'Rglpk' (<https://cran.R-project.org/package=Rglpk>) is
    installed. A bicriterion anticlustering method proposed by Brusco et al. 
    (2020; <doi:10.1111/bmsp.12186>) is available through the function
    bicriterion_anticlustering(). Some other functions are available to
    solve classical clustering problems. The function
    balanced_clustering() applies a cluster analysis under size
    constraints, i.e., creates equal-sized clusters. The function
    matching() can be used for (unrestricted, bipartite, or K-partite)
    matching. The function wce() can be used optimally solve the
    (weighted) cluster editing problem, also known as correlation
    clustering, clique partitioning problem or transitivity clustering.
A collection of tools for antitrust practitioners, including the ability to calibrate different consumer demand systems and simulate the effects of mergers under different competitive regimes.
Wraps the 'AntiWord' utility to extract text from Microsoft
    Word documents. The utility only supports the old 'doc' format, not the 
    new xml based 'docx' format. Use the 'xml2' package to read the latter.
Fits finite Bayesian mixture models with a random number of components. The MCMC algorithm implemented is based on point processes as proposed by Argiento and De Iorio (2019) <arXiv:1904.09733> and offers a more computationally efficient alternative to reversible jump. Different mixture kernels can be specified: univariate Gaussian, multivariate Gaussian, univariate Poisson, and multivariate Bernoulli (latent class analysis). For the parameters characterising the mixture kernel, we specify conjugate priors, with possibly user specified hyper-parameters. We allow for different choices for the prior on the number of components: shifted Poisson, negative binomial, and point masses (i.e. mixtures with fixed number of components).
You can use this package to create custom pipeline badges in a standard 'svg' format.
    This is useful for a company to use internally, where it may not be possible
    to create badges through external providers.
    This project was inspired by the 'anybadge' library in python.
Supplies a set of functions to query air travel data for user-
    specified years and airports. Datasets include on-time flights, airlines,
    airports, planes, and weather.
Made to make your life simpler with packages, by
    installing and loading a list of packages, whether they are on CRAN,
    Bioconductor or github. For github, if you do not have the full path, with 
    the maintainer name in it (e.g. "achateigner/topReviGO"), it will be able to
    load it but not to install it.
Convert input in any one of character, integer, numeric, factor,
 or ordered type into 'POSIXct' (or 'Date') objects, using one of a number of
 predefined formats, and relying on Boost facilities for date and time parsing.
Alternating optimization of (high-dimensional) functions is an 
    iterative procedure for minimizing (or maximizing) jointly over all 
    parameters by alternately optimizing parameter subsets. For a reference, 
    see Bezdek and Hathaway (2002) "Some Notes on Alternating Optimization" 
    <doi:10.1007/3-540-45631-7_39>.
Provides a set of functions to analyse
        overdispersed counts or proportions. Most of the methods are
        already available elsewhere but are scattered in different
        packages. The proposed functions should be considered as
        complements to more sophisticated methods such as generalized
        estimating equations (GEE) or generalized linear mixed effect
        models (GLMM).
Provides functions to analyse overdispersed
        counts or proportions.  These functions should be considered as
        complements to more sophisticated methods such as generalized
        estimating equations (GEE) or generalized linear mixed effect
        models (GLMM). aods3 is an S3 re-implementation of the
        deprecated S4 package aod.
A breakpoint-based method to detect ontogenetic shifts in 
  univariate time-activity budget series of central-place foraging insects. 
  The method finds a single breakpoint according to the likelihood function. 
  The method was developed with honey bees in order to detect the Age at 
  Onset of Foraging (AOF), but can be used for the detection of other 
  ontogenetic shifts in other central-place foraging insects.
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.
A collection of functions to construct A-optimal block designs for comparing test treatments with one or more control(s). Mainly A-optimal balanced treatment incomplete block designs, weighted A-optimal balanced treatment incomplete block designs, A-optimal group divisible treatment designs and A-optimal balanced bipartite block designs can be constructed using the package. The designs are constructed using algorithms based on linear integer programming. To the best of our knowledge, these facilities to construct A-optimal block designs for comparing test treatments with one or more controls are not available in the existing R packages. For more details on designs for tests versus control(s) comparisons, please see Hedayat, A. S. and Majumdar, D. (1984) <doi:10.1080/00401706.1984.10487989> A-Optimal Incomplete Block Designs for Control-Test Treatment Comparisons, Technometrics, 26, 363-370 and Mandal, B. N. , Gupta, V. K., Parsad, Rajender. (2017) <doi:10.1080/03610926.2015.1071394> Balanced treatment incomplete block designs through integer programming. Communications in Statistics - Theory and Methods 46(8), 3728-3737.
It can sometimes be difficult to ascertain when some events (such as property crime)
    occur because the victim is not present when the crime happens. As a result, police databases often
    record a 'start' (or 'from') date and time, and an 'end' (or 'to') date and time. The time span between
    these date/times can be minutes, hours, or sometimes days, hence the term 'Aoristic'. 
    Aoristic is one of the past tenses in Greek and represents an uncertain occurrence in time. 
    For events with a location describes with either a latitude/longitude, or X,Y coordinate pair, 
    and a start and end date/time, this package generates an aoristic data frame with aoristic weighted
    probability values for each hour of the week, for each observation. The coordinates are not 
    necessary for the program to calculate aoristic weights; however, they are part of this package 
    because a spatial component has been integral to aoristic analysis from the start. Dummy 
    coordinates can be introduced if the user only has temporal data. Outputs include an aoristic 
    data frame, as well as summary graphs and displays.   
        For more information see:
    Ratcliffe, JH (2002) Aoristic signatures and the temporal analysis of high volume crime patterns, 
    Journal of Quantitative Criminology. 18 (1): 23-43.
    Note: This package replaces an original 'aoristic' package (version 0.6) by George Kikuchi that 
    has been discontinued with his permission.
Trigger animation effects on scroll on any HTML element 
    of 'shiny' and 'rmarkdown', such as any text or plot, thanks to 
    the 'AOS' Animate On Scroll jQuery library.
Provides functions to perform statistical inference in the balanced one-way ANOVA model with a random factor: confidence intervals, prediction interval, and Weerahandi generalized pivotal quantities. References: Burdick & Graybill (1992, ISBN-13: 978-0824786441); Weerahandi (1995) <doi:10.1007/978-1-4612-0825-9>; Lin & Liao (2008) <doi:10.1016/j.jspi.2008.01.001>.
It covers various approaches to analysis of variance, provides an assumption testing section in order to provide a decision diagram that allows selecting the most appropriate technique. It provides the classical analysis of variance, the nonparametric equivalent of Kruskal Wallis, and the Bayesian approach. These results are shown in an interactive shiny panel, which allows modifying the arguments of the tests, contains interactive graphics and presents automatic conclusions depending on the tests in order to contribute to the interpretation of these analyzes. 'AovBay' uses 'Stan' and 'FactorBayes' for Bayesian analysis and 'Highcharts' for interactive charts.
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).
R interface for 'Apache Sedona' based on 'sparklyr'
    (<https://sedona.apache.org>).
Provides capabilities to process Apache HTTPD Log files.The main functionalities are to extract data from access and error log files to data frames.
A common task faced by researchers is the creation of APA style
    (i.e., American Psychological Association style) tables from statistical
    output. In R a large number of function calls are often needed to obtain all of
    the desired information for a single APA style table. As well, the process of
    manually creating APA style tables in a word processor is prone to transcription
    errors. This package creates Word files (.doc files) containing APA style tables
    for several types of analyses. Using this package minimizes transcription errors
    and reduces the number commands needed by the user.
Create APA style text from analyses for use within R Markdown documents. Descriptive statistics, confidence intervals, and cell sizes are reported.
Maps of the 'area potentially available' (APA) of trees is calculated from
  mapped forest stands using the approach from Gspaltl et al. (2012) <doi:10.1093/forestry/cps052>.
  This is done by computing a rasterized version of
  'weighted voronoi diagrams' using a an approximation of the trees competitive
  ability (e.g., crown radius, leaf area) as weight. The main output are 'Raster*'-
  objects from the 'raster' package that are stored together with the raw data in
  apa_list's, the main class of the 'APAtree' package. Aggregation functions are
  provided to calculate stand characteristics based on APA-maps such as relative
  proportions according to APA-size and the neighborhood diversity index NDiv
  (Glatthorn (2021) <doi:10.1016/j.ecolind.2021.108073>).
Functions for age-period-cohort analysis. Aggregate 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. Individual-level data should have a row for each individual and columns for each of age, period, and cohort. The statistical model for repeated cross-section is a generalized linear model. The statistical model for panel data is ordinary least squares. The canonical parametrisation of Kuang, Nielsen and Nielsen (2008) <DOI:10.1093/biomet/asn026> is used. Thus, the analysis does not rely on ad hoc identification.
Analysis of  data from unreplicated orthogonal experiments such as 2-level factorial and fractional factorial designs and Plackett-Burman designs using the all possible comparisons (APC) methodology developed by Miller (2005) <doi:10.1198/004017004000000608>.
The adapted pair correlation function transfers the concept of the
  pair correlation function from point patterns to patterns of objects of 
  finite size and irregular shape (e.g. lakes within a country). This is a 
  reimplementation of the method suggested by Nuske et al. (2009) 
  <doi:10.1016/j.foreco.2009.09.050> using the library 'GEOS'.
It implemented Age-Period-Interaction Model (APC-I Model) proposed in the paper of Liying Luo and James S. Hodges in 2019. A new age-period-cohort model for describing and investigating inter-cohort differences and life course dynamics.
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.
In fields such as ecology, microbiology, and genomics, non-Euclidean distances are widely applied to describe pairwise dissimilarity between samples. Given these pairwise distances, principal coordinates analysis (PCoA) is commonly used to construct a visualization of the data. However, confounding covariates can make patterns related to the scientific question of interest difficult to observe. We provide 'aPCoA' as an easy-to-use tool to improve data visualization in this context, enabling enhanced presentation of the effects of interest. Details are described in Yushu Shi, Liangliang Zhang, Kim-Anh Do, Christine Peterson and Robert Jenq (2020) Bioinformatics, Volume 36, Issue 13, 4099-4101.
Age-Period-Cohort (APC) analyses are used to differentiate relevant drivers for long-term developments.
    The 'APCtools' package offers visualization techniques and general routines to simplify the workflow of an APC analysis.
    Sophisticated functions are available both for descriptive and regression model-based analyses.
    For the former, we use density (or ridgeline) matrices and (hexagonally binned) heatmaps as innovative visualization
    techniques building on the concept of Lexis diagrams.
    Model-based analyses build on the separation of the temporal dimensions based on generalized additive models,
    where a tensor product interaction surface (usually between age and period) is utilized
    to represent the third dimension (usually cohort) on its diagonal.
    Such tensor product surfaces can also be estimated while accounting for
    further covariates in the regression model.
    See Weigert et al. (2021) <doi:10.1177/1354816620987198> for methodological details.
An implementation of the additive polynomial (AP) design matrix. It
    constructs and appends an AP design matrix to a data frame for use with
    longitudinal data subject to seasonality.
Functions for reading, writing, plotting, and manipulating phylogenetic trees, analyses of comparative data in a phylogenetic framework, ancestral character analyses, analyses of diversification and macroevolution, computing distances from DNA sequences, reading and writing nucleotide sequences as well as importing from BioConductor, and several tools such as Mantel's test, generalized skyline plots, graphical exploration of phylogenetic data (alex, trex, kronoviz), estimation of absolute evolutionary rates and clock-like trees using mean path lengths and penalized likelihood, dating trees with non-contemporaneous sequences, translating DNA into AA sequences, and assessing sequence alignments. Phylogeny estimation can be done with the NJ, BIONJ, ME, MVR, SDM, and triangle methods, and several methods handling incomplete distance matrices (NJ*, BIONJ*, MVR*, and the corresponding triangle method). Some functions call external applications (PhyML, Clustal, T-Coffee, Muscle) whose results are returned into R.
The goal is to print an "aperçu", a short view of a vector, a
    matrix, a data.frame, a list or an array. By default, it prints the first 5
    elements of each dimension. By default, the number of columns is equal to
    the number of lines. If you want to control the selection of the elements,
    you can pass a list, with each element being a vector giving the selection
    for each dimension.
Toolkit for the analysis of multiple gene data (Jombart et al. 2017) <doi:10.1111/1755-0998.12567>. 
    Apex implements the new S4 classes 'multidna', 'multiphyDat' and associated methods to handle aligned DNA sequences from multiple genes.
Provides an 'htmlwidgets' interface to 'apexcharts.js'. 
  'Apexcharts' is a modern JavaScript charting library to build interactive charts and visualizations with simple API.
  'Apexcharts' examples and documentation are available here: <https://apexcharts.com/>.
Implements a multiple testing approach to the
    choice of a threshold gamma on the p-values using the
    Average Power Function (APF) and Bayes False Discovery
    Rate (FDR) robust estimation. Function apf_fdr() 
    estimates both quantities from either raw data or
    p-values. Function apf_plot() produces smooth graphs 
    and tables of the relevant results. Details of the methods
    can be found in Quatto P, Margaritella N, et al. (2019) 
    <doi:10.1177/0962280219844288>.
Helper tools for facilitating basic geo-processing tasks, such as reading/writing
  Shapefiles, merging polygons or generating terrain contours.
Designed for the development and application of
    hidden Markov models and profile HMMs for biological sequence analysis. 
    Contains functions for multiple and pairwise sequence alignment, 
    model construction and parameter optimization, file import/export,
    implementation of the forward, backward and Viterbi algorithms for 
    conditional sequence probabilities, tree-based sequence weighting, 
    and sequence simulation. 
    Features a wide variety of potential applications including 
    database searching, gene-finding and annotation, phylogenetic 
    analysis and sequence classification.
    Based on the models and algorithms described in Durbin et 
    al (1998, ISBN: 9780521629713).
Implements a parsimonious evolutionary model to analyze and
  predict gene-functional annotations in phylogenetic trees as described in Vega
  Yon et al. (2021) <doi:10.1371/journal.pcbi.1007948>. With a focus on
  computational efficiency, 'aphylo' makes it possible to estimate pooled
  phylogenetic models, including thousands (hundreds) of annotations (trees) in
  the same run. The package also provides the tools for visualization of
  annotated phylogenies, calculation of posterior probabilities (prediction,)
  and goodness-of-fit assessment featured in Vega Yon et al. (2021).
Parentage assignment package.
    Parentage assignment is performed based on observed average Mendelian transmission probability distributions.
    The main function of this package is the function APIS(), which is the parentage assignment function.
API for using 'episensr', Basic sensitivity analysis of the observed relative risks adjusting for unmeasured confounding and misclassification of the exposure/outcome, or both. See <https://cran.r-project.org/package=episensr>.
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.
For many times, we are not just aligning plots as what 'cowplot' and 'patchwork' did. Users would like to align associated information that requires axes to be exactly matched in subplots, e.g. hierarchical clustering with a heatmap. This package provides utilities to aligns associated subplots to a main plot at different sides (left, right, top and bottom) with axes exactly matched.
Some functions for drawing some special plots:
   The function 'bagplot' plots a bagplot,
   'faces' plots chernoff faces,
   'iconplot' plots a representation of a frequency table or a data matrix,
   'plothulls' plots hulls of a bivariate data set,
   'plotsummary' plots a graphical summary of a data set,
   'puticon' adds icons to a plot,
   'skyline.hist' combines several histograms of a one dimensional data set in one plot,
   'slider' functions supports some interactive graphics,
   'spin3R' helps an inspection of a 3-dim point cloud,
   'stem.leaf' plots a stem and leaf plot,
   'stem.leaf.backback' plots back-to-back versions of stem and leaf plot.
We include
            1) data cleaning including variable scaling, missing values and unbalanced variables identification and removing, and strategies for variable balance improving; 
			2) modeling based on random forest and gradient boosted model including feature selection, model training, cross-validation and external testing.
			For more information, please see Deng X (2021). <doi:10.1016/j.scitotenv.2020.144746>; H2O.ai (Oct. 2016). R Interface for H2O, R package version 3.10.0.8. <https://github.com/h2oai/h2o-3>; Zhang W (2016). <doi:10.1016/j.scitotenv.2016.02.023>.
Fit linear, logistic and Cox models regularized with L0, lasso (L1), elastic-net (L1 and L2), or net (L1 and Laplacian) penalty, and their adaptive forms, such as adaptive lasso / elastic-net and net adjusting for signs of linked coefficients. It solves L0 penalty problem by simultaneously selecting regularization parameters and performing hard-thresholding or selecting number of non-zeros. This augmented and penalized minimization method provides an approximation solution to the L0 penalty problem, but runs as fast as L1 regularization problem. The package uses one-step coordinate descent algorithm and runs extremely fast by taking into account the sparsity structure of coefficients. It could deal with very high dimensional data and has superior selection performance.
Convert several png files into an animated png file.
  This package exports only a single function `apng'. Call the
  apng function with a vector of file names (which should be
  png files) to convert them to a single animated png file.
Choice models are a widely used technique across numerous scientific disciplines. The Apollo package is a very flexible tool for the estimation and application 
    of choice models in R. Users are able to write their own 
    model functions or use a mix of already available ones. Random heterogeneity, 
    both continuous and discrete and at the level of individuals and 
    choices, can be incorporated for all models. There is support for both standalone 
    models and hybrid model structures.  Both classical 
    and Bayesian estimation is available, and multiple discrete 
    continuous models are covered in addition to discrete choice. 
    Multi-threading processing is supported for estimation and a large
    number of pre and post-estimation routines, including for computing posterior
    (individual-level) distributions are available. 
    For examples, a manual, and a support forum, visit 
    <http://www.ApolloChoiceModelling.com>. For more information on choice 
    models see Train, K. (2009) <isbn:978-0-521-74738-7> and Hess, 
    S. & Daly, A.J. (2014) <isbn:978-1-781-00314-5> for an overview 
    of the field.
Calculating predictive model performance measures adjusted for predictor distributions using density ratio method (Sugiyama et al., (2012, ISBN:9781139035613)). L1 and L2 error for continuous outcome and C-statistics for binomial outcome are computed.
Using 'Apple App Store' <https://www.apple.com/app-store/> web scraping and 'iTunes' API 
    <https://affiliate.itunes.apple.com/resources/documentation/itunes-store-web-service-search-api/>
    to extract content information, app ratings and reviews.
A modeling package compiling applicability domain methods in R.
    It combines different methods to measure the amount of extrapolation new
    samples can have from the training set. See Netzeva et al (2005) 
    <doi:10.1177/026119290503300209> for an overview of applicability domains.
A few functions and several data set for the Springer book 'Applied Predictive Modeling'.
Amyloid propensity prediction neural network (APPNN) is an amyloidogenicity propensity predictor based on a machine learning approach through recursive feature selection and feed-forward neural networks, taking advantage of newly published sequences with experimental, in vitro, evidence of amyloid formation.
Performs Bayesian prediction of complex computer codes when fast approximations are available.  It uses a hierarchical version of the Gaussian process, originally proposed by Kennedy and O'Hagan (2000), Biometrika 87(1):1.
Tools for constructing a matched design with multiple comparison groups.
 Further specifications of refined covariate balance restriction and exact match on 
 covariate can be imposed. Matches are approximately optimal in  the sense that the 
 cost of the solution is at most twice the optimal cost, Crama and Spieksma (1992) 
 <doi:10.1016/0377-2217(92)90078-N>, Karmakar, Small and Rosenbaum (2019)
 <doi:10.1080/10618600.2019.1584900>.
R and C++ functions to perform exact and 
  approximate optimal transport. All C++ methods are linkable 
  to other R packages via their header files.
An unofficial companion to the textbook "Applied Regression
    Analysis" by N.R. Draper and H. Smith (3rd Ed., 1998) including all the
    accompanying datasets.
Assists the evaluation of whether and
    where to focus code optimization, using Amdahl's law and visual aids
    based on line profiling. Amdahl's profiler organizes profiling output
    files (including memory profiling) in a visually appealing way.
    It is meant to help to balance development
    vs. execution time by helping to identify the most promising sections
    of code to optimize and projecting potential gains. The package is
    an addition to R's standard profiling tools and is not a wrapper for them.
The functions in this package inspect, read, edit and run files for 'APSIM' "Next Generation" ('JSON')
             and 'APSIM' "Classic" ('XML'). The files with an 'apsim' extension correspond to
	     'APSIM' Classic (7.x) - Windows only - and the ones with an 'apsimx' extension correspond to 'APSIM' "Next Generation".
	     For more information about 'APSIM' see (<https://www.apsim.info/>) and for 'APSIM'
	     next generation (<https://apsimnextgeneration.netlify.app/>).
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.
We provide tools to estimate two prediction accuracy metrics,
    the average positive predictive values (AP) as well as the well-known AUC
    (the area under the receiver operator characteristic curve) for risk scores. 
    The outcome of interest is either binary or censored event time.
    Note that for censored event time, our functions' estimates, the AP and the
    AUC, are time-dependent for pre-specified time interval(s). A function that
    compares the APs of two risk scores/markers is also included. Optional
    outputs include positive predictive values and true positive fractions at
    the specified marker cut-off values, and a plot of the time-dependent AP
    versus time (available for event time data).
Simulation and analysis
        of phylogenetic tree topologies using statistical indices. It
        is a companion library of the 'ape' package. It provides
        additional functions for reading, plotting, manipulating
        phylogenetic trees. It also offers convenient web-access to
        public databases, and enables testing null models of
        macroevolution using corrected test statistics.  Trees of class
        "phylo" (from 'ape' package) can be converted easily.
        Implements methods described in Bortolussi et al. (2005)
        <doi:10.1093/bioinformatics/bti798> and Maliet et al. (2017) 
        <doi:10.1101/224295>.
Provides a quick method for visualizing non-aggregated line-list
    or aggregated census data stratified by age and one or two categorical
    variables (e.g. gender and health status) with any number of values. It
    returns a 'ggplot' object, allowing the user to further customize the
    output. This package is part of the 'R4Epis' project 
    <https://r4epis.netlify.com>.
Developed for use by those tasked with the routine detection,
        characterisation and quantification of discrete changes in air
        quality time-series, such as identifying the impacts of air quality
        policy interventions. The main functions use signal isolation then
        break-point/segment (BP/S) methods based on 'strucchange' and 
        'segmented' methods detect and quantify change events (Ropkins & 
        Tate, 2021, <doi:10.1016/j.scitotenv.2020.142374>).
Functions are included for recalling AQL (Acceptable Quality Level or Acceptance Quality Level) 
 Based single, double, and multiple attribute sampling plans from the Military Standard (MIL-STD-105E) - American 
 National Standards Institute/American Society for Quality (ANSI/ASQ Z1.4) tables and for retrieving variable
 sampling plans from Military Standard (MIL-STD-414) - American National Standards Institute/American Society
 for Quality (ANSI/ASQ Z1.9) tables. The sources for these tables are listed in the URL: field. Also included
 are functions for computing the OC (Operating Characteristic) and ASN (Average Sample Number) coordinates
 for the attribute plans it recalls, and functions for computing the estimated proportion nonconforming and
 the maximum allowable proportion nonconforming for variable sampling plans. The MIL-STD AQL Sampling schemes
 were the most used and copied set of standards in the world. They are intended to be used for sampling a stream
 of lots, and were used in contract agreements between supplier and customer companies. When the US military
 dropped support of MIL-STD 105E and 414, The American National Standards Institute (ANSI) and the International
 Standards Organization (ISO) adopted the standard with few changes or no changes to the central tables. This
 package is useful because its computer implementation of these tables duplicates that available in other
 commercial software and subscription online calculators.
The Algorithms for Quantitative Pedology (AQP) project was started in 2009 to organize a loosely-related set of concepts and source code on the topic of soil profile visualization, aggregation, and classification into this package (aqp). Over the past 8 years, the project has grown into a suite of related R packages that enhance and simplify the quantitative analysis of soil profile data. Central to the AQP project is a new vocabulary of specialized functions and data structures that can accommodate the inherent complexity of soil profile information; freeing the scientist to focus on ideas rather than boilerplate data processing tasks <doi:10.1016/j.cageo.2012.10.020>. These functions and data structures have been extensively tested and documented, applied to projects involving hundreds of thousands of soil profiles, and deeply integrated into widely used tools such as SoilWeb <https://casoilresource.lawr.ucdavis.edu/soilweb-apps/>. Components of the AQP project (aqp, soilDB, sharpshootR, soilReports packages) serve an important role in routine data analysis within the USDA-NRCS Soil Science Division. The AQP suite of R packages offer a convenient platform for bridging the gap between pedometric theory and practice.
Breeding programs can be simulated with this package. The functions are written to simulate production and reproduction systems encountered in aquaculture and are easy to combine with custom functions. Simulating breeding programs is useful to predict the expected genetic gain, rate of inbreeding and the effect of changes in the breeding program. AquaBPsim does not simulate genome wide-markers and QTLs, but it simulates estimated breeding values as values correlated to the true breeding values. The correlation equals the accuracy, which can be provided or calculated using deterministic formulas. For genomic selection, the accuracy can be calculate using the formula of Deatwyler et al. (2010) <doi:10.1534/genetics.110.116855>. Without genomic selection, accuracy can be calculated with the selection index method (Mrode, 2014. ISBN:978-1-84593-981-6).
Provides an automatic aggregation tool to manage point data privacy, 
    intended to be helpful for the production of official spatial data and for researchers.
    The package pursues the data accuracy at the smallest possible areas preventing 
    individual information disclosure. The methodology, based on hierarchical geographic 
    data structures performs aggregation and local suppression of point data to ensure privacy
    as described in Lagonigro, R., Oller, R., Martori J.C. (2017) <doi:10.2436/20.8080.02.55>.
    The data structures are created following the guidelines for grid datasets from the
    European Forum for Geography and Statistics.
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.
The Aquo Standard is the Dutch Standard for the exchange of 
    data in water management. With *aquodom* (short for aquo domaintables) 
    it is easy to exploit the API (<https://www.aquo.nl/index.php/Hoofdpagina>) to download domaintables 
    of the Aquo Standard and use them in R.
In mathematics, 'rejection sampling' is a basic technique used to generate observations from a distribution. It is also commonly called 'the Acceptance-Rejection method' or 'Accept-Reject algorithm' and is a type of Monte Carlo method. 'Acceptance-Rejection method' is based on the observation that to sample a random variable one can perform a uniformly random sampling of the 2D cartesian graph, and keep the samples in the region under the graph of its density function. Package 'AR' is able to generate/simulate random data from a probability density function by Acceptance-Rejection method. Moreover, this package is a useful teaching resource for graphical presentation of Acceptance-Rejection method. From the practical point of view, the user needs to calculate a constant in Acceptance-Rejection method, which package 'AR' is able to compute this constant by optimization tools. Several numerical examples are provided to illustrate the graphical presentation for the Acceptance-Rejection Method.
Using sparse precision matricies and Choleski factorization simulates data that is auto-regressive.
Simple functions to convert given Arabic numerals to Kansuji  
    numerical figures that represent numbers written in Chinese characters.
Allows users to stem Arabic texts for text analysis.
Allows the user to connect with the World Spider Catalogue (WSC; <https://wsc.nmbe.ch/>) and the World Spider Trait (WST; <https://spidertraits.sci.muni.cz/>) databases. Also performs several basic functions such as checking names validity, retrieving coordinate data from the Global Biodiversity Information Facility (GBIF; <https://www.gbif.org/>), and mapping.
Processes noble gas mass spectrometer data to determine the isotopic composition of argon (comprised of Ar36, Ar37, Ar38, Ar39 and Ar40) released from neutron-irradiated potassium-bearing minerals. Then uses these compositions to calculate precise and accurate geochronological ages for multiple samples as well as the covariances between them. Error propagation is done in matrix form, which jointly treats all samples and all isotopes simultaneously at every step of the data reduction process. Includes methods for regression of the time-resolved mass spectrometer signals to t=0 ('time zero') for both single- and multi-collector instruments, blank correction, mass fractionation correction, detector intercalibration, decay corrections, interference corrections, interpolation of the irradiation parameter between neutron fluence monitors, and (weighted mean) age calculation. All operations are performed on the logs of the ratios between the different argon isotopes so as to properly treat them as 'compositional data', sensu Aitchison [1986, The Statistics of Compositional Data, Chapman and Hall].
Implements the Classification-based on
    Association Rules (CBA) (Bing Liu, Wynne Hsu,	Yiming Ma (1999) <https://dl.acm.org/doi/10.5555/3000292.3000305>) algorithm for association rule classification (ARC).
    The package also contains several convenience methods that allow to automatically
    set CBA parameters (minimum confidence, minimum support) and it also natively
    handles numeric attributes by integrating a pre-discretization step.
    The rule generation phase is handled by the 'arules' package. 
    To further decrease the size of the CBA models produced by the 'arc' package, postprocessing by the 
    'qCBA' package is suggested. QCBA package can be obtained from CRAN archive or from <https://github.com/kliegr/QCBA>.
It fits an univariate left or right censored linear regression model
    with autoregressive errors under the normal distribution. It provides estimates
    and standard errors of the parameters, prediction of future observations and
    it supports missing values on the dependent variable.
    It also performs influence diagnostic through local influence for three possible
    perturbation schemes.
Provides a list of functions for the Bayesian modeling of archaeological chronologies. The Bayesian models are implemented in 'JAGS' ('JAGS' stands for Just Another Gibbs Sampler. It is a program for the analysis of Bayesian hierarchical models using Markov Chain Monte Carlo (MCMC) simulation. See <http://mcmc-jags.sourceforge.net/> and "JAGS Version 4.3.0 user manual", Martin Plummer (2017) <https://sourceforge.net/projects/mcmc-jags/files/Manuals/>.). The inputs are measurements with their associated standard deviations and the study period. The output is the MCMC sample of the posterior distribution of the event date with or without radiocarbon calibration.
Provides a list of functions for the statistical analysis of archaeological dates and groups of dates. It is based on the post-processing of the Markov Chains whose stationary distribution is the posterior distribution of a series of dates. Such output can be simulated by different applications as for instance 'ChronoModel' (see <https://chronomodel.com/>), 'Oxcal' (see <https://c14.arch.ox.ac.uk/oxcal.html>) or 'BCal' (see <https://bcal.shef.ac.uk/>). The only requirement is to have a csv file containing a sample from the posterior distribution.  Note that this package interacts with data available through the 'ArchaeoPhases.dataset' package which is available in a separate repository.  The size of the 'ArchaeoPhases.dataset' package is approximately 4 MB.
Provides the data sets used to build the 'ArchaeoPhases' vignettes.  The data sets were formerly distributed with 'ArchaeoPhases', however they exceed current CRAN policy for package size.
The archdata package provides several types of data that are typically used in archaeological research. It provides all of the data sets used in "Quantitative Methods in Archaeology Using R" by David L Carlson, one of the Cambridge Manuals in Archaeology.
Methods to analyse fragmented objects in archaeology using refitting relationships between fragments scattered in archaeological spatial units (e.g. stratigraphic layers). Graphs and graph theory are used to model archaeological observations. The package is mainly based on the 'igraph' package for graph analysis. Functions can: 1) create, manipulate, and simulate fragmentation graphs, 2) measure the cohesion and admixture of archaeological spatial units, and 3) characterise the topology of a specific set of refitting relationships. An empirical dataset is also provided as an example.
A project template to support the data science workflow.
The main function archetypes implements a
    framework for archetypal analysis supporting arbitrary
    problem solving mechanisms for the different conceptual
    parts of the algorithm.
Provides a set of tools to make quantitative structural model of trees (i.e the so-called 'QSM') from LiDAR point cloud, to manipulate and visualize the QSMs as well as to compute metrics from them. It can be used in various context of forest ecology (i.e biomass estimation) and tree architecture (i.e architectural metrics), see Martin-Ducup et al. (2020) <doi:10.1111/1365-2435.13678>. The package is based on a new S4 class called 'aRchi'.
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>.
Bindings to 'libarchive' <http://www.libarchive.org> the
    Multi-format archive and compression library. Offers R connections and
    direct extraction for many archive formats including 'tar', 'ZIP',
    '7-zip', 'RAR', 'CAB' and compression formats including 'gzip',
    'bzip2', 'compress', 'lzma' and 'xz'.
Scraping content from archived web pages stored in
    the 'Internet Archive' (<https://archive.org>) using a systematic
    workflow.  Get an overview of the mementos available from the
    respective homepage, retrieve the Urls and links of the page and
    finally scrape the content. The final output is stored in tibbles,
    which can be then easily used for further analysis.
Data exploration and modelling is a process in which a lot of data
    artifacts are produced. Artifacts like: subsets, data aggregates, plots,
    statistical models, different versions of data sets and different versions
    of results. The more projects we work with the more artifacts are produced
    and the harder it is to manage these artifacts. Archivist helps to store
    and manage artifacts created in R. Archivist allows you to store selected
    artifacts as a binary files together with their metadata and relations.
    Archivist allows to share artifacts with others, either through shared
    folder or github. Archivist allows to look for already created artifacts by
    using it's class, name, date of the creation or other properties. Makes it
    easy to restore such artifacts. Archivist allows to check if new artifact
    is the exact copy that was produced some time ago. That might be useful
    either for testing or caching.
The extension of the 'archivist' package integrating the archivist with GitHub via GitHub API, 'git2r' packages and 'httr' package.
Set of functions to analyse and estimate Artificial Counterfactual models from Carvalho, Masini and Medeiros (2016) <DOI:10.2139/ssrn.2823687>.
For emulating multifidelity computer models. The major methods include univariate autoregressive cokriging and multivariate autoregressive cokriging. The autoregressive cokriging methods are implemented for both hierarchically nested design and non-nested design. For hierarchically nested design, the model parameters are estimated via standard optimization algorithms; For non-nested design, the model parameters are estimated via Monte Carlo expectation-maximization (MCEM) algorithms. In both cases, the priors are chosen such that the posterior distributions are proper. Notice that the uniform priors on range parameters in the correlation function lead to improper posteriors. This should be avoided when Bayesian analysis is adopted. The development of objective priors for autoregressive cokriging models can be found in Pulong Ma (2020) <DOI:10.1137/19M1289893>. The development of the multivariate autoregressive cokriging models with possibly non-nested design can be found in Pulong Ma, Georgios Karagiannis, Bledar A Konomi, Taylor G Asher, Gabriel R Toro, and Andrew T Cox (2019) <arXiv:1909.01836>.
Functions to efficiently query 'ArcGIS REST' APIs 
  <https://developers.arcgis.com/rest/>. 
  Both spatial and SQL queries can be used to retrieve data. 
  Simple Feature (sf) objects are utilized to perform spatial queries. 
  This package was neither produced nor is maintained by Esri.
Provides functions to process minute level actigraphy-measured activity counts data and extract commonly used physical activity volume and fragmentation metrics.
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.
Creates complex autoregressive distributed lag (ARDL) models
    providing just the order and automatically constructs the underlying
    unrestricted and restricted error correction model (ECM). It also performs
    the bounds-test for cointegration as described in Pesaran et al. (2001) <doi:10.1002/jae.616> and provides the multipliers and the cointegrating
    equation.
Considering an (n x m) data matrix X, this package is based on the method proposed 
             by Gower, Groener, and Velden (2010) <doi:10.1198/jcgs.2010.07134>, and 
             utilize the  resulting matrices from the extended version of the NIPALS decomposition 
             to determine n triangles whose areas are used  to  visually  estimate the elements of
             a specific column of X. After a 90-degree rotation of the sample points, the triangles
             are drawn regarding the following points: 1.the origin of the axes; 2.the sample points;
             3. the vector endpoint representing some variable.
A pipeable, transparent implementation of areal weighted interpolation
    with support for interpolating multiple variables in a single function call.
    These tools provide a full-featured workflow for validation and estimation
    that fits into both modern data management (e.g. tidyverse) and spatial 
    data (e.g. sf) frameworks.
Plot stacked areas and confidence bands as filled polygons, or add
  polygons to existing plots. A variety of input formats are supported,
  including vectors, matrices, data frames, formulas, etc.
Reads Arena <https://www.arenasimulation.com/> CSV output files and generates nice tables and plots. The package contains a Shiny App that can be used to interactively visualize Arena's results.
Generates data for challenging machine learning models in 'Arena'
    <https://arena.drwhy.ai> - an interactive web application. You can start
    the server with XAI (Explainable Artificial Intelligence) plots to be
    generated on-demand or precalculate and auto-upload data file beside
    shareable 'Arena' URL.
Simulates, fits, and predicts long-memory and anti-persistent
	time series, possibly mixed with ARMA, regression, transfer-function
	components.
	Exact methods (MLE, forecasting, simulation) are used.
	Bug reports should be done via GitHub (at
	<https://github.com/JQVeenstra/arfima>), where the development version
	of this package lives; it can be installed using devtools.
Augmented Regression with General Online data (ARGO) for accurate estimation of influenza epidemics in United States on national level, regional level and state level. It replicates the method introduced in paper Yang, S., Santillana, M. and Kou, S.C. (2015) <doi:10.1073/pnas.1515373112>; Ning, S., Yang, S. and Kou, S.C. (2019) <doi:10.1038/s41598-019-41559-6>; Yang, S., Ning, S. and Kou, S.C. (2021) <doi:10.1038/s41598-021-83084-5>.
Supports the analysis of oceanographic data recorded by Argo autonomous drifting profiling floats. Functions are provided to (a) download and cache data files, (b) subset data in various ways, (c) handle quality-control flags and (d) plot the results according to oceanographic conventions. A shiny app is provided for easy exploration of datasets. The package is designed to work well with the 'oce' package, providing a wide range of processing capabilities that are particular to oceanographic analysis. See Kelley, Harbin, and Richards (2021) <doi:10.3389/fmars.2021.635922> for more on the scientific context and applications.
Utilities for secure password hashing via the argon2 algorithm.
    It is a relatively new hashing algorithm and is believed to be very secure.
    The 'argon2' implementation included in the package is the reference
    implementation.  The package also includes some utilities that should be
    useful for digest authentication, including a wrapper of 'blake2b'.  For
    similar R packages, see sodium and 'bcrypt'.  See
    <https://en.wikipedia.org/wiki/Argon2> or
    <https://eprint.iacr.org/2015/430.pdf> for more information.
Create awesome 'Bootstrap 4' dashboards powered by 'Argon'.
   See more here <https://rinterface.github.io/argonDash/>.
R wrapper around the argon HTML library.
    More at <https://demos.creative-tim.com/argon-design-system/>.
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.
Perform the Adaptable Regularized Hotelling's T^2 test (ARHT) proposed by
    Li et al., (2016) <arXiv:1609.08725>. Both one-sample and two-sample mean test are available with
    various probabilistic alternative prior models. It contains a function to consistently
    estimate higher order moments of the population covariance spectral distribution using
    the spectral of the sample covariance matrix (Bai et al. (2010) <doi:10.1111/j.1467-842X.2010.00590.x>).
    In addition, it contains a function to sample from 3-variate chi-squared random vectors approximately 
    with a given correlation matrix when the degrees of freedom are large.
Create videos from 'R Markdown' documents, or images and audio
    files. These images can come from image files or HTML slides, and the audio
    files can be provided by the user or computer voice narration can be created
    using 'Amazon Polly'. The purpose of this package is to allow users to create
    accessible, translatable, and reproducible lecture videos. See
    <https://aws.amazon.com/polly/> for more information.
It performs All-Resolutions Inference (ARI) on functional Magnetic Resonance Image (fMRI) data. As a main feature, 
 it estimates lower bounds for the proportion of active voxels in a set of clusters as, for example, given by a cluster-wise analysis. 
 The method is described in Rosenblatt, Finos, Weeda, Solari, Goeman (2018) <doi:10.1016/j.neuroimage.2018.07.060>.
Implements an efficient O(n) algorithm based on bucket-sorting for 
    fast computation of standard clustering comparison measures. Available measures
    include adjusted Rand index (ARI), normalized information distance (NID), 
    normalized mutual information (NMI), adjusted mutual information (AMI), 
    normalized variation information (NVI) and entropy, as described in Vinh et al (2009) 
    <doi:10.1145/1553374.1553511>. Include AMI (Adjusted Mutual Information) since version 0.1.2, 
    a modified version of ARI (MARI) and simple Chi-square distance since version 1.0.0.
Leverages the 'ari' package and other tools to create automated 
    courses from slides and a script.  Also, uploads these to 
    'YouTube' and other services using 'tuber' package.
Flat text files provide a robust, compressible, and portable
  way to store tables from databases.  This package provides convenient
  functions for exporting tables from relational database connections
  into compressed text files and streaming those text files back into
  a database without requiring the whole table to fit in working memory.
A collection of classes that represent
    archaeological data. This package provides a set of S4 classes that
    represent different special types of matrix (absolute/relative
    frequency, presence/absence data, co-occurrence matrix, etc.) upon
    which package developers can build subclasses. It also provides a set
    of generic methods (mutators and coercion mechanisms) and functions
    (e.g. summary statistics, predicates). In addition, a few classes of 
    general interest (e.g. that represent stratigraphic relationships) 
    are implemented.
Functions to accompany A. Gelman and J. Hill, Data Analysis Using Regression and Multilevel/Hierarchical Models, Cambridge University Press, 2007.
