c(
    person(given = "Alasdair",
           family = "Warwick",
           role = c("aut", "cre", "cph"),
           email = "alasdair.warwick.19@ucl.ac.uk",
           comment = c(ORCID = "0000-0002-0800-2890")),
    person("Robert", "Luben", role = "aut",
           comment = c(ORCID = "0000-0002-5088-6343")),
    person("Abraham", "Olvera-Barrios", role = "aut",
           comment = c(ORCID = "0000-0002-3305-4465")),
    person("Chuin Ying", "Ung", role = "aut",
           comment = c(ORCID = "0000-0001-8487-4589")),
    person(given = "Jon",
           family = "Clayden",
           role = c("rev"),
           comment=c(ORCID="0000-0002-6608-0619")),
    person(given = "Alexandros",
           family = "Kouretsis",
           role = c("rev"))
    )
person(given = "Gustavo",
                      family = "Landfried",
                      role = c("aut", "cre"),
                      email = "gustavolandfried@gmail.com")
person(given = "Joshua",
                      family = "Callaway",
                      role = c("aut", "cre"),
                      email = "Callaway@roar-analytics.com")
c(person(given = "Nathan",
           family = "Smith",
           role = c("aut", "cre"),
           email = "nathan_smith_99@ku.edu"),
           person(given = "Trambak",
           family = "Banerjee",
           role = c("aut"),
           email = "trambak@ku.edu"),
           person(given = "Bhaswar",
           family = "Bhattacharya",
           role = c("aut"),
           email = "bhaswar@wharton.upenn.edu"),
           person(given = "Gourab",
           family = "Mukherjee",
           role = c("aut"),
           email = "gourab@usc.edu"))
c(person(given = "Judit", family = "García-González", email = "judit.garciagonzalez@mssm.edu", role = c("aut", "cre"), comment = c(ORCID = "0000-0001-6245-740X")),
      person(given = "Lathan", family = "Liou", email = "lathan.liou@icahn.mssm.edu", role = c("ctb")))
c(
    person("Yuyao", "Wang", email = "yuw079@ucsd.edu", role = c("cre","aut")),
    person("Andrew", "Ying", email = "aying9339@gmail.com", role = "aut"),
	person("Ronghui", "Xu", email = "rxu@health.ucsd.edu", role = "aut"))
c(person(given="Zdravko", family="Botev", role = "aut", email = "botev@unsw.edu.au", comment = c(ORCID = "0000-0001-9054-3452")), person(given="Leo", family="Belzile", role = c("aut", "cre"), email = "belzilel@gmail.com", comment = c(ORCID = "0000-0002-9135-014X")))
c(
		person(
			given = "René",
			family = "Holst",
			role = "aut",
			email = "rene.holst@medisin.uio.no"
		),
		person(
			given = "Waldir",
			family = "Leoncio",
			role = c("cre", "aut"),
			email = "w.l.netto@medisin.uio.no"
		)
	)
c(person("Olaf", "Mersmann", role=c("aut", "cre"), 
                    email="olafm@p-value.net"),
             person("Heike", "Trautmann", role=c("aut")),
             person("Detlef", "Steuer", role=c("aut")),
             person("Björn", "Bornkamp", role=c("aut")))
c(
  person("Mika", "Braginsky", role = c("aut"), email = "mika.br@gmail.com"),
  person("Leon", "Tran", role = "aut", email = "leontk@stanford.edu"),
  person("Maya", "Mathur", role = "ctb", email = "mmathur@stanford.edu"),
  person("Peter", "Solymos", email = "peter@analythium.io", role = c("cre", "ctb"), comment = c(ORCID = "0000-0001-7337-1740"))
  )
c(person("Yves", "Croissant", role = c("aut", "cre"), email = "yves.croissant@univ-reunion.fr"),
             person("Achim", "Zeileis", role = "aut", email = "Achim.Zeileis@R-project.org"))
person(given = "Peter",
           family = "Muller",
           role = c("aut", "cre"),
           email = "ttspackage@gmail.com",
           comment = c(ORCID = "0000-0002-5748-6270"))
c(
    person("Annemarie", "Pläschke", , "anneplaeschke@gmail.com", role = c("aut", "cre", "cph"),
      comment = c(ORCID = "0009-0005-7115-8790")
    ),
    person("Tobias", "Brändle", role = "aut",
      comment = c(ORCID = "0000-0001-8872-9872")
    )
  )
person(given="Michael", family="Braun", email="braunm@smu.edu", role=c("aut","cre","cph"), comment=c(ORCID="0000-0003-4774-2119"))
c(
  person("Juergen", "Altfeld", email = "jaltfeld@altfeld-im.de", role = c("aut", "cre", "cph")),
  person("Charles", "Epaillard", email = "charles.epaillard@gmail.com", role = "ctb"),
  person("Brandon", "Bertelsen", email = "brandon@bertelsen.ca", role = "ctb"),
  person("Valerian", "Wrobel", email = "valerian.wrobel@gmail.com", role = "ctb")
  )
c(person("Christophe", "Dutang", role = c("aut", "cre"), 
                email = "dutangc@gmail.com", 
                comment = c(ORCID = "0000-0001-6732-1501")),
            person("Cosma", "Shalizi", role = "aut", 
                comment = c(ORCID = "0000-0002-9195-1308"))
                )
c(person("Md", "Yeasin", role = c("aut","cre"), email = "yeasin.iasri@gmail.com"),
    person("Ranjit Kumar", "Paul", role = "aut", email = "ranjitstat@gmail.com"),
    person("Dipro", "Sinha", role = "aut", email = "diprosinha@gmail.com"))
c(person("Alexios", "Galanos", role = c("aut", "cre","cph"), email = "alexios@4dscape.com", comment = c(ORCID = "0009-0000-9308-0457")))
c(
    person("Christoph", "Sax", email = "christoph.sax@gmail.com", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-7192-7044")),
    person("Cathy", "Chamberlin", role = c("rev")),
    person("Nunes", "Matt", role = c("rev"))
    )
c(person(given = "Markus", family = "Matilainen", role = c("cre","aut"),
             email = "markus.matilainen@outlook.com", comment = c(ORCID = "0000-0002-5597-2670")),
      person(given = "Christophe", family = "Croux", role = "aut"),
      person(given = "Jari", family = "Miettinen", role = "aut", comment = c(ORCID = "0000-0002-3270-7014")),
      person(given = "Klaus", family = "Nordhausen", role = c("aut"), comment = c(ORCID = "0000-0002-3758-8501")),
      person(given = "Hannu", family = "Oja", role = "aut"),
      person(given = "Sara", family = "Taskinen", role = "aut", comment = c(ORCID = "0000-0001-9470-7258")),
	  person(given = "Joni", family = "Virta", role = "aut", comment = c(ORCID = "0000-0002-2150-2769")))
c(
    person(given="David",
            family = "Carl",
            role = c("aut", "cre"),
            email = "david.carl@phd.unibocconi.it",
            comment = c(ORCID = "0000-0003-0615-0133")),
    person(given = "Corinne",
            family = "Emmenegger",
            role = c("aut"),
            email = "emmenegger@stat.math.ethz.ch", 
            comment = c(ORCID = "0000-0003-0353-8888")),
    person(given = "Wei",
            family = "Yuan",
            role = c("aut"),
            email = "wy204@stat.rutgers.edu"),
    person(given = "Mengchu",
            family = "Zheng",
            role = c("aut"),
            email = "mengchuzheng@link.cuhk.edu.cn"),
    person(given = "Zijian",
            family = "Guo",
            role = c("aut"),
            email = "zijguo@stat.rutgers.edu",
            comment = c(ORCID = "0000-0002-2888-7016")))
c(person("Pablo", "Montero Manso", role= c("cre"), email="pmontm@gmail.com"),
             person("Jose", "Vilar Fernandez", role="aut"))
c(person(given = "Alexander", 
             family = "McNeil", 
             email = "alexanderjmcneil@gmail.com", 
             role = c("aut", "cre")),
     person(given = "Martin", 
            family = "Bladt", 
            email = "martinbladt@gmail.com", 
            role = "aut"))
c(
  person("Tobias", "Liboschik", role=c("aut", "cre"), email="liboschik@statistik.tu-dortmund.de"),
  person("Roland", "Fried", role=c("aut"), email="fried@statistik.tu-dortmund.de"),
  person("Konstantinos", "Fokianos", role=c("aut"), email="fokianos@ucy.ac.cy"),
  person("Philipp", "Probst", role=c("aut"), email="philipp.probst@tu-dortmund.de"),
  person("Jonathan", "Rathjens", role=c("ctb"), email="jonathan.rathjens@tu-dortmund.de"))
person("Tianjian", "Yang", email = "yangtj5@mail2.sysu.edu.cn",
    role = c("aut", "cre"))
c(
    person(given = "Thiyanga S.",
           family = "Talagala",
           role = c("aut", "cre"),
           email = "ttalagala@sjp.ac.lk",
           comment = c(ORCID = "0000-0002-0656-9789")))
person(given = "Enrico", family = "Schumann",
                  role = c("aut", "cre"),
                  email = "es@enricoschumann.net",
                  comment = c(ORCID = "0000-0001-7601-6576"))
c(person(given = "Ronit",
         family = "Jaiswal",
         role = c("aut", "cre"),
         email = "ronitjaiswal2912@gmail.com"),
  person(given = "Girish Kumar",
         family = "Jha",
         role =  c("aut", "ths", "ctb")),
  person(given = "Rajeev Ranjan ",
         family = "Kumar",
         role =  c("aut", "ctb")),
  person(given = "Kapil",
         family = "Choudhary",
         role = c("aut", "ctb")))
c(
        person("Jen-Hsiang", "Ou", 
            email = "jen-hsiang.ou@imbim.uu.se", 
            role = c("aut", "cre"), 
            comment = c(ORCID = "0000-0001-9305-2931")),
        person("Po-Ya", "Wu", 
            email = "Po-Ya.Wu@hhu.de", 
            role = c("aut"), 
            comment = c(ORCID = "0000-0002-7342-2867")),
        person("Chen-Tuo", "Liao", 
            email = "ctliao@ntu.edu.tw", 
            role = c("aut", "ths"), 
            comment = c(ORCID = "0000-0001-9777-3701")))
c(person(given = "Luke",
           family = "Mosley",
           role = c("aut","cre"),
           email = "l.mosley@lancaster.ac.uk"),
      person(given = "Kaveh",
           family = "S. Nobari",
           role = c("aut"),
           email = "k.salehzadehnobari@lancs.ac.uk",
           comment = c(ORCID = "0000-0002-4053-0781")))
c(person("Usue", "Mori", email = "usue.mori@ehu.eus",role = c("aut", "cre")),person("Alexander", "Mendiburu", role = c("aut")),person("Jose A.", "Lozano", role = c("aut")),person("Duarte", "Folgado", role = c("ctb")))
c(
  person(family = "Battioui",
         given = "Chakib",
         email = "battioui_chakib@lilly.com",
         role = c("aut")),
  person(family = "Denton",
         given = "Brian",
         email = "denton_brian_david@lilly.com",
         role = c("aut","cre")),
  person(family = "Shen",
         given = "Lei",
         email = "shen_lei@lilly.com",
         role = c("ctb")),
  person(family = "Eli Lilly and Company",
         role = "cph"))
c(person("Antonio Fabio", "Di Narzo", role = "aut",
                    comment =c(ORCID="0000-0002-4033-5038")),
             person("Jose Luis", "Aznarte", role = "ctb",
                    comment =c(ORCID="0000-0002-1636-244X"),
                    email = "jlaznarte@dia.uned.es"),
             person("Matthieu", "Stigler", role = c("aut","cre"), email="Matthieu.Stigler@gmail.com",
                    comment =c(ORCID="0000-0002-6802-4290")))
c(person("Joshua", "Miller", role = c("aut", "cre"), email = "joshlmiller@msn.com"))
person("Iván", "Velasco", , "ivan.velasco@urjc.es", role = c("aut", "cre", "cph"),
           comment = c(ORCID = "0000-0002-2314-5670"))
c(person("Vitor", "Cerqueira", email = "cerqueira.vitormanuel@gmail.com", role = c("aut", "cre")),
			 person("Luis", "Torgo", role = "ctb"),
       person("Carlos", "Soares", role = "ctb"))
person("Jiri", "Tomcala", email = "jiri.tomcala@vsb.cz", role = c("aut", "cre"))
c(person("Adrian", "Trapletti", role = "aut",
                    email = "adrian@trapletti.org"),
             person("Kurt", "Hornik", role = c("aut", "cre"),
                    email = "Kurt.Hornik@R-project.org",
		    comment = c(ORCID = "0000-0003-4198-9911")),
             person("Blake", "LeBaron", role = "ctb",
                    comment = "BDS test code"))
c(
   person('Simone', 'Giannerini', email = 'simone.giannerini@unibo.it', role = c('aut','cre'),
    comment = c(ORCID = '0000-0002-0710-668X'))
    )
c(person("Vishakh", "Padmakumar", email = "vishakhpadmakumar@gmail.com", role = c("aut", "cre")), person("Rishab", "Ranga", email = "rishab.ranga1996@gmail.com", role = c("aut")), person("Amogha", "Udupa", email = "amogha15@gmail.com", role = c("aut")), person("Param", "Hanji", email = "param.catchchaos@gmail.com", role = c("aut")))
c(
   person('Simone', 'Giannerini', email = 'simone.giannerini@uniud.it', role = c('aut','cre'),
    comment = c(ORCID = '0000-0002-0710-668X')), 
    person('Greta', 'Goracci', email = 'greta.goracci@unibz.it', role = c('aut'),
    comment = c(ORCID = '0000-0001-5212-0539'))
    )
c(
    person("Rob", "Hyndman", email = "Rob.Hyndman@monash.edu", role = c("aut","cre"), comment = c(ORCID = "0000-0002-2140-5352")),
    person("Yanfei", "Kang", role = "aut", comment = c(ORCID = "0000-0001-8769-6650")),
    person("Pablo", "Montero-Manso", email="p.montero.manso@udc.es", role="aut"),
    person("Mitchell", "O'Hara-Wild", role="aut", comment=c(ORCID = "0000-0001-6729-7695")),
    person("Thiyanga", "Talagala", role = "aut", comment=c(ORCID = "0000-0002-0656-9789")),
    person("Earo", "Wang", role = "aut", comment=c(ORCID = "0000-0001-6448-5260")),
    person("Yangzhuoran", "Yang", email = "Fin.Yang@monash.edu", role = "aut"),
    person("Souhaib", "Ben Taieb", role = "ctb"),
    person("Cao", "Hanqing", role="ctb"),
    person("D K", "Lake", role="ctb"),
    person("Nikolay", "Laptev", role="ctb"),
    person("J R", "Moorman", role="ctb"),
    person("Bohan", "Zhang", role = "ctb"))
c(
    person("Maria Pilar", "Frias-Bustamante", role = c("aut"), email = "mpfrias@ujaen.es"),
    person("Ana Maria", "Martinez-Rodriguez", role = c("aut"), email = "ammartin@ujaen.es"),
    person("Antonio", "Conde-Sanchez", role = c("aut"), email = "aconde@ujaen.es"),
    person("Francisco", "Martinez", role = c("aut", "cre"), email = "fmartin@ujaen.es"))
person("Francisco", "Martinez", email = "fmartin@ujaen.es",
    role = c("aut", "cre"))
c(person("Mrinmoy", "Ray", role = c("aut", "cre"),
                     email = "mrinmoy4848@gmail.com"),
              person("K. N.", "Singh", role = "ctb"),
              person("Kanchan", "Sinha", role = "ctb"),
              person("Rajeev Ranjan", "Kumar", role = "ctb"),
              person("Prakash", "Kumar", role = "ctb"))
c(person(given = "Craig", family = "Thamotheram", role = c("aut", "cre"), email = "cpt@tacindex.com"))
c(person("Md. Samir", "Farooqi", email = "ms.Farooqi@icar.gov.in", role = "aut"),
    person("K.K.", "Chaturvedi", email = "kk.Chaturvedi@icar.gov.in", role = "aut"),
    person("D.C.", "Mishra", email = "Dwijesh.Mishra@icar.gov.in", role = "aut"),
    person("Sudhir", "Srivastava", email = "Sudhir.Srivastava@icar.gov.in", role = c("cre","aut")))
c(person(given = "Earo",
             family = "Wang",
             role = c("aut", "cre"),
             email = "earo.wang@gmail.com",
             comment = c(ORCID = "0000-0001-6448-5260")),
      person(given = "Di",
             family = "Cook",
             role = c("aut", "ths"),
             comment = c(ORCID = "0000-0002-3813-7155")),
      person(given = "Rob",
             family = "Hyndman",
             role = c("aut", "ths"),
             comment = c(ORCID = "0000-0002-2140-5352")),
      person(given = "Mitchell",
             family = "O'Hara-Wild",
             role = "aut",
             comment = c(ORCID = "0000-0001-6729-7695")),
      person(given = "Tyler",
             family = "Smith",
             role = "ctb"),
      person(given = "Wil",
             family = "Davis",
             role = "ctb",
             email = "william.davis@worthingtonindustries.com"))
c(
    person("Mitchell", "O'Hara-Wild", email = "mail@mitchelloharawild.com", role = c("aut", "cre")),
    person("Rob", "Hyndman", role = "aut"),
    person("Earo", "Wang", role = "aut"),
    person("Rakshitha", "Godahewa", role = "aut"),
    person("Christoph", "Bergmeir", role = "ctb")
    )
c(person(given = "Earo",
             family = "Wang",
             role = c("aut", "cre"),
             email = "earo.wang@gmail.com",
             comment = c(ORCID = "0000-0001-6448-5260")),
      person(given = "Di",
             family = "Cook",
             role = "aut",
             comment = c(ORCID = "0000-0002-3813-7155")))
c(
    person("Nikolaos", "Kourentzes", email = "nikolaos@kourentzes.com", role = c("cre","aut"), comment = c(ORCID = "0000-0003-0211-5218")),
    person("Fotios", "Petropoulos", email = "F.Petropoulos@bath.ac.uk", role = "ctb", comment = c(ORCID = "0000-0003-3039-4955")))
c(person("Alexios", "Galanos", role = c("aut", "cre"), comment = c(ORCID = "0009-0000-9308-0457"), email = "alexios@4dscape.com"))
c(person("Jianmin", "Chen", role = c("aut", "cre"), email = "jianminc000@gmail.com"), person("Kun", "Chen", role = "aut"))
c(person("Dr. Ranjit Kumar", "Paul", role = c("aut","cre"), email = "ranjitstat@gmail.com"),person("Dr. Md", "Yeasin", role = "aut", email = "yeasin.iasri@gmail.com"))
c(
    person(given = "Jaime", family = "Pizarroso Gonzalo",
      email = "jpizarroso@comillas.edu", role = c("aut","ctb", "cre")),
	person(given = "Antonio", family = "Muñoz San Roque",
      email = "antonio.munoz@iit.comillas.edu", role = "aut")
    )
c(person("Sandip", "Garai", role = c("aut","cre"), email = "sandipnicksandy@gmail.com"),
    person("Krishna Pada", "Sarkar", role = "aut", email = "kpsarkarrbi@gmail.com"))
c(person(given = "Francisco",
             family = "Bischoff",
             role = c("aut", "cre"),
             email = "fbischoff@med.up.pt",
             comment = c(ORCID = "https://orcid.org/0000-0002-5301-8672")),
      person(given = "Michael",
             family = "Yeh",
             role = c("res", "ccp", "ctb"),
             email = "myeh003@ucr.edu",
             comment = c(ORCID = "https://orcid.org/0000-0002-9807-2963")),
      person(given = "Diego",
             family = "Silva",
             role = c("res", "ccp", "ctb"),
             email = "diegofs@ufscar.br",
             comment = c(ORCID = "https://orcid.org/0000-0002-5184-9413")),
      person(given = "Yan",
             family = "Zhu",
             role = c("res", "ccp", "ctb"),
             email = "yzhu015@ucr.edu",
             comment = c(ORCID = "https://orcid.org/0000-0002-5952-2108")),
      person(given = "Hoang",
             family = "Dau",
             role = c("res", "ccp", "ctb"),
             email = "hdau001@ucr.edu",
             comment = c(ORCID = "https://orcid.org/0000-0003-2439-5185")),
      person(given = "Michele",
             family = "Linardi",
             role = c("res", "ccp", "ctb"),
             email = "michele.linardi@orange.fr",
             comment = c(ORCID = "https://orcid.org/0000-0002-3249-2068")))
c(person(given = "Eraldo",
           family = "B. dos Anjos Filho",
           role = c("aut", "cre"),
           email = "ebdaf1@de.ufpe.br",
           comment = c(ORCID = "0000-0002-1272-2794")),
           person(given = "Aldo",
           family = "M. Garay",
           role = c("aut"),
           email = "agaray@de.ufpe.br",
           comment = c(ORCID = "0000-0002-4510-639X")))
c(
           person("Skye", "Bender-deMoll", role=c("aut","cre"), email="skyebend@uw.edu"),
           person("Martina", "Morris", role=c("aut"), email="morrism@u.washington.edu"),
           person("James", "Moody", role=c("ctb"), email="jmoody77@soc.duke.edu")
           )
c(person("Björn S.", "Siepe", 
  email = "bjoernsiepe@gmail.com", 
  role = c("aut", "cre", "cph"),
  comment = c(ORCID = "0000-0002-9558-4648")),
  person("Matthias", "Kloft",
  email = "kloft@uni-marburg.de",
  role = c("aut"),
  comment = c(ORCID = "0000-0003-1845-6957")),
  person("Daniel W.", "Heck",
  email = "daniel.heck@uni-marburg.de",
  role = c("ctb"),
  comment = c(ORCID = "0000-0002-6302-9252")))
c(person("Michael", "Hahsler", 
    role = c("aut", "cre", "cph"),
	email = "mhahsler@lyle.smu.edu",
	comment = c(ORCID = "0000-0003-2716-1405")),
    person("Kurt", "Hornik", 
	role = c("aut", "cph"),
	comment = c(ORCID = "0000-0003-4198-9911"))
    )
c(person(c("Rebecca", "Pontes"),"Salles", role = c("aut", "cre","cph"),email = "rebeccapsalles@acm.org", comment = "CEFET/RJ"),
			 person("Eduardo","Ogasawara", role = c("ths"),email = "eogasawara@ieee.org", comment = "CEFET/RJ"))
c(    person(given = "Eduardo", family = "Ogasawara", role = c("aut", "ths", "cre"), 
               email = "eogasawara@ieee.org", comment = c(ORCID = "0000-0002-0466-0626")),
        person(given = "Cristiane", family = "Gea", role = "aut", email = "cristiane.gea@eic.cefet-rj.br"),
        person(given = "Diego", family = "Carvalho", role = "ctb", email = "diego.carvalho@cefet-rj.br"),
        person(given = "Diogo", family = "Santos", role = "aut", email = "diogo.santos@eic.cefet-rj.br"),
        person(given = "Eduardo", family = "Bezerra", role = "ctb", email = "ebezerra@cefet-rj.br"),
        person(given = "Esther", family = "Pacitti", role = "ctb", email = "Esther.Pacitti@lirmm.fr"),
        person(given = "Fabio", family = "Porto", role = "ctb", email = "fporto@lncc.br"),
        person(given = "Fernando", family = "Alexandrino", role = "aut", email = "fernando.alexandrino@ifsp.edu.br"),
        person(given = "Rebecca", family = "Salles", role = "aut", email = "rebecca.salles@eic.cefet-rj.br"),
        person(given = "Vitoria", family = "Birindiba", role = "aut", email = "vitoria.birindiba@eic.cefet-rj.br"),
        person(given = "CEFET/RJ", role = "cph")
  )
person("Peter", "Laurinec", email = "tsreprpackage@gmail.com",
  role = c("aut", "cre"), 
  comment = c(ORCID = "0000-0002-3501-8783"))
person("Andrew", "Ying", email = "aying9339@gmail.com", role = c("aut", "cre"))
c(
    person(given = "Michał",
           family = "Narajewski",
           role = c("aut", "cre"),
           email = "michal.narajewski@uni-due.de",
           comment = c(ORCID = "https://orcid.org/0000-0002-3115-0162")),
    person(given = "Jens",
           family = "Kley-Holsteg",
           role = "aut",
           email = "jens.kley-holsteg@hs-ruhrwest.de"),
    person(given = "Florian",
           family = "Ziel",
           role = "aut",
           email = "florian.ziel@uni-due.de",
           comment = c(ORCID = "https://orcid.org/0000-0002-2974-2660")))
person("Arden", "Burrell", email = "arden.burrell@gmail.com", role = c("aut", "cre"))
person("Daniel", "Ollech", , "daniel.ollech@bundesbank.de", role = c("aut", "cre"))
c(
    person("L. Leticia", "Ramirez-Ramirez", email = "leticia.ramirez@cimat.mx", role = c("aut", "cre")),
    person("Alejandro", "Islas-Camargo", email = "", role = "aut"),
    person("Victor M.", "Guerrero", email = "", role = "aut"))
c(person(" Mrinmoy", "Ray", role = c("aut", "cre"),email = "mrinmoy4848@gmail.com"),
            person("Samir", "Barman", role = c("aut", "ctb")),
            person("Kanchan", "Sinha", role = c("aut", "ctb")),
           person("K. N.", "Singh", role = c("aut", "ctb")))
c(
    person("Matthias", "Bannert", , "bannert@kof.ethz.ch", role = "aut"),
    person("Severin", "Thoeni", , "thoenis@kof.ethz.ch", role = "aut"),
    person("Stéphane", "Bisinger", , "bisinger@kof.ethz.ch", role = c("aut", "cre"))
  )
c(
    person("Nikolaos", "Kourentzes",, "nikolaos@kourentzes.com", c("aut", "cre")),
    person("Ivan", "Svetunkov",, "ivan@svetunkov.ru", c("ctb")),
    person("Oliver", "Schaer",, "info@oliverschaer.ch", c("ctb")))
c(person(given=c("Bradley", "M"), family="Broom", role=c("aut"), email="bmbroom@mdanderson.org", comment=c(ORCID="0000-0002-0915-3164")),
    person(given=c("Mary", "A"), family="Rohrdanz", role=c("ctb", "cre"), email="marohrdanz@mdanderson.org"),
    person(given="Chris", family="Wakefield", role=c("ctb"), email="cwakefield@mdanderson.org"),
    person(given="James", family="Melott", role=c("ctb"), email="jmmelott@mdanderson.org"),
    person(given="Paul", family="Hsieh", role=c("ctb")),
    person(given="MD Anderson Cancer Center", role=c("cph")))
c(person(
        "Marta", 
        "Peroni", 
        email = "marta.peroni@xtreamers.io", 
        role = "aut"),
     person(
        "Emanuele", 
        "Fabbiani", 
        email = "emanuele.fabbiani@xtreamers.io", 
        role = "cre"))
c(
    person("Daniel C.", "Reuman", email = "reuman@ku.edu", role = c("aut", "cre")),
    person("Lei", "Zhao", email = "lei.zhao@cau.edu.cn", role=c("aut")),
    person("Shaopeng", "Wang", email = "shaopeng.wang@pku.edu.cn", role=c("aut"))
    )
c(person(given = "Wayne", family = "Woodward", role = c("aut")), person(given = "Bivin", family = "Sadler", role = c("cre"), email = "bsadler@smu.edu"))
person("Thomas", "Lugrin", role = c("aut", "cre", "cph"), email = "thomas.lugrin@alumni.epfl.ch")
c(person(given="David", family="DEVREKER", role = c("aut", "cre"), email =
        "David.Devreker@ifremer.fr" ), person(given="Alain", family="LEFEBVRE",
        role = c("aut"), email = "Alain.Lefebvre@ifremer.fr" ) )
c(person("Raoul", "Müller", email = "raoul.mueller@uni-goettingen.de", role = "aut"),
             person("Dominic", "Schuhmacher", email = "dominic.schuhmacher@mathematik.uni-goettingen.de", role = c("aut","cre"), comment = c(ORCID = "0000-0001-7079-6313")))
person("Jasmine", "Dumas", email = "jasmine.dumas@gmail.com", role = c("aut", "cre"))
c(person(family="Kiang", given="Hao Chi", role=c("cre","aut"), email="hello@hckiang.com"))
c(person("Dirk", "Eddelbuettel", role = c("aut", "cre"), email = "edd@debian.org",
                    comment = c(ORCID = "0000-0001-6419-907X")),
             person("Alton", "Barbehenn", role = "aut",
                    comment = c(ORCID = "0009-0000-3364-7204")))
c(person(c("Ian", "H."), "Hutchinson", role = "aut", comment = "author of tth/ttm C sources"),
	     person("Friedrich", "Leisch", role = "aut", email = "Friedrich.Leisch@R-project.org",
                    comment = c(ORCID = "0000-0001-7278-1983", "author of R wrappers to tth/ttm, maintainer 2012-2024")),
	     person("Achim", "Zeileis", role = c("aut", "cre"), email = "Achim.Zeileis@R-project.org",
                    comment = c(ORCID = "0000-0003-0918-3766", "author of R wrappers to tth/ttm")))
c(
  person(given="Joshua", family="Ulrich", role=c("cre","aut"), email="josh.m.ulrich@gmail.com"),
  person(given=c("Ethan","B."), family="Smith", role="ctb")
  )
c(person(given = "Steven",
                      family = "Higgins",
                      role = c("aut", "cre", "cph"),
                      email = "steven.higgins@uni-bayreuth.de"),
               person(given = "Arne",
                      family = "Burkhardt",
                      role = "ctb"))
c(person(given="Meredith", family="Ray", email="maray@memphis.edu", role = c("aut", "cre")),
    person(given="Xin", family="Tong", role = c("aut")),
    person(given="Hongmei", family="Zhang", role = c("aut")))
person(given = "Stefano",
                  family = "Mangiola",
                  role = c("aut", "cre"),
                  email = "mangiolastefano@gmail.com")
c(
    person("Gaurav", "Sood", , "gsood07@gmail.com", role = c("aut", "cre")),
    person("Kate", "Lyons", , "k.lyons7@gmail.com", role = "ctb"),
    person("John", "Muschelli", , "muschellij2@gmail.com", role = "ctb")
  )
c(
      person("Marta","Marticorena",role = "aut"),
      person("Gustavo","Gimenez",email = "gustavo.gimenez@faea.uncoma.edu.ar",role="cre"),
      person("Cecilia","Gonzalez",role = "ctb"),
      person("Sergio","Bramardi",role = "aut"))
c(
    person("Yihui", "Xie", role = "aut", email = "xie@yihui.name", comment = c(ORCID = "0000-0003-0645-5666")),
    person("Christophe", "Dervieux", role = c("ctb", "cre"), email = "cderv@posit.co", comment = c(ORCID = "0000-0003-4474-2498")),
    person("JJ", "Allaire", role = "aut"),
    person("Andrzej", "Oles", role = "ctb"),
    person("Dave", "Liepmann", role = "ctb", comment = "Tufte CSS in inst/rmarkdown/templates/tufte_html/resources"),
    person(given = "Posit Software, PBC", role = c("cph", "fnd"))
  )
c(
    person("Álvaro", "de Prado Saborido", , "alvarodepradosaborido@gmail.com", role = c("aut", "cre"),
           comment = "Departamento de Estatística e Investigación Operativa. Universidade de Vigo. Spain"),
    person("Alejandro", "Bernárdez Ferradás", , "alejandro.bernardez@uvigo.gal", role = "ctb",
           comment = c(ORCID = "0009-0006-0960-3555", "SiDOR. Departamento de Estatística e Investigación Operativa. Universidade de Vigo. CITMAga. Spain")),
    person("Miguel Ángel", "Mirás Calvo", , "mmiras@uvigo.es", role = "aut",
           comment = c(ORCID = "0000-0001-7247-1926", "RGEAF. Departamento de Matemáticas. Universidade de Vigo. Spain")),
    person("Iago", "Núñez Lugilde", , "iago.nunez.lugilde@udc.es", role = "aut",
           comment = c(ORCID = "0000-0003-3382-0737", "Departamento de Matemáticas. MODES. Universidade da Coruña. Spain")),
    person("Carmen", "Quinteiro Sandomingo", , "quinteir@uvigo.gal", role = "aut",
           comment = c(ORCID = "0000-0002-2711-1945", "Departamento de Matemáticas. Universidade de Vigo. Spain")),
    person("Estela", "Sánchez Rodríguez", , "esanchez@uvigo.es", role = "aut",
           comment = c(ORCID = "0000-0002-0933-6411", "SiDOR. Departamento de Estatística e Investigación Operativa. Universidade de Vigo. CITMAga. Spain")),
    person("MCIN/AEI/10.13039/501100011033", role = "fnd",
           comment = "Project PID2021-124030NB-C33. ERDF A way of making Europe/EU")
  )
c(person(given = "Tingting", family = "Zhan", role =
       c("aut", "cre"), email = c("tingtingzhan@gmail.com")),
       person(given = "Inna", family = "Chervoneva", role =
       c("ctb"), email = "Inna.Chervoneva@jefferson.edu"))
c(person("Frank","Schaarschmidt", role=c("aut","cre"), email="schaarschmidt@biostat.uni-hannover.de"),
 person("Christian", "Ritz", role="aut"),
 person("Ludwig", "Hothorn", role="ctb"))
c(
    person("Max", "Kuhn", , "max@posit.co", role = c("aut", "cre"),
           comment = c(ORCID = "0000-0003-2402-136X")),
    person(given = "Posit Software, PBC", role = c("cph", "fnd"))
  )
c(person("Uwe", "Ligges", email="ligges@statistik.tu-dortmund.de", role = c("aut", "cre", "cph"), comment = c(ORCID = "0000-0001-5875-6167")),
             person("Sebastian", "Krey", role = c("aut", "cph")),
             person("Olaf", "Mersmann", role = c("aut", "cph")),
             person("Sarah", "Schnackenberg", role = c("aut", "cph")),
             person("Guillaume", "Guénard", role = c("aut", "cph"), comment = "for the 'pulse' functionality"),
             person("Daniel P. W.", "Ellis", role = c("aut", "cph"), comment = "functions ported from 'rastamat'"),
             person("", "Underbit Technologies", role = c("aut", "cph"), comment = "for the included 'libmad MPEG audio decoder library'"),
             person("Andrea", "Preusser", role = c("ctb")),
             person("Anita", "Thieler", role = c("ctb")),
             person("Johanna", "Mielke", role = c("ctb")),
			 person("Claus", "Weihs", role = c("ctb")),
			 person("Brian D.", "Ripley", role = c("ctb")),			 
			 person("Matthias", "Heymann", role = c("ctb"), comment = "for ideas from the former 'sound' package"))
c(
    person("Philipp", "Probst", email = "philipp_probst@gmx.de", role = c("aut", "cre")),
    person("Simon", "Klau", email = "simonklau@ibe.med.uni-muenchen.de", role = "ctb"))
c(person(given = "Emmanuel", family = "Lazaridis",
    email="emmanuel@lazaridis.eu", role = c("aut", "cre")))
c( person(given = c("Jennifer", "F."), family = "Bobb", role =
        "aut", email = "jbobb@hsph.harvard.edu"), person(given =
        "Ravi", family = "Varadhan", role = c("aut", "cre"), email =
        "ravi.varadhan@jhu.edu"), person(given = "Hui", family =
        "Zhao", role = "ctb"))
c(
    person("Anna", "Cena", role = c("aut")),
    person("Marek", "Gagolewski", role = c("aut")),
    person("Barbara", "Zogala-Siudem", role = c("aut", "cre"),
    email = "zogala@rexamine.com"),
    person("Marcin", "Kosinski", role = c("aut")),
    person("Natalia", "Potocka", role = c("aut"))
    )
c(
    person(given = c("Maria","D."), 
           family = "Guillen", 
           role = c("cre", "aut"),
           email = "maria.guilleng@umh.es",
           comment = c(ORCID = "0000-0002-2445-5654")),
    person(given = "Juan Carlos", 
           family = "Gonçalves", 
           role = c("aut"),
           email = "jgoncalves@umh.es",
           comment = c(ORCID = "0000-0002-0867-0004"))
    )
c(person("Alberto", "Viglione", role=c("aut", "cre"), email="alberto.viglione@polito.it"),
             person("Juraj", "Parajka", role=c("aut"), email="parajka@hydro.tuwien.ac.at"))
c(
    person("Ethan", "Heinzen", email = "heinzen.ethan@mayo.edu", role = c("aut", "cre")),
    person("Patrick", "Wilson", role="ctb"),
    person("Brendan", "Broderick", role="ctb"),
    person("Peter", "Martin", role="ctb")
  )
c(
    person("Addison", "Hu", role = "ctb"),
    person("Daniel", "McDonald", role = "ctb"),
    person("Ryan", "Tibshirani", , "ryantibs@gmail.com", 
        role = c("aut", "cre", "cph"))
  )
c(person(given="John J.", family="Dziak",
                     role = c("aut", "cre"),
                     email = "dziakj1@gmail.com",
					 comment = c(ORCID = "0000-0003-0762-5495")),
              person(given="Donna L.", family="Coffman", role = "aut",comment=c(ORCID="0000-0001-6305-6579")),
              person(given="Runze", family="Li", role = "aut",comment=c(ORCID="0000-0002-0154-2202")),
			  person(given="Kaylee", family="Litson", role = "aut",  comment=c(ORCID="0000-0003-1296-4811")),
              person(given="Yajnaseni",family="Chakraborti",role="aut"))
c(person("Guillermo", "Ferreira", role = "aut",
                    email = "gferreir@udec.cl"),
            person("Tomás", "Arancibia", role = c("aut", "cre"),
                    email = "tarancibia2016@udec.cl"))
c(
    person(given = "Vincent", family = "Landau", role = c("aut", "cre"),
           email = "vincent@csp-inc.org"),
    person(given = "Luke", family = "Zachmann", role = c("ctb"),
           email = "luke@csp-inc.org"),
    person(given = "Conservation Science Partners, Inc.", role = c("cph"),
           email = "info@csp-inc.org")
           )
c(person(given = "Donna",
          family = "Coffman",
          role = c("aut", "cre"),
          email = "dcoffman@temple.edu",
          comment = c(ORCID = "0000-0001-6305-6579")),
   person(given = "Xizhen",
          family = "Cai",
          role = "aut",
          email = "cai.xizhen@gmail.com",
          comment = c(ORCID = "0000-0003-2097-1755")),
   person(given = "Yajnaseni",
          family = "Chakraborti",
          role = "aut",
          email = "yajnaseni.chakraborti@temple.edu",
          comment = c(ORCID = "0000-0002-6747-8821")),
   person(given = "Harry",
          family = "Zobel",
          role = "ctb",
          email = "harryzobeljr@gmail.com")
   )
c(
  person("Henrique", "Jose de Paula Alves", email = "jpahenrique@gmail.com", role= c("aut", "cre")),
  person("Ben", "Deivide de Oliveira Batista", email = "ben.deivide@gmail.com", role = "ctb"),
  person("Daniel", "Furtado Ferreira", email = "danielff@ufla.br", role= "aut"))
c(
  person(given = "Erik", family = "Lillrank",
         email = "erik.lillrank@gmail.com",
         role = c("aut", "cre"),
         comment = c(ORCID = "0009-0001-3345-7694")),
  person(given = "Yukai", family = "Yang",
         email = "yukai.yang@statistik.uu.se",
         role = c("aut"),
         comment = c(
           ORCID = "0000-0002-2623-8549")))
c(person("Isabel", "Casas", email = "casasis@gmail.com", role = c("aut", "cre")),
        person("Ruben", "Fernandez-Casal", email = "rubenfcasal@gmail.com", role = "aut"))
person(given = "Ryo",
         family = "Nakagawara",
         role = c("aut", "cre"),
         email = "ryonakagawara@gmail.com")
c(
    person("David", "Shilane", , "david.shilane@columbia.edu", c("aut")),
    person("Mayur", "Bansal", , "mb4511@columbia.edu", c("ctb")),
    person("Mehak", "Khara", , "mehakkhara90@gmail.com", c("ctb")),
    person("Srivastav", "Budugutta", , "sb4788@columbia.edu", c("ctb","cre"))
    )
c(
        person("Donna", "Coffman", email = "donna.coffman@gmail.com", 
        role=c("aut", "cre"), comment = c(ORCID = "0000-0001-6305-6579")), 
        person("Brian", "Vegetabile", role = c("ctb")))
c(person(given = "Dan",
           family = "McCaffrey",
           role = c("aut", "cre"),
           email = "dmccaffrey@ets.org"),
    person(given = "Katherine",
           family = "Castellano",
           role = c("aut"),
           email = "kecastellano@ets.org"),
    person(given = "Donna",
           family = "Coffman",
           role = c("aut"),
           email = "donna.coffman@temple.edu"),
    person(given = "Brian",
           family = "Vegetabile",
           role = c("aut"),
           email = "bvegetab@rand.org"),
    person(given = "Megan",
           family = "Schuler",
           role = c("aut"),
           email = "mschuler@rand.org"),
    person(given = "Haoyu",
           family = "Zhou",
           role = c("aut"),
           email = "haoyu.zhou@temple.edu"))
c(person(given = "Mijael Rodrigo", family = "Vargas Godoy",
           email = "mirovago@gmail.com", role = c("aut", "cre"),
           comment = c(ORCID = "0000-0002-1828-9266")),
    person(given = "Yannis", family = "Markonis",
           email = "imarkonis@gmail.com", role = "aut",
           comment = c(ORCID = "0000-0003-0144-8969")))
c(
      person(
        given = "Victor", 
        family = "Maus", 
        role = c("aut", "cre"),
        email = "vwmaus1@gmail.com",
        comment = c(ORCID = "0000-0002-7385-4723")
      )
    )
person("Peter K.", "Dunn", email = "pdunn2@usc.edu.au", role = c("cre", "aut") )
c(person(given = "Thomas Lin",
             family = "Pedersen",
             role = c("aut", "cre"),
             email = "thomasp85@gmail.com",
             comment = c(ORCID = "0000-0002-5147-4711")))
c(
    person("Jacob", "Scott", , "jscott2718@gmail.com", role = c("aut", "cre")),
    person("Twitter, Inc", role = "aut"),
    person("Other contributors", role = "aut")
  )
person("Will", "Lowe", email = "wlowe@princeton.edu", role = c("aut", "cre"))
person(given = "Hawre",
                  family = "Jalal",
                  role = c("aut", "cre"),
                  email = "hjalal@uottawa.ca",
                  comment = c(ORCID = "0000-0002-8224-6834"))
person("Sean", "Kross", email = "sean@seankross.com", role = c("aut", "cre"))
c(person("Akhil", "Vakayil", role=c("aut", "cre"), email="akhilv@gatech.edu", comment=c(ORCID="0000-0003-3684-6617")),
	person(c("V.", "Roshan"), "Joseph", role=c("aut", "ths"), comment=c(ORCID="0000-0002-9430-5301")),
    person(c("Jose", "L."), "Blanco", role="ctb", comment="nanoflann author"))
c(person("Akhil", "Vakayil", role = c("aut", "cre"), email = "akhilv@gatech.edu"),
	person("Roshan", "Joseph", role = c("aut", "ths")))
person("Guido", "Volpi", email="idrivefoxes@gmail.com",
  role=c("aut", "cre"))
c(
    person("Bo", "Zhang", email = "bzhang@pumabiotechnology.com", role = c("cre","aut")),
    person("Yi", "Zhong", email = "yi.zhong1006@gmail.com", role = c("aut","ctb")),
    person("Ginny", "Peng", email = "gpeng@pumabiotechnology.com", role = c("aut","ctb")),
    person("Bin", "Yao", email = "BYao@pumabiotechnology.com", role = c("aut","ctb"))
    )
c(person("Yajie", "Duan", email = "yajieritaduan@gmail.com", role = c("aut","cre")),
  person("Birol", "Emir", email = "birol.emir@pfizer.com", role = "aut"),
  person("Griffith", "Bell", email = "griffith.bell@pfizer.com", role = "aut"),
  person("Javier", "Cabrera", email = "cabrera@stat.rutgers.edu", role = "aut"),
  person("Pfizer Inc.", role = c("cph", "fnd")))
c(person("Atchanut", "Rattanalertnusorn", role = c("aut", "cre"),
                     email = "atchanut_r@rmutt.ac.th"))
c(person(given = "Akhilesh", family = "Jha", email = "jha.akhilesh09@gmail.com", role = c("aut", "cre")), person(given = "Cini", family = "Varghese", email = "cini2204@gmail.com", role = "aut"), person(given = "Seema", family = "Jaggi", email = "seema.jaggi@icar.gov.in", role = "aut"), person(given = "Eldho", family = "Varghese", email = "eldoiasri@gmail.com", role = "aut"), person(given = "Mohd", family = "Harun", email = "mohd.harun@icar.gov.in", role = "aut"), person(given = "Med Ram", family = "Verma", email = "drmrverma@gail.com", role = "aut"))
person("Connor", "Dowd", , "cd@codowd.com", role = c("aut", "cre"),
           comment = c(ORCID = "0000-0002-9782-0931"))
c(
  person("Marta", "Cousido Rocha", email = "martacousido@uvigo.es", role = c("aut", "cre")),
  person("José Carlos", "Soage González", email = "jsoage@uvigo.es", role = "ctr"),
  person("Jacobo", "de Uña Álvarez", email = "jacobo@uvigo.es", role = c("aut", "ths")),
  person("Jeffrey", "D. Hart", email = "hart@stat.tamu.edu", role = "aut"))
c(person("Eric", "Van Buren", email = "edvanburen@gmail.com",
  role = c("aut", "cre")),person("Yun", "Li",role="aut"),person("Di", "Wu",role="aut"),person("Ming", "Hu",role="aut"))
c(person("Susan", "Gruber", role = c("aut", "cre"), 
				email = "sgruber@TLrevolution.com"),
                    person("Mark", "van der Laan", role = "aut",
                                email = "laan@TLrevolution.com"))
c(person(given = c("Radu", "V."),
                    family = "Craiu",
                    role = "aut"),
             person(given = "Thierry",
                    family = "Duchesne",
                    role = c("aut", "cre"),
                    email = "thierry.duchesne@mat.ulaval.ca"),
             person(given = "Daniel",
                    family = "Fortin",
                    role = "aut"),
             person(given = "Sophie",
                    family = "Baillargeon",
                    role = "aut"))
c(person("Angela", "Carollo", email = "carollo@demogr.mpg.de",  role = c("aut", "cre", "cph"), comment = c(ORCID = "0000-0002-5625-6553")),
           person("Paul H.C.", "Eilers", email = "p.eilers@erasmusmc.nl", role = "aut"),
           person("Jutta", "Gampe", email = "gampe@demogr.mpg.de", role = "aut"))
c(person(given = "Michael", family = "Friendly", role=c("aut", "cre"), email="friendly@yorku.ca",
             comment=c(ORCID="0000-0002-3237-0941")),
            person(given = c("Richard", "M."), family = "Heiberger", role="aut", email="rmh@temple.edu"),
            person(given = "John", family = "Fox", role="ctb", email="jfox@mcmaster.ca")
            )
c(
    person("Diego", "Ciccia", role = c("aut", "cre"), email = "diego.ciccia@kellogg.northwestern.edu"),
    person("Felix", "Knau", role = "aut", email = "felix.knau@sciencespo.fr"),
    person("Mélitine", "Malezieux", role = "aut", email = "melitine.malezieux@sciencespo.fr"),	
    person("Doulo", "Sow", role = "aut", email = "doulo.sow@ensae.fr"),
    person("Shuo", "Zhang", role = "aut", email = "shuo_zhang@ucsb.edu"),
    person("Clément", "de Chaisemartin", role = "aut", email = "clement.dechaisemartin@sciencespo.fr"),
    person("Grant", "McDermott", role = "ctb", email = "gmcd@amazon.com")
    )
c(person("Osman", "Dag", role = c("aut", "cre"), email = "osman.dag@outlook.com"), person("Sam", "Weerahandi", role = "aut"), person("Malwane", "Ananda", role = "aut"), person("Muhammed Ali", "Yilmaz", role = "aut"), person("Merve", "Kasikci", role = "aut"))
c( person("Mohammad", "Tabatabai", email = "mtabatabai@mmc.edu",
                     role = c("aut")), 
              person("Derek", "Wilus", email = "dwilus@mmc.edu",
                     role = c("aut", "cre")) )
c(
    person("Nima", "Hejazi", email = "nh@nimahejazi.org",
           role = c("aut", "cre", "cph"),
           comment = c(ORCID = "0000-0002-7127-2789")),
    person("David", "Benkeser", email = "benkeser@emory.edu",
           role = "aut",
           comment = c(ORCID = "0000-0002-1019-8343")),
    person("Iván", "Díaz", email = "ild2005@med.cornell.edu",
           role = "ctb",
           comment = c(ORCID = "0000-0001-9056-2047")),
    person("Jeremy", "Coyle", email = "jeremy.coyle@gmail.com",
           role = "ctb",
           comment = c(ORCID = "0000-0002-9874-6649")),
    person("Mark", "van der Laan", email = "laan@berkeley.edu",
           role = c("ctb", "ths"),
           comment = c(ORCID = "0000-0003-1432-5511"))
  )
c(person("Bjoern", "Bornkamp", email = "bbnkmp@mail.de",
                  role = c("aut", "cre")),
             person("Ivan", "Krylov", email = "krylov.r00t@gmail.com", role = c("ctb"), comment = "txtimage"))
c(
  person(
    family = "Landau",
    given = c("William", "Michael"),
    email = "will.landau@gmail.com",
    role = c("aut", "cre"),
    comment = c(ORCID = "0000-0003-1878-3253")
  ),
  person(
    given = c("Ian","E."),
    family = "Fellows", 
    role = c("ctb"), 
    email = "ian@fellstat.com"
  ),
  person(
    family = "Eli Lilly and Company",
    role = "cph"
  ))
person("Jim", "Hester", email = "james.f.hester@gmail.com", role = c("aut", "cre"))
c(
    person("Mark", "Padgham", , "mark.padgham@email.com", role = c("aut", "cre"),
           comment = c(ORCID = "0000-0003-2172-5265")),
    person("Filip", "Krikava", role = "ctb",
           comment = "Author of original 'injectr' code on which this package builds; https://github.com/PRL-PRG/injectr"),
    person("covr authors", role = "cph",
            comment = "Original authors of sections of code from 'covr' package included here in modified form.")
  )
c(
    person("Davis", "Vaughan", , "davis@posit.co", role = c("aut", "cre")),
    person("Howard", "Hinnant", role = "cph",
           comment = "Author of the included date library"),
    person("Posit Software, PBC", role = c("cph", "fnd"))
  )
c(person(given = "Andres",
             family = "Missiego Manjon",
             role = "aut"),
      person(given = "Juan Jose",
             family = "Cuadrado Gallego",
             role = "aut",
             comment = c(ORCID = "0000-0001-8178-5556")),
      person(given = "Andriy",
             family = "Protsak Protsak",
             role = c("aut", "cre"),
             email = "andriy.protsak@edu.uah.es"),
      person(given = "Universidad de Alcala de Henares",
             role = "cph"))
c(
                person(
                    "Víctor",
                    "Amador Padilla",
                    email = "victor.amador@edu.uah.es",
                    role = c("aut")
                ),
                person(
                    "Juan Jose",
                    "Cuadrado Gallego",
                    email = "jjcg@uah.es",
                    role = c("ctb"),
                    comment = c(ORCID = "0000-0001-8178-5556")
                ),
                person("Andriy",
                       "Protsak Protsak",
                       email = "andriy.protsak@edu.uah.es",
                       role = c("aut", "cre")),
                person(
                    "Universidad de Alcala",
                    role = c("cph")
                )
            )
c(
    person("Carlos Javier", "Hellin Asensio", role = "aut",
           email = "carlos.hellin@uah.es"),
    person("Jose Manuel", "Gomez Caceres", role = "aut",
           email = "josemanuel.gomezc@edu.uah.es"),
    person("Dennis", "Monheimius", role = "aut",
           email = "dennis.monhemimius@edu.uah.es"),
    person("Eduardo", "Benito", role = "aut",
           email = "eduardo.benito@edu.uah.es"),
    person("Juan Jose", "Cuadrado", role = "aut",
           email = "jjcg@uah.es"),
    person("Andriy", "Protsak Protsak", role = c("aut", "cre"),
           email = "andriy.protsak@edu.uah.es"),
    person("Universidad de Alcala de Henares", role = "cph")
    )
c(
       person(
            "Eduardo",
            "Ruiz Sabajanes",
            email = "eduardo.ruizs@edu.uah.es",
            role = c("aut")
        ),
        person(
            "Roberto", 
            "Alcantara",
            email = "roberto.alcantara@edu.uah.es",
            role = c("aut")
        ),
        person(
            "Juan Jose",
            "Cuadrado Gallego",
            email = "jjcg@uah.es",
            role = c("aut"),
            comment = c(ORCID = "0000-0001-8178-5556")
        ),
        person(
            "Andriy",
            "Protsak Protsak",
            email = "andriy.protsak@edu.uah.es",
            role = c("aut", "cre")
        ),
        person(
            "Universidad de Alcala",
            role = c("cph")
        )
    )
c(
    person("Bob", "Rudis", , "bob@rud.is", c("aut", "cre")),
    person("Lindsey", "Simon", role = "aut", comment = "uap-core"),
    person("Tobie", "Langel", role = "aut", comment = "uap-core"),
    person("Colman", "Humphrey", role = "ctb"))
c(person("Chris", "Reudenbach", email = "reudenbach@uni-marburg.de", role = c("cre", "aut")),
             person("Marvin", "Ludwig", role = c("ctb")),
             person("Sebastian", "Richter", role = c("ctb")),
             person("Florian", "Detsch", role = c("ctb")),
             person("Hanna", "Meyer", role = c("ctb"))
             )
c(
    person("Raphael", "Franke", , "raphael.franke@uba.de", role = c("aut")),
    person("Imke", "Voss", , "imke.voss@uba.de", role = c("aut", "cre")))
c(
    person('Anna', 'Jenul', , 'anna.jenul@nmbu.no', role = c('aut', 'cre'),
           comment = c(ORCID = '0000-0002-6919-3483')),
    person('Stefan', 'Schrunner', , 'stefan.schrunner@nmbu.no', role = 'aut',
           comment = c(ORCID = '0000-0003-1327-4855')),
    person('Kristian Hovde', 'Liland', role = 'rev'),
    person('Oliver', 'Tomic', role = 'ctb'),
    person('Jürgen', 'Pilz', role = 'ctb')
  )
c(person("John", "Harrold", 
                     role    = c("aut", "cre"),
                     email   = "john.m.harrold@gmail.com",
                     comment = c(ORCID="0000-0003-2052-4373")))
c(
  person("Raffaella", "Piccarreta", role = "aut", email = "raffaella.piccarreta@unibocconi.it"),
  person("Sergio", "Venturini", role = "cre", email = "sergio.venturini@unicatt.it"))
c(
    person("Rafael H. M.", "Pereira", , "rafa.pereira.br@gmail.com", role = c("aut", "cre"),
           comment = c(ORCID = "0000-0003-2125-7465"))
           )
c(person(given = "Mikaela",
           family = "Koutrouli",
           role = c("aut", "cre"),
           email = "mikaela.koutrouli@cpr.ku.dk"
           ),
    person(given = "Lars Juhl",
           family = "Jensen",
           role = "aut",
           email = "lars.juhl.jensen@cpr.ku.dk"))
c(
    person("James Joseph", "Balamuta", email = "james.balamuta@gmail.com", 
           role = c("aut", "cre", "cph"), 
           comment = c(ORCID = "0000-0003-2826-8458")),
    person("Philip", "Truong",
           email = "ucirepository@gmail.com", 
           role = c("aut", "cph"))
    )
c(person("Gabriela","Cybis",email="gcybis@gmail.com",role=c("aut","cre")),
           person("Marcio","Valk",email="marciovalk@gmail.com",role="aut"),
           person("Kazuki","Yokoyama",email="gcybis@gmail.com",role="ctb"),
           person("Debora","Zava Bello",email="gcybis@gmail.com",role="ctb"))
c(
    person("K Hervé", "Dakpo", email = "k-herve.dakpo@inrae.fr", 
    role = c("ctb", "cre")),
    person("Hans Bruun", "Nielsen", role = "aut"),
    person("Stig Bousgaard", "Mortensen", role = "aut")
  )
c(person("Diego J.", "Pedregal", role = c("aut", "cre"),
                     email = "Diego.Pedregal@uclm.es",
                     comment = c(ORCID = "0000-0003-4958-0969")))
person("Jacob", "Kaplan", email = "jacobkap@sas.upenn.edu", role = c       ("aut", "cre"))
c(
    person("Shixiang", "Wang", , "w_shixiang@163.com", role = c("aut", "cre"),
           comment = c(ORCID = "0000-0001-9855-7357")),
    person("Shensuo", "Li", , "lishensuo@163.com", role = "aut"),
    person("Yi", "Xiong", , "neuroxiong@openbiox.com", role = "aut",
           comment = c(ORCID = "0000-0002-4370-9824")),
    person("Longfei", "Zhao", , "longfei8533@126.com", role = "aut",
           comment = c(ORCID = "0000-0002-6277-0137")),
    person("Kai", "Gu", , "gukai1212@163.com", role = "aut",
           comment = c(ORCID = "0000-0002-0177-0774")),
    person("Yin", "Li", , "yinli18@fudan.edu.cn", role = "aut"),
    person("Fei", "Zhao", , "zhaofei415@mails.ucas.edu.cn", role = "aut")
  )
c(person(given = "Shixiang",
             family = "Wang",
             role = c("aut", "cre"),
             email = "w_shixiang@163.com",
             comment = c(ORCID = "0000-0001-9855-7357")),
      person(given = "Xue-Song",
             family = "Liu",
             role = "aut",
             comment = c(ORCID = "0000-0002-7736-0077")),
      person(given = "Martin",
             family = "Morgan",
             role = "ctb"),
      person(given = "Christine",
             family = "Stawitz",
             role = "rev",
             comment = "Christine reviewed the package for ropensci, see <https://github.com/ropensci/software-review/issues/315>"),
      person(given = "Carl",
             family = "Ganz",
             role = "rev",
             comment = "Carl reviewed the package for ropensci, see <https://github.com/ropensci/software-review/issues/315>"))
person("Rolando", "Rodriguez", , "rolando@ad.unc.edu", role = c("aut", "cre"))
c(person("James", "Hiebert", email = "hiebert@uvic.ca",
	             role = c("aut", "cre")),
	     person("CRAN team",  role = c("ctb", "cph"),
	            comment = "revisions/corrections in 2022"))
c(person("Mr. Ankit Kumar", "Singh", role = "aut", email = "ankitsinghvns32@gmail.com"),
            person("Dr. Ranjit Kumar", "Paul", role = c("aut","cre"), email = "ranjitstat@gmail.com"),
            person("Dr. Md", "Yeasin", role = "aut"),
            person("Ms. Anita", "Sarkar", role = "aut"),
            person("Dr. Amrit Kumar", "Paul", role = "aut")
              )
c(
  person("Yuanhua", "Feng", role = "aut", 
    comment = "Paderborn University, Germany"),
  person("Xuehai", "Zhang", role = "aut",
    comment = "Former research associate at Paderborn University, Germany"),
  person("Christian", "Peitz", role = "aut",
    comment = "Paderborn University, Germany"),
  person("Dominik", "Schulz", role = "aut", 
    comment = "Paderborn University, Germany"),
  person("Shujie", "Li", role = "aut", 
    comment = "Paderborn Universtiy, Germany"),
  person("Sebastian", "Letmathe", role = c("aut", "cre"),
    email = "sebastian.letmathe@uni-paderborn.de",
    comment = "Paderborn University, Germany"))
c(
        person(given = "Gjalt-Jorn",
               family = "Peters",
               role = c("aut", "cre"),
               email = "ufs@opens.science",
               comment = c(ORCID = "0000-0002-0336-9589")),
        person(given = "Stefan",
               family = "Gruijters",
               role = c("ctb"),
               email = "mail@stefangruijters.nl",
               comment = c(ORCID = "0000-0003-0141-0071"))
    )
c(person(given = "Josmar",
             family = "Mazucheli",
             role = c("aut", "cre"),
             email = "jmazucheli@gmail.com"),
      person(given = "Bruna",
             family = "Alves",
             role = "ctb",
             email = "pg402900@uem.br"))
c(
  person("Taban", "Baghfalaki", email = "t.baghfalaki@gmail.com", role =c("cre","aut"), comment = c(ORCID = "0000-0002-2100-4532")),
  person("Mojtaba", "Ganjali", email = "m-Ganjali@sbu.ac.ir", role = "aut", comment = c(ORCID = "0000-0002-8574-1750")),
  person("Narayanaswamy", "Balakrishnan", email = "bala@mcmaster.ca", role = "aut"))
c(person(given="Emilie", family="POISSON-CAILLAULT", role = c("aut"), email =
        "emilie.caillault@lisic.univ-littoral.fr" ), person(given="Paul", family="TERNYNCK",
        role = c("aut", "cre"), email = "ternynck@lisic.univ-littoral.fr" ) )
c(
  person("James", "Balamuta", 
         email = "balamut2@illinois.edu", role = c("aut", "cre"),
         comment = c(ORCID = "0000-0003-2826-8458")),
  person("Steven Andrew", "Culpepper" ,
         email = "sculpepp@illinois.edu", role = "ctb",
         comment = "Provided the Minimal Orange Beamer Theme"),
  person("David", "Dalpiaz" ,
         email = "dalpiaz2@illinois.edu", role = "ctb",
         comment = "Collaborated on the LaTeX Journal Theme"),
  person("Jose Luis", "Rodriguez",
         email = "jlroo@illinois.edu", role = "ctb",
         comment = "Provided the Market Information Lab (MIL) Beamer Theme")
  )
person(given = "Longfei", 
		family = "Wang", 
		email = "wang.lo@wehi.edu.au", 
		role = c("aut", "cre"),
		comment = c(ORCID = "https://orcid.org/0000-0002-5143-4146"))
c(
    person("Mine", "\u00C7etinkaya-Rundel", email = "cetinkaya.mine@gmail.com", role = c("aut", "cre"), comment = c(ORCID = "0000-0001-6452-2420")),
    person("Thomas J.", "Leeper", email = "thosjleeper@gmail.com", role = c("aut")),
    person("Nicholas", "Goguen-Compagnoni", email = "s2013017@ed.ac.uk", role = c("aut")),
    person("Sara", "Lemus", email = "saralemus70@gmail.com", role = c("aut"))
    )
person(given=c("Scott", "C"), family="Ritchie", email="sritchie73@gmail.com", 
           role=c("aut", "cre"), comment=(ORCID="0000-0002-8454-9548"))
person(given = "Anthony",
                    family = "Hammond",
                    role = c("aut", "cre"),
                    email = "agqhammond@gmail.com")
person("Timothy", "Wong", email = "timothy.wong@hotmail.co.uk", role = c("aut", "cre"))
c(person("Rami", "Krispin", 
                    email = "rami.krispin@gmail.com",
                    role = c("aut", "cre")),
             person("Lior", "Krispin", 
                    email = "lior.krispin1@gmail.com",
                    role = c("ctb"))
             )
c(
    person("Ollie", "Lloyd", email = "o.lloyd@doctors.org.uk", role = c("aut", "cre", "cph"), comment = c(ORCID = "0000-0002-9385-1634")),
    person("Pouria", "Hadjibagheri", role = "ctb", comment = "Author of the original 'ukcovid19' package."))
c(
    person(given = "Evan",
           family = "Odell",
           role = c("aut", "cre"),
           email = "evanodell91@gmail.com",
           comment = c(ORCID = "0000-0003-1845-808X")),
    person(family = "Nicholas", 
           given = "Tierney",
           role = "aut",
           email = "nicholas.tierney@gmail.com",
           comment = c(ORCID = "https://orcid.org/0000-0003-1460-8722")))
c(
  person("Bob", "Rudis", role = "aut", comment = c(ORCID = "0000-0001-5670-2640")),
  person("Suyash", "Verma", role = "aut", comment = "ULID C++ <https://github.com/suyash/ulid/>"),
  person("Dirk", "Eddelbuettel", role = "cre", comment = c(ORCID = "0000-0001-6419-907X"),
         email = "edd@debian.org"))
c(
    person("Francisco", "Pascoal",,"fpascoal1996@gmail.com", role = c("aut","cre"), comment = c(ORCID = "0000-0003-2315-358X")),
    person("Paula", "Branco",,"paobranco@gmail.com", role = c("aut"), comment = c(ORCID = "0000-0002-9917-3694")),
    person("Luís", "Torgo",,"ltorgo@dal.ca", role = c("aut"), comment = c(ORCID = "0000-0002-6892-8871")),
    person("Rodrigo", "Costa",,"rodrigoscosta@tecnico.ulisboa.pt", role = c("aut"), comment = c(ORCID = "0000-0002-5932-4101")),
    person("Catarina", "Magalhães",,"catarinamagalhaes1972@gmail.com", role = c("aut"), comment = c(ORCID = "0000-0001-9576-2398")))
c(person("Yana", "Outkin", role = c("aut", "cre"), email =
    "youtkin1@swarthmore.edu"), person("Jonathan", "Washington", role = "aut", email = 
    "jwashin1@swarthmore.edu"))
c(person(given = "Florian", family = "Lerch", role = "aut"),
               person(given = "Michael", family = "Thrun", role = "aut"),
               person(given = "Felix", family = "Pape", role = "ctb"),
               person(given = "Jorn", family = "Lotsch", role = c("aut", "cre"), email = "j.lotsch@em.uni-frankfurt.de"),
               person(given = "Raphael", family = "Paebst", role = "ctb"),
               person(given = "Alfred", family = "Ultsch", role = "aut"))
person("Linus", "Seelinger", , "mail@linusseelinger.de", role = c("aut", "cre"),
           comment = c(ORCID = "0000-0001-8632-8493"))
c(
    person("Stefan", "Filges", , "stefan.filges@gu.se", role = c("aut", "cre"),
           comment = c(ORCID = "0000-0002-5994-6699")),
    person("Gustav", "Johansson", , "gustav.johansson@gu.se", role = "ctb")
  )
c(
    person("Inna", "Gerlovina", email = "innager@berkeley.edu",
           role = c("aut", "cre")),
    person("Alan", "Hubbard", email = "hubbard@berkeley.edu",
           role = "aut"))
c(person("Kevin R.", "Coombes", role = c("aut", "cre"),
                    email = "krc@silicovore.com"),
             person("Jiexin", "Zhang", role = c("aut")),
             person("Caitlin E.", "Coombes", role = c("aut")))
c(
	person(c("Timothy", "C."), "Bates"    , role = c("aut", "cre"), email = "timothy.c.bates@gmail.com", comment = c(ORCID = "0000-0002-1153-9007")),
	person("Nathan"          , "Gillespie", role = c("wit"), email = "nathan.gillespie@vcuhealth.org"),
	person("Hermine"         , "Maes"     , role = c("ctb"), email = "hmeaes@vcu.edu"),
	person(c("Michael", "C."), "Neale"    , role = c("ctb"), email = "neale@vcu.edu"),
	person(c("Joshua", "N.") , "Pritikin" , role = c("ctb"), email = "jpritikin@pobox.com"),
	person("Luis"            , "De Araujo", role = c("ctb"), email = "ldearaujo@unimelb.edu.au"),
	person("Brenton"         , "Wiernik"  , role = c("ctb"), email = "wiernik@umn.edu"),
	person("Michael"         , "Zakharin" , role = c("wit"), email = "s1775682@sms.ed.ac.uk")
	)
c(
  person("Jeison", "Alarcón", email = "jmalarconbe@unal.edu.co", role = c("aut", "cre", "ctr"), comment = c(ORCID = "0000-0002-1517-7840")),
  person("Alberto", "Rodríguez", email = "albrodriguezr@unal.edu.co", role = c("aut", "rev")),
  person("Dirección Nacional de Planeación y Estadística", "Universidad Nacional de Colombia", email = "estadistica_nal@unal.edu.co", role = c("cph", "fnd"))
  )
c(person("Seonghyeon", "Kim", email = "shkim93@snu.ac.kr", role = c("aut", "cre")),
	person("Yongdai", "Kim", role = "aut"),
	person("Ilsang", "Ohn", role = "aut"))
person("David", "Randahl", , "david.randahl@pcr.uu.se", role = c("cre", "aut"))
c(person("Jay", "Lee", email = "joongsup@gmail.com", role = c("aut", "cre")))
c(person(given  = "Bernardo",
             family = "Reckziegel",
             email  = "bernardo_cse@hotmail.com", 
             role   = c("aut", "cre"))
    )
person("Samuel", "Emerson", email = "samuel.emerson@hotmail.co.uk",
                    role = c("aut", "cre"))
c(person("Jonas","Lieben", role = c("aut"), email = "jonas.lieben@uhasselt.be"), person("Gert","Janssenswillen", email = "gert.janssenswillen@uhasselt.be", 
  role = c("cre")))
person("Eric", "Jamieson", email = "ericbrucejamieson@gmail.com", role = c("aut", "cre", "cph"))
c(
  person("Marius D.", "Pascariu", role = c("aut", "cre"), email = "rpascariu@outlook.com", comment = c(ORCID = "0000-0002-2568-6489")),
  person("Silvia", "Rizzi", role = "aut", email = "srizzi@health.sdu.dk"),
  person("Jonas", "Schoeley", role = "aut", comment = c(ORCID = "0000-0002-3340-8518")),
  person("Maciej J.", "Danko", role = "aut", email = "Danko@demogr.mpg.de", comment = c(ORCID = "0000-0002-7924-9022"))
  )
c(
    person("Cédric", "Vidonne", , "cedric@vidonne.me", role = c("aut", "cre")),
    person("Ahmadou", "Dicko", , "mail@ahmadoudicko.com", role = "aut"),
    person("Edouard", "Legoupil", , "legoupil@unhcr.org", role = "aut"),
    person("UNHCR", role = "cph")
  )
c(person("Cédric", "Vidonne",
             email = "cedric@vidonne.me",
	     role = c("aut", "cre")),
      person("Ahmadou", "Dicko",
             email = "mail@ahmadoudicko.com",
	     role = "aut"),
      person("UNHCR",
         role = "cph"))
person("Luis D.", "Verde Arregoitia", email = "luis@liomys.mx",
        role=c("aut","cre"),comment = c(ORCID = "0000-0001-9520-6543"))
c(person(given = "Nanami",
                        family = "Taketomi",
                        role = c("aut", "cre"),
                        email = "nnmamikrn@gmail.com"),
                 person(given = "Jia-Han",
                        family = "Shih",
                        role = "aut"),
                 person(given = "Takeshi",
                        family = "Emura",
                        role = "aut"))
c(
    person("Zeyuan", "Chen", , "johnsonchen@cs.ucla.edu", role = c("aut", "cre")),
    person("Elior", "Rahmani", role = "aut")
  )
c(person("Hansjoerg", "Neth", role = c("aut", "cre"), email = "h.neth@uni.kn", comment = c(ORCID = "0000-0001-5427-3141")), 
             person("Constantin", "Basler", role = "ctb"),
             person("Paula", "Bauer", role = "ctb"),
             person("Kaethe", "Bodenstein", role = "ctb"),
             person("Franziska", "Drechsel", role = "ctb"),
             person("Gina-Loretta", "Franz", role = "ctb"),
             person("Karolin", "Heiss", role = "ctb"),
             person("Julia", "Koenig", role = "ctb"),
             person("Isabelle", "Krist", role = "ctb"),
             person("Larissa", "Schwab", role = "ctb"),
             person("Alina", "Sohst", role = "ctb"),
             person("Isabel", "Staatz", role = "ctb"),
             person("Lisa", "Trueb", role = "ctb")
             )
c(
 person(
 "Yiyi", "Li", role = c("aut", "cre"), 
 email = "gtarex@nmsu.edu", 
 comment = c(ORCID = "0000-0001-8859-3987")
 ),
 person(
 "Joe", "Song", role = c("aut"),
 email = "joemsong@cs.nmsu.edu",
 comment = c(ORCID = "0000-0002-6883-6547")))
c(person("Markus","Kalisch",
                    email="kalisch@stat.math.ethz.ch", role=c("aut","cre")),
                    person("Manuel", "Schuerch", role="ctb"))
c(
    person(given = "Michael", 
           family = "Mahoney", 
           email = "mike.mahoney.218@gmail.com", 
           role = c("aut", "cre"),
           comment = c(ORCID = "0000-0003-2402-304X")),
    person(given = "Will",
           family = "Jones",
           role = c("rev"),
           comment = c("Will reviewed the package (v. 0.2.0) for rOpenSci, see <https://github.com/ropensci/software-review/issues/521>")),
           person(given = "Tan",
           family = "Tran",
           role = c("rev"),
           comment = c("Tan reviewed the package (v. 0.2.0) for rOpenSci, see <https://github.com/ropensci/software-review/issues/521>")))
c(
      person(given = "Dominik", family = "Weber", role = c("aut", "cre"), email = "dominik.weber@uni-saarland.de"),
      person(given = "Marco", family = "Koch", role = "aut"),
      person(given = "Frank M.", family = "Spinath", role = "aut"),
        person(given = "Nicolas", family = "Becker", role = "aut")
    )
c(person("Hansjoerg", "Neth", role = c("aut", "cre"), email = "h.neth@uni.kn", comment = c(ORCID = "0000-0001-5427-3141")), 
             person("Nico", "Gradwohl", role = "aut", comment = c(ORCID = "0000-0002-8703-905X")))
c(person("Michaël", "Benesty", role = c("aut", "cre", "cph"), email = "michael@benesty.fr"),
  person("Jacques Savoy", role = c("cph"), email = "Jacques.Savoy@unine.ch"))
c(person("Mohamed", "Soudy", email = "MohmedSoudy2009@gmail.com", role=c("aut", "cre")), person("Ali", "Mostafa", email = "ali.mo.anwar@std.agr.cu.edu.eg", role = "aut"))
person("Shaun", "Jackman", email = "sjackman@gmail.com", role = c("aut", "cph", "cre"))
c(
	person("Martin", "Penzel", email = "Martin.Penzel@movisens.com", role = c("ctb","cre")),
	person("Jürgen", "Stumpp", email = "Stumpp@movisens.com", role = "aut"),
	person("Jörg", "Ottenbacher", role = "ctb"),
	person("Stephan", "Grund", role = "ctb"),
	person("movisens GmbH", role = "cph")
	)
c(
    person(
    "Brodie", "Gaslam", email="brodie.gaslam@yahoo.com",
    role=c("aut", "cre")),
    person(
    "Michael", "https://github.com/MichaelChirico", 
    email="michaelchirico4@gmail.com",
    role=c("ctb")),
    person(family="R Core Team",
    email="R-core@r-project.org", role="cph",
    comment="Traceback function sources."