Two steps variable selection procedure in a context of high-dimensional dependent data
  but few observations. First step is dedicated to eliminate dependence between variables (clustering
  of variables, followed by factor analysis inside each cluster).
  Second step is a variable selection using by aggregation of adapted methods.
  Bastien B., Chakir H., Gegout-Petit A., Muller-Gueudin A., Shi Y.
  A statistical methodology to select covariates in high-dimensional data under dependence.
  Application to the classification of genetic profiles associated with outcome of a non-small-cell
  lung cancer treatment. 2018. <https://hal.archives-ouvertes.fr/hal-01939694>.
An efficient 'Rcpp' implementation of the Adaptive Rejection Metropolis Sampling (ARMS) algorithm proposed by Gilks, W. R., Best, N. G. and Tan, K. K. C. (1995) <doi:10.2307/2986138>. This allows for sampling from a univariate target probability distribution specified by its (potentially unnormalised) log density.
Arnold Schwarzenegger movie weekend box office records from
    1982-2014
Estimates the covariate-adjusted Receiver Operating Characteristic (AROC) curve and pooled (unadjusted) ROC curve by different methods. Inacio de Carvalho, V., and Rodriguez-Alvarez, M. X. (2018) <arXiv:1806.00473>. NOTE: We have created a new package, 'ROCnReg', with more functionalities. It also implements all the methods included in 'AROC'. We, therefore, recommend using 'ROCnReg' ('AROC' will no longer be maintained).
A cross-platform R framework that facilitates processing of any number of Affymetrix microarray samples regardless of computer system.  The only parameter that limits the number of chips that can be processed is the amount of available disk space.  The Aroma Framework has successfully been used in studies to process tens of thousands of arrays.  This package has actively been used since 2006.
DEPRECATED. Do not start building new projects based on this package. (The (in-house) APD file format was initially developed to store Affymetrix probe-level data, e.g. normalized CEL intensities.  Chip types can be added to APD file and similar to methods in the affxparser package, this package provides methods to read APDs organized by units (probesets).  In addition, the probe elements can be arranged optimally such that the elements are guaranteed to be read in order when, for instance, data is read unit by unit.  This speeds up the read substantially.  This package is supporting the Aroma framework and should not be used elsewhere.)
Methods for analyzing DNA copy-number data.  Specifically,
  this package implements the multi-source copy-number normalization (MSCN)
  method for normalizing copy-number data obtained on various platforms and
  technologies.  It also implements the TumorBoost method for normalizing
  paired tumor-normal SNP data.
Core methods and classes used by higher-level 'aroma.*' packages
        part of the Aroma Project, e.g. 'aroma.affymetrix' and 'aroma.cn'.
Tools for geometric morphometric analysis. The package includes tools of virtual anthropology to align two not articulated parts belonging to the same specimen, to build virtual cavities as endocast (Profico et al, 2021 <doi:10.1002/ajpa.24340>).
Contains functions for retrieving, managing and analysing air quality and weather data from Regione Lombardia open database (<https://www.dati.lombardia.it/>).
    Data are collected by ARPA Lombardia (Lombardia Environmental Protection Agency), Italy, through its ground monitoring network.
    See the webpage <https://www.arpalombardia.it/Pages/ARPA_Home_Page.aspx> for further information on ARPA Lombardia's activities and history.
    Data quality (e.g. missing values, exported values, graphical mapping) has been checked involving members of the ARPA Lombardia's office for air quality control.
    The package makes available observations since 1989 (for weather) and 1996 (for air quality) and are updated with daily frequency by the regional agency.
Left, right or interval censored mixed-effects linear model with autoregressive errors of order p or DEC correlation structure using the type-EM algorithm. The error distribution  can be Normal or t-Student. It provides the  parameter estimates, the standard errors  and prediction of future observations (available only for the normal case). Olivari et all (2021) <doi:10.1080/10543406.2020.1852246>.
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, whole interval recording, and
    augmented interval recording.
Provides convenience functions for programming with
    'magrittr' pipes. Conditional pipes, a string prefixer and a function
    to pipe the given object into a specific argument given by character
    name are currently supported. It is named after the dadaist Hans Arp,
    a friend of Rene Magritte.
Functions for Arps decline-curve analysis on oil and gas data. Includes exponential, hyperbolic, harmonic, and hyperbolic-to-exponential models as well as the preceding with initial curtailment or a period of linear rate buildup. Functions included for computing rate, cumulative production, instantaneous decline, EUR, time to economic limit, and performing least-squares best fits.
Fast generators and iterators for permutations, combinations,
    integer partitions and compositions. The arrangements are in
    lexicographical order and generated iteratively in a memory efficient manner. 
    It has been demonstrated that 'arrangements' outperforms most existing
    packages of similar kind. Benchmarks could be found at
    <https://randy3k.github.io/arrangements/articles/benchmark.html>.
High performance variant of apply() for a fixed set of functions.
  Considerable speedup is a trade-off for universality, user defined
  functions cannot be used with this package. However, 21 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 '...'.
Some convenient functions to work with arrays.
'Apache' 'Arrow' <https://arrow.apache.org/> is a cross-language
    development platform for in-memory data. It specifies a standardized
    language-independent columnar memory format for flat and hierarchical data,
    organized for efficient analytic operations on modern hardware. This
    package provides an interface to the 'Arrow C++' library.
Adaptive Rejection Sampling, Original version.
A method for quantifying resilience after a stress event. A set of functions
    calculate the area of resilience that is created by the departure of baseline 
    'y' (i.e., robustness) and the time taken 'x' to return to baseline 
    (i.e., rapidity) after a stress event using the Cartesian coordinates of the 
    data. This package has the capability to calculate areas of resilience,
    growth, and cases in which resilience is not achieved 
    (e.g., diminished performance without return to baseline).
An Arsenal of 'R' functions for large-scale statistical summaries,
  which are streamlined to work within the latest reporting tools in 'R' and
  'RStudio' and which use formulas and versatile summary statistics for summary
  tables and models. The primary functions include tableby(), a Table-1-like
  summary of multiple variable types 'by' the levels of one or more categorical
  variables; paired(), a Table-1-like summary of multiple variable types paired across
  two time points; modelsum(), which performs simple model fits on one or more endpoints
  for many variables (univariate or adjusted for covariates);
  freqlist(), a powerful frequency table across many categorical variables;
  comparedf(), a function for comparing data.frames; and
  write2(), a function to output tables to a document.
An implementation of the Aligned Rank Transform technique for
    factorial analysis (see references below for details) including models with
    missing terms (unsaturated factorial models). The function first
    computes a separate aligned ranked response variable for each effect of the
    user-specified model, and then runs a classic ANOVA on each of the aligned
    ranked responses. For further details, see Higgins, J. J. and Tashtoush, S.
    (1994). An aligned rank transform test for interaction. Nonlinear World 1
    (2), pp. 201-211. Wobbrock, J.O., Findlater, L., Gergle, D. and
    Higgins,J.J. (2011). The Aligned Rank Transform for nonparametric factorial
    analyses using only ANOVA procedures. Proceedings of the ACM Conference on
    Human Factors in Computing Systems (CHI '11). New York: ACM Press, pp.
    143-146. <doi:10.1145/1978942.1978963>.
Fit and simulate ARTFIMA. Theoretical autocovariance function and spectral density function for stationary ARTFIMA.
For instructions, check <https://github.com/Hzhang-ouce/ARTofR>. This is a wrapper of 'bannerCommenter', for inserting neat comments, headers and dividers.
The aligned rank transform for nonparametric
    factorial ANOVAs as described by Wobbrock,
    Findlater, Gergle, and Higgins (2011) <doi:10.1145/1978942.1978963>. 
    Also supports aligned rank transform contrasts as described
    by Elkin, Kay, Higgins, and Wobbrock (2021)
    <doi:10.1145/3472749.3474784>.
Pathway and gene level association test using raw data or summary statistics.
Provides algorithms for creating artworks in the 'ggplot2' language that incorporate some form of randomness.
Provides the infrastructure for representing, manipulating and analyzing 
  transaction data and patterns (frequent itemsets and association rules). 
  Also provides C implementations of the association mining algorithms Apriori and Eclat. 
  Hahsler, Gruen and Hornik (2005) <doi:10.18637/jss.v014.i15>.
Provides the infrastructure for association rule-based classification including the algorithms CBA, CMAR, CPAR, C4.5, FOIL, PART, PRM, RCAR, and RIPPER to build associative classifiers.
NBMiner is an implementation of the model-based mining algorithm for mining NB-frequent itemsets and NB-precise rules. Michael Hahsler (2006) <doi:10.1007/s10618-005-0026-2>.
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. Michael Hahsler (2017) <doi:10.32614/RJ-2017-047>.
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.
Datasets to be used primarily in conjunction with Ascent
    training materials but also for the book 'SAMS Teach Yourself R in 24 Hours' (ISBN: 978-0-672-33848-9).
    Version 1.0-7 is largely for use with the book; however, version 1.1 has a much greater focus on use with
    training materials, whilst retaining compatibility with the book.
Coerce R object to 'asciidoc', 'txt2tags',
    'restructuredText', 'org', 'textile' or 'pandoc' syntax.
    Package comes with a set of drivers for 'Sweave'.
Record 'asciicast' screen casts from R scripts. Convert them
    to animated SVG images, to be used in 'README' files, or blog posts.
    Includes 'asciinema-player' as an 'HTML' widget, and an 'asciicast'
    'knitr' engine, to embed 'ascii' screen casts in 'Rmarkdown' documents.
Create ASCII line graphs of a time series directly on
    your terminal in an easy way. There are some configurations you
    can add to make the plot the way you like. This project was
    inspired by the original 'asciichart' package by Igor Kroitor.
An ASCII ruler is for measuring text and is especially useful for sequence analysis. Included in this package are methods to create ASCII rulers and associated GenBank sequence blocks, multi-column text displays that make it easy for viewers to locate nucleotides by position.
Lets you open a fixed-width ASCII file (.txt or
    .dat) that has an accompanying setup file (.sps or .sas). These file
    combinations are sometimes referred to as .txt+.sps, .txt+.sas,
    .dat+.sps, or .dat+.sas. This will only run in a txt-sps or txt-sas
    pair in which the setup file contains instructions to open that text
    file. It will NOT open other text files, .sav, .sas, or .por data
    files. Fixed-width ASCII files with setup files are common in older
    (pre-2000) government data.
A spatiotemporal model that simulates the spread of Ascochyta
    blight in chickpea fields based on location-specific weather conditions.
    This model is adapted from a model developed by Diggle et al. (2002)
   <doi:10.1094/PHYTO.2002.92.10.1110> for simulating the spread of anthracnose
   in a lupin field.
Package runs simulations for adaptive seamless designs with and without early outcomes 
                           for treatment selection and subpopulation type designs.
A simple driver that reads binary data created by the ASD Inc.
    portable spectrometer instruments, such as the FieldSpec (for more information,
    see <http://www.asdi.com/products/fieldspec-spectroradiometers>). Spectral data
    can be extracted from the ASD files as raw (DN), white reference, radiance, or
    reflectance. Additionally, the metadata information contained in the ASD file
    header can also be accessed.
The Australian Statistical Geography Standard ('ASGS') is 
  a set of shapefiles by the Australian Bureau of Statistics. This package
  provides an interface to those shapefiles, as well as methods for converting
  coordinates to shapefiles.
David Scott's ASH routines ported from S-PLUS to R.
The R package 'ashr' implements an Empirical Bayes
    approach for large-scale hypothesis testing and false discovery
    rate (FDR) estimation based on the methods proposed in
    M. Stephens, 2016, "False discovery rates: a new deal",
    <DOI:10.1093/biostatistics/kxw041>. These methods can be applied
    whenever two sets of summary statistics---estimated effects and
    standard errors---are available, just as 'qvalue' can be applied
    to previously computed p-values. Two main interfaces are
    provided: ash(), which is more user-friendly; and ash.workhorse(),
    which has more options and is geared toward advanced users. The
    ash() and ash.workhorse() also provides a flexible modeling
    interface that can accommodate a variety of likelihoods (e.g.,
    normal, Poisson) and mixture priors (e.g., uniform, normal).
Gives some hypothesis test functions (sign test, median and other quantile tests, Wilcoxon signed rank test, coefficient of variation test, test of normal variance, test on weighted sums of Poisson [see Fay and Kim <doi:10.1002/bimj.201600111>], sample size for t-tests with different variances and non-equal n per arm, Behrens-Fisher test, nonparametric ABC intervals, Wilcoxon-Mann-Whitney test [with effect estimates and confidence intervals, see Fay and Malinovsky <doi:10.1002/sim.7890>], two-sample melding tests [see Fay, Proschan, and Brittain <doi:10.1111/biom.12231>], one-way ANOVA allowing var.equal=FALSE [see Brown and Forsythe, 1974, Biometrics]), prevalence confidence intervals that adjust for sensitivity and specificity [see Lang and Reiczigel, 2014 <doi:10.1016/j.prevetmed.2013.09.015>]). The focus is on hypothesis tests that have compatible confidence intervals, but some functions only have confidence intervals (e.g., prevSeSp).
'Asio' is a cross-platform C++ library for network and low-level
 I/O programming that provides developers with a consistent asynchronous model
 using a modern C++ approach. It is also included in Boost but requires linking
 when used with Boost. Standalone it can be used header-only (provided a recent
 compiler). 'Asio' is written and maintained by Christopher M. Kohlhoff, and
 released under the 'Boost Software License', Version 1.0.
Efficiently perform complex satellite image processes automatically with minimum inputs. 
  Functions are providing more control on the user to specify how the function needs to be executed by offering more customization options and facilitate more functionalities. 
  The functions are designed to identify the type of input satellite images and perform accordingly. 
  Also, some functions are giving options to perform multiple satellite data (even from different types) in single run.
  Package currently supports satellite images from most widely used Landsat 4,5,7 and 8 and Sentinel-2 MSI data. 
  The primary applications of this package are given below.
  1. Conversion of optical bands to top of atmosphere reflectance.
  2. Conversion of thermal bands to corresponding temperature images. 
  3. Derive application oriented products directly from source satellite image bands.
  4. Compute user defined equation and produce corresponding image product.
  5. Other basic tools for satellite image processing.
  REFERENCES.
  i. Chander and Markham (2003) <doi:10.1109/TGRS.2003.818464>.
  ii. Roy et.al, (2014) <doi:10.1016/j.rse.2014.02.001>.
  iii. Abrams (2000) <doi:10.1080/014311600210326>.
Cross-platform utilities for prompting the user for credentials or a 
    passphrase, for example to authenticate with a server or read a protected key.
    Includes native programs for MacOS and Windows, hence no 'tcltk' is required. 
    Password entry can be invoked in two different ways: directly from R via the 
    askpass() function, or indirectly as password-entry back-end for 'ssh-agent' 
    or 'git-credential' via the SSH_ASKPASS and GIT_ASKPASS environment variables.
    Thereby the user can be prompted for credentials or a passphrase if needed 
    when R calls out to git or ssh.
Provides an interface to the algorithm selection benchmark library
    at <http://www.aslib.net> and the 'LLAMA' package
    (<https://cran.r-project.org/package=llama>) for building
    algorithm selection models; see Bischl et al. (2016)
    <doi:10.1016/j.artint.2016.04.003>.
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, as described in Whitehead (2007) Analyzing Animal Societies <University of Chicago Press> and Farine & Whitehead (2015) <doi: 10.1111/1365-2656.12418>. 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.
The aSPC test is designed to test global association between two groups of variables potentially with moderate to high dimension (e.g. in hundreds). The aSPC is particularly useful when the association signals between two groups of variables are sparse.
Contains various functions for optimal scaling. One function performs optimal scaling by maximizing an aspect (i.e. a target function such as the sum of eigenvalues, sum of squared correlations, squared multiple correlations, etc.) of the corresponding correlation matrix. Another function performs implements the LINEALS approach for optimal scaling by minimization of an aspect based on pairwise correlations and correlation ratios. The resulting correlation matrix and category scores can be used for further multivariate methods such as structural equation models.
Tools for the analysis and visualization of bilateral asymmetry in parasitic infections.
R codes for the (adaptive) Sum of Powered Score ('SPU' and 'aSPU')
    tests, inverse variance weighted Sum of Powered score ('SPUw' and 'aSPUw') tests
    and gene-based and some pathway based association tests (Pathway based Sum of
    Powered Score tests ('SPUpath'), adaptive 'SPUpath' ('aSPUpath') test, 'GEEaSPU'
    test for multiple traits - single 'SNP' (single nucleotide polymorphism)
    association in generalized estimation equations, 'MTaSPUs' test for multiple
    traits - single 'SNP' association with Genome Wide Association Studies ('GWAS')
    summary statistics, Gene-based Association Test that uses an extended 'Simes'
    procedure ('GATES'), Hybrid Set-based Test ('HYST') and extended version
    of 'GATES' test for pathway-based association testing ('GATES-Simes'). ).
    The tests can be used with genetic and other data sets with covariates. The
    response variable is binary or quantitative. Summary; (1) Single trait-'SNP' set
    association with individual-level data ('aSPU', 'aSPUw', 'aSPUr'), (2) Single trait-'SNP'
    set association with summary statistics ('aSPUs'), (3) Single trait-pathway
    association with individual-level data ('aSPUpath'), (4) Single trait-pathway
    association with summary statistics ('aSPUsPath'), (5) Multiple traits-single
    'SNP' association with individual-level data ('GEEaSPU'), (6) Multiple traits-
    single 'SNP' association with summary statistics ('MTaSPUs'), (7) Multiple traits-'SNP' set association with summary statistics('MTaSPUsSet'), (8) Multiple traits-pathway association with summary statistics('MTaSPUsSetPath').
Assists in automating the selection of terms to include in mixed models when  
  'asreml' is used to fit the models. Also used to display, in tables and graphs, predictions   
  obtained using any model fitting function and to explore differences between predictions. 
  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 (for further 
  details see 'asremlPlus-package' in help). 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 'asreml' package provides a 
  computationally efficient algorithm for fitting mixed models using Residual Maximum 
  Likelihood. It is a commercial package that can be purchased from 
  'VSNi' <https://www.vsni.co.uk/> as 'asreml-R', who will supply a zip file for local 
  installation/updating (see <https://asreml.kb.vsni.co.uk/>). It is not needed for functions that are 
  methods for 'alldiffs'  and 'data.frame' objects. The package 'asremPlus' can also be 
  installed from <http://chris.brien.name/rpackages/>.
Functions to model and decompose time series into principal components using singular spectrum analysis (de Carvalho and Rua (2017) <doi:10.1016/j.ijforecast.2015.09.004>; de Carvalho et al (2012) <doi:10.1016/j.econlet.2011.09.007>).
Construct pharmacometric nonlinear mixed effect models by combining 
    predefined model components and automatically generate model code for NONMEM. 
    Models are created by combining parameter and observation models, algebraic 
    relationships, compartments, and flows. Pharmacokinetic models can be assembled
    from the higher-order components: absorption, distribution, and elimination. 
    The generated code is optimized for performance by recognizing, for example,
    linear differential equations or differential equations with an analytic 
    solution.
Lightweight validation tool for checking function arguments and 
  validating data analysis scripts. This is an alternative to stopifnot() from 
  the 'base' package  and to assert_that() from the 'assertthat' package. It 
  provides more informative error messages and facilitates debugging.
Simple, flexible, assertions on data.frame or data.table objects with verbose output for vetting. While other assertion packages apply towards more general use-cases, assertable is tailored towards tabular data. It includes functions to check variable names and values, whether the dataset contains all combinations of a given set of unique identifiers, and whether it is a certain length. In addition, assertable includes utility functions to check the existence of target files and to efficiently import multiple tabular data files into one data.table.
Lots of predicates (is_* functions) to check the state of your
    variables, and assertions (assert_* functions) to throw errors if they
    aren't in the right form.
A minimal set of predicates and assertions used by the assertive
    package.  This is mainly for use by other package developers who want to
    include run-time testing features in their own packages.  End-users will
    usually want to use assertive directly.
A set of predicates and assertions for checking the properties of
    code. This is mainly for use by other package developers who want to include
    run-time testing features in their own packages. End-users will usually want to
    use assertive directly.
A set of predicates and assertions for checking the properties of
    (country independent) complex data types.  This is mainly for use by other
    package developers who want to include run-time testing features in
    their own packages.  End-users will usually want to use assertive directly.
A set of predicates and assertions for checking the properties of
    UK-specific complex data types.  This is mainly for use by other package
    developers who want to include run-time testing features in their own
    packages.  End-users will usually want to use assertive directly.
A set of predicates and assertions for checking the properties of
    US-specific complex data types.  This is mainly for use by other package
    developers who want to include run-time testing features in their own
    packages.  End-users will usually want to use assertive directly.
A set of predicates and assertions for checking the properties of
    dates and times.  This is mainly for use by other package developers who
    want to include run-time testing features in their own packages.  End-users
    will usually want to use assertive directly.
A set of predicates and assertions for checking the properties of
    files and connections.  This is mainly for use by other package developers
    who want to include run-time testing features in their own packages.
    End-users will usually want to use assertive directly.
A set of predicates and assertions for checking the properties of
    matrices.  This is mainly for use by other package developers who want to
    include run-time testing features in their own packages.  End-users will
    usually want to use assertive directly.
A set of predicates and assertions for checking the properties of
    models.  This is mainly for use by other package developers who want to
    include run-time testing features in their own packages.  End-users will
    usually want to use assertive directly.
A set of predicates and assertions for checking the properties of
    numbers.  This is mainly for use by other package developers who want to
    include run-time testing features in their own packages.  End-users will
    usually want to use assertive directly.
A set of predicates and assertions for checking the properties of
    variables, such as length, names and attributes.  This is mainly for use by
    other package developers who want to include run-time testing features in
    their own packages.  End-users will usually want to use assertive directly.
A set of predicates and assertions for checking the state and
    capabilities of R, the operating system it is running on, and the IDE
    being used.  This is mainly for use by other package developers who
    want to include run-time testing features in their own packages.
    End-users will usually want to use assertive directly.
A set of predicates and assertions for checking the properties of
    sets.  This is mainly for use by other package developers who want to
    include run-time testing features in their own packages.  End-users will
    usually want to use assertive directly.
A set of predicates and assertions for checking the properties of
    strings.  This is mainly for use by other package developers who want to
    include run-time testing features in their own packages.  End-users will
    usually want to use assertive directly.
A set of predicates and assertions for checking the types of
    variables.  This is mainly for use by other package developers who want to
    include run-time testing features in their own packages.  End-users will
    usually want to use assertive directly.
Provides functionality to assert conditions
    that have to be met so that errors in data used in
    analysis pipelines can fail quickly. Similar to
    'stopifnot()' but more powerful, friendly, and easier
    for use in pipelines.
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's gone
    wrong.
Easy and quick testing of customizable asset allocation strategies.
    Users can rely on their own data, or have the package automatically
    download data from Yahoo Finance (<https://finance.yahoo.com/>). Several 
    pre-loaded portfolios with data are available, including some which are 
    discussed in Faber (2015, ISBN:9780988679924).
Functions for the estimation of intra- and inter-cohort correlations in the Vasicek credit portfolio model. For intra-cohort correlations, the package covers the two method of moments estimators of Gordy (2000) <doi:10.1016/S0378-4266(99)00054-0>, the method of moments estimator of Lucas (1995) <https://jfi.pm-research.com/content/4/4/76> and a Binomial approximation extension of this approach. Moreover, the maximum likelihood estimators of Gordy and Heitfield (2010) <http://elsa.berkeley.edu/~mcfadden/e242_f03/heitfield.pdf> and Duellmann and Gehde-Trapp (2004) <http://hdl.handle.net/10419/19729> are implemented. For inter-cohort correlations, the method of moments estimator of Bluhm and Overbeck (2003) <doi:10.1007/978-3-642-59365-9_2>/Bams et al. (2016) <https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2676595> is provided and the maximum likelihood estimators comprise the approaches of Gordy and Heitfield (2010)/Kalkbrener and Onwunta (2010) <ISBN: 978-1906348250> and Pfeuffer et al. (2020). Bootstrap and Jackknife procedures for bias correction are included as well as the method of moments estimator of Frei and Wunsch (2018) <doi:10.21314/JCR.2017.231> for auto-correlated time series.
Calculates the optimal price of assets (such as
	airline flight seats, hotel room bookings) whose value
	becomes zero after a fixed ``expiry date''.  Assumes
	potential customers arrive (possibly in groups) according
	to a known inhomogeneous Poisson process.  Also assumes a
	known time-varying elasticity of demand (price sensitivity)
	function.  Uses elementary techniques based on ordinary
	differential equations.  Uses the package deSolve to effect
	the solution of these differential equations.
Use Monte-Carlo and K-fold cross-validation coupled with machine-
    learning classification algorithms to perform population assignment, with
    functionalities of evaluating discriminatory power of independent training
    samples, identifying informative loci, reducing data dimensionality for genomic
    data, integrating genetic and non-genetic data, and visualizing results.
Routines for re-scaling isotope maps using known-origin tissue isotope data, assigning origin of unknown samples, and summarizing and assessing assignment results. Methods are adapted from Wunder (2010, in ISBN:9789048133536) and Vander Zanden, H. B. et al. (2014) <doi:10.1111/2041-210X.12229> as described in Ma, C. et al. (2020) <doi:10.1111/2041-210X.13426>.
Clinical trial design for subgroup selection in three-stage group
    sequential trial. Includes facilities for design, exploration and analysis of
    such trials. An implementation of the initial DEFUSE-3 trial is also provided
    as a vignette.
When doing association analysis one does not always have the 
   genotypes for the control population.  In such cases it may be 
   necessary to fall back on frequency based tests using well known sources 
   for the frequencies in the control population, for instance, from the 
   1000 Genomes Project.  The Allele Frequency Comparison ('AssocAFC') package 
   performs multiple rare variant association analyses in both population and 
   family-based GWAS (Genome-Wide Association Study) designs. It includes 
   three score tests that are based on the difference of the sum of allele 
   frequencies between cases and controls.  Two of these tests, Wcorrected() 
   and Wqls(), are collapsing-based tests and suffer from having protective 
   and risk variants. The third test, afcSKAT(), is a score test that 
   overcomes the mix of SNP (Single-Nucleotide Polymorphism) effect 
   directions. For more details see 
   Saad M and Wijsman EM (2017) <doi:10.1093/bib/bbx107>.
Implements several new association indices that can control for various types of errors. Also includes existing association indices and functions for simulating the effects of different rates of error on estimates of association strength between individuals using each method.
An interactive document on  the topic of association rule mining analysis using 'rmarkdown' and 'shiny' packages. Runtime examples are provided in the package function as well as at  <https://kartikeyabolar.shinyapps.io/ASSOCShiny/>.
Some procedures including EIGENSTRAT (a procedure for detecting and correcting 
	for population stratification through searching for the eigenvectors in genetic association 
	studies), PCoC (a procedure for correcting for population stratification through calculating 
	the principal coordinates and the clustering of the subjects), Tracy-Widom test (a procedure 
	for detecting the significant eigenvalues of a matrix), distance regression (a procedure for 
	detecting the association between a distance matrix and some independent variants of interest), 
	single-marker test (a procedure for identifying the association between the genotype at a 
	biallelic marker and a trait using the Wald test or the Fisher's exact test), MAX3 (a procedure 
	for testing for the association between a single nucleotide polymorphism and a binary phenotype 
	using the maximum value of the three test statistics derived for the recessive, additive, and 
	dominant models), nonparametric trend test (a procedure for testing for the association between 
	a genetic variant and a non-normal distributed quantitative trait based on the nonparametric 
	risk), and nonparametric MAX3 (a procedure for testing for the association between a biallelic 
	single nucleotide polymorphism and a quantitative trait using the maximum value of the three 
	nonparametric trend tests derived for the recessive, additive, and dominant models), which are 
	commonly used in genetic association studies. To cite this package in publications use: 
	Lin Wang, Wei Zhang, and Qizhai Li. AssocTests: An R Package for Genetic Association Studies. 
	Journal of Statistical Software. 2020; 94(5): 1-26.
Functions to calculate the assortment of vertices in social networks.  This can be measured on both weighted and binary networks, with discrete or continuous vertex values.
R package with statistical tests and methods for genetic
    association studies with emphasis on rare variants and binary (dichotomous)
    traits
Fits a model to adjust and consider additional variations in three dimensions of age groups, time, and space on residuals excluded from a prediction model that have residual such as: linear regression, mixed model and so on. Details are given in Foreman et al. (2015) <doi:10.1186/1478-7954-10-1>.
Enable translation of a tiny subset of R to C++. The user has to define a R function which gets translated. For a full list of possible functions check the documentation. After translation an external pointer to the C++ function is returned to the user. The intention of the package is to generate fast functions which can be used as ode-system or during optimization.
Aster models (Geyer, Wagenius, and Shaw, 2007,
    <doi:10.1093/biomet/asm030>; Shaw, Geyer, Wagenius, Hangelbroek, and
    Etterson, 2008, <doi:10.1086/588063>; Geyer, Ridley, Latta, Etterson,
    and Shaw, 2013, <doi:10.1214/13-AOAS653>) 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, life table analysis,
    zero-inflated Poisson regression, and
    generalized linear models that are exponential family (e. g., logistic
    regression and Poisson regression with log link) are special cases.
    Main use is for data in which there is survival over discrete time periods
    and there is additional data about what happens conditional on survival
    (e. g., number of offspring).  Uses the exponential family canonical
    parameterization (aster transform of usual parameterization).
    There are also random effects versions of these models.
Aster models are exponential family regression models for life
    history analysis.  They are like generalized linear models except that
    elements of the response vector can have different families (e. g.,
    some Bernoulli, some Poisson, some zero-truncated Poisson, some normal)
    and can be dependent, the dependence indicated by a graphical structure.
    Discrete time survival analysis, zero-inflated Poisson regression, and
    generalized linear models that are exponential family (e. g., logistic
    regression and Poisson regression with log link) are special cases.
    Main use is for data in which there is survival over discrete time periods
    and there is additional data about what happens conditional on survival
    (e. g., number of offspring).  Uses the exponential family canonical
    parameterization (aster transform of usual parameterization).
    Unlike the aster package, this package does dependence groups (nodes of
    the graph need not be conditionally independent given their predecessor
    node), including multinomial and two-parameter normal as families.  Thus
    this package also generalizes mark-capture-recapture analysis.
Provides basic functionalities to calculate the position of
    satellites given a known state vector. The package includes implementations
    of the SGP4 and SDP4 simplified perturbation models to propagate orbital
    state vectors, as well as utilities to read TLE files and convert coordinates
    between different frames of reference. Several of the functionalities of the
    package (including the high-precision numerical orbit propagator) require
    the coefficients and data included in the 'asteRiskData' package, available
    in a 'drat' repository. To install this data package, run 
    'install.packages("asteRiskData", repos="https://rafael-ayala.github.io/drat/")'.
    Felix R. Hoots, Ronald L. Roehrich and T.S. Kelso (1988) <https://celestrak.com/NORAD/documentation/spacetrk.pdf>.
    David Vallado, Paul Crawford, Richard Hujsak and T.S. Kelso (2012) <doi:10.2514/6.2006-6753>.
    Felix R. Hoots, Paul W. Schumacher Jr. and Robert A. Glover (2014) <doi:10.2514/1.9161>.
Data sets and examples from National Health and Nutritional Examination Survey (NHANES).
Routines for astrochronologic testing, astronomical time scale construction, and time series analysis <doi:10.1016/j.earscirev.2018.11.015>. 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.
Data sets and scripts to accompany Time Series Analysis and Its Applications: With R Examples (4th ed), by R.H. Shumway and D.S. Stoffer. Springer Texts in Statistics, 2017, <DOI:10.1007/978-3-319-52452-8>, and Time Series: A Data Analysis Approach Using R. Chapman-Hall, 2019, <DOI:10.1201/9780429273285>.
Provides the ASUS procedure for estimating a high dimensional sparse parameter in the presence of auxiliary data that encode side information on sparsity.
    It is a robust data combination procedure in the sense that even when pooling non-informative auxiliary data ASUS would be at least as efficient as competing soft thresholding based methods that do not use auxiliary data.
    For more information, please see the website <http://www-bcf.usc.edu/~wenguans/Papers/ASUS.htm> and the accompanying paper.
A collection of functions related to density estimation by using Chen's (2000) idea. Mean Squared Errors (MSE) are calculated for estimated curves. For this purpose, R functions allow the distribution to be Gamma, Exponential or Weibull. For details see Chen (2000), Scaillet (2004) <doi:10.1080/10485250310001624819> and Khan and Akbar.
Data on Asylum and Resettlement for the UK,
    provided by the Home Office <https://www.gov.uk/government/statistics/immigration-statistics-year-ending-june-2021>.
Computes asymmetric LD measures (ALD) for multi-allelic genetic data. These measures are identical to the correlation measure (r) for bi-allelic data.
Response surface designs (RSDs) are widely used for Response Surface Methodology (RSM) based optimization studies, which aid in exploring the relationship between a group of explanatory variables and one or more response variable(s) (G.E.P. Box and K.B. Wilson (1951), "On the experimental attainment of optimum conditions" ; M. Hemavathi, Shashi Shekhar, Eldho Varghese, Seema Jaggi, Bikas Sinha & Nripes Kumar Mandal (2022) <DOI: 10.1080/03610926.2021.1944213>."Theoretical developments in response surface designs: an informative review and further thoughts".). Second order rotatable designs are the most prominent and popular class of designs used for process and product optimization trials but it is suitable for situations when all the number of levels for each factor is the same. In many practical situations, RSDs with asymmetric levels (J.S. Mehta and M.N. Das (1968). "Asymmetric rotatable designs and orthogonal transformations" ; M. Hemavathi, Eldho Varghese, Shashi Shekhar & Seema Jaggi (2020) <DOI: 10.1080/02664763.2020.1864817>. "Sequential asymmetric third order rotatable designs (SATORDs)" .) are more suitable as these designs explore more regions in the design space.This package contains functions named Asords() ,CCD_coded(), CCD_original(), SORD_coded() and SORD_original() for generating asymmetric/symmetric RSDs along with the randomized layout. It also contains another function named Pred.var() for generating the variance of predicted response as well as the moment matrix based on a second order model.
Multidimensional scaling models and methods for the visualization for asymmetric data <doi:10.1111/j.2044-8317.1996.tb01078.x>. An asymmetric matrix has the same number of rows and columns, and these rows and columns refer to the same set of objects. At least some elements in the upper-triangle are different from the corresponding elements in the lower triangle. An example is a  student migration table, where the rows correspond to the countries of origin of the students and the columns to the destination countries. This package provides the slide-vector model <doi:10.1007/BF02294474>, a scaling model with unique dimensions and the asymscal model for asymmetric multidimensional scaling. Furthermore, a heat map for skew-symmetric data, and the decomposition of asymmetry are provided for the analysis of asymmetric tables.
Provides functions and examples for the weak and strong density asymmetry measures in the articles: "A measure of asymmetry", Patil, Patil and Bagkavos (2012) <doi:10.1007/s00362-011-0401-6> and "A measure of asymmetry based on a new necessary and sufficient condition for symmetry", Patil, Bagkavos and Wood (2014) <doi:10.1007/s13171-013-0034-z>. The measures provided here are useful for quantifying the asymmetry of the shape of a density of a random variable. The package facilitates implementation of the measures which are applicable in a variety of fields including e.g. probability theory, statistics and economics.
One and two sample mean and variance
 tests (differences and ratios) are considered.
 The test statistics are all expressed in the same
 form as the Student t-test, which facilitates their
 presentation in the classroom. This contribution
 also fills the gap of a robust (to non-normality)
 alternative to the chi-square single variance test
 for large samples, since no such procedure is implemented
 in standard statistical software.
Estimate the lower and upper bound of asymptomatic cases in an 
    epidemic using the capture/recapture methods from Böhning et al. (2020) 
    <doi:10.1016/j.ijid.2020.06.009> and Rocchetti et al. (2020) 
    <doi:10.1101/2020.07.14.20153445>.
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. Cao, H., Li, J., and Fine, J. P. (2016) <doi:10.1214/16-EJS1141>. Cao, H., Zeng, D., and Fine, J. P. (2015) <doi:10.1111/rssb.12086>.
A set of routines written in the S language
             that calculate power and related quantities utilizing asymptotic
             likelihood ratio methods.
Provides a collection of psychometric methods to process item metadata
 and use target assessment and measurement blueprint constraints to assemble a test form. Currently two automatic
 test assembly (ata) approaches are enabled. For example, the weighted (positive) deviations method, wdm(), proposed
 by Swanson and Stocking (1993) <doi:10.1177/014662169301700205> was implemented in its full specification allowing
 for both item selection as well as test form refinement. The linear constraint programming approach, atalp(), uses the 
 linear equation solver by Berkelaar et. al (2014) <http://lpsolve.sourceforge.net/5.5/>
 to enable a variety of approaches to select items.
Create Tables for Reporting Clinical Trials.
  Calculates descriptive statistics and hypothesis tests, 
  arranges the results in a table ready for reporting with LaTeX, HTML or Word.
The Ata method (Yapar et al. (2019) <doi:10.15672/hujms.461032>), an alternative to exponential smoothing (described in Yapar (2016) <doi:10.15672/HJMS.201614320580>, 
	Yapar et al. (2017) <doi:10.15672/HJMS.2017.493>), is a new univariate time series forecasting method which provides innovative solutions to issues faced during the 
	initialization and optimization stages of existing forecasting methods. Forecasting performance of the Ata method is superior to existing methods both in terms of easy 
	implementation and accurate forecasting. It can be applied to non-seasonal or seasonal time series which can be decomposed into four components (remainder, level, trend 
	and seasonal). This methodology performed well on the M3 and M4-competition data. This package was written based on Ali Sabri Taylan’s PhD dissertation.
Point-scale variogram deconvolution from irregular/regular spatial support according to Goovaerts, P., (2008) <doi: 10.1007/s11004-007-9129-1>; ordinary area-to-area (co)Kriging and area-to-point (co)Kriging.
Estimation and inference methods for bounding average treatment effects (on the treated) that are valid under an unconfoundedness assumption. 
    The bounds are designed to be robust in challenging situations, for example, when the conditioning variables take on a large number of different values in the observed sample, or when the overlap condition is violated. 
    This robustness is achieved by only using limited "pooling" of information across observations. 
    For more details, see the paper by Lee and Weidner (2021), "Bounding Treatment Effects by Pooling Limited Information across Observations," <arXiv:2111.05243>.
Nonparametric estimation and inference for average treatment effects based on covariate balancing.
Stanford 'ATLAS' (Advanced Temporal Search Engine) is a powerful tool that allows constructing
             cohorts of patients extremely quickly and efficiently. This package is designed to interface directly
             with an instance of 'ATLAS' search engine and facilitates API queries and data dumps. Prerequisite
             is a good knowledge of the temporal language to be able to efficiently construct a query.
             More information available at <https://shahlab.stanford.edu/start>.
Download air quality and meteorological information of Chile from  the National Air Quality System (S.I.N.C.A.)<https://sinca.mma.gob.cl/> dependent on the Ministry of the Environment and the Meteorological Directorate of Chile (D.M.C.)<http://www.meteochile.gob.cl/> dependent on the Directorate General of Civil Aeronautics.
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.
A collection of functions for smart sampling and sensitivity analysis for metrology applications, including computationally expensive problems.
Provides functions for implementing the Analysis-of-marginal-Tail-Means (ATM) method, a robust optimization method for discrete black-box optimization. Technical details can be found in Mak and Wu (2018+) <arXiv:1712.03589>. This work was supported by USARO grant W911NF-17-1-0007.
Calculates acoustic traveltimes and ray paths in 1-D,
        linear atmospheres.  Later versions will support arbitrary 1-D
        atmospheric models, such as radiosonde measurements and
        standard reference atmospheres.
Implements the differential equations associated to different versions of Allometric Trophic Models (ATN) to estimate the temporal dynamics of species biomasses in food webs. It offers several features to generate synthetic food webs and to parametrise models as well as a wrapper to the ODE solver deSolve.
Provides tools to read/write/publish metadata based on the 'Atom' XML syndication format. This includes
 support of 'Dublin Core' XML implementation, and a client to API(s) implementing the 'AtomPub' 'SWORD' API specification.
Plot party trees in left-right orientation instead of the
  classical top-down layout.
Contains some tools for testing, analyzing time series data and
    fitting popular time series models such as ARIMA, Moving Average and Holt
    Winters, etc. Most functions also provide nice and clear outputs like SAS
    does, such as identify, estimate and forecast, which are the same statements
    in PROC ARIMA in SAS.
Provides functions for retrieving time-series and related
    meta-data such as entities, metrics, and tags from the Axibase
    Time-Series Database (ATSD). ATSD is a non-relational clustered
    database used for storing performance measurements from IT infrastructure
    resources: servers, network devices, storage systems, and applications.
Tools to help manage dependencies during package
    development.  This can retrieve all dependencies that are used in R
    files in the "R" directory, in Rmd files in "vignettes" directory and
    in 'roxygen2' documentation of functions. There is a function to
    update the Description file of your package and a function to create a
    file with the R commands to install all dependencies of your package.
    All functions to retrieve dependencies of R scripts and Rmd files can
    be used independently of a package development.