    )    )
c(person(given = "André F. B.", family = "Menezes", role = c("aut", "cre"), email = "andrefelipemaringa@gmail.com", comment = c(ORCID = "0000-0002-3320-9834")),
    person(given = "Josmar", family = "Mazucheli", role = c("aut"), comment = c(ORCID = "0000-0001-6740-0445")))
c(person("Edzer", "Pebesma", role = c("aut", "cre"), email = "edzer.pebesma@uni-muenster.de", comment = c(ORCID = "0000-0001-8049-7069")),
    person("Thomas", "Mailund", role = "aut", email = "mailund@birc.au.dk"),
    person("Tomasz", "Kalinowski", role = "aut"),
    person("James", "Hiebert", role = "ctb"),
    person("Iñaki", "Ucar", role = "aut", email = "iucar@fedoraproject.org", comment = c(ORCID = "0000-0001-6403-5550")),
    person("Thomas Lin", "Pedersen", role = "ctb")
    )
person(given = "Ankita",
           family = "Sharma",
           role = c("aut", "cre"),
           email = "ankita.sh49@gmail.com")
c(
    person("Jonas", "Moss", , "jonas.gjertsen@gmail.com", role = c("aut", "cre"), 
      comment = c(ORCID = "0000-0002-6876-6964")),
    person("Thomas", "Nagler", , "mail@tnagler.com", role = c("ctb")),
    person("Chitu", "Okoli", , "chitu.okoli@skema.edu", role = c("ctb"))
    )
c(person(given = "Nathakhun",
            family = "Wiroonsri",
            email  = "nathakhun.wir@kmutt.ac.th",
            role   = c("cre","aut"),
            comment = c(ORCID = "0000-0003-2167-9641")),
      person(given ="Onthada",
            family = "Preedasawakul",
            email  = "o.preedasawakul@gmail.com",
            role   = "aut",
            comment = c(ORCID = "0000-0002-4186-3158")))
c(
    person("Joe", "Thorley", , "joe@poissonconsulting.ca", role = c("aut", "cre"),
           comment = c(ORCID = "0000-0002-7683-4592")),
    person("Kirill", "Müller", role = "ctb",
           comment = c(ORCID = "0000-0002-1416-3412")),
    person("Poisson Consulting", role = c("cph", "fnd"))
  )
person("Jeroen", "Ooms", email = "jeroenooms@gmail.com", 
    comment = c(ORCID = "0000-0002-4035-0289"), role = c("aut", "cre"))
c(person("Michael D.","Sumner", role = c("aut", "cre"), email = "mdsumner@gmail.com"), 
             person("Simon", "Wotherspoon", role = "ctb"),
             person("Hadley", "Wickham", role = "ctb", comment = "named the concept, provided excellent guidance via tidyr source code"))
c(
    person("Richard", "Chandler", role="aut"), 
    person("Ken", "Kellner", email="contact@kenkellner.com", role=c("cre", "aut")),
    person("Ian", "Fiske", role="aut"), 
    person("David", "Miller", role="aut"),
    person("Andy", "Royle", role="aut"),
    person("Jeff", "Hostetler", role="aut"),
    person("Rebecca", "Hutchinson", role="aut"),
    person("Adam", "Smith", role="aut"),
    person("Lea", "Pautrel", role="aut"),
    person("Marc", "Kery", role="ctb"),
    person("Mike", "Meredith", role="ctb"),
    person("Auriel", "Fournier", role="ctb"),
    person("Ariel", "Muldoon", role="ctb"),
    person("Chris", "Baker", role="ctb")
    )
c(person(given = "Ryan", family = "Hebdon", role = c("aut"),
           email = "ryan_hebdon@baylor.edu"),
    person(given = "James", family = "Stamey", role = c("aut"),
           email = "james_stamey@baylor.edu",
           comment = c(ORCID = "0000-0002-3787-6490")),
    person(given = "David", family = "Kahle", role = c("aut", "cre"),
           email = "david@kahle.io",
           comment = c(ORCID = "0000-0002-9999-1558")),
    person(given = "Xiang", family = "Zhang", role = c("aut"),
           email = "Xiang.Zhang@cslbehring.com"))
person(given = "Vitalie",
           family = "Spinu",
           role = c("aut", "cre"),
           email = "spinuvit@gmail.com")
c(
    person("Duncan", "Garmonsway", email = "nacnudus@gmail.com", role = c("aut", "cre"))
    )
c(person(given = "Yunhe",
             family = "Liu",
             role = c("aut","cre"),
             email = "yunhe.liu@tamu.edu"),
      person(given = "Haitao",
             family = "Pan",
             role = c("aut"),
             email = "haitao.pan@stjude.org"))
c(
    person("Jeroen", "Ooms", ,"jeroenooms@gmail.com", role = c("aut", "cre")),
    person("Free Software Foundation, Inc", role = "cph"))
person('Stefanie', 'Schneider', email = 'stefanie.schneider@itg.uni-muenchen.de', role = c('cre', 'aut'), comment = c(ORCID = "0000-0003-4915-6949"))
c(
    person("Lukas", "Jung", , "jung-lukas@gmx.net", role = c("aut", "cre")),
    person("Nathanael", "Larigaldie", role = "ctb")
  )
person("Haeran", "Cho", email = "haeran.cho@bristol.ac.uk", role=c("aut", "cre"))
c(
    person("W. Joel", "Schneider", , "w.joel.schneider@gmail.com", role = c("aut", "cre"),
           comment = c(ORCID = "0000-0002-8393-5316")),
    person("Feng", "Ji", , "fengji@berkeley.edu", role = "aut")
  )
c(person("David", "Robinson", email = "admiral.david@gmail.com", role = c("aut", "cre")),
             person("Nicholas", "Goguen-Compagnoni", email = "n.goguen-compagnoni@sms.ed.ac.uk", role = "ctb"))
c(person(given = "Tianbao", family = "Zhou", role = c("aut", "cre"),
                    email = "michaelzhou@buaa.edu.cn"),
             person("Xinghao", "Li", role="aut", email="lixinghao@bjfu.edu.cn"),
             person("Lin", "Liu*", role="aut", email="liulin@pku.edu.cn"))
c(person("Heming Deng", "Developer", role = c("aut", "cre", "cph"), email = "dheming@ruc.edu.cn"), person("Sunan Gao", "Dev", role = "aut"))
c(person("Antony", "Unwin", email="unwin@math.uni-augsburg.de", role=c("aut", "cre", "cph")), person("Bill", "Venables", role="ctb"))
person("Jacob", "Scott", email = "jscott2718@gmail.com", role = c("aut", "cre"))
person("Daniel D.", "Sjoberg", , "danield.sjoberg@gmail.com", role = c("aut", "cre", "cph"),
           comment = c(ORCID = "0000-0003-0862-2018"))
person("David", 
                  "Gerard",
                  email = "gerard.1787@gmail.com", 
                  role = c("aut", "cre"), 
                  comment = c(ORCID = "0000-0001-9450-5023"))
c(
    person("Tom", "Rohmer",, "tom.rohmer@inrae.fr", role = c("aut", "cre")),
    person("Vincent", "Le",, "giang-nam.le@inrae.fr", role = "aut"),
    person("Ingrid", "David",, "Ingrid.david@inrae.fr",role = "aut")
    )
c(person("Gregor","Zens",role=c("aut","cre"),email="zens@iiasa.ac.at"),
             person("Sylvia","Frühwirth-Schnatter", role="aut"),
             person("Helga","Wagner", role="aut"))
c(person("Jiangshan", "Lai", , "lai@njfu.edu.cn", role = c("aut", "cre"),
           comment = c(ORCID = "0000-0002-0279-8816")),person("Yao", "Liu", role="aut"),person("Bangken", "Ying", role="aut"))
c(
    person("Jake", "Conway", email = "jake_conway@hms.harvard.edu", role = "cre"),
    person("Nils", "Gehlenborg", email = "nils@hms.harvard.edu", role = "aut"))
c(person(given = "Tom",
             family = "Cardoso",
             email = "tcardoso@globeandmail.com",
             role = c("aut", "cre"),
             comment = "creator and maintainer"),
      person(given = "Michael",
             family = "Pereira",
             email = "hello@monkeycycle.org",
             role = c("ctb")),
      person(given = "The Globe and Mail Inc.",
             role = c("cph")))
person("Roberto", "Sichera", email = "rob.sichera@gmail.com", role = c("aut", "cre") )
c(
    person("James", "Lamb", email = "jaylamb20@gmail.com", role = c("aut", "cre")),
    person("Nick", "Paras", role = c("aut")),
    person("Austin", "Dickey", role = c("aut")),
    person("Michael", "Frasco", email = "mfrasco6@gmail.com", role = c("ctb")),
    person("Weiwen", "Gu", role = c("ctb")),
    person("Will", "Dearden", role = c("ctb")),
    person("Uptake Technologies Inc.", role = c("cph")))
person("Benjamin", "Goehring", email = "bengoehr@umich.edu", role = c("aut", "cre"), comment = c(ORCID = "0000-0001-8450-6065"))
c(
    person( "Arne", "Henningsen", 
      email = "arne.henningsen@gmail.com", 
      role = c( "aut", "cre" ),
      comment = c( ORCID = "0000-0002-6720-0264" ) ),
    person( "Geraldine", "Henningsen", 
      role = "aut",
      comment = c( ORCID = "0000-0002-4651-1039" ) ) )
c(person("Bernhard", "Pfaff", email = "bernhard@pfaffikus.de", role = c("aut", "cre")), person("Eric", "Zivot",email = "ezivot@u.washington.edu", role = "ctb"), person("Matthieu", "Stigler", role = "ctb"))
c(
    person("R Core team", role = "aut",
           comment = "The code in urltools.R adapted from the tools package"),
    person("Jim", "Hester", role = "aut",
           comment = c(ORCID = "0000-0002-2739-7082")),
    person("Gábor", "Csárdi", , "csardi.gabor@gmail.com", role = c("aut", "cre")),
    person("RStudio", role = c("cph", "fnd"))
  )
person("Marek", "Prokop", , "mprokop@prokopsw.cz", role = c("aut", "cre"))
person("Dyfan", "Jones", , "dyfan.r.jones@gmail.com", role = c("aut", "cre"))
c(
    person("John", "Malc", email = "cincenko@outlook.com", role = c("aut", "cre"),  comment = "@dmpe"),
    person("Andrea", "Dodet", role = c("ctb"), comment = "@andodet"),
    person("Stephen", "Synchronicity", role = c("ctb"), comment="@yogat3ch"))
c(person(given = "Os",
                    family = "Keyes",
                    role = c("aut", "cre"),
                    email = "ironholds@gmail.com"),
             person(given = "Jay",
                    family = "Jacobs",
                    role = "aut"),
             person(given = "Drew",
                    family = "Schmidt",
                    role = "aut"),
             person(given = "Mark",
                    family = "Greenaway",
                    role = "ctb"),
             person(given = "Bob",
                    family = "Rudis",
                    role = "ctb"),
             person(given = "Alex",
                    family = "Pinto",
                    role = "ctb"),
             person(given = "Maryam",
                    family = "Khezrzadeh",
                    role = "ctb"),
             person(given = "Peter",
                    family = "Meilstrup",
                    role = "ctb"),
             person(given = c("Adam", "M."),
                    family = "Costello",
                    role = "cph"),
             person(given = "Jeff",
                    family = "Bezanson",
                    role = "cph"),
             person(given = "Peter",
                    family = "Meilstrup",
                    role = "ctb"),
             person(given = "Xueyuan",
                    family = "Jiang",
                    role = "ctb"))
c( person(given = "Javier", family = "López-de-Lacalle",
                     role = c("aut"), 
                     email = "javlacalle@yahoo.es"),
              person(given = c("Georgi", "N."), family = "Boshnakov", 
                     role = c("cre"), 
                     email = "georgi.boshnakov@manchester.ac.uk") )
c(person("Greg", "Minshall", role = c("aut", "cph", "cre"),
                    email = "minshall@acm.org"),
             person("United States Census Bureau", "", role = c("dtc")) )
c(person(given = "Bowen", family = "Chen", role = c("aut", "cre"), email = "bwchen0719@gmail.com", comment = c(ORCID = "0000-0003-0370-2756")),
            person(given = "Elliott", family = "Dennis", role = "aut"))
c(person(given = "Robert",
             family = "Dinterman",
             role = c("cre", "aut"),
             email = "robert.dinterman@gmail.com",
             comment = c(ORCID = "0000-0002-9055-6082")),
      person(given = "Jonathan",
             family = "Eyer",
             role = "aut",
             email = "jeyer@usc.edu"))
c(
    person(given = "Mine",
           family = "Çetinkaya-Rundel",
           email = "cetinkaya.mine@gmail.com", 
           role = c("aut", "cre"), 
           comment = c(ORCID = "0000-0001-6452-2420")),
    person(given = "David", 
           family = "Diez", 
           email = "david@openintro.org", 
           role = c("aut")),
    person(given = "Leah", 
           family = "Dorazio", 
           email = "leah.dorazio@sfuhs.org", 
           role = c("aut"))    
    )
person("Steph", "Buongiorno", , "steph.buon@gmail.com", role = c("aut", "cre"),
           comment = c(ORCID = "0000-0002-6965-0787"))
person("Carl", "Goodwin", , "carl.goodwin@quantumjitter.com", role = c("aut", "cre", "cph"))
c(
    person(c("Jared", "P."), "Lander", email = "packages@jaredlander.com", role = c("cre", "aut")),
    person("Nicholas", "Galasinao", email = "nicholas.galasinao@gmail.com", role = "ctb"),
    person("Joshua", "Kraut", email = "joshua.kraut@gmail.com", role = "ctb"),
    person("Daniel", "Chen", email = "dan dcheny@gmail.com", role = "ctb"))
person("John", "Zobolas", , "bblodfon@gmail.com", role = c("aut", "cph", "cre"),
           comment = c(ORCID = "0000-0002-3609-8674"))
c(
    person("Max", "Kuhn", , "max@rstudio.com", role = c("aut", "cre"),
           comment = c(ORCID = "0000-0003-2402-136X")),
    person("RStudio", role = "cph")
  )
c(
    person("Hadley", "Wickham", , "hadley@posit.co", role = "aut",
           comment = c(ORCID = "0000-0003-4757-117X")),
    person("Jennifer", "Bryan", , "jenny@posit.co", role = c("aut", "cre"),
           comment = c(ORCID = "0000-0002-6983-2759")),
    person("Malcolm", "Barrett", , "malcolmbarrett@gmail.com", role = "aut",
           comment = c(ORCID = "0000-0003-0299-5825")),
    person("Andy", "Teucher", , "andy.teucher@posit.co", role = "aut",
           comment = c(ORCID = "0000-0002-7840-692X")),
    person("Posit Software, PBC", role = c("cph", "fnd"))
  )
c(
    person("Wenlong", "Liu", role = c("aut","cre"),
        email = 'wliu14@ncsu.edu')
    )
c(person("Sinead E.", "Morris", role = c("aut", "cre"),
                     email = "sinead.morris@columbia.edu",
		     comment = c(ORCID = "0000-0001-8626-1698")),
              person("Luise", "Dziobek-Garrett", role = "ctb"),
              person("Andrew", "Yates", role = "ctb"))
c(
    person("Shane", "Orr", email = "shane.j.orr@gmail.com", role = c("aut", "cre", "cph")),
    person("Thomas", "Wells", email = "thomascwells@gmail.com", role = "ctb")
    )
c(person(given="Neil J.", family="Gunther", role
    = "aut"), person(given="Stefan", family="Moeding", role
    = c("aut", "cre"), email = "stm@moeding.net"))
person("Paolo", "Di Lorenzo", email = "paolo@dilorenzo.org", role = c("aut", "cph", "cre"))
person("Paolo", "Di Lorenzo", email = "paolo@dilorenzo.org", role = c("aut", "cre"))
person(given = "Nik",
           family = "Krieger",
           role = c("aut", "cre"),
           email = "nk@case.edu")
person(given = "Daniel",
           family = "Eness",
           role = c("aut", "cre"),
           email = "dpauleness@gmail.com",
           comment = c(ORCID = "https://orcid.org/0000-0001-9740-4690"))
person("Guillermo", "Roditi Dominguez", , "guillermo@newriverinvestments.com", role = c("aut", "cre"),
           comment = c(ORCID = "0000-0002-7127-8742"))
c(
    person(given = "Jo Thori",
           family = "Lind",
           role = c("aut", "cre"),
           email = "j.t.lind@econ.uio.no",
           comment = c(ORCID = "0000-0002-2980-3814")),
    person(given = "Halvor",
           family = "Mehlum",
           role = "aut",
           email = "halvor.mehlum@econ.uio.no"))
c(person(given = c("Patrick", "O."),
             family = "Perry",
             role = c("aut", "cph")),
      person(given = "Kirill",
             family = "M\u00fcller",
             role = "cre",
             email = "kirill@cynkra.com",
             comment = c(ORCID = "0000-0002-1416-3412")),
      person(given = "Unicode, Inc.",
             role = c("cph", "dtc"),
             comment = "Unicode Character Database"))
c(person('Eric', 'Finnesgard', email = 'efinite@outlook.com', role = c('aut', 'cre')), person('Jennifer', 'Grauberger', role = c('aut')))
c(person('Eric', 'Finnesgard', email = 'efinite@outlook.com', role = c('aut', 'cre')))
c(
    person("Sebastian O.", "Schneider", email = "sschneider@coll.mpg.de", role = c("aut", "cre", "prg")),
    person("Giulia", "Baldini", email = "giulia.baldini@uni-bonn.de", role = c("edt", "prg")),
    person("Paul", "Eilers", comment = "Functions tpower and bbase, see Package JOPS at http://statweb.lsu.edu/faculty/marx/JOPS_0.1.0.tar.gz", role = "ctb"))
c(
    person("Gustavo","Coelho", role=c("aut","cre"), email="gustavo.coelho@ipea.gov.br"),
    person("Lucas","Mation", role=c("aut"), email="lucas.mation@ipea.gov.br"),
    person("Daniel","Lima", role=c("ctb"), email="daniel.viegas@ipea.gov.br"),
    person("Igor","Noberto", role=c("ctb"), email="igor.noberto@ipea.gov.br"),
    person("João Victor","Machado", role=c("ctb"), email="joao.machado@ipea.gov.br")
    )
c(
    person("Maria Pilar", "Frias-Bustamante", , "mpfrias@ujaen.es", role = "aut",
           comment = c(ORCID = "0000-0001-6886-0953")),
    person("Francisco", "Martinez", , "fmartin@ujaen.es", role = c("aut", "cre", "cph"),
           comment = c(ORCID = "0000-0002-5206-1898"))
  )
c(person("Simon", "Urbanek", role=c("aut","cre","cph"), email="Simon.Urbanek@r-project.org", comment=c("https://urbanek.org", ORCID="0000-0003-2297-1732")), person("Theodore","Ts'o", email="tytso@thunk.org", role=c("aut","cph"), comment="libuuid"))
c(
    person("James", "Melville", , "jlmelville@gmail.com", role = c("aut", "cre", "cph")),
    person("Aaron", "Lun", role = "ctb"),
    person("Mohamed Nadhir", "Djekidel", role = "ctb"),
    person("Yuhan", "Hao", role = "ctb"),
    person("Dirk", "Eddelbuettel", role = "ctb"),
    person("Wouter", "van der Bijl", role = "ctb")
  )
c(
    person("Jeroen", "Ooms", role = c("aut", "cre"), email = "jeroenooms@gmail.com",
      comment = c(ORCID = "0000-0002-4035-0289")),
    person("Jan Marvin", "Garbuszus", role = "ctb"))
c(person("Sandipan", "Pramanik", role = c("aut", "cre"),
                    email = "sandy.pramanik@gmail.com",
                    comment = c(ORCID = "0000-0002-7196-155X")),
             person("Emily", "Wilson", role = "aut",
                    email = "wilsonem@gmail.com"),
             person("Jacob", "Fiksel", role = "aut",
                    email = "jfiksel@gmail.com"),
             person("Brian", "Gilbert", role = "aut",
                    email = "bgilbert345@gmail.com"),
             person("Abhirup", "Datta", role = "aut",
                    email = "abhidatta@jhu.edu"))
person("Avi", "Kenny", email = "avi.kenny@gmail.com", role = c("aut", "cre"))
c(
    person("David Santiago", "Quevedo", , "ex-dsquevedo@javeriana.edu.co", role = "aut",
           comment = c(ORCID = "0000-0003-1583-4262")),
    person("Zulma M.", "Cucunubá", , "tracelacdev@gmail.com", role = c("aut", "cre"),
           comment = c(ORCID = "0000-0002-8165-3198")),
    person("International Development Research Center (IDRC)", role = "fnd"),
    person("Geraldine", "Gómez Millán", , "geralidine.gomez@javeriana.edu.co", role = "ctb",
           comment = c(ORCID = "0009-0007-8701-0568")),
    person("Pratik", "Gupte", , "pratik.gupte@lshtm.ac.uk", role = "ctb",
           comment = c(ORCID = "0000-0001-5294-7819")),
    person("Érika J", "Cantor", , "erika.cantor@javeriana.edu.co", role = "ctb",
           comment = c(ORCID = "0000-0003-3320-6032")),
    person("Santiago", "Loaiza", , "santiago.loaiza@javeriana.edu.co", role = "ctb",
           comment = c(ORCID = "0000-0002-2092-3262")),
    person("Jaime", "Pavlich-Mariscal", , "jpavlich@javeriana.edu.co", role = "ctb",
           comment = c(ORCID = "0000-0002-3892-6680")),
    person("Hugo", "Gruson", , "hugo.gruson@data.org", role = "ctb",
           comment = c(ORCID = "0000-0002-4094-1476")),
    person("Chris", "Hartgerink", , "chris@data.org", role = "ctb",
           comment = c(ORCID = "0000-0003-1050-6809")),
    person("Felipe Segundo", "Abril", , "fsabrilb@unal.edu.co", role = "ctb",
           comment = c(ORCID = "0000-0002-2512-4929")),
    person("Joshua W.", "Lambert", , "joshua.lambert@lshtm.ac.uk", role = "ctb",
           comment = c(ORCID = "0000-0001-5218-3046")),
    person("Julian", "Otero", , "jd.otero10@uniandes.edu.co", role = "ctb",
           comment = c(ORCID = "0009-0006-0429-7747")),
    person("Pontificia Universidad Javeriana", role = "cph")
  )
c(
      person("Jos", "Lommerse", email = "jos.lommerse@gmail.com", role = c("aut")),
      person("Anna", "Largajolli", email = "anna.largajolli@certara.com", role = c("aut")),
      person("James", "Craig", email = "james.craig@certara.com", role = c("aut", "cre")),
      person("Nele", "Mueller-Plock", email = "nele.mueller-plock@certara.com", role = c("ctb")),
      person("Jeff", "Sachs", email = "jeff_sachs@merck.com", role = c("ctb")),
      person("Amy", "Cheung", email = "amy.cheung@certara.com", role = c("ctb")),
      person("Certara USA, Inc.", role = c("cph","fnd"))
      )
person(given = "Katherine",
           family = "Roehrick",
           role = c("aut", "cre"),
           email = "kr.gitcode@gmail.com")
c(
	person("Han Lin", "Shang", email="hanlin.shang@anu.edu.au", role=c("aut", "cre", "cph"), comment = c(ORCID = "0000-0003-1769-6430")),
	person("Francis K.C.", "Hui", role="aut", comment = c(ORCID = "0000-0003-0765-3533"))
	)
c(
  person("Benjamin", "Christoffersen", 
         email = "boennecd@gmail.com", 
         role = c("cre", "aut"), 
         comment = c(ORCID = "0000-0002-7182-1346")), 
  person("Mark", "Clements", 
         role = "aut"),
  person("Birzhan", "Akynkozhayev", 
         role = "aut"),
  person("Antoine", "Savine", 
         role = "cph"))
person("Eugene", "Ha", email = "eha@posteo.de", role = c("aut", "cre"))
c(
    person("Kieran", "Harrison", email = "k.harrison@vald.com", role = c("aut", "cre")), 
    person(given = "VALD Support", email = "support@vald.com", role = "ctb"),
    person("VALD", role = "cph", comment = "Copyright holder"))
c(
  person("Chris", "Cooper", role = "aut"),
  person("Dorota", "H. Sendorek", role = "ctb"),
  person("Paul", "C. Boutros", role = c("cre", "cph"), email = "Paul.Boutros@oicr.on.ca")
  )
c(person(given = "Corey", family = "Runkel",
             email = "runkelcorey@gmail.com", role = c("aut", "cre"),
             comment = c(ORCID = "0000-0001-8992-4434")))
c(person("Timothée", "Giraud", email = "timothee.giraud@cnrs.fr",
    role = c("cre","aut"),  comment = c(ORCID = "0000-0002-1932-3323")),
    person("Matthieu", "Viry", role = c("aut"),
    comment = c(ORCID = "0000-0002-0693-8556")))
person("Greer B.", "Humphrey",
                  email = "greer.humphrey@student.adelaide.edu.au",
                  role = c("aut", "cre"))
c(person("Mark", "van der Loo"
                   , role   = c("cre","aut")
                   , email  = "mark.vanderloo@gmail.com"
                   , comment= c(ORCID="0000-0002-9807-4686"))
           , person("Edwin", "de Jonge"
                   , role = "aut"
                   , comment=c(ORCID="0000-0002-6580-4718"))
           , person("Paul","Hsieh"
                   , role = "ctb" ))
c(
	person(given = "Luwei",
           family = "Ying",
           role = c("aut", "cre"),
           email = "triads.developers@wustl.edu",
           comment = c(ORCID = "0000-0001-7307-4834")),
    person(given = "Jacob",
           family = "Montgomery",
           role = c("aut")),
    person(given = "Brandon",
           family = "Stewart",
           role = c("aut")))
c(person(given = "Edwin",
           family = "de Jonge",
           role = c("aut", "cre"),
           email = "edwindjonge@gmail.com",
           comment = c(ORCID="0000-0002-6580-4718")
           ),
         person(given = "Olav",
         family = "ten Bosch",
         role = c("aut")
         )
      )
c( person("Edwin", "de Jonge", email = "edwindjonge@gmail.com", role = c("aut", "cre")
            , comment=c(ORCID="0000-0002-6580-4718"))
            , person("Mark", "van der Loo", email = "mark.vanderloo@gmail.com",role = c("aut"))
            , person("Jacco", "Daalmans", role = c("ctb"))
            )
c(person("Andres", "Rodriguez", role = c("aut", "cre","cph"),
                      email = "andres@validmind.ai"))
c(person("Kristin", "Piikki", 
                email = "kristin.piikki@slu.se",
                role = c("aut", "cre", "cph")
                ),
              person("Johanna", "Wetterlind", 
                email = "johanna.wetterlind@slu.se",
                role = c("aut", "cph")
                    ),
              person("Mats", "Soderstrom",
                email = "mats.soderstrom@slu.se",
                role = c("aut", "cph")
                ),
              person("Bo", "Stenberg",
                email = "bo.stenberg@slu.se",
                role = c("aut", "cph")
                )
            )
c(person("Victor", "Trevino", role = c("aut", "cre"),
    email = "vtrevino@itesm.mx"))
person("Clay", "Ford", , "joe.clayton.ford@gmail.com", role = c("cre", "aut"))
c(
    person("Jay", "Hesselberth", , "jay.hesselberth@cuanschutz.edu", role = c("aut"),
           comment = c(ORCID = "0000-0002-6299-179X")),
    person("Kent", "Riemondy", , "kent.riemondy@gmail.com", role = c("aut", "cre"),
           comment = c(ORCID = "0000-0003-0750-1273")),
    person("RNA Bioscience Initiative", role = c("fnd", "cph"),
           comment = c(ROR = "https://ror.org/03wmf1y16"))
  )
c(person("Sheeja", "Manchira Krishnan", , "sheejamk@gmail.com", c("aut", "cre")))
person(given = "Huncheol",
           family = "Ha",
           role = c("aut", "cre"),
           email = "hancury@gmail.com",
           comment = c(ORCID = "0000-0001-7095-3841"))
c(
    person("Hengxin", "Li", email = "h343li@uwaterloo.ca", 
    role = c("aut", "cph")),
    person("Ben", "Feng", email = "ben.feng@uwaterloo.ca", 
    role = c("aut", "cph")),
    person("Mingyi", "Jiang", email = "m64jiang@uwaterloo.ca", 
    role = c("aut" , "cph", "cre")),
    person("GuoJun", "Gan", email = "Guojun.Gan@uconn.edu", 
    role = c("ctb")))
c(person(given = "Alberto", family = "Jiménez-Valverde",
           role = c("aut", "cre"),
           email = "alberto.jimenez.valverde@gmail.com",
           comment = c(ORCID = "0000-0001-9962-2106")))
person(given = "Cheryl", family = "Isabella", role = c("aut", "cre"), email = "cheryl.academic@gmail.com")
person(given = "Burrel",
	 		family = "Vann Jr", 
	 		email = "bvannjr@sdsu.edu", 
	 		role = c("aut", "cre"),
	 		comment = c(ORCID = "0000-0003-3066-5815"))
c(person("Michael", "Sumner", email = "mdsumner@gmail.com", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-2471-7511")), 
             person("Simon", "Wotherspoon", role = "ctb", comment = "RasterIO configuration for resampling options"), 
             person("Mark", "Padgham", role = "ctb", comment = "helped get started :)"), 
             person("Edzer", "Pebesma", role = "ctb", comment = "wrote the field-read handling, adapted here from sf"),
             person("Roger", "Bivand", role = "ctb", comment = "wrote configure.ac, adapted here from rgdal"),
             person("Jim", "Hester", role = c("ctb", "cph"), comment = "wrote CollectorList.h, copied here from fs package"), 
             person("Timothy", "Keitt", role = c("ctb"), comment = "wrote GetPointsInternal copied here from rgdal2 package"), 
             person("Jeroen", "Ooms", role = c("ctb"), comment = "tweaked build process, provided Windows build tools"), 
             person("Dale", "Maschette", role = c("ctb"), comment = "created the hex logo"), 
             person("Joseph", "Stachelek", role = "ctb"), 
             person("Even", "Rouault", role = "ctb", comment = "primary author of the COG format and its use of the GDALwarp app-library, example code used by the warper function here"), 
             person("Robert", "Hijmans", role = "ctb", comment = "code in terra package used as example/inspiration"), 
             person("Dewey", "Dunnington", role = "ctb", comment = "wrote the columnar-access mode streaming Arrow support"), 
             person("Tomas", "Kalibera", role = "ctb"))
c(
    person("Evangelos", "Ioannidis", email = "eioannid@aueb.gr", role =  c("cre", "aut", "cph") ),
    person("Panagiotis", "Papastamoulis", email = "papapast@yahoo.gr", role = c("aut", "cph")))
c(
    person("Yuhan", "Tian", email = "yuhan.tian@ufl.edu", role = c("aut", "cre")),
    person("Abolfazl", "Safikhani", email = "asafikha@gmu.edu", role = "aut"))
c(
  person("Juris",   "Breidaks",   role = "aut"),
  person("Martins", "Liberts",    role = c("aut", "cre"),
         email = "martins.liberts@csb.gov.lv"),
  person("Santa",   "Ivanova",    role = "aut"),
  person("Aleksis", "Jursevskis", role = "ctb"),
  person("Anthony", "Damico",     role = "ctb"),
  person("Central Statistical Bureau of Latvia", role = c("cph", "fnd"),
         email = "rcsb@csb.gov.lv"))
c(person(given="Leo", family="Belzile", role = "cre", email = "belzilel@gmail.com", comment = c(ORCID = "0000-0002-9135-014X")), person(family="Nadarajah", given = "Saralees", role = "aut", email = "Saralees.Nadarajah@manchester.ac.uk"), person(given="Stephen", family="Chan", role= "aut"), person(given="Emmanuel", family="Afuecheta", role="aut", comment = c(ORCID = "0000-0002-9223-0799")))
c(person("Mehrad", "Mahmoudian", role = c("aut", "cre"),
		     email = "m.mahmoudian@gmail.com"))
c(person("Raj", "Popat", email = "popatrajc@gmail.com", role = c("aut", "cre")), person("Rumit", "Patel", email = "rumitpatel01@gmail.com ", role = "aut"),person("Dinesh", "Parmar", email = "djparmar.a@gmail.com", role = "aut"))
c(
    person("Runze","Li", email = "runzelipsu@gmail.com", role = "aut"),
    person("Liying","Huang", email = "lxh37@PSU.EDU", role = "aut"),
    person("John","Dziak", email = "dziakj1@gmail.com", role = c("aut","cre")))
c(
    person("Samuel", "Macêdo", email = "samuelmacedo@recife.ifpe.edu.br", 
           role = c("aut", "cre")),
    person("Javier", "Luraschi", email = "javier@rstudio.com", role = "aut")
    )
c(person("Keiichiro", "Hijikata", email = "k.hijikata.1120@outlook.jp", role = c("aut", "cre")),
    person("Motonori", "Oka", role = "aut", comment = c(ORCID = "0000-0002-9867-8922")),
    person("Kazuhiro", "Yamaguchi", role = "aut", comment = c(ORCID = "0000-0001-8011-8575")),
    person("Kensuke", "Okada", role = "aut", comment = c(ORCID = "0000-0003-1663-5812")))
c(
    person("Philipp", "Probst", email = "philipp_probst@gmx.de", role = c("aut", "cre")),
    person("Silke","Janitza", email = "silke.janitza@gmail.com", role = "ctb"))
c(
    person("Craig", "Wang", email = "craig.wang@math.uzh.ch", role = c("aut","cre"),comment = c(ORCID = "0000-0003-1804-2463")),
    person("Reinhard", "Furrer", email = "reinhard.furrer@math.uzh.ch", role = "ctb",comment = c(ORCID = "0000-0002-6319-2332")))
c(
    person("Xiaofei", "Chen", email = "bill.nj08873@gmail.com", role = c("aut","cre")),
    person("Haekyung", "Jeon-Slaughter", email = "Haekyung.Jeon-Slaughter@UTSouthwestern.edu", role = "aut"))
c(
  person("Adam", "Mills", role = c("aut", "cre"), email = "Adam.Mills@icr.ac.uk"),
  person("Erle", "Holgersen", role = "aut"),
  person("Ros", "Cutts", role = "aut"),
  person("Syed", "Haider", role = "aut", email = "Syed.Haider@icr.ac.uk")
  )
c(person("Naimin", "Jing", email = "naimin.jing@temple.edu",
    role = c("aut", "cre")),
    person("Hexin", "Bai", role = "aut"),
    person("Tong", "Wang", role = "aut"),
    person("Cheng Yong", "Tang", role = "aut"))
c(person(given = "Onur",
           family = "Boyar",
           role = c("aut", "cre"),
           email = "boyaronur@gmail.com"),
           person(given = "Wolfgang",
           family = "Hörmann",
           role = c("aut"),
           email = "hormannw@boun.edu.tr")
   )
c(person("Kristy","Robledo", email = "robledo.kristy@gmail.com", role = c("aut", "cre")),person("Ian", "Marschner", role = c("ths")))
c(person("Bernhard", "Pfaff", email = "bernhard@pfaffikus.de", role = c("aut", "cre")), person("Matthieu", "Stigler", role = "ctb"))
c(person("Alex", "Martinez", role = c("aut", "cre"), email = "ahmartinez283@gmail.com"),
             person("Andrew", "Chapple", role = "aut"))
c(
    person("Namgil", "Lee", role = c("aut", "cre"),
           email = "namgil.lee@kangwon.ac.kr",
           comment = c(ORCID = "0000-0003-0593-9028")),
    person("Heon Young", "Yang", role = c("ctb"),
           email = "hyyang@kangwon.ac.kr"),
    person("Sung-Ho", "Kim", role = c("aut"),
           email = "sung-ho.kim@kaist.edu")
    )
c(person("Gozde", "Cosar", role = "aut"), person("Merve", "Kasikci", role = "aut"), person("Osman", "Dag", role = c("aut", "cre"), email = "osman.dag@outlook.com"))
c(
    person("Charlotte","Baey", ,"charlotte.baey@univ-lille.fr", role = c("aut", "cre"),
       comment = c(ORCID= "0000-0002-1413-1058")
       ), 
    person("Estelle","Kuhn", ,"estelle.kuhn@inra.fr", role = c("aut"))
  )
c(
      person(
        given = "Markus",
        family = "Belfrage",
        role   = c("aut","cre"),
        email  = "markus.belfrage@gmail.com"
      ),
      person(
        given  = "Paul",
        family = "Catani",
        role   = "ctb"
      ),
      person(
        given  = "Niklas",
        family = "Ahlgren",
        role   = "ctb"
      )
    )
c(
    person("Jakob A.", "Dambon", , "jakob.dambon@math.ethz.ch", role = c("aut", "cre"),
           comment = c(ORCID = "0000-0001-5855-2017")),
    person("Fabio", "Sigrist", role = "ctb",
           comment = c(ORCID = "0000-0002-3994-2244")),
    person("Reinhard", "Furrer", role = "ctb",
           comment = c(ORCID = "0000-0002-6319-2332"))
  )
c(person("Rich", "FitzJohn", role = c("aut", "cre"),
                    email = "rich.fitzjohn@gmail.com"),
             person("Robert", "Ashton", role = "aut"),
             person("Wes", "Hinsley", role = "aut"),
             person("Imperial College of Science, Technology and Medicine",
                    role = "cph"))
c(
    person("Julie", "Dudasova (MSD)", email = "julie.dudasova@merck.com", role = c("aut", "cre"))
    )