Tools for defensive programming, inspired by 'purrr' mappers and 
    based on 'rlang'.'attempt' extends and facilitates defensive programming by 
    providing a consistent grammar, and provides a set of easy to use functions 
    for common tests and conditions. 'attempt' only depends on 'rlang', and 
    focuses on speed, so it can be easily integrated in other functions and 
    used in data analysis.
Confidence curves, confidence intervals and p-values for 
   correlation coefficients corrected for attenuation due to measurement error.
   Implements the methods described in Moss (2019, <arxiv:1911.01576>).
Provides functions for estimating the attributable burden of disease due to risk factors. The posterior simulation is performed using arm::sim as described in Gelman, Hill (2012) <doi:10.1017/CBO9780511790942> and the attributable burden method is based on Nielsen, Krause, Molbak <doi:10.1111/irv.12564>.
Various 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). Support for visualization and partial areas is included.
Implements methods for identifying linear and nonlinear marker combinations that maximizes the Area Under the AUC Curve (AUC).
Performs statistical testing to compare predictive 
	models based on multiple observations of the A' statistic (also known as 
	Area Under the Receiver Operating Characteristic Curve, or AUC). 
	Specifically, it implements a testing method based on the equivalence 
	between the A' statistic and the Wilcoxon statistic. 
	For more information, see Hanley and McNeil (1982) <doi:10.1148/radiology.143.1.7063747>.
Estimates a first-price auction model with conditionally independent
  private values as described in MacKay (2020) <doi:10.2139/ssrn.3096534>. The
  model allows for unobserved heterogeneity that is common to all bidders in
  addition to observable heterogeneity.
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).
Facilitates plotting audiometric data (mostly) by preparing the coordinate system according to standards, given e. g. in American Speech-Language-Hearing Association (2005), <doi:10.1044/policy.GL2005-00014>.
Find an upper bound for the total amount of overstatement
    of assets in a set of accounts, or estimate the amount of sales tax
    owed on a collection of transactions (Meeden and Sargent, 2007,
    <doi:10.1080/03610920701386802>).
Provides an easy to use unified interface for creating validation plots for any model. 
  The 'auditor' helps to avoid repetitive work consisting of writing code needed to create residual plots. 
  This visualizations allow to asses and compare the goodness of fit, performance, and similarity of models.
Dynamic regression for time series using Extreme Gradient Boosting with hyper-parameter tuning via Bayesian Optimization or Random Search.
A collection of Japanese text processing tools for filling
    Japanese iteration marks, Japanese character type conversions,
    segmentation by phrase, and text normalization which is based on rules
    for the 'Sudachi' morphological analyzer and the 'NEologd' (Neologism
    dictionary for 'MeCab').  These features are specific to Japanese and
    are not implemented in 'ICU' (International Components for Unicode).
Functions for analysis of data generated from experiments in
    augmented randomised complete block design according to Federer, W.T. (1961)
    <doi:10.2307/2527837>. Computes analysis of variance, adjusted means,
    descriptive statistics, genetic variability statistics etc. Further includes
    data visualization and report generation functions.
Implementation of a data augmentation technique based on conditional entropy
    It was devised by both authors during their masters and is discussed in detail in the second author dissertation.
    It is able to create novel samples conditioned on a desired value of a categorical attribute, as a way to augment data for classification tasks
    Tests discussed in the dissertation and future paper present that the technique satisfies several statistical assumptions for the novel samples.
    It also shows significant improvement for machine learning models trained on small data.
Implementation of the augmented
            Simulation-Extrapolation (SIMEX) algorithm proposed by Yi et al. (2015) <doi:10.1080/01621459.2014.922777>
            for analyzing the data with mixed measurement error and misclassification. The main
            function provides a similar summary output as that of glm() function. Both parametric and
            empirical SIMEX are considered in the package.
Extract and process bird sightings records from
    eBird (<http://ebird.org>), an online tool for recording bird
    observations.  Public access to the full eBird database is via the
    eBird Basic Dataset (EBD; see <http://ebird.org/ebird/data/download>
    for access), a downloadable text file. This package is an interface to
    AWK for extracting data from the EBD based on taxonomic, spatial, or
    temporal filters, to produce a manageable file size that can be
    imported into R.
Standard template library sort is
 used to implement an efficient  
 algorithm <arXiv:2107.01285> for computing Area Under Minimum and
 directional derivatives.
Uses locality sensitive hashing and creates a neighbourhood graph for a data set and calculates the adjusted rank index value for the same. It uses Gaussian random planes to decide the nature of a given point. Datar, Mayur, Nicole Immorlica, Piotr Indyk, and Vahab S. Mirrokni(2004) <doi:10.1145/997817.997857>.
'AURIN' <https://aurin.org.au/resources/aurin-apis/> is "Australia's single
    largest resource for accessing clean, integrated, spatially enabled and research-ready
    data on issues surrounding health and wellbeing, socio-economic metrics, transportation,
    and land-use.". This package provides functions to download and search datasets from
    the AURIN API (it's free to use!).
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.
Extraction, preparation, visualisation and analysis of TERN AusPlots ecosystem monitoring data. Direct access to plot-based data on vegetation and soils across Australia, including physical sample barcode numbers. Simple function calls extract the data and merge them into species occurrence matrices for downstream analysis, or calculate things like basal area and fractional cover. TERN AusPlots is a national field plot-based ecosystem surveillance monitoring method and dataset for Australia. The data have been collected across a national network of plots and transects by the Terrestrial Ecosystem Research Network (TERN - <https://www.tern.org.au>), an Australian Government NCRIS-enabled project, and its Ecosystem Surveillance platform (<https://www.tern.org.au/tern-observatory/tern-ecosystem-surveillance/>).
Provides access to biographical and political data about Australian 
    federal politicians who served between 1901 and 2021. This enhances how 
    reproducible research is that uses this data.
Performs approximate unconditional and permutation testing for
    2x2 contingency tables. Motivated by testing for disease association with rare
    genetic variants in case-control studies. When variants are extremely rare,
    these tests give better control of Type I error than standard tests.
Uses Auth0 API (see <https://auth0.com> for more 
    information) to use a simple and secure authentication system. It provides 
    tools to log in and out a shiny application using social networks or a list
    of e-mails.
PCA done by eigenvalue decomposition of a data correlation matrix, here it automatically determines the number of factors by eigenvalue greater than 1 and it gives the uncorrelated variables based on the rotated component scores, Such that in each principal component variable which has the high variance are selected. It will be useful for non-statisticians in selection of variables. For more information, see the <http://www.ijcem.org/papers032013/ijcem_032013_06.pdf> web page.
A framework for automated machine learning. Concretely, the focus is on the optimisation of bagging workflows. A bagging workflows is composed by three phases: (i) generation: which and how many predictive models to learn; (ii) pruning: after learning a set of models, the worst ones are cut off from the ensemble; and (iii) integration: how the models are combined for predicting a new observation. autoBagging optimises these processes by combining metalearning and a learning to rank approach to learn from metadata. It automatically ranks 63 bagging workflows by exploiting past performance and dataset characterization. A complete description of the method can be found in: Pinto, F., Cerqueira, V., Soares, C., Mendes-Moreira, J. (2017): "autoBagging: Learning to Rank Bagging Workflows with Metalearning" arXiv preprint arXiv:1706.09367.
A modified version of the classification and regression tree (CART) 
    algorithm for modelling spatial data that features coordinate information.
    Coordinate information can be used to evaluate measures of spatial
    autocorrelation and spatial compactness during the splitting phase of the
    tree, leading to better predictions and more physically realistic predictions
    on these types of datasets. These methods are described in Ancell and Bean (2021)
    <arXiv:2101.08258>.
Automatically calculates cognostic groups for plot objects and list column plot objects.  Results are returned in a nested data frame.
Contains functions to implement automated covariate selection using methods described in the
             high-dimensional propensity score (HDPS) algorithm by Schneeweiss et.al. Covariate adjustment in real-world-observational-data (RWD) is important for
             for estimating adjusted outcomes and this can be done by using methods such as, but not limited to, propensity score 
             matching, propensity score weighting and regression analysis. While these methods strive to statistically adjust for 
             confounding, the major challenge is in selecting the potential covariates that can bias the outcomes comparison estimates 
             in observational RWD (Real-World-Data). This is where the utility of automated covariate selection comes in. 
             The functions in this package help to implement the three major steps of automated covariate selection as described by
             Schneeweiss et. al elsewhere. These three functions, in order of the steps required to execute automated covariate 
             selection are, get_candidate_covariates(), get_recurrence_covariates() and get_prioritised_covariates(). 
             In addition to these functions, a sample real-world-data from publicly available de-identified medical claims data is 
             also available for running examples and also for further exploration. The original article where the algorithm is described 
             by Schneeweiss et.al. (2009) <doi:10.1097/EDE.0b013e3181a663cc> .
An interface to the 'AutoDesk' 'API' Platform including the Authentication 
    'API' for obtaining authentication to the 'AutoDesk' Forge Platform, Data Management 
    'API' for managing data across the platform's cloud services, Design Automation 'API'
    for performing automated tasks on design files in the cloud, Model
    Derivative 'API' for translating design files into different formats, sending
    them to the viewer app, and extracting design data, and Viewer for rendering
    2D and 3D models (see <https://developer.autodesk.com> for more information).
Implementation of the sparse autoencoder in R environment, following the notes of Andrew Ng (http://www.stanford.edu/class/archive/cs/cs294a/cs294a.1104/sparseAutoencoder.pdf). The features learned by the hidden layer of the autoencoder (through unsupervised learning of unlabeled data) can be used in constructing deep belief neural networks.
Forced-choice (FC) response has gained increasing popularity and interest for its resistance to faking when well-designed (Cao & Drasgow, 2019 <doi:10.1037/apl0000414>). To established well-designed FC scales, typically each item within a block should measure different trait and have similar level of social desirability (Zhang et al., 2020 <doi:10.1177/1094428119836486>). Recent study also suggests the importance of high inter-item agreement of social desirability between items within a block (Pavlov et al., 2021 <doi:10.31234/osf.io/hmnrc>). In addition to this, FC developers may also need to maximize factor loading differences (Brown & Maydeu-Olivares, 2011 <doi:10.1177/0013164410375112>) or minimize item location differences (Cao & Drasgow, 2019 <doi:10.1037/apl0000414>) depending on scoring models. Decision of which items should be assigned to the same block, termed item pairing, is thus critical to the quality of an FC test. This pairing process is essentially an optimization process which is currently carried out manually. However, given that we often need to simultaneously meet multiple objectives, manual pairing becomes impractical or even not feasible once the number of latent traits and/or number of items per trait are relatively large. To address these problems, autoFC is developed as a practical tool for facilitating the automatic construction of FC tests, essentially exempting users from the burden of manual item pairing and reducing the computational costs and biases induced by simple ranking methods. Given characteristics of each item (and item responses), FC tests can be automatically constructed based on user-defined pairing criteria and weights as well as customized optimization behavior. Users can also construct parallel forms of the same test following the same pairing rules.
Automatic fixed rank kriging for (irregularly located)
    spatial data using a class of basis functions with multi-resolution features
    and ordered in terms of their resolutions. The model parameters are estimated
    by maximum likelihood (ML) and the number of basis functions is determined
    by Akaike's information criterion (AIC). For spatial data with either one
    realization or independent replicates, the ML estimates and AIC are efficiently
    computed using their closed-form expressions when no missing value occurs. Details 
    regarding the basis function construction, parameter estimation, and AIC calculation  
    can be found in Tzeng and Huang (2018) <doi:10.1080/00401706.2017.1345701>. For
    data with missing values, the ML estimates are obtained using the expectation-
    maximization algorithm. Apart from the number of basis functions, there are
    no other tuning parameters, making the method fully automatic. Users can also
    include a stationary structure in the spatial covariance, which utilizes
    'LatticeKrig' package.
A customisable set of tools for assessing and grading 
    R or R-markdown scripts from students. It allows for checking correctness 
    of code output, runtime statistics and static code analysis. The latter 
    feature is made possible by representing R expressions using a tree
    structure.
Perform mediation analysis for time to event high-dimensional data.
             Mediation Analysis proposed by Miocevic et al.(2017)  <doi:10.1080/10705511.2017.1342541> 
             as a statistical tool in the Bayesian framework. 
             Time to event data analysis methods like Cox proportional hazard model, 
             accelerated failure time model to work with high dimensional data 
             with Bayesian approaches are provided. Missing data imputation techniques tool
             to work with high dimensional data coupled for mediation analysis by 
             presented by the active mediator variables.
Functions for displaying multiple images  or scatterplots with a color 
    scale, i.e., heat maps, possibly with projected coordinates.  The
    package relies on the base graphics system, so graphics are
    rendered rapidly.
R Interface to 'AutoKeras' <https://autokeras.com/>.
    'AutoKeras' is an open source software library for Automated Machine
    Learning (AutoML). The ultimate goal of AutoML is to provide easily
    accessible deep learning tools to domain experts with limited data science
    or machine learning background. 'AutoKeras' provides functions to
    automatically search for architecture and hyperparameters of deep
    learning models.
Parse R code in a given directory for R packages and attempt to install them from CRAN or GitHub. Optionally use a dependencies file for tighter control over which package versions to install.
Performs an automatic interpolation by automatically estimating the variogram and then calling gstat.
Provides methods for fitting the Mixture of Factor Analyzers (MFA) model automatically. 
    The MFA model is a mixture model where each sub-population is assumed to follow the Factor Analysis model. The Factor Analysis (FA) model is a latent variable model which assumes that observations are normally distributed, but imposes constraints on their covariance matrix. The MFA model contains two hyperparameters; g (the number of components in the mixture) and q (the number of factors in each component Factor Analysis model). Usually, the Expectation-Maximisation algorithm would be used to fit the MFA model, but this requires g and q to be known. This package treats g and q as unknowns and provides several methods which infer these values with as little input from the user as possible.
Fits from simple regression to highly customizable deep neural networks 
    either with gradient descent or metaheuristic, using automatic hyper parameters 
    tuning and custom cost function.
    A mix inspired by the common tricks on Deep Learning and Particle Swarm Optimization.
A tool that improves the prediction performance of multilevel
    regression with post-stratification (MrP) by combining a number of machine
    learning methods. For information on the method, please refer to Broniecki, 
	Wüest, Leemann (2020) ''Improving Multilevel Regression with 
	Post-Stratification Through Machine Learning (autoMrP)'' forthcoming in 
	'Journal of Politics'. Final pre-print version: 
	<https://lucasleemann.files.wordpress.com/2020/07/automrp-r2pa.pdf>.
An unsupervised fully-automated pipeline for transcriptome analysis or a supervised option to identify characteristic genes from predefined subclasses.
              We rely on the 'pamr' <http://www.bioconductor.org/packages//2.7/bioc/html/pamr.html> clustering algorithm to cluster the Data and then draw a heatmap of the clusters with the most significant genes and the 
              least significant genes according to the 'pamr' algorithm. This way we get easy to grasp heatmaps that show us for each cluster which are the clusters most defining genes.
Functionalities to automatically generate interactive visualizations for
             statistical results supported by 'ggfortify', such as time series, PCA,
             clustering and survival analysis, with 'plotly.js' <https://plotly.com/>  and
             'ggplot2' style. The generated visualizations can also be easily extended
             using 'ggplot2' and 'plotly' syntax while staying interactive.
The image of the amino acid transform on the protein level is drawn, and the automatic routing of the functional elements such as the domain and the mutation site is completed.
Performs Rasch analysis (semi-)automatically, which has been shown to be comparable with the standard Rasch analysis (Feri Wijayanto et al. (2021) <doi:10.1111/bmsp.12218>, Feri Wijayanto et al. (2022) <https://psyarxiv.com/erxuh/>).
Make summary tables for descriptive statistics and select explanatory variables 
    automatically in various regression models. Support linear models, generalized linear 
    models and cox-proportional hazard models. Generate publication-ready tables summarizing 
    result of regression analysis and plots. The tables and plots can be exported in "HTML", 
    "pdf('LaTex')", "docx('MS Word')" and "pptx('MS Powerpoint')" documents.
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.
A novel interpretable machine learning-based framework to automate the development of a clinical scoring model for predefined outcomes. Our novel framework consists of six modules: variable ranking with machine learning, variable transformation, score derivation, model selection, domain knowledge-based score fine-tuning, and performance evaluation.The details are described in our research paper<doi:10.2196/21798>. Users or clinicians could seamlessly generate parsimonious sparse-score risk models (i.e., risk scores), which can be easily implemented and validated in clinical practice. We hope to see its application in various medical case studies.
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.
For researchers to quickly and comprehensively acquire disease genes, so as to understand the mechanism of disease,
  we developed this program to acquire disease-related genes.
  The data is integrated from three public databases. The three databases are 'eDGAR', 'DrugBank'
  and 'MalaCards'. The 'eDGAR' is a comprehensive database, containing data on the relationship between disease and genes.
  'DrugBank' contains information on 13443 drugs and 5157 targets. 'MalaCards' integrates human disease information, including disease-related genes.
Static code compilation of a 'shiny' app given an R function (into 'ui.R' and 'server.R' files or into a 'shiny' app object). See examples at <https://github.com/alekrutkowski/autoshiny>.
Automatically do statistical exploration. Create formulas using 'tidyselect' syntax, and then determine cross-validated model accuracy and variable contributions using 'glm' and 'xgboost'. Contains additional helper functions to create and modify formulas. Has a flagship function to quickly determine relationships between categorical and continuous variables in the data set.
Randomly splits data into testing and training sets. Then, uses stepwise selection to fit numerous multiple regression models on the training data, and tests them on the test data. Returned for each model are plots comparing model Akaike Information Criterion (AIC), Pearson correlation coefficient (r) between the predicted and actual values, Mean Absolute Error (MAE), and R-Squared among the models. Each model is ranked relative to the other models by the model evaluation metrics (i.e., AIC, r, MAE, and R-Squared) and the model with the best mean ranking among the model evaluation metrics is returned. Model evaluation metric weights for AIC, r, MAE, and R-Squared are taken in as arguments as aic_wt, r_wt, mae_wt, and r_squ_wt, respectively. They are equally weighted as default but may be adjusted relative to each other if the user prefers one or more metrics to the others, Field, A. (2013, ISBN:978-1-4462-4918-5).
Automatic model selection for structural time series decomposition into trend, cycle, and seasonal components, plus optionality for structural interpolation, using the Kalman filter. 
  Koopman, Siem Jan and Marius Ooms (2012) "Forecasting Economic Time Series Using Unobserved Components Time Series Models" <doi:10.1093/oxfordhb/9780195398649.013.0006>.
  Kim, Chang-Jin and Charles R. Nelson (1999) "State-Space Models with Regime Switching: Classical and Gibbs-Sampling Approaches with Applications" <doi:10.7551/mitpress/6444.001.0001><http://econ.korea.ac.kr/~cjkim/>.
Algorithms for automatically finding appropriate
    thresholds for numerical data, with special functions for thresholding
    images. Provides the 'ImageJ' 'Auto Threshold' plugin functionality to
    R users. See <https://imagej.net/plugins/auto-threshold> and Landini et al.
    (2017) <DOI:10.1111/jmi.12474>.
Offers a set of functions to easily make predictions for univariate time series. 
             'autoTS' is a wrapper of existing functions of the 'forecast' and 'prophet' packages, 
             harmonising their outputs in tidy dataframes and using default values for each.
             The core function getBestModel() allows the user to effortlessly benchmark seven 
             algorithms along with a bagged estimator to identify which one performs the best 
             for a given time series.
Bindings to 'FFmpeg' <http://www.ffmpeg.org/> AV library for working with 
    audio and video in R. Generates high quality video from images or R graphics with 
    custom audio. Also offers high performance tools for reading raw audio, creating
    'spectrograms', and converting between countless audio / video formats. This package 
    interfaces directly to the C API and does not require any command line utilities.
Check if a given package name is available to use. It checks the
  name's validity. Checks if it is used on 'GitHub', 'CRAN' and 'Bioconductor'. Checks
  for unintended meanings by querying Urban Dictionary, 'Wiktionary' and Wikipedia.
Implements the allan variance and allan variance linear regression estimator for latent time series models. More details about the method can be found, for example, in Guerrier, S., Molinari, R., & Stebler, Y. (2016) <doi:10.1109/LSP.2016.2541867>.
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.
It provides users of the R programming language with easy-to-use
  tools for performing complex queries on avidaDB, a semantic database that
  stores genomic and transcriptomic data of self-replicating computer programs
  (known as digital organisms) that mutate and evolve within a user-defined
  computational environment. The functions send SPARQL queries to the
  avidaDB endpoint <https://graphdb.fortunalab.org/repositories/avidaDB>.
Tools to compute the center of gravity and moment of inertia tensor of any 
    flying bird. The tools function by modeling a bird as a composite structure 
    of simple geometric objects. This requires detailed morphological 
    measurements of bird specimens although those obtained for the associated 
    paper have been included in the package for use. Refer to the vignettes and 
    supplementary material for detailed information on the package function.
Research of subgroups in random clinical trials with binary outcome and two treatments groups. This is an adaptation of the Jared Foster method (<https://www.ncbi.nlm.nih.gov/pubmed/21815180>).
Provides a weekly summary of Hass Avocado sales for the 
    contiguous US from January 2017 to November 2020. 
    See the package website for more information, documentation,
    and examples. Data source: Haas Avocado Board 
    <https://hassavocadoboard.com/category-data/>.
Which day a week starts depends heavily on the either the local or
  professional context. This package is designed to be a lightweight solution
  to easily switching between week-based date definitions.
Self-organizing maps (also known as SOM, see Kohonen (2001) <doi:10.1007/978-3-642-56927-2>) are a method for dimensionality reduction and clustering of continuous data. This package introduces interactive (html) graphics for easier analysis of SOM results. It also features an interactive interface, for push-button training and visualization of SOM on numeric, categorical or mixed data, as well as tools to evaluate the quality of SOM.
Make the compiled Java modules of the Amazon Web Services ('AWS') 'SDK' available to be used in downstream R packages interacting with 'AWS'. See <https://aws.amazon.com/sdk-for-java> for more information on the 'AWS' 'SDK' for Java.
'RJDBC' based 'DBI' driver to Amazon Athena, which is an interactive
    query service to analyze data in Amazon 'S3' using standard 'SQL'.
Fetching data from Amazon 'Kinesis' Streams using the Java-based 'MultiLangDaemon' interacting with Amazon Web Services ('AWS') for easy stream processing from R. For more information on 'Kinesis', see <https://aws.amazon.com/kinesis>.
Encrypt plain text and 'decrypt' cipher text using encryption keys hosted at Amazon Web Services ('AWS') Key Management Service ('KMS'), on which see <https://aws.amazon.com/kms> for more information.
We provide a collection of R-functions implementing
    adaptive smoothing procedures in 1D, 2D and 3D. This includes the
    Propagation-Separation Approach to adaptive smoothing,
   the Intersecting Confidence Intervals (ICI), variational approaches and a non-local means filter.
   The package is described in detail in Polzehl J, Papafitsoros K, Tabelow K (2020). 
   Patch-Wise Adaptive Weights Smoothing in R. Journal of Statistical Software, 95(6), 1-27. 
   <doi:10.18637/jss.v095.i06>,
    Usage of the package in neuroimaging is illustrated in Polzehl and Tabelow (2019),
   Magnetic Resonance Brain Imaging, Appendix A, Springer, Use R! Series.
   <doi:10.1007/978-3-030-29184-6_6>.
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.
Client for 'AWS Comprehend' <https://aws.amazon.com/comprehend>, a cloud natural language processing service that can perform a number of quantitative text analyses, including language detection, sentiment analysis, and feature extraction.
Retrieve Amazon EC2 instance metadata from within the running instance.
Providing the functions for communicating with Amazon Web Services(AWS)
    Elastic Compute Cloud(EC2) and Elastic Container Service(ECS).
    The functions will have the prefix 'ecs_' or 'ec2_' depending on the class 
    of the API. The request will be sent via the REST API and the parameters are
    given by the function argument. The credentials can be set via 'aws_set_credentials'.
    The EC2 documentation can be found at <https://docs.aws.amazon.com/AWSEC2/latest/APIReference/Welcome.html>
    and ECS can be found at <https://docs.aws.amazon.com/AmazonECS/latest/APIReference/Welcome.html>.
A simple client for the Amazon Web Services ('AWS') Identity
    and Access Management ('IAM') 'API' <https://aws.amazon.com/iam/>.
Client package for the 'AWS Key Management Service' <https://aws.amazon.com/kms/>, a cloud service for managing encryption keys.
A simple client package for the Amazon Web Services ('AWS') Lambda 
    API <https://aws.amazon.com/lambda/>.
A client for AWS Polly <http://aws.amazon.com/documentation/polly>, a speech synthesis service.
A simple client package for the Amazon Web Services ('AWS') Simple
    Storage Service ('S3') 'REST' 'API' <https://aws.amazon.com/s3/>.
Generates version 2 and version 4 request signatures for Amazon Web Services ('AWS') <https://aws.amazon.com/> Application Programming Interfaces ('APIs') and provides a mechanism for retrieving credentials from environment variables, 'AWS' credentials files, and 'EC2' instance metadata. For use on 'EC2' instances, users will need to install the suggested package 'aws.ec2metadata' <https://cran.r-project.org/package=aws.ec2metadata>.
Client for 'AWS Transcribe' <https://aws.amazon.com/documentation/transcribe>, a cloud transcription service that can convert an audio media file in English and other languages into a text transcript.
A client for 'AWS Translate' <https://aws.amazon.com/documentation/translate>, a machine translation service that will convert a text input in one language into a text output in another language.
Defines the method extract and provides 'openMP' support as needed in several packages.
Computes maximum likelihood estimate of general, zero inflated, and zero altered models for discrete and continuous distributions. Computes Kolmogorov-Smirnov (KS) test and 
             likelihood ratio test for general, zero-inflated, and zero-altered data.
             It obtains the inverse of fisher information matrix and confidence interval for the parameters of general, zero inflated, and zero altered models. It simulate random deviates from zero inflated or hurdle models to obtain maximum likelihood estimate.
             It is based on the work of Aldirawi et al. (2022) <doi:10.1007/s42519-021-00230-y>.
Testing for heterogeneous effects in a case-control setting.
    The aim here to discover an association that is beyond a mean difference
    between all cases and all controls. Instead, the signal of interest here
    is present in only a proportion of the cases. This test should be more 
    powerful than a t-test or Wilcoxon test in this heterogeneous setting.
    Please cite the corresponding paper: Mezlini et al. (2020) 
    <doi:10.1101/2020.03.23.002972>.
Imports Azure Application Insights for web pages into Shiny apps
    via Microsoft's JavaScript snippet. 
    Allows app developers to submit page tracking and submit events.
Provides Azure Active Directory (AAD) authentication functionality for R users of Microsoft's 'Azure' cloud <https://azure.microsoft.com/>. Use this package to obtain 'OAuth' 2.0 tokens for services including Azure Resource Manager, Azure Storage and others. It supports both AAD v1.0 and v2.0, as well as multiple authentication methods, including device code and resource owner grant. Tokens are cached in a user-specific directory obtained using the 'rappdirs' package. The interface is based on the 'OAuth' framework in the 'httr' package, but customised and streamlined for Azure. Part of the 'AzureR' family of packages.
An interface to Azure Cognitive Services <https://docs.microsoft.com/en-us/azure/cognitive-services/>. Both an 'Azure Resource Manager' interface, for deploying Cognitive Services resources, and a client framework are supplied. While 'AzureCognitive' can be called by the end-user, it is meant to provide a foundation for other packages that will support specific services, like Computer Vision, Custom Vision, language translation, and so on. Part of the 'AzureR' family of packages.
An interface to container functionality in Microsoft's 'Azure' cloud: <https://azure.microsoft.com/en-us/product-categories/containers/>. Manage 'Azure Container Instance' (ACI), 'Azure Container Registry' (ACR) and 'Azure Kubernetes Service' (AKS) resources, push and pull images, and deploy services. On the client side, lightweight shells to the 'docker', 'docker-compose', 'kubectl' and 'helm' commandline tools are provided. Part of the 'AzureR' family of packages.
An interface to 'Azure CosmosDB': <https://azure.microsoft.com/en-us/services/cosmos-db/>. On the admin side, 'AzureCosmosR' provides functionality to create and manage 'Cosmos DB' instances in Microsoft's 'Azure' cloud. On the client side, it provides an interface to the 'Cosmos DB' SQL API, letting the user store and query documents and attachments in 'Cosmos DB'. Part of the 'AzureR' family of packages.
A simple interface to the 'Microsoft Graph' API <https://docs.microsoft.com/en-us/graph/overview>. 'Graph' is a comprehensive framework for accessing data in various online Microsoft services. This package was originally intended to provide an R interface only to the 'Azure Active Directory' part, with a view to supporting interoperability of R and 'Azure': users, groups, registered apps and service principals. However it has since been expanded into a more general tool for interacting with Graph. Part of the 'AzureR' family of packages.
Manage keys, certificates, secrets, and storage accounts in Microsoft's 'Key Vault' service: <https://azure.microsoft.com/services/key-vault/>. Provides facilities to store and retrieve secrets, use keys to encrypt, decrypt, sign and verify data, and manage certificates. Integrates with the 'AzureAuth' package to enable authentication with a certificate, and with the 'openssl' package for importing and exporting cryptographic objects. Part of the 'AzureR' family of packages.
An interface to 'Azure Data Explorer', also known as 'Kusto', a fast, highly scalable data exploration service from Microsoft: <https://azure.microsoft.com/en-us/services/data-explorer/>. Includes 'DBI' and 'dplyr' interfaces, with the latter modelled after the 'dbplyr' package, whereby queries are translated from R into the native 'KQL' query language and executed lazily. On the admin side, the package extends the object framework provided by 'AzureRMR' to support creation and deletion of databases, and management of database principals. Part of the 'AzureR' family of packages.
Interface to the 'Azure Machine Learning' Software Development Kit
    ('SDK'). Data scientists can use the 'SDK' to train, deploy, automate, and
    manage machine learning models on the 'Azure Machine Learning' service. To
    learn more about 'Azure Machine Learning' visit the website:
    <https://docs.microsoft.com/en-us/azure/machine-learning/service/overview-what-is-azure-ml>.
An interface to 'Azure Queue Storage'. This is a cloud service for storing large numbers of messages, for example from automated sensors, that can be accessed remotely via authenticated calls using HTTP or HTTPS. Queue storage is often used to create a backlog of work to process asynchronously. Part of the 'AzureR' family of packages.
A lightweight but powerful R interface to the 'Azure Resource Manager' REST API. The package exposes a comprehensive class framework and related tools for creating, updating and deleting 'Azure' resource groups, resources and templates. While 'AzureRMR' can be used to manage any 'Azure' service, it can also be extended by other packages to provide extra functionality for specific services. Part of the 'AzureR' family of packages.
Manage storage in Microsoft's 'Azure' cloud: <https://azure.microsoft.com/en-us/product-categories/storage/>. On the admin side, 'AzureStor' includes features to create, modify and delete storage accounts. On the client side, it includes an interface to blob storage, file storage, and 'Azure Data Lake Storage Gen2': upload and download files and blobs; list containers and files/blobs; create containers; and so on. Authenticated access to storage is supported, via either a shared access key or a shared access signature (SAS). Part of the 'AzureR' family of packages.
An interface to the table storage service in 'Azure': <https://azure.microsoft.com/en-us/services/storage/tables/>. Supplies functionality for reading and writing data stored in tables, both as part of a storage account and from a 'CosmosDB' database with the table service API. Part of the 'AzureR' family of packages.
An interface to 'Azure Computer Vision' <https://docs.microsoft.com/azure/cognitive-services/Computer-vision/Home> and 'Azure Custom Vision' <https://docs.microsoft.com/azure/cognitive-services/custom-vision-service/home>, building on the low-level functionality provided by the 'AzureCognitive' package. These services allow users to leverage the cloud to carry out visual recognition tasks using advanced image processing models, without needing powerful hardware of their own. Part of the 'AzureR' family of packages.
Functionality for working with virtual machines (VMs) in Microsoft's 'Azure' cloud: <https://azure.microsoft.com/en-us/services/virtual-machines/>. Includes facilities to deploy, startup, shutdown, and cleanly delete VMs and VM clusters. Deployment configurations can be highly customised, and can make use of existing resources as well as creating new ones. A selection of predefined configurations is provided to allow easy deployment of commonly used Linux and Windows images, including Data Science Virtual Machines. With a running VM, execute scripts and install optional extensions. Part of the 'AzureR' family of packages.
A simple interface to the instance metadata for a virtual machine running in Microsoft's 'Azure' cloud. This provides information about the VM's configuration, such as its processors, memory, networking, storage, and so on. Part of the 'AzureR' family of packages.
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.
Genomic analysis of model organisms frequently requires the
    use of databases based on human data or making comparisons to
    patient-derived resources. This requires harmonization of gene names
    into the same gene space. The 'babelgene' R package converts between
    human and non-human gene orthologs/homologs. The package integrates
    orthology assertion predictions sourced from multiple databases as
    compiled by the HGNC Comparison of Orthology Predictions (HCOP)
    (Wright et al. 2005 <doi:10.1007/s00335-005-0103-2>, Eyre et al. 2007
    <doi:10.1093/bib/bbl030>, Seal et al. 2011 <doi:10.1093/nar/gkq892>).
Provides a unified interface to interact with
    'docker' and 'singularity' containers.  You can execute a command
    inside a container, mount a volume or copy a file.
'BEAST2' (<https://www.beast2.org>) is a widely used
    Bayesian phylogenetic tool, that uses DNA/RNA/protein data
    and many model priors to create a posterior of jointly estimated 
    phylogenies and parameters.
    'BEAST2' is commonly accompanied by 'BEAUti 2', 'Tracer' and 'DensiTree'.
    'babette' provides for an alternative workflow of using 
    all these tools separately. This allows doing complex Bayesian
    phylogenetics easily and reproducibly from 'R'.
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.
Implements a discrete-event simulation model for a hospital resource planning problem.
    The project is motivated by the challenges faced by health care institutions in the current COVID-19 pandemic. 
    It can be used by health departments to forecast demand for intensive care beds, ventilators, and staff resources.
    Our modelling approach is inspired by "A novel modelling technique to predict resource requirements in critical care - a case study" (Lawton and McCooe 2019) and combines two powerful technologies: 
      (i) discrete event simulation using the 'simmer' package and 
      (ii) model-based optimization using 'SPOT'.
    Ucar I, Smeets B, Azcorra A (2019) <doi:10.18637/jss.v090.i02>.
    Bartz-Beielstein T, Lasarczyk C W G, Preuss M (2005) <doi:10.1109/CEC.2005.1554761>.
    Lawton T, McCooe M (2019) <doi:10.7861/futurehosp.6-1-17>.
US baby names provided by the SSA. This package contains all
    names used for at least 5 children of either sex.
Can be used for simulation of organisms living in
    communities (Bauer and Zimmermann (2017) <doi:10.1371/journal.pcbi.1005544>). 
    Each organism is represented individually and genome scale
    metabolic models determine the uptake and release of compounds. Biological
    processes such as movement, diffusion, chemotaxis and kinetics are available
    along with data analysis techniques.
The BACCO bundle of packages is replaced by the BACCO
 package, which provides a vignette that illustrates the constituent
 packages (emulator, approximator, calibrator) in use.
Implements the Bayesian Augmented Control (BAC, a.k.a. Bayesian historical data borrowing) method under clinical trial setting by calling 'Just Another Gibbs Sampler' ('JAGS') software. In addition, the 'BACCT' package evaluates user-specified decision rules by computing the type-I error/power, or probability of correct go/no-go decision at interim look. The evaluation can be presented numerically or graphically. Users need to have 'JAGS' 4.0.0 or newer installed due to a compatibility issue with 'rjags' package. Currently, the package implements the BAC method for binary outcome only. Support for continuous and survival endpoints will be added in future releases. We would like to thank AbbVie's Statistical Innovation group and Clinical Statistics group for their support in developing the 'BACCT' package.
Provides the design of multi-group phase
    II clinical trials with binary outcomes using the hierarchical Bayesian
    classification and information sharing (BaCIS) model. Subgroups are classified
    into two clusters on the basis of their outcomes mimicking the hypothesis
    testing framework. Subsequently, information sharing takes place within
    subgroups in the same cluster, rather than across all subgroups. This method can
    be applied to the design and analysis of multi-group clinical trials with binary
    outcomes. Reference: Nan Chen and J. Jack Lee (2019) <doi:10.1002/bimj.201700275>.
An implementation of methods for extracting an unweighted unipartite
   graph (i.e. a backbone) from an unweighted unipartite graph (e.g., Hamann et al.,
   2016  <doi:10.1007/s13278-016-0332-2>), a weighted  unipartite graph (e.g.,
   Serrano et al.,  2009 <doi:10.1073/pnas.0808904106>), the projection of an unweighted
   bipartite graph (e.g., Neal et al., <doi:10.1038/s41598-021-03238-3>, or the projection
   of a weighted bipartite graph (e.g., Neal, 2017 <doi:10.1177/0308518X16631339>).
Provides a backward-pipe operator for 'magrittr' (%<%) or 
    'pipeR' (%<<%) that allows for a performing operations from right-to-left. 
    This allows writing more legible code where right-to-left ordering is 
    natural. This is common with hierarchies and nested structures such as 
    trees, directories or markup languages (e.g. HTML and XML). 
    The package also includes a R-Studio add-in that can be bound to a keyboard 
    shortcut.
Functions introduced or changed since R v3.0.0 are re-implemented in this
    package. The backports are conditionally exported in order to let R resolve
    the function name to either the implemented backport, or the respective base
    version, if available. Package developers can make use of new functions or
    arguments by selectively importing specific backports to
    support older installations.
Code for 'backShift', an algorithm to estimate the connectivity
    matrix of a directed (possibly cyclic) graph with hidden variables. The
    underlying system is required to be linear and we assume that observations
    under different shift interventions are available. For more details,
    see <arXiv:1506.02494>.
The backtest package provides facilities for exploring
        portfolio-based conjectures about financial instruments
        (stocks, bonds, swaps, options, et cetera).
Decomposition for differences-in-differences with variation in
    treatment timing from Goodman-Bacon (2018) <doi:10.3386/w25018>.
The Bayesian Adjustment for Confounding (BAC) algorithm (Wang et al., 2012)
 can be used to estimate the causal effect of a continuous exposure on a continuous outcome.
 This package provides an approximate sensitivity analysis of BAC with regards to the
 hyperparameter omega. BACprior 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.
Query information and generate badge for using in README and GitHub Pages.
Bagged OutlierTrees is an explainable unsupervised outlier detection method based on an ensemble implementation of the existing OutlierTree procedure (Cortes, 2020). This implementation takes advantage of bootstrap aggregating (bagging) to improve robustness by reducing the possible masking effect and subsequent high variance (similarly to Isolation Forest), hence the name "Bagged OutlierTrees". To learn more about the base procedure OutlierTree (Cortes, 2020), please refer to <arXiv:2001.00636>.
Bagged cross-validation for bandwidth selection 
  in kernel density estimation (Hall and Marron (1987) <doi:10.1007/BF00363516>). This bandwidth selector can achieve greater statistical precision than standard cross-validation while being computationally fast.
Bagging bandwidth selection methods for the Parzen-Rosenblatt and Nadaraya-Watson estimators. These 
  bandwidth selectors can achieve greater statistical precision than their non-bagged counterparts while being 
    computationally fast. See Barreiro-Ures et al. (2020) <doi:10.1093/biomet/asaa092> and 
  Barreiro-Ures et al. (2021) <arXiv:2105.04134>.
Running and comparing meta-analyses of data with hierarchical 
    Bayesian models in Stan, including convenience functions for formatting
    data, plotting and pooling measures specific to meta-analysis. This implements many models
    from Meager (2019) <doi:10.1257/app.20170299>.
The BAGofT assesses the goodness-of-fit of binary classifiers. Details can be found in Zhang, Ding and Yang (2021) <arXiv:1911.03063v2>.
Tree- and rule-based models can be bagged using this package
    and their predictions equations are stored in an efficient format to
    reduce the model objects size and speed.
A method to filter correlation and covariance matrices by averaging
     bootstrapped filtered hierarchical clustering and boosting. See Ch. Bongiorno and D. Challet,
     Covariance matrix filtering with bootstrapped hierarchies (2020) <arXiv:2003.05807> and
     Ch. Bongiorno and D. Challet, Reactive Global Minimum Variance Portfolios with k-BAHC covariance cleaning
     (2020) <arXiv:2005.08703>.
Computes approximated adjusted fractional Bayes factors for
    equality, inequality, and about equality constrained hypotheses.
    For a tutorial on this method, see Hoijtink, Mulder, van Lissa, & Gu,
    (2019) <doi:10.31234/osf.io/v3shc>. For applications in structural equation
    modeling, see: Van Lissa, Gu, Mulder, Rosseel, Van Zundert, &
    Hoijtink, (2021) <doi:10.1080/10705511.2020.1745644>. For the statistical
    underpinnings, see Gu, Mulder, and Hoijtink (2018) <doi:10.1111/bmsp.12110>;
    Hoijtink, Gu, & Mulder, J. (2019) <doi:10.1111/bmsp.12145>; Hoijtink, Gu, 
    Mulder, & Rosseel, (2019) <doi:10.31234/osf.io/q6h5w>.
Provides functions to specify, fit and visualize
    nested partially-latent class models (Wu et al., 2016,
    'JRSS-C' <doi:10.1111/rssc.12101>; 
    Wu et al., 2017, 'Biostatistics' <doi:10.1093/biostatistics/kxw037>;
    Wu and Chen, 2021, Statistics in Medicine <doi:10.1002/sim.8804>) for 
    inference of population disease etiology and individual diagnosis. In the motivating
    Pneumonia Etiology Research for Child Health (PERCH) study, because both quantities
    of interest sum to one hundred percent, the PERCH scientists frequently refer to
    them as "population etiology pie" and "individual etiology pie", hence the name of the package.
Two practical tests are provided for assessing whether multiple covariates in a treatment group and a matched control group are balanced in observational studies.
Select balanced and spatially balanced probability samples in multi-dimensional spaces with any prescribed inclusion probabilities. It contains fast (C++ via Rcpp) implementations of the included sampling methods. The local pivotal method and spatially correlated Poisson sampling (for spatially balanced sampling) are included. Also the cube method (for balanced sampling) and the local cube method (for doubly balanced sampling) are included.
Facilitates the evolutionary analysis and structure conservation study of specified amino acids in proteins.
Hypothesis tests and sure independence screening (SIS) procedure based on ball statistics, including ball divergence <doi:10.1214/17-AOS1579>, ball covariance <doi:10.1080/01621459.2018.1543600>, and ball correlation <doi:10.1080/01621459.2018.1462709>, are developed to analyze complex data in metric spaces, e.g, shape, directional, compositional and symmetric positive definite matrix data. The ball divergence and ball covariance based distribution-free tests are implemented to detecting distribution difference and association in metric spaces <doi:10.18637/jss.v097.i06>. Furthermore, several generic non-parametric feature selection procedures based on ball correlation, BCor-SIS and all of its variants, are implemented to tackle the challenge in the context of ultra high dimensional data. A fast implementation for large-scale multiple K-sample testing with ball divergence <doi: 10.1002/gepi.22423> is supported, which is particularly helpful for genome-wide association study.
Analysis of gene expression RNA-seq data using Bartlett-Adjusted Likelihood-based LInear model (BALLI). Based on likelihood ratio test, it provides comparisons for effect of one or more variables. See Kyungtaek Park (2018) <doi:10.1101/344929> for more information.
The core algorithm is described in "Ball mapper: a shape summary for topological data analysis" by Pawel Dlotko, (2019) <arXiv:1901.07410>. Please consult the following youtube video <https://www.youtube.com/watch?v=M9Dm1nl_zSQfor> the idea of functionality. Ball Mapper provide a topologically accurate summary of a data in a form of an abstract graph. To create it, please provide the coordinates of points (in the points array), values of a function of interest at those points (can be initialized randomly if you do not have it) and the value epsilon which is the radius of the ball in the Ball Mapper construction. It can be understood as the minimal resolution on which we use to create the model of the data.
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) <doi:10.1201/b17888>.
Perform mediation analysis in the presence of high-dimensional
    mediators based on the potential outcome framework. Bayesian Mediation
    Analysis (BAMA), developed by Song et al (2019) <doi:10.1111/biom.13189> and
    Song et al (2020) <arXiv:2009.11409>,
    relies on two Bayesian sparse linear mixed models to simultaneously analyze
    a relatively large number of mediators for a continuous exposure and outcome
    assuming a small number of mediators are truly active. This sparsity
    assumption also allows the extension of univariate mediator analysis by
    casting the identification of active mediators as a variable selection
    problem and applying Bayesian methods with continuous shrinkage priors on
    the effects.
Fit (using Bayesian methods) and simulate mixtures of univariate and bivariate angular distributions. Chakraborty and Wong (2021) <doi:10.18637/jss.v099.i11>.
Implementation of the Bamboo methods described in Li, Dahl, Vannucci, Joo, and Tsai (2014) <DOI:10.1371/journal.pone.0109832>.
Enables a user to consume the 'BambooHR' API endpoints using R. The
  actual URL of the API will depend on your company domain, and will be handled
  by the package automatically once you setup the config file. The API documentation
  can be found here <https://documentation.bamboohr.com/docs>.
Provides a new class of Bayesian meta-analysis models that incorporates a model for internal and external validity bias. In this way, it is possible to combine studies of diverse quality and different types. For example, we can combine the results of randomized control trials (RCTs) with the results of observational studies (OS).
Infrastructure for estimating probabilistic distributional regression models in a Bayesian framework.
  The distribution parameters may capture location, scale, shape, etc. and every parameter may depend
  on complex additive terms (fixed, random, smooth, spatial, etc.) similar to a generalized additive model.
  The conceptual and computational framework is introduced in Umlauf, Klein, Zeileis (2019)
  <doi:10.1080/10618600.2017.1407325> and the R package in Umlauf, Klein, Simon, Zeileis (2021)
  <doi:10.18637/jss.v100.i04>.
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.
Bayesian Age-Period-Cohort Modeling and Prediction using efficient Markov Chain Monte Carlo Methods. This is the R version of the previous BAMP software as described in Volker Schmid and Leonhard Held (2007) <DOI:10.18637/jss.v021.i08> Bayesian Age-Period-Cohort Modeling and Prediction - BAMP, Journal of Statistical Software 21:8. This package includes checks of convergence using Gelman's R.
A set of functions for doing analysis of A/B split test data and web metrics in general.
Provides functions for the Bayesian analysis of some simple 
    commonly-used models, without using Markov Chain Monte Carlo (MCMC) 
    methods such as Gibbs sampling.  The 'rust' package
    <https://cran.r-project.org/package=rust> is used to simulate a random 
    sample from the required posterior distribution, using the generalized 
    ratio-of-uniforms method.  See Wakefield, Gelfand and Smith (1991) 
    <DOI:10.1007/BF01889987> for details. At the moment three conjugate 
    hierarchical models are available: beta-binomial, gamma-Poisson and a 1-way 
    analysis of variance (ANOVA).
A convenience package for use while drafting code.
    It facilitates making stand-out comment lines decorated with
    bands of characters.  The input text strings are converted into
    R comment lines, suitably formatted. These are then displayed in
    a console window and, if possible, automatically transferred to a
    clipboard ready for pasting into an R script.  Designed to save
    time when drafting R scripts that will need to be navigated and
    maintained by other programmers.
It covers several Bayesian Analysis of Variance (BANOVA) models used in analysis of experimental designs in which both within- and between- subjects factors are manipulated. They can be applied to data that are common in the behavioral and social sciences. The package includes: Hierarchical Bayes ANOVA models with normal response, t response, Binomial (Bernoulli) response, Poisson response, ordered multinomial response and multinomial response variables. All models accommodate unobserved heterogeneity by including a normal distribution of the parameters across individuals. Outputs of the package include tables of sums of squares, effect sizes and p-values, and tables of predictions, which are easily interpretable for behavioral and social researchers. The floodlight analysis and mediation analysis based on these models are also provided. BANOVA uses 'Stan' and 'JAGS' as the computational platform. References: Dong and Wedel (2017) <doi:10.18637/jss.v081.i09>; Wedel and Dong (2020) <doi:10.1002/jcpy.1111>.
Create a hierarchical acoustic event species classifier out of  
  multiple call type detectors as described in 
  Rankin et al (2017) <doi:10.1111/mms.12381>.
Provides functions to scrape IQY calls to Bank of Mexico,
    downloading and ordering the data conveniently.
Bayesian estimation and prediction for stochastic processes based
    on the Euler approximation. Considered processes are: jump diffusion,
    (mixed) diffusion models, hidden (mixed) diffusion models, non-homogeneous
    Poisson processes (NHPP), (mixed) regression models for comparison and a
    regression model including a NHPP.
Tool to find where a function has its lowest value(minimum). The
    functions can be any dimensions. Recommended use is with eps=10^-10, but can be
    run with 10^-20, although this depends on the function. Two more methods are in
    this package, simple gradient method (Gradmod) and Powell method (Powell). These
    are not recommended for use, their purpose are purely for comparison.
The function \code{barcode()}
        produces a histogram-like plot of a distribution that shows
        granularity in the data.
Tools to generate unique identifier codes and
    printable barcoded labels for the management of biological samples.
    The creation of unique ID codes and printable PDF files can be
    initiated by standard commands, user prompts, or through a GUI addin
    for R Studio. Biologically informative codes can be included for
    hierarchically structured sampling designs.
To perform species identification using DNA barcodes.
Provides R data structures for Shakespeare's complete works, 
  as provided by Project Gutenberg <https:www.gutenberg.org/ebooks/100>.
Allows the user to access and import data from the rich French open data portal through the provided free API <https://doc.data.gouv.fr/api/reference/>. 
    The portal is free, and no credential is required for extracting datasets.
Barnard's unconditional test for 2x2 contingency tables.
Creates 3D barplots. Includes a function for sequence context plots used in DNA sequencing analysis.
Bayesian Additive Regression Trees (BART) provide flexible nonparametric modeling of covariates for continuous, binary, categorical and time-to-event outcomes.  For more information see Sparapani, Spanbauer and McCulloch <doi:10.18637/jss.v097.i01>.
"BART-BMA Bayesian Additive Regression Trees using Bayesian Model Averaging" (Hernandez B, Raftery A.E., Parnell A.C. (2018) <doi:10.1007/s11222-017-9767-1>) is an extension to the original BART sum-of-trees model (Chipman et al 2010). BART-BMA differs to the original BART model in two main aspects in order to implement a greedy model which 
  will be computationally feasible for high dimensional data. Firstly BART-BMA uses a greedy search for the best split points and variables when growing decision trees within each sum-of-trees 
  model. This means trees are only grown based on the most predictive set of split rules. Also rather than using Markov chain Monte Carlo (MCMC), BART-BMA uses a greedy implementation of Bayesian Model Averaging called Occam's Window 
  which take a weighted average over multiple sum-of-trees models to form its overall prediction. This means that only the set of sum-of-trees for which there is high support from the data
  are saved to memory and used in the final model.
Contains a variety of methods to generate typical causal inference estimates using Bayesian Additive Regression Trees (BART) as the underlying regression model (Hill (2012) <doi:10.1198/jcgs.2010.08162>).
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.
Bayesian additive regression trees (BART) provides flexible non-parametric modeling of mixed-type predictors for continuous and binary responses. This package is built upon CRAN R package 'BART', version 2.7 (<https://github.com/cran/BART>). It implements the three proposed variable selection approaches in the paper: Luo, C and Daniels, M. J. (2021), "Variable Selection Using Bayesian Additive Regression Trees." <arXiv:2112.13998>, and other three existing BART-based variable selection approaches.
Intended to facilitate acoustic analysis of (animal) sound transmission experiments, which typically aim to quantify changes in signal structure when transmitted in a given habitat by broadcasting and re-recording animal sounds at increasing distances. The package offers a workflow with functions to prepare the data set for analysis as well as to calculate and visualize several degradation metrics, including blur ratio, signal-to-noise ratio, excess attenuation and envelope correlation among others (Dabelsteen et al 1993 <doi:10.1121/1.406682>).
Computations of entropy regularized Wasserstein Distances, a.k.a. dual-Sinkhorn divergences, and entropy regularized Wasserstein Barycenters. Relevant papers are Marco Cuturi (2013) <arXiv:1306.0895>, Marco Cuturi (2014) <arXiv:1310.4375> and Jason Altschuler et al. <arXiv:1705.09634>.
Package for Bayesian Variable Selection and  Model Averaging 
    in linear models and generalized linear models using stochastic or
    deterministic sampling without replacement from posterior
    distributions.  Prior distributions on coefficients are
    from Zellner's g-prior or mixtures of g-priors
    corresponding to the Zellner-Siow Cauchy Priors or the
    mixture of g-priors from Liang et al (2008)
    <DOI:10.1198/016214507000001337>
    for linear models or mixtures of g-priors from  Li and Clyde
    (2019) <DOI:10.1080/01621459.2018.1469992> in generalized linear models.
    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 which
    samples models using a tree structure of the model space 
    as an efficient hash table.  See  Clyde, Ghosh and Littman (2010) 
    <DOI:10.1198/jcgs.2010.09049> for  details on the sampling algorithms.
    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 to enforce sampling models that are full rank.  
    The user may force variables to always be included in addition to imposing
    constraints that higher order interactions are included only if their 
    parents are included in the model.
    This material is based upon work supported by the National Science
    Foundation under Division of Mathematical Sciences grant 1106891.
    Any opinions, findings, and
    conclusions or recommendations expressed in this material are those of
    the author(s) and do not necessarily reflect the views of the
    National Science Foundation.
Provides a Bayesian variable selection approach using continuous spike and slab prior distributions. The prior choices here are motivated by the shrinking and diffusing priors studied in Narisetty & He (2014) <DOI:10.1214/14-AOS1207>.
We perform linear, logistic, and cox regression using the base functions lm(), 
    glm(), and coxph() in the R software and the 'survival' package. Likewise, we 
    can use ols(), lrm() and cph() from the 'rms' package for the same 
    functionality. Each of these two sets of commands has a different focus.
  In many cases, we need to use both sets of commands in the same situation, 
    e.g. we need to filter the full subset model using AIC, and we need to build 
    a visualization graph for the final model. 'base.rms' package can help you to switch 
    between the two sets of commands easily.
Compatibility wrapper to replace the orphaned package by
    Romain Francois. New applications should use the 'openssl' or
    'base64enc' package instead.
This package provides tools for handling base64 encoding. It is more flexible than the orphaned base64 package.
In contrast to RFC3548, the 62nd character ("+") is replaced with
    "-", the 63rd character ("/") is replaced with "_". Furthermore, the encoder
    does not fill the string with trailing "=". The resulting encoded strings
    comply to the regular expression pattern "[A-Za-z0-9_-]" and thus are
    safe to use in URLs or for file names.
    The package also comes with a simple base32 encoder/decoder suited for
    case insensitive file systems.
A tool for gathering and analyzing data from the Baseball Databank <http://www.baseball-databank.org/>, which includes player performance statistics from major league baseball in the United States beginning in the year 1871.
Provides numerous utilities for acquiring and analyzing
    baseball data from online sources such as Baseball Reference <https://www.baseball-reference.com/>,
    FanGraphs <https://www.fangraphs.com/>, and the MLB Stats API <https://www.mlb.com/>.
Provides functions streamlining the data analysis workflow: 
  Outsourcing data import, renaming and type casting to a *.csv.
  Manipulating imputed datasets and fitting models on them. Summarizing models.
An R interface to the 'Base dos Dados' API <https:basedosdados.github.io/mais/py_reference_api/>). Authenticate your project, query our tables, save data to disk and memory, all from R.
Computes hydrograph separation using the conceptual automated process from Pelletier and Andreassian (2020) <doi:10.5194/hess-24-1171-2020>. Contains scalar and vectorized functions to compute correlation criterion to calibrate the baseflow separation model.
Some very simple infrastructure for basis functions.
Collection of baseline correction algorithms, along with a framework and a Tcl/Tk enabled 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.
A lightweight package to access spatial basemaps from open sources such as OpenStreetMap, Carto, Mapbox and others in R.
Implements a class and methods to work with sets,
    doing intersection, union, complementary sets, power sets, cartesian
    product and other set operations in a "tidy" way. These set operations
    are available for both classical sets and fuzzy sets. Import sets from
    several formats or from other several data structures.
Functions to create and select graphical themes for the base plotting system. Contains: 1) several custom pre-made themes 2) mechanism for creating new themes by making persistent changes to the graphical parameters of base plots.
Resurrects the standard plot for shapes established by the
 'base' and 'graphics' packages. This is suited to workflows that require
 plotting using the established and traditional idioms of plotting spatially
 coincident data where it belongs. This package depends on 'sf' and only replaces 
 the plot method.
Provides methods for examining posterior MCMC samples
    from a single chain using trace plots and density plots, and from
    multiple chains by comparing posterior medians and credible intervals
    from each chain.  These plotting functions have a variety of options,
    such as figure sizes, legends, parameters to plot, and saving plots to file.
    Functions interface with the NIMBLE software package, see
    de Valpine, Turek, Paciorek, Anderson-Bergman, Temple Lang and Bodik (2017)
    <doi:10.1080/10618600.2016.1172487>.
Conducts Aldrich-McKelvey and Blackbox Scaling (Poole et al 2016)
		<doi:10.18637/jss.v069.i07> to recover latent dimensions of judgment.
Easily create tables from data 
    frames/matrices.  Create/manipulate tables 
    row-by-row, column-by-column or cell-by-cell.  
    Use common formatting/styling to output 
    rich tables as 'HTML', 'HTML widgets' or to 
    'Excel'.
Data sets of the Spanish National Forest Inventory <https://www.miteco.gob.es/es/biodiversidad/servicios/banco-datos-naturaleza/informacion-disponible/> are processed to compute tree metrics and statistics. Function metrics2Vol() controls most of the routines.
It makes the creation of networks from sequences of RNA, with this is done the abstraction of characteristics of these networks with a methodology of threshold for the purpose of making a classification between the classes of the sequences. There are four data present in the 'BASiNET' package, "sequences", "sequences2", "sequences-predict" and "sequences2-predict" with 11, 10, 11 and 11 sequences respectively. These sequences were taken from the data set used in the article (LI, Aimin; ZHANG, Junying; ZHOU, Zhongyin, 2014) <doi:10.1186/1471-2105-15-311>, these sequences are used to run examples. The BASiNET was published on Nucleic Acids Research, (ITO, Eric; KATAHIRA, Isaque; VICENTE, Fábio; PEREIRA, Felipe; LOPES, Fabrício, 2018) <doi:10.1093/nar/gky462>.
It makes the creation of networks from sequences of RNA, with this is done the abstraction of characteristics of these networks with a methodology of maximum entropy for the purpose of making a classification between the classes of the sequences. There are two data present in the 'BASiNET' package, "mRNA", and "ncRNA" with 100 sequences. These sequences were taken from the data set used in the article (LI, Aimin; ZHANG, Junying; ZHOU, Zhongyin, 2014) <doi:10.1186/1471-2105-15-311>, these sequences are used to run examples.
BASIX provides some efficient C/C++ implementations to speed up calculations in R.
Tool to perform Bayesian inference of carcass processing/transport strategy and bone attrition from archaeofaunal skeletal profiles characterized by percentages of MAU (Minimum Anatomical Units). The approach is based on a generative model for skeletal profiles that replicates the two phases of formation of any faunal assemblage: initial accumulation as a function of human transport strategies and subsequent attrition.Two parameters define this model: 1) the transport preference (alpha), which can take any value between - 1 (mostly axial contribution) and 1 (mostly appendicular contribution) following strategies constructed as a function of butchering efficiency of different anatomical elements and the results of ethnographic studies, and 2) degree of attrition (beta), which can vary between 0 (no attrition) and 10 (maximum attrition) and relates the survivorship of bone elements to their maximum bone density. Starting from uniform prior probability distribution functions of alpha and beta, a Monte Carlo Markov Chain sampling based on a random walk Metropolis-Hasting algorithm is adopted to derive the posterior probability distribution functions, which are then available for interpretation. During this process, the likelihood of obtaining the observed percentages of MAU given a pair of parameter values is estimated by the inverse of the Chi2 statistic, multiplied by the proportion of elements within a 1 percent of the observed value. See Ana B. Marin-Arroyo, David Ocio (2018).<doi:10.1080/08912963.2017.1336620>.
Implementation of multisource exchangeability models for Bayesian analyses of prespecified subgroups arising in the context of basket trial design and monitoring.  The R 'basket' package facilitates implementation of the binary, symmetric multi-source exchangeability model (MEM) with posterior inference arising from both exact computation and Markov chain Monte Carlo sampling. Analysis output includes full posterior samples as well as posterior probabilities, highest posterior density (HPD) interval boundaries, effective sample sizes (ESS), mean and median estimations, posterior exchangeability probability matrices, and maximum a posteriori MEMs. In addition to providing "basketwise" analyses, the package includes similar calculations for "clusterwise" analyses for which subgroups are combined into meta-baskets, or clusters, using graphical clustering algorithms that treat the posterior exchangeability probabilities as edge weights. In addition plotting tools are provided to visualize basket and cluster densities as well as their exchangeability.  References include Hyman, D.M., Puzanov, I., Subbiah, V., Faris, J.E., Chau, I., Blay, J.Y., Wolf, J., Raje, N.S., Diamond, E.L., Hollebecque, A. and Gervais, R (2015) <doi:10.1056/NEJMoa1502309>; Hobbs, B.P. and Landin, R. (2018) <doi:10.1002/sim.7893>; Hobbs, B.P., Kane, M.J., Hong, D.S. and Landin, R. (2018) <doi:10.1093/annonc/mdy457>; and Kaizer, A.M., Koopmeiners, J.S. and Hobbs, B.P. (2017) <doi:10.1093/biostatistics/kxx031>.
Contains data and code to accompany  the book 
             P. Zuccolotto and M. Manisera (2020) Basketball Data Science. Applications with R. CRC Press. ISBN 9781138600799.
Calculates the exact operating characteristics of a single-stage
    basket trial with the design of
	Fujikawa, K., Teramukai, S., Yokota, I., & Daimon, T. (2020). <doi:10.1002/bimj.201800404>.
Bayesian fitting and sensitivity analysis methods for adaptive
    spline surfaces described in <doi:10.18637/jss.v094.i08>. Built to handle continuous and categorical inputs as well as
    functional or scalar output. An extension of the methodology in Denison, Mallick
    and Smith (1998) <doi:10.1023/A:1008824606259>.
Estimates survival and mortality with covariates from capture-recapture/recovery data in a Bayesian framework when many individuals are of unknown age. It includes tools for data checking, model diagnostics and outputs such as life-tables and plots.
Big data statistical analysis for high-dimensional models is made possible by modifying lasso.proj() in 'hdi' package by replacing its nodewise-regression with sparse precision matrix computation using 'BigQUIC'.
Includes algorithms to assess alpha and beta diversity
    in all their dimensions (taxonomic, phylogenetic and functional).
    It allows performing a number of analyses based on species
    identities/abundances, phylogenetic/functional distances, trees,
    convex-hulls or kernel density n-dimensional hypervolumes
    depicting species relationships.
    Cardoso et al. (2015) <doi:10.1111/2041-210X.12310>.
Allows the user to manage easily R packages removal and installation. It offers many functions to display installed packages according to
    specific dates and removes them if needed. The user is always prompted when running the removal functions in order to confirm
    the required action. It also provides functions that will install 'Github' starred R packages whether available on 'CRAN' or not.
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 financial data from Yahoo Finance <https://finance.yahoo.com/>.
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.
Handy frameworks, such as error handling and log generation, for batch scripts.
	Use case: in scripts running in remote servers, set error handling mechanism for downloading and uploading and record operation log.
Different adjustment methods for batch effects in biomarker data,
  such as from tissue microarrays. Some methods attempt to retain differences 
  between batches that may be due to between-batch differences in "biological"
  factors that influence biomarker values.
As a successor of the packages 'BatchJobs' and 'BatchExperiments',
    this package provides a parallel implementation of the Map function for high
    performance computing systems managed by schedulers 'IBM Spectrum LSF'
    (<https://www.ibm.com/products/hpc-workload-management>),
    'OpenLava' (<https://www.openlava.org/>), 'Univa Grid Engine'/'Oracle Grid
    Engine' (<https://www.univa.com/>), 'Slurm' (<https://slurm.schedmd.com/>),
    'TORQUE/PBS'
    (<https://adaptivecomputing.com/cherry-services/torque-resource-manager/>),
    or 'Docker Swarm' (<https://docs.docker.com/engine/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.
Compute bounds for the treatment effect 
    after adjusting for the presence of omitted variables in linear  
    econometric models, according to the method of Basu (2022) <arXiv:2203.12431>. 
    You supply the data, identify the outcome and 
    treatment variables and additional regressors. The main functions
    will compute bounds for the bias-adjusted treatment effect. Many
    plot functions allow easy visualization of results.
Survey systems and other third-party data sources commonly use non-standard representations of logical values when
    it comes to qualitative data - "Yes", "No" and "N/A", say. batman is a package designed to seamlessly convert these into logicals.
    It is highly localised, and contains equivalents to boolean values in languages including German, French, Spanish, Italian,
    Turkish, Chinese and Polish.
Battery reduction is a method used in data reduction. It uses Gram-Schmidt orthogonal rotations to find out a subset of variables best representing the original set of variables.
Collection of tools to work with basketball data. Functions available are related to friendly 
	web scraping and visualization. Data were obtained from <https://www.euroleague.net/>, 
	<https://www.eurocupbasketball.com/> and <https://www.acb.com/>, following the instructions 
        of their respectives robots.txt files, when available. Tools for visualization include a population pyramid, 2D plots, 
        circular plots of players' percentiles, plots of players' monthly/yearly stats,
	team heatmaps, team shooting plots, team four factors plots, cross-tables with the results of regular season games
	and maps of nationalities. Please see Vinue (2020) <doi:10.1089/big.2018.0124>.
A Bayesian hybrid approach for inferring Directed Acyclic Graphs
    (DAGs) for continuous, discrete, and mixed data. The algorithm can use the 
    graph inferred by another more efficient graph inference method as input;
    the input graph may contain false edges or undirected edges but can help
    reduce the search space to a more manageable size. A Bayesian Markov chain
    Monte Carlo algorithm is then used to infer the probability of direction and
    absence for the edges in the network.
    References:
    Martin and Fu (2019) <arXiv:1909.10678>.
Implements the Bayesian FDR control described by 
    Newton et al. (2004), <doi:10.1093/biostatistics/5.2.155>.
    Allows optimisation and visualisation of expected error rates based on
    tail posterior probability tests.
    Based on code written by Catalina Vallejos for BASiCS, see
    Beyond comparisons of means: understanding changes in gene expression at the
    single-cell level Vallejos et al. (2016) <doi:10.1186/s13059-016-0930-3>.
Contains several Bayesian models for data analysis of psychological tests. A user friendly interface for these models should enable students and researchers to perform professional level Bayesian data analysis without advanced knowledge in programming and Bayesian statistics. This package is based on the Stan platform (Carpenter et el. 2017 <doi:10.18637/jss.v076.i01>).
A suite of functions that allow the user to analyze A/B test
    data in a Bayesian framework. Intended to be a drop-in replacement for
    common frequentist hypothesis test such as the t-test and chi-sq test.
Performs Bayesian estimation of the additive main effects and multiplicative interaction (AMMI) model. The method is explained in Crossa, J., Perez-Elizalde, S., Jarquin, D., Cotes, J.M., Viele, K., Liu, G. and Cornelius, P.L. (2011) (<doi:10.2135/cropsci2010.06.0343>).
Provides a Bayesian version of the analysis of variance based on a three-component Gaussian mixture for which a Gibbs sampler produces posterior draws. For details about the Bayesian ANOVA based on Gaussian mixtures, see Kelter (2019) <arXiv:1906.07524>.
The Autoregressive Integrated Moving Average (ARIMA) model is very popular univariate time series model. Its application has been widened by the incorporation of exogenous variable(s) (X) in the model and modified as ARIMAX by Bierens (1987) <doi:10.1016/0304-4076(87)90086-8>. In this package we estimate the ARIMAX model using Bayesian framework.
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.
Smoothed lexis diagrams with Bayesian method specifically tailored to cancer 
             incidence data. Providing to calculating slope and constructing credible interval.
             LC Chien et al. (2015) <doi:10.1080/01621459.2015.1042106>. 
             LH Chien et al. (2017) <doi:10.1002/cam4.1102>.
Applies the Beta regression model in the Bayesian statistical view with the possibility of adding a spatial effect in the parameters, the Beta regression is used when the response variable is a proportion variable, that is, it only accepts values between 0 and 1.
    The package 'bayesbr' uses 'rstan' package to build the Bayesian statistical models. The main function of the package receives as a parameter a form informing the independent variable and the co-variables of the model to be made, as output it returns a list with the results of the model. For more details see Ferrari and Cribari-Neto (2004) <doi:10.1080/0266476042000214501> and Hoffman and Gelman (2014) <arXiv:1111.4246>.
Performs CACE (Complier Average Causal Effect analysis) on either a single study or meta-analysis of datasets with binary outcomes, using either complete or incomplete noncompliance information. Our package implements the Bayesian methods proposed in Zhou et al. (2019) <doi:10.1111/biom.13028>, which introduces a Bayesian hierarchical model for estimating CACE in meta-analysis of clinical trials with noncompliance, and Zhou et al. (2021) <doi:10.1080/01621459.2021.1900859>, with an application example on Epidural Analgesia.
Combine diverse evidence across multiple studies to test a high level scientific theory. The methods can also be used as an alternative to a standard meta-analysis.
Bayesian multivariate binary (probit) regression
    models for analysis of ecological communities.
Tools for Bayesian copula generalized linear models (GLMs). 
             The sampling scheme is based on Pitt, Chan, and Kohn (2006) <doi:10.1093/biomet/93.3.537>. 
             Regression parameters (including coefficients and dispersion parameters) are
             estimated via the adaptive random walk Metropolis approach developed by
             Haario, Saksman, and Tamminen (1999) <doi:10.1007/s001800050022>.
             The prior for the correlation matrix is based on Hoff (2007) <doi:10.1214/07-AOAS107>.
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.
Simulation and analysis of Bayesian adaptive clinical trials for binomial, Gaussian, and time-to-event data types, 
      incorporates historical data and allows early stopping for futility or early success. The package uses novel 
      and efficient Monte Carlo methods for estimating Bayesian posterior probabilities, evaluation of loss to follow up, 
      and imputation of incomplete data. The package has the functionality for dynamically incorporating historical data 
      into the analysis via the power prior or non-informative priors.
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>.
The proposed event-driven approach for Bayesian two-stage single-arm phase II trial design is a novel clinical trial design and can be regarded as an extension of the Simon’s two-stage design with the time-to-event endpoint. This design is motivated by cancer clinical trials with immunotherapy and molecularly targeted therapy, in which time-to-event endpoint is often a desired endpoint.
Implements Bayesian dynamic factor analysis with 'Stan'. Dynamic 
    factor analysis is a dimension reduction tool for multivariate time series.
    'bayesdfa' extends conventional dynamic factor models in several ways. 
    First, extreme events may be estimated in the latent trend by modeling
    process error with a student-t distribution. Second, alternative constraints
    (including proportions are allowed). Third, the estimated
    dynamic factors can be analyzed with hidden Markov models to evaluate
    support for latent regimes.
Implements Bayesian Distribution Regression methods. This package contains functions for three estimators (non-asymptotic, semi-asymptotic and asymptotic) and related routines for Bayesian Distribution Regression in Huang and Tsyawo (2018) <doi:10.2139/ssrn.3048658> which is also the recommended reference to cite for this package. The functions can be grouped into three (3) categories. The first computes the logit likelihood function and posterior densities under uniform and normal priors. The second contains Independence and Random Walk Metropolis-Hastings Markov Chain Monte Carlo (MCMC) algorithms as functions and the third category of functions are useful for semi-asymptotic and asymptotic Bayesian distribution regression inference.
Functions for data augmentation using the Bayesian discount prior 
    method for single arm and two-arm clinical trials, as described in Haddad 
    et al. (2017) <doi:10.1080/10543406.2017.1300907>. The discount power prior 
    methodology was developed in collaboration with the The Medical Device 
    Innovation Consortium (MDIC) Computer Modeling & Simulation Working Group.
Determines effective sample size of a parametric prior distribution
    in Bayesian models. For a web-based Shiny application related to this package, see <https://implement.shinyapps.io/bayesess/>.
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) from G. Conti, S. Frühwirth-Schnatter, J.J. Heckman, 
    R. Piatek (2014) <doi:10.1016/j.jeconom.2014.06.008>, 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.
Fit Bayesian time series models using 'Stan' for full Bayesian inference. A wide range 
  of distributions and models are supported, allowing users to fit Seasonal ARIMA, ARIMAX, Dynamic 
  Harmonic Regression, GARCH, t-student innovation GARCH models, asymmetric GARCH, Random Walks, stochastic 
  volatility models for univariate time series.  Prior specifications are flexible and explicitly encourage 
  users to apply prior distributions that actually reflect their beliefs. Model fit can easily be assessed 
  and compared with typical visualization methods, information criteria such as loglik, AIC, BIC WAIC, Bayes 
  factor and leave-one-out cross-validation methods. References: Hyndman (2017)
    <doi:10.18637/jss.v027.i03>; Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>.
The 'bayesGAM' package is designed to provide a user friendly option to fit univariate and multivariate response Generalized Additive Models (GAM) using Hamiltonian Monte Carlo (HMC) with few technical burdens.  The functions in this package use 'rstan' (Stan Development Team 2020)  to call 'Stan' routines that run the HMC simulations. The 'Stan' code for these models is already pre-compiled for the user. The programming formulation for models in 'bayesGAM' is designed to be familiar to analysts who fit statistical models in 'R'.
    Carpenter, B., Gelman, A., Hoffman, M. D., Lee, D., Goodrich, B., Betancourt, M., ... & Riddell, A. (2017). Stan: A probabilistic programming language. Journal of statistical software, 76(1). 
    Stan Development Team. 2018. RStan: the R interface to Stan. R package version 2.17.3.   <https://mc-stan.org/>
    Neal, Radford (2011) "Handbook of Markov Chain Monte Carlo" ISBN: 978-1420079418.
    Betancourt, Michael, and Mark Girolami. "Hamiltonian Monte Carlo for hierarchical models." Current trends in Bayesian methodology with applications 79.30 (2015): 2-4.
    Thomas, S., Tu, W. (2020) "Learning Hamiltonian Monte Carlo in R" <arXiv:2006.16194>,
    Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013) "Bayesian Data Analysis" ISBN: 978-1439840955, 
    Agresti, Alan (2015) "Foundations of Linear and Generalized Linear Models ISBN: 978-1118730034, 
    Pinheiro, J., Bates, D. (2006) "Mixed-effects Models in S and S-Plus" ISBN: 978-1441903174.
    Ruppert, D., Wand, M. P., & Carroll, R. J. (2003). Semiparametric regression (No. 12). Cambridge university press. ISBN: 978-0521785167.
Provides the bayesGARCH() function which performs the
    Bayesian estimation of the GARCH(1,1) model with Student's t innovations as described in Ardia (2008) <doi:10.1007/978-3-540-78657-3>.
Set of tools to perform the statistical inference based on the Bayesian approach for regression models under the assumption that independent additive errors follow normal, Student-t, slash, contaminated normal, Laplace or symmetric hyperbolic distributions, i.e., additive errors follow a scale mixtures of normal distributions. The regression models considered in this package are: (i) Generalized elliptical semi-parametric models, where both location and dispersion parameters of the response variable distribution include non-parametric additive components described by using B-splines; and (ii) Flexible measurement error models under the presence of homoscedastic and heteroscedastic random errors, which admit explanatory variables with and without measurement additive errors as well as the presence of a non-parametric components approximated by using B-splines.
A Bayesian data modeling scheme that performs four interconnected tasks: (i) characterizes the uncertainty of the elicited parametric prior; (ii) provides exploratory diagnostic for checking prior-data conflict; (iii) computes the final statistical prior density estimate; and (iv) executes macro- and micro-inference. Primary reference is Mukhopadhyay, S. and Fletcher, D. 2018 paper "Generalized Empirical Bayes via Frequentist Goodness of Fit" (<https://www.nature.com/articles/s41598-018-28130-5 >).
Bayesian inferences on nonparametric regression via Gaussian Processes with a modified exponential square kernel using a basis expansion approach.
Fits Bayesian grouped weighted quantile sum (BGWQS) regressions for one or more chemical groups with binary outcomes. Wheeler DC et al. (2019) <doi:10.1016/j.sste.2019.100286>.
Fit Bayesian models using 'brms'/'Stan' with 'parsnip'/'tidymodels'
    via 'bayesian' <doi:10.5281/zenodo.5884879>. 'tidymodels' is a collection of
    packages for machine learning; see Kuhn and Wickham (2020) <https://www.tidymodels.org>).
    The technical details of 'brms' and 'Stan' are described in Bürkner (2017)
    <doi:10.18637/jss.v080.i01>, Bürkner (2018) <doi:10.32614/RJ-2018-017>,
    and Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>.
Bayesian melding approach to combine the GPS observations and Dead-Reckoned path for an accurate animal's track, or equivalently, use the GPS observations to correct the Dead-Reckoned path. It can take the measurement errors in the GPS observations into account and provide uncertainty statement about the corrected path. The main calculation can be done by the BMAnimalTrack function.
The Epidemic Type Aftershock Sequence  (ETAS) model is one of the best-performing methods for modeling and forecasting earthquake occurrences. This package implements Bayesian estimation routines to draw samples from the full posterior distribution of the model parameters, given an earthquake catalog. The paper on which this package is based is Gordon J. Ross - Bayesian Estimation of the ETAS Model for Earthquake Occurrences (2016), available from the below URL.
Adjust the Gamma regression models from a Bayesian perspective described by Cepeda and Urdinola (2012) <doi:10.1080/03610918.2011.600500>, modeling the parameters of mean and shape and using different link functions for the parameter associated to the mean. And calculates different adjustment statistics such as the Akaike information criterion and Bayesian information criterion.
Implements a data-augmented block Gibbs sampler for simulating the posterior distribution of concentration matrices for specifying the topology and parameterization of a Gaussian Graphical Model (GGM). This sampler was originally proposed in Wang (2012) <doi:10.1214/12-BA729>.
Functional differences between the cerebral hemispheres 
    are a fundamental characteristic of the human brain. Researchers 
    interested in studying these differences often infer underlying 
    hemispheric dominance for a certain function (e.g., language) from 
    laterality indices calculated from observed performance or brain 
    activation measures . However, any inference from observed measures 
    to latent (unobserved) classes has to consider the prior probability 
    of class membership in the population. The provided functions 
    implement a Bayesian model for predicting hemispheric dominance from
    observed laterality indices (Sorensen and Westerhausen, Laterality: 
    Asymmetries of Body, Brain and Cognition, 2020, <doi:10.1080/1357650X.2020.1769124>).
A 'Shiny' web application for creating interactive Bayesian Network models,
    learning the structure and parameters of Bayesian networks, and utilities for classic
    network analysis.
A collection of methods to determine the required sample size for
    the evaluation of inequality constrained hypotheses by means of a Bayes 
    factor. Alternatively, for a given sample size, the unconditional error 
    probabilities or the expected conditional error probabilities can be
    determined. Additional material on the methods in this package is 
    available in Klaassen, F., Hoijtink, H. & Gu, X. (2019) 
    <doi:10.31219/osf.io/d5kf3>.
Functions to plot and help understand positive and negative
    predictive values (PPV and NPV), and their relationship with
    sensitivity, specificity, and prevalence. See Akobeng, A.K. (2007)
    <doi:10.1111/j.1651-2227.2006.00180.x> for a theoretical overview of
    the technical concepts and Navarrete et al. (2015) for a practical
    explanation about the importance of their understanding
    <doi:10.3389/fpsyg.2015.01327>.
General-purpose MCMC and SMC samplers, as well as plot and
    diagnostic functions for Bayesian statistics, with a particular focus on
    calibrating complex system models. Implemented samplers include various
    Metropolis MCMC variants (including adaptive and/or delayed rejection MH), the
    T-walk, two differential evolution MCMCs, two DREAM MCMCs, and a sequential
    Monte Carlo (SMC) particle filter.
Performs Bayesian t Regression where mean and scale parameters are modeling by lineal regression structures, and the degrees of freedom parameters are estimated.
Various algorithms for segmentation of 2D and 3D images, such
    as computed tomography and satellite remote sensing. This package implements
    Bayesian image analysis using the hidden Potts model with external field
    prior of Moores et al. (2015) <doi:10.1016/j.csda.2014.12.001>.
    Latent labels are sampled using chequerboard updating or Swendsen-Wang.
    Algorithms for the smoothing parameter include pseudolikelihood, path sampling,
    the exchange algorithm, approximate Bayesian computation (ABC-MCMC and ABC-SMC),
    and the parametric functional approximate Bayesian (PFAB) algorithm. Refer to
    <doi:10.1007/978-3-030-42553-1_6> for an overview and also to <doi:10.1007/s11222-014-9525-6>
    and <doi:10.1214/18-BA1130> for further details of specific algorithms.
Bayesian Latent Class Analysis using several different
        methods.
Making probabilistic projections of life expectancy for all countries of the world, using a Bayesian hierarchical model <doi:10.1007/s13524-012-0193-x>. Subnational projections are also supported.
Computes point estimates, standard deviations, and credible intervals 
             for linear combinations of posterior samples. Optionally performs region
             practical equivalence (ROPE) tests as described in 
             Kruschke and Liddell (2018) <doi:10.3758/s13423-016-1221-4>.
Bayesian inference under log-normality assumption must be performed very carefully. In fact, under the common priors for the variance, useful quantities in the original data scale (like mean and quantiles) do not have posterior moments that are finite (Fabrizi et al. 2012 <doi:10.1214/12-BA733>). This package allows to easily carry out a proper Bayesian inferential procedure by fixing a suitable distribution (the generalized inverse Gaussian) as prior for the variance. Functions to estimate several kind of means (unconditional, conditional and conditional under a mixed model) and quantiles (unconditional and conditional) are provided.