c(person("Alexander", "Coppock", , "acoppock@gmail.com", role = c("aut", "cre", "cph"),
           comment = c(ORCID = "0000-0002-5733-2386")),
           person("Elias", "Hyde", , "eliasworrallhyde@gmail.com", role = "ctb"))
c(
    person("Weichang", "Yu", , "weichang.yu@unimelb.edu.au", role = c("aut"),
    comment = c(ORCID = "0000-0002-0399-3779")),
    person("Jeremy", "Lim", , "jeremy.lim@unimelb.edu.au", role = c("cre", "aut"))
  )
c(person("Chen Zhang", role=c("aut", "cre", "cph"),
 email="chen.zhang_06sept@foxmail.com", comment=(ORCID="0009-0007-7689-5030")))
c(
    person("Detlef", "Steuer", , "steuer@hsu-hh.de", role = c("aut", "cre"), comment=c(ORCID="0000-0003-2676-5290")),
    person("Hartmut", "Hebbel", , "hebbel@hsu-hh.de", role = c("aut")))
c(person(given = "Thomas",
            family = "Nagler",
            role = c("aut", "cre"),
            email = "mail@tnagler.com"),
      person(given = "Benedikt",
             family = "Graeler",
             role = "ctb"))
c(person(given = "Xin",
           family = "Xiong",
           role = c("aut", "cre"),
           email = "xinxiong@hsph.harvard.edu"),
       person(given = "Ivor",
           family = "Cribben",
           role = c("aut"),
           email = "cribben@ualberta.ca"))
c(person(given = "David", family = "Meyer", role = c("aut", "cre"),
                    email = "David.Meyer@R-project.org",
		    comment = c(ORCID = "0000-0002-5196-3048")),	    
             person(given = "Achim", family = "Zeileis", role = "aut",
	            email = "Achim.Zeileis@R-project.org",
                    comment = c(ORCID = "0000-0003-0918-3766")),
             person(given = "Kurt", family = "Hornik", role = "aut",
	            email = "Kurt.Hornik@R-project.org",
                    comment = c(ORCID = "0000-0003-4198-9911")),
             person(given = "Florian", family = "Gerber", role = "ctb"),
             person(given = "Michael", family = "Friendly", role = "aut",
	            email = "friendly@yorku.ca",
		    comment = c(ORCID = "0000-0002-3237-0941")))
person(
    "Calvin", "Deutschbein", , 
    email = "ckdeutschbein@willamette.edu", 
    role = c("aut", "cre"),
    comment = c(ORCID = "0000-0003-1354-7200"))
c(
            person(given = "Michael", 
                   family = "Friendly", 
                   role=c("aut", "cre"), 
                   email="friendly@yorku.ca",
                   comment = c(ORCID = "0000-0002-3237-0941")),
            person(given = "Heather", family = "Turner", role="ctb"),
            person(given = "David",
                   family = "Meyer", 
                   role="ctb"),
            person(given = "Achim", 
                   family = "Zeileis", 
                   role="ctb",
                   comment = c(ORCID = "0000-0003-0918-3766")),
            person(given = "Duncan", 
                   family = "Murdoch", 
                   role="ctb"),
            person(given = "David", 
                   family = "Firth", 
                   role="ctb"),
            person(given = "Matt", family = "Kumar", role="ctb"),
            person(given = "Shuguang", family = "Sun", role="ctb")
            )
c(
    person("Zilong", "Li", , "zilong.dk@gmail.com", role = c("aut", "cre"),
           comment = c(ORCID = "0000-0001-5859-2078")),
    person("Bonfield, James K and Marshall, John and Danecek, Petr and Li, Heng and Ohan, Valeriu and Whitwham, Andrew and Keane, Thomas and Davies, Robert M",  role = "cph",
           comment = "Authors of included htslib library")
           )
c(
    person(c("Brian", "J."), "Knaus", role = c("cre", "aut"),
    email = "briank.lists@gmail.com", comment = c(ORCID = "0000-0003-1665-4343")),
    person(c("Niklaus", "J."), "Grunwald", role = "aut",
    email = "grunwaln@science.oregonstate.edu", comment = c(ORCID = "0000-0003-1656-7602")),
    person(c("Eric", "C."), "Anderson", role = "ctb",
    email = "eric.anderson@noaa.gov", comment = c(ORCID = "0000-0003-1326-0840")),
    person(c("David", "J."), "Winter", role = "ctb",
    email = "david.winter@gmail.com"),
    person(c("Zhian", "N."), "Kamvar", role = "ctb",
    email = "zkamvar@gmail.com", comment = c(ORCID = "0000-0003-1458-7108")),
    person(c("Javier", "F."), "Tabima", role = "ctb",
    email = "caifaz01@gmail.com", comment = c(ORCID = "0000-0002-3603-2691"))
    )
c(
  person("Victor", "Perrier", email = "victor.perrier@dreamrs.fr", role = c("aut", "cre")),
  person("Fanny", "Meyer", role = "aut"))
c(
    person("Sang Kyu", "Lee", role = c("aut", "cre"), email = "lsk0816@gmail.com"),
    person("Seonjin", "Kim", role = "aut", email = "kims20@miamioh.edu"),
    person("Hyokyoung (Grace)", "Hong", role = "aut", email = "grace.hong@nih.gov"),
    person("Mi-Ok", "Kim", role = "aut", email = "miok.kim@ucsf.edu")
    )
c(person("Jason", "Sinnwell", email = "sinnwell.jason@mayo.edu", comment=c(ORCID="0000-0003-1964-5522"), role = c("aut","cre"),), person("Daniel", "Schaid", email = "schaid.daniel@mayo.edu", comment=c(ORCID="0000-0003-1457-6433"), role = c("aut")))
c(
    person("Scott", "Chamberlain", , "myrmecocystus@gmail.com", role = c("aut", "cre"),
           comment = c(ORCID = "0000-0003-1444-9135")),
    person("Aaron", "Wolen", role = "aut",
           comment = c(ORCID = "0000-0003-2542-2202")),
    person("Maëlle", "Salmon", role = "aut",
           comment = c(ORCID = "0000-0002-2815-0399")),
    person("Daniel", "Possenriede", role = "aut",
           comment = c(ORCID = "0000-0002-6738-9845")),
    person("Hadley", "Wickham", , "hadley@posit.co", role = "aut"),
    person("rOpenSci", role = "fnd",
           comment = c(ROR = "019jywm96"))
  )
c(
  person("Reto", "Burgin", role = c("aut", "cre"), email = "rbuergin@gmx.ch", comment = c(ORCID = "0000-0002-6212-1567")),
  person("Gilbert", "Ritschard", role = c("ctb"), email = "gilbert.ritschard@unige.ch", comment = c(ORCID = "0000-0001-7776-0903")))
c(
    person("Hadley", "Wickham", , "hadley@posit.co", role = "aut"),
    person("Lionel", "Henry", , "lionel@posit.co", role = "aut"),
    person("Davis", "Vaughan", , "davis@posit.co", role = c("aut", "cre")),
    person("data.table team", role = "cph",
           comment = "Radix sort based on data.table's forder() and their contribution to R's order()"),
    person("Posit Software, PBC", role = c("cph", "fnd"))
  )
c(
    person("Maria Pilar", "Frias-Bustamante", email = "mpfrias@ujaen.es",
            role = c("aut"), comment = c(ORCID = "0000-0001-6886-0953")),
    person("Francisco", "Martinez", email = "fmartin@ujaen.es",
           role = c("aut", "cre", "cph"), 
           comment = c(ORCID = "0000-0002-5206-1898")))
c(
    person("Anne", "Le Maitre", email = "anne.le.maitre@univie.ac.at", role = c("aut", "cre"), comment = c(ORCID = "0000-0003-2690-7367")),
    person("Philipp", "Mitteroecker", email = "philipp.mitteroecker@univie.ac.at", role = "aut"))
c(person("Pieter", "Schoonees", email = "schoonees@gmail.com",
    role = c("aut", "cre", "cph")), person("Niel", "le Roux", role = "ctb"))
c(
    person("Lionel", "Henry", , "lionel@posit.co", role = c("cre", "aut")),
    person("Thomas Lin", "Pedersen", , "thomas.pedersen@posit.co", role = "aut",
           comment = c(ORCID = "0000-0002-5147-4711")),
    person("Posit Software, PBC", role = c("cph", "fnd")),
    person("T Jake", "Luciani", , "jake@apache.org", role = "aut",
           comment = "svglite"),
    person("Matthieu", "Decorde", , "matthieu.decorde@ens-lyon.fr", role = "aut",
           comment = "svglite"),
    person("Vaudor", "Lise", , "lise.vaudor@ens-lyon.fr", role = "aut",
           comment = "svglite"),
    person("Tony", "Plate", role = "ctb",
           comment = "svglite: Early line dashing code"),
    person("David", "Gohel", role = "ctb",
           comment = "svglite: Line dashing code and raster code"),
    person("Yixuan", "Qiu", role = "ctb",
           comment = "svglite: Improved styles; polypath implementation"),
    person("Håkon", "Malmedal", role = "ctb",
           comment = "svglite: Opacity code")
  )
c(
    person("Pendy", "Tok", email = "pendytok0518@gmail.com", role = c("aut", "cre")),
    person("Li Chuin", "Chong", role = "aut"),
    person("Evgenia", "Chikina", role = "aut"),
    person("Yin Cheng", "Chen", role = "aut"),
    person("Mohammad Asif", "Khan", role = "aut"))
c(
    person(given = "Pavel", family = "Hernandez", role = c("aut", "cre"),
           email = "pavel.amaro96@gmail.com"),
    person(given = "Jose Ignacio", family = "Diez", role = c("ctr"),
           email = "jodiezru@pa.uc3m.es"),
    person(given = "Maria", family = "Durban", role = c("ctb"),
           email = "mdurban@est-econ.uc3m.es"),
    person(given = "Maria del Carmen", family = "Aguilera-Morillo", role = c("ctb"),
           email = "mdagumor@eio.upv.es")
    )
c(
      person("Linna", "Wang", email="wang2l9@mail.uc.edu",
             role=c("aut", "cre")),
      person("Yichen", "Qin",
             role="aut"))
c(
	person("German", "Carrillo", role = c("cre", "aut"),
                   email = "geotux_tuxman@linuxmail.org")
    )
c(
    person("Jacob Gerard", "Levernier", email = "jlevern@upenn.edu", role = c("aut", "cre"), comment = "Designed and authored the package source code and documentation. Roles: author, creator, designer, engineer, programmer"), # Additional MARC Relator roles: "dsr", "eng", "prg"
    person("Heather Gaile", "Wacha", email = "wacha2@wisc.edu", role = c("aut"), comment = "Provided intellectual overview and consultation during development for use with medieval cartographic datasets. Roles: conceptor, consultant, data contributor")) # Additional MARC Relator roles: "ccp", "csp", "dtc"
c(
    person("Steven", "De Keyser",, "steven.dekeyser@kuleuven.be", role = c("aut", "cre"),
           comment = c(ORCID = "0000-0002-3469-8692")),
    person("Irène", "Gijbels",, email = "irene.gijbels@kuleuven.be", role = "ctb",
           comment = c(ORCID = "0000-0002-4443-9803")))
person("Mateusz", "Kolek", , "mati.kolek13@gmail.com", role = c("aut", "cre", "cph"),
           comment = c(ORCID = "0000-0001-6470-4830"))
person("Samuel", "Sapire", , "sapires@protonmail.com", role = c("aut", "cre", "cph"))
c(
    person("pwh", "kwong", email = "wai-hang.kwong@polyu.edu.hk", role = "aut"),
    person("ananda", "sidarta", email = "ananda.sidarta@ntu.edu.sg", role = "ctb"),
    person("zhexuan", "gu", email = "22054513g@connect.polyu.hk", role = "cre"))
person("Christina", "De Cesaris", email = "cmdecesaris@ucdavis.edu", role = c("aut", "cre"))
c(
      person(
        given = "Mitchell",
        family = "O'Hara-Wild",
        role = c("aut", "cre"),
        email = "mail@mitchelloharawild.com",
        comment = c(ORCID = "0000-0001-6729-7695")
      ))
c(
    person("Neeraj Dhanraj", "Bokde", , "neerajdhanraj@gmail.com", role = c("aut", "cre", "cph")),
    person("Prajwal Kailasnath", "Patil", , "prajwalpatilcodes@gmail.com", role = "aut"),
    person("Saradindu", "Sengupta", ,"saradindu.mi1@iiitmk.ac.in" ,role = "aut"),
    person("Andrés Elías Feijóo", "Lorenzo", , "Afeijoo@uvigo.gal", role = "aut")
  )
c(
  person("Katherine", "Goode", email = "kjgoode@sandia.gov", role = c("cre", "aut")),
  person("J. Derek", "Tucker", email = "jftuck@sandia.gov", role = c("aut")),
  person("Sandia National Laboratories", role = c("cph", "fnd")))
c(
      person("Bob", "Rudis", email = "bob@rudis.net", role = c("aut", "cre")),
      person("Kanit", "Wongsuphasawat,", comment = "Vega-Lite library", role = c("aut")),
      person("Jeffrey", "Heer", comment = "Vega-Lite library", role = c("aut")),
      person("Arvind", "Satyanarayan", comment = "Vega-Lite library", role = c("aut")),
      person("Mike", "Bostock", comment = "D3 library", role = c("aut"))
    )
c(person("Jari", "Oksanen", role=c("aut","cre"),
                    email="jhoksane@gmail.com"),
	   person("Gavin L.", "Simpson", role="aut",
	            email="ucfagls@gmail.com"),
	   person("F. Guillaume", "Blanchet", role="aut"),
	   person("Roeland", "Kindt", role="aut"),
	   person("Pierre", "Legendre", role="aut"),
	   person("Peter R.", "Minchin", role="aut"),
	   person("R.B.", "O'Hara", role="aut"),
	   person("Peter", "Solymos", role="aut"),
	   person("M. Henry H.", "Stevens", role="aut"),
	   person("Eduard", "Szoecs", role="aut"),
	   person("Helene", "Wagner", role="aut"),
	   person("Matt", "Barbour", role="aut"),
	   person("Michael", "Bedward", role="aut"),
	   person("Ben", "Bolker", role="aut"),
	   person("Daniel", "Borcard", role="aut"),
	   person("Tuomas", "Borman", role="aut"),
	   person("Gustavo", "Carvalho", role="aut"),
	   person("Michael", "Chirico", role="aut"),
	   person("Miquel", "De Caceres", role="aut"),
	   person("Sebastien", "Durand", role="aut"),
	   person("Heloisa Beatriz Antoniazi", "Evangelista", role="aut"),
	   person("Rich", "FitzJohn", role="aut"),
	   person("Michael", "Friendly", role="aut"),
	   person("Brendan","Furneaux", role="aut"),
	   person("Geoffrey", "Hannigan", role="aut"),
	   person("Mark O.", "Hill", role="aut"),
	   person("Leo", "Lahti", role="aut"),
	   person("Cameron", "Martino", role="aut"),
	   person("Dan", "McGlinn", role="aut"),
	   person("Marie-Helene", "Ouellette", role="aut"),
	   person("Eduardo", "Ribeiro Cunha", role="aut"),
	   person("Tyler", "Smith", role="aut"),
	   person("Adrian", "Stier", role="aut"),
	   person("Cajo J.F.", "Ter Braak", role="aut"),
	   person("James", "Weedon", role="aut"))
c(person("Jari", "Oksanen", role=c("aut","cre"),
	     email="jhoksane@gmail.com"),
             person("Roeland", "Kindt", role="aut"),
             person(given=c("Gavin", "L."), family="Simpson", role="aut",
             email="ucfagls@gmail.com"),
             person("Duncan", "Murdoch", role="ctb", 
                    email="murdoch.duncan@gmail.com"))
c(
    person(
      given = "Ian", 
      family = "Lyttle", 
      email = "ijlyttle@me.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'))
c(person(given = "Dibyendu", family = "Deb", role = c("aut", "ctb"),email = "debiasri@gmail.com"),
  person(given = "Arpan", family = "Bhowmik", role = c("aut", "ctb"), email = "arpan.stat@gmail.com"),
  person(given = "Bijoy", family = "Chanda", role = c("aut", "cre", "ctb"),email = "bijoychanda08@gmail.com" ),
  person(given = "J.P.", family = "Singh", role = "aut",email ="jpsingh.igfri@gmail.com" ),
  person(given = "Sunil", family = "Mandi", role = "aut",email = "mandi.sunil@gmail.com"),
  person(given = "Alemwati", family = "Pongener", role = "aut",email = "alemwati@gmail.com" ))
c(
  person(given="Robert", 
         family="Nuske", 
         role=c("aut", "cre"),
         email="robert.nuske@mailbox.org",
         comment=c(ORCID="0000-0001-9773-2061")),
  person("Nordwestdeutsche Forstliche Versuchsanstalt (NW-FVA)", 
         role=c("cph", "fnd")))
c(person("Avijit", "Ghosh", role = c("aut","ctb","cph"),email = "avijitghosh19892@gmail.com"),
               person("Ram", "S Chaurasia", role = c("aut","ctb","cph"), email = "sharan25.jhs@gmail.com"),
               person("Tanuj", "Misra",  role = c("aut", "cre", "ctb", "cph"), email = "tanujmisra102@gmail.com"),
               person("Amit", "Kumar Singh", role = c("aut","ctb","cph"), email = "amit09bhu@gmail.com"),
               person("Shailendra", "Kumar", role = c("aut","ctb","cph"), email = "shailendrabujhansi@gmail.com"),
               person("Abhishek", "Chakraborty", role = c("aut","ctb","cph"), email = "abhishekchakraborty392@gmail.com"),
               person("Devanand", role = c("aut","ctb","cph"), email = "dvnd456@gmail.com"),
               person("J P Singh", role = c("aut","ctb","cph"), email = "jpsingh.igfri@gmail.com"),
               person("A K Shukla", role = c("aut","ctb","cph"), email = "akshuklahort@gmail.com")
              )
c( 
  person(given = "Sergio", family = "Ibarra-Espinosa",
         role = c("aut", "cre"),
         email = "zergioibarra@gmail.com",
         comment = c(ORCID = "0000-0002-3162-1905")),
  person(given="Daniel", family="Schuch", 
         role="ctb", 
         email="underschuch@gmail.com",
         comment = c(ORCID = "0000-0001-5977-4519")),
  person(given="Joao", family="Bazzo", 
         role="ctb", 
         email="joao.bazzo@gmail.com",
         comment = c(ORCID = "0000-0002-7371-1116")),
  person(given="Mario", family="Gavidia-Calderón", 
         role="ctb", 
         email="mario.calderon@iag.usp.br",
         comment = c(ORCID = "0000-0003-4536-5006")),
  person(given="Karl", family="Ropkins", 
         role="ctb", 
         email="k.ropkins@its.leeds.ac.uk",
         comment = c(ORCID = "0000-0002-0294-6997")))
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"))
  )
person(given = "Adrian", family = "Dusa",
    role = c("aut", "cre", "cph"),
    email = "dusa.adrian@unibuc.ro",
    comment = c(ORCID = "0000-0002-3525-9253"))
c(person("Zehao", "Xu", email = "z267xu@uwaterloo.ca", role = c("aut", "cre")), person("R. Wayne", "Oldford", role = "aut"), person("Martin", "Lysy", role = "ctb"))
person(given = "Cara",
           family = "Thompson",
           role = c("aut", "cre"),
           email = "cara.r.thompson@gmail.com",
           comment = c(ORCID = "0000-0002-6472-5073"))
c(
    person("Adriana", "Robles", email = "adriana@demografiando.pro",
           role = c("aut"), comment = c(ORCID = "0000-0001-7202-6523")),
    person("Javier", "Borja", email = "javier@demografiando.pro",
           role = c("aut", "cre")))
c(
    person("Maria", "Gargiulo", , "mariag@hrdag.org", role = c("aut", "cre")),
    person("María Juliana", "Durán", , "mduranfedullo@gmail.com", role = "aut"),
    person("Paula Andrea", "Amado", , "pamadoa1@gmail.com", role = "aut"),
    person("Patrick", "Ball", role = "rev")
  )
c(person("Eric", "Gilleland", role = c("aut","cre"), email = "eric.gilleland@colostate.edu", comment = c(ORCID="0000-0002-8058-7643") ),
	     person("Matt", "Pocernich", role = "ctb", email = "matt_pocernich@hotmail.com" ),
	     person("Sabrina", "Wahl", role = "ctb", email = "sabrina.wahl@dwd.de"),
	     person( "Ronald", "Frenette", role = "ctb", email = "Ronald.Frenette@ec.gc.ca") )
c(
    person("Juha", "Javanainen", email = "ejuhjav@gmail.com", role = c("aut", "cre")),
    person("Tiina", "Kirsilä", email = "tiina.kirsila@gmail.com", role = "ctb"),
    person("Anna", "Wiksten", email = "a.s.wiksten@gmail.com", role = "ctb"))
person("Nathaniel", "Henry", , "nat@henryspatialanalysis.com", role = c("aut", "cre"),
           comment = c(ORCID = "0000-0001-8150-4988"))
c(person("Laura", "Bakala",
             email = "erdaradun.gaztea@gmail.com",
             role = c("cre", "aut", "cph")))
person("Ryan", "Dickerson", , "fresh.tent5866@fastmail.com", role = c("aut", "cre", "cph"))
c(
    person("Junhong", "Yu", , "junhong.yu@ntu.edu.sg", role = "aut",
           comment = c(ORCID = "0000-0002-2563-9658")),
    person("Charly", "Billaud", , "charly.billaud@ntu.edu.sg", role = c("aut", "cre"),
           comment = c(ORCID = "0009-0001-3466-9963"))
  )
c(
    person("Julia", "Silge", , "julia.silge@posit.co", role = c("cre", "aut"),
           comment = c(ORCID = "0000-0002-3671-836X")),
    person("Posit Software, PBC", role = c("cph", "fnd"))
  )
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/r-copied.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 T.", "Holloway", role = c("aut", "cre"),
                     email = "shannon.t.holloway@gmail.com"))
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"),
                    comment = c(ORCID = "0000-0002-9970-3907"),
                    email = "t.yee@auckland.ac.nz"),
             person("Cleve", "Moler", role = "ctb",
                    comment = "LINPACK routines in src"))
c(person("Thomas", "Yee", role = c("aut", "cre", "cph"),
                    comment = c(ORCID = "0000-0002-9970-3907"),
                    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"))
person("Max", "Goplerud", email = "mgoplerud@austin.utexas.edu", 
    role = c("aut", "cre"))
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"))
person(given = "Arnaud",
                    family = "Le Rouzic",
                    role = c("aut", "cre"),
                    email = "arnaud.le-rouzic@universite-paris-saclay.fr")
c(person(given = "Alper",
             family = "Kucukural",
             role = c("aut", "cre"),
             email = "alper@viascientific.com"),
      person(family = "Via Scientific",
             role = c("aut", "cph")))
c(person(given = "VIBASS Team",
             role = c("aut", "cph")),
      person(given = "Facundo",
             family = "Muñoz",
             role = c("ctb", "cre"),
             email = "facundo.munoz@cirad.fr",
             comment = c("Cirad, Package developer", ORCID = "0000-0002-5061-4241")),
      person(given = "Carmen",
             family = "Armero",
             role = "ctb",
             email = "carmen.armero@uv.es",
             comment = "Universitat de València"),
      person(given = "Anabel",
             family = "Forte",
             role = "ctb",
             email = "anabel.forte@uv.es",
             comment = "Universitat de València"),
      person(given = "David",
             family = "Conesa",
             role = "ctb",
             email = "David.V.Conesa@uv.es",
             comment = "Universitat de València"),
      person(given = "Mark",
             family = "Brewer",
             role = "ctb",
             email = "mark.brewer@bioss.ac.uk",
             comment = c("Biomathematics and Statistics Scotland (BioSS)",
             ORCID = "0000-0001-7559-389X")),
      person(given = "Virgilio",
             family = "Gómez-Rubio",
             role = "ctb",
             email = "Virgilio.Gomez@uclm.es",
             comment = c("Universidad Castilla-La Mancha",
             ORCID = "0000-0002-4791-3072")))
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"))
    )
c(person("Jackie", "Rao", email = "jackie.rao@mrc-bsu.cam.ac.uk", role = c("aut", "cre")),
    person(c("Paul", "D.W"), "Kirk", email = "paul.kirk@mrc-bsu.cam.ac.uk", role = "ths"),
    person("Sara", "Wade", email = "sara.wade@ed.ac.uk", role = "ctb"),
    person("Colin", "Starr", email = "colin.starr@mrc-bsu.cam.ac.uk", role = "ctb"),
    person("John", "Maddock", email = "john@johnmaddock.co.uk", role = "cph", comment = "Author of original version of digamma header (digamma.h)."))
c(person('Boris', 'Hejblum', email = 'boris.hejblum@u-bordeaux.fr', role = c('cre', 'aut')),
             person('Melanie', 'Huchon', email = 'melanie.huchon@u-bordeaux.fr', role = c('aut')),
             person('Clement', 'Nerestan', email = 'clement.nerestan@u-bordeaux.fr', role = c('aut'))
           )
c(
    person("Ashley", "Baldry", email = "arbaldry91@gmail.com", role = c("aut", "cre")),
    person("Steve", "Heffernan", role = "aut", comment = "Creator of video.js")
    )
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 = "de Siqueira Gesteira",
                  role = c("aut"),
                  email = "gdesiqu@ncsu.edu"),
           person(given = "Jeekin",
                  family = "Lau",
                  role = c("aut")),
           person(given = "Olivia",
                  family = "Angelin-Bonnet",
                  role = c("aut")),
           person(given = "Susan",
                  family = "Thomson",
                  role = c("ctb")),
           person(given = "Guilherme",
                  family = "da Silva 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("Charles", "Dolladille", email = "cdolladille@hotmail.com", role = c("aut", "cre"),
           comment = c(ORCID = "0000-0003-0449-6261")),
      person("Basile", "Chrétien", role = c("aut"),
           comment = c(ORCID = "0000-0002-7483-2489")),
       person("Universite de Caen Normandie", role = c("cph"), comment = "Caen, France"),
       person("Unite de pharmaco-epidemiologie", role = c("cph") , comment = "Service de pharmacologie, Centre Hospitalier Universitaire de Caen, Caen, France")
       )
c(person("Akio", "Onogi", role = c("aut", "cre", "cph"), email = "onogiakio@gmail.com"), person("Hiroyoshi", "Iwata", role = "cph"), person("Takuji", "Nishimura", role = "ctb", comment = "Developer of Mersenne twister in header1.h"), person("Makoto", "Matsumoto", role = "ctb", comment = "Developer of Mersenne twister in header1.h"), person("STRUCTURE software contributors", role = "ctb", comment="Provide snorm and RNormal functions in header2.h"), person("Alan", "Miller", role = "ctb", comment = "Program mylgamma function in header2.h"), person("Peter", "Beerli", role = "ctb", comment = "Translate mylgamma function in header2.h"))
c(
  person(
    given = "Thomas",
    family = "Thelen",
    role = c("aut", "cre"),
    email = "tommythelen@gmail.com"
  ),
  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 = "brian.d.williamson@kp.org",
             comment = c(ORCID = "0000-0002-7024-548X")),
      person(given = "Jean",
             family = "Feng",
             role = "ctb"),
      person(given = "Charlie",
             family = "Wolock",
             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"))
person("Meredith", "Wallace", , "lotzmj@upmc.edu", role = c("aut", "cre"),
           comment = c(ORCID = "0000-0003-3951-890X"))
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("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("aut", "ctb")),
           person("Jordan", "Adamson", email = "adamson@wifa.uni-leipzig.de", role = c("aut", "ctb")))
c(
  person(c("Brandon", "M."), family = "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"))
  )
c(person("Tatsiana", "Bylund", email = "tatsiana.bylund@nih.gov", role = c("aut", "cre")))
person("Juan Pablo", "Acuña González", , "22253567@uagro.mx", role = c("aut", "cre"),
           comment = c(ORCID = "0009-0003-6029-6560"))
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("Frans", "Willekens", role = c("aut", "cre"),email="willekens@nidi.nl",
           comment = c(ORCID = "0000-0001-6125-0212")),
   	   person("Tim", "Riffe", role = "ctb", 
           email = "tim.riffe@gmail.com", comment = c(ORCID = "0000-0002-2673-4622")))
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")))
c(
    person("Ruff", "Sergej", email = "serijnh@gmail.com", role = c("aut", "cre"),
           comment = c(ORCID = "0009-0000-8264-6347")),
    person("Lauber", "Chris", email = "chris.lauber@twincore.de", role = "ctb",
           comment = c(ORCID = "0000-0002-2265-2953")),
    person("Chong", "Li Chuin",email = "lichuin.chong@twincore.de", role = "ctb",
           comment = c(ORCID = "0000-0002-3574-1365")))
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 = "Stephanie",
             family = "Modica",
             role = c("aut"),
             email = "steph.modica.npm@gmail"),
      person(given = "David",
             family = "Kutas",
             role = c("aut"),
             email = "david.kutas.npm@gmail.com"),
      person(given = "Jaime",
             family = "Tanner",
             role = c("aut"),
             email = "jaimeatanner@gmail.com"),
      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@mountsinai.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")))
c(
    person(given = "Zishu",
           family = "Zhan",
           role = c("aut", "cre"),
           email = "zishu927@hotmail.com"),
    person(given = "Jingxiao",
           family = "Zhang",
           role = "aut",
           email = "zhjxiao@ruc.edu.cn")
    )
c(person(given = "Georgios", 
         family = "Seitidis", 
         role = c("aut", "cre"),
         email = "g.seitidis@uoi.gr", 
         comment = c(ORCID = "0000-0003-0856-1892")),
  person(given = "Sofia",
         family = "Tsokani",
         role = c("aut"),
         email = "s.tsokani@uoi.gr",
         comment = c(ORCID = "0000-0002-6258-8861")),
  person(given = "Christos",
         family = "Christogiannis",
         role = c("aut"),
         email = "xristogiannhsjr@gmail.com"),
  person(given = "Katerina Maria",
         family = "Kontouli",
         role = c("aut"),
         email = "kmkontouli@gmail.com"),
  person(given = "Alexandros",
         family = "Fyraridis",
         role = c("aut"),
         email = "alexandros.fyraridis@unityhealth.to"),
  person(given = "Stavros",
         family = "Nikolakopoulos",
         role = c("aut"),
         email = "snikolakopoulos@uoi.gr",
         comment = c(ORCID = "0000-0002-9769-3725")),
  person(given = "Areti Angeliki",
         family = "Veroniki",
         role = c("aut"),
         email = "areti-angeliki.veroniki@unityhealth.to",
         comment = c(ORCID = "0000-0001-6388-4825")),
  person(given = "Dimitris",
         family = "Mavridis",
         role = c("aut"),
         email = "snikolakopoulos@uoi.gr",
         comment = c(ORCID = "0000-0003-1041-4592")))
person("Peter", "Biber", email = "castor.fiber@gmx.de", 
                  role = c("aut", "cre"),
                  comment = c(ORCID = "0000-0002-9700-8708"))
c(
            person("Tomoki", "Tokuda", email = "t-tokuda@eri.u-tokyo.ac.jp", role= c("aut")),
            person("Ben", "Goodrich", email = "bg2382@columbia.edu", role= c("ctb")),
            person("Iven", "Van Mechelen", email = "iven.vanmechelen@kuleuven.be", role= c("ctb")),
            person("Andrew", "Gelman", email = "gelman@stat.columbia.edu", role= c("ctb")),
            person("Francis", "Tuerlinckx", email = "francis.tuerlinckx@kuleuven.be", role= c("ctb")),
            person("Kristof", "Meers", email = "kristof.meers+cran@kuleuven.be", role=c("cre"))
            )
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")),
  person("Christophe", "Regouby", role=c("ctb"))
      )
c(person("Erin M.", "Buchanan", role = c("aut", "cre"),
                     email = "buchananlab@gmail.com",
                     comment = c(ORCID = "0000-0002-9689-4189")))
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("Robert", "Bowen", email = "robertbowen.bham@gmail.com", role = c("aut", "cre")),
        person("Russell", "Goebel", email = "rgoebel@bu.edu", role = "aut"),
        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("Janko", "Richter", role = c("aut", "cre"), email = "janko@richtej.de"))
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 = "bthieurmel@gmail.com")
    )
c(
    person(
      "Martí", "Català", , "marti.catalasabate@ndorms.ox.ac.uk", 
      role = c("aut"), comment = c(ORCID = "0000-0003-3308-9905")
    ),
    person(
      "Núria", "Mercadé-Besora", , "nuria.mercadebesora@ndorms.ox.ac.uk", 
      role = c("aut", "cre"), comment = c(ORCID = "0009-0006-7948-3747")
    ),
    person(
      "Yuchen", "Guo", , "yuchen.guo@ndorms.ox.ac.uk", 
      role = c("ctb"), comment = c(ORCID = "0000-0002-0847-4855")
    ),
    person(
      "Elin", "Rowlands", , "elin.rowlands@ndorms.ox.ac.uk", 
      role = c("ctb"), comment = c(ORCID = "0009-0007-6629-4661")
    ),
    person(
      "Marta", "Alcalde-Herraiz", , "marta.alcaldeherraiz@ndorms.ox.ac.uk", 
      role = c("ctb"), comment = c(ORCID = "0009-0002-4405-1814")
    ),
    person(
      "Edward", "Burn", , "edward.burn@ndorms.ox.ac.uk", 
      role = c("ctb"), comment = c(ORCID = "0000-0002-9286-1128")
    )
  )
c(
    person("Claudiu", "Forgaci", , "c.forgaci@tudelft.nl", role = c("aut", "cre", "cph"),
           comment = c(ORCID = "0000-0003-3218-5102")),
    person("Francesco", "Nattino", , "f.nattino@esciencecenter.nl", role = "aut",
           comment = c(ORCID = "0000-0003-3286-0139")),
    person("Netherlands eScience Center", , , "info@esciencecenter.nl", role = "fnd")
  )
c(
  person("Patrick", "Breheny", role=c("aut","cre"),
         email="patrick-breheny@uiowa.edu",
         comment=c(ORCID="0000-0002-0650-1119")),
  person("Woodrow", "Burchett", role=c("ctb")))
c(
    person("Sabine", "Schilling", , "sabineschilling@gmx.ch", 
    role = c("cre", "aut", "cph"),comment = c(ORCID = "0000-0002-8318-9421", year = "2025")),
    person("Peter", "Kauf", , "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")
	         )
	  )
c(person(given = "Tingting", family = "Zhan", role =
       c("aut", "cre"), email = "tingtingzhan@gmail.com", comment
       = c(ORCID = '0000-0001-9971-4844')))
person(given = "Javier",
           family = "Portela",
           role = c("aut", "cre"),
           email = "javipgm@gmail.com",
		   comment = c(ORCID = "0000-0002-5284-5123"))
c(
    person("Ramesh", "Ramasamy", , "ramesh.rahu96@gmail.com", role = c("aut", "cre","cph"),
           comment = c(ORCID = "0000-0002-5433-1417")),
    person("Mathiyarasi", "Kulandaivadivel", , "mathiarasi6@gmail.com", role = "ctb"),
    person("Tamilselvan", "Arumugam", , "tamilselvan372511@gmail.com", role ="ctb")
    )
c(person(given = "Wilbert",
             family = "Heeringa",
             role = c("aut", "cre"),
             email = "wheeringa@fryske-akademy.nl"),
      person(given = "Hans",
             family = "Van de Velde",
             role = "aut",
             email = "hvandevelde@fryske-akademy.nl"))
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")),
  person("Isabelle", "Greco", role="ctb", comment = c(ORCID = "0000-0003-1604-6639")))
c(
  person("Rob", "Hyndman", email="Rob.Hyndman@monash.edu", role=c("aut", "cre", "cph"), comment = c(ORCID = "0000-0002-2140-5352")),
  person("Sixian", "Tang", role = "aut", comment = c(ORCID = "0000-0002-7292-462X")),
  person("Miles", "McBain", role = "ctb", comment = c(ORCID = "0000-0003-2865-2548")),
  person("Mitchell", "O'Hara-Wild", role = "ctb", comment = c(ORCID = "0000-0001-6729-7695"))
  )
c(
    person("Simon", "Couch", , "simon.couch@posit.co", role = c("aut", "cre"),
           comment = c(ORCID = "0000-0001-5676-5107")),
    person("Max", "Kuhn", , "max@posit.co", role = "ctb"),
    person("Hadley", "Wickham", , "hadley@posit.co", role = "ctb",