Tools for sampling from the PolyaGamma distribution based on Polson, Scott, and Windle (2013) <doi:10.1080/01621459.2013.829001>.  Useful for logistic regression.
The function MC3() searches for log-linear models with the highest posterior probability. The function gibbsSampler() is a blocked Gibbs sampler for sampling from the posterior distribution of the log-linear parameters. The functions findPostMean() and findPostCov() compute the posterior mean and covariance matrix for decomposable models which, for these models, is available in closed form.
Adjusts longitudinal regression models using Bayesian methodology 
            for covariance structures of composite symmetry (SC), 
            autoregressive ones of order 1 AR (1) and 
            autoregressive moving average of order (1,1) ARMA (1,1).
Covers many important models used
  in marketing and micro-econometrics applications. 
  The package includes:
  Bayes Regression (univariate or multivariate dep var),
  Bayes Seemingly Unrelated Regression (SUR),
  Binary and Ordinal Probit,
  Multinomial Logit (MNL) and Multinomial Probit (MNP),
  Multivariate Probit,
  Negative Binomial (Poisson) Regression,
  Multivariate Mixtures of Normals (including clustering),
  Dirichlet Process Prior Density Estimation with normal base,
  Hierarchical Linear Models with normal prior and covariates,
  Hierarchical Linear Models with a mixture of normals prior and covariates,
  Hierarchical Multinomial Logits with a mixture of normals prior
     and covariates,
  Hierarchical Multinomial Logits with a Dirichlet Process prior and covariates,
  Hierarchical Negative Binomial Regression Models,
  Bayesian analysis of choice-based conjoint data,
  Bayesian treatment of linear instrumental variables models,
  Analysis of Multivariate Ordinal survey data with scale
     usage heterogeneity (as in Rossi et al, JASA (01)),
  Bayesian Analysis of Aggregate Random Coefficient Logit Models as in BLP (see
  Jiang, Manchanda, Rossi 2009)
  For further reference, consult our book, Bayesian Statistics and
  Marketing by Rossi, Allenby and McCulloch (Wiley 2005) and Bayesian Non- and Semi-Parametric
  Methods and Applications (Princeton U Press 2014).
An implementation of the Bayesian version of the Mallows rank model
    (Vitelli et al., Journal of Machine Learning Research, 2018 <https://jmlr.org/papers/v18/15-481.html>;
    Crispino et al., Annals of Applied Statistics, 2019 <doi:10.1214/18-AOAS1203>). Both Metropolis-Hastings 
    and sequential Monte Carlo algorithms for estimating the models are available. Cayley, footrule,
    Hamming, Kendall, Spearman, and Ulam distances are supported in the models. The rank data to be
    analyzed can be in the form of complete rankings, top-k rankings, partially missing rankings, as well
    as consistent and inconsistent pairwise preferences. Several functions for plotting and studying the
    posterior distributions of parameters are provided. The package also provides functions for estimating
    the partition function (normalizing constant) of the Mallows rank model, both with the importance
    sampling algorithm of Vitelli et al. and asymptotic approximation with the IPFP algorithm
    (Mukherjee, Annals of Statistics, 2016 <doi:10.1214/15-AOS1389>).
Bayesian tools that can be used to reconcile, or mass balance, mass flow rate data collected from chemical or particulate separation processes aided by constraints governed by the conservation of mass.
    Functions included in the package aid the user in organizing and constraining data, using Markov chain Monte Carlo methods to obtain samples from Bayesian models, and in computation of the marginal likelihood of the data, given a particular model, for model selection.  Marginal likelihood is approximated by methods in Chib S (1995) <doi:10.2307/2291521>.
A collection of functions allowing to derive the posterior distribution of the two parameters in a random-effects meta-analysis, and providing functionality to evaluate joint and marginal posterior probability distributions, predictive distributions, shrinkage effects, posterior predictive p-values, etc.; For more details, see also Roever C (2020) <doi:10.18637/jss.v093.i06>.
Contains a split population survival estimator that models the 
    misclassification probability of failure versus right-censored events.  
    The split population survival estimator is described in 
    Bagozzi et al. (2019) <doi:10.1017/pan.2019.6>.
Fits finite mixture models of univariate Gaussian distributions using JAGS within a Bayesian framework.
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.
Methods for assessing animal movement from telemetry and biologging
    data using non-parametric Bayesian methods. This includes features for pre-
    processing and analysis of data, as well as the visualization of results
    from the models. This framework does not rely on standard parametric density
    functions, which provides flexibility during model fitting. Further details 
    regarding part of this framework can be found in Cullen et al. (2021) <doi:10.1101/2020.11.05.369702>.
Software for fitting sparse Bayesian multi-resolution spatial models using Markov Chain Monte Carlo.
Implementation of No-Effect-Concentration estimation that uses 'brms' (see Burkner (2017)<doi:10.18637/jss.v080.i01>; Burkner (2018)<doi:10.32614/RJ-2018-017>; Carpenter 'et al.' (2017)<doi:10.18637/jss.v076.i01> to fit concentration(dose)-response data using Bayesian methods for the purpose of estimating 'ECX' values, but more particularly 'NEC' (see Fox (2010)<doi:10.1016/j.ecoenv.2009.09.012>. This package expands and supersedes an original version implemented in R2jags, see Fisher, Ricardo and Fox (2020)<doi:10.5281/ZENODO.3966864>.
Belief propagation methods in Bayesian Networks to propagate evidence through the network. The implementation of these methods are based on the article: Cowell, RG (2005). Local Propagation in Conditional Gaussian Bayesian Networks <https://www.jmlr.org/papers/v6/cowell05a.html>. For details please see Yu et. al. (2020) BayesNetBP: An R Package for Probabilistic Reasoning in Bayesian Networks <doi:10.18637/jss.v094.i03>. The optional 'cyjShiny' package for running the Shiny app is available at <https://github.com/cytoscape/cyjShiny>. Please see the example in the documentation of 'runBayesNetApp' function for installing 'cyjShiny' package from GitHub.
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
Enables off-the-shelf functionality for fully Bayesian, nonstationary Gaussian process modeling. The approach to nonstationary modeling involves a closed-form, convolution-based covariance function with spatially-varying parameters; these parameter processes can be specified either deterministically (using covariates or basis functions) or stochastically (using approximate Gaussian processes). Stationary Gaussian processes are a special case of our methodology, and we furthermore implement approximate Gaussian process inference to account for very large spatial data sets (Finley, et al (2017) <arXiv:1702.00434v2>). Bayesian inference is carried out using Markov chain Monte Carlo methods via the 'nimble' package, and posterior prediction for the Gaussian process at unobserved locations is provided as a post-processing step.
The proposed group-sequential trial design is based on Bayesian methods for ordinal endpoints, 
             including three methods, the proportional-odds-model (PO)-based, non-proportional-odds-model (NPO)-based, 
             and PO/NPO switch-model-based designs, which makes our proposed methods generic to be able to deal with 
             various scenarios.
             Richard J. Barker, William A. Link (2013) <doi:10.1080/00031305.2013.791644>.
             Thomas A. Murray, Ying Yuan, Peter F. Thall, Joan H. Elizondo, Wayne L.Hofstetter (2018) <doi:10.1111/biom.12842>.
             Chengxue Zhong, Haitao Pan, Hongyu Miao (2021) <arXiv:2108.06568>.
Fits a piecewise exponential hazard to survival data using a
    Hierarchical Bayesian model with an Intrinsic Conditional Autoregressive
    formulation for the spatial dependency in the hazard rates for each piece.
    This function uses Metropolis- Hastings-Green MCMC to allow the number of split
    points to vary and also uses Stochastic Search Variable Selection to determine
    what covariates drive the risk of the event. This function outputs trace plots
    depicting the number of split points in the hazard and the number of variables
    included in the hazard. The function saves all posterior quantities to the
    desired path.
Fits a piecewise exponential hazard to survival data using a
    Hierarchical Bayesian model with an Intrinsic Conditional Autoregressive
    formulation for the spatial dependency in the hazard rates for each piece.
    This function uses Metropolis- Hastings-Green MCMC to allow the number of split
    points to vary. This function outputs graphics that display the histogram of
    the number of split points and the trace plots of the hierarchical parameters.
    The function outputs a list that contains the posterior samples for the number
    of split points, the location of the split points, and the log hazard rates
    corresponding to these splits. Additionally, this outputs the posterior samples
    of the two hierarchical parameters, Mu and Sigma^2.
A lightweight modelling syntax for defining likelihoods and priors
    and for computing Bayes factors for simple one parameter models. It includes
    functionality for computing and plotting priors, likelihoods, and model
    predictions. Additional functionality is included for computing and plotting
    posteriors.
Plotting functions for posterior analysis, MCMC diagnostics,
    prior and posterior predictive checks, and other visualizations
    to support the applied Bayesian workflow advocated in
    Gabry, Simpson, Vehtari, Betancourt, and Gelman (2019) <doi:10.1111/rssa.12378>.
    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'.
Presence-Only data is best modelled with a Point Process Model.
    The work of Moreira (2020) provides a way to use exact Bayesian
    inference to model this type of data, which is implemented in this package.
Generating population projections for all countries of the world using several probabilistic components, such as total fertility rate and life expectancy (Raftery et al., 2012 <doi:10.1073/pnas.1211452109>).
An implementation of functions to generate and plot postestimation quantities after estimating Bayesian regression models using Markov chain Monte Carlo (MCMC). Functionality includes the estimation of the Precision-Recall curves (see Beger, 2016 <doi:10.2139/ssrn.2765419>), the implementation of the observed values method of calculating predicted probabilities by Hanmer and Kalkan (2013) <doi:10.1111/j.1540-5907.2012.00602.x>, the implementation of the average value method of calculating predicted probabilities (see King, Tomz, and Wittenberg, 2000 <doi:10.2307/2669316>), and the generation and plotting of first differences to summarize typical effects across covariates (see Long 1997, ISBN:9780803973749; King, Tomz, and Wittenberg, 2000 <doi:10.2307/2669316>). This package can be used with MCMC output generated by any Bayesian estimation tool including 'JAGS', 'BUGS', 'MCMCpack', and 'Stan'.
Bayesian power/type I error calculation and model fitting using 
  the power prior and the normalized power prior for generalized linear models.
  Detailed examples of applying the package are available at <arXiv:2112.14616>. 
  The Bayesian clinical trial design methodology is described in Chen et al. (2011) 
  <doi:10.1111/j.1541-0420.2011.01561.x>, and Psioda and Ibrahim (2019) 
  <doi:10.1093/biostatistics/kxy009>. The normalized power prior is described in Duan et al. (2006) 
  <doi:10.1002/env.752> and Ibrahim et al. (2015) <doi:10.1002/sim.6728>.
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.
Implementations in 'cpp' of the BayesProject algorithm (see G. Hahn, P. Fearnhead, I.A. Eckley (2020) <doi:10.1007/s11222-020-09966-2>) which implements a fast approach to compute a projection direction for multivariate changepoint detection, as well as the sum-cusum and max-cusum methods, and a wild binary segmentation wrapper for all algorithms.
Bayesian quantile regression using the asymmetric Laplace distribution, both continuous as well as binary dependent variables are supported. The package consists of implementations of the methods of Yu & Moyeed (2001) <doi:10.1016/S0167-7152(01)00124-9>, Benoit & Van den Poel (2012) <doi:10.1002/jae.1216> and Al-Hamzawi, Yu & Benoit (2012) <doi:10.1177/1471082X1101200304>. To speed up the calculations, the Markov Chain Monte Carlo core of all algorithms is programmed in Fortran and called from R.
Fits linear or generalized linear regression models using Bayesian global-local shrinkage prior hierarchies as described in Polson and Scott (2010) <doi:10.1093/acprof:oso/9780199694587.003.0017>. Provides an efficient implementation of ridge, lasso, horseshoe and horseshoe+ regression with logistic, Gaussian, Laplace, Student-t, Poisson or geometric distributed targets using the algorithms summarized in Makalic and Schmidt (2016) <arXiv:1611.06649>.
Functionality for reliability estimates. For 'unidimensional' tests:
    Coefficient alpha, 'Guttman's' lambda-2/-4/-6, the Greatest lower
    bound and coefficient omega_u ('unidimensional') in a Bayesian and a frequentist version. 
    For multidimensional tests: omega_t (total) and omega_h (hierarchical). 
    The results include confidence and credible intervals, the 
    probability of a coefficient being larger than a cutoff, 
    and a check for the factor models, necessary for the omega coefficients. 
    The method for the Bayesian 'unidimensional' estimates, except for omega_u, 
    is sampling from the posterior inverse 'Wishart' for the 
    covariance matrix based measures (see 'Murphy', 2007, 
    <https://groups.seas.harvard.edu/courses/cs281/papers/murphy-2007.pdf>. 
    The Bayesian omegas (u, t, and h) are obtained by 
    'Gibbs' sampling from the conditional posterior distributions of 
    (1) the single factor model and (2) the second-order factor model
    ('Lee', 2007, <https://onlinelibrary.wiley.com/doi/book/10.1002/9780470024737>).
Contains posterior samplers for the Bayesian piecewise linear log-hazard and piecewise exponential hazard models, including Cox models. Posterior mean restricted survival times are also computed for non-Cox an Cox models with only treatment indicators. The ApproxMean() function can be used to estimate restricted posterior mean survival times given a vector of patient covariates in the Cox model. Functions included to return the posterior mean hazard and survival functions for the piecewise exponential and piecewise linear log-hazard models. Chapple, AG, Peak, T, Hemal, A (2020). Under Revision.
To perform model estimation using MCMC algorithms with Bayesian methods for incomplete longitudinal studies on binary and ordinal outcomes that are measured repeatedly on subjects over time with drop-outs. Details about the method can be found in the vignette or <https://sites.google.com/view/kuojunglee/r-packages/bayesrgmm>.
Runs hierarchical linear Bayesian models. Samples from the posterior
    distributions of model parameters in JAGS (Just Another Gibbs Sampler;
	Plummer, 2017, <http://mcmc-jags.sourceforge.net>). Computes Bayes factors for group
	parameters of interest with the Savage-Dickey density ratio (Wetzels,
	Raaijmakers, Jakab, Wagenmakers, 2009, <doi:10.3758/PBR.16.4.752>).
Provides datasets and functions used for analysis 
  and visualizations in the Bayes Rules! book (<https://www.bayesrulesbook.com>). 
  The package contains a set of functions that summarize and plot Bayesian models from some conjugate families 
  and another set of functions for evaluation of some Bayesian models.
bayess contains a collection of functions that allows the
        reenactment of the R programs used in the book "Bayesian
        Essentials with R" (revision of "Bayesian Core") without
        further programming. R code being available as well, they can
        be modified by the user to conduct one's own simulations.
In p >> n settings, full posterior sampling using existing Markov chain Monte
    Carlo (MCMC) algorithms is highly inefficient and often not feasible from a practical
    perspective. To overcome this problem, we propose a scalable stochastic search algorithm that is called the Simplified Shotgun Stochastic Search (S5) and aimed at rapidly explore interesting regions of model space and finding the maximum a posteriori(MAP) model. Also, the S5 provides an approximation of posterior probability of each model (including the marginal inclusion probabilities). This algorithm is a part of an article titled "Scalable Bayesian Variable Selection Using Nonlocal Prior Densities in Ultrahigh-dimensional Settings" (2018) by Minsuk Shin, Anirban Bhattacharya, and Valen E. Johnson and "Nonlocal Functional Priors for Nonparametric Hypothesis Testing and High-dimensional Model Selection" (2020+) by Minsuk Shin and Anirban Bhattacharya.
Provides a variety of methods from Rao (2003, ISBN:0-471-41374-7)
  and some other research articles to deal with several specific small area area-
  level models in Bayesian framework. Models provided range from the basic Fay-Herriot model to 
  its improvement such as You-Chapman models, unmatched models, spatial models and so on. 
  Different types of priors for specific parameters could be chosen to obtain MCMC posterior 
  draws. The main sampling function is written in C with GSL lab so as to facilitate the 
  computation. Model internal checking and model comparison criteria are also involved.
Allows the user to apply the Bayes Linear approach to finite population with the Simple Random Sampling - BLE_SRS() - and
    the Stratified Simple Random Sampling design - BLE_SSRS() - (both without replacement), to the Ratio estimator (using auxiliary
    information) - BLE_Ratio() - and to categorical data - BLE_Categorical().
    The Bayes linear estimation approach is applied to a general linear regression model for finite population prediction in BLE_Reg()
    and it is also possible to achieve the design based estimators using vague prior distributions.    
    Based on Gonçalves, K.C.M, Moura, F.A.S and  Migon, H.S.(2014) <https://www150.statcan.gc.ca/n1/en/catalogue/12-001-X201400111886>.
Generates different posterior distributions of adjusted odds ratio under different priors of sensitivity and specificity, and plots the models for comparison. It also provides estimations for the specifications of the models using diagnostics of exposure status with a non-linear mixed effects model. It implements the methods that are first proposed in <doi:10.1016/j.annepidem.2006.04.001> and <doi:10.1177/0272989X09353452>.
An implementation of methods for spectral analysis using the Bayesian framework. It includes functions for modelling spectrum as well as appropriate plotting and output estimates. There is segmentation capability with RJ MCMC (Reversible Jump Markov Chain Monte Carlo). The package takes these methods predominantly from the 2012 paper "AdaptSPEC: Adaptive Spectral Estimation for Nonstationary Time Series" <DOI:10.1080/01621459.2012.716340>.
Parametric spatial split-population (SP) survival models for clustered 
    event processes. The models account for structural and spatial heterogeneity among 
    “at risk” and “immune” populations, and incorporate time-varying covariates. 
    This package currently implements Weibull, Exponential and Log-logistic forms for 
    the duration component. It also includes functions for a series of diagnostic 
    tests and plots to easily visualize spatial autocorrelation, convergence, and spatial effects. Users can 
    create their own spatial weights matrix based on their units and adjacencies of 
    interest, making the use of these models flexible and broadly applicable to a 
    variety of research areas. Joo et al. (2020) <https://github.com/Nicolas-Schmidt/BayesSPsurv/blob/master/man/figures/SPcure.pdf> describe 
    the estimators included in this package.
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. See Miroshnikov, Savel'ev and Conlon (2015) <arXiv:1503.00635>.
Bayesian seemingly unrelated regression with general variable selection and dense/sparse covariance matrix. The sparse seemingly unrelated regression is described in Bottolo et al. (2021) <doi:10.1111/rssc.12490>, and the software paper is in Zhao et al. (2021) <arXiv:2104.14008>.
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.
Performs unadjusted Bayesian survival analysis for right censored time-to-event data. The main function, BayesSurv(), computes the posterior mean and a credible band for the survival function and for the cumulative hazard, as well as the posterior mean for the hazard, starting from a piecewise exponential (histogram) prior with Gamma distributed heights that are either independent, or have a Markovian dependence structure.
    A function, PlotBayesSurv(), is provided to easily create plots of the posterior means of the hazard, cumulative hazard and survival function, with a credible band accompanying the latter two.
    The priors and samplers are described in more detail in Castillo and Van der Pas (2020) "Multiscale Bayesian survival analysis" <arXiv:2005.02889>. In that paper it is also shown that the credible bands for the survival function and the cumulative hazard can be considered confidence bands (under mild conditions) and thus offer reliable uncertainty quantification.
Provides an Markov-Chain-Monte-Carlo algorithm for Bayesian t-tests on the effect size. The underlying Gibbs sampler is based on a two-component Gaussian mixture and approximates the posterior distributions of the effect size, the difference of means and difference of standard deviations. A posterior analysis of the effect size via the region of practical equivalence is provided, too. For more details about the Gibbs sampler see Kelter (2019) <arXiv:1906.07524>.
Provides utilities to describe posterior
    distributions and Bayesian models. It includes point-estimates such as
    Maximum A Posteriori (MAP), measures of dispersion (Highest Density
    Interval - HDI; Kruschke, 2015 <doi:10.1016/C2012-0-00477-2>) and
    indices used for null-hypothesis testing (such as ROPE percentage, pd
    and Bayes factors).
Making probabilistic projections of total fertility rate for all countries of the world, using a Bayesian hierarchical model <doi:10.1007/s13524-011-0040-5>. Subnational probabilistic projections are also supported.
Provides tools for conducting Bayesian analyses. The package contains 
    functions for creating a wide range of prior distribution objects, mixing posterior 
    samples from 'JAGS' and 'Stan' models, plotting posterior distributions, and etc...
    The tools for working with prior distribution span from visualization, generating 'JAGS' 
    and 'bridgesampling' syntax to basic functions such as rng, quantile, and distribution functions.
This is an implementation of BART:Bayesian Additive Regression Trees,
        by Chipman, George, McCulloch (2010).
Provides a way to simulate from the prior distribution of Bayesian trees by Chipman et al. (1998) <DOI:10.2307/2669832>. The prior distribution of Bayesian trees is highly dependent on the design matrix X, therefore using the suggested hyperparameters by Chipman et al. (1998) <DOI:10.2307/2669832> is not recommended and could lead to unexpected prior distribution. This work is part of my master thesis (expected 2016).
Bayesian analysis of item-level hierarchical twin data using an integrated item response theory model. Analyses are based on Schwabe & van den Berg (2014) <doi:10.1007/s10519-014-9649-7>, Molenaar & Dolan (2014) <doi:10.1007/s10519-014-9647-9>, Schwabe, Jonker & van den Berg (2016) <doi:10.1007/s10519-015-9768-9> and Schwabe, Boomsma & van den Berg (2016) <doi:10.1016/j.lindif.2017.01.018>.
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>. It also allows problems with p>n and p>>n and also incorporates routines to handle problems with variable selection with factors.
Provides users with its associated functions for pedagogical purposes in visually learning Bayesian networks and Markov chain Monte Carlo (MCMC) computations. It enables users to: a) Create and examine the (starting) graphical structure of Bayesian networks; b) Create random Bayesian networks using a dataset with customized constraints; c) Generate 'Stan' code for structures of Bayesian networks for sampling the data and learning parameters; d) Plot the network graphs; e) Perform Markov chain Monte Carlo computations and produce graphs for posteriors checks. The package refers to one reference item, which describes the methods and algorithms: Vuong, Quan-Hoang and La, Viet-Phuong (2019) <doi:10.31219/osf.io/w5dx6> The 'bayesvl' R package. Open Science Framework (May 18).
Functions for exploring and visualising estimation results
  obtained with BayesX, a free software for estimating structured additive
  regression models (<http://www.BayesX.org>). In addition,
  functions that allow to read, write and manipulate map objects that are required in spatial
  analyses performed with BayesX.
BayesX performs Bayesian inference in structured additive regression (STAR) models.
	The R package BayesXsrc provides the BayesX command line tool for easy installation.
	A convenient R interface is provided in package R2BayesX.
Fits a Bayesian zero-inflated Bernoulli regression model handling (potentially) different covariates for the zero-inflated
    and non zero-inflated parts. See Moriña D, Puig P, Navarro A. (2021) <doi:10.1186/s12874-021-01427-2>.
A Bayesian, global planktic foraminifera core top calibration to 
    modern sea-surface temperatures. Includes four calibration models, 
    considering species-specific calibration parameters and seasonality.
Bayesian analysis of luminescence data and C-14 age estimates. Bayesian models are based on the following publications: Combes, B. & Philippe, A. (2017) <doi:10.1016/j.quageo.2017.02.003> and Combes et al (2015) <doi:10.1016/j.quageo.2015.04.001>. This includes, amongst others, data import, export, application of age models and palaeodose model.
Bayesian approach to multidimensional scaling. The package consists of implementations of the methods of Oh and Raftery (2001)  <doi:10.1198/016214501753208690>.
BAYesian inference for MEDical designs in R. Functions for the 
    computation of Bayes factors for common biomedical research designs. 
    Implemented are functions to test the equivalence (equiv_bf), 
    non-inferiority (infer_bf), and superiority (super_bf) of an experimental 
    group compared to a control group on a continuous outcome measure. Bayes 
    factors for these three tests can be computed based on raw data (x, y) or 
    summary statistics (n_x, n_y, mean_x, mean_y, sd_x, sd_y [or ci_margin 
    and ci_level]).
Performs general Bayesian estimation method of linear–bilinear models for genotype × environment interaction. The method is explained in Perez-Elizalde, S., Jarquin, D., and Crossa, J. (2011) (<doi:10.1007/s13253-011-0063-9>).
Fit two-regime threshold autoregressive (TAR) models by Markov chain Monte Carlo methods.
Enable users to evaluate long-term trends using a Generalized 
    Additive Modeling (GAM) approach. The model development includes selecting a 
    GAM structure to describe nonlinear seasonally-varying changes over time, 
    incorporation of hydrologic variability via either a river flow or salinity, 
    the use of an intervention to deal with method or laboratory changes 
    suspected to impact data values, and representation of left- and 
    interval-censored data. The approach has been applied to water quality data 
    in the Chesapeake Bay, a major estuary on the east coast of the United 
    States to provide insights to a range of management- and research-focused 
    questions.  Methodology described in Murphy (2019) 
    <doi:10.1016/j.envsoft.2019.03.027>.
A collection of miscellaneous functions for 
    copying objects to the clipboard ('Copy');
    manipulating strings ('concat', 'mgsub', 'trim', 'verlan'); 
    loading or showing packages ('library_with_dep', 'require_with_dep', 
    'sessionPackages'); 
    creating or testing for named lists ('nlist', 'as.nlist', 'is.nlist'), 
    formulas ('is.formula'), empty objects ('as.empty', 'is.empty'), 
    whole numbers ('as.wholenumber', 'is.wholenumber'); 
    testing for equality ('almost.equal', 'almost.zero') and computing 
    uniqueness ('almost.unique'); 
    getting modified versions of usual functions ('rle2', 'sumNA'); 
    making a pause or a stop ('pause', 'stopif'); 
    converting into a function ('as.fun'); 
    providing a C like ternary operator ('condition %?% true %:% false'); 
    finding packages and functions ('get_all_pkgs', 'get_all_funs');
    and others ('erase', '%nin%', 'unwhich', 'top', 'bot', 'normalize').
Barzilai-Borwein spectral methods for solving nonlinear
        system of equations, and for optimizing nonlinear objective
        functions subject to simple constraints. A tutorial style
        introduction to this package is available in a vignette on the
        CRAN download page or, when the package is loaded in an R
        session, with vignette("BB").
Efficiently draw samples from the posterior distribution of various correlation coefficients 
             with the Bayesian bootstrap described in Rubin (1981) <doi:10.1214/aos/1176345338>. 
             There are six correlation coefficients, including 
             Pearson, Kendall, Spearman, Gaussian Rank, Blomqvist, and polychoric.
Implements two algorithms of detecting Bull and Bear markets in stock prices: the algorithm of Pagan and Sossounov (2002, <doi:10.1002/jae.664>) and the algorithm of Lunde and Timmermann (2004, <doi:10.1198/073500104000000136>).  
       The package also contains functions for printing out the dating of the Bull and Bear states of the market, the descriptive statistics of the states, and functions for plotting the results. 
       For the sake of convenience, the package includes the monthly and daily data on the prices (not adjusted for dividends) of the S&P 500 stock market index.
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. The description of the corresponding approach 
        could be found at Gagin and Levin (2014) <DOI:10.1107/S1600576714023796>.
Set of functions to calculate Benthic Biotic Indices from
	composition data, obtained whether from morphotaxonomic inventories or
	sequencing data. Based on reference ecological weights publicly available for
	a set of commonly used marine biotic indices, such as AMBI (A Marine Biotic Index, Borja et al., 2000) <doi:10.1016/S0025-326X(00)00061-8>
	NSI (Norwegian Sensitivity Index) and ISI (Indicator Species Index) (Rygg 2013, <ISBN:978-82-577-6210-0>). It provides the ecological quality status of the samples based on each BBI as well as the normalized Ecological Quality Ratio.
Supervised learning using Boltzmann Bayes model inference, 
    which extends naive Bayes model to include interactions. Enables 
    classification of data into multiple response groups based on a large 
    number of discrete predictors that can take factor values of 
    heterogeneous levels. Either pseudo-likelihood or mean field 
    inference can be used with L2 regularization, cross-validation, and 
    prediction on new data. 
    <doi:10.18637/jss.v101.i05>.
Miscellaneous helper functions for and from B. Bischl and
    some other guys, mainly for package development.
Methods and functions for fitting maximum likelihood models in R. This package modifies and extends the 'mle' classes in the 'stats4' package.
The model provides an empirical estimate of a movement
        path using discrete location data obtained at relatively short
        time intervals.
Provides a set of functions to fit general macroevolutionary models for continuous traits evolving in adaptive landscapes of any shape. This package implements the Fokker-Planck-Kolmogorov model (FPK), in which the trait evolves under random diffusion but is also subject to a force that pulls it towards specific values - this force can be of any shape. FPK has a version in which hard reflective bounds exist at the extremes of the trait interval: this second model is called BBMV.
Provides a common framework for optimization of black-box
    functions for other packages, e.g. 'mlr3tuning' or 'mlr3fselect'. It
    offers various optimization methods e.g. grid search, random search,
    generalized simulated annealing and iterated racing.
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.
Functions to fit, via Expectation-Maximization (EM) algorithm, the Bessel and Beta regressions to a data set with a bounded continuous response variable. The Bessel regression is a new and robust approach proposed in the literature. The EM version for the well known Beta regression is another major contribution of this package. See details in the references Barreto-Souza, Mayrink and Simas (2022) <doi:10.1111/anzs.12354> and Barreto-Souza, Mayrink and Simas (2020) <arXiv:2003.05157>.
A set of frequently used Bayesian parametric and nonparametric model structures, as well as a set of tools for common analytical tasks. Structures include linear Gaussian systems, Gaussian and Normal-Inverse-Wishart conjugate structure, Gaussian and Normal-Inverse-Gamma conjugate structure, Categorical and Dirichlet conjugate structure, Dirichlet Process on positive integers, Dirichlet Process in general, Hierarchical Dirichlet Process ... Tasks include updating posteriors, sampling from posteriors, calculating marginal likelihood, calculating posterior predictive densities, sampling from posterior predictive distributions, calculating "Maximum A Posteriori" (MAP) estimates ... See <https://chenhaotian.github.io/Bayesian-Bricks/> to get started.
The North American Breeding Bird Survey (BBS) is a long-running
  program that seeks to monitor the status and trends of the breeding birds in
  North America. Since its start in 1966, the BBS has accumulated over 50 years 
  of data for over 500 species of North American Birds. Given the temporal and 
  spatial structure of the data, hierarchical Bayesian models are used to assess 
  the status and trends of these 500+ species of birds. 'bbsBayes' allows you to perform 
  hierarchical Bayesian analysis of BBS data. You can run a full
  model analysis for one or more species that you choose, or you can take
  more control and specify how the data should be stratified, prepared
  for 'JAGS', or modelled. The functions provided here allow you to replicate
  analyses performed by the United State Geological Survey (USGS, see Link 
  and Sauer (2011) <doi:10.1525/auk.2010.09220>) and Canadian Wildlife Service
  (CWS, see Smith and Edwards (2020) <doi:10.1101/2020.03.26.010215>).
Posterior sampling for Spike-and-Slab LASSO prior in linear models from Nie and Rockova <arXiv:2011.14279>.
The blocked weighted bootstrap (BBW) is an estimation technique 
    for use with data from two-stage cluster sampled surveys in which either 
    prior weighting (e.g. population-proportional sampling or PPS as used in 
    Standardized Monitoring and Assessment of Relief and Transitions or SMART 
    surveys) or posterior weighting (e.g. as used in rapid assessment method or
    RAM and simple spatial sampling method or S3M surveys). The method was 
    developed by Accion Contra la Faim, Brixton Health, Concern Worldwide, 
    Global Alliance for Improved Nutrition, UNICEF Sierra Leone,  UNICEF Sudan
    and Valid International. It has been tested by the Centers for Disease
    Control (CDC) using infant and young child feeding (IYCF) data. See Cameron
    et al (2008) <doi:10.1162/rest.90.3.414> for application of bootstrap
    to cluster samples. See Aaron et al (2016) <doi:10.1371/journal.pone.0163176> 
    and Aaron et al (2016) <doi:10.1371/journal.pone.0162462> for application 
    of the blocked weighted bootstrap to estimate indicators from two-stage 
    cluster sampled surveys.
Implementation of the BC3NET algorithm for gene regulatory network inference (de Matos Simoes and Frank Emmert-Streib, Bagging Statistical Network Inference from Large-Scale Gene Expression Data, PLoS ONE 7(3): e33624, <doi:10.1371/journal.pone.0033624>).
Implementing the Block Coordinate Ascent with One-Step Generalized Rosen (BCA1SG) algorithm on the semiparametric models for panel count data, interval-censored survival data, and degradation data. A comprehensive description of the BCA1SG algorithm can be found in Wang et al. (2020) <https://github.com/yudongstat/BCA1SG/blob/master/BCA1SG.pdf>. For details of the semiparametric models for panel count data, interval-censored survival data, and degradation data, please see Wellner and Zhang (2007) <doi:10.1214/009053607000000181>, Huang and Wellner (1997) <ISBN:978-0-387-94992-5>, and Wang and Xu (2010) <doi:10.1198/TECH.2009.08197>, respectively.
Computation of bootstrap confidence intervals in an almost automatic fashion as described in Efron and Narasimhan (2020, <doi:10.1080/10618600.2020.1714633>).
Fully Bayesian Classification with a subset of high-dimensional features, such as expression levels of genes. The data are modeled with a hierarchical Bayesian models using heavy-tailed t distributions as priors. When a large number of features are available, one may like to select only a subset of features to use, typically those features strongly correlated with the response in training cases. Such a feature selection procedure is however invalid since the relationship between the response and the features has be exaggerated by feature selection. This package provides a way to avoid this bias and yield better-calibrated predictions for future cases when one uses F-statistic to select features.
Calculates the prices of European options based on the universal solution provided by Bakshi, Cao and Chen (1997) <doi:10.1111/j.1540-6261.1997.tb02749.x>. This solution considers stochastic volatility, stochastic interest and random jumps. Please cite their work if this package is used.
Developed for the following tasks. Simulating, computing maximum likelihood 
             estimator, computing the Fisher information matrix, computing goodness-of-fit measures,
             and correcting bias of the ML estimator for a wide range of distributions fitted to units
             placed on progressive type-I interval censoring and progressive type-II censoring plans.  
             The methods of Cox and Snell (1968) <doi:10.1111/j.2517-6161.1968.tb00724.x> and
             bootstrap method for computing the bias-corrected maximum likelihood estimator.
A collection of functions for structure learning of causal networks and estimation of joint 
    causal effects from observational Gaussian data. Main algorithm consists of a Markov chain Monte Carlo 
    scheme for posterior inference of causal structures, parameters and causal effects between variables.
    References:
    F. Castelletti and A. Mascaro (2021) <doi:10.1007/s10260-021-00579-1>,
    F. Castelletti and A. Mascaro (2022) <arXiv:2201.12003>.
Search, query, and download tabular and
    'geospatial' data from the British Columbia Data Catalogue
    (<https://catalogue.data.gov.bc.ca/>).  Search catalogue data records
    based on keywords, data licence, sector, data format, and B.C.
    government organization. View metadata directly in R, download many
    data formats, and query 'geospatial' data available via the B.C.
    government Web Feature Service ('WFS') using 'dplyr' syntax.
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 estimate taxonomic compositions from biomarker data, using a Bayesian approach.
Produces an economic evaluation of a sample of suitable variables of
             cost and effectiveness / utility for two or more interventions,
             e.g. from a Bayesian model in the form of MCMC simulations.
             This package computes the most cost-effective alternative and
             produces graphical summaries and probabilistic sensitivity analysis.
A Bayesian model averaging approach to causal effect estimation
    based on the BCEE algorithm. Currently supports binary or continuous
    exposures and outcomes. For more details, see 
    Talbot et al. (2015) <doi:10.1515/jci-2014-0035>
    Talbot and Beaudoin (2020) <arXiv:2003.11588>.
Causal inference for a binary treatment and continuous outcome using Bayesian Causal Forests. See Hahn, Murray and Carvalho (2017) <arXiv:1706.09523> for additional information. This implementation relies on code originally accompanying Pratola et. al. (2013) <arXiv:1309.1906>.
Fit and simulate a semiparametric bivariate correlated frailty models with
    proportional hazard structure. Frailty distributions, such as gamma and
    lognormal models are supported. Bivariate gamma fit is obtained  
    using the approach given in Iachine (1995) and lognormal fit is based 
    on the approach by Ripatti and Palmgren (2000) <doi:10.1111/j.0006-341X.2000.01016.x>.
Fit and simulate bivariate correlated frailty models with
    proportional hazard structure. Frailty distributions, such as gamma and
    lognormal models are supported. Frailty variances of the two subjects 
    can be varied or equal. Details on the models are available in book of 
    Wienke (2011,ISBN:978-1-4200-7388-1). Bivariate gamma fit is obtained using the approach given 
    in Iachine (1995) with modifications. Lognormal fit is based on the approach
    by Ripatti and Palmgren (2000) <doi:10.1111/j.0006-341X.2000.01016.x>. Univariate
    and bivariate shared gamma frailty model fits are possible. Standard errors of
    the estimated covariate coefficients and frailty variance parameter are obtained using
    the approach given in Klein and Moeschberger (2003,ISBN:0-387-95399-X).
Fits generalised partial linear regression models using a Bayesian approach, where shape and
			smoothness constraints are imposed on nonparametrically modelled predictors through shape-restricted splines, and 
			no constraints are imposed on optional parametrically modelled covariates. See Meyer et al. (2011) <doi/10.1080/10485252.2011.597852> 
			for more details. IMPORTANT: before installing 'bcgam', you need to install 'Rtools' (Windows) or 'Xcode' (Mac OS X). These are	
			required for the correct installation of 'nimble' (<https://r-nimble.org/download>).
Provides bias-corrected estimates for the regression coefficients of a marginal model estimated with generalized estimating equations. Details about the bias formula used are in Lunardon, N., Scharfstein, D. (2017) <doi:10.1002/sim.7366>.
Users can estimate the treatment effect for multiple subgroups basket trials based on the Bayesian Cluster Hierarchical Model (BCHM). In this model, a Bayesian non-parametric method is applied to dynamically calculate the number of clusters by conducting the multiple cluster classification based on subgroup outcomes. Hierarchical model is used to compute the posterior probability of treatment effect with the borrowing strength determined by the Bayesian non-parametric clustering and the similarities between subgroups. To use this package, 'JAGS' software and 'rjags' package are required, and users need to pre-install them.
Enables quick calibration of radiocarbon dates under various 
  calibration curves (including user generated ones); age-depth modelling 
  as per the algorithm of Haslett and Parnell (2008) <DOI:10.1111/j.1467-9876.2008.00623.x>; Relative sea level 
  rate estimation incorporating time uncertainty in polynomial regression 
  models (Parnell and Gehrels 2015) <DOI:10.1002/9781118452547.ch32>; non-parametric phase modelling via 
  Gaussian mixtures as a means to determine the activity of a site 
  (and as an alternative to the Oxcal function SUM; currently 
  unpublished), and reverse calibration of dates from calibrated into 
  un-calibrated years (also unpublished).
Many clustering methods have been proposed, but
    most of them cannot work for longitudinal gene expression data. 
    'BClustLonG' is a package that allows us to perform clustering analysis for 
    longitudinal gene expression data. It adopts a linear-mixed effects framework 
    to model the trajectory of genes over time, while clustering is jointly 
    conducted based on the regression coefficients obtained from all genes. 
    To account for the correlations among genes and alleviate the 
    high dimensionality challenges, factor analysis models are adopted
    for the regression coefficients. The Dirichlet process prior distribution 
    is utilized for the means of the regression coefficients to induce clustering.
    This package allows users to specify which variables to use for clustering 
    (intercepts or slopes or both) and whether a factor analysis model is desired.
    More details about this method can be found in Jiehuan Sun, et al. (2017) <doi:10.1002/sim.7374>.
Provides access to various spatial layers for B.C., such as 
    administrative boundaries, natural resource management boundaries, etc. 
    Most layers are imported from the 'bcdata' package as 'sf' or 'Spatial' objects
    through function calls in this package.
Inference on the marginal model of the mixed effect model with 
    the Box-Cox transformation and on the model median differences between
    treatment groups for longitudinal randomized clinical trials. These 
    statistical methods are proposed by Maruo et al. (2017) 
    <doi:10.1002/sim.7279>.
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.
Provides functions to utilize a command line utility that does bulk inserts and exports from SQL Server databases.
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.
             Gail MH, Brinton LA, et al (1989) <doi:10.1093/jnci/81.24.1879>. 
             Marthew PB, Gail MH, et al (2016) <doi:10.1093/jnci/djw215>.
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. See Sweeting et al. (2013): <doi:10.18637/jss.v054.i13>.
The bias-corrected estimation methods for the receiver operating characteristics
  ROC surface and the volume under ROC surfaces (VUS) under missing at random (MAR)
  assumption.
Bindings to the 'blowfish' password hashing algorithm derived from
    the 'OpenBSD' implementation.
Gene expression profiles are commonly utilized to infer disease
             subtypes and many clustering methods can be adopted for this task.
             However, existing clustering methods may not perform well when
             genes are highly correlated and many uninformative genes are included
             for clustering. To deal with these challenges, we develop a novel
             clustering method in the Bayesian setting. This method, called BCSub,
             adopts an innovative semiparametric Bayesian factor analysis model
             to reduce the dimension of the data to a few factor scores for
             clustering. Specifically, the factor scores are assumed to follow
             the Dirichlet process mixture model in order to induce clustering.
An implementation of a collection of tools for exact Bayesian inference with discrete times series. This package contains functions that can be used for prediction, model selection, estimation, segmentation/change-point detection and other statistical tasks. Specifically, the functions provided can be used for the exact computation of the prior predictive likelihood of the data, for the identification of the a posteriori most likely (MAP) variable-memory Markov models, for calculating the exact posterior probabilities and the AIC and BIC scores of these models, for prediction with respect to log-loss and 0-1 loss and segmentation/change-point detection. Example data sets from finance, genetics, animal communication and meteorology are also provided. Detailed descriptions of the underlying theory and algorithms can be found in [Kontoyiannis et al. 'Bayesian Context Trees: Modelling and exact inference for discrete time series.' Journal of the Royal Statistical Society: Series B (Statistical Methodology), April 2022. Available at: <arXiv:2007.14900> [stat.ME], July 2020] and [Lungu et al. 'Change-point Detection and Segmentation of Discrete Data using Bayesian Context Trees' <arXiv:2203.04341> [stat.ME], March 2022].
Implements the projected t-SNE method for batch correction of 
  high-dimensional data. Please see Aliverti et al. (2020) 
  <doi:10.1093/bioinformatics/btaa189> for more information.
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 <arXiv:0908.2062>) and Perry's 2009 PhD thesis
    (at <arXiv:0909.3052>).
Algorithms developed for binned data analysis,
  gene expression data analysis and
  measurement error models for ordinal data analysis.
The bdc package brings together several aspects of
    biodiversity data-cleaning in one place. It is organized in thematic
    modules related to different biodiversity dimensions, including 1)
    Merge datasets: standardization and integration of different datasets; 2) Pre-filter:
    flagging and removal of invalid or non-interpretable information,
    followed by data amendments; 3) Taxonomy: cleaning, parsing, and
    harmonization of scientific names from several taxonomic groups
    against taxonomic databases locally stored through the application of
    exact and partial matching algorithms; 4) Space: flagging of
    erroneous, suspect, and low-precision geographic coordinates; and 5)
    Time: flagging and, whenever possible, correction of inconsistent
    collection date. In addition, the package contains features to
    visualize, document, and report data quality – which is essential for
    making data quality assessment transparent and reproducible.
Supplies a Shiny app and a set of functions to perform and managing data checks for biodiversity data.
Provides features to manage the complete workflow for biodiversity data cleaning. Uploading data, gathering input from users (in order to adjust cleaning procedures), cleaning data and finally, generating various reports and several versions of the data. Facilitates user-level data cleaning, designed for the inexperienced R user. T Gueta et al (2018) <doi:10.3897/biss.2.25564>. T Gueta et al (2017) <doi:10.3897/tdwgproceedings.1.20311>.
The 'shiny' application 'bdDwC' makes biodiversity data field names Darwin Core compatible.
A collection of S4 classes which implements different methods to estimate and deal with densities in bounded domains. That is, densities defined within the interval [lower.limit, upper.limit], where lower.limit and upper.limit are values that can be set by the user.
For a balanced design of experiments, this package calculates the sample size required to detect a certain standardized effect size, under a significance level. This package also provides three graphs; detectable standardized effect size vs power, sample size vs detectable standardized effect size, and sample size vs power, which show the mutual relationship between the sample size, power and the detectable standardized effect size. The detailed procedure is described in R. V. Lenth (2006-9) <https://homepage.divms.uiowa.edu/~rlenth/Power/>, Y. B. Lim (1998), M. A. Kastenbaum, D. G. Hoel and K. O. Bowman (1970) <doi:10.2307/2334851>, and Douglas C. Montgomery (2013, ISBN: 0849323312).
Statistical tools for Bayesian structure learning in undirected graphical models for continuous, discrete, and mixed data. The package is implemented the recent improvements in the Bayesian graphical models' literature, including Mohammadi and Wit (2015) <doi:10.1214/14-BA889>, Mohammadi et al. (2021) <doi:10.1080/01621459.2021.1996377>, and Mohammadi and Wit (2019) <doi:10.18637/jss.v089.i03>.
Interface to Local Data Bank ('Bank Danych Lokalnych' - 'bdl') API 
    <https://api.stat.gov.pl/Home/BdlApi?lang=en> with set of useful tools like 
    quick plotting and map generating using data from bank.
The main function generateDataset() processes a user-supplied .R file that 
  contains metadata parameters in order to generate actual data. The metadata parameters 
  have to be structured in the form of metadata objects, the format of which is 
  outlined in the package vignette. This approach allows to generate artificial data 
  in a transparent and reproducible manner.
Analyze differences among time series curves with p-value
        adjustment for multiple comparisons introduced in Oleson et al
        (2015) <DOI:10.1177/0962280215607411>.
Tools and workflow to choose design parameters in Bayesian adaptive single-arm phase II trial designs with binary endpoint (response, success) with possible stopping for efficacy and futility at interim analyses. Also contains routines to determine and visualize operating characteristics. See Kopp-Schneider et al. (2018) <doi:10.1002/bimj.201700209>.
Provide a tool to easily build customized data flows to pre-process large volumes 
    of information from different sources. To this end, 'bdpar' allows to (i) easily use and 
    create new functionalities and (ii) develop new data source extractors according to the 
    user needs. Additionally, the package provides by default a predefined data flow 
    to extract and pre-process the most relevant information (tokens, dates, ... ) from some textual 
    sources (SMS, Email, tweets, YouTube comments).
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.
Fits a discharge rating curve based on the power-law and the generalized power-law from data on paired stage and discharge measurements in a given river using a Bayesian hierarchical model as described in Hrafnkelsson et al. (2020) <arXiv:2010.04769>.
Implements Bayesian inference to detect signal from blinded 
    clinical trial when total number of adverse events of special 
    concerns and total risk exposures from all patients are available in the study.  
    For more details see the article by Mukhopadhyay et. al. (2018) 
    titled 'Bayesian Detection of Potential Risk Using Inference on Blinded Safety Data', 
    in Pharmaceutical Statistics (to appear).
Provides a continuous date scale, omitting weekends and holidays.
This is a special case of sparse matrices, used by coxme.
A Bayesian regression model for discrete response, where the conditional distribution is modelled via a discrete Weibull distribution. This package provides an implementation of Metropolis-Hastings and Reversible-Jumps algorithms to draw samples from the posterior. It covers a wide range of regularizations through any two parameter prior. Examples are Laplace (Lasso), Gaussian (ridge), Uniform, Cauchy and customized priors like a mixture of priors. An extensive visual toolbox is included to check the validity of the results as well as several measures of goodness-of-fit.
The package bdynsys for panel/longitudinal data combines methods to model 
             changes in up to four indicators over times as a function of the indicators
             themselves and up to three predictors using ordinary differential equations 
             (ODEs) with polynomial terms that allow to model complex and nonlinear 
             effects. A Bayesian model selection approach is implemented. The package 
             provides also visualisation tools to plot phase portraits of the dynamic 
             system, showing the complex co-evolution of two indicators over time with the
             possibility to highlight trajectories for specified entities (e.g. countries, 
             individuals). Furthermore the visualisation tools allow for making 
             predictions of the trajectories of specified entities with respect to the 
             indicators.
Analyze bioequivalence study data with industrial strength. Sample size could be determined for various crossover designs, such as 2x2 design, 2x4 design, 4x4 design, Balaam design, Two-sequence dual design, and William design.
             Reference: Chow SC, Liu JP. Design and Analysis of Bioavailability and Bioequivalence Studies. 3rd ed. (2009, ISBN:978-1-58488-668-6).
Provides an R interface for the Bureau of Economic Analysis (BEA) 
		API (see <http://www.bea.gov/API/bea_web_service_api_user_guide.htm> for 
		more information) that serves two core purposes - 
    1. To Extract/Transform/Load data [beaGet()] from the BEA API as R-friendly 
		formats in the user's work space [transformation done by default in beaGet() 
		can be modified using optional parameters; see, too, bea2List(), bea2Tab()].
		2. To enable the search of descriptive meta data [beaSearch()].
		Other features of the library exist mainly as intermediate methods 
		or are in early stages of development.
		Important Note - You must have an API key to use this library.  
		Register for a key at <http://www.bea.gov/API/signup/index.cfm> .
A platform is provided for interactive analyses with a goal of totally easy to develop, deploy, interact, and explore (TEDDIE). Using this package, users can create customized analyses and make them available to end users who can perform interactive analyses and save analyses to RTF or HTML files. It allows developers to focus on R code for analysis, instead of dealing with html or shiny code.
Reproducible and automated analysis of multiplex bead assays such
    as CBA (Morgan et al. 2004; <doi: 10.1016/j.clim.2003.11.017>), LEGENDplex
    (Yu et al. 2015; <doi: 10.1084/jem.20142318>), and MACSPlex (Miltenyi
    Biotec 2014; Application note: Data acquisition and analysis without the
    MACSQuant analyzer;
    <https://www.miltenyibiotec.com/upload/assets/IM0021608.PDF>). The
    package provides functions for streamlined reading of fcs files, and
    identification of bead clusters and analyte expression. The package eases
    the calculation of standard curves and the subsequent calculation of the
    analyte concentration.
A minimalist web framework for developing application programming 
    interfaces in R that provides a flexible framework for handling common 
    HTTP-requests, errors, logging, and an ability to integrate any R code as 
    server middle-ware.
Fast Bayesian inference of marginal and conditional independence structures from high-dimensional data. Leday and Richardson (2019), Biometrics, <doi:10.1111/biom.13064>.
Plots univariate comparison graphs, an alternative to
        boxplot/stripchart/violin plot.
These data contain morphological image measurements for dried
    beans from Koklu and Ozkan (2020) <doi:10.1016/j.compag.2020.105507>.
It is vital to assess the heterogeneity of treatment effects
    (HTE) when making health care decisions for an individual patient or a group
    of patients. Nevertheless, it remains challenging to evaluate HTE based
    on information collected from clinical studies that are often designed and
    conducted to evaluate the efficacy of a treatment for the overall population.
    The Bayesian framework offers a principled and flexible approach to estimate
    and compare treatment effects across subgroups of patients defined by their
    characteristics. This package allows users to explore a wide range of Bayesian
    HTE analysis models, and produce posterior inferences about HTE. See Wang et al.
    (2018) <DOI:10.18637/jss.v085.i07> for further details.
Assume that a temporal process is composed of contiguous segments with differing slopes and replicated noise-corrupted time series measurements are observed. The unknown mean of the data generating process is modelled as a piecewise linear function of time with an unknown number of change-points. The package infers the joint posterior distribution of the number and position of change-points as well as the unknown mean parameters per time-series by MCMC sampling. A-priori, the proposed model uses an overfitting number of mean parameters but, conditionally on a set of change-points, only a subset of them influences the likelihood. An exponentially decreasing prior distribution on the number of change-points gives rise to a posterior distribution concentrating on sparse representations of the underlying sequence, but also available is the Poisson distribution. See Papastamoulis et al (2017) <arXiv:1709.06111> for a detailed presentation of the method.
'BEAST2' (<https://www.beast2.org>) is a widely used
    Bayesian phylogenetic tool, that uses DNA/RNA/protein data
    and many model priors to create a posterior of jointly estimated 
    phylogenies and parameters.
    'BEAST2' is a command-line tool.
    This package provides a way to call 'BEAST2' 
    from an 'R' function call.
Provides JAR to perform Markov chain Monte Carlo (MCMC) inference using
    the popular Bayesian Evolutionary Analysis by Sampling Trees 'BEAST' software library
    of Suchard et al (2018) <doi:10.1093/ve/vey016>. 'BEAST' supports auto-tuning
    Metropolis-Hastings, slice, Hamiltonian Monte Carlo and Sequential Monte Carlo
    sampling for a large variety of composable standard and phylogenetic statistical
    models using high performance computing.  By placing the 'BEAST' JAR in this package,
    we offer an efficient distribution system for 'BEAST' use by other R packages using
    CRAN.
'BEAST2' (<https://www.beast2.org>) is a widely used
  Bayesian phylogenetic tool, that uses DNA/RNA/protein data
  and many model priors to create a posterior of jointly estimated 
  phylogenies and parameters.
  'BEAUti 2' (which is part of 'BEAST2') is a GUI tool 
  that allows users to specify the many possible setups
  and generates the XML file 'BEAST2' needs to run.
  This package provides a way to create 'BEAST2' input
  files without active user input, but using
  R function calls instead.
An interface for the 'Neo4j' database providing
    mapping between different identifiers of biological entities.
    This Biological Entity Dictionary (BED)
    has been developed to address three main challenges.
    The first one is related to the completeness of identifier mappings.
    Indeed, direct mapping information provided by the different systems
    are not always complete and can be enriched by mappings provided by other
    resources.
    More interestingly, direct mappings not identified by any of these
    resources can be indirectly inferred by using mappings to a third reference.
    For example, many human Ensembl gene ID are not directly mapped to any
    Entrez gene ID but such mappings can be inferred using respective mappings
    to HGNC ID. The second challenge is related to the mapping of deprecated
    identifiers. Indeed, entity identifiers can change from one resource
    release to another. The identifier history is provided by some resources,
    such as Ensembl or the NCBI, but it is generally not used by mapping tools.
    The third challenge is related to the automation of the mapping process
    according to the relationships between the biological entities of interest.
    Indeed, mapping between gene and protein ID scopes should not be done
    the same way than between two scopes regarding gene ID.
    Also, converting identifiers from different organisms should be possible
    using gene orthologs information.
    A ready to use database is provided as
    a 'Docker' image <https://hub.docker.com/r/patzaw/bed-ucb-human/>.
    The method has been published by
    Godard and van Eyll (2018) <doi:10.12688/f1000research.13925.3>.
Provides functions that allow users to quantify the relative 
	contributions of geographic and ecological distances to empirical patterns of genetic 
	differentiation on a landscape.  Specifically, we use a custom Markov chain 
	Monte Carlo (MCMC) algorithm, which is used to estimate the parameters of the 
	inference model, as well as functions for performing MCMC diagnosis and assessing 
	model adequacy.
A matrix-like data structure that allows for efficient,
    convenient, and scalable subsetting of binary genotype/phenotype files
    generated by PLINK (<https://www.cog-genomics.org/plink2>), the whole
    genome association analysis toolset, without loading the entire file into
    memory.
Genomic regions processing using open-source command line tools such as 'BEDTools', 'BEDOPS' and 'Tabix'. 
  These tools offer scalable and efficient utilities to perform genome arithmetic e.g indexing, formatting and merging.
  bedr API enhances access to these tools as well as offers additional utilities for genomic regions processing.
The main function of this package is beep(), with the purpose to
    make it easy to play notification sounds on whatever platform you are on.
    It is intended to be useful, for example, if you are running a long analysis
    in the background and want to know when it is ready.
The bee swarm plot is a one-dimensional scatter plot like
    "stripchart", but with closely-packed, non-overlapping points.
Facilitates many of the analyses performed in studies of
    behavioral economic demand. The package supports commonly-used options for
		modeling operant demand including (1) data screening proposed by Stein,
		Koffarnus, Snider, Quisenberry, & Bickel (2015; <doi:10.1037/pha0000020>),
		(2) fitting models of demand such as linear (Hursh, Raslear, Bauman,
		& Black, 1989, <doi:10.1007/978-94-009-2470-3_22>), exponential	(Hursh & Silberberg, 2008,
		<doi:10.1037/0033-295X.115.1.186>) and modified exponential (Koffarnus,
		Franck, Stein, & Bickel, 2015, <doi:10.1037/pha0000045>), and (3) calculating
		numerous measures	relevant to applied behavioral economists (Intensity,
		Pmax, Omax). Also	supports plotting and comparing data.
This is a sub national population projection model for calculating
    population development. The model uses a cohort component method.
    Further reading: Stanley K. Smith: A Practitioner's Guide to State and 
    Local Population Projections. 2013.  <doi:10.1007/978-94-007-7551-0>.
Useful functions for R beginners, including hints for the arguments of the 'plot()' function, self-defined functions for error bars, user-customized pair plots and hist plots, enhanced linear regression figures, etc.. This package could be helpful to R experts as well.
Contains specialised analyses and
    visualisation tools for behavior change science.
    These facilitate conducting determinant studies
    (for example, using confidence interval-based
    estimation of relevance, CIBER, or CIBERlite
    plots, see Crutzen, Noijen & Peters (2017)
    <doi:10.3389/fpubh.2017.00165>),
    systematically developing, reporting,
    and analysing interventions (for example, using
    Acyclic Behavior Change Diagrams), and reporting
    about intervention effectiveness (for example, using
    the Numbers Needed for Change, see Gruijters & Peters
    (2017) <doi:10.31234/osf.io/2bau7>), and computing the
    required sample size (using the Meaningful Change
    Definition, see Gruijters & Peters (2020)
    <doi:10.1080/08870446.2020.1841762>).
    This package is especially useful for
    researchers in the field of behavior change or
    health psychology and to behavior change
    professionals such as intervention developers and
    prevention workers.
Implements an S3 class based on 'data.table' to store and process efficiently ethomics (high-throughput behavioural) data.
Methods and tools for estimating, simulating and forecasting of so-called BEKK-models (named after Baba, Engle, Kraft and Kroner) based on the fast Berndt–Hall–Hall–Hausman (BHHH) algorithm described in Hafner and Herwartz (2008) <doi:10.1007/s00184-007-0130-y>.
Tools for downloading historical financial data from the www.belex.rs.
Calculates the Boltzmann entropy of a landscape gradient.
    This package uses the analytical method created by Gao, P., Zhang, H.
    and Li, Z., 2018 (<doi:10.1111/tgis.12315>) and by Gao, P. and Li, Z., 2019
    (<doi:10.1007/s10980-019-00854-3>). It also extend the original ideas by
    allowing calculations on data with missing values.
Bell regression models for count data with overdispersion. The implemented models account for ordinary and zero-inflated regression models under both frequentist and Bayesian approaches. Theoretical details regarding the models implemented in the package can be found in Castellares et al. (2018) <doi:10.1016/j.apm.2017.12.014> and Lemonte et al. (2020) <doi:10.1080/02664763.2019.1636940>.
Tools to accurately benchmark and analyze execution times for
    R expressions.
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 for comparative methods based
    on Stochastic Frontier Analyses (SFA) and for convex nonparametric 
    least squares of convex functions (STONED). 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.
R client for Bender Hyperparameters optimizer : <https://bender.dreem.com>
    The R client allows you to communicate with the Bender API and therefore submit some new trials within your R script itself.
Perform the Benford's Analysis to a data set in order to evaluate if it contains human fabricated data. For more details on the method see Moreau, 2021, Model Assist. Statist. Appl., 16 (2021) 73–79. <doi:10.3233/MAS-210517>.
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.
Analysis tool for evaluating benthic multimetric indices (BENMMIs). 
    It generates reproducible reports on the analysis of benthic data, e.g., 
    validation and correction of species names, sample pooling, automatic
    conversion of genus to species names, outlier detection, benthic 
    indicator calculation, optimization of single and 
    multimetric indicators against a pressure gradient, and spatial aggregation
    of benthic indicators. One of its use cases was the development of a common 
    benthic indicator for <https://www.ospar.org> (publication accepted by 
    Ecological Indicators). See Van Loon et al. (2018) <doi:10.1016/j.ecolind.2017.09.029> for details.
Included are two main interfaces for fitting and diagnosing
	bent-cable regressions for autoregressive time-series data or
	independent data (time series or otherwise): 'bentcable.ar()' and
	'bentcable.dev.plot()'. Some components in the package can also be
	used as stand-alone functions. The bent cable
	(linear-quadratic-linear) generalizes the broken stick
	(linear-linear), which is also handled by this package. Version 0.2
	corrects a glitch in the computation of confidence intervals for the
	CTP. References that were updated from Versions 0.2.1 and 0.2.2 appear
	in Version 0.2.3 and up. Version 0.3.0 improves robustness of the
	error-message producing mechanism. It is the author's intention to
	distribute any future updates via GitHub.
Preprocessing tools and biodiversity measures 
    (species abundance, species richness, population heterogeneity and 
    sensitivity) for analysing marine benthic data. See Van Loon et al.
    (2015) <doi:10.1016/j.seares.2015.05.002> for an application of 
    these tools.
Bayesian analysis for exponential random graph models using advanced computational algorithms. More information can be found at: <https://acaimo.github.io/Bergm/>.
Draw horizontal histograms, color scattered points by 3rd dimension,
    enhance date- and log-axis plots, zoom in X11 graphics, trace errors and warnings, 
    use the unit hydrograph in a linear storage cascade, convert lists to data.frames and arrays, 
    fit multiple functions.
An implementation of best subset selection in generalized linear model and Cox proportional hazard model via the primal dual active set algorithm proposed by Wen, C., Zhang, A., Quan, S. and Wang, X. (2020) <doi:10.18637/jss.v094.i04>. The algorithm formulates coefficient parameters and residuals as primal and dual variables and utilizes efficient active set selection strategies based on the complementarity of the primal and dual variables.
Computations for Bessel function for complex, real and partly
  'mpfr' (arbitrary precision) 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. It implements the method of Kruschke (2013) Bayesian estimation supersedes the t test. Journal of Experimental Psychology: General, 142(2):573-603 <doi: 10.1037/a0029146>.
Best subset glm using information criteria or cross-validation,
        carried by using 'leaps' algorithm (Furnival and Wilson, 1974) <doi:10.2307/1267601>
        or complete enumeration (Morgan and Tatar, 1972) <doi:10.1080/00401706.1972.10488918>.
        Implements PCR and PLS using AIC/BIC.
        Implements one-standard deviation rule for use with the 'caret' package.
An implementation of intervention effect estimation for DAGs (directed acyclic graphs) learned from binary or continuous data. First, parameters are estimated or sampled for the DAG and then interventions on each node (variable) are propagated through the network (do-calculus). Both exact computation (for continuous data or for binary data up to around 20 variables) and Monte Carlo schemes (for larger binary networks) are implemented.
Estimate a suite of normalizing transformations, including 
    a new adaptation of a technique based on ranks which can guarantee 
    normally distributed transformed data if there are no ties: ordered 
    quantile normalization (ORQ). ORQ normalization combines a rank-mapping
    approach with a shifted logit approximation that allows
    the transformation to work on data outside the original domain. It is 
    also able to handle new data within the original domain via linear 
    interpolation. The package is built to estimate the best normalizing 
    transformation for a vector consistently and accurately. It implements 
    the Box-Cox transformation, the Yeo-Johnson transformation, three types 
    of Lambert WxF transformations, and the ordered quantile normalization 
    transformation. It estimates the normalization efficacy of other
    commonly used transformations, and it allows users to specify 
    custom transformations or normalization statistics. Finally, functionality
    can be integrated into a machine learning workflow via recipes.
Decision tree algorithm with a major feature added. Allows for users to define an ordering on the partitioning process.
    Resulting in Branch-Exclusive Splits Trees (BEST). Cedric Beaulac and Jeffrey S. Rosentahl (2019) <arXiv:1804.10168>.
The bestridge package is designed to provide a one-stand service for users to successfully carry out best ridge regression in various complex situations via the primal dual active set algorithm proposed by Wen, C., Zhang, A., Quan, S. and Wang, X. (2020) <doi:10.18637/jss.v094.i04>. This package allows users to perform the regression, classification, count regression and censored regression for (ultra) high dimensional data, and it also supports advanced usages like group variable selection and nuisance variable selection.
Provides a practical tool for estimating the burden of common communicable diseases in settlements of displaced populations. An online version of the tool can be found at <http://who-refugee-bod.ecdf.ed.ac.uk/shiny/app/>. Estimates of burden of disease aim to synthesize data about cause-specific morbidity and mortality through a systematic approach that enables evidence-based decisions and comparisons across settings. The focus of this tool is on four acute communicable diseases and syndromes, including Acute respiratory infections, Acute diarrheal diseases, Acute jaundice syndrome and Acute febrile illnesses.
Nonparametric detection of nonuniformity and dependence with Binary Expansion Testing (BET). See Kai Zhang (2019) BET on Independence, Journal of the American Statistical Association, 114:528, 1620-1637, <DOI:10.1080/01621459.2018.1537921>, Kai Zhang, Zhigen Zhai, and Wen Zhou. (2021). BEAUTY Powered BEAST, <arXiv:2103.00674>  and Zhigen Zhao, Michael Baiocchi, Kai Zhang. SorBET: A Fast and Powerful Algorithm to Test Dependence of Variables.
Provides a class of Bayesian beta regression models for the analysis of continuous data with support restricted to an unknown finite support. The response variable is modeled using a four-parameter beta distribution with the mean or mode parameter depending linearly on covariates through a link function. When the response support is known to be (0,1), the above class of models reduce to traditional (0,1) supported beta regression models. Model choice is carried out via the logarithm of the pseudo marginal likelihood (LPML), the deviance information criterion (DIC), and the Watanabe-Akaike information criterion (WAIC). See Zhou and Huang (2022) <doi:10.1016/j.csda.2021.107345>.
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 <https://betabit.wiki>.
Fit beta calibration models and obtain calibrated probabilities from
    them.
Package providing a number of functions for working with Two- and 
    Four-parameter Beta and closely related distributions (i.e., the Gamma-
    Binomial-, and Beta-Binomial distributions), including parameterization in
    terms of moments, and fitting of Beta distributions to vectors of values.
    Includes d/p/r- and a function and for calculating moments of Beta-Binomial
    distributions. Also includes functions for estimating classification 
    accuracy, diagnostic performance, and consistency, making use of what is
    generally known as the 'Livingston and Lewis approach' in the psychometric 
    literature which models observed-score distributions in terms of the Beta-
    Binomial distribution. A shiny app is available, providing a GUI for the
    Livingston and Lewis approach when used for binary classifications. For
    url to the app, see documentation for the LL.CA() function. 
    Livingston and Lewis (1995) <doi:10.1111/j.1745-3984.1995.tb00462.x>.
    Lord (1965) <doi:10.1007/BF02289490>.
    Hanson (1991) <https://files.eric.ed.gov/fulltext/ED344945.pdf>.
    Tharwat (2020) <doi:10.1016/j.aci.2018.08.003>.
Measures of beta-diversity in networks, and easy visualization of why two networks are different.
Functions to compute pair-wise dissimilarities (distance matrices) and multiple-site dissimilarities, separating the turnover and nestedness-resultant components of taxonomic (incidence and abundance based), functional and phylogenetic beta diversity.
Power calculations are a critical component of any research study to determine the 
	minimum sample size necessary to detect differences between multiple groups. 
	Researchers often work with data taking the form of proportions that can be modeled with 
	a beta distribution. Here we present an R package, 'BetaPASS', that perform power and 
	sample size calculations for data following a beta distribution with comparative 
	nonparametric output. This package allows flexibility with multiple options for link 
	functions to fit the data and graphing functionality for visual comparisons.
Permutational method to incorporate taxonomic uncertainty  and some functions to assess its effects on parameters of some widely used multivariate methods in ecology, as explained in Cayuela et al. (2011) <doi:10.1111/j.1600-0587.2009.05899.x>.
Beta regression for modeling beta-distributed dependent variables, e.g., rates and proportions.
  In addition to maximum likelihood regression (for both mean and precision of a beta-distributed
  response), bias-corrected and bias-reduced estimation as well as finite mixture models and
  recursive partitioning for beta regressions are provided.
Simulation, estimation and forecasting of first-order Beta-Skew-t-EGARCH models with leverage (one-component, two-component, skewed versions).
The sample size according to the Bethel's procedure.
It provides access to and information about the most important
    Brazilian economic time series - from the Getulio Vargas Foundation <http://portal.fgv.br/en>,
    the Central Bank of Brazil <http://www.bcb.gov.br> and the Brazilian Institute of Geography
    and Statistics <http://www.ibge.gov.br>. It also presents tools for managing, analysing (e.g.
    generating dynamic reports with a complete analysis of a series) and exporting
    these time series.
Implements likelihood inference for early epidemic analysis. BETS is short for the four key epidemiological events being modeled: Begin of exposure, End of exposure, time of Transmission, and time of Symptom onset. The package contains a dataset of the trajectory of confirmed cases during the coronavirus disease (COVID-19) early outbreak. More detail of the statistical methods can be found in Zhao et al. (2020) <arXiv:2004.07743>.
Drop-in replacement for 'parallel::mclapply()' adding e.g.
    tracebacks, crash dumps, retries, condition handling, improved seeding,
    progress bars and faster inter process communication. Some of the internal
    functions are also exported for other use: 'etry()' (extended try),
    'copy2shm()/allocate_from_shm()' (copy to and allocate from POSIX shared
    memory), 'char_map/map2char()' (split a character vector into its unique
    elements and a mapping on these) and various semaphore related functions.
Provides squared semi partial correlations, tolerance, Mahalanobis, Likelihood Ratio Chi Square, and Pseudo R Square. Aberson, C. L. (2022) <doi:10.31234/osf.io/s2yqn>.
A fast integrative genetic association test for rare diseases based on a model for disease status given allele counts at rare variant sites. Probability of association, mode of inheritance and probability of pathogenicity for individual variants are all inferred in a Bayesian framework - 'A Fast Association Test for Identifying Pathogenic Variants Involved in Rare Diseases', Greene et al 2017 <doi:10.1016/j.ajhg.2017.05.015>.
Allows to compare the goodness of fit of Benford's and Blondeau Da Silva's digit distributions in a dataset. It is used to check whether the data distribution is consistent with theoretical distributions highlighted by Blondeau Da Silva or not (through the dat.distr() function): this ideal theoretical distribution must be at least approximately followed by the data for the use of Blondeau Da Silva's model to be well-founded. It also enables to plot histograms of digit distributions, both observed in the dataset and given by the two theoretical approaches (with the digit.ditr() function). Finally, it proposes to quantify the goodness of fit via Pearson's chi-squared test (with the chi2() function).
Implementations of Bayesian parametric, nonparametric and semiparametric procedures for univariate and multivariate time series. The package is based on the methods presented in C. Kirch et al (2018) <doi:10.1214/18-BA1126> and A. Meier (2018) <https://opendata.uni-halle.de//handle/1981185920/13470>. It was supported by DFG grant KI 1443/3-1.
The bezier package is a toolkit for working with Bezier curves and splines. The package provides functions for point generation, arc length estimation, degree elevation and curve fitting.
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 described
    in Verbesselt et al. (2010) <doi:10.1016/j.rse.2009.08.014>. 'BFAST monitor'
    provides functionality to detect disturbance in near real-time based on 'BFAST'-
    type models, and is described in Verbesselt et al. (2012) <doi:10.1016/j.rse.2012.02.022>.
    'BFAST Lite' approach is a flexible approach that handles missing data
    without interpolation, and will be described in an upcoming paper.
    Furthermore, different models can now be used to fit the
    time series data and detect structural changes (breaks).
Implements the Bayesian paradigm for fractional
 polynomial models under the assumption of normally distributed error terms, see
 Sabanes Bove, D. and Held, L. (2011) <doi:10.1007/s11222-010-9170-7>.
Implementation of various default Bayes factors
    for testing statistical hypotheses. The package is
    intended for applied quantitative researchers in the
    social and behavioral sciences, medical research,
    and related fields. The Bayes factor tests can be
    executed for statistical models such as 
    univariate and multivariate normal linear models,
    generalized linear models, special cases of 
    linear mixed models, survival models, relational
    event models. Parameters that can be tested are
    location parameters (e.g., group means, regression coefficients),
    variances (e.g., group variances), and measures of 
    association (e.g,. bivariate correlations), among others.
    The statistical underpinnings are
    described in 
    Mulder, Hoijtink, and Xin (2019) <arXiv:1904.00679>,
    Mulder and Gelissen (2019) <arXiv:1807.05819>,
    Mulder (2016) <DOI:10.1016/j.jmp.2014.09.004>,
    Mulder and Fox (2019) <DOI:10.1214/18-BA1115>,
    Mulder and Fox (2013) <DOI:10.1007/s11222-011-9295-3>,
    Boeing-Messing, van Assen, Hofman, Hoijtink, and Mulder (2017) <DOI:10.1037/met0000116>,
    Hoijtink, Mulder, van Lissa, and Gu, (2018) <DOI:10.31234/osf.io/v3shc>,
    Gu, Mulder, and Hoijtink, (2018) <DOI:10.1111/bmsp.12110>,
    Hoijtink, Gu, and Mulder, (2018) <DOI:10.1111/bmsp.12145>, and
    Hoijtink, Gu, Mulder, and Rosseel, (2018) <DOI:10.1037/met0000187>. When using the
    packages, please refer to Mulder et al. (2021) <DOI:10.18637/jss.v100.i18>.
Search and download data from the Swiss Federal Statistical Office <https://www.bfs.admin.ch/>.
How to fit a straight line through a set of points with errors in
  both coordinates? The 'bfsl' package implements the York regression 
  (York, 2004 <doi:10.1119/1.1632486>). It provides unbiased estimates of the 
  intercept, slope and standard errors for the best-fit straight line to 
  independent points with (possibly correlated) normally distributed errors in 
  both x and y. Other commonly used errors-in-variables methods, such as 
  orthogonal distance regression, geometric mean regression or Deming regression
  are special cases of the 'bfsl' solution.
At the Swiss Federal Statistical Office (SFSO), spatial maps of Switzerland are available free of charge as 'Cartographic bases for small-scale thematic mapping'. This package contains convenience functions to import ESRI (Environmental Systems Research Institute) shape files using the package 'rgdal' and to plot them easily and quickly without having to worry too much about the technical details.
      It contains utilities to combine multiple areas to one single polygon and to find neighbours for single regions. For any point on a map, a special locator can be used to determine to which municipality, district or canton it belongs.
Derived from the work of Kruschke (2015,
    <ISBN:9780124058880>), the present package aims to provide a framework
    for conducting Bayesian analysis using Markov chain Monte Carlo (MCMC)
    sampling utilizing the Just Another Gibbs Sampler ('JAGS', Plummer,
    2003, <https://mcmc-jags.sourceforge.io>).  The initial version
    includes several modules for conducting Bayesian equivalents of
    chi-squared tests, analysis of variance (ANOVA), multiple
    (hierarchical) regression, softmax regression, and for fitting data
    (e.g., structural equation modeling).
An umbrella package providing a phenotype/genotype data structure
    and scalable and efficient computational methods for large genomic datasets
    in combination with several other packages: 'BEDMatrix', 'LinkedMatrix',
    and 'symDMatrix'.
Routine for fitting regression models for binary rare events with linear and nonlinear covariate effects when using the quantile function of the Generalized Extreme Value random variable.
Tools for analysing board game data. Mainly focused on providing 
    an interface for BoardGameGeek's XML API2 through R6 class system objects.
    More details about the BoardGameGeek's API can be obtained here
    <https://boardgamegeek.com/wiki/page/BGG_XML_API2>.
Application of genome prediction for a continuous variable, focused 
             on genotype by environment (GE) genomic selection models (GS). It consists a group of functions 
             that help to create regression kernels for some GE genomic models proposed by Jarquín et al. (2014) <doi:10.1007/s00122-013-2243-1>
             and Lopez-Cruz et al. (2015) <doi:10.1534/g3.114.016097>. Also, it computes genomic predictions based on Bayesian approaches.
             The prediction function uses an orthogonal transformation of the data and specific priors
             present by Cuevas et al. (2014) <doi:10.1534/g3.114.013094>.
Fit Bayesian Gaussian graphical models. The methods are separated into 
    two Bayesian approaches for inference: hypothesis testing and estimation. There are 
    extensions for confirmatory hypothesis testing, comparing Gaussian graphical models, 
    and node wise predictability. These methods were recently introduced in the Gaussian 
    graphical model literature, including 
    Williams (2019) <doi:10.31234/osf.io/x8dpr>, 
    Williams and Mulder (2019) <doi:10.31234/osf.io/ypxd8>,
    Williams, Rast, Pericchi, and Mulder (2019) <doi:10.31234/osf.io/yt386>.
Provides a Metropolis-coupled Markov chain Monte Carlo sampler,
    post-processing and parameter estimation functions, and plotting utilities
    for the generalized graded unfolding model of Roberts, Donoghue, and
    Laughlin (2000) <doi:10.1177/01466216000241001>.
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).
Functions for behavior genetic modeling, including model identification, calculating relatedness, and various others (e.g., Hunter, Garrison, et al, 2019 <doi:10.1007/s10519-019-09973-8>).
Two partially supervised mixture modeling methods: 
        soft-label and belief-based modeling are implemented.
        For completeness, we equipped the package also with the
        functionality of unsupervised, semi- and fully supervised
        mixture modeling.  The package can be applied also to selection
        of the best-fitting from a set of models with different
        component numbers or constraints on their structures.
        For detailed introduction see:
        Przemyslaw Biecek, Ewa Szczurek, Martin Vingron, Jerzy
        Tiuryn (2012), The R Package bgmm: Mixture Modeling with
        Uncertain Knowledge, Journal of Statistical Software 
        <doi:10.18637/jss.v047.i03>.
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.
Implementation of Bayesian multi-task regression models and was developed within the context of imaging genetics. The package can currently fit two models. The Bayesian group sparse multi-task regression model of Greenlaw et al. (2017)<doi:10.1093/bioinformatics/btx215> can be fit with implementation using Gibbs sampling. An extension of this model developed by Song, Ge et al. to accommodate both spatial correlation as well as correlation across brain hemispheres can also be fit using either mean-field variational Bayes or Gibbs sampling. The model can also be used more generally for multivariate (non-imaging) phenotypes with spatial correlation.
Bimodal Gumbel distribution. General functions for performing extreme value analysis.
Estimation of Bayesian Global Vector Autoregressions (BGVAR) with different prior setups and the possibility to introduce stochastic volatility. Built-in priors include the Minnesota, the stochastic search variable selection and Normal-Gamma (NG) prior. For a reference see also Crespo Cuaresma, J., Feldkircher, M. and F. Huber (2016) "Forecasting with Global Vector Autoregressive Models: a Bayesian Approach", Journal of Applied Econometrics, Vol. 31(7), pp. 1371-1391 <doi:10.1002/jae.2504>. Post-processing functions allow for doing predictions, structurally identify the model with short-run or sign-restrictions and compute impulse response functions, historical decompositions and forecast error variance decompositions. Plotting functions are also available.
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 packages. 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 1.78.0-0, the following Boost libraries are
 included: 'accumulators' 'algorithm' 'align' 'any' 'atomic' 'beast'
 'bimap' 'bind' 'circular_buffer' 'compute' 'concept' 'config'
 'container' 'date_time' 'detail' 'dynamic_bitset' 'exception'
 'flyweight' 'foreach' 'functional' 'fusion' 'geometry' 'graph' 'heap'
 'icl' 'integer' 'interprocess' 'intrusive' 'io' 'iostreams'
 'iterator' 'lambda2' 'math' 'move' 'mp11' 'mpl' 'multiprecision'
 'numeric' 'pending' 'phoenix' 'polygon' 'preprocessor' 'process'
 'propery_tree' 'random' 'range' 'scope_exit' 'smart_ptr' 'sort'
 'spirit' 'tuple' 'type_traits' 'typeof' 'unordered' 'utility' 'uuid'.
Provides an approach which is based on the methodology of the Burden of Communicable Diseases in Europe (BCoDE) and can be used for large and small samples such as individual countries. The Burden of Healthcare-Associated Infections (BHAI) is estimated in disability-adjusted life years, number of infections as well as number of deaths per year. Results can be visualized with various plotting functions and exported into tables.
Provides functions for Maximum Likelihood
             Estimation, Markov Chain Monte Carlo, finding confidence
             intervals.  The implementation is heavily based on the
             original Fortran source code translated to R.
Functions and data sets reproducing some examples in
             Box, Hunter and Hunter II.  Useful for statistical design
             of experiments, especially factorial experiments.
Contains tools to fit both predictive and prognostic biomarker effects using biomarker threshold models and continuous threshold models. Evaluate the treatment effect, biomarker effect and treatment-biomarker interaction using probability index measurement. Test for treatment-biomarker interaction using residual bootstrap method.
Provides functions for the evaluation of basket
    trial designs with binary endpoints. Operating characteristics of a
    basket trial design are assessed by simulating trial data according to
    scenarios, analyzing the data with Bayesian hierarchical models (BHMs), and
    assessing decision probabilities on stratum and trial-level based on Go / No-go decision making.
    The package is build for high flexibility regarding decision rules,
    number of interim analyses, number of strata, and recruitment.
    The BHMs proposed by
    Berry et al. (2013) <doi:10.1177/1740774513497539>
    and Neuenschwander et al. (2016) <doi:10.1002/pst.1730>,
    as well as a model that combines both approaches are implemented.
    Functions are provided to implement Bayesian decision rules as for example
    proposed by Fisch et al. (2015) <doi:10.1177/2168479014533970>.