           comment = c(ORCID = "0000-0003-4757-117X")),
    person("Mine", "Cetinkaya-Rundel", , "mine@posit.co", role = "ctb",
           comment = c(ORCID = "0000-0001-6452-2420")),
    person("Posit Software, PBC", role = c("cph", "fnd"),
           comment = c(ROR = "03wc8by49"))
  )
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")
	)
c(
    person("Marissa", "Knoll", role = "aut"),
    person("Katherine", "Johnson", role = "aut"),
    person("Megan", "Hockman", role = "aut"),
    person("Eric", "Borenstein", role = "aut"),
    person("Mohammed", "Khalfan", role = "aut"),
    person("Elodie", "Ghedin", role = "aut"),
    person("David", "Gresham", , "dg107@nyu.edu", role = c("aut", "cre", "cph"))
  )
c(
    person(given = "Alan", family = "Inglis", email = "alan.n.inglis@gmail.com", 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("Dave", "Armstrong", , email="davearmstrong.ps@gmail.com", role = c("aut", "cre"),
           comment = c(ORCID = "0000-0002-9358-2489")),
    person("William", "Poirier", , "wpoirier@uwo.ca", role = c("aut"),
           comment = c(ORCID = "0000-0002-3274-1351")))
c(
    person("Dmitriy", "Sorokin", role = c("aut", "cre"),
           email = "dementiy@yandex.ru"),
    person("Anton", "Antonov", role = "ctb",
           email = "tonytonov@gmail.com"))
c(person(given = c("Taryn", "B.", "T."),
                        family = "Athey",
                        role = "cre",
                        email = "taryn.athey@gmail.com"),
                 person(given = c("Paul", "D."),
                        family = "McNicholas",
                        role = "aut"),
                 person(given = c("Jarrett", "D."),
                        family = "Phillips",
                        role = "ctb"))
person("Martin", "Maechler", role = c("aut", "cre"),
		  email = "maechler@stat.math.ethz.ch", comment = c(ORCID = "0000-0002-8685-9910"))
c(person("Adriano Zanin Zambom", "Developer", role = c("aut", "cre","cph"), email = "adriano.zambom@gmail.com"), person("Seonjin Kim", "Developer", role = "aut"), person("Nancy Lopes Garcia", "Developer", role = "aut") )
person(given = "Chainarong",
           family = "Amornbunchornvej",
           role = c("aut", "cre"),
           email = "grandca@gmail.com",
           comment =c(ORCID = "0000-0003-3131-0370"))
c( person("Lampros", "Mouselimis", email = "mouselimislampros@gmail.com", role = c("aut", "cre"), comment = c(ORCID = "https://orcid.org/0000-0002-8024-1546")), person("Dominique", "Zosso", email = "dominique.zosso@montana.edu", role = "cph", comment = "Author of the Variational Mode Decomposition Matlab code"), person("Konstantin", "Dragomiretskiy", role = "cph", comment = "Author of the Variational Mode Decomposition Matlab code") )
c(person("Pankaj", "Das", role = c("aut","cre"),email="pankaj.das2@icar.gov.in"), 
        person("Girish", "Kumar Jha", role = "aut",email="grish.stat@gmail.com"),
        person("Tauqueer", "Ahmad", role = "aut",email="Tauqueer.Ahmad@icar.gov.in"),
        person("Achal", "Lama", role = "aut",email="achal.lama@icar.gov.in"),
        person("Lampros", "Mouselimis", email = "mouselimislampros@gmail.com", role = "cph"))
c(person(given = "Kapil",
         family = "Choudhary",
         role = c("aut", "cre"),
         email = "kapiliasri@gmail.com"),
  person(given = "Girish Kumar",
         family = "Jha",
         role =  c("aut", "ths", "ctb")),
  person(given = "Rajender ",
         family = "Parsad",
         role =  c("aut", "ctb")),
  person(given = "Ronit",
         family = "Jaiswal",
         role = c("aut", "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"))
c(
    person("Zhenhua", "Wang", email = "zhenhua.wang@missouri.edu", role = c("aut", "cre")),
    person("Paul A.", "Parker", email = "pparker1@ucsc.edu", role = c("aut", "res")),
    person("Scott H.", "Holan", email = "holans@missouri.edu", role = c("aut", "res")))
c(
    person("Sam", "Byrne", email = "ssbyrne@uw.edu"
    , role = c("aut", "cre", "cph")
    , comment = c(ORCID = "0009-0008-1067-307X"))
           )
person(given = "Lennart",
           family = "Oelschl\U00E4ger",
           role = c("aut", "cre"),
           email = "lennart.oelschlaeger@uni-bielefeld.de",
           comment = c(ORCID = "0000-0001-5421-9313"))
person("Saturnino", "Luz", email = "luzs@acm.org", role = c("aut", "cre"))
c(person(given = "Christopher", family = "Jackson",role = c("aut", "cre"),email = "chris.jackson@mrc-bsu.cam.ac.uk"),
	   person(given = "Anna", family = "Heath", role = c("aut"), email = "anna.heath@sickkids.ca"),
	   person(given = "Gianluca", family = "Baio", role = c("ctb"), email = "g.baio@ucl.ac.uk", comment="Author of code taken from the BCEA package"),
	   person(given = "Mark", family = "Strong", role = c("ctb"), email = "mark.strong@sheffield.ac.uk", comment="Author of code taken from the SAVI package"),
	   person(given="Kofi Placid", family="Adragni", role="ctb", comment="Author of code taken from the ldr package"),
	   person(given="Andrew", family="Raim", role="ctb",comment="Author of code taken from the ldr package")
	   )
person("Zabala", "Filipe J.", email = "filipezabala@gmail.com", role = c("cre","aut"))
c(person(given = "Francesc",
          					family = "Busquet", 
          					email = "francesc.busquet@unisg.ch",
          					role = c("aut", "cre")),
			    person(given = "Christian", 
        					family = "Hildebrand",
        					email = "christian.hildebrand@unisg.ch",
        					role = c("aut")))
c(person("Massimo", "Cannas", role = c("aut", "cre"),
                     email = "massimo.cannas@unica.it"),
              person("Nicola", "Piras", role = c("aut"),
                     email = "nicola.piras97@unica.it"),
              person("Daniele", "Chiriu", role = c("ctb"),
                     email = "daniele.chiriu@dsf.unica.it"))
c(person("Katriona", "Goldmann", role = c("aut", "cre"),
                       email = "katrionagoldmann@gmail.com",
                       comment = c(ORCID = "0000-0002-9073-6323")),
                person("Myles", "Lewis", role = c("aut"),
                comment = c(ORCID = "0000-0001-9365-5345")))
c(
	person("Jeremy", "Wildfire", email = "jwildfire@gmail.com", role = c("cre","aut")),
	person("Becca", "Krouse", role="aut"),
	person("Natalia","Andriychuk", role="aut"),
	person("Anh","Tran", role = "aut"),
	person("Isaac","Zhao", role="aut"))
c(
    person("Vissarion", "Fisikopoulos", , "vissarion.fisikopoulos@gmail.com", role = c("aut", "cre", "cph"),
           comment = c(ORCID = "0000-0002-0780-666X")),
    person("Apostolos", "Chalkis", , "tolis.chal@gmail.com", role = c("aut", "cph"),
           comment = c(ORCID = "0000-0002-4628-1907")))
c(
    person("Jakob", "Jünger", email = "jakob.juenger@uni-muenster.de", role = c("aut", "cre", "cph"),
      comment = c(ORCID = "0000-0003-1860-6695")),
    person("Henrieke", "Kotthoff", email = "henrieke.kotthoff@uni-muenster.de", role = c("aut","ctb")),
    person("Chantal", "Gärtner", email = "chantal.gaertner@uni-muenster.de", role = "ctb", 
      comment = c(ORCID = "0000-0002-3653-6013"))
  )
person("Yoshida", "Takuji", email = "t.yoshida.science.kyoto@gmail.com", 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 = "Emmaline",
             family = "Sheahan",
             role = "aut",
             email = "sheahane@ufl.edu",
             comment = c(ORCID = "0000-0003-3358-9758")),
      person(given = "Carsten", 
             family = "Rahbek",,,
             role="aut",
             comment = c(ORCID = "0000-0003-4585-0300")))
c(person(given = "Jorn", family = "Lotsch",
                    role = c("aut", "cre"),
                    email = "j.lotsch@em.uni-frankfurt.de",
                    comment = c(ORCID = "0000-0002-5818-6958")))
c(person("Emmanuel", "Paradis", role = c("aut", "cre", "cph"), email = "Emmanuel.Paradis@ird.fr", comment = c(ORCID = "0000-0003-3092-2199")),
  person("Steven", "Fortune", role = c("aut", "ctb")), person("Plan 9 Foundation", role = "cph"))
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("Bryan", "Gertzel",
           email = "bryan.gertzel@anu.edu.au",
           role = c("aut", "cre")),
    person("Robert", "Ackland",
           email = "robert.ackland@anu.edu.au",
           role = "aut",
           comment = c(ORCID = "0000-0002-0008-1766")),
    person("Timothy", "Graham",
           role = "aut",
           comment = c(ORCID = "0000-0002-4053-9313")),
    person("Francisca", "Borquez",
           role = "ctb"))
c(person(given = "Hana",
                      family = "Sevcikova",
                      role = c("aut", "cre"),
                      email = "hanas@uw.edu"),
               person(given = "Bernard",
                      family = "Silverman",
                      role = "aut"),
               person(given = "Adrian",
                      family = "Raftery",
                      role = "aut"))
c(
    person("Bob", "Rudis", email = "bob@rud.is", role = c("aut", "cre"), 
           comment = c(ORCID = "0000-0001-5670-2640")),
      person("ProPublica", comment = "Cartogram SVGs used to make the plot data.frames", role = c("cph")),
      person("GovTrack", comment = "Cartogram SVG used to make the plot data.frame", role = c("cph"))
    )
c(person(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("David", "Clifford", , email="David.Clifford+CRAN@gmail.com", role = c("aut")),
           person("Glenn", "Stone", role = c("aut")),
           person("Ethan", "Bass", email = "ethanbass@gmail.com", role = c("ctb", "cre"), comment = c(ORCID = "0000-0002-6175-6739"))
           )
c(person("Emily O'Grady",  role = c("aut", "cre","cph"), email = "vprrcontact@gmail.com"), person("Kevin Sorochan", role = "aut", "dev"), person("Catherine Johnson", 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("Jim", "Hester", role = "aut",
           comment = c(ORCID = "0000-0002-2739-7082")),
    person("Hadley", "Wickham", , "hadley@posit.co", role = "aut",
           comment = c(ORCID = "0000-0003-4757-117X")),
    person("Jennifer", "Bryan", , "jenny@posit.co", role = c("aut", "cre"),
           comment = c(ORCID = "0000-0002-6983-2759")),
    person("Shelby", "Bearrows", role = "ctb"),
    person("https://github.com/mandreyel/", role = "cph",
           comment = "mio library"),
    person("Jukka", "Jylänki", role = "cph",
           comment = "grisu3 implementation"),
    person("Mikkel", "Jørgensen", role = "cph",
           comment = "grisu3 implementation"),
    person("Posit Software, PBC", role = c("cph", "fnd"))
  )
c(person("Jeffrey L.", "Andrews", role = c("aut")),
  person("Mackenzie R.", "Neal", role = "aut"),
  person("Paul D.", "McNicholas", role = c("aut", "cre"), email = "mcnicholas@math.mcmaster.ca",
  comment = c(ORCID = "0000-0002-2482-523X")))
c(
    person("Mengyang", "Zheng", , "mengyang.zheng@outlook.com", role = c("aut", "cre")),
    person("Hui", "Fu", , "hui_fu@hotmail.com", role = "aut")
  )
c(
  person('Jiaqi', 'Liang',  email = 'liangjq27@mail2.sysu.edu.cn', role = c('aut')),
  person('Chaoye', 'Wang',  email = 'wangcy1@sysucc.org.cn', role = c('aut', 'cre'))
  )
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")
  )
c(person("Karolina", "Klockmann", email="karolina.klockmann@gmx.de", role = c("aut", "cre")), person("Tatyana", "Krivobokova",  role = c("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("Nilson", "Chapagain", email = "nilson.chapagain@gmail.com", role = c("aut", "cre"), comment = c(ORCID = "0000-0003-2962-2949")),
            person("Debdeep", "Pati", email = "debdeep@tamu.edu", role = c("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("Tomer", "Iwan", email = "t.iwan@vu.nl", role = c("aut", "cre", "cph")),
    person("Hajo", "Bons", email = "h.b.bons@vu.nl", role = c("aut", "cph")))
c(person("Hajo", "Bons", , "h.b.bons@vu.nl", role = c("aut", "cre")),
    person("Tomer", "Iwan", email = "t.iwan@vu.nl", role = "aut"))
c(person(given = "Tomer", 
           family = "Iwan", 
           role = c("aut", "cre"), 
           email = "t.iwan@vu.nl"
           ),
    person(given = "Niels",
           family = "Smits",
           role = "ctb",
           email = "n.smits@uva.nl",
           comment = c(ORCID = "0000-0003-3669-9266")
           ),
    person(given = "VU Analytics",
           role = "cph"
           ))
c(person(given = "Tomer", 
           family = "Iwan", 
           role = c("aut", "cre"), 
           email = "t.iwan@vu.nl"
           ),
    person(given = "Corneel",
           family = "Den Hartogh",
           role = "ctb",
           email = "c.f.den.hartogh@vu.nl",
           comment = c(ORCID = "0000-0001-8347-7944")
           ),
    person(given = "Jesús",
           family = "Blanco",
           role = "ctb",
           email = "j.e.blanco@vu.nl"
           ),
    person(given = "VU Analytics",
           role = "cph"
           ))
c(person(given = "Corneel", 
           family = "Den Hartogh", 
           role = c("aut", "cre"), 
           email = "c.f.den.hartogh@vu.nl",
           comment = c(ORCID = "0000-0001-8347-7944")
           ),
    person(given = "Tomer",
           family = "Iwan",
           role = "ctb"
           ),
    person(given = "VU Analytics",
           role = "cph"
           ))
c(person(given = "Tomer", 
           family = "Iwan", 
           role = c("aut", "cre"), 
           email = "t.iwan@vu.nl"
           ),
    person(given = "Ruben",
           family = "Korvinus",
           role = "ctb"
           ),
    person(given = "Jorn",
           family = "Amatdoelrasit",
           role = "ctb"
           ),
     person(given = "VU Analytics",
           role = "cph"
           ))
person(given = "Paul",
                  family = "Murrell",
                  role = c("aut", "cre"),
                  email = "paul@stat.auckland.ac.nz")
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("Benjamin", "Listyg", , "listyg.ben@gmail.com", role = c("aut", "cre")),
    person("Brennah V.", "Ross", , "bross28@gsu.edu", role = "aut")
  )
c(
    person("Bob", "Rudis", email = "bob@rud.is", role = c("aut", "cre"), 
           comment = c(ORCID = "0000-0001-5670-2640")),
    person("Dave", "Gandy", role = "aut", comment = "Font Awesome"),
    person("Andrew", "Breza", email = "andrew.breza@gmail.com", role = "ctb"),
    person("Matthias", "Jütte", role = "ctb"),
    person("Paul", "Campbell", email = "pacampbell91@gmail.com", 
           role = "ctb", comment = "geom_bar flip")
    )
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("Lauren Blair", "Wilner", 
    email = "wilnerl@uw.edu", 
    role = c("aut", "cre", "cph"),
    comment = c(ORCID = "0000-0003-4439-3734")),
    person("Stephen J", "Mooney",
    email = "sjm2186@uw.edu",
    role = c("aut")))
c(person("Bethany A.", "Johnson", email = "bjohnso005@citymail.cuny.edu", role = "aut"),
    person("Daniel F.", "Lopez-Lozano", email = "dlopezlozano@amnh.org", role = "aut"),
    person("Jamie M.", "Kass", email = "jamie.m.kass@gmail.com", role = "aut"),
    person("Gonzalo E.", "Pinilla-Buitrago", email = "gepinillab@gmail.com", role = "aut"),
    person("Andrea", "Paz", email = "paz.andreita@gmail.com", role = "aut"),
    person("Valentina", "Grisales-Betancur", email = "vgrisale@eafit.edu.co", role = "aut"),
    person("Dean", "Attali", email = "daattali@gmail.com", role = "aut"),
    person("Matthew E.", "Aiello-Lammens", email = "matt.lammens@gmail.com", role = "aut"),
    person("Cory", "Merow", email = "corymerow@gmail.com", role = "aut"),
    person("Mary E.", "Blair", email = "mblair1@amnh.org", role = c("aut", "cre")),
    person("Robert P.", "Anderson", email = "randerson@ccny.cuny.edu", role = "aut"),
    person("Sarah I.", "Meenan", email = "sarah.meenan@gmail.com", role = "ctb"),
    person("Olivier", "Broennimann", email = "olivier.broennimann@unil.ch", role = "ctb"),
    person("Peter J.", "Galante", email = "pgalante@amnh.org", role = "ctb"),
    person("Brian S.", "Maitner", email = "bmaitner@gmail.com", role = "ctb"),
    person("Hannah L.", "Owens", email = "hannah.owens@gmail.com", role = "ctb"),
    person("Sara", "Varela", email = "sara_varela@yahoo.com", role = "ctb"),
    person("Bruno", "Vilela", email = "bvilela@wustl.edu", role = "ctb"),
    person("Robert", "Muscarella", email = "bob.muscarella@gmail.com", role = "ctb"))
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"))
person("Kevin", "Huynh", email = "kevin.huynh-dev@gmx.ch",
           role = c("aut", "cre"),
           comment = c(ORCID = "0000-0002-4621-2274"))
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")) )
person(given = c("Niklas"),
                   family = "Burgard",
                   role = c("aut", "cre"),
                   email = "info@burgard.online")
c(person(given = "Javier",
           family = "Sanchez Alvarez",
           role = c("aut", "cre"),
           email = "javiersanchezeco@gmail.com"),
      person(given = "Gabriel",
      family = "Lemyre",
      role = "ctb",
      email = "gabriel.lemyre@evidera.com"),                    
      person(given = "Valerie",
      family = "Aponte Ribero",
      role = "ctb",
      email = ""))
c(person(given = "Andrzej",
           family = "Dudek",
           role = c("aut","cre"),
           email = "andrzej.dudek@ue.wroc.pl",
           comment = c(ORCID = "0000-0002-4943-8703")),
           person(given = "Krzysztof",
           family = "Dmytrów",
           role = c("aut"),
           email = "<krzysztof.dmytrow@usz.edu.pl",
           comment = c(ORCID = "0000-0001-7657-6063")))
person(given = "Takumi",
		family = "Tsutaya",
		role = c("aut", "cre"),
		email = "tsutayatakumi@gmail.com")
c(
    person(
      "Xiaoli", "Wang",
      role = c("aut")
    ),
    person(
      "Mingyue", "Pan",
      email = "panmyue18@163.com",
      role = c("aut", "cre")
    )
  )
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")
  )
c(
    person("Jadey", "Ryan", , "jryan@agr.wa.gov", role = c("aut", "cre")),
    person("Molly", "Mcilquham", , "molly.mcilquham@wsu.edu", role = "aut"),
    person("Dani", "Gelardi", , "dgelardi@agr.wa.gov", role = "aut"),
    person("Washington State Department of Agriculture", role = c("cph", "fnd"))
  )
c(
    person("Mian", "Zhong", , "mzhong@ethz.ch", role = "aut",
           comment = c(ORCID = "0009-0009-4546-7214")),
    person("Margaux", "Götschmann", , "margauxg@ethz.ch", role = "aut",
           comment = c(ORCID = "0009-0002-2567-3343")),
    person("Colin", "Walder", , "cwalder@ethz.ch", role = c("aut", "cre"),
           comment = c(ORCID = "0009-0006-0969-1954")),
    person("Lars", "Schöbitz", , "lschoebitz@ethz.ch", role = "aut",
           comment = c(ORCID = "0000-0003-2196-5015")),
    person("Global Health Engineering, ETH Zurich", role = "cph")
  )
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")))
c(
    person("Flavio", "Barbara", , "flavio.barbara@gmail.com", role = c("cre", "aut")),
    person("Marcos", "Santos", , "marcosdossantos_doutorado_uff@yahoo.com.br", role = "ctb")
  )
c(
    person("Charlie", "Gao", , "charlie.gao@posit.co", role = c("aut", "cre"),
           comment = c(ORCID = "0000-0002-0750-061X")),
    person("Posit Software, PBC", role = c("cph", "fnd"),
           comment = c(ROR = "03wc8by49"))
  )
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("Will", "Beasley", role = c("aut", "cre", "cph"), email =
    "wibeasley@hotmail.com", comment = c(ORCID = "0000-0002-5613-5006")),
     person("Joe", "Rodgers", role = "aut", email =
    "joseph.l.rodgers@vanderbilt.edu"), person("Matthew", "Schuelke", role =
    "ctb", email = "schuelkem@slu.edu"), person("Ronnie",
    "Coleman", role = "ctb"), person("Mark Joseph", "Lachowicz", role =
    "ctb", email = "mark.j.lachowicz@vanderbilt.edu"),
    person("Oklahoma Shared Clinical and Translational Resource (OSCTR)", role = "fnd"))
c(
    person("Lukas", "Sablica", email = "lsablica@wu.ac.at", role = c("aut", "cre"),
           comment = c(ORCID = "0000-0001-9166-4563")), 
    person("Kurt", "Hornik", email = "Kurt.Hornik@wu.ac.at", role = "aut",
           comment = c(ORCID = "0000-0003-4198-9911")),
    person("Josef", "Leydold", email = "Josef.Leydold@wu.ac.at", role = "aut",
           comment = c(ORCID = "0000-0002-9076-4893")),
    person("Gerard", "Jungman", role = c("ctb","cph"), comment = "Author and copyright holder of included GNU GSL code"),
    person("Brian", "Gough", role = c("ctb","cph"), comment = "Author and copyright holder of included GNU GSL code")
  )
c(
    person("Daniel", "Falbel", email = "daniel@rstudio.com", role = c("aut", "cre", "cph")),
    person(family = "Posit, PBC", role = c("cph")),
    person("David", "Reid", role = c("cph"), comment = "For the vendored dr_wav.h code.")
    )
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(" Dr. Ranjit Kumar", "Paul", role = c("aut","cre"), email = "ranjitstat@gmail.com"),
    person("Dr. Md", "Yeasin", role = "aut", email = "yeasin.iasri@gmail.com"))
c(person(" Dr. Ranjit Kumar", "Paul", role = c("aut","cre"), email = "ranjitstat@gmail.com"),
    person("Mr. Sandipan", "Samanta", role = "aut", email = "sondiponsamanta@gmail.com"),
    person("Dr. Md", "Yeasin", role = "aut", email = "yeasin.iasri@gmail.com"))
c(person(given = "Angi",
                    family = "Roesch",
                    role = c("aut", "cre"),
                    email = "angi@angi-stat.com"),
             person(given = "Harald",
                    family = "Schmidbauer",
                    role = "aut",
                    email = "harald@hs-stat.com"))
c(person(" Dr. Ranjit Kumar", "Paul", role = "aut", email = "ranjitstat@gmail.com"),
    person("Dr. Md", "Yeasin", role = c("aut","cre"), email = "yeasin.iasri@gmail.com"))
c(person(" Mr. Sandip", "Garai", role = c("aut","cre"), email = "sandipnicksandy@gmail.com"),
    person("Dr. Ranjit Kumar", "Paul", role = "aut", email = "ranjitstat@gmail.com"))
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'))
person(given = "Michiel",
           family = "Arts",
           role = c("aut", "cre"),
           email = "michiel.arts@stratigraphy.eu",
		   comment = c(ORCID = "0000-0003-3181-4608"))
c(
  person("Raphaël", "Jauslin", email = "raphael.jauslin@bfs.admin.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"),
	person("Stuart", "Barber", role="ctb", email="s.barber@leeds.ac.uk"),
	person("Tim", "Downie", role="ctb", email="tim.downie@bht-berlin.de"),
	person("Piotr", "Frylewicz", role="ctb", email="p.fryzlewicz@lse.ac.uk"),
	person("Arne", "Kovac", role="ctb", email="A.Kovac@bristol.ac.uk"),
	person("Todd", "Ogden", role="ctb", email="todd.ogden@columbia.edu"),
	person("Bernard", "Silverman", role="ctb"))
c(person("Kevin R.", "Coombes", role = c("aut", "cre"),
                    email = "krc@silicovore.com"),
             person("Polina", "Bombina", role = c("aut"),
	            email = "pbombina@augusta.edu"))
person("Mark", "Eisler", email = "mark.eisler@bristol.ac.uk", role = c("aut", "cre", "cph"),
           comment = c(ORCID = "0000-0001-6843-3345"))
c(
    person("Michael", "Mahoney", , "mike.mahoney.218@gmail.com", role = c("aut", "cre"),
           comment = c(ORCID = "0000-0003-2402-304X")),
    person("Lucas", "Johnson", , "lucas.k.johnson03@gmail.com", role = "ctb",
           comment = c(ORCID = "0000-0002-7953-0260")),
    person("Virgilio", "Gómez-Rubio", role = "rev",
           comment = "Virgilio reviewed the package (v. 0.2.0.9000) for rOpenSci, see <https://github.com/ropensci/software-review/issues/571>"),
    person("Jakub", "Nowosad", role = "rev",
           comment = "Jakub reviewed the package (v. 0.2.0.9000) for rOpenSci, see <https://github.com/ropensci/software-review/issues/571>"),
    person("Posit Software, PBC", role = c("cph", "fnd"))
  )
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("Teal", "Emery", , "lte@tealinsights.com", role = c("aut", "cre")),
      person("Teal Insights", role = c("cph")),
      person("Christoph", "Scheuch", , "christoph@tidy-intelligence.com",
             role = c("aut"), comment = c(ORCID = "0009-0004-0423-6819")),
      person("Christopher", "Smith", email = "chriscarrollsmith@gmail.com", 
             role = c("ctb")))
c(person(given = "Rafal",
                    family = "Baranowski",
                    role = c("aut", "cre"),
                    email = "package_maintenance@rbaranowski.com"),
             person(given = "Piotr",
                    family = "Fryzlewicz",
                    role = "aut"))
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(given = "Marie-Pierre", family = "Sylvestre", role = c("aut", "cre"), email = "marie-pierre.sylvestre@umontreal.ca", comment = c(ORCID = "0000-0001-5803-4615")),
    person(given = "Marie-Eve", family = "Beauchamp", role =  "ctb"), person(given = "Ryan Patrick", family = "Kyle", role =  "ctb"),
    person(given = "Michal", family = "Abrahamowicz", role =  "ctb"))
c(person("Majid", "Nabipoor", email = "nabipoor@ualberta.ca", role=c("aut"),
           comment=c(ORCID = "0000-0003-2578-8300")), 
           person("Cynthia", "Westerhout", email="cindy.westerhout@ualberta.ca", role=c("aut"),
           comment=c(ORCID = "0000-0003-1082-5981")),
           person("Jeffrey", "Bakal", email="jbakal@ualberta.ca", role=c("aut"),
           comment=c(ORCID = "0000-0002-3658-2554")),
           person("Sarah", "Rathwell", email="srathwel@ualberta.ca", role=c("cre"),
           comment=c(ORCID = "0000-0003-3176-994X")))
c(
    person("Rituparna", "Dey", email = "rituparnadey525@gmail.com", role = c("aut", "cre")),
    person("Yajie", "Duan", email = "yajieritaduan@gmail.com", role = "aut"),
    person("Javier", "Cabrera", email = "xavier.cabrera@gmail.com", role = "aut"),
    person("Ge", "Cheng", email = "gc651@scarletmail.rutgers.edu", role = "aut"))
person(given = "Alexandru",
           family = "Monahov",
           role = c("aut", "cre", "cph"),
           email = "alexandru.monahov@proton.me",
           comment = c(ORCID = "0000-0001-6204-9131"))
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(given = "Vera",
           family = "Arntzen",
           role = c("aut", "cre"),
           email = "v.h.arntzen@math.leidenuniv.nl",
           comment = c(ORCID = "0000-0002-2642-9898")), 
           person(given = "Mar",
           family = "Rodriguez-Girondo",
           role = c("aut"),
           comment = c(ORCID = "0000-0003-3126-1003")))
c(
    person("Sánchez Martínez", "Noelia", , "noelia.sanchez.martinez@rai.usc.es", role = c("cre", "aut"),
           comment = c(ORCID = "0000-0002-2869-1347")),
    person("Borrajo Garcia", "Maria Isabel", , "mariaisabel.borrajo@usc.es", role = "aut",
           comment = c(ORCID = "0000-0002-3372-1993")),
    person("Conde Amboage", "Mercedes", , "mercedes.amboage@usc.es", role = "aut",
           comment = c(ORCID = "0000-0003-0306-8142"))
  )
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(given = "Gaelle", family = "Hamelin", role = c("aut", "cre"), email = "gaelle.hamelin@institut-agro.fr", comment = c(ORCID = "0009-0007-2148-7937"))
person("Thomas", "Nagler", email = "mail@tnagler.com", role = c("aut", "cre"))
c(
    person("Yelie", "Yuan", email = "yelie.yuan@uconn.edu",
           role = c("aut", "cre")),
    person("Tiandong", "Wang", email = "td_wang@fudan.edu.cn ",
           role = "aut"),
    person("Jun", "Yan", email = "jun.yan@uconn.edu",
           role = c("aut")),
    person("Panpan", "Zhang", email = "panpan.zhang@vumc.org",
           role = "aut"))
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("Akhilesh Kumar", "Gupta",,"akhileshgupta.ouat@gmail.com", 
    role =  c("aut","cph","cre"),comment = c(ORCID = "0000-0001-9380-1103")),
    person("Kader Ali", "Sarkar",,"kader804@gmail.com", role ="ths"),
    person("Digvijay Singh", "Dhakre",,"dhakreds@gmail.com", role =  "ths"),
    person("Debasis", "Bhattacharya",,"debasis_us@yahoo.com", role =  "ths"))
c(person("Brooke", "Anderson",
    email = "brooke.anderson@colostate.edu", role = c("aut", "cre")),
    person("Roger", "Peng", email = "rdpeng@gmail.com", role = c("aut")),
    person("Joshua", "Ferreri", email = "joshua.m.ferreri@gmail.com",
    role = c("aut")))
c(
      person(given = " Andriette",
        family = "Bekker",
        role = c("aut"),
        email = "andriette.bekker@up.ac.za"
        ),
      person(given = "Mohammad",
        family = "Arashi",
        role = c("aut"),
        email = "m_arashi_stat@yahoo.com"
        ),
      person(given = "Leila",
        family = "Marvian Mashhad",
        role = c("aut","cre"),
        email = "Leila.marveian@gmail.com"
        ),
      person(given = "Priyanka",
        family = "Nagar",
        role = c("aut"),
        email = "pnagar@sun.ac.za"
        )
    )
person("Jeffrey", "Fowler", email = "JeffreyF6120@gmail.com", 
       role = c("aut", "cre", "cph"), 
       comment = c(ORCID = "0009-0003-9448-0980"))
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("Ethan", "Bass", 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")),
    person("Lisa", "DeBruine",
           email = "debruine@gmail.com",
           role = c("aut", "cre")))
c(
    person("Gábor", "Csárdi", , "csardi.gabor@gmail.com", role = c("aut", "cre")),
    person("Posit Software, PBC", role = c("cph", "fnd"),
           comment = c(ROR = "03wc8by49")),
    person(, "Civetweb contributors", role = "ctb",
           comment = "see inst/credits/ciwetweb.md"),
    person(, "Redoc contributors", role = "ctb",
           comment = "see inst/credits/redoc.md"),
    person("L. Peter", "Deutsch", role = "ctb",
           comment = "src/md5.h"),
    person("Martin", "Purschke", role = "ctb",
           comment = "src/md5.h"),
    person(, "Aladdin Enterprises", role = "cph",
           comment = "src/md5.h"),
    person("Maëlle", "Salmon", , "maelle.salmon@yahoo.se", role = "ctb",
           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(given = c("Jason", "C."),
             family = "Fisher",
             role=c("aut", "cre"),
             email = "jfisher@usgs.gov",
             comment = c(ORCID="0000-0001-9032-8912")),
      person(given = "Stefano",
             family = "Cudini",
             role = c("ctb", "cph"),
             comment = "leaflet-search library"),
      person(given = "John",
             family = "Firebaugh",
             role = c("ctb", "cph"),
             comment = "Leaflet.fullscreen library")
    )
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 = c(ROR = "019jywm96"))
    )
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"),
 	      person("Ziqian", "Xu", role = "ctb"),
 	      person("Conor", "McNamara", role = "ctb"))
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("Giovanni", "Poggiato",, "giov.poggiato@gmail.com", role = c("aut","cre","cph")),
      person("Jérémy", "Andréoletti", role = c("aut")))
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", , "winston@posit.co", role = c("aut", "cre")),
    person("Barret", "Schloerke", , "barret@posit.co", role = "ctb",
           comment = c(ORCID = "0000-0001-9986-114X")),
    person("Posit Software, PBC", role = c("cph", "fnd"),
           comment = c(ROR = "03wc8by49"))
  )
c(
    person("Winston", "Chang", role = c("aut", "cre"), email = "winston@posit.co"),
    person("Joe", "Cheng", role = "aut", email = "joe@posit.co"),
    person("Alan", "Dipert", role = "aut"),
    person("Barbara", "Borges", role = "aut"),
    person(family = "Posit, PBC", 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("David", "Schoch", email = "david@schochastics.net", role = c("aut", "cre"),
           comment = c(ORCID = "0000-0003-2952-4812")),
    person("Bernhard", "Clemm von Hohenberg", email = "bernhard.clemm@gesis.org", role = "aut",
            comment = c(ORCID = "0000-0002-6976-9745")),
    person("Frank", "Mangold", email = "frank.mangold@gesis.org", role = "aut",
           comment = c(ORCID = "0000-0002-9776-3113")),
    person("Sebastian", "Stier", email = "sebastian.stier@gesis.org", role = "aut",
           comment = c(ORCID = "0000-0002-1217-5778"))
    )
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('Geoffery','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@gmx.de",
      role = "aut")
    )
person("Aristidis K.", "Nikoloulopoulos", role = c("aut","cre"), email = "a.nikoloulopoulos@uea.ac.uk")
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("Thomas J.", "Fisher", email = "fishert4@miamioh.edu", 
    role = c("aut", "cre"),
    comment = c(ORCID = "0000-0001-5885-7646") ),
    person("Colin M.", "Gallagher", email = "cgallag@g.clemson.edu", role = "aut") )
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("Michael", "Jahn", , "jahn@mpusp.mpg.de", role = c("aut", "cre"),
           comment = c(ORCID = "0000-0002-3913-153X")),
    person("David", "Leslie", , "david.leslie@scilifelab.se", role = "aut"),
    person("Ahmadou", "Dicko", , "mail@ahmadoudicko.com", role = "aut",
           comment = c(ORCID = "0000-0002-9654-7582")),
    person("Dunipace", "Eric", , "edunipace@mail.harvard.edu", role = "aut",
           comment = c(ORCID = "0000-0001-8909-213X")),
    person("Paul", "Murrell", , "p.murrell@auckland.ac.nz", role = c("aut", "cph"),
           comment = c(ORCID = "0000-0002-3224-8858"))
  )
person("Abdullah Faruk", "Kilic", email = "abdullahfarukkilic@gmail.com", role = c("aut", "cre"))
c(
  person(c("Kathleen", "M."), "Coburn", email = "kcoburn@ucmerced.edu", role = c("aut", "cre")),
  person(c("Jack", "L."), "Vevea", email = "jvevea@ucmerced.edu", role = c("aut")))
c(person(given = "Josh",
                 family = "Pasek",
                 role = c("aut", "cre"),
                 email = "josh@joshpasek.com",
		 comment = c(ORCID = "0000-0001-6099-6119")),
           person(given = "Alex",
                 family = "Tahk",