    In addition, posterior point estimates (mean/median) and credible intervals
    for response rates and some model parameters can be calculated.
    For simulated trial data, bias and mean squared errors of posterior
    point estimates for response rates can be provided.
Performs Bayesian hierarchical multi-subject multiscale analysis of fMRI data as described in Sanyal & Ferreira (2012) <DOI:10.1016/j.neuroimage.2012.08.041> using wavelet based prior that borrows strength across subjects and returns posterior smoothed versions of the fMRI data and samples from the posterior distribution.
Bayesian hierarchical methods for the detection of differences in rates of related outcomes for multiple treatments for clustered observations. Theoretical background for the models is given in Carragher (2017) <https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.736866>. The models in this package are extensions for multiple treatments and clusters. This software was developed for the Precision Drug Theraputics: Risk Prediction in Pharmacoepidemiology project as part of a Rutherford Fund Fellowship at Health Data Research (UK), Medical Research Council (UK) award reference MR/S003967/1 (<https://gtr.ukri.org/>). Principal Investigator: Raymond Carragher.
Provides a function for estimating the parameters of Structural Bayesian Vector Autoregression models with the method developed by Baumeister and Hamilton (2015) <doi:10.3982/ECTA12356>, Baumeister and Hamilton (2017) <doi:10.3386/w24167>, and Baumeister and Hamilton (2018) <doi:10.1016/j.jmoneco.2018.06.005>. Functions for plotting impulse responses, historical decompositions, and posterior distributions of model parameters are also provided.
Can be used for joint identification of candidate compound hits from multiple assays, in drug discovery. This package implements the framework of I. D. Shterev, D. B. Dunson, C. Chan and G. D. Sempowski. "Bayesian Multi-Plate High-Throughput Screening of Compounds", Scientific Reports 8(1):9551, 2018. This project was funded by the Division of Allergy, Immunology, and Transplantation, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Department of Health and Human Services, under contract No. HHSN272201400054C entitled "Adjuvant Discovery For Vaccines Against West Nile Virus and Influenza", awarded to Duke University and lead by Drs. Herman Staats and Soman Abraham.
Generate the James Blinding Index, as described in James et al (1996) 
             <https://pubmed.ncbi.nlm.nih.gov/8841652/> and the Bang Blinding Index, 
             as described in Bang et al (2004) <https://pubmed.ncbi.nlm.nih.gov/15020033/>.
             These are measures to assess whether or not satisfactory blinding has been 
             maintained in a randomized, controlled, clinical trial. These can be generated 
             for trial subjects, research coordinators and principal investigators, based 
             upon standardized questionnaires that have been administered, to assess whether
             or not they can correctly guess to which treatment arm (e.g. placebo or treatment) 
             subjects were assigned at randomization.
Bias correction of second order of the maximum likelihood
        estimators of the parameters of the beta regression model.
A GUI to correct measurement bias in DNA methylation
    analyses. The 'BiasCorrector' package just wraps the functions
    implemented in the 'R' package 'rBiasCorrection' into a shiny web
    application in order to make them more easily accessible. Publication:
    Kapsner et al. (2021) <doi:10.1002/ijc.33681>.
Statistical models of biased sampling in the form of 
  univariate and multivariate noncentral hypergeometric distributions, 
  including Wallenius' noncentral hypergeometric distribution and
  Fisher's noncentral hypergeometric distribution 
  (also called extended hypergeometric distribution).  
  See vignette("UrnTheory") for explanation of these distributions.
Parse a BibTeX file to a data.frame to make it accessible for further analysis and visualization.
A simple R wrapper for the Java BiBit algorithm from "A
    biclustering algorithm for extracting bit-patterns from binary datasets"
    from Domingo et al. (2011) <DOI:10.1093/bioinformatics/btr464>. An simple adaption for the BiBit algorithm which allows noise in the biclusters is also introduced as well as a function to guide the algorithm towards given (sub)patterns. Further, a workflow to derive noisy biclusters from discoverd larger column patterns is included as well.
Reading and writing BibTeX files using data frames in R sessions.
Tool for quantitative research in scientometrics and bibliometrics.
    It provides various routines for importing bibliographic data from 'SCOPUS' (<https://scopus.com>),
    'Clarivate Analytics Web of Science' (<https://www.webofknowledge.com/>), 'Digital Science Dimensions' 
	(<https://www.dimensions.ai/>), 'Cochrane Library' (<https://www.cochranelibrary.com/>), 'Lens' (<https://lens.org>), 
	and 'PubMed' (<https://pubmed.ncbi.nlm.nih.gov/>) databases, performing bibliometric analysis 
    and building networks for co-citation, coupling, scientific collaboration and co-word analysis.
It contains some example datasets used in 'bibliometrix'. The data are bibliographic datasets exported from the 'SCOPUS' (<https://scopus.com>) and 'Clarivate Analytics Web of Science' (<https://www.webofscience.com/>) databases. They can be used to test the different features of the package 'bibliometrix' (<https://bibliometrix.org>).
Functions to find edges for bibliometric networks like bibliographic coupling network, co-citation network and co-authorship network. The weights of network edges can be calculated according to different methods, depending on the type of networks, the type of nodes, and what you want to analyse. These functions are optimized to be be used on large dataset. The package contains functions inspired by: Leydesdorff, Loet and Park, Han Woo (2017) <doi:10.1016/j.joi.2016.11.007>; Perianes-Rodriguez, Antonio, Ludo Waltman, and Nees Jan Van Eck (2016) <doi:10.1016/j.joi.2016.10.006>; Sen, Subir K. and Shymal K. Gan (1983) <http://nopr.niscair.res.in/handle/123456789/28008>; Shen, Si, Zhu, Danhao, Rousseau, Ronald, Su, Xinning and Wang, Dongbo (2019) <doi:10.1016/j.joi.2019.01.012>; Zhao, Dangzhi and Strotmann, Andreas (2008) <doi:10.1002/meet.2008.1450450292>.
Currently, the package provides several functions for plotting and analyzing bibliometric data (JIF, Journal Impact Factor, and paper percentile values), beamplots with citations and percentiles, and three plot functions to visualize the result of a reference publication year spectroscopy (RPYS) analysis performed in the free software 'CRExplorer' (see <http://crexplorer.net>). Further extension to more plot variants is planned.
Developed for the following tasks. 1- Simulating and computing the maximum likelihood 
              estimator for the Birnbaum-Saunders (BS) distribution, 2- Computing the Bayesian estimator for
              the parameters of the BS distribution based on reference prior proposed by Xu and Tang (2010)
              <doi:10.1016/j.csda.2009.08.004> and conjugate prior. 3- Computing the Bayesian estimator for
              the BS distribution based on conjugate prior. 4- Computing the Bayesian estimator for the BS
              distribution based on Jeffrey prior given by Achcar (1993) <doi:10.1016/0167-9473(93)90170-X>
              5- Computing the Bayesian estimator for the BS distribution under progressive type-II censoring
              scheme.
Implements methods for sample size 
  reduction within Linear and Quadratic Discriminant Analysis in Lapanowski and Gaynanova (2020) <arXiv:2005.03858>.
  Also includes methods for non-linear discriminant analysis with simultaneous sparse feature selection in Lapanowski and Gaynanova (2019) PMLR 89:1704-1713.
A tool for enumerating maximal complete bipartite graphs. The input should be a edge list file or a binary matrix file. 
             The output are maximal complete bipartite graphs. Algorithms used can be found in this paper Y. Lu et al. BMC Res Notes 13, 88 (2020) <doi:10.1186/s13104-020-04955-0>.
The main function biclust() provides several algorithms to
        find biclusters in two-dimensional data: Cheng and Church (2000, ISBN:1-57735-115-0),
        spectral (2003) <doi:10.1101/gr.648603>, plaid model (2005) <doi:10.1016/j.csda.2004.02.003>, xmotifs (2003) <doi:10.1142/9789812776303_0008> and bimax (2006) <doi:10.1093/bioinformatics/btl060>. In addition, the
        package provides methods for data preprocessing (normalization
        and discretisation), visualisation, and validation of bicluster
        solutions.
Biclustering is a statistical learning technique that simultaneously 
    partitions and clusters rows and columns of a data matrix. Since the solution 
    space of biclustering is in infeasible to completely search with current 
    computational mechanisms, this package uses a greedy heuristic. The algorithm 
    featured in this package is, to the best our knowledge, the first biclustering 
    algorithm to work on data with missing values. Li, J., Reisner, J., Pham, H., 
    Olafsson, S., and Vardeman, S. (2020) Biclustering with Missing Data. Information 
    Sciences, 510, 304–316.
Prior transcription factor binding knowledge and target gene expression data are integrated in a Bayesian framework for functional cis-regulatory module inference. Using Gibbs sampling, we iteratively estimate transcription factor associations for each gene, regulation strength for each binding event and the hidden activity for each transcription factor.
Implementation of a collection of MCMC methods for Bayesian structure learning
    of directed acyclic graphs (DAGs), both from continuous and discrete data. For efficient
    inference on larger DAGs, the space of DAGs is pruned according to the data. To filter
    the search space, the algorithm employs a hybrid approach, combining constraint-based 
    learning with search and score. A reduced search space is initially defined on the basis
    of a skeleton obtained by means of the PC-algorithm, and then iteratively improved with
    search and score. Search and score is then performed following two approaches:
    Order MCMC, or Partition MCMC.
    The BGe score is implemented for continuous data and the BDe score is implemented
    for binary data or categorical data. The algorithms may provide the maximum a posteriori 
    (MAP) graph or a sample (a collection of DAGs) from the posterior distribution given the data.
    All algorithms are also applicable for structure learning and sampling for dynamic Bayesian networks.
    References:
    J. Kuipers, P. Suter, G. Moffa (2022) <doi:10.1080/10618600.2021.2020127>,
    N. Friedman and D. Koller (2003) <doi:10.1023/A:1020249912095>, 
    J. Kuipers and G. Moffa (2017) <doi:10.1080/01621459.2015.1133426>, 
    M. Kalisch et al. (2012) <doi:10.18637/jss.v047.i11>,
    D. Geiger and D. Heckerman (2002) <doi:10.1214/aos/1035844981>.
Implements an efficient estimation procedure of bid-ask spreads from open, high, low, and close prices
  as described in Ardia-Guidotti-Kroencke <https://www.ssrn.com/abstract=3892335>. 
  Moreover, it provides an implementation of the estimators proposed in 
  Roll (1984) <doi:10.1111/j.1540-6261.1984.tb03897.x>, 
  Corwin-Schultz (2012) <doi:10.1111/j.1540-6261.2012.01729.x>,
  and Abdi-Ranaldo (2017) <doi:10.1093/rfs/hhx084>.
Calculates the bidimensional regression between two 2D configurations following the approach by Tobler (1965).
Provides Tools for Accessing the Botanical Information and Ecology Network Database.  The BIEN database contains cleaned and standardized botanical data including occurrence, trait, plot and taxonomic data (See <https://bien.nceas.ucsb.edu/bien/> for more Information).  This package provides functions that query the BIEN database by constructing and executing optimized SQL queries.
The calculator computes bifactor indices such as explained common variance (ECV), hierarchical Omega (OmegaH), percentage of uncontaminated correlations (PUC), item explained common variance (I-ECV), and more. This package is an R version of the 'Excel' based 'Bifactor Indices Calculator' (Dueber, 2017)  <doi:10.13023/edp.tool.01> with added convenience features for directly utilizing output from several programs that can fit confirmatory factor analysis or item response models.
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 an asymptotic bias correction proposed by Fernandez-Val (2009) 
  <doi:10.1016/j.jeconom.2009.02.007>.
Contains tools for survey statistics (especially in educational
    assessment) for datasets with replication designs (jackknife, 
    bootstrap, replicate weights; see Kolenikov, 2010;
    Pfefferman & Rao, 2009a, 2009b, <doi:10.1016/S0169-7161(09)70003-3>,
    <doi:10.1016/S0169-7161(09)70037-9>); Shao, 1996, 
    <doi:10.1080/02331889708802523>). 
    Descriptive statistics, linear and logistic regression, 
    path models for manifest variables with measurement error 
    correction and two-level hierarchical regressions for weighted 
    samples are included. Statistical inference can be conducted for 
    multiply imputed datasets and nested multiply imputed datasets
    and is in particularly suited for the analysis of plausible values
    (for details see George, Oberwimmer & Itzlinger-Bruneforth, 2016; 
    Bruneforth, Oberwimmer & Robitzsch, 2016; Robitzsch, Pham &
    Yanagida, 2016). The package development was supported by BIFIE 
    (Federal Institute for Educational Research, Innovation and Development 
    of the Austrian School System; Salzburg, Austria).
Estimation of bifurcating autoregressive models of any order, p, BAR(p) as
    well as several types of bias correction for the least squares estimators of
    the autoregressive parameters as described in Zhou and Basawa (2005)
    <doi:10.1016/j.spl.2005.04.024> and Elbayoumi and Mostafa (2020)
    <doi:10.1002/sta4.342>. Currently, the bias correction methods supported
    include bootstrap (single, double and fast-double) bias correction and
    linear-bias-function-based bias correction. Functions for generating and
    plotting bifurcating autoregressive data from any BAR(p) model are also
    included.
Provides arithmetic functions for R matrix and 'big.matrix' objects as well as functions for QR factorization, Cholesky factorization, General eigenvalue, and Singular value decomposition (SVD). A method matrix multiplication and an arithmetic method -for matrix addition, matrix difference- allows for mixed type operation -a matrix class object and a big.matrix class object- and pure type operation for two big.matrix class 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'.
Enhanced message functions (cat() / message() / warning() / error()) 
    using wrappers around sprintf(). Also, multiple assertion functions 
    (e.g. to check class, length, values, files, arguments, etc.).
Provides functions for reading *.PGN files with more than one game, including large files without copying it into RAM (using 'ff' package or 'RSQLite' package). Handle chess data and chess aggregated data, count figure moves statistics, create player profile, plot winning chances, browse openings. Set of functions of R API to communicate with UCI-protocol based chess engines.
Functions to compute distances between probability measures or any other data object than can be posed in this way, entropy measures for samples of curves, distances and depth measures for functional data, and the Generalized Mahalanobis Kernel distance for high dimensional data. For further details about the metrics please refer to Martos et al (2014) <doi:10.3233/IDA-140706>; Martos et al (2018) <doi:10.3390/e20010033>; Hernandez et al (2018, submitted); Martos et al (2018, submitted).
Data sets and functions for chi-squared Hardy-Weinberg and case-control association tests of highly polymorphic genetic data [e.g., human leukocyte antigen (HLA) data]. Performs association tests at multiple levels of polymorphism (haplotype, locus and HLA amino-acids) as described in Pappas DJ, Marin W, Hollenbach JA, Mack SJ (2016) <doi:10.1016/j.humimm.2015.12.006>. Combines rare variants to a common class to account for sparse cells in tables as described by Hollenbach JA, Mack SJ, Thomson G, Gourraud PA (2012) <doi:10.1007/978-1-61779-842-9_14>.
Provides utilities to compute, store and access distance matrices on disk as file-backed matrices provided by the 'bigstatsr' package. File-backed distance matrices are stored as a symmetric matrix to facilitate out-of-memory operations on file-backed matrix while the in-memory 'dist' object stores only the lower diagonal elements. 'disto' provides an unified interface to work with in-memory and disk-based distance matrices.
Implements several spatial and spatio-temporal scalable disease mapping models for high-dimensional count data using the INLA technique for approximate Bayesian inference in latent Gaussian models (Orozco-Acosta et al., 2021 <doi:10.1016/j.spasta.2021.100496> and Orozco-Acosta et al., 2022 <arXiv:2201.08323>).
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.
Response surface methods for drug synergy analysis. Available
    methods include generalized and classical Loewe formulations as well as Highest
    Single Agent methodology. Response surfaces can be plotted in an interactive
    3-D plot and formal statistical tests for presence of synergistic effects are
    available. Implemented methods and tests are described in the article 
    "BIGL: Biochemically Intuitive Generalized Loewe null model for prediction 
    of the expected combined effect compatible with partial agonism and antagonism"
    by Koen Van der Borght, Annelies Tourny, Rytis Bagdziunas, Olivier Thas, 
    Maxim Nazarov, Heather Turner, Bie Verbist & Hugo Ceulemans (2017) 
    <doi:10.1038/s41598-017-18068-5>.
Extend lasso and elastic-net model fitting for ultrahigh-dimensional, 
    multi-gigabyte data sets that cannot be loaded into memory. It's much more 
    memory- and computation-efficient as compared to existing lasso-fitting packages 
    like 'glmnet' and 'ncvreg', thus allowing for very powerful big data analysis 
    even with an ordinary laptop.
Calculation of physical (e.g. aerodynamic conductance, surface temperature), 
             and physiological (e.g. canopy conductance, water-use efficiency) ecosystem properties
			 from eddy covariance data and accompanying meteorological measurements. Calculations
			 assume the land surface to behave like a 'big-leaf' and return bulk ecosystem/canopy variables.
Regression for data too large to fit in memory.
Regression for data too large to fit in memory. This package functions exactly like the 'biglm' package, but works with later versions of R.
Unsupervised clustering protocol for large scale structured data, based on a low dimensional representation of the data. Dimensionality reduction is performed using a parallelized implementation of the t-Stochastic Neighboring Embedding algorithm (Garriga J. and Bartumeus F. (2018), <arXiv:1812.09869>).
MDS is a statistic tool for reduction of dimensionality, using as input a distance
    matrix of dimensions n × n. When n is large, classical algorithms suffer from
    computational problems and MDS configuration can not be obtained.
    With this package, we address these problems by means of three algorithms:
        - Divide-and-conquer MDS proposed by Delicado P. and C. Pachón-García (2021)
        <arXiv:2007.11919>.
        - Interpolation MDS, also proposed by Delicado P. and C. Pachón-García (2021)
        <arXiv:2007.11919>, which uses Gower's interpolation formula as described in
        Gower, J. C. and D. J. Hand (1995).
        - Fast MDS, which is an implementation of the algorithm proposed by 
        Yang, T., J. Liu, L. McMillan, and W. Wang (2006).
    The main idea of these algorithms is based on partitioning the data set into small 
    pieces, where classical methods can work. In order to align all the solutions, 
    Procrustes formula is used as described in Borg, I. and P. Groenen (2005).
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.
Classes for storing and manipulating arbitrary-precision
    integer vectors and high-precision floating-point vectors. These
    extend the range and precision of the 'integer' and 'double' data
    types found in R. This package utilizes the 'Boost.Multiprecision' C++
    library. It is specifically designed to work well with the 'tidyverse'
    collection of R packages.
Utility functions for easy parallelism in R. Include some reexports
    from other packages, utility functions for splitting and parallelizing over
    blocks, and choosing and setting the number of cores used.
A computationally-efficient leading-eigenvalue approximation to tail probabilities and quantiles of large quadratic forms, in particular for the Sequence Kernel Association Test (SKAT) used in genomics <doi:10.1002/gepi.22136>. Also provides stochastic singular value decomposition for dense or sparse matrices.
Interface with 'Google BigQuery',
    see <https://cloud.google.com/bigquery/> for more information.
    This package uses 'googleAuthR' so is compatible with similar packages, 
    including 'Google Cloud Storage' (<https://cloud.google.com/storage/>) for result extracts.
Use Newton's method, coordinate descent, and METIS clustering
    to solve the L1 regularized Gaussian MLE inverse covariance
    matrix estimation problem.
Read large text files by splitting them in smaller files.
    Package 'bigreadr' also provides some convenient wrappers around fread()
    and fwrite() from package 'data.table'.
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.
Simulate multivariate data with arbitrary marginal distributions.
  'bigsimr' is an package for simulating high-dimensional multivariate data with a target correlation and arbitrary marginal distributions via Gaussian copula. 
  It utilizes a Julia package named 'Bigsimr.jl' for its core routines.
Easy-to-use, efficient, flexible and scalable tools for analyzing 
    massive SNP arrays. Privé et al. (2018) <doi:10.1093/bioinformatics/bty185>.
Provides a sparse matrix format with data stored on disk, to be
    used in both R and C++. This is intended for more efficient use of sparse 
    data in C++ and also when parallelizing, since data on disk does not need
    copying. Only a limited number of features will be implemented. For now,
    conversion can be performed from a 'dgCMatrix' or a 'dsCMatrix' from R 
    package 'Matrix'. A new compact format is also now available.
Fits smoothing spline regression models using scalable algorithms designed for large samples. Seven marginal spline types are supported: linear, cubic, different cubic, cubic periodic, cubic thin-plate, ordinal, and nominal. Random effects and parametric effects are also supported. Response can be Gaussian or non-Gaussian: Binomial, Poisson, Gamma, Inverse Gaussian, or Negative Binomial.
Easy-to-use, efficient, flexible and scalable statistical tools.
  Package bigstatsr provides and uses Filebacked Big Matrices via memory-mapping.
  It provides for instance matrix operations, Principal Component Analysis,
  sparse linear supervised models, utility functions and more
  <doi:10.1093/bioinformatics/bty185>.
Selecting linear and generalized linear models for large data sets
    using modified stepwise procedure and modern selection criteria (like
    modifications of Bayesian Information Criterion). Selection can be
    performed on data which exceed RAM capacity.
Fits Cox model via stochastic gradient descent. This implementation avoids computational instability of the standard Cox Model when dealing large datasets. Furthermore, it scales up with large datasets that do not fit the memory. It also handles large sparse datasets using proximal stochastic gradient descent algorithm. For more details about the method, please see Aliasghar Tarkhan and Noah Simon (2020) <arXiv:2003.00116v2>.
Extend the bigmemory package with 'table', 'tapply', and 'split'
    support for 'big.matrix' objects. The functions may also be used with native R
    matrices for improving speed and memory-efficiency.
For studying recurrent disease and death with competing
    risks, comparisons based on the well-known cumulative incidence function
    can be confounded by different prevalence rates of the competing events.
    Alternatively, comparisons of the conditional distribution of the survival
    time given the failure event type are more relevant for investigating the
    prognosis of different patterns of recurrence disease. This package implements
    a nonparametric estimator for the conditional cumulative incidence function
    and a nonparametric conditional bivariate cumulative incidence function for the
    bivariate gap times proposed in Huang et al. (2016) <doi:10.1111/biom.12494>.
Estimation of large Vector AutoRegressive (VAR), Vector AutoRegressive with Exogenous Variables X (VARX) and Vector AutoRegressive Moving Average (VARMA) Models with Structured Lasso Penalties, see Nicholson, Wilms, Bien and Matteson (2020) <https://jmlr.org/papers/v21/19-777.html> and Wilms, Basu, Bien and Matteson (2021) <doi:10.1080/01621459.2021.1942013>.
Utility functions for large-scale data. For now, package 'bigutilsr'
    mainly includes functions for outlier detection and unbiased PCA projection.
Estimates VAR and VARX models with Structured Penalties using the methods developed by Nicholson et al (2017)<doi:10.1016/j.ijforecast.2017.01.003> and Nicholson et al (2020) <doi:10.48550/arXiv.1412.5250>.
Anonymised Bay Area bike share trip data for the year 2014. 
    Also contains additional metadata on stations and weather.
Co-clustering of the rows and columns of a contingency or binary matrix, or double binary matrices and model selection for the number of row and column clusters. Three models are considered: the Poisson latent block model for contingency matrix, the binary latent block model for binary matrix and a new model we develop: the multiple latent block model for double binary matrices. A new procedure named bikm1 is implemented to investigate more efficiently the grid of numbers of clusters. Then, the studied model selection criteria are the integrated completed likelihood (ICL) and the Bayesian integrated likelihood (BIC). Finally, the co-clustering adjusted Rand index (CARI) to measure agreement between co-clustering partitions is implemented. Robert Valerie, Vasseur Yann, Brault Vincent (2021) <doi:10.1007/s00357-020-09379-w>.
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. 
  M. Helena Gonçalves et al.(2007) <DOI: 10.18637/jss.v046.i09>.
Contains data sets regarding songs on the Billboard Hot 100 list
    from 1960 to 2016. The data sets include the ranks for the given year,
    musical features of a lot of the songs and lyrics for several of the songs
    as well.
Provides an 'htmlwidgets' interface to 'billboard.js', 
    a re-usable easy interface JavaScript chart library, based on D3 v4+.
    Chart types include line charts, scatterplots, bar/lollipop charts, histogram/density plots, pie/donut charts and gauge charts.
    All charts are interactive, and a proxy method is implemented to smoothly update a chart without rendering it again in 'shiny' apps.
Time series analysis, (dis)aggregation and manipulation, e.g. time series extension, merge, projection, lag, lead, delta, moving and cumulative average and product, selection by index, date and year-period, conversion to daily, monthly, quarterly, (semi)annually. Simultaneous equation models definition, estimation, simulation and forecasting with coefficient restrictions, error autocorrelation, exogenization, add-factors, impact and interim multipliers analysis, conditional equation evaluation, endogenous targeting and model renormalization, structural stability, stochastic simulation and forecast, optimal control.
Defines the functions used to compute the
  bimodal index as defined by Wang et al. (2009)
  <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2730180/>,
  <doi:10.4137/CIN.S2846>.
R client to the 'Binance' Public Rest API for data collection on cryptocurrencies, portfolio management and trading: <https://github.com/binance/binance-spot-api-docs/blob/master/rest-api.md>.
Provides methods for the binarization of one-dimensional data and some visualization functions.
Allows to view the optimal probability cut-off point at which the Sensitivity and Specificity meets and its a best way to minimize both Type-1 and Type-2 error for a binary Classifier in determining the Probability threshold.
Tools to create binary dosage from either VCF
    or GEN files, merge binary dosage files, and read
    binary dosage files.
Implements variable selection for high dimensional datasets with a binary response
    variable using the EM algorithm.  Both probit and logit models are supported.  Also included 
    is a useful function to generate high dimensional data with correlated variables.
Modeling under- and over-dispersed binary data using extended Poisson process models (EPPM) as in the article Faddy and Smith (2012) <doi:10.1002/bimj.201100214> .
Allows the estimation and prediction for binary Gaussian process model. The mean function can be assumed to have time-series structure. The estimation methods for the unknown parameters are based on penalized quasi-likelihood/penalized quasi-partial likelihood and restricted maximum likelihood. The predicted probability and its confidence interval are computed by Metropolis-Hastings algorithm. More details can be seen in Sung et al (2017) <arXiv:1705.02511>.
A collection of 'LaTeX' styles using 'Beamer' customization for
 pdf-based presentation slides in 'RMarkdown'. At present it contains
 'RMarkdown' adaptations of the LaTeX themes 'Metropolis' (formerly 'mtheme')
 theme by Matthias Vogelgesang and others (now included in 'TeXLive'), the
 'IQSS' by Ista Zahn (which is included here), and the 'Monash' theme by
 Rob J Hyndman. Additional (free) fonts may be needed: 'Metropolis' prefers
 'Fira', and 'IQSS' requires 'Libertinus'.
Estimate the correlation between two irregular time series that are not necessarily sampled on identical time points. This program is also applicable to the situation of two evenly spaced time series that are not on the same time grid. 'BINCOR' is based on a novel estimation approach proposed by Mudelsee (2010, 2014) to estimate the correlation between two climate time series with different timescales. The idea is that autocorrelation (AR1 process) allows to correlate values obtained on different time points. 'BINCOR' contains four functions: bin_cor() (the main function to build the binned time series), plot_ts() (to plot and compare the irregular and binned time series, cor_ts() (to estimate the correlation between the binned time series) and ccf_ts() (to estimate the cross-correlation between the binned time series).
Implements functions for multi-class
   discriminant analysis using binary predictors, for corresponding 
   variable selection, and for dichotomizing continuous data.
Generation of correlated artificial binary data.
Provides a simple interface for creating active bindings where the
    bound function accepts additional arguments.
Provides an easy way to fill an environment with active bindings
    that call a C++ function.
Methods for model selection, model averaging, and calculating metrics, such as the Gini, Theil, Mean Log Deviation, etc, on binned income data where the topmost bin is right-censored.  We provide both a non-parametric method, termed the bounded midpoint estimator (BME), which assigns cases to their bin midpoints; except for the censored bins, where cases are assigned to an income estimated by fitting a Pareto distribution. Because the usual Pareto estimate can be inaccurate or undefined, especially in small samples, we implement a bounded Pareto estimate that yields much better results.  We also provide a parametric approach, which fits distributions from the generalized beta (GB) family. Because some GB distributions can have poor fit or undefined estimates, we fit 10 GB-family distributions and use multimodel inference to obtain definite estimates from the best-fitting distributions. We also provide binned income data from all United States of America school districts, counties, and states.
Binford's hunter-gatherer data includes more than 200 variables
    coding aspects of hunter-gatherer subsistence, mobility, and social organization
    for 339 ethnographically documented groups of hunter-gatherers.
Methods for estimation and hypothesis testing of proportions
 in group testing designs: methods for estimating a proportion in a single
        population (assuming sensitivity and specificity equal to 1 in designs
        with equal group sizes), as well as hypothesis tests and
        functions for experimental design for this situation. For
        estimating one proportion or the difference of proportions, a
        number of confidence interval methods are included, which can
        deal with various different pool sizes. Further, regression
        methods are implemented for simple pooling and matrix pooling
        designs.
        Methods for identification of positive items in group
        testing designs: Optimal testing configurations can be found 
        for hierarchical and array-based algorithms. Operating 
        characteristics can be calculated for testing configurations 
        across a wide variety of situations.
Methods for the group testing identification problem: 1) Operating 
    characteristics (e.g., expected number of tests) for commonly used 
    hierarchical and array-based algorithms, and 2) Optimal testing 
    configurations for these same algorithms. Calculations for algorithms with 
    single-disease assays are described in Hitt et al. (2019) 
    <doi:10.1002/sim.8341> and with multiplex assays are described in Bilder 
    et al. (2019) <doi:10.1111/biom.12988> and Hou et al. (2020) 
    <doi:10.1093/biostatistics/kxy058>. Methods for the group testing 
    estimation problem: 1) Estimation and inference procedures for an overall 
    prevalence, and 2) Regression modeling for commonly used hierarchical and 
    array-based algorithms. Estimation and confidence interval methods are 
    described in Biggerstaff (2008) <doi:10.1198/108571108X379055> and 
    Hepworth & Biggerstaff (2017) <doi:10.1007/s13253-017-0297-2>. 
    Regression modeling is described in Xie (2001) <doi:10.1002/sim.817>.
Consider an at-most-K-stage group sequential design with only an upper bound for the last analysis and non-binding lower bounds.With binary endpoint, two kinds of test can be applied, asymptotic test based on normal distribution and exact test based on binomial distribution. This package supports the computation of boundaries and conditional power for single-arm group sequential test with binary endpoint, via either asymptotic or exact test. The package also provides functions to obtain boundary crossing probabilities given the design.
Binomial Haar-Fisz transforms for Gaussianization as in Nunes and Nason (2009).
Tools and functions for managing the download of binary files.
    Binary repositories are defined in 'YAML' format. Defining new 
    pre-download, download and post-download templates allow additional 
    repositories to be added.
A molecular genetics tool that processes binary data from fragment analysis. It consolidates replicate sample pairs, outputs summary statistics, and produces hierarchical clustering trees and nMDS plots. This package was developed from the publication available here: <https://www.sciencedirect.com/science/article/pii/S1049964420306538>. The GUI version of this package is available on the R Shiny online server at: <https://clarkevansteenderen.shinyapps.io/BINMAT/> or it is accessible via GitHub by typing: shiny::runGitHub("BinMat", "clarkevansteenderen") into the console in R. Two real-world datasets accompany the package: an AFLP dataset of Bunias orientalis samples from Tewes et. al. (2017) <https://besjournals.onlinelibrary.wiley.com/doi/full/10.1111/1365-2745.12869>, and an ISSR dataset of Nymphaea specimens from Reid et. al. (2021) <https://www.sciencedirect.com/science/article/pii/S0304377021000218> . The authors of these publications are thanked for allowing the use of their data.
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) <DOI:10.1002/sim.5362>.
Generating multiple binary and normal variables simultaneously given marginal characteristics and association structure based on the methodology proposed by Demirtas and Doganay (2012) <DOI:10.1080/10543406.2010.521874>.
Constructs confidence intervals on the probability of
        success in a binomial experiment via several parameterizations.
Binomial random numbers are generated via the perfect
        sampling algorithm. At each iteration dual markov chains are
        generated and coalescence is checked. In case coalescence
        occurs, the resulting number is outputted. In case not, then
        the algorithm is restarted from T(t)=2*T(t) until coalescence
        occurs.
The 'binomialRF' is a new feature selection technique for decision trees that aims at providing an alternative approach to identify significant feature subsets using binomial distributional assumptions (Rachid Zaim, S., et al. (2019)) <doi:10.1101/681973>. Treating each splitting variable selection as a set of exchangeable correlated Bernoulli trials, 'binomialRF' then tests whether a feature is selected more often than by random chance.
The R package contains different MCMC schemes to estimate the regression coefficients of a binomial (or binary) logit model within a Bayesian framework: a data-augmented independence MH-sampler, an auxiliary mixture sampler and a hybrid auxiliary mixture (HAM) sampler. All sampling procedures are based on algorithms using data augmentation, where the regression coefficients are estimated by rewriting the logit model as a latent variable model called difference random utility model (dRUM).
A suite of functions to compute confidence intervals and necessary
  sample sizes for the parameter p of the Bernoulli B(p)
  distribution under simple random sampling or under pooled
  sampling. Such computations are e.g. of interest when investigating
  the incidence or prevalence in populations.
  The package contains functions to compute coverage probabilities and
  coverage coefficients of the provided confidence intervals
  procedures. Sample size calculations are based on expected length.
Generation of samples from a mix of binary, ordinal and continuous random variables with a pre-specified correlation matrix and marginal distributions. The details of the method are explained in Demirtas et al. (2012) <DOI:10.1002/sim.5362>.
Simulation and visualization depth-dependent integrated visual fields. Visual fields are measured monocularly at a single depth, yet real-life activities involve predominantly binocular vision at multiple depths. The package provides functions to simulate and visualize binocular visual field impairment in a depth-dependent fashion from monocular visual field results based on Ping Liu, Allison McKendrick, Anna Ma-Wyatt, Andrew Turpin (2019) <doi:10.1167/tvst.9.3.8>. At each location and depth plane, sensitivities are linearly interpolated from corresponding locations in monocular visual field and returned as the higher value of the two. Its utility is demonstrated by evaluating DD-IVF defects associated with 12 glaucomatous archetypes of 24-2 visual field pattern in the included 'shiny' apps.
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.
Provides methods for piecewise smooth regression. A piecewise smooth signal is estimated by applying a bootstrapped test recursively (binary segmentation approach). Each bootstrapped test decides whether the underlying signal is smooth on the currently considered subsegment or contains at least one further change-point.
Standard template library 
 containers are used to implement an efficient binary segmentation
 algorithm, which is log-linear on average and quadratic in the
 worst case.
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. Methods include step function, recursive
    subdivision, and optimized spline. Data are assumed to be nonnegative, 
    the top bin is assumed to have no upper bound, but the bin widths need
    be equal. 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.) In practice, an estimate for 
    the mean of the distribution should be supplied as an optional argument.
    Doing so greatly improves the reliability of statistics computed from 
    the smoothed density functions. Includes methods for estimating the Gini 
    coefficient, the Theil index, percentiles, and random deviates from a 
    smoothed distribution. Among the three methods, the optimized spline 
    (splinebins) is recommended for most purposes. The percentile and 
    random-draw methods should be regarded as experimental, and these methods 
    only support splinebins.
Provides tools for statistical analysis using the binscatter methods developed by Cattaneo, Crump, Farrell and Feng (2021a) <arXiv:1902.09608> and Cattaneo, Crump, Farrell and Feng (2021b) <arXiv:1902.09615>. Binscatter provides a flexible way of describing the relationship between two variables based on partitioning/binning of the independent variable of interest. binsreg(), binsqreg() and binsglm() implement binscatter least squares regression, quantile regression and generalized linear regression respectively, with particular focus on constructing binned scatter plots. They also implement robust (pointwise and uniform) inference of regression functions and derivatives thereof. binstest() implements hypothesis testing procedures for parametric functional forms of and nonparametric shape restrictions on the regression function. binspwc() implements hypothesis testing procedures for pairwise group comparison of binscatter estimators. binsregselect() implements data-driven procedures for selecting the number of bins for binscatter estimation. All the commands allow for covariate adjustment, smoothness restrictions and clustering.
Various supervised and unsupervised binning tools
    including using entropy, recursive partition methods
    and clustering.
A recently proposed Bayesian BIN model disentangles the underlying processes 
    that enable forecasters and forecasting methods to improve, decomposing forecasting accuracy into 
    three components: bias, partial information, and noise. By describing the differences between two 
    groups of forecasters, the model allows the user to carry out useful inference, such as calculating 
    the posterior probabilities of the treatment reducing bias, diminishing noise, or increasing information.
    It also provides insight into how much tamping down bias and noise in judgment or enhancing the efficient 
    extraction of valid information from the environment improves forecasting accuracy. This package provides 
    easy access to the BIN model. For further information refer to the paper Ville A. Satopää, Marat Salikhov,
    Philip E. Tetlock, and Barbara Mellers (2021) "Bias, Information, Noise: The BIN 
    Model of Forecasting" <doi:10.1287/mnsc.2020.3882>.
Imports benthic count data, reformats this data, and
        computes environmental inferences from this data.
Utilities to process, organize and explore protein structure,
    sequence and dynamics data. Features include the ability to read and write
    structure, sequence and dynamic trajectory data, perform sequence and structure
    database searches, data summaries, atom selection, alignment, superposition,
    rigid core identification, clustering, torsion analysis, distance matrix
    analysis, structure and sequence conservation analysis, normal mode analysis,
    principal component analysis of heterogeneous structure data, and correlation
    network analysis from normal mode and molecular dynamics data. In addition,
    various utility functions are provided to enable the statistical and graphical
    power of the R environment to work with biological sequence and structural data.
    Please refer to the URLs below for more information.
Contains all the necessary tools to process audio recordings of
             various formats (e.g., WAV, WAC, MP3, ZC), filter noisy files, 
             display audio signals, detect and extract automatically acoustic
             features for further analysis such as classification.
The goal is to help users to analyse data from multi wells with minimum effort. Using these functions several plates can be analyzed automatically.
Download stats reported from the BioConductor.org stats website.
Implement in 'R' interactive Circos-like visualizations of genomic data, to map information
	such as genetic variants, genomic fusions and aberrations to a circular genome, as proposed by the
	'JavaScript' library 'BioCircos.js', based on the 'JQuery' and 'D3' technologies. The output is by 
	default displayed in stand-alone HTML documents or in the 'RStudio' viewer pane. Moreover it can be
	integrated in 'R Markdown' documents and 'Shiny' applications.
Using numeric or raster data, this package contains functions to 
    calculate: complete water balance, bioclimatic balance, bioclimatic 
    intensities, reports for individual locations, multi-layered rasters for
    spatial analysis.
A convenient tool to install and update Bioconductor packages.
Contains functions for the data analysis with the emphasis on biological data, including several algorithms for feature ranking, feature selection, classification
 algorithms with the embedded validation procedures.
 The functions can deal with numerical as well as with nominal features. Includes also the functions for calculation
 of feature AUC (Area Under the ROC Curve) and HUM (hypervolume under manifold) values and construction 2D- and 3D- ROC curves.
 Provides the calculation of Area Above the RCC (AAC) values and construction of Relative Cost Curves
 (RCC) to estimate the classifier performance under unequal misclassification costs problem.
 There exists the special function to deal with missing values, including different imputing schemes.
Tools to create, validate, and export BioCompute Objects
    described in King et al. (2019) <doi:10.17605/osf.io/h59uh>.
    Users can encode information in data frames, and compose
    BioCompute Objects from the domains defined by the standard.
    A checksum validator and a JSON schema validator are provided.
    This package also supports exporting BioCompute Objects as JSON,
    PDF, HTML, or 'Word' documents, and exporting to cloud-based platforms.
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.
A tool to perform all different statistical tests and calculations 
    needed by Biological Dosimetry Laboratories.
Multilevel ecological data series (MEDS) are sequences of observations ordered according to temporal/spatial hierarchies that are defined by sample designs, with sample variability confined to ecological factors. Dendroclimatic MEDS of tree rings and climate are modeled into normalized fluctuations of tree growth and aridity.  Modeled fluctuations (model frames) are compared with Mantel correlograms on multiple levels defined by sample design. Package implementation can be understood by running examples in modelFrame(), and muleMan() functions.
The main drawback of the most common biodiversity indices is that different measures may lead to different rankings among communities. This instrument overcomes this limit using some functional tools with the diversity profiles. In particular, the derivatives, the curvature, the radius of curvature, the arc length, and the surface area are proposed. The goal of this method is to interpret in detail the diversity profiles and obtain an ordering between different ecological communities on the basis of diversity. In contrast to the typical indices of diversity, the proposed method is able to capture the multidimensional aspect of biodiversity, because it takes into account both the evenness and the richness of the species present in an ecological community.