                 role = c("ctb"),
                 email = "atahk@wisc.edu",
		 comment = c(ORCID = "0000-0001-7895-9420")),
	   person(given="Gene",
                 family = "Culter",
                 role = c("ctb"),
		 email = "gcutler@amgen.com"),
           person(given = "Marcus",
                 family = "Schwemmle",
                 role = c("ctb"),
		 email="marcus.schwemmle@gfk.com"),
           person(given = c("Some", "code", "modified", "from", "R-core"), family = "", role = "ctb"))
c(person(given = "Rob",
           family = "Hyndman",
           role = c("aut", "cre", "cph"),
           email = "Rob.Hyndman@monash.edu",
           comment = c(ORCID = "0000-0002-2140-5352")),
    person(given = "RStudio",
           role = c("cph")))
c(person("Emmanuel", "Sikali", role="pdr", email = "Emmanuel.Sikali@ed.gov"),
             person("Paul", "Bailey", role=c("aut", "cre"), email="pbailey@air.org"),
             person("Blue", "Webb", role="aut"),
             person("Claire", "Kelley", role="aut"),
             person("Trang", "Nguyen", role="aut"),
             person("Huade", "Huo", role="aut"),
             person("Steve","Walker",role="cph",comment="lme4pureR PIRLS function"),
             person("Doug","Bates",role="cph",comment="lme4pureR PIRLS function"),
             person("Eric", "Buehler", role="ctb"),
             person(c("Christian", "Christrup"), "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(
      "Tim", "Ginker",
      email = "timginker@gmail.com",
      role = c("aut", "cre","cph"),
      comment = c(ORCID = "0000-0002-7138-5417")
    )
  )
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("Carsten", "Croonenbroeck", email = "carsten.croonenbroeck@uni-rostock.de",
				role = c("aut", "cre")),
			 person("David", "Hennecke", email = "david.hennecke@uni-rostock.de", role = "ctb"))
c(person(given = "Julian",
                      family = "Taylor",
                      role = c("aut", "cre"),
                      email = "julian.taylor@adelaide.edu.au"),
               person(given = "Ari",
                      family = "Verbyla",
                      role = "aut",
                      email = "averbyla@avdataanalytics.com.au"))
c(person(given = "Peter",
                    family = "Langfelder",
                    role = c("aut", "cre"),
                    email = "Peter.Langfelder@gmail.com"),
             person(given = "Steve", 
                    family = "Horvath", 
                    role = "aut"),
             person(given = "Chaochao",
                    family = "Cai",
                    role = "aut"),
             person(given = "Jun",
                    family = "Dong",
                    role = "aut"),
             person(given = "Jeremy",
                    family = "Miller",
                    role = "aut"),
             person(given = "Lin",
                    family = "Song",
                    role = "aut"),
             person(given = "Andy",
                    family = "Yip",
                    role = "aut"),
             person(given = "Bin",
                    family = "Zhang",
                    role = "aut"))
person("Guangchuang", "Yu",     email = "guangchuangyu@gmail.com", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-6485-8781"))
person("Guillaume", "Biessy", email = "guillaume.biessy78@gmail.com",
  role = c("aut", "cre", "cph"), comment = c(ORCID = "0000-0003-3756-7345"))
person("Julian", "Kohne", email = "julian.kohne@gesis.org",
                  role = c("aut", "cre"))
person("David", "Smith", , "david.alex.smith@gmail.com", 
           role = c("aut", "cre", "cph"),
           comment = c(ORCID = "0009-0000-8482-3220"))
c(
    person("Catalin", "Covaci", , "catalin.covaci@csic.es", role = c("aut", "cre"), comment = c(ORCID = "0009-0005-2186-5972")),
    person("Eduardo", "Aguilera", , "eduardo.aguilera@csic.es", role = c("aut", "cph"), comment = c(ORCID = "0000-0003-4382-124X")),
    person("João", "Serra", , "jserra@agro.au.dk", role = c("ctb"), comment = c(ORCID = "0000-0002-3561-5350")),
    person("European Research Council", role = c("fnd"))
  )
person("Matt", "Hendtlass", , "m.hendtlass@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("Aksel", "Thomsen", , "oath@novonordisk.com", role = c("aut", "cre")),
    person("Lovemore", "Gakava", , "lvgk@novonordisk.com", role = "aut"),
    person("Cervan", "Girard", , "cgid@novonordisk.com", role = "aut"),
    person("Kristian", "Troejelsgaard", , "ktqn@novonordisk.com", role = "aut"),
    person("Steffen Falgreen", "Larsen", , "sffl@novonordisk.com", role = "aut"),
    person("Vladimir", "Obucina", , "vlob@novonordisk.com", role = "aut"),
    person("Michael", "Svingel", , "wymz@novonordisk.com", role = "aut"),
    person("Skander", "Mulder", , "sktf@novonordisk.com", role = "aut"),
    person("Oliver", "Lundsgaard", , "ovlr@novonordisk.com", role = "aut"),
    person("Novo Nordisk A/S", role = "cph")
  )
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("Chad", "Milando", , "cmilando@bu.edu", role = c("aut", "cre")),
      person(given = "Tenglong", family = "Li", role = "ctb"),
      person(given = "Zhenwei", family = "Zhou", role = "ctb"),
      person(given = "Laura", family = "White", role = "ctb"))
c(
    person("Jorge", "Lopez Perez", ,"jorge@loperez.com", c("aut", "cre", "cph")),
    person("Jinook", "Jeong", ,"jinookjeong@gmail.com", c("ctb"))
    )
c(
    person(given = "R. Taki",
       family = "Shinohara",
       email = "russell.shinohara@pennmedicine.upenn.edu",
       role = c("aut")),
    person(given = "John",
       family = "Muschelli",
       email = "muschellij2@gmail.com",
       role = c("aut", "cre"),
       comment = c(ORCID = "0000-0001-6469-1750"))
    )
person(given = "Josh",
           family = "Erickson",
           role = c('aut', 'cre', 'cph'),
           email = "joshualerickson@gmail.com"
           )
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"))
c(person("Lindsay", "Lee", email = "lindsayevanslee@gmail.com", role = c("aut","cre")), person("Carolina", "Fellinghauer", email = "carolina.fellinghauer@data-fittery.ch", role = c("ctb")),  person("World Health Organization", email = "disability@who.int", role = "cph"))
person("Paul", "West", role=c("aut", "cre"), email="fp8049@gmail.com")
person(given = "Dave", family = "Zes", role = c("aut", "cre"), email = "zesdave@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", , "admiral.david@gmail.com", role = "aut"),
    person("Kanishka", "Misra", role = "ctb"),
    person("Julia", "Silge", , "julia.silge@gmail.com", role = c("aut", "cre"),
           comment = c(ORCID = "0000-0002-3671-836X"))
  )
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 = "jemma.stachelek@gmail.com", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-5924-2464"))
    )
c(
       person("Guangchuang", "Yu",     email = "guangchuangyu@gmail.com", role = c("aut", "cre", "cph"), comment = c(ORCID = "0000-0002-6485-8781")),
       person("Yihao", "Chen",       email = "olivercyh@foxmail.com",      role = "aut")
       )
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")
    )
person("Michael", "Falk", , "michaelgfalk@gmail.com", role = c("aut", "cre", "cph"),
           comment = c(ORCID = "0000-0001-9261-8390"))
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","cph"),
                  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 = "Pierce", 
           family = "Cappelli", 
           email = "pierce.cappelli@utexas.edu", 
           role = c("aut"),
           comment = c(ORCID = "0000-0001-8426-8132")))
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 = "kirill@cynkra.com",
             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 = "Kevin",
                      family = "Arbuckle",
                      role = c("aut", "cre"),
                      email = "kevin.arbuckle@swansea.ac.uk"),
               person(given = "Amanda",
                      family = "Minter",
                      role = "aut"))
c(
    person("Sebastian", "Gatscha", , email="sebastian_gatscha@gmx.at", c("aut", "cre", "cph"))
    )
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("Sebastian", "Wójcik", , "S.Wojcik@stat.gov.pl", role = c("aut", "cre"), comment = c(ORCID = "0000-0003-2425-9626")),
    person("Agnieszka", "Giemza", , "A.Giemza@stat.gov.pl", role = c("aut")),
    person("Katarzyna", "Machowska", , "K.Machowska@stat.gov.pl", role = c("aut")),
    person("Jarosław", "Napora", , "J.Napora@stat.gov.pl", role = c("aut")))
c(person("Ilaria", "Prosdocimi", 
                    email = "prosdocimi.ilaria@gmail.com",
                    role = c("aut", "cre"),
                    comment = c(ORCID = "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("Anusha", "Bishop", , email = "anusha.bishop@berkeley.edu", role = c("aut", "cre"), 
                   comment = c(ORCID = "0000-0003-1731-8683")),
            person("Anne", "Chambers", , email = "eachambers@berkeley.edu", role ="aut", 
                   comment = c(ORCID = "0000-0002-7369-0108")),
            person("Ian", "Wang", , email = "ianwang@berkeley.edu", role = "aut",
                   comment = c(ORCID = "0000-0003-2554-9414"))
            )
c(
    person("Obafèmi Philippe", "Koutchade", , "obafemi-philippe.koutchade@inrae.fr", role = c("aut", "cre", "cph"),
           comment = c(ORCID = "0000-0001-7327-3139")),
    person("Fabienne", "Femenia", , "fabienne.femenia@inrae.fr", role = "aut"),
    person("Alain", "Carpentier", , "alain.carpentier@inrae.fr", role = "aut")
  )
c(person("Ann Marie K.", "Weideman", email = "anndo1@umbc.edu", role = c("aut", "cre")),
    person("Elaine K.", "Kowalewski", role = "aut"),
    person("Gary G.", "Koch", role = "aut"))
c(person(given = "Ying",
             family = "Cui",
             role = c("aut", "cre"),
             email = "cuiyingbeicheng@gmail.com"),
             person(given = "Bo",
             family = "Huang",
             role = "aut"))
c(person("James", "Troendle", email = "james.troendle@nih.gov", role = c("aut", "cre")),
           person("Samuel", "Lawrence", email = "roaders79@gmail.com", role = "aut"))
c(person(given = "Jose M.",
           family = "Pavía",
           role = c("aut", "cre"),
           email = "jose.m.pavia@uv.es",
           comment = c(ORCID = "0000-0002-0129-726X")))
c(
  person("Ngumbang", "Juat", role="cre", email="ngumbangjuat@hotmail.com"),
  person("Mike", "Meredith", role="aut"),
  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("Raymond", "Kan", email = "raymond.kan@rotman.utoronto.ca",
           role = c("aut", "cre")),
           person("Preston", "Liang", email = "preston.liang.2003@gmail.com",
           role = c("aut")))
person(given = c("Nikolas", "Ivan"),
           family = "Krieger", 
           email = "nk@case.edu",
           role = c("aut", "cre"))
c(person("Torbjørn", "Lindahl", email="torbjorn.lindahl@gmail.com", role = c("aut", "cre", "cph"),
           comment = c(ORCID = "0009-0007-5538-0783")),
    person("Anja", "Minsaas", email="anja.minsaas@gmail.com", role = c("aut")))
c(
    person("Jim", "Hester", role = "aut"),
    person("Lionel", "Henry", , "lionel@posit.co", role = c("aut", "cre")),
    person("Kirill", "Müller", , "krlmlr+r@mailbox.org", role = "aut"),
    person("Kevin", "Ushey", , "kevinushey@gmail.com", role = "aut"),
    person("Hadley", "Wickham", , "hadley@posit.co", role = "aut"),
    person("Winston", "Chang", role = "aut"),
    person("Jennifer", "Bryan", role = "ctb"),
    person("Richard", "Cotton", role = "ctb"),
    person("Posit Software, PBC", role = c("cph", "fnd"))
  )
c(
    person("Joshua G.", "Kenney", email = "joshua.kenney@yale.edu", role = c("aut", "cre")),
    person("Trevor F.", "Williams", email = "trevormsu@gmail.com", role = "aut"),
    person("Minerva K.", "Pappu", email = "minerva.pappu@yale.edu", role = "aut"),
    person("Michael J.", "Spilka", email = "michael.j.spilka@gmail.com", role = "aut"),
    person("Danielle N. Pratt", email = "danielle.pratt33@gmail.com", role = "ctb"),
    person("Victor J.", "Pokorny", email = "victor.pokorny@northwestern.edu", role = "ctb"),
    person("Santiago", "Castiello de Obeso", email = "santiago.castiellodeobeso@yale.edu", role = "ctb"),
    person("Praveen", "Suthaharan", email = "praveen.suthaharan@yale.edu", role = "ctb"),
    person("Christian R.", "Horgan", email = "christian.horgan@yale.edu", role = "ctb")
  )
c(
      person(given = "Dewey",
             family = "Dunnington",
             role = c("aut", "cre"),
             email = "dewey@fishandwhistle.net",
             comment = c(ORCID = "0000-0002-9415-4582")),
       person(given = "Edzer",
              family = "Pebesma",
              role = c("aut"),
              email = "edzer.pebesma@uni-muenster.de",
              comment = c(ORCID = "0000-0001-8049-7069")),
      person(given = "Anthony",
             family = "North",
             email = "anthony.jl.north@gmail.com",
             role = c("ctb"))
    )
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"))
c(
  person("Subharup", "Guha", , "s.guha@ufl.edu", role = c("aut","cre")),
  person("Mengqi", "Xu", , "m332xu@uwaterloo.ca", role = c("aut")),
  person("Kashish", "Priyam", , "25kashishp@students.harker.org", role = c("aut")),
  person("Yi", "Li", , "yili@umich.edu", role = c("aut")))
person("Will", "Frierson", email = "will.frierson@gmail.com",
  role = c("aut", "cre"))
c( 
    person("Yijin", "Zeng", , "yijinzeng98@gmail.com", role = c("aut", "cre", "cph")),
    person("Dean", "Bodenham", , "deanbodenhampkgs@gmail.com", role = c("aut")),
    person("Niall", "Adams", , "n.adams@imperial.ac.uk", role = c("aut")))
c(
    person("Arne C.", "Bathke", role = "aut"),
    person("Edgar", "Brunner", role = "aut"),
    person("Martin", "Happ", role = c("aut", "cre"),
                      email = "statistics@happ.co.at",
                      comment = c(ORCID = "0000-0003-0009-2665")),
    person("Frank", "Konietschke", role = "aut")
  )
c(person(given = "Chenan",
             family = "Huang",
             role = c("aut", "cre"),
             email = "hualianchan@gmail.com"),
      person(given = "Narumasa",
             family = "Tsutsumida",
             role = c("aut"),
             email = "rsnaru.jp@gmail.com",
             comment = c(ORCID = "0000-0002-6333-0301")))
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(given = "Jasper M.",
           family = "Fuchs",
           role = c("aut", "cre"),
           email = "jasper.fuchs@usys.ethz.ch",
           comment=c(ORCID="0000-0001-5951-7897")),
    person(given = "Kai",
           family = "Husmann",
           role = c("aut"),
           email = "kai.husmann@uni-goettingen.de",
           comment=c(ORCID="0000-0003-2970-4709")),
    person(given = "Hilmar",
           family = "v. Bodelschwingh",
           role = "aut"),
    person(given = "Roman",
           family = "Koster",
           role = "aut",
           comment=c(ORCID="0000-0002-0439-2086")),
    person(given = "Kai",
           family = "Staupendahl",
           role = "aut",
           comment=c(ORCID="0000-0001-6595-3601")),
    person(given = "Armin",
           family = "Offer",
           role = "aut"),
    person(given = "Bernhard",
           family = "Möhring",
           role = "aut"),
    person(given = "Carola",
           family = "Paul",
           role = "aut",
           comment=c(ORCID="0000-0002-6257-2026")),
    person("University Goettingen - Department of Forest Economics and Sustainable Land-use Planning",
           role = "fnd"),
    person("Federal Ministry of Education and Research Germany (BMBF)",
           role = "fnd"),
    person("BiodivClim ERA-Net COFUND BiodivERsA call",
           role = "fnd"),
    person("ETH Zurich - Institute of Terrestrial Ecosystems - Forest Resources Management",
           role = "fnd")
    )
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@tilburguniversity.edu",
      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('Kohei', 'Watanabe', role = 'aut', email = 'watanabe.kohei@gmail.com', comment = c(ORCID = '0000-0001-6519-5265')), 
    person('BNOSAC', role = 'cph', comment = "R wrapper"),
    person('Max', 'Fomichev', role = c('ctb', 'cph'), comment = "Code in src/word2vec"))
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("Sebastian","Moreno",role = c("aut", "cre"),
           email = "sebastian.moreno.araya@gmail.com"),
    person("Cristobal", "Heredia", role = "aut"), 
    person("Enrique", "Canessa", role = "ths"),
    person("Sergio", "Chaigneau", role = "ths")
    )
c(person("Kohei", "Watanabe", email = "watanabe.kohei@gmail.com", role = c("aut", "cre", "cph")))
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")
    )
c(person(given = "Michael", family = "Friendly", role=c("aut", "cre"), email="friendly@yorku.ca", 
            comment=c(ORCID="0000-0002-3237-0941")),
            person(given = "Matthew", family="Dubins", role="ctb")
            )
person(given = "Nadir",
           family = "Latif",
           role = c("aut", "cre"),
           email = "pakjiddat@gmail.com",
           comment = c(ORCID = "0000-0002-7543-7405"))
c(person("Xiurui", "Zhu", , "zxr6@163.com", role = c("aut", "cre"),
             comment = NULL),
      person("@olivory", , , "olivroy@fosstodon.org", role = c("ctb"),
             comment = "Add `URL` and `BugReports` fields to `DESCRIPTION`."))
person(given = "Conor",
           family = "Neilson",
           role = c("aut", "cre"),
           email = "condwanaland@gmail.com")
c(
    person('Kohei', 'Watanabe', role = c('aut', 'cre', 'cph'), email = 'watanabe.kohei@gmail.com', comment = c(ORCID = '0000-0001-6519-5265')), 
    person('Jan', 'Wijffels', role = 'aut', email = 'jwijffels@bnosac.be', comment = "Original R code"), 
    person('BNOSAC', role = 'cph', comment = "Original R code"),
    person('Max', 'Fomichev', role = c('ctb', 'cph'), comment = "Original C++ code"))
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()"),
    person("Zaynaib", "Giwa", role = c("ctb"),
           comment = "Multiple enhancements"),
    person("Xiongbing", "Jin", role = c("ctb"),
           comment = "Fixed bug in versions table for figures"),
    person("Yihui", "Xie", role = c("ctb"),
           comment = c(ORCID = "0000-0003-0645-5666",
                       "Design advice; maintenance"))
    )
c(
    person("Davis", "Vaughan", , "davis@posit.co", role = "aut"),
    person("Simon", "Couch", , "simon.couch@posit.co", role = c("aut", "cre"),
           comment = c(ORCID = "0000-0001-5676-5107")),
    person(given = "Posit Software, PBC", role = c("cph", "fnd"))
  )
c(
    person("Hannah", "Frick", , "hannah@posit.co", role =c("aut", "cre"),
           comment = c(ORCID =  "0000-0002-6049-5258")),
    person("Max", "Kuhn", , "max@posit.co", role = "aut",
           comment = c(ORCID = "0000-0003-2402-136X")),
    person("Simon", "Couch", , "simon.couch@posit.co", role = "aut",
           comment = c(ORCID = "0000-0001-5676-5107")),
    person("Posit Software, PBC", role = c("cph", "fnd"),
           comment = c(ROR = "03wc8by49"))
  )
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("Jason", "Zivkovic", , "jaseziv83@gmail.com", role = c("aut", "cre", "cph")),
    person("Tony", "ElHabr", , "anthonyelhabr@gmail.com", role = "ctb"),
    person(given = "Tan", family = "Ho", , "tan@tanho.ca", role = "ctb"),
    person("Samuel","H", , "saintsbynumbers@gmail.com", role = "ctb")
  )
c(
    person("David", "Carslaw", , "david.carslaw@york.ac.uk", role = c("aut", "cre"),
           comment = c(ORCID = "0000-0003-0991-950X")),
    person("Jack", "Davison", , "jack.davison@ricardo.com", role = "aut",
           comment = c(ORCID = "0000-0003-2653-6615"))
  )
c(person("Kentaro", "Yamamoto", role = c("aut", "cre"),
                      email = "yamaken37.the.answer@gmail.com"),
                      person("Koki", "Tsuyuzaki", role = "aut"),
                      person("Itoshi", "Nikaido", role = "aut"))
c(
    person("Scott", "Chamberlain", role = "aut",
        email = "myrmecocystus@gmail.com",
        comment = c(ORCID = "0000-0003-1444-9135")),
    person("Bart", "Vanhoorne.", email = "bart.vanhoorne@vliz.be", role = c("aut","cre"))
    )
c(
    person("Anthony", "Pileggi", , "apileggi20@gmail.com", c("aut", "cre", "cph")),
    person("Shannon", "Pileggi", , "shannon.pileggi@gmail.com", role = c("aut"),
           comment = c(ORCID = "0000-0002-7732-4164"))
    )
c(person("Gabriele", "Lubatti", role = c("aut", "cre","cph"),email = "gabriele.lubatti@helmholtz-muenchen.de"))
c(person("Gleb", "Beliakov", role = c("aut", "cre"),
		email = "gleb@deakin.edu.au"),
		person("Daniela", "Calderon", role = "aut"))
c(person("Zola", "Batsaikan", email = "zoljargal11@gmail.com", role = c("aut"),
           comment = c(ORCID = "0009-0005-0055-1448")),
      person("Dianne", "Cook", email = "dicook@monash.edu", role = c("aut", "cre"),
           comment = c(ORCID = "0000-0002-3813-7155")),
      person("Ursula", "Laa", email = "ursula.laa@boku.ac.at", role = c("aut"),
           comment = c(ORCID = "0000-0002-0249-6439")))
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("Tomaz", "Skrbinsek", role = "aut"),
    person("Gregor", "Simcic", , "grega0simcic@gmail.com", role = c("aut", "cre"))
  )
c(
    person("Xiaotong", "Liu", email = "xiaotongliu@link.cuhk.edu.cn", role = c("aut", "cre")),
    person("Kunyu", "Ye", role = "aut"),
    person("Siyao", "Wang", role = "aut"),
    person("Xudong", "Liu", role = "aut"),
    person("Tianwei", "Yu", role = c("aut", "ths"))
    )
c(
  person("Population Division, Department of Economic and Social Affairs, United Nations", role = "aut"),
  person(given = "Hana", family = "Sevcikova", role = "cre", email = "hanas@uw.edu"))
c(
  person(given = "Eric", 
    family = "Dunipace", 
    email = "edunipace@mail.harvard.edu", 
    role = c("aut", "cre"), 
    comment = c(ORCID = "0000-0001-8909-213X")),
  person(given = "Clemens",
    family = "Schmid",
    role = c("ctb"),
    email = "clemens@nevrome.de",
    comment = c(ORCID = "0000-0003-3448-5715", 
    "ETA progres bar is adapted from their code")),
  person(given = "Espen",
    family = "Bernton",
    role = c("ctb"),
    comment = "'Hilbert Sort' adapted from their code"),
  person(given = "Mathieu",
    family = "Gerber",
    role = c("ctb"),
    comment = "'Hilbert Sort' adapted from their code"),
  person(given = "Pierre",
    family = "Jacob",
    role = c("ctb"),
    comment = "'Hilbert Sort' adapted from their code"),
  person(given = "Bin", 
    family = "Dai", 
    email = "bdai@uwalumni.com", 
    role = c("ctb"),
    comment = "W2 projections adapted from their 'OEM' code"),
  person(given = "Jared", 
    family = "Huling",
    email = "jaredhuling@gmail.com", 
    role = c("ctb"),
    comment = c(ORCID = "0000-0003-0670-4845","W2 projections adapted from their 'OEM' code")),
  person(given = "Yixuan", 
    family = "Qiu",
    role = c("ctb"),
    comment = "W2 projections adapted from their 'OEM' code"),
  person(given = "Dominic", 
    family = "Schuhmacher", 
    email = "dominic.schuhmacher@mathematik.uni-goettingen.de", 
    role = c("ctb"),
    comment = "'Shortsimplex 'optimal transport method adapted from their code"),
  person(given = "Nicolas", 
    family = "Bonneel", 
    role = "ctb", 
    comment = "'Network Simplex' algorithm adapted from their code")
  )
person( "Anoop S", "Kumar", role = c( "aut", "cre" ), email = "akumar.sasikumar@gmail.com" )
c(
  person("Alan", "Jassby", role = c("aut")),
  person("James", "Cloern", role = c("ctb")),
  person("Jemma", "Stachelek", role = c("ctb", "cre"), email = "jemma.stachelek@gmail.com")
  )
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 = "Fiona",
           family = "Johnson",
           role = c("aut"),
           comment = c(ORCID = "0000-0001-5708-1807")))
c(
    person("Drew", "Day", email = "dday612@gmail.com", role = c("aut", "cre")),
    person("James", "Peng", email = "jpspeng@uw.edu", role = "aut"),
    person("Adam", "Szpiro", email = "aszpiro@uw.edu", role = "aut"))
c(
    person(given = "Marcus",
      family = "Beck",
      role = c("aut", "cre"),
      email = "mbeck@tbep.org",
      comment = c(ORCID = "0000-0002-4996-0059")
    ), 
    person(given = "Perry",
      family = "de Valpine",
      role = c("aut"),
      email = "pdevalpine@gmail.com"
    ),
    person(given = "Rebecca",
      family = "Murphy",
      role = c("aut"),
      email = "rmurphy@chesapeakebay.net"
    ),
    person(given = "Ian",
      family = "Wren",
      role = c("aut"),
      email = "ianw@sfei.org"
    ),
    person(given = "Ariella",
      family = "Chelsky",
      role = c("aut"),
      email = "ariellac@sfei.org"
    ),
    person(given = "Melissa",
      family = "Foley",
      role = c("aut"),
      email = "melissaf@sfei.org",
      comment = c(ORCID = "0000-0002-5832-6404")
    ),  
    person(given = "David",
      family = "Senn",
      role = c("aut"),
      email = "davids@sfei.org",
      comment = c(ORCID = "0000-0002-4869-3550")
    )
  )
c(
    person(given = "Andreas",
           family = "Busjahn",
           role = c("cre", "aut"),
           email = "andreas@busjahn.net",
           comment = c(ORCID = "0000-0001-9650-6919")),
    person(given = "Franziska",
           family = "Eidloth",
           role = c("aut"),
           email = "franziska.eidloth@gmail.com",
           comment = ""),
    person(given = "Bilal",
           family = "Asser",
           role = c("aut"),
           email = "billyasser@hotmail.co.uk",
           comment = ""))
c(
    person("John", "Piper", email = "john.piper.using.r@gmail.com", role = c("aut", "cre"),
    comment = c(ORCID = "0009-0000-6003-7671"))
    )
c(
    person("Raphael", "Winkelmann", ,"raphael@phonetik.uni-muenchen.de", role = c("aut")),
    person("Lasse", "Bombien", , "lasse@phonetik.uni-muenchen.de", role = c("aut")),
    person("Michel", "Scheffers", role = c("aut")),
    person("Markus", "Jochim", ,"markusjochim@phonetik.uni-muenchen.de", role = c("cre", "aut"), comment = c(ORCID = "0000-0002-5638-4870")))
person("Benjamin", "Uveges", , "uvegesbti@gmail.com", role = c("aut", "cre"),
           comment = c(ORCID = "0000-0002-8981-8970"))
person(given = "Autumn", family = "O'Donnell", role=c("aut", "cre", "cph"), email="autumn.research@gmail.com")
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("Chao", "Zhang", email = "czhang@pstat.ucsb.edu", role = "aut"),
    person("Matthew", "Coleman", email = "matthewbcoleman@ucsb.edu", role = "aut"),
    person("Alexander", "Petersen", email = "petersen@pstat.ucsb.edu", role = "aut"))
c(person(given = c("David"),
                      family = "Torres Irribarra",
                      role = c("aut", "cre"),
                      email = "david@torresirribarra.me"),
                      person(given = "Rebecca",
                      family = "Freund",
                      role = "aut"))
person(
    "Komala Sheshachala", "Srikanth",
    email = "sri.teach@gmail.com",
    role = c("aut", "cre"),
    comment = c(ORCID = "0000-0002-1865-5668")
    )
c(
    person("Jeroen", "Ooms", ,"jeroenooms@gmail.com", 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 = "Marc",
                    family = "Schwartz",
                    role = c("aut", "cre"),
                    email = "marc_schwartz@me.com"),
             person(given = c("Various authors for Perl modules listed in each .pm"),
                    family = "file.",
                    role = "aut"))
c(person(given="Patrick", family="Mair", email="mair@fas.harvard.edu", role=c("cre","aut")),
             person(given="Rand", family="Wilcox", email="rwilcox@usc.edu", role=c("aut")),
             person(given="Indrajeet", family="Patil", email="patilindrajeet.science@gmail.com", role=c("ctb")))
person(given = "Kirill",
           family = "M\u00fcller",
           role = c("aut", "cre"),
           email = "krlmlr+r@mailbox.org")
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", , "kabirkhanna@gmail.com", role = "aut"),
    person("Brandon", "Bertelsen", , "brandon@bertelsen.ca", role = c("aut", "cre")),
    person("Santiago", "Olivella", , "olivella@unc.edu", role = "aut"),
    person("Evan", "Rosenman", , "etrrosenman@gmail.com", role = "aut"),
    person("Alexander", "Rossell Hayes", , "alexander@rossellhayes.com", role = "aut"),
    person("Kosuke", "Imai", , "imai@harvard.edu", role = "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("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("Mucyo", "Karemera", role = "aut"),
    person("Nathanael", "Claussen", role = "ctb"),
    person("Haotian", "Xu", role = "ctb"),
    person("Lionel", "Voirol", role = "ctb")
    )
c(
    person("Mihyun", "Kim", email = "mihyun.kim@mail.wvu.edu", role = c("aut", "cre")),
    person("Daniel", "Petoukhov", email = "dvpetouk@uwaterloo.ca", role = c("aut")))
c(person(given = "Subhrendu",
           family = "Gangopadhyay",
           role = "aut"),
    person(given = "Lindsay",
           family = "Bearup",
           role = "aut"),
    person(given = "David",
           family = "Woodson",
           role = c("aut", "cre"),
           email = "dwoodson@usbr.gov"),  
    person(given = "Marketa",
           family = "McGuire",
           role = "aut"),
    person(given = "Andrew",
           family = "Verdin",
           role = "aut"), 
    person(given = "Eylon",
           family = "Shamir",
           role = "aut"),
    person(given = "Eve",
           family = "Halper",
           role = "aut"))
c(
    person("Dirk", "Eddelbuettel", email = "edd@debian.org", role = c("aut", "cre"), comment = c(ORCID = "0000-0001-6419-907X")),
    person("Christoph", "Sax", role = "aut", comment = c(ORCID = "0000-0002-7192-7044")),
    person("Kirill", "Müller", role = "ctb", comment = c(ORCID = "0000-0002-1416-3412")),
    person("Jeroen", "Ooms", role = "ctb", comment = c(ORCID = "0000-0002-4035-0289")),
    person("Michael", "Antonov", role = "ctb")
    )
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"))
            )
c(person(given = "Michael C",
             family = "Sachs",
             role = c("aut", "cre"),
             email = "sachsmc@gmail.com"),
      person(given = "Michael P", 
             family = "Fay", role = c("aut")), 
      person(given = "Erin E", 
             family = "Gabriel", role = c("aut")), 
      person(given = "David B", 
              family = "Dahl", role = "ctb", comment = "(rbindings.rs)"))
person("Lukas", "Schönmann", , "lukas.schoenmann@outlook.com", role = c("aut", "cre"),
           comment = c(ORCID = "0000-0002-3161-4270"))
c(
    person("Garrick", "Aden-Buie", , "garrick@adenbuie.com", role = c("aut", "cre"),
           comment = c(ORCID = "0000-0002-7111-0077")),
    person("Matthew T.", "Warkentin", , "warkentin@lunenfeld.ca", role = "aut",
           comment = c("Contributed scribble", ORCID = "0000-0001-8730-3511")),
    person("Yotam", "Mann", role = "cph",
           comment = "tone.js"),
    person("Daniel", "Eden", role = "cph",
           comment = "animate.css"),
    person(, "Tachyons authors", role = "cph"),
    person(, "Klaus Hartl, Fagner Brack, GitHub Contributors", role = "cph",
           comment = "js-cookie"),
    person(, "Chris Andrejewski", role = "cph",
           comment = "himalaya"),
    person("Eric", "Londaits", role = "cph",
           comment = "text-poster.js"),
    person("Zeno", "Rocha", role = "cph",
           comment = "clipboard.js"),
    person("Nikita", "Karamov", role = "cph",
           comment = "shareon.js"),
    person("Ross", "Zurowski", role = "cph",
           comment = "fitvids.js"),
    person(, "Michelle Bu and Eric Zhang", role = "cph",
           comment = "peerjs"),
    person("Kiril", "Vatev", role = "cph",
           comment = "tiny.toast"),
    person("André", "Restive", role = "cph",
           comment = "remark.search"),
    person(, "Printio (Juriy Zaytsev, Maxim Chernyak)", role = "cph",
           comment = "fabric.js"),
    person("Christpher", "Antonellis", role = "cph",
           comment = "freezeframe.js")
  )
c(person(given = "Joshua",
                      family = "Hewitt",
                      role = "aut"),
               person(given = "K.C.",
                      family = "Bierlich",
                      role = c("aut", "cre"),
                      email = "bierlick@oregonstate.edu"),
               person(given = "Enrico",
                      family = "Pirotta",
                      role = "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"))
          )
person("Paolo", "Manildo", email = "paolo.manildo@phd.unipd.it",
  role = c("aut", "cre"), comment = c(ORCID = "0009-0006-7056-7012"))
person("Paul", "Murrell", email = "paul@stat.auckland.ac.nz",
                  comment = c(ORCID = "0000-0002-3224-8858"), 
                  role = c("aut", "cre"))
person(given = "Andreas",
           family = "Geyer-Schulz",
           role = c("aut", "cre"),
           email = "Andreas.Geyer-Schulz@kit.edu",
           comment = c(ORCID = "0009-0000-5237-3579"))
person(given = "Andreas",
           family = "Geyer-Schulz",
           role = c("aut", "cre"),
           email = "Andreas.Geyer-Schulz@kit.edu",
          comment = c(ORCID = "0009-0000-5237-3579"))
person(given = "Andreas",
           family = "Geyer-Schulz",
           role = c("aut", "cre"),
           email = "Andreas.Geyer-Schulz@kit.edu",
    comment = c(ORCID = "0009-0000-5237-3579"))
c(person(given = "Philip", family = "Leifeld", email = "philip.leifeld@essex.ac.uk", role = c("aut", "cre")), person(given = c("Skyler", "J."), family = "Cranmer", email = "cranmer.12@osu.edu", role = "ctb"), person(given = c("Bruce", "A."), family = "Desmarais", email = "bdesmarais@psu.edu", role = "ctb"))
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", URL = "https://yihui.org")),
  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")
  )
person(given = "Gero",
                  family = "Szepannek",
                  role = c("aut", "cre"),
                  email = "gero.szepannek@web.de",
                  comment = c(ORCID = "0000-0001-8456-1283")
                  )
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("Felix", "Luginbuhl", ,"felix.luginbuhl@protonmail.ch", role = c("aut", "cre", "cph"),
      comment = c(ORCID = "0009-0008-6625-2899")),
    person("Eric", "Gazoni", role = c("ctb", "cph"),
      comment = "Author of openpyxl (see AUTHORS and COPYRIGHT files for details)"),
    person("Charlie", "Clark", role = c("ctb", "cph"),
      comment = "Author of openpyxl (see AUTHORS and COPYRIGHT files for details)"),
    person("openpyxl authors", role = c("ctb", "cph"),
      comment = "Authors of openpyxl (see AUTHORS and COPYRIGHT files for details)")
    )
c(person("Mirai Solutions GmbH", role = "aut",
                    email = "xlconnect@mirai-solutions.com"),
             person("Martin", "Studer", role = "cre",
                    email = "martin.studer@mirai-solutions.com",
                    comment = c(ORCID = "0009-0004-8250-7303")),
             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"))
person(
      "Hugo", "Gruson", , "hugo.gruson+R@normalesup.org", 
      role = c("aut", "cre", "cph"),
      comment = c(ORCID = "0000-0002-4094-1476")
    )
person(given = "Nicholas",
           family = "Hilderson",
           role = c("aut", "cre", "cph"),
           email = "nhilderson.code@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 = "NyAvo", 
           family = "RATOVO-ANDRIANARISOA", 
           role = c("aut", "cre"), 
           email = "ratovoandry1@gmail.com"))
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("Cole", "Arendt", 
                    role = c("aut", "cre"),
                    email = "cole.arendt@outlook.com"),
             person(c("Adrian", "A."), "Dragulescu", 
                    role = "aut",
                    email = "adrian.dragulescu@gmail.com"), 
             person("The Apache Software Foundation", 
                    role = c("ctb", "cph"), 
                    comment = "Apache POI, Apache XMLBeans, Apache Commons Codec"))
c(
    person(
      given = "Cynthia A.", 
      family = "Huang",
      email = "cynthiahqy@gmail.com", 
      role = c("aut", "cre"), 
      comment = c(ORCID = "0000-0002-9218-987X")
    ),
    person(
      given = "Laura", 
      family = "Puzzello",
      role = c("aut", "fnd")
    )
  )
c(person("Chuan", "Hong", role="aut"),
            person("Chongliang", "Luo", role="aut"),
            person("Jiayi", "Tong", role=c("aut"), 
                    email="Jiayi.Tong@pennmedicine.upenn.edu"),
            person("Rui", "Duan", role="ctb"),
            person("Haitao", "Chu", role="ctb"),
			      person("Yulun", "Liu", role="ctb"),
            person("Yuqing", "Lei", role=c("cre"),
                     email ="lyqlei@pennmedicine.upenn.edu"),
            person("Yong", "Chen", role=c("aut"),
                     email ="ychen123@upenn.edu"))
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"),
	     person("Ivan", "Krylov", role = "ctb"))
c(
    person("Hadley", "Wickham", role = "aut"),
    person("Jim", "Hester", role = "aut"),
    person("Jeroen", "Ooms", email = "jeroenooms@gmail.com", role = c("aut", "cre")),
    person("Posit Software, PBC", role = c("cph", "fnd")),
    person("R Foundation", role = "ctb",
           comment = "Copy of R-project homepage cached as example")
  )
person("Carson", "Sievert", , "cpsievert1@gmail.com", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-4958-2844"))
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("Edwin", "de Jonge", , "edwindjonge@gmail.com", role = c("aut", "cre"),
           comment = c(ORCID = "0000-0002-6580-4718"))
c(person("Trevor L", "Davis", role=c("aut", "cre"), 
                    email="trevor.l.davis@gmail.com", comment = c(ORCID = "0000-0001-6341-4639")),
             person("Linux Foundation", role="dtc",
                    comment = 'Uses some data from the "SPDX License List" <https://github.com/spdx/license-list-XML>'))
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"))
person(given = "Bill",
                  family = "Engels",
                  role = c("aut", "cre"),
                  email = "wrengels@wisc.edu")
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", role = c("aut", "cre")),
    person("Fathi", "Boudra", role = "aut"),
    person("Rex", "Dieter", role = "aut"),
    person("Kevin", "Krammer", role = "aut"),
    person("Jeremy", "White", role = "aut"),
    person("Posit Software, PBC", role = c("cph", "fnd"))
  )
c(person(given = "Ludvig Renbo",
             family = "Olsen",
             role = c("aut", "cre"),
             email = "r-pkgs@ludvigolsen.dk",
             comment = c(ORCID = "0009-0006-6798-7454")),
      person(given = "R. Mark",
             family = "Sharp",
             role = c("ctb")))
c(
           person(given="Ramón",family="Alvarez-Esteban", email="ramon.alvarez@unileon.es", role = c("aut", "cre"), comment = c(ORCID="0000-0002-4751-2797")),
           person(given="Mónica",family="Bécue-Bertaut", email="monica.becue@upc.edu", role = "aut", comment = c(ORCID = "0000-0002-6027-3655")),
            person(given="Josep-Anton",family="Sánchez-Espigares", email="josep.a.sanchez@upc.edu",role="ctb",  comment = c(ORCID = "0000-0001-8195-1913")),
            person(given="Belchin Adriyanov", family="Kostov", email="belchin.adriyanov.kostov@upc.edu",role="ctb", comment = c(ORCID ="0000-0002-2126-3892")))
c(
    person("Eli", "Miller", , "Eli.Miller@AtorusResearch.com", role = c("aut", "cre"),
           comment = c(ORCID = "0000-0002-2127-9456")),
    person("Ben", "Straub", role = "aut"),
    person("Zelos", "Zhu", role = "aut"),
    person("Ethan", "Brockmann", role = "aut"),
    person("Vedha", "Viyash", role = "aut"),
    person("Andre", "Verissimo", role = "aut"),
    person("Sophie", "Shapcott", role = "aut"),
    person("Celine", "Piraux", role = "aut"),
    person("Kangjie", "Zhang", role = "aut"),
    person("Adrian", "Chan", role = "aut"),
    person("Sadchla", "Mascary", role = "aut"),
    person("Atorus/GSK JPT", role = "cph")
  )
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'))
person("John", "Prybylski", , "jprybylski@gmail.com", role = c("aut", "cre", "cph"),
           comment = c(ORCID = "0000-0001-5802-0539"))
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("Dixin", "Shen", email = "dixinshe@usc.edu", role = c("aut")),
  person("Juan Pablo", "Lewinger", email = "lewinger@usc.edu", role = c("ctb", "ths")))
person(given = "Erick",
                  family = "Marroquin",
                  role = c("aut", "cre"),
                  email = "ericksuhel@gmail.com")
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"))
c(person(given = "Fernando",
           family = "DePaolis",
           role = c("aut", "cre"),
           email = "fdepaolis@middlebury.edu",
           comment = c(ORCID = "0000-0003-2296-8381")))
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")
  )
c(person(given = "Jingxuan",
           family = "He",
           role = c("aut", "cre"),
           email = "hejingxu@usc.edu"),
    person(given = "Chubing",
           family = "Zeng",
           role = "aut"))
person(given = "BeiHao",family = "Li",email = "szright2000@gmail.com",role = c("aut","cre"))
person("Willem", "van den Boom", role = c("aut", "cre"), email = "willem@wvdboom.nl")
c(
    person("Nilabhra Rohan", "Das", , "nr.das@yahoo.com", role = c("cre", "aut"), comment = c(ORCID = "0000-0001-8187-0080")),
    person("Abhirup", "Nandy", , "abhirupnandy.online@gmail.com", role = "aut", comment = c(ORCID = "0000-0001-8618-0847"))
  )
c(
    person("Mike", "Cheng", role = c("aut", "cre", 'cph'), email = "mikefc@coolbutuseless.com"),
    person("Yann", "Collet", role = c("ctb", "cph"), comment = "Author of the embedded xxhash library")
    )
c(
  person(family="Evans", given="Jeffrey S.", email = "jeffrey_evans@tnc.org", 
         role = c("aut", "cre"), comment = c(ORCID = "0000-0002-5533-7044")),
  person(family="Crookston", given="Nicholas L.", role = c("aut")),
  person(family="Finley", given="Andrew O.", role = c("aut")),			 
  person(family="Coulston (Sunil Arya and David Mount for ANN)", given="John", 
         role = c("ctb", "com")) )
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("Max", "Kuhn", , "max@posit.co", role = "aut"),
    person("Davis", "Vaughan", , "davis@posit.co", role = "aut"),
    person("Emil", "Hvitfeldt", , "emil.hvitfeldt@posit.co", role = c("aut", "cre"),
           comment = c(ORCID = "0000-0002-0679-1945")),
    person("Posit Software, PBC", role = c("cph", "fnd"))
  )
person("Nathaniel", "Phillips", email = "nathaniel.d.phillips.is@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 = "ctb"),
      person(given = "Kai-Yang",
             family = "Goh",
             role = "ctb"),
      person(given = "Yangzhuoran Fin",
             family = "Yang",
             role = c("aut", "cre"),
             email = "yangyangzhuoran@gmail.com",
             comment = c(ORCID = "0000-0002-1232-8017")))
c(
    person("Emmanuel", "Alcala", , "jealcalat@gmail.com", role = c("aut", "cre")),
    person("Rodrigo", "Sosa", , "rsosas@up.edu.mx", role = "aut"),
	person("Victor", "Reyes", , "zickvic@hotmail.com", role = "aut")
  )
c(
    person("Ayla", "Pearson", , "ayla@poissonconsulting.ca", role = "cre",
           comment = c(ORCID = "0000-0001-7388-1222")),
    person("Joe", "Thorley", , "joe@poissonconsulting.ca", role = c("aut", "cph"),
           comment = c(ORCID = "0000-0002-7683-4592")),
    person("Hadley", "Wickham", , "hadley@rstudio.com", role = c("aut", "cph"),
           comment = c(ORCID = "0000-0003-4757-117X"))
  )
person("Giovanni", "Colitti", email = "g.a.colitti@gmail.com", role = c("aut", "cre", "cph"))
c(person("Marcelo", "Perlin", email = "marceloperlin@gmail.com", role = c("aut", "cre")),
             person("Nic", "Crane", role = "rev",
                    comment = "Nic reviewed the package (v. 0.0.5) for 
                    rOpenSci, see <https://github.com/ropensci/software-review/issues/523>"),
            person("Alexander", "Fischer", role = "rev",
                    comment = "Alexander reviewed the package (v. 0.0.5) for 
                    rOpenSci, see <https://github.com/ropensci/software-review/issues/523>"))
c(person(given = "Kim", family = "Nimon", role = c("aut", "cre"), email = "kim.nimon@gmail.com"), person(given = "Fred", family = "Oswald", role = "aut"), person(given = c("J.", "Kyle"), family = "Roberts", role = "aut"))
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")),
    person(given = "The authors of the dependency Rust crates",
           role = c("ctb"),
           comment = "see inst/AUTHORS file for details")
    )
person(c("Yoo Ri","Hwang"), email = "yrhwang89@gmail.com", role = c("aut","cre"))
c(
    person("Malcolm", "Barrett", , "malcolmbarrett@gmail.com", role = c("aut", "cre"),
           comment = c(ORCID = "0000-0003-0299-5825")),
    person("Richard", "Iannone", , "rich@rstudio.com", role = "aut",
           comment = c(ORCID = "0000-0003-3925-190X")),
    person("RStudio", role = c("cph", "fnd"))
  )
c(
    person(
        given = "Tim",
        family = "Taylor",
        role = c("aut", "cre", "cph"),
        email = "tim.taylor@hiddenelephants.co.uk",
        comment = c(ORCID = "0000-0002-8587-7113")
    ),
    person(
        given = "R Core Team",
        role = "cph",
        comment = "fstrcapture uses code from strcapture"
    ),
    person(
        given = "Toby",
        family = "Hocking",
        role = "cph",
        comment = "fstrcapture uses code from nc::capture_first_vec"
    ))
c(
    person(given = "Richard",
           family = "Payne",
           role = c("aut", "cre"),
           email = "paynestatistics@gmail.com"),
    person(family = "Eli Lilly and Company",
           role = "cph"))
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")
    )
person(given = "Fabio", family = "Demarqui", email = "fndemarqui@est.ufmg.br", role = c("aut", "cre"))
c(
    person("Joe", "Thorley", , "joe@poissonconsulting.ca", role = c("aut", "cre"),
           comment = c(ORCID = "0000-0002-7683-4592")),
    person("Ayla", "Pearson", role = "ctb",
           comment = c(ORCID = "0000-0001-7388-1222")),
    person("Poisson Consulting", role = c("cph", "fnd"))
  )
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("Jake", "Davis", email = "jacobrdavis95@gmail.com", role = c("aut", "cre", "cph"))
c(person(given = c("YUIMA", "Project"),
                       family = "Team",
                       role = "aut"),
                person(given = c("Stefano", "M."),
                       family = "Iacus",
                       role = "cre",
                       email = "siacus@iq.harvard.edu"))
person(given = "Gjalt-Jorn",
           family = "Peters",
           role = c("aut", "cre"),
           email = "gjalt-jorn@userfriendlyscience.com")
c(
    person("Mike", "Cheng", role = c("aut", "cre", 'cph'), 
    email = "mikefc@coolbutuseless.com"),
    person("Yao", "Yuan", role = c("aut", "cph"), email = "ibireme@gmail.com", 
    comment="Author of bundled yyjson"),
    person("Murat", "Tasan", role = c("ctb"), 
    comment="'json_verbatim' handling")
    )
person("Jonas", "Lieth", , "jonas.lieth@gesis.org", role = c("cre", "aut", "cph"),
           comment = c(ORCID = "0000-0002-3451-3176"))
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("Ari Lamstein", role = c("aut", "cre"), email = "alamstein@market-bridge.com"),
    person("MarketBridge LLC", role = c("cph", "fnd")))
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("Nathan", "Teetor", email = "nate@haufin.ch",
           role = c("aut", "cre")),
    person("Paul", "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")),
  person("Jemma", "Stachelek", role = c("ctb"), comment = c(ORCID = "0000-0002-5924-2464")))
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("Maciej", "Romaniuk", email = "mroman@ibspan.waw.pl",
    role = c("cre","aut"),comment = c(ORCID = "0000-0001-9649-396X",
    "(Systems Research Institute Polish Academy of Sciences, Newelska 6, 01-447 Warszawa, Poland)")),
    person("Abbas", "Parchami", email = "parchami@uk.ac.ir", role = "aut",
    comment = c(ORCID = "0000-0002-0593-7324", 
    "(Mahani Math Center, Afzalipour Research Institute, Shahid Bahonar University of Kerman, Kerman, Iran)")),
    person("Przemyslaw", "Grzegorzewski", email = "przemyslaw.grzegorzewski@ibspan.waw.pl",
    role = "ctb",comment = c(ORCID = "0000-0002-5191-4123",
    "(Faculty of Mathematics and Information Science, Warsaw University of Technology, Koszykowa 75, 00-662 Warsaw, Poland)")))
c(
    person("Aksel", "Thomsen", , "oath@novonordisk.com", role = c("aut", "cre")),
    person("Mathias Lerbech", "Jeppesen", , "nmlj@novonordisk.com", role = "aut"),
    person("Cervan", "Girard", , "cgid@novonordisk.com", role = "aut"),
    person("Kristian", "Troejelsgaard", , "ktqn@novonordisk.com", role = "aut"),
    person("Lovemore", "Gakava", , "lvgk@novonordisk.com", role = "aut"),
    person("Steffen Falgreen", "Larsen", , "sffl@novonordisk.com", role = "aut"),
    person("Vladimir", "Obucina", , "vlob@novonordisk.com", role = "aut"),
    person("Novo Nordisk A/S", role = "cph")
  )
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("Yexian", "Zhang", role = c("cre", "aut"), email = "yxzhang@bethbio.com"),
    person("Haoyi", "Weng", role = c("aut"), email = "hyweng@link.cuhk.edu.hk"),
    person("Maggie","Wang", role = c("aut", "cph"), email = "maggiew@cuhk.edu.hk"))
c(person("Eric", "Zhang Chen", email = "chvlyl@gmail.com", role = c("aut", "cph")),
             person("Charlie", "Bushman", email = "ctbushman@gmail.com", role = c("cre")))
c(
    person("Ming", "Yang", email = "mingyang@biostatstudio.com", role = c("aut", "cre")),
    person("Gideon", "Zamba", role = "aut"),
    person("Joseph", "Cavanaugh", role = "aut")
    )
person(given = "Xiaomin",
                    family = "Liu",
                    role = c("aut", "cre","cph"),
                    email = "e0717571@u.nus.edu")
c(person(given = "Aldo M.",
           family = "Garay",
           role = c("aut"),
           email = "agaray@de.ufpe.br"),
    person(given = "João Vitor",
           family = "Ribeiro",
           role = c("aut","cre"),
           email = "joao.vitorribeiro@ufpe.br"))
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("Gábor", "Csárdi", , "csardi.gabor@gmail.com", role = c("aut", "cre")),
    person("Kuba", "Podgórski", role = "ctb"),
    person("Rich", "Geldreich", role = "ctb"),
    person("Posit Software, PBC", role = c("cph", "fnd"),
           comment = c(ROR = "03wc8by49"))
  )
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")) )
person(given = "Gavin",
           family = "Rozzi",
           role = c("aut", "cre"),
           email = "gr@gavinrozzi.com",
           comment = c(ORCID = "0000-0002-9969-8175"))
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")))
c(person("Roulan", "Jiang", role = c("aut", "cre"),email = "roulan2000@gmail.com"),
             person("Tianying", "Wang", role = "aut",email = "tianyingw0905@outlook.com"))
c(
    person(
      given = "Christopher", family = "Prener", email = "Christopher.Prener@pfizer.com", 
      role = c("aut", "cre"), comment = c(ORCID = "0000-0002-4310-9888")
    ),
    person(
      given = "Timothy", family = "Wiemken", email = "Timothy.Wiemken@pfizer.com", 
      role = "aut", comment = c(ORCID = "0000-0002-8251-3007")
    ),
    person(
      given = "Angela", family = "Cook", email = "Angela.Cook@pfizer.com", 
      role = "aut"
    )
  )
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"
                  )
        )
c(
  person("CD", "Durrant", email = "cbdurrantant@gmail.com", role = c("com","cre","cph")),
  person("Tucker", "Gilman", email = "tucker.gilman@manchester.ac.uk", role = c("aut", "cph")))
person("Semjon", "Geist", role = c("aut", "cre"),
                     email = "mail@semjon-geist.de")
person(given = "Sebastian", family = "Gibb",
                  email = "mail@sebastiangibb.de",
                  role = c("aut", "cre"),
		          comment = c(ORCID = "0000-0001-7406-4443"))
c(person(given = "Fang",
                      family = "Liu",
                      role = c("aut", "cre"),
                      email = "fang.liu.131@nd.edu"),
               person(given = "Yunchuan",
                      family = "Kong",
                      role = "ctb"))
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(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("Arick", "Grootveld", email = "arick.grootveld@gmail.com", role = c("cre","aut")),
  person("Eva", "Kozak", email = "eva.kozak@academics.udg.mx", role = "aut")
  )
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"),comment=c(ORCID="0000-0001-6869-5345")),
    person(given="Sebastian", family="Gibb", role="ctb",
    email="mail@sebastiangibb.de"))
c(
    person("Beniamino", "Green", , "beniamino.green@yale.edu", role = c("aut", "cre", "cph")),
    person("Etienne", "Bacher", email = "etienne.bacher@protonmail.com", role = "ctb",
           comment = c(ORCID = "0000-0002-9271-5075")),
    person(given = "The authors of the dependency Rust crates",
             role = c("ctb", "cph"),
             comment = "see inst/AUTHORS file for details")
    )
person(given = "Andrew",
           family = "Knight",
           role = c("aut", "cre"),
           email = "knightap@wustl.edu")
c(
      person(
        given = "Chris",
        family = "Umphlett",
        role = c("aut", "cre"),
        email = "christopher.umphlett@gmail.com"
      ),
      person(
        given = "Paul",
        family = "Schmidt",
        role = c("ctb"),
        email = "schmidtpaul1989@outlook.com"
      )      
    )
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 = "kisungyou@outlook.com",comment=c(ORCID="0000-0002-8584-459X"))
person("Zach", "Peagler", , "zachpeagler00@gmail.com", role = c("aut", "cre", "cph"))
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("David", "Bronaugh", email="bronaugh@uvic.ca", role="aut"), person("Arelia", "Schoeneberg", email="wernera@uvic.ca", role="aut"), person("Lee", "Zeman", email="lzeman@uvic.ca", role="cre"))
c(person("Angelo", "Canty", role = "aut",
email = "cantya@mcmaster.ca",
comment = "author of original code for S"),
person("Brian", "Ripley", role = c("aut", "trl"),
email = "ripley@stats.ox.ac.uk",
comment = "conversion to R, maintainer 1999--2022, author of parallel support"),
person("Alessandra R.", "Brazzale", role = c("ctb", "cre"),
email = "brazzale@stat.unipd.it",
comment = "minor bug fixes"))
c(person("Brian", "Ripley", role = c("aut", "cre", "cph"),
email = "Brian.Ripley@R-project.org"),
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})")
#not yet ,person("Fischer-Rasmussen", "Kasper", role = "ctb", comment = "Gower distance for CLARA")
)
c(
person("R Core Team", email = "R-core@R-project.org",
role = c("aut", "cph", "cre"),
comment = c(ROR = "02zz1nj61")),
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 = "Brian.Ripley@R-project.org",
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 = "miscellaneous improvements"),
person("Zhijian (Jason)", "Wen", role = "cph", comment = "filled contour code"),
person("Paul", "Murrell", role = "ctb", email = "paul@stat.auckland.ac.nz"),
person("Stefan", "Eng", role = "ctb", comment = "violin plot improvements"),
person("Achim", "Zeileis", role = "ctb", comment = "modern colors"),
person("Alexandre", "Courtiol", role = "ctb", comment = "generics for larrows, lpolygon, lrect and lsegments")
)
c(person("Brian", "Ripley", role = c("aut", "cre", "cph"),
email = "Brian.Ripley@R-project.org"),
person("Bill", "Venables", role = c("aut", "cph")),
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",
comment = "support functions for polr"))
c(person("Douglas", "Bates", role = "aut",
comment = c(ORCID = "0000-0001-8316-9503")),
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 = c(ORCID = "0000-0002-3542-2938")),
person("Timothy A.", "Davis", role = "ctb",
comment = c(ORCID = "0000-0001-7614-6899",
"SuiteSparse libraries",
"collaborators listed in dir(system.file(\"doc\", \"SuiteSparse\", package=\"Matrix\"), pattern=\"License\", full.names=TRUE, recursive=TRUE)")),
person("George", "Karypis", role = "ctb",
comment = c(ORCID = "0000-0003-2753-1437",
"METIS library",
"Copyright: Regents of the University of Minnesota")),
person("Jason", "Riedy", role = "ctb",
comment = c(ORCID = "0000-0002-4345-4200",
"GNU Octave's condest() and onenormest()",
"Copyright: Regents of the University of California")),
person("Jens", "Oehlschlägel", role = "ctb",
comment = "initial nearPD()"),
person("R Core Team", role = "ctb",
comment = c(ROR = "02zz1nj61", "base R's matrix implementation")))
person(given = "Simon",
family = "Wood",
role = c("aut", "cre"),
email = "simon.wood@r-project.org")
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"), comment = c(ROR = "02zz1nj61")))
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 = "Brian.Ripley@R-project.org"),
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"))
Computing methodologies ~ Classification and
regression trees, Computing methodologies ~ Supervised learning
by classification, Computing methodologies ~ Massively parallel
and high-performance simulations, Computing methodologies ~
Distributed simulation
'adaptive-gmrf-2covar.R' 'adaptive-gmrf.R'
'plot-weights-real.R' 'plot-weights-sim.R'
'sim-adaptive-gmrf-2covar.R' 'sim-adaptive-gmrf.R'
'sim-fmri-2covar.R' 'sim-fmri.R'
'adbi-package.R' 'AdbiDriver.R' 'AdbiConnection.R'
'AdbiResult.R' 'AdbiResultArrow.R'
'dbAppendTable_AdbiConnection_character_data.frame.R'
'dbBegin_AdbiConnection.R' 'dbBindArrow_AdbiResult.R'
'dbBindArrow_AdbiResultArrow.R' 'dbBind_AdbiResult.R'
'dbBind_AdbiResultArrow.R' 'dbClearResult_AdbiResult.R'
'dbClearResult_AdbiResultArrow.R' 'dbColumnInfo_AdbiResult.R'
'dbColumnInfo_AdbiResultArrow.R' 'dbCommit_AdbiConnection.R'
'dbConnect_AdbiDriver.R' 'dbDataType_AdbiConnection.R'
'dbDataType_AdbiDriver.R' 'dbDisconnect_AdbiConnection.R'
'dbExistsTable_AdbiConnection_Id.R'
'dbExistsTable_AdbiConnection_SQL.R'
'dbExistsTable_AdbiConnection_character.R'
'dbFetchArrowChunk_AdbiResultArrow.R'
'dbFetchArrow_AdbiResultArrow.R' 'dbFetch_AdbiResult.R'
'dbGetInfo_AdbiConnection.R' 'dbGetInfo_AdbiDriver.R'
'dbGetRowCount_AdbiResult.R' 'dbGetRowCount_AdbiResultArrow.R'
'dbGetRowsAffected_AdbiResult.R'
'dbGetRowsAffected_AdbiResultArrow.R'
'dbGetStatement_AdbiResult.R'
'dbGetStatement_AdbiResultArrow.R'
'dbHasCompleted_AdbiResult.R'
'dbHasCompleted_AdbiResultArrow.R' 'dbIsValid_AdbiConnection.R'
'dbIsValid_AdbiDriver.R' 'dbIsValid_AdbiResult.R'
'dbIsValid_AdbiResultArrow.R'
'dbListFields_AdbiConnection_Id.R'
'dbListFields_AdbiConnection_SQL.R'
'dbListFields_AdbiConnection_character.R'
'dbListTables_AdbiConnection.R'
'dbQuoteIdentifier_AdbiConnection_character.R'
'dbQuoteLiteral_AdbiConnection_character.R'
'dbQuoteString_AdbiConnection_character.R'
'dbRemoveTable_AdbiConnection_character.R'
'dbRemoveTable_AdbiConnection_Id.R'
'dbRollback_AdbiConnection.R'
'dbSendQueryArrow_AdbiConnection.R'
'dbSendQuery_AdbiConnection_character.R'
'dbSendStatement_AdbiConnection_character.R'
'dbUnquoteIdentifier_AdbiConnection.R'
'dbWriteTable_AdbiConnection_Id_data.frame.R'
'dbWriteTable_AdbiConnection_SQL_data.frame.R'
'dbWriteTable_AdbiConnection_character_data.frame.R' 'export.R'
'nanoarrow.R' 'show_AdbiConnection.R' 'show_AdbiDriver.R'
'show_AdbiResult.R' 'show_AdbiResultArrow.R' 'utils.R'
'adegenet.package.R' 'datasets.R' 'dist.genlight.R'
'orthobasis.R' 'classes.R' 'constructors.R' 'accessors.R'
'basicMethods.R' 'handling.R' 'auxil.R' 'minorAllele.R'
'setAs.R' 'SNPbin.R' 'strataMethods.R' 'hierarchyMethods.R'
'glHandle.R' 'glFunctions.R' 'glSim.R' 'find.clust.R'
'hybridize.R' 'scale.R' 'fstat.R' 'import.R' 'seqTrack.R'
'chooseCN.R' 'genind2genpop.R' 'loadingplot.R' 'sequences.R'
'gstat.randtest.R' 'makefreq.R' 'colorplot.R' 'monmonier.R'
'spca.R' 'coords.monmonier.R' 'haploGen.R' 'old2new.R'
'global_local_tests.R' 'dapc.R' 'compoplot.R' 'xvalDapc.R'
'haploPop.R' 'PCtest.R' 'dist.genpop.R' 'Hs.R' 'propShared.R'
'export.R' 'HWE.R' 'propTyped.R' 'inbreeding.R' 'glPlot.R'
'gengraph.R' 'simOutbreak.R' 'mutations.R' 'snpposi.R'
'snpzip.R' 'pairDist.R' 'snapclust.R' 'AIC.snapclust.R'
'AICc.snapclust.R' 'BIC.snapclust.R' 'KIC.snapclust.R'
'snapclust.choose.k.R' 'servers.R' 'showmekittens.R'
'spca_randtest.R' 'export_to_mvmapper.R' 'doc_C_routines.R'
'zzz.R'
adeGsenv.R parameter.R utils.R utilstriangle.R genericMethod.R
utilsclass.R panelfunctions.R ADEg.R ADEgS.R utilsADEgS.R
ADEg.C1.R C1.barchart.R C1.curve.R C1.curves.R C1.density.R
C1.gauss.R C1.dotplot.R C1.hist.R C1.interval.R ADEg.S1.R
S1.boxplot.R S1.class.R S1.distri.R S1.label.R S1.match.R
ADEg.S2.R S2.arrow.R S2.class.R S2.corcircle.R S2.density.R
S2.distri.R S2.image.R S2.label.R S2.logo.R S2.match.R
S2.traject.R S2.value.R ADEg.T.R T.image.R T.value.R T.cont.R
ADEg.Tr.R Tr.class.R Tr.label.R Tr.match.R Tr.traject.R
addhist.R addline.R addpoint.R addsegment.R addtext.R
ade4-kplot.R ade4-scatter.R ade4-score.R ade4-plot.R
multiplot.R s.Spatial.R utilskey.R
'adestr_package.R' 'twostagedesign_with_cache.R' 'analyze.R'
'estimators.R' 'densities.R' 'evaluate_estimator.R'
'fastmonoHFC.R' 'fisher_information.R' 'hcubature.R'
'helper_functions.R' 'integrate_over_sample_space.R'
'reference_implementation.R' 'mle_distribution.R'
'mlmse_score.R' 'n2c2_helpers.R' 'plot.R' 'priors.R' 'print.R'
'adfExplorer-package.R' 'blocks.R' 'compress.R' 'cpp11.R'
'connect_adf.R' 'demo.R' 'deprecated.R' 'move.R'
'device_info.R' 'device_create.R' 'helpers.R' 'directory.R'
'entry_info.R' 'remove.R' 's3_conn.R' 's3_methods.R'
'virtual_path.R'
'DataDistribution.R' 'BinomialDistribution.R'
'ChiSquaredDistribution.R' 'Prior.R' 'TwoStageDesign.R'
'OneStageDesign.R' 'util.R' 'Scores.R' 'CompositeScore.R'
'ConditionalPower.R' 'ConditionalSampleSize.R'
'ContinuousPrior.R' 'FDistribution.R' 'GroupSequentialDesign.R'
'MaximumSampleSize.R' 'NormalDistribution.R' 'PointMassPrior.R'
'StudentDistribution.R' 'Survival.R' 'adoptr.R' 'constraints.R'
'minimize.R' 'regularization.R'
'adproclus-package.R' 'adproclus_classes.R' 'clustering.R'
'data.R' 'get_starts.R' 'model_selection.R' 'utils.R'
'visualize.R'
'utils.R' 'checks.R' 'R6_Model.R' 'R6_AgghooCV.R' 'agghoo.R'
'compareTo.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'
'AllClasses.R' 'AllGenerics.R' 'aion-internal.R'
'aion-package.R' 'axis.R' 'calendar-gregorian.R'
'calendar-julian.R' 'calendar.R' 'coerce.R' 'convert.R'
'data.R' 'format.R' 'intervals.R' 'mutators.R' 'operators.R'
'overlap.R' 'plot.R' 'series.R' 'show.R' 'span.R' 'subset.R'
'time.R' 'validate.R' 'year.R' 'zzz.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'
'generics.R' 'rcppRoxygen.R' 'helper-loadMapsRoxygen.R'
'helper-processResult.R' 'ShapeMap.R' 'AxialShapeGraph.R'
'AllLineShapeGraph.R' 'SegmentShapeGraph.R' 'PointMap.R'
'TraversalType.R' 'AgentLookMode.R' 'RcppExports.R'
'agentAnalysis.R' 'allFewestLineMap.R' 'axialAnalysis.R'
'generateRandomCapString.R' 'getTopFeatures.R' 'isovist.R'
'matchPointsToLines.R' 'palettes.R' 'prepareVGA.R'
'readMetaGraph.R' 'refIDtoIndex.R' 'segmentAnalysis.R'
'sfConversions.R' 'shapegraphToGraphData.R'
'oneToOneTraverse.R' 'oneToAllTraverse.R' 'allToAllTraverse.R'
'vgaLocal.R'
'Acl.r' 'Client.r' 'Algorithm.r' 'AlgorithmHandler.r'
'DataDirectory.r' 'DataFile.r' 'data_utilities.r'
'AllGenerics.R' 'alkahest-internal.R' 'alkahest-package.R'
'baseline.R' 'data.R' 'integrate.R' 'peaks.R' 'replace.R'
'resample.R' 'rescale.R' 'signal.R' 'smooth.R' 'windows.R'
'xrd.R'
'UPN_914.R' 'UPN_915.R' 'UPN_916.R' 'rounding_off.R'
'assess_anemia.R' 'assess_increased_doses.R'
'assess_neutropenia.R' 'assess_reduced_doses.R'
'assess_stop_doses.R' 'assess_thrombocytopenia.R'
'compare_cohorts.R' 'convert_external_format.R'
'convert_tmc_format.R' 'plot_progression.R'
'summarize_cohortMT.R' 'summarize_cycle_progression.R'
'time_to_first_dose_increase.R'
'AMAPVox.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' 'VersionManager.R' 'Run.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'
'AllClasses.R' 'AllGenerics.R' 'ananke-internal.R'
'ananke-package.R' 'c14_calibrate.R' 'c14_combine.R'
'c14_curve.R' 'c14_ensemble.R' 'c14_f14c.R' 'c14_sample.R'
'c14_spd.R' 'c14_uncalibrate.R' 'c14_validate.R' 'coerce.R'
'data.R' 'describe.R' 'interval.R' 'mutators.R' 'pb_age.R'
'plot.R' 'proxy.R' 'statistics.R' 'subset.R' 'validate.R'
'zzz.R'
'RcppExports.R' 'anomaly-package.R' 'generics.R' 'bard.R'
'capa.R' 'data.R' 'pass.R' 'pass.class.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-package.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' 'AuctionClasses.R' 'BargainingClasses.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
'reexport.R' 'AllClasses.R' 'AllGenerics.R'
'ArchaeoPhases-defunct.R' 'ArchaeoPhases-deprecated.R'
'ArchaeoPhases-internal.R' 'ArchaeoPhases-package.R'
'activity.R' 'allen-mcmc.R' 'allen-relations.R' 'bind.R'
'boundaries.R' 'coerce.R' 'data.R' 'depth.R' 'duration.R'
'elapse.R' 'events.R' 'hiatus.R' 'interpolate.R' 'interval.R'
'mutators.R' 'occurrence.R' 'phases.R' 'plot.R' 'read.R'
'sensitivity.R' 'show.R' 'sort.R' 'subset.R' 'summary.R'
'tempo.R' 'test.R' 'transition.R' 'validate.R' 'zzz.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' 'body.R' 'skel.R' 'toy.R'
'AllGenerics.R' 'append.R' 'predicates.R' 'assert.R'
'arkhe-deprecated.R' 'arkhe-internal.R' 'arkhe-package.R'
'assign.R' 'clean.R' 'compact.R' 'conditions.R' 'count.R'
'describe.R' 'detect.R' 'discard.R' 'get.R' 'keep.R'
'mathematics.R' 'remove.R' 'replace.R' 'scale.R' 'seek.R'
'sparsity.R' 'statistics.R' 'utilities.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-object.R' 'type.R'
'array-data.R' 'arrow-datum.R' 'array.R' 'arrow-info.R'
'arrow-package.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' 'dictionary.R' 'dplyr-across.R'
'dplyr-arrange.R' 'dplyr-by.R' 'dplyr-collect.R'
'dplyr-count.R' 'dplyr-datetime-helpers.R' 'dplyr-distinct.R'
'dplyr-eval.R' 'dplyr-filter.R' 'dplyr-funcs-agg.R'
'dplyr-funcs-augmented.R' 'dplyr-funcs-conditional.R'
'dplyr-funcs-datetime.R' 'dplyr-funcs-doc.R'
'dplyr-funcs-math.R' 'dplyr-funcs-simple.R'
'dplyr-funcs-string.R' 'dplyr-funcs-type.R' 'expression.R'
'dplyr-funcs.R' 'dplyr-glimpse.R' 'dplyr-group-by.R'
'dplyr-join.R' 'dplyr-mutate.R' 'dplyr-select.R'
'dplyr-slice.R' 'dplyr-summarize.R' 'dplyr-union.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' 'udf.R' 'util.R'
'AAA_arules-package.R' 'AAA_check_installed.R'
'AAA_definitions.R' 'AAA_nodots.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' 'affinity.R' 'appearance.R' 'apriori.R' 'c.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'
'assert.R' 'assert_class.R' 'is_comparisons.R'
'is_functions.R' 'utils.R' 'assert_create.R' 'assert_type.R'
'assert_compare.R' 'assert_dataframe.R' 'assert_files.R'
'assert_functions.R' 'set_operations.R' 'assert_includes.R'
'assert_length.R' 'assert_names.R' 'assert_null.R'
'assert_numerical.R' 'assert_set.R' 'coverage_testing.R'
'export_testing.R' 'has.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'
'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' 'autocogs-package.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' 'extracton_section_text.R'
'forestharp.R' 'forestharp_helpers.R' 'generate_thumbnails.R'
'join_treeharp.R' 'lang_2_tree.R' 'lang_2_tree_helpers.R'