High- and low-level functions for processing biogas data and predicting biogas production. Molar mass and calculated oxygen demand (COD') can be determined from a chemical formula. Measured gas volume can be corrected for water vapor and to (possibly user-defined) standard temperature and pressure. Gas quantity can be converted between volume, mass, and moles. Gas composition, cumulative production, or other variables can be interpolated to a specified time. Cumulative biogas and methane production (and rates) can be calculated using volumetric, manometric, gravimetric, or gas density methods for any number of bottles. With cumulative methane production data and data on bottle contents, biochemical methane potential (BMP) can be calculated and summarized, including subtraction of the inoculum contribution and normalization by substrate mass. Cumulative production and production rates can be summarized in several different ways (e.g., omitting normalization) using the same function. Biogas quantity and composition can be predicted from substrate composition and additional, optional data. Lastly, inoculum and substrate mass can be determined for planning BMP experiments.
Functions for error detection and correction in point data quality datasets that are used in species distribution modelling. Includes functions for parsing and converting coordinates into decimal degrees from various formats.
Is used to simulate and fit biological geometries. biogeom incorporates several novel universal parametric equations that can generate the profiles of bird eggs, flowers, linear and lanceolate leaves, seeds, starfish, and tree-rings (Gielis (2003) <doi:10.3732/ajb.90.3.333>; Shi et al. (2020) <doi:10.3390/sym12040645>), three growth-rate curves representing the ontogenetic growth trajectories of animals and plants against time, and the axially symmetrical and integral forms of all these functions (Shi et al. (2017) <doi:10.1016/j.ecolmodel.2017.01.012>; Shi et al. (2021) <doi:10.3390/sym13081524>). The optimization method proposed by Nelder and Mead (1965) <doi:10.1093/comjnl/7.4.308> was used to estimate model parameters. biogeom includes several real data sets of the boundary coordinates of natural shapes, including avian eggs, fruit, lanceolate and ovate leaves, tree rings, seeds, and sea stars,and can be potentially applied to other natural shapes. biogeom can quantify the conspecific or interspecific similarity of natural outlines, and provides information with important ecological and evolutionary implications for the growth and form of living organisms.
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.
Modelling of population growth under static and dynamic environmental conditions. 
    Includes functions for model fitting and making prediction under isothermal and 
    dynamic conditions. The methods (algorithms & models) are based on
    predictive microbiology (See Perez-Rodriguez and Valero (2012, ISBN:978-1-4614-5519-6)).
Single linkage clustering and connected component analyses are often performed on biological images. 'Bioi' provides a set of functions for performing these tasks. This functionality is implemented in several key functions that can extend to from 1 to many dimensions. The single linkage clustering method implemented here can be used on n-dimensional data sets, while connected component analyses are limited to 3 or fewer dimensions.
Tools for 3D imaging, mostly for biology/microscopy. 
    Read and write TIFF stacks. Functions for segmentation, filtering and analyzing 3D point patterns.
Functions for modelling microbial inactivation under
    isothermal or dynamic conditions. The calculations are based on several mathematical models broadly
    used by the scientific community and industry. Functions enable to make predictions for cases where the
    kinetic parameters are known. It also implements functions for parameter estimation for isothermal and
    dynamic conditions. The model fitting capabilities include an Adaptive Monte Carlo method for a Bayesian
    approach to parameter estimation.
Analyze and plot the abundance of different RNA biotypes present in a count matrix, this evaluation can be useful if you want to test different strategies of normalization or analyze a particular biotype in a differential gene expression analysis.
The bi-objective k-nearest neighbors method (biokNN) is an imputation method designed to estimate missing values on data with a multilevel structure. The original algorithm is an extension of the k-nearest neighbors method proposed by Bertsimas et al. (2017) (<https://jmlr.org/papers/v18/17-073.html>) using a bi-objective approach.  A brief description of the method can be found in Cubillos (2021) (<https://pure.au.dk/portal/files/214627979/biokNN.pdf>). The 'biokNN' package provides an R implementation of the method for datasets with continuous variables (e.g. employee productivity, student grades) and a categorical class variable (e.g. department, school). Given an incomplete dataset with such structure, this package produces complete datasets using both single and multiple imputation, including visualization tools to better understand the pattern of the missing values.
Generate urls and hyperlinks to commonly used biological databases
    and resources based on standard identifiers. This is primarily useful when
    writing dynamic reports that reference things like gene symbols in text or
    tables, allowing you to, for example, convert gene identifiers to hyperlinks
    pointing to their entry in the 'NCBI' Gene database. Currently supports
    'NCBI' Gene, 'PubMed', Gene Ontology, 'KEGG', CRAN and Bioconductor.
Analysing and forecasting biological invasions time series with a stochastic approach that accounts for human-aided dispersal, habitat suitability and provides estimates confidence level.
Provides utilities to facilitate import, export and computation with the 
 BIOM (Biological Observation Matrix) format (http://biom-format.org).
Variable selection methods are provided for several classification methods: the lasso/elastic net, PCLDA, PLSDA, and several t-tests. Two approaches for selecting cutoffs can be used, one based on the stability of model coefficients under perturbation, and the other on higher criticism.
Perform large scale genomic data retrieval and functional annotation retrieval. This package aims to provide users with a standardized
                way to automate genome, proteome, 'RNA', coding sequence ('CDS'), 'GFF', and metagenome
                retrieval from 'NCBI RefSeq', 'NCBI Genbank', 'ENSEMBL', 
                and 'UniProt' databases. Furthermore, an interface to the 'BioMart' database
                (Smedley et al. (2009) <doi:10.1186/1471-2164-10-22>) allows users to retrieve
                functional annotation for genomic loci. In addition, users can download entire databases such
                as 'NCBI RefSeq' (Pruitt et al. (2007) <doi:10.1093/nar/gkl842>), 'NCBI nr',
                'NCBI nt', 'NCBI Genbank' (Benson et al. (2013) <doi:10.1093/nar/gks1195>), etc. with only one command.
Contains functions to estimate aboveground biomass/carbon and its uncertainty in tropical forests. 
	These functions allow to (1) retrieve and to correct taxonomy, (2) estimate wood density and its uncertainty, 
	(3) construct height-diameter models, (4) manage tree and plot coordinates, 
	(5) estimate the aboveground biomass/carbon at the stand level with associated uncertainty. 
	To cite 'BIOMASS', please use citation("BIOMASS"). 
	See more in the article of Réjou-Méchain et al. (2017) <https://besjournals.onlinelibrary.wiley.com/doi/10.1111/2041-210X.12753>.
Provides functions to aid in the design and analysis of
    agronomic and agricultural experiments through easy access to
    documentation and helper functions, especially for users who are
    learning these concepts.
Functions for species distribution modeling, calibration and evaluation, 
  ensemble of models, ensemble forecasting and visualization. The package permits to run
  consistently up to 10 single models on a presence/absences (resp presences/pseudo-absences)
  dataset and to combine them in ensemble models and ensemble projections. Some bench of other 
  evaluation and visualisation tools are also available within the package.
Data Package that includes several examples of chemical and biological data networks, i.e. data graph structured.
Extends the bioinactivation package with functions for Sensitivity
    Analysis and Optimum Experiment Design.
Enables the user to systematically identify and visualize impulse-like gene expression changes within short genomic series experiments. In order to detect such activation peaks, the gene expression is treated as a signal that propagates along an experimental axis (time, temperature or other series conditions). Peaks are selected by exhaustive identification of local maximums and subsequent filtering based on a range of controllable parameters. Moreover, the 'Biopeak' package provides a series of data exploration tools including: expression profile plots, correlation heat maps and clustering functionalities.
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.
Prognostic Enrichment is a strategy of enriching a clinical trial for testing an intervention intended to prevent or delay an unwanted clinical event.  A prognostically enriched trial enrolls only patients who are more likely to experience the unwanted clinical event than the broader patient population (R. Temple (2010) <doi:10.1038/clpt.2010.233>). By testing the intervention in an enriched study population, the trial may be adequately powered with a smaller sample size, which can have both practical and ethical advantages.
    This package provides tools to evaluate biomarkers for prognostic enrichment of clinical trials with survival/time-to-event outcomes.
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.
Several tools for analyzing diagnostic tests and 2x2 contingency tables are provided. In particular, positive and negative predictive values for a diagnostic tests can be calculated from prevalence, sensitivity and specificity values. For contingency tables, relative risk and odds ratio measures are estimated. Furthermore, confidence intervals are provided.
Extract, visualize and summarize aerial movements of birds and
    insects from weather radar data. See <doi:10.1111/ecog.04028>
    for a software paper describing package and methodologies.
Utilities for computation and analysis of correlation/covariation in multiple sequence alignments and in side chain motions during molecular dynamics simulations. Features include the computation of correlation/covariation scores using a variety of scoring functions between either sequence positions in alignments or side chain dihedral angles in molecular dynamics simulations and utilities to analyze the correlation/covariation matrix through a variety of tools including network representation and principal components analysis. In addition, several utility functions are based on the R graphical environment to provide friendly tools for help in data interpretation. Examples of sequence covariation analysis are provided in: (1) Pele J, Moreau M, Abdi H, Rodien P, Castel H, Chabbert M (2014) <doi:10.1002/prot.24570> and (2) Taddese B, Deniaud M, Garnier A, Tiss A, Guissouma H, Abdi H, Henrion D, Chabbert M (2018) <doi: 10.1371/journal.pcbi.1006209>. An example of side chain correlated motion analysis is provided in: Taddese B, Garnier A, Abdi H, Henrion D, Chabbert M (2020) <doi: 10.1038/s41598-020-72766-1>. This work was supported by the French National Research Agency (Grant number: ANR-11-BSV2-026) and by GENCI (Grant number: 100567).
Utilities 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.
Unified and user-friendly framework for using new 
             distributional representations of biosensors data in different statistical modeling 
             tasks: regression models, hypothesis testing, cluster analysis, visualization, and 
             descriptive analysis. Distributional representations are a functional extension of 
             compositional time-range metrics and we have used them successfully so far in modeling 
             glucose profiles and accelerometer data. However, these functional representations can 
             be used to represent any biosensor data such as ECG or medical imaging such as fMRI.
             Matabuena M, Petersen A, Vidal JC, Gude F. "Glucodensities: A new representation of 
             glucose profiles using distributional data analysis" (2021) 
             <doi:10.1177/0962280221998064>.
Classes and functions to work with biological sequences (DNA, RNA and amino acid sequences).
    Implements S3 infrastructure to work with biological sequences as described in Keck (2020) <doi:10.1111/2041-210X.13490>.
    Provides a collection of functions to perform biological conversion among classes
    (transcription, translation) and basic operations on sequences
    (detection, selection and replacement based on positions or patterns).
    The package also provides functions to import and export sequences from and to other package formats.
Data processing tools to compute the rectified, integrated and the averaged EMG. Routines for automatic detection of activation phases. A routine to compute and plot the ensemble average of the EMG. An EMG signal simulator for general purposes.
Provides some tools for developing and validating prediction models, estimate expected survival of patients and visualize them graphically. Most of the implemented methods are based on penalized regressions such as: the lasso (Tibshirani R (1996)), the elastic net (Zou H et al. (2005) <doi:10.1111/j.1467-9868.2005.00503.x>), the adaptive lasso (Zou H (2006) <doi:10.1198/016214506000000735>), the stability selection (Meinshausen N et al. (2010) <doi:10.1111/j.1467-9868.2010.00740.x>), some extensions of the lasso (Ternes et al. (2016) <doi:10.1002/sim.6927>), some methods for the interaction setting (Ternes N et al. (2016) <doi:10.1002/bimj.201500234>), or others. A function generating simulated survival data set is also provided.
Utility functions, datasets and extended examples for survival analysis. This extends a range of other packages, some simple wrappers for time-to-event analyses, datasets, and extensive examples in HTML with R scripts. The package also supports the course Biostatistics III entitled "Survival analysis for epidemiologists in R".
Tutorials for statistics, aimed at biological scientists. 
    Subjects range from basic descriptive statistics 
    through to complex linear modelling. The tutorials
    include text, videos, interactive coding exercises
    and multiple choice quizzes. The package also 
    includes 19 datasets which are used in the 
    tutorials.
Datasets and functions for the book "Initiation à la Statistique avec R", F. Bertrand and M. Maumy-Bertrand (2018, ISBN:978-2100782826 Dunod, 3eme edition).
A collection of tools that allows users to plan systems of sampling 
    sites, increasing the efficiency of biodiversity monitoring by considering  
    the relationship between environmental and geographic conditions in a  
    region. The options for selecting sampling sites included here differ from 
    other implementations in that they consider the environmental and geographic 
    conditions of a region to suggest sampling sites that could increase the 
    efficiency of efforts dedicated to monitoring biodiversity. The methods 
    proposed here are new in the sense that they combine various criteria and 
    points previously made in related literature; some of the theoretical and 
    methodological bases considered are described in:
    Arita et al. (2011) <doi:10.1111/j.1466-8238.2011.00662.x>,
    Soberón and Cavner (2015) <doi:10.17161/bi.v10i0.4801>, and Soberón et al. 
    (2021).
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 evaluate 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. 
	A test for seed sample heterogeneity is now available. Mantel's permutation test can be found in this package. 
	A new approach for calculating its power is implemented. biotools also contains tests for genetic covariance components.
	Heuristic approaches for performing non-parametric spatial predictions of generic response variables and 
	spatial gene diversity are implemented.
Creates an area-proportional Venn diagram of 2 or 3 circles. 'BioVenn' is the only R package that can automatically generate an accurate area-proportional Venn diagram by having only lists of (biological) identifiers as input. Also offers the option to map Entrez and/or Affymetrix IDs to Ensembl IDs. In SVG mode, text and numbers can be dragged and dropped. Based on the BioVenn web interface available at <https://www.biovenn.nl>. Hulsen (2021) <doi:10.3233/DS-210032>.
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.
Generates interactive bipartite graphs using the D3 library.
    Designed for use with the 'bipartite' analysis package.
    Sources open source 'viz-js' library 
    Adapted from examples at <https://bl.ocks.org/NPashaP> (released under GPL-3).
Function bipmod() that partitions a bipartite network into non-overlapping biclusters by maximizing bipartite modularity defined in Barber (2007) <doi:10.1103/PhysRevE.76.066102> using the bipartite version of the algorithm described in Treviño (2015) <doi:10.1088/1742-5468/2015/02/P02003>.
We use a Bayesian approach to run individual patient data meta-analysis and network meta-analysis using 'JAGS'. The methods incorporate shrinkage methods and calculate patient-specific treatment effects as described in Seo et al. (2021) <DOI:10.1002/sim.8859>. This package also includes user-friendly functions that impute missing data in an individual patient data using mice-related packages.
A GUI with which the user can construct and interact with Bootstrap methods on Classical Biplots and with Clustering and/or Disjoint Biplot. This GUI is also aimed for estimate any numerical data matrix using the Clustering and Disjoint Principal component (CDPCA) methodology.
Provides a GUI with which users can construct and interact
        with biplots.
Logistic Biplot is a method that allows representing multivariate binary data on a subspace of low dimension, where each individual is represented by a point and each variable as vectors directed through the origin. The orthogonal projection of individuals onto these vectors predicts the expected probability that the characteristic occurs. The package contains new techniques to estimate the model parameters and constructs in each case the 'Logistic-Biplot'. References can be found in the help of each procedure.
A suite of functions to estimate, summarize
  and perform predictions with the bivariate probit subject to partial observability.
  The frequentist and Bayesian probabilistic philosophies are both supported. The 
  frequentist method is estimated with maximum likelihood and the Bayesian method is 
  estimated with a Markov Chain Monte Carlo (MCMC) algorithm developed by 
  Rajbanhdari, A (2014) <doi:10.1002/9781118771051.ch13>.
Highly efficient functions for estimating various rank (centrality) measures of nodes in bipartite graphs (two-mode networks). Includes methods for estimating HITS, CoHITS, BGRM, and BiRank with implementation primarily inspired by He et al. (2016) <doi:10.1109/TKDE.2016.2611584>. Also provides easy-to-use tools for efficiently estimating PageRank in one-mode graphs, incorporating or removing edge-weights during rank estimation, projecting two-mode graphs to one-mode, and for converting edgelists and matrices to sparseMatrix format. Best of all, the package's rank estimators can work directly with common formats of network data including edgelists (class data.frame, data.table, or tbl_df) and adjacency matrices (class matrix or dgCMatrix).
R functions to read EURING data and analyse re-encounter data of birds marked by metal rings. For a tutorial, go to <http://www.tandfonline.com/doi/full/10.1080/03078698.2014.933053>.
Collection of tools to make R more convenient. Includes tools to
    summarize data using statistics not available with base R and manipulate
    objects for analyses.
R functions for "The Basics of Item Response Theory Using R" by Frank B. Baker and Seock-Ho Kim (Springer, 2017, ISBN-13: 978-3-319-54204-1) including iccplot(), icccal(), icc(), iccfit(), groupinv(), tcc(), ability(), tif(), and rasch().  For example, iccplot() plots an item characteristic curve under the two-parameter logistic model.
Provides a 'ggplot2' centric approach to bivariate mapping. This is a 
    technique that maps two quantities simultaneously rather than the single value 
    that most thematic maps display. The package provides a suite of tools 
    for calculating breaks using multiple different approaches, a selection of 
    palettes appropriate for bivariate mapping and a scale function for 'ggplot2' 
    calls that adds those palettes to maps. A tool for creating bivariate legends 
    is also included.
Functions for downloading data from the Bank for
     International Settlements (BIS; <https://www.bis.org/>) in
     Basel.  Supported are only full datasets in (typically) CSV
     format.  The package is lightweight and without dependencies;
     suggested packages are used only if data is to be transformed
     into particular data structures, for instance into 'zoo'
     objects. Downloaded data can optionally be cached, to avoid
     repeated downloads of the same files.
An implementation of Bisect, a method for inferring cell type composition of samples based on methylation sequencing data (Whole Genome Bisulfite Sequencing and Reduced Representation Sequencing). The method is specifically tailored for sequencing data, and therefore works better than methods developed for methylation arrays. It contains a supervised mode that requires a reference (the methylation probabilities in the pure cell types), and a semi-supervised mode, that requires cell counts for a subset of the samples, but does not require a reference.
Tools to find bad commits with git bisect. See
        https://github.com/wch/bisectr for examples and test script
        templates.
Enables the user to infer potential synthetic lethal relationships
    by analysing relationships between bimodally distributed gene pairs in big
    gene expression datasets.  Enables the user to visualise these candidate
    synthetic lethal relationships.
Implementation of the 'bisque' strategy for approximate Bayesian posterior inference.  See Hewitt and Hoeting (2019) <arXiv:1904.07270> for complete details.  'bisque' combines conditioning with sparse grid quadrature rules to approximate marginal posterior quantities of hierarchical Bayesian models.  The resulting approximations are computationally efficient for many hierarchical Bayesian models.  The 'bisque' package allows approximate posterior inference for custom models; users only need to specify the conditional densities required for the approximation.
Provides tools to accurately estimate cell type abundances 
    from heterogeneous bulk expression. A reference-based method utilizes
    single-cell information to generate a signature matrix and transformation
    of bulk expression for accurate regression based estimates. A marker-based
    method utilizes known cell-specific marker genes to measure relative
    abundances across samples.
    For more details, see Jew and Alvarez et al (2019) <doi:10.1101/669911>.
Bisulfite-treated RNA non-conversion in a set of samples is
  analysed as follows : each sample's non-conversion distribution is
  identified to a Poisson distribution. P-values adjusted for multiple
  testing are calculated in each sample. Combined non-conversion P-values
  and standard errors are calculated on the intersection of the set of
  samples. For further details, see C Legrand, F Tuorto, M Hartmann, 
  R Liebers, D Jakob, M Helm and F Lyko (2017) <doi:10.1101/gr.210666.116>.
Estimates cumulative history for time-series for continuously
    viewed bistable perceptual rivalry displays. Computes cumulative history
    via a homogeneous first order differential process. I.e., it assumes
    exponential growth/decay of the history as a function time and perceptually
    dominant state, Pastukhov & Braun (2011) <doi:10.1167/11.10.12>.
    Supports Gamma, log normal, and normal distribution families.
    Provides a method to compute history directly and example of using the
    computation on a custom Stan code.
Provided are classes for boolean and skewed boolean vectors,
    fast boolean methods, fast unique and non-unique integer sorting,
    fast set operations on sorted and unsorted sets of integers, and
    foundations for ff (range index, compression, chunked processing).
Package 'bit64' provides serializable S3 atomic 64bit (signed) integers. 
 These are useful for handling database keys and exact counting in +-2^63.
 WARNING: do not use them as replacement for 32bit integers, integer64 are not
 supported for subscripting by R-core and they have different semantics when 
 combined with double, e.g. integer64 + double => integer64. 
 Class integer64 can be used in vectors, matrices, arrays and data.frames. 
 Methods are available for coercion from and to logicals, integers, doubles, 
 characters and factors as well as many elementwise and summary functions. 
 Many fast algorithmic operations such as 'match' and 'order' support inter-
 active data exploration and manipulation and optionally leverage caching.
Contains the JIVE (joint inter and intra-specific model of variance evolution) model and other Bayesian models aimed at understanding trait evolution. The goal of the package is to join phylogenetic comparative models (PCM) that tend to integrate various type of data (individual observations, environmental data, fossil data) into a hierarchical Bayesian framework. It contains various PCMs as well as functions to join those models into a hierarchical Bayesian framework in a flexible and user friendly way. It contains various Markov chain Monte-Carlo (MCMC) algorithms, methods for model comparison and many plotting function for pre- and post-processing data visualization. Finally, this package integrates functions allowing bridges between 'R' and the 'BEAST2' implementations of PCMs. Kostikova A, Silvestro D, Pearman PB, Salamin N (2016) <doi:10.1093/sysbio/syw010>. Gaboriau T, Mendes FK, Joly S, Silvestro D, Salamin N (in prep).
A client for cryptocurrency exchange BitMEX
    <https://www.bitmex.com/> including the ability to obtain historic
    trade data and place, edit and cancel orders. BitMEX's Testnet and
    live API are both supported.
Functions for bitwise operations on integer vectors.
Provides methods for the binarization and trinarization of one-dimensional data and some visualization functions.
Provides a implementation of floating-point quantization algorithms for use in precision-preserving 
             compression, similar to the approach taken in the 'netCDF operators' (NCO) software package and 
             described in Zender (2016) <doi:10.5194/gmd-2016-63>.
Provides functions for creating and modifying bitmaps with special emphasis on bitmap fonts and their glyphs.  Provides native read/write support for the 'hex' and 'yaff' bitmap font formats and if 'Python' is installed can also read/write several more bitmap font formats using an embedded version of 'monobit'.
Perform competing risks analysis under bivariate Pareto models. See Shih et al. (2019) <doi:10.1080/03610926.2018.1425450> for details.
Contains functions to plot bivariate maps and to generate grids from shapefiles based on area coverage. For more info, see: Hidasi-Neto, J (2015) <https://rfunctions.blogspot.com/2015/03/bivariate-maps-bivariatemap-function.html>, Hidasi-Neto, J (2014) <https://rfunctions.blogspot.com/2014/12/gridfilter-intersect-grid-with-shape.html>.
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.
Computes the joint probability mass function (pmf), the joint cumulative function (cdf), the joint survival function (sf), the correlation coefficient, the covariance, the cross-factorial moment and generate random deviates for the Basu-Dhar bivariate geometric distribution as well the joint probability mass, cumulative and survival function assuming the presence of a cure fraction given by the standard bivariate mixture cure fraction model. The package also computes the estimators based on the method of moments.
Implements Roy's bivariate geometric model (Roy (1993) <doi:10.1006/jmva.1993.1065>): joint probability mass function, distribution function, survival function, random generation, parameter estimation, and more.
A collection of models for bivariate alternating recurrent event data analysis. 
             Includes non-parametric and semi-parametric methods.
Assess the agreement in method comparison studies by tolerance intervals and errors-in-variables (EIV) regressions. The Ordinary Least Square regressions (OLSv and OLSh), the Deming Regression (DR), and the (Correlated)-Bivariate Least Square regressions (BLS and CBLS) can be used with unreplicated or replicated data. The BLS() and CBLS() are the two main functions to estimate a regression line, while XY.plot() and MD.plot() are the two main graphical functions to display, respectively an (X,Y) plot or (M,D) plot with the BLS or CBLS results. Four hyperbolic statistical intervals are provided: the Confidence Interval (CI), the Confidence Bands (CB), the Prediction Interval and the Generalized prediction Interval. Assuming no proportional bias, the (M,D) plot (Band-Altman plot) may be simplified by calculating univariate tolerance intervals (beta-expectation (type I) or beta-gamma content (type II)). Major updates from last version 1.0.0 are: title shortened, include the new functions BLS.fit() and CBLS.fit() as shortcut of the, respectively, functions BLS() and CBLS(). References: B.G. Francq, B. Govaerts (2016) <doi:10.1002/sim.6872>, B.G. Francq, B. Govaerts (2014) <doi:10.1016/j.chemolab.2014.03.006>, B.G. Francq, B. Govaerts (2014) <http://publications-sfds.fr/index.php/J-SFdS/article/view/262>, B.G. Francq (2013), PhD Thesis, UCLouvain, Errors-in-variables regressions to assess equivalence in method comparison studies, <https://dial.uclouvain.be/pr/boreal/object/boreal%3A135862/datastream/PDF_01/view>.
Generates bivariate residual plots with simulation polygons for any diagnostics and bivariate model from which functions to extract the desired diagnostics, simulate new data and refit the models are available.
Simulation of bivariate uniform data with a full range of correlations based on 
             two beta densities and computation of the tetrachoric correlation 
             (correlation of bivariate uniform data) from the phi coefficient 
             (correlation of bivariate binary data) and vice versa.
This is a port of the WTC MATLAB package written by Aslak Grinsted
    and the wavelet program written by Christopher Torrence and Gibert P.
    Compo. This package can be used to perform univariate and bivariate
    (cross-wavelet, wavelet coherence, wavelet clustering) analyses.
Compute multivariate location, scale, and correlation
        estimates based on Tukey's biweight M-estimator.
Business days calculations based on a list of holidays and
    nonworking weekdays. Quite useful for fixed income and derivatives pricing.
Maximum likelihood estimation of copula-based zero-inflated 
    (and non-inflated) Poisson and negative binomial count models. Supports Frank 
    and Gaussian copulas. Allows for mixed margins (e.g., one margin Poisson, the 
    other zero-inflated negative binomial), and several marginal link functions. 
    Built-in methods for publication-quality tables using 'texreg', and post-estimation 
    diagnostics using 'DHARMa'. For information on copula regression for count data, 
    see Genest and Nešlehová (2007) <doi:10.1017/S0515036100014963> as well as 
    Nikoloulopoulos (2013) <doi:10.1007/978-3-642-35407-6_11>. For information on zero-inflated
    count regression generally, see Lambert (1992) <https:www.jstor.org/stable/1269547?origin=crossref>. The author 
    acknowledges support by NSF DMS-1925119 and DMS-212324.
Drawing heavy influence from 'blscrapeR', this package scrapes crime data from <https://www.bjs.gov/>. Specifically, it scrapes data from the National Crime Victimization Survey which tracks personal and household crime in the USA. The idea is to utilize the 'tidyverse' methodology to create an efficient work flow when dealing with crime statistics.
Implementation of a statistical approach 
  for estimating the joint health effects of multiple 
  concurrent exposures, as described in Bobb et al (2015) 
  <doi:10.1093/biostatistics/kxu058>.
Bayesian kernel machine regression (from the 'bkmr' package)
    is a Bayesian semi-parametric generalized linear model approach under
    identity and probit links. There are a number of functions in this
    package that extend Bayesian kernel machine regression fits to allow
    multiple-chain inference and diagnostics, which leverage functions
    from the 'future', 'rstan', and 'coda' packages.  Reference: Bobb, J.
    F., Henn, B. C., Valeri, L., & Coull, B. A. (2018). Statistical
    software for analyzing the health effects of multiple concurrent
    exposures via Bayesian kernel machine regression. ;
    <doi:10.1186/s12940-018-0413-y>.
Performs prediction of a response function from simulated response values, allowing black-box optimization of functions estimated with some error. Includes a simple user interface for such applications, as well as more specialized functions designed to be called by the Migraine software (Rousset and Leblois, 2012 <doi:10.1093/molbev/MSR262>; Leblois et al., 2014 <doi:10.1093/molbev/msu212>; and see URL). The latter functions are used for prediction of likelihood surfaces and implied likelihood ratio confidence intervals, and for exploration of predictor space of the surface. Prediction of the response is based on ordinary Kriging (with residual error) of the input. Estimation of smoothing parameters is performed by generalized cross-validation.
The functions in this package are designed to be used in the processing of black carbon concentration collected by a specific type of a personal monitor. The package includes functions for processing of the data by applying Optimized noise reduction averaging algorithm along with filter loading correction and with an option of filter change adjustment.
Can be used to read and write a fwf with an accompanying 'Blaise' datamodel.
    Blaise is the software suite built by Statistics Netherlands (CBS). It is essentially a 
    way to write and collect surveys and perform statistical analysis on the data. It stores its data in 
    fixed width format with an accompanying metadata file, this is the Blaise format. The package automatically 
    interprets this metadata and reads the file into an R dataframe.
    When supplying a datamodel for writing, the dataframe will be automatically converted 
    to that format and checked for compatibility.
    Supports dataframes, tibbles and LaF objects.
    For more information about 'Blaise', see <https://blaise.com/products/general-information>.
Fast and accurate calculation of Blaker's binomial and Poisson confidence limits (and some related stuff).
Bland-Altman Plots using either base graphics or ggplot2,
    augmented with confidence intervals, with detailed return values and
    a sunflowerplot option for data with ties.
Carries out Bland Altman analyses (also known as a Tukey
    mean-difference plot) as described by JM Bland and DG Altman in
    1986 <doi:10.1016/S0140-6736(86)90837-8>. This package was created in 
    2015 as existing Bland-Altman analysis functions did not calculate 
    confidence intervals. This package was created to rectify this, 
    and create reproducible plots. This package is also available as a module
    for the 'jamovi' statistical spreadsheet (see <https://www.jamovi.org>
    for more information).
Build and compare nested statistical models with sets of equal and different independent variables. An analysis using this package is Marquardt et al. (2021) <https://github.com/p-mq/Percentile_based_averaging>.
Laplace approximations and penalized B-splines are combined
    for fast Bayesian inference in latent Gaussian models. The routines can be
    used to fit survival models, especially proportional hazards and promotion 
    time cure models (Gressani, O. and Lambert, P. (2018) 
    <doi:10.1016/j.csda.2018.02.007>). The Laplace-P-spline methodology can also
    be implemented for inference in (generalized) additive models
    (Gressani, O. and Lambert, P. (2021) <doi:10.1016/j.csda.2020.107088>).
    See the associated website for more information and examples.
Implementation of an efficient BLAST-like sequence
             comparison algorithm, written in C++11 and using
             native R datatypes. Blaster is based on 'nsearch' -
             Schmid et al 2018; <doi:10.1101/399782>.
Compose and send out responsive HTML email messages that render
    perfectly across a range of email clients and device sizes. Helper functions
    let the user insert embedded images, web link buttons, and 'ggplot2' plot
    objects into the message body. Messages can be sent through an 'SMTP'
    server, through the 'RStudio Connect' service, or through the 'Mailgun' API
    service <http://mailgun.com/>.
Estimation of latent variable models using Bayesian methods. Currently estimates the loglinear cognitive diagnosis model of Henson, Templin, and Willse (2009) <doi:10.1007/s11336-008-9089-5>.
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.
Fit a variety of Bayesian latent variable models, including confirmatory
   factor analysis, structural equation models, and latent growth curve models. References: Merkle & Rosseel (2018) <doi:10.18637/jss.v085.i04>; Merkle et al. (2021) <doi:10.18637/jss.v100.i06>.
An implementation of the Black-Litterman Model and Attilio
    Meucci's copula opinion pooling framework as described in
    Meucci, Attilio (2005) <doi:10.2139/ssrn.848407>,
    Meucci, Attilio (2006) <doi:10.2139/ssrn.872577> and
    Meucci, Attilio (2008) <doi:10.2139/ssrn.1117574>.
A new link function that equals one specified link function up to a cutover then a linear rescaling of another specified link function. For use in glm() or glm2(). The intended use is in binary regression, in which case the first link should be set to "log" and the second to "logit". This ensures that fitted probabilities are between 0 and 1 and that exponentiated coefficients can be interpreted as relative risks for probabilities up to the cutoff.
Tools for assessing exotic species' contributions to
        landscape homogeneity using average pairwise Jaccard similarity
        and an analytical approximation derived in Harris et al. (2011,
        "Occupancy is nine-tenths of the law," The American
        Naturalist). Also includes a randomization method for assessing
        sources of model error.
Performs a joint analysis of experiments with mixtures and random effects, taking on a process variable represented by a covariable.
Computation of key characteristics and plots for blinded sample size recalculation.
   Continuous as well as binary endpoints are supported in superiority and non-inferiority trials.
   The implemented methods include the approaches by
   Lu, K. (2019) <doi:10.1002/pst.1737>,
   Kieser, M. and Friede, T. (2000) <doi:10.1002/(SICI)1097-0258(20000415)19:7%3C901::AID-SIM405%3E3.0.CO;2-L>,
   Friede, T. and Kieser, M. (2004) <doi:10.1002/pst.140>, 
   Friede, T., Mitchell, C., Mueller-Veltern, G. (2007) <doi:10.1002/bimj.200610373>, and
   Friede, T. and Kieser, M. (2011) <doi:10.3414/ME09-01-0063>.
An implementation of the model in Steorts (2015) <DOI:10.1214/15-BA965SI>, which performs Bayesian entity resolution for categorical and text data, for any distance function defined by the user. In addition, the precision and recall are in the package to allow one to compare to any other comparable method such as logistic regression, Bayesian additive regression trees (BART), or random forests. The experiments are reproducible and illustrated using a simple vignette. LICENSE: GPL-3 + file license.
A method for the Bayesian functional linear regression model (scalar-on-function),
  including two estimators of the coefficient function and an estimator of its support.
  A representation of the posterior distribution is also available. Grollemund P-M., Abraham C., 
  Baragatti M., Pudlo P. (2019) <doi:10.1214/18-BA1095>.
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, implementing the methods of Chung, et al. (2013) <doi:10.1007/s11336-013-9328-2>. Extends package 'lme4' (Bates, Maechler, Bolker, and Walker (2015) <doi:10.18637/jss.v067.i01>).
Data files and functions accompanying the book Korner-Nievergelt, Roth, von Felten, Guelat, Almasi, Korner-Nievergelt (2015) "Bayesian Data Analysis in Ecology using R, BUGS and Stan", Elsevier, New York.
Posterior distribution in the Black-Litterman model is computed from a prior distribution given in the form of a time series of asset returns and a continuous distribution of views provided by the user as an external function.
R's raw vector is useful for storing a single binary object.
    What if you want to put a vector of them in a data frame? The 'blob'
    package provides the blob object, a list of raw vectors, suitable for
    use as a column in data frame.
Simultaneous clustering of rows and columns, usually designated by
    biclustering, co-clustering or block clustering, is an important technique
    in two way data analysis. It consists of estimating a mixture model which
    takes into account the block clustering problem on both the individual and
    variables sets. The 'blockcluster' package provides a bridge between the C++
    core library build on top of the 'STK++' library, and the R statistical
    computing environment. This package allows to co-cluster binary
    <doi:10.1016/j.csda.2007.09.007>, contingency <doi:10.1080/03610920903140197>,
    continuous <doi:10.1007/s11634-013-0161-3> and categorical data-sets
    <doi:10.1007/s11222-014-9472-2>. It also provides utility functions to
    visualize the results. This package may be useful for various applications
    in fields of Data mining, Information retrieval, Biology, computer vision
    and many more. More information about the project and comprehensive tutorial
    can be found on the link mentioned in URL.
Computation of large covariance matrices having a block structure up to a permutation of their columns and rows 
    from a small number of samples with respect to the dimension of the matrix.
 The method is described in the paper Perrot-Dockès et al. (2019) <arXiv:1806.10093>.
Creating spatially or environmentally separated folds for cross-validation to provide a robust error estimation in spatially structured environments; Investigating and visualising the effective range of spatial autocorrelation in continuous raster covariates to find an initial realistic distance band to separate training and testing datasets spatially described in Valavi, R. et al. (2019) <doi:10.1111/2041-210X.13107>.
An R implementation of an extension of the 'BayeScan' software (Foll, 2008) <DOI:10.1534/genetics.108.092221> for codominant markers, adding the option to group individual SNPs into pre-defined blocks. A typical application of this new approach is the identification of genomic regions, genes, or gene sets containing one or more SNPs that evolved under directional selection.
A random forest variant 'block forest' ('BlockForest') tailored to the 
  prediction of binary, survival and continuous outcomes using block-structured 
  covariate data, for example, clinical covariates plus measurements of a certain 
  omics data type or multi-omics data, that is, data for which measurements of 
  different types of omics data and/or clinical data for each patient exist. Examples 
  of different omics data types include gene expression measurements, mutation data
  and copy number variation measurements.
  Block forest are presented in Hornung & Wright (2019). The package includes four
  other random forest variants for multi-omics data: 'RandomBlock', 'BlockVarSel', 
  'VarProb', and 'SplitWeights'. These were also considered in Hornung & Wright (2019), 
  but performed worse than block forest in their comparison study based on 20 real 
  multi-omics data sets. Therefore, we recommend to use block forest ('BlockForest') 
  in applications. The other random forest variants can, however, be consulted for 
  academic purposes, for example, in the context of further methodological 
  developments. 
  Reference: Hornung, R. & Wright, M. N. (2019) Block Forests: random forests for blocks of clinical and omics covariate data. BMC Bioinformatics 20:358. <doi:10.1186/s12859-019-2942-y>.
A set of functions to select the optimal block-length for a 
    dependent bootstrap (block-bootstrap). Includes the Hall, Horowitz, and Jing
    (1995) <doi:10.1093/biomet/82.3.561> cross-validation method and the 
    Politis and White (2004) <doi:10.1081/ETC-120028836> Spectral Density 
    Plug-in method, including the Patton, Politis, and White (2009)
    <doi:10.1080/07474930802459016> correction with a corresponding set of S3
    plot methods.
Some elementary matrix algebra tools are implemented to manage
    block matrices or partitioned matrix, i.e. "matrix of matrices"
    (http://en.wikipedia.org/wiki/Block_matrix). The block matrix is here
    defined as a new S3 object. In this package, some methods for "matrix"
    object are rewritten for "blockmatrix" object. New methods are implemented.
    This package was created to solve equation systems with block matrices for
    the analysis of environmental vector time series .
    Bugs/comments/questions/collaboration of any kind are warmly welcomed.
Creates strings that show a text message in 8 by 8 block
        letters
This is primarily meant as an implementation of generalized blockmodeling for valued networks.
 In addition, measures of similarity or dissimilarity based on structural equivalence and 
 regular equivalence (REGE algorithms) can be computed and partitioned matrices can be plotted:
 Žiberna (2007)<doi:10.1016/j.socnet.2006.04.002>, Žiberna (2008)<doi:10.1080/00222500701790207>,
 Žiberna (2014)<doi:10.1016/j.socnet.2014.04.002>.
Latent and Stochastic Block Model estimation by a Variational EM algorithm.
             Various probability distribution are provided (Bernoulli,
             Poisson...), with or without covariates.
Create randomizations for block random clinical trials.  Can also produce a pdf file of randomization cards.
Constructs treatment and block designs for linear treatment models
  with crossed or nested block factors. The treatment design can be any feasible 
  linear model and the block design can be any feasible combination of crossed or 
  nested block factors. The block design is a sum of one or more block factors
  and the block design is optimized sequentially with the levels of each successive
  block factor optimized conditional on all previously optimized block factors. 
  D-optimality is used throughout except for square or rectangular lattice block designs 
  which are constructed algebraically using mutually orthogonal Latin squares.
  Crossed block designs with interaction effects are optimized using a weighting scheme
  which allows for differential weighting of first and second-order block effects. 
  Outputs include a table showing the allocation of treatments to blocks and tables showing
  the achieved D-efficiency factors for each block and treatment design.  
  Edmondson, R.N. Multi-level Block Designs for Comparative Experiments. 
  JABES 25, 500–522 (2020) <doi:10.1007/s13253-020-00416-0>.
Write blog posts and web pages in R Markdown. This package
    supports the static site generator 'Hugo' (<https://gohugo.io>) best,
    and it also supports 'Jekyll' (<https://jekyllrb.com>) and 'Hexo'
    (<https://hexo.io>).
It includes estimating the area under the concentrations
        versus time curve (AUC) and its standard error for data with
        Below the Limit of Quantification (BLOQ) observations. Two 
        approaches are implemented: direct estimation using censored maximum 
        likelihood, also by first imputing the BLOQ's 
        using various methods, then compute AUC and its standard 
        error using imputed data. Technical details can found in 
        Barnett, Helen Yvette, Helena Geys, Tom Jacobs, and Thomas Jaki. 
		"Methods for Non-Compartmental Pharmacokinetic Analysis With Observations 
		Below the Limit of Quantification." Statistics in Biopharmaceutical 
		Research (2020): 1-12.
        (available online: 
        <https://www.tandfonline.com/doi/full/10.1080/19466315.2019.1701546>).