'log_summary.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'
'RcppExports.R' 'aloocv.R' 'utilities.R' 'cohortODX.R'
'computeEndpoints.R' 'fit_baclava.R' 'predictODX_helpers.R'
'predictODX.R' 's3_methods.R' 'screen_data.R'
'C5.0.R' 'bag_mars_data.R' 'bag_nnet_data.R' 'bag_tree_data.R'
'import-standalone-types-check.R' 'validate.R' 'bagger.R'
'baguette-package.R' 'bridge.R' 'cart.R' 'class_cost.R'
'constructor.R' 'cost_models.R' 'import-standalone-obj-type.R'
'mars.R' 'misc.R' 'model_info.R' 'nnet.R' 'out-of-bag.R'
'predict.R' 'var_imp.R' 'zzz.R'
'appearance.R' 'barrks-package.R' 'bso-phenology-results.R'
'bso-phenology.R' 'utils.R' 'bso-plot.R' 'create-daylengths.R'
'create-events.R' 'create-suntimes.R' 'data.R'
'utils-model-functions.R' 'model.R' 'model-bso.R'
'model-phenips.R' 'model-rity.R' 'model-chapy.R'
'model-combine.R' 'model-joensson.R' 'model-lange.R'
'model-phenips-clim.R' 'phenology-properties.R'
'phenology-results.R' 'phenology.R'
'plot-development-diagram.R' 'stations.R' 'utils-doc.R'
'utils-log.R' 'utils-storage.R'
'validity.R' 'AllClasses.R' 'AllGenerics.R'
'BaseSet-package.R' 'GMT.R' 'GeneSetCollection.R' 'activate.R'
'add.R' 'add_column.R' 'add_relation.R' 'adjacency.R'
'arrange.R' 'c.R' 'cardinality.R' 'cartesian.R' 'complement.R'
'data_frame.R' 'deactivate.R' 'droplevels.R' 'elements.R'
'extract.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'
'union_closed.R' 'utils.R' 'zzz.R'
'RcppExports.R' 'class.R' 'adjust_lambda.R' 'analysis.R'
'basket_test.R' 'baskexact-package.R' 'borrowing.R' 'check.R'
'ecd.R' 'ess.R' 'estim.R' 'globalweights.R' 'helper.R'
'interim.R' 'monotonicity.R' 'opt_design.R' 'plot.R' 'pow.R'
'rejection_probabilities.R' 'toer.R' 'validate.R' 'weights.R'
'api_check.R' 'bayes_colors.R' 'bayes_label.R' 'bayes_plot.R'
'bayes_read.R' 'bayes_smooth.R' 'bayes_utils.R'
'bayes_variance.R' 'RcppExports.R' 'zzz.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'
'Heatmap_default.R' 'PT.R' 'USA.R' 'blc.R' 'ffbs.R' 'dlm.R'
'gompertz_dlm.R' 'dlm_close.R' 'expectancy_blc.R'
'expectancy_default.R' 'fitted_dlm.R' 'expectancy_dlm.R'
'fun_aux.R' 'fitted_hp.R' 'expectancy_hp.R' 'fitted_blc.R'
'globals.R' 'heatmap_blc.R' 'heatmap_dlm.R' 'heatmap_hp.R'
'heatmap_list.R' 'hp_poisson_reduced.R'
'hp_lognormal_reduced.R' 'hp_binomial_reduced.R' 'hp_poisson.R'
'hp_lognormal.R' 'hp_binomial.R' 'hp.R' 'sir_gompertz.R'
'hp_close.R' 'hp_mix.R' 'improvement.R' 'kd_filter.R'
'kd_smoother.R' 'mean_blc.R' 'mean_blc_pred.R' 'plot_blc.R'
'plot_blc_pred.R' 'plot_chain.R' 'plot_dlm.R' 'plot_hp.R'
'plot_list.R' 'predict_blc.R' 'predict_dlm.R' 'print_blc.R'
'print_blc_pred.R' 'print_dlm.R' 'print_hp.R' 'quantile_blc.R'
'quantile_blc_pred.R' 'summary_dlm.R' 'summary_hp.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' 'ArchiveAsync.R' 'ArchiveAsyncFrozen.R'
'ArchiveBatch.R' 'CallbackAsync.R' 'CallbackBatch.R'
'Codomain.R' 'ContextAsync.R' 'ContextBatch.R' 'Objective.R'
'ObjectiveRFun.R' 'ObjectiveRFunDt.R' 'ObjectiveRFunMany.R'
'OptimInstance.R' 'OptimInstanceAsync.R'
'OptimInstanceAsyncMultiCrit.R'
'OptimInstanceAsyncSingleCrit.R' 'OptimInstanceBatch.R'
'OptimInstanceBatchMultiCrit.R'
'OptimInstanceBatchSingleCrit.R' 'OptimInstanceMultiCrit.R'
'OptimInstanceSingleCrit.R' 'mlr_optimizers.R' 'Optimizer.R'
'OptimizerAsync.R' 'OptimizerAsyncDesignPoints.R'
'OptimizerAsyncGridSearch.R' 'OptimizerAsyncRandomSearch.R'
'OptimizerBatch.R' 'OptimizerBatchChain.R'
'OptimizerBatchCmaes.R' 'OptimizerBatchDesignPoints.R'
'OptimizerBatchFocusSearch.R' 'OptimizerBatchGenSA.R'
'OptimizerBatchGridSearch.R' 'OptimizerBatchIrace.R'
'OptimizerBatchLocalSearch.R' 'OptimizerBatchNLoptr.R'
'OptimizerBatchRandomSearch.R' 'Progressor.R'
'mlr_terminators.R' 'Terminator.R' 'TerminatorClockTime.R'
'TerminatorCombo.R' 'TerminatorEvals.R' 'TerminatorNone.R'
'TerminatorPerfReached.R' 'TerminatorRunTime.R'
'TerminatorStagnation.R' 'TerminatorStagnationBatch.R'
'TerminatorStagnationHypervolume.R' 'as_terminator.R'
'assertions.R' 'bb_optimize.R' 'bbotk_reflections.R'
'bibentries.R' 'helper.R' 'mlr_callbacks.R' 'nds_selection.R'
'reexport.R' 'sugar.R' 'worker_loops.R' 'zzz.R'
'bcdata-package.R' 'bcdc-get-citation.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' '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' 'emojisData.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'
'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'
'bigrquery-package.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' 'connections-page.R' 'cpp11.R'
'dbi-driver.R' 'dbi-connection.R' 'dbi-result.R' 'dplyr.R'
'gs-object.R' 'import-standalone-obj-type.R'
'import-standalone-s3-register.R'
'import-standalone-types-check.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'
'biomod2-package.R' 'biomod2_globalVariables.R'
'biomod2_classes_0.R' 'biomod2_classes_1.R'
'biomod2_classes_2.R' 'biomod2_classes_3.R'
'biomod2_classes_4.R' 'biomod2_classes_5.R'
'biomod2_internal.R' 'biomod2_data.R'
'BIOMOD_EnsembleForecasting.R' 'BIOMOD_EnsembleModeling.R'
'BIOMOD_FormatingData.R' 'BIOMOD_LoadModels.R'
'BIOMOD_Modeling.R' 'BIOMOD_Projection.R' 'BIOMOD_RangeSize.R'
'DEPRECATED.R' 'bm_BinaryTransformation.R'
'bm_CrossValidation.R' 'bm_FindOptimStat.R' 'bm_MakeFormula.R'
'bm_ModelingOptions.R' 'bm_PlotEvalBoxplot.R'
'bm_PlotEvalMean.R' 'bm_PlotRangeSize.R'
'bm_PlotResponseCurves.R' 'bm_PlotVarImpBoxplot.R'
'bm_PseudoAbsences.R' 'bm_RunModelsLoop.R' 'bm_SRE.R'
'bm_SampleBinaryVector.R' 'bm_SampleFactorLevels.R'
'bm_Tuning.R' 'bm_VariablesImportance.R' 'zzz.R'
'data.R' 'globals.R' 'as_numeric_parse.R' 'covsum_nested.R'
'dsmb_ccru.R' 'nice_mcmcglmm.R' 'nice_mcmcglmm_icc.R'
'ae_timeline_plot.R' 'caterpillar_plot.R' 'redcap_data_out.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'
'samplers.R' 'tensor_ops.R' 'result_logger.R'
'likelihood_evaluator.R' 'distances.R' 'kernels.R' 'bktr.R'
'examples.R' 'plots.R' 'utils.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'
'blandr.statistics.formula.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'
'print.blandr.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'
block.sub.R periodic.R utils.R jackVarField.R ppw.R boots.R
plot.boodd.R summary.boodd.R confint.boodd.R generate_process.R
bootsemi.R sieveboot.R blockboot.R blockboot.seasonal.R
regenboot.R freqboot.R aidedboot.R bootglm.R fieldboot.R
tboot_dist.R boot_dist.R pkc.R compute_power.R field.sub.R
findBestEpsilon.R fastNadaraya.R smallEnsemble.R jackVar.R
jackVarBlock.R jackVarRegen.R jackVarRegen.atom.R
jackVarRegen.smallEnsemblle.R jackFunc.R jackFuncBlock.R
jackFuncRegen.R qVar.R mark_boot.R fieldbootP.R
GetPseudoBlocks.R bandw1.R GetBlocks.R func_fdb.R naradamar.R
ftrunc.R thetaRB.R boot_wild.R boot_local.R boot_res.R
per_boo.R tft_boot.R para.boot.R rate.sub.R rate.block.sub.R
best.block.sub.size.R best.sub.size.iid.R f_PseudoBlocks.R
thetaARBB.R boodd-package.R boodd-class.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_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'
'utils.R' 'utils_matrix.R' 'vertex_roles.R' 'vulnerability.R'
'write_brainnet.R' 'zzz.R'
'bridge.R' 'bridgr-package.R' 'data-indicators.R' 'forecast.R'
'summary.R' 'utils.R'
'aaa-documentation-helper.R' 'null-and-default.R' 'aer.R'
'auc.R' 'base.R' 'bbmle.R' 'betareg.R' 'biglm.R' 'bingroup.R'
'boot.R' 'broom-package.R' 'broom.R' 'btergm.R' 'car.R'
'caret.R' 'cluster.R' 'cmprsk.R' 'data-frame.R'
'deprecated-0-7-0.R' 'drc.R' 'emmeans.R' 'epiR.R' 'ergm.R'
'fixest.R' 'gam.R' 'geepack.R' 'glmnet-cv-glmnet.R'
'glmnet-glmnet.R' 'gmm.R' 'hmisc.R'
'import-standalone-obj-type.R'
'import-standalone-types-check.R' 'joinerml.R' 'kendall.R'
'ks.R' 'lavaan.R' 'leaps.R' 'lfe.R' 'list-irlba.R'
'list-optim.R' 'list-svd.R' 'list-xyz.R' 'list.R' 'lm-beta.R'
'lmodel2.R' 'lmtest.R' 'maps.R' 'margins.R' 'mass-fitdistr.R'
'mass-negbin.R' 'mass-polr.R' 'mass-ridgelm.R' 'stats-lm.R'
'mass-rlm.R' 'mclust.R' 'mediation.R' 'metafor.R' 'mfx.R'
'mgcv.R' 'mlogit.R' 'muhaz.R' 'multcomp.R' 'nnet.R' 'nobs.R'
'ordinal-clm.R' 'ordinal-clmm.R' 'plm.R' 'polca.R' 'psych.R'
'stats-nls.R' 'quantreg-nlrq.R' 'quantreg-rq.R'
'quantreg-rqs.R' 'robust-glmrob.R' 'robust-lmrob.R'
'robustbase-glmrob.R' 'robustbase-lmrob.R' 'sp.R' 'spdep.R'
'speedglm-speedglm.R' 'speedglm-speedlm.R' 'stats-anova.R'
'stats-arima.R' 'stats-decompose.R' 'stats-factanal.R'
'stats-glm.R' 'stats-htest.R' 'stats-kmeans.R' 'stats-loess.R'
'stats-mlm.R' 'stats-prcomp.R' 'stats-smooth.spline.R'
'stats-summary-lm.R' 'stats-time-series.R' 'survey.R'
'survival-aareg.R' 'survival-cch.R' 'survival-coxph.R'
'survival-pyears.R' 'survival-survdiff.R' 'survival-survexp.R'
'survival-survfit.R' 'survival-survreg.R' 'systemfit.R'
'tseries.R' 'utilities.R' 'vars.R' 'zoo.R' 'zzz.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'
'import-standalone-obj-type.R'
'import-standalone-types-check.R' 'inputs.R' 'skinSelector.R'
'utils.R' 'zzz.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'
'accordion.R' 'breakpoints.R' 'bs-current-theme.R'
'bs-dependencies.R' 'bs-global.R' 'bs-remove.R'
'bs-theme-layers.R' 'bs-theme-preset-bootswatch.R'
'bs-theme-preset-brand.R' 'bs-theme-preset-builtin.R'
'bs-theme-preset.R' 'utils.R' 'bs-theme-preview.R'
'bs-theme-update.R' 'bs-theme.R' 'bslib-package.R' 'buttons.R'
'card.R' 'deprecated.R' 'files.R' 'fill.R' 'imports.R'
'input-dark-mode.R' 'input-switch.R' 'layout.R' 'nav-items.R'
'nav-update.R' 'navbar_options.R' 'navs-legacy.R' 'navs.R'
'onLoad.R' 'page.R' 'popover.R' 'precompiled.R' 'print.R'
'shiny-devmode.R' 'sidebar.R' 'staticimports.R' 'tooltip.R'
'utils-deps.R' 'utils-shiny.R' 'utils-tags.R' 'value-box.R'
'version-default.R' 'versions.R'
'data.R' 'BTYD.R' 'bgbb.R' 'bgnbd.R' 'pnbd.R' 'dc.R' 'spend.R'
'00_environment.R' '01_class_01_operations.R'
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'01_class_04_expense.R' '01_class_05_loan.R'
'01_class_06_depreciation.R' '01_class_07_transaction_plan.R'
'02_method_barplot.R' '02_method_condense.R'
'02_method_get_set_as.R' '02_method_kable_bpR.R'
'02_method_kbl_by_types.R' '02_method_model2df.R'
'02_method_update_operations.R' '02_method_update_plan.R'
'businessPlanR-package.R' 'businessPlanR_internal.R'
'calc_staff.R' 'fin_needs.R' 'first_last.R' 'growth.R'
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'regularly.R' 'regularly_delayed.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'
'CasinoTest.R' 'PairScore.R' 'RcppExports.R' 'S4-BuysePower.R'
'S4-BuysePower-model.tables.R' 'S4-BuysePower-nobs.R'
'S4-BuysePower-summary.R' 'S4-BuysePower-print.R'
'S4-BuysePower-show.R' 'S4-BuyseTest.R' 'S4-BuyseTest-coef.R'
'S4-BuyseTest-confint.R' 'S4-BuyseTest-get.R'
'S4-BuyseTest-model.tables.R' 'S4-BuyseTest-nobs.R'
'S4-BuyseTest-plot.R' 'S4-BuyseTest-summary.R'
'S4-BuyseTest-print.R' 'S4-BuyseTest-sensitivity.R'
'S4-BuyseTest-show.R' 'S4-BuyseTest-update.R'
'S4-BuyseTest.options.R' 'S4-BuyseTest.vcov.R'
'as.data.table.performance.R' 'auc.R' 'autoplot.S4BuyseTest.R'
'brier.R' 'constStrata.R' 'discreteRoot.R' 'doc-data.R'
'efronlim.R' 'iid.S3sensitivity.R' 'iid.prodlim.R' 'mover.R'
'normexp.R' 'performance.R' 'performanceResample.R'
'plot.S3sensitivity.R' 'powerBuyseTest.R' 'predict.logit.R'
'rbind.performanceResample.R' 'sim.simBuyseTest.R'
'simBuyseTest.R' 'simCompetingRisks.R' 'summary.performance.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-package.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.bvarlist.R'
'plot.bvarprd.R' 'plot.bvec.R' 'plot.dfm.R'
'post_normal_covar_const.R' 'post_normal_covar_tvp.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'
'HD2022.R' 'HDI2020.R' 'MAC1975.R' 'PKH1982.R' 'RAND.R'
'ANCCR.R' 'RW1972.R' 'SM2007.R' 'TD.R' 'rsa_functions.R'
'compare_models.R' 'data.R' 'fit_helpers.R' 'fit_model.R'
'model_parsers.R' 'model_plots.R' 'plotting_functions.R'
'model_graphs.R' 'model_support_functions.R' 'parse_design.R'
'run_experiment.R' 'phase_parser.R' 'information_functions.R'
'make_experiment.R' 'assertions.R' 'get_parameters.R'
'get_timings.R' 'get_design.R' 'heidi_helpers.R'
'anccr_helpers.R' 'td_helpers.R' 'calmr_verbosity.R'
'parallel_helpers.R' 'maps.R' 'set_calmr_palette.R'
'class_model.R' 'class_design.R' 'class_result.R'
'class_experiment.R' 'class_rsa.R' 'class_fit.R'
'calmr-package.R'
'global.R' 'utilities.R' 'time_utilities.R' 'check.R'
'generic.R' 'data.R' 'seed.R' 'distribution.R'
'dataset_config.R' 'time_entry.R' 'repeated_schedule.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' 'event_logic.R'
'dataset_summary.R' 'outfun.R' 'hardware_settings.R'
'simulation_progress.R' 'solver_settings.R' 'nocb_settings.R'
'declare_settings.R' 'progress_settings.R'
'internal_settings.R' 'simulation_settings.R' 'plan_setup.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'
'omega_block.R' 'omega_blocks.R'
'parameter_uncertainty_utils.R' 'replication_settings.R'
'replicated_campsis_model.R' 'model_add_suffix.R'
'rxode_model.R' 'rxode_conversion.R' 'mrgsolve_model.R'
'mrgsolve_conversion.R'
'cartographer-global.R' 'cartographer-package.R'
'add_geometry.R' 'data.R' 'resolve.R' 'zzz.R'
Cascade-package.R global.R micro_array.R network.R
micro_array-network.R micropredict.R datasets.R
'background-color.R' 'theme.R' 'border-all.R' 'border-color.R'
'border-width.R' 'breakpoints.R' 'cascadess.R' 'display.R'
'flex-align.R' 'flex-content.R' 'flex-direction.R'
'flex-display.R' 'flex-justify.R' 'flex-wrap.R' 'float.R'
'focus-color.R' 'gap-all.R' 'height-relative.R' 'item-align.R'
'item-fill.R' 'item-grow.R' 'item-order.R' 'margin-all.R'
'overflow-all.R' 'padding-all.R' 'position-centered.R'
'position-offset.R' 'position-sticky.R' 'position.R'
'reexports.R' 'rounded-all.R' 'shadow.R' 'stack-vertical.R'
'style-pronoun.R' 'text-alignment.R' 'text-break.R'
'text-color.R' 'text-decoration.R' 'text-height.R'
'text-selection.R' 'text-size.R' 'text-style.R'
'text-transform.R' 'text-weight.R' 'text-wrap.R' 'utils-docs.R'
'utils.R' 'vertical-alignment.R' 'visible.R' 'width-relative.R'
'zzz.R'
'DataSimClass.R' 'dataHolder.R' 'weightsClass.R' 'ESS.R'
'OT.R' 'PSIS.R' 'RcppExports.R' 'balanceFunctions.R'
'barycentricProjection.R' 'calc_weight.R' 'causalOT-package.R'
'cost_functions.R' 'scmClass.R' 'gridSearch.R' 'cotClass.R'
'cotOOP.R' 'cot_opts.R' 'likelihoodClass.R' 'mean_balance.R'
'summary.R' 'supportedMethods.R' 'treatment_effect.R' 'utils.R'
'zzz.R'
'category.R' 'ceg.R' 'event_tree.R' 'staged_tree.R'
'ceg_model.R' 'cont_table.R' 'data_documentation.R'
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'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'
'BootstrapParams.R' 'NlmeParallelMethod.R'
'NlmeUserAuthentication.R' 'NlmeParallelHost.R'
'SimpleNlmeJob.R' 'BootNlmeJob.R' 'CovariateEffectModel.R'
'GenerateControlfile.R' 'GenerateParamsfile.R'
'NlmeColumnMapping.r' 'NlmeCovariateParameter.r'
'NlmeDataset.r' 'NlmeDoseMapping.R' 'NlmeEmaxParameters.R'
'NlmeIndirectParameters.R' 'NlmeModelAbsorption.R'
'NlmeModelParameterization.R' 'NlmeModelType.R'
'NlmeParamsMapping.R' 'NlmePkParameters.R' 'NlmePmlModelInfo.R'
'NlmeRandParamsMapping.R' 'NlmeRandomEffectBlock.r'
'NlmeRemoteExecutor.R' 'NlmeScenario.R' 'NlmeTableDef.R'
'SortColumns.R' 'ProfileParameters.R' 'ProfileNlmeJob.R'
'ProfileVar.R' 'error_model.r' 'pml_model.r'
'RandomEffectsMethods.R' 'ShotgunNlmeJob.R' 'SortByNlmeJob.R'
'StepwiseParams.R' 'StepwiseNlmeJob.R' 'acceptAllEffects.R'
'addInfusion.R' 'addLabel.R' 'addTablesToColumnMapping.R'
'add_input_dosingCycles.R' 'bootstrap.r' 'built_in_models.r'
'checkHostParams.R' 'colMapping.R' 'copyModel.R'
'covariateModel.R' 'covariateNames.R' 'createInitialMapping.R'
'create_model_from_metamodel.R' 'create_model_info.R' 'data.r'
'dosing.r' 'editModel.R' 'emaxmodel.R' 'engine_params.r'
'extract_mmdl.R' 'fitmodel.R' 'fitmodelHelperFunctions.R'
'fixedEffect.R' 'generateCovarSearchArgsFile.R' 'getThetas.R'
'get_omega_omegaSE.R' 'globals.R' 'hostParams.R' 'job.r'
'linearmodel.R' 'log_Execution.R' 'map_covariates.R'
'map_dosepoints.R' 'modelVariableNames.R' 'observation.r'
'obtain_NLMELicense.R' 'parameterNames.R' 'parseControlFile.R'
'parsePMLColMap.R' 'parse_mmdl.R' 'pkemaxmodel.R'
'pkindirectmodel.R' 'pklinearmodel.R' 'pkmodel.R'
'profile_estimation.r' 'randomEffect.R'
'readInitialEstimatesParams.R' 'run_metamodel.R' 'saveModel.R'
'saveUpdatedMetamodel.R' 'secondary_variable.r'
'shotgunSearch.R' 'simParams.R' 'sortfit.R' 'stepwiseSearch.R'
'structural_param.r' 'tableParams.R' 'update_PMLwithThetas.R'
'vpc.r' 'writeColumnMapping.R' 'writeDefaultFiles.R'
'api.R' 'CFCalendar.R' 'CFCalendar360.R' 'CFCalendar365.R'
'CFCalendar366.R' 'CFCalendarJulian.R' 'CFCalendarNone.R'
'CFCalendarProleptic.R' 'CFCalendarStandard.R'
'CFCalendarTAI.R' 'CFCalendarUTC.R' 'CFClimatology.R'
'CFtime-package.R' 'CFtime.R' 'deprecated.R' 'helpers.R'
'zzz.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'
'checkPermutation.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
'report_null.R' 'utils.R' 'chevron_tlg-S4class.R'
'ael01_nollt.R' 'ael02.R' 'ael03.R' 'aet01.R' 'aet01_aesi.R'
'aet02.R' 'aet03.R' 'aet04.R' 'aet05.R' 'aet05_all.R' 'aet10.R'
'assertions.R' 'cfbt01.R' 'checks.R' 'chevron_tlg-S4methods.R'
'cml02a_gl.R' 'cmt01a.R' 'cmt02_pt.R' 'coxt01.R' 'coxt02.R'
'data.R' 'dmt01.R' 'dst01.R' 'dtht01.R' 'dummy_template.R'
'egt01.R' 'egt02.R' 'egt03.R' 'egt05_qtcat.R' 'ext01.R'
'fstg01.R' 'fstg02.R' 'gen_args.R' 'kmg01.R' 'lbt01.R'
'lbt04.R' 'lbt05.R' 'lbt06.R' 'lbt07.R' 'lbt14.R' 'lbt15.R'
'mht01.R' 'mng01.R' 'package.R' 'pdt01.R' 'pdt02.R'
'reexports.R' 'rmpt01.R' 'rmpt03.R' 'rmpt04.R' 'rmpt05.R'
'rmpt06.R' 'rspt01.R' 'rtables_utils.R' 'standard_rules.R'
'ttet01.R' 'unwrap.R' 'vst01.R' 'vst02.R' 'zzz.R'
'acs.R' 'admin1.R' 'choropleth.R' 'country.R' 'county.R'
'data.R' 'get_usa_demographics.R' 'init.R' 'internal-docs.R'
'state.R' 'tract.R'
global.R attributes.R call.R datasets.R fetching.R info.R
utility.R zzz.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'
'track.R' 'heatmap.R' 'cell-geom.R' 'track-geom.R' 'link.R'
'param.R' 'utils.R' 'initialize.R' 'add.R'
'circlizePlus-package.R' 'data.R'
'utils.R' 'internals.R' 'checks.R' 'cleanr-package.R'
'options.R' 'throw.R' 'wrappers.R' 'zzz.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'
'answer_check.r' 'baseline_table.r' 'calculate_index.r'
'check_nonnum.r' 'classif_model_compare.r' 'combine_files.r'
'cut_by.r' 'extract_num.r' 'get_valid.r' 'get_valid_subset.r'
'importance_plot.r' 'initial_cleaning.r' 'interactions.r'
'mark_outlier.r' 'utils.R' 'misc.r' 'multichoice.r'
'rcs_plot.r' 'regressions.r' 'subgroup_forest.r'
'subject_standardize.r' 'to_date.r' 'unit_standardize.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'
'utils.R' 'probPred.R' 'linearPred.R' 'logLH.R' 'loss.R'
'helpful_tests.R' 'ctnsSolution.R' 'rwlsSolution.R'
'Wtsmth_Fit.R' 'nfoldSplit.R' 'WTsmth_nFold_CV.R' 'breakCNV.R'
'datadescrip.R' 'wideDataRaw.R' 'wideFrequency.R'
'weightMatrix.R' 'prep.R'
'RcppExports.R' 'cache.R' 'coala.R' 'model.R' 'feature.R'
'feature_growth.R' 'feature_ignore_singletons.R'
'feature_migration.R' 'feature_mutation.R' 'feature_outgroup.R'
'feature_pop_merge.R' 'feature_recombination.R'
'feature_sample.R' 'feature_selection.R'
'feature_size_change.R' 'feature_sumstats.R'
'feature_unphased.R' 'import_popgenome.R' 'interface_abc.R'
'locus.R' 'model_build.R' 'model_check.R' 'model_examples.R'
'model_getters.R' 'model_print.R' 'model_scale.R'
'model_simulate.R' 'onLoad.R' 'parameter.R' 'parameter_prior.R'
'parameter_variation.R' 'parameter_zero_inflation.R'
'search_executable.R' 'segsites.R' 'simulation_tasks.R'
'simulator_class.R' 'simulator_ms.R' 'simulator_msms.R'
'simulator_scrm.R' 'simulator_seqgen.R' 'sumstat.R'
'sumstat_dna.R' 'sumstat_file.R' 'sumstat_four_gamete.R'
'sumstat_ihh.R' 'sumstat_jsfs.R' 'sumstat_mcmf.R'
'sumstat_nucleotide_div.R' 'sumstat_omega.R'
'sumstat_seg_sites.R' 'sumstat_sfs.R' 'sumstat_tajimas_d.R'
'sumstat_trees.R' 'tools.R'
'CFR_estimation.R' 'dic.fit.mcmc.R' 'dic.fit.R'
'get.obs.type.R' 'S4stuff.R' 'sampleSizeSimulation.R'
'coarseDataTools-package.R'
'0helper.r' 'as.htest.r' 'cocor-package.R'
'cocor.dep.groups.nonoverlap.r' 'cocor.dep.groups.overlap.r'
'cocor.indep.groups.r' 'cocor.r' 'get.cocor.input.r'
'get.cocor.results.r'
classes.R librarySymbols.R functionHandlers.R codeDepends.R
sectionDepends.R sweave.R xml.R jss.R frags.R codeTypes.R gc.R
graph.R parallel.R deps.R separateBlocks.R callGraph.R isPlot.R
isOutput.R refScript.R sideEffects.R highlightCode.R
freeVariables.R convenienceFuns.R
'cohortBuilder-package.R' 'cohort_methods.R'
'source_methods.R' 'step.R' 'filter.R' 'attrition.R'
'bind_keys.R' 'hooks.R' 'list_operators.R' 'repro_code_utils.R'
'source_tblist.R' 'data.R'
'utils.R' 'collatz_function.R' 'hailstone_sequence.R'
'reverse_function.R' 'stopping_time.R' 'tree_graph.R'
'coloc-package.R' 'boundaries.R' 'check.R' 'claudia.R'
'plot.R' 'private.R' 'sensitivity.R' 'split.R' 'susie.R'
'testdata.R' 'zzz.R'
'sum_every_n1.R' 'sum_every_n.R' 'pistar_compute.R'
'pi_compute.R' 'COMBO_data.R' 'expit.R'
'perfect_sensitivity_EM.R' 'w_j.R' 'q_gamma_f.R' 'q_beta_f.R'
'em_function.R' 'loglik.R' 'COMBO_EM.R' 'pitilde_compute.R'
'COMBO_data_2stage.R' 'w_j_2stage.R' 'q_delta_f.R'
'em_function_2stage.R' 'loglik_2stage.R' 'COMBO_EM_2stage.R'
'COMBO_EM_data.R' 'label_switch.R' 'check_and_fix_chains.R'
'mean_pistarjj_compute.R' 'pistar_compute_for_chains.R'
'pistar_by_chain.R' 'naive_jags_picker.R'
'naive_model_picker.R' 'jags_picker.R' 'model_picker.R'
'COMBO_MCMC.R' 'model_picker_2stage.R' 'COMBO_MCMC_2stage.R'
'LSAC_data.R' 'VPRAI_synthetic_data.R'
'check_and_fix_chains_2stage.R' 'jags_picker_2stage.R'
'label_switch_2stage.R' 'misclassification_prob.R'
'misclassification_prob2.R' 'naive_jags_picker_2stage.R'
'naive_loglik_2stage.R' 'naive_model_picker_2stage.R'
'pistar_by_chain_2stage.R' 'pistar_compute_for_chains_2stage.R'
'pitilde_by_chain.R' 'pitilde_compute_for_chains.R'
'true_classification_prob.R'
'sum_every_n1.R' 'sum_every_n.R' 'pistar_compute.R'
'pi_compute.R' 'COMBO_weight.R' 'COMBO_EM_function.R'
'COMBO_EM_algorithm.R' 'COMMA_data.R' 'w_m_normalY.R'
'w_m_binaryY.R' 'EM_function_normalY_XM.R'
'EM_function_normalY.R' 'EM_function_bernoulliY_XM.R'
'EM_function_bernoulliY.R' 'COMMA_EM.R' 'COMMA_boot_sample.R'
'COMMA_EM_bootstrap_SE.R' 'COMMA_OLS.R'
'COMMA_OLS_bootstrap_SE.R' 'COMMA_PVW.R'
'COMMA_PVW_bootstrap_SE.R' 'EM_function_poissonY.R'
'EM_function_poissonY_XM.R' 'NCHS2022_sample.R'
'misclassification_prob.R' 'theta_optim.R' 'theta_optim_XM.R'
'true_classification_prob.R' 'w_m_poissonY.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'
'audits.R' 'browse.R' 'connect.R' 'connectapi-package.R'
'connectapi.R' 'content.R' 'deploy.R' 'get.R' 'git.R'
'groups.R' 'integrations.R' 'lazy.R' 'page.R' 'parse.R'
'promote.R' 'ptype.R' 'remote.R' 'runtime-caches.R'
'schedule.R' 'tags.R' 'utils.R' 'thumbnail.R' 'user.R'
'utils-ci.R' 'utils-pipe.R' 'variant.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"
"postProcessCqSysfile.R" "ReadConQuestState_createDF.R"
"conquestr.R" "conquestrFunc.R" "generateItems.R"
"generateHelpers.R" "infoHelpers.R" "itanalHelpers.R"
"plotGeneral.R" "plotRout.R" "pvHelpers.R" "residHelpers.R"
"showHelpers.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'
'ConversationAlign-package.R' 'compute_auc.R'
'compute_lagcorr.R' 'corpus_analytics.R' 'data.R' 'globals.R'
'prep_dyads.R' 'read_1file.R' 'read_dyads.R'
'replacements_25.R' 'summarize_dyads.R' 'utils.R' 'zzz.R'
'CopernicusMarine-package.r' 'cms_catalogue_entry.R'
'cms_cite_product.r' 'cms_download_native.R'
'cms_download_stac.r' 'cms_login.r' 'cms_download_subset.r'
'cms_list_stac_files.r' 'cms_product_details.r'
'cms_product_metadata.r' 'cms_product_services.r'
'cms_products_list.r' 'cms_stac_properties.r' 'generics.r'
'cms_wmts.r' 'import.r'
'cophescan-package.R' 'singlevar.R' 'multivarsusie.R'
'multitrait.R' 'cophe_hyp_predict.R' 'copheplots.R'
'testdata.R' 'RcppExports.R' 'metrop_hier_priors.R' 'zzz.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
obs.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
'coranking-package.R' 'coranking.R' 'coranking_internals.R'
'lcmc.R' 'criteria.R' 'image.R' 'rankmatrix.R' 'submatrix.R'
'r_nx.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' 'probpredict.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' 'enc-utils.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'
'plot_rp.R' 'simts.R' 'spdiags.R' 'wincrqa.R' 'windowdrp.R'
'helpers.R' 'as_csquares.R' 'as_stars.R' 'csquares-package.R'
'csquares_methods.R' 'drop.R' 'expand.R' 'ices.R' 'in.R'
'init.R' 'joins_tidy.R' 'new_csquares.R' 'orca.R' 'resample.R'
'tidyverse.R' 'st_as_sf.R' 'validate.R' 'vctrs.R'
'utils.R' 'curry.R' 'partial.R' 'scaffold.R' 'tail_curry.R'
'batch.R' 'cache_controls.R' 'class-CalibrationSet.R'
'class-ExposureSeries.R' 'class-EffectScenario.R' 'calibrate.R'
'class-ParameterSet.R' 'class-Transferable.R' 'data.R'
'dose_response.R' 'effect.R' 'epx.R' 'explore_space.R' 'fx.R'
'get.R' 'globals.R' 'has.R' 'import_morse.R' 'import_toxswa.R'
'is.R' 'lik_profile.R' 'log.R' 'man-deb.R' 'man-lemna.R'
'man-macrophytes.R' 'solver.R' 'model-algae.R'
'model-deb_abj.R' 'model-debtox.R' 'model-deb_daphnia.R'
'model-guts_red.R' 'model-lemna_setac.R'
'model-lemna_schmitt.R' 'model-myriophyllum.R' 'package.R'
'pll.R' 'plot.R' 'plotting.r' 'pull.R' 'sequence.R' 'set.R'
'set_bounds.R' 'set_exposure.R' 'set_forcings.R' 'set_init.R'
'set_param.R' 'set_transfer.R' 'set_window.R' 'show.R'
'simulate.R' 'survival.R' 'utils-pipe.R'
'CVXR-package.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' 'clarabel.R' 'eliminate_pwl.R' 'dcp2cone.R'
'dgp2dcp.R' 'sparse_utils.R' 'qp2quad_form.R' 'qp_solvers.R'
'utilities.R' 'coll_utils.R' 'solver_utilities.R'
'transforms.R' 'exports.R' 'rcppUtils.R' 'LinOp.R'
'LinOpVector.R' 'RcppExports.R' 'CVXcanon.R' 'canonInterface.R'
'Cyclotomic.R' 'arithmetic.R' 'conjugate.R' 'imports.R' 'is.R'
'maputils.R' 'mkCyclotomic.R' 'polar.R' 'rational.R'
'showCyclotomic.R' 'sqrt.R' 'trigonometry.R' 'zzz.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' 'deprecated.R' 'fit_cop_corr.R' 'fit_cop_mle.R'
'fit_margin.R' 'gof.R' 'joint_distr.R' 'mixed_linear.R'
'mixedvonmises.R' 'opt_auto.R' 'plotting_functions.R'
'simulate_trajectory.R' 'utils.R' 'wrappedcauchy.R' 'zzz.R'
'DAISIEprep-package.R' 'add_asr_node_states.R'
'add_island_colonist.R' 'add_missing_species.R'
'add_multi_missing_species.R' 'add_outgroup.R'
'all_descendants_conspecific.R' 'any_back_colonisation.R'
'any_outgroup.R' 'any_polyphyly.R' 'as_daisie_datatable.R'
'benchmark.R' 'bind_colonist_to_tbl.R' 'check_phylo_data.R'
'count_missing_species.R' 'create_daisie_data.R'
'create_endemicity_status.R' 'create_test_phylod.R' 'data.R'
'default_params_doc.R' 'endemicity_to_sse_states.R'
'extract_asr_clade.R' 'extract_clade_name.R'
'extract_endemic_clade.R' 'extract_endemic_singleton.R'
'extract_island_species.R' 'extract_multi_tip_species.R'
'extract_nonendemic.R' 'extract_species_asr.R'
'extract_species_min.R' 'extract_stem_age.R'
'extract_stem_age_asr.R' 'extract_stem_age_genus.R'
'extract_stem_age_min.R' 'get_endemic_species.R'
'is_back_colonisation.R' 'is_duplicate_colonist.R'
'is_identical_island_tbl.R' 'is_multi_tip_species.R'
'island_colonist-class.R' 'island_colonist-accessors.R'
'island_tbl-class.R' 'island_tbl-accessors.R'
'island_tbl-methods.R' 'multi_extract_island_species.R'
'multi_island_tbl-class.R' 'multi_island_tbl-methods.R'
'plot_colonisation.R' 'plot_performance.R' 'plot_phylod.R'
'plot_sensitivity.R' 'print-methods.R' 'read_performance.R'
'read_sensitivity.R' 'rm_duplicate_island_species.R'
'rm_island_colonist.R' 'rm_multi_missing_species.R'
'rm_nonendemic_in_clade.R' 'sensitivity.R' 'translate_status.R'
'unique_island_genera.R' 'utils.R' 'write_biogeobears_input.R'
'Class-Dasst.R' 'Dasst-methods.R' 'Dasst.R' 'dasstOptions.R'
'extractData.R' 'extractHeader.R' 'buildContents.R' 'data.R'
'gatherTables.R' 'read.dssat.R' 'stackTables.R' 'write.dssat.R'
'NAMESPACE.R' 'FormulaList.R' 'helper.R' 'DataFrame.R'
'as.function.R' 'bindRows.R' 'dataTableBackend.R' 'deparse.R'
'extract.R' 'map.R' 'mutar.R' 'pipeExport.R' 'replace.R'
'useDplyr.R' 'verboseApply.R'
'results_parsers.R' 'semantic_report_constructors.R' 'utils.R'
'report.R' 'assertions.R'
'aggregateForBarplot.R' 'aggregateForHistogram.R'
'allCheckFunctions.R' 'allClasses.R' 'allSummaryFunctions.R'
'allVisualFunctions.R' 'allXFunctions.R' 'makeXFunction.R'
'visualFunction.R' 'basicVisual.R' 'summaryFunction.R'
'centralValue.R' 'check.R' 'checkResult.R' 'messageGenerator.R'
'checkFunction.R' 'identifyMissing.R' 'minMax.R' 'classes.R'
'countMissing.R' 'dataMaid-package.R' 'dataMaid_as_factor.R'
'description.R' 'identifyCaseIssues.R' 'identifyLoners.R'
'identifyNums.R' 'identifyOutliers.R'
'identifyOutliersTBStyle.R' 'identifyWhitespace.R' 'isCPR.R'
'isSingular.R' 'isEmpty.R' 'isKey.R' 'isSupported.R'
'makeCodebook.R' 'makeDataReport.R' 'misc.R' 'quartiles.R'
'refCat.R' 'render.R' 'setChecks.R' 'setSummaries.R'
'setVisuals.R' 'smartNum.R' 'standardVisual.R' 'summarize.R'
'summaryResult.R' 'tableVisual.R' 'uniqueValues.R'
'unpackLabelled.R' 'utility.R' 'variableType.R' 'visualize.R'
'AbstractTableDescriber.R' 'auth_request.R' 'D1Node.R'
'AuthenticationManager.R' 'CNode.R' 'CertificateManager.R'
'D1Object.R' 'MNode.R' 'D1Client.R' 'EMLParser.R'
'dataone-defunct.R' 'dataone-deprecated.R' 'dataone-package.R'
'Constants.R' 'SystemMetadata.R' 'dmsg.R' 'DataObject.R'
'DataPackage.R' 'ResourceMap.R' 'datapack-package.r' 'zzz.R'
'aggregateForBarplot.R' 'aggregateForHistogram.R'
'allCheckFunctions.R' 'allClasses.R' 'allSummaryFunctions.R'
'allVisualFunctions.R' 'allXFunctions.R' 'makeXFunction.R'
'visualFunction.R' 'basicVisual.R' 'summaryFunction.R'
'centralValue.R' 'check.R' 'checkResult.R' 'messageGenerator.R'
'checkFunction.R' 'identifyMissing.R' 'minMax.R' 'classes.R'
'countMissing.R' 'dataReporter-package.R'
'dataReporter_as_factor.R' 'description.R'
'identifyCaseIssues.R' 'identifyLoners.R' 'identifyNums.R'
'identifyOutliers.R' 'identifyOutliersTBStyle.R'
'identifyWhitespace.R' 'isCPR.R' 'isSingular.R' 'isEmpty.R'
'isKey.R' 'isSupported.R' 'makeCodebook.R' 'makeDataReport.R'
'misc.R' 'quartiles.R' 'refCat.R' 'render.R' 'setChecks.R'
'setSummaries.R' 'setVisuals.R' 'smartNum.R' 'standardVisual.R'
'summarize.R' 'summaryResult.R' 'tableVisual.R'
'uniqueValues.R' 'unpackLabelled.R' 'utility.R'
'variableType.R' 'visualize.R'
'DatastreamDSWS2R.R' 'common.R' 'classConstructor.R'
'wrapper.R' 'UCTSUpload.R' 'cbindRobust.R' 'data.R'
'Common.R' 'DSConnect.R' 'DSRequests.R' 'DSResponse.R'
'DSUserDataObjectBase.R' 'Datastream.R' 'DatastreamR.R'
'DatastreamUserCreated_TimeSeries.R'
'db-sql.R' 'utils-check.R' 'import-standalone-types-check.R'
'import-standalone-obj-type.R' 'utils.R' 'sql.R' 'escape.R'
'translate-sql-cut.R' 'translate-sql-quantile.R'
'translate-sql-string.R' 'translate-sql-paste.R'
'translate-sql-helpers.R' 'translate-sql-window.R'
'translate-sql-conditional.R' 'backend-.R' 'backend-access.R'
'backend-hana.R' 'backend-hive.R' 'backend-impala.R'
'verb-copy-to.R' 'backend-mssql.R' 'backend-mysql.R'
'backend-odbc.R' 'backend-oracle.R' 'backend-postgres.R'
'backend-postgres-old.R' 'backend-redshift.R'
'backend-snowflake.R' 'backend-spark-sql.R' 'backend-sqlite.R'
'backend-teradata.R' 'build-sql.R' 'data-cache.R'
'data-lahman.R' 'data-nycflights13.R' 'db-escape.R' 'db-io.R'
'db.R' 'dbplyr.R' 'explain.R' 'ident.R'
'import-standalone-s3-register.R' 'join-by-compat.R'
'join-cols-compat.R' 'lazy-join-query.R' 'lazy-ops.R'
'lazy-query.R' 'lazy-select-query.R' 'lazy-set-op-query.R'
'memdb.R' 'optimise-utils.R' 'pillar.R' 'progress.R'
'sql-build.R' 'query-join.R' 'query-select.R'
'query-semi-join.R' 'query-set-op.R' 'query.R' 'reexport.R'
'remote.R' 'rows.R' 'schema.R' 'simulate.R' 'sql-clause.R'
'sql-expr.R' 'src-sql.R' 'src_dbi.R' 'table-name.R'
'tbl-lazy.R' 'tbl-sql.R' 'test-frame.R' 'testthat.R'
'tidyeval-across.R' 'tidyeval.R' 'translate-sql.R'
'utils-format.R' 'verb-arrange.R' 'verb-compute.R'
'verb-count.R' 'verb-distinct.R' 'verb-do-query.R' 'verb-do.R'
'verb-expand.R' 'verb-fill.R' 'verb-filter.R' 'verb-group_by.R'
'verb-head.R' 'verb-joins.R' 'verb-mutate.R'
'verb-pivot-longer.R' 'verb-pivot-wider.R' 'verb-pull.R'
'verb-select.R' 'verb-set-ops.R' 'verb-slice.R'
'verb-summarise.R' 'verb-uncount.R' 'verb-window.R' 'zzz.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'
'paramtnormci_numeric.R' 'rtnorm90ci.R' 'rdistq_fit.R'
'rdist90ci_exact.R' 'estimate1d.R' 'random.R'
'rmvnorm90ci_exact.R' 'estimate.R' 'mcSimulation.R'
'welfareDecisionAnalysis.R' 'eviSimulation.R'
'individualEvpiSimulation.R' 'estimate_read_csv_old.R'
'decisionSupport.R' 'decisionSupport-package.R' 'discount.R'
'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' 'find-package-root.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'
'AttachMessage.R' 'RcppExports.R' 'generics.R'
'helper_functions.R' 'subplot_functions.R'
'accuracy_measures.R' 'class-smoothing_options.R'
'class-decomp.R' 'class-deseats.R' 'class-s_semiarma.R'
'class-bv41.R' 'class-hfilter.R' 'class-lmdecomp.R'
'class-madecomp.R' 'class-llindecomp.R' 'data_documentation.R'
'fitting_functions.R' 'runDecomposition.R' 'class-deseats_fc.R'
'forecasting_functions.R' 'bwidth_bootstrap.R'
'gain_function.R' 'linear_filters.R' 'ts_conversion.R'
'hA_calc.R' 'arima_no_warn.R' 'deseats-package.R'
'seasonplot.R'
'checks.R' 'computations.R' 'data.R' 'in-line.R'
'desirability.R' 'desirability2-package.R'
'import-standalone-obj-type.R'
'import-standalone-types-check.R' 'overall.R' 'tune.R' 'zzz.R'
'DEXiR.R' 'DexiClasses.R' 'DexiAlternatives.R' 'DexiUtils.R'
'DexiAnalysis.R' 'DexiFunctions.R' 'DexiScales.R'
'DexiAttributes.R' 'DexiCharts.R' 'DexiData.R' 'DexiEvaluate.R'
'DexiModels.R' 'DexiValues.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'
'mechanisms.R' 'bernstein_mechanism.R' 'diffpriv.R'
'exponential_mechanism.R' 'numeric_mechanism.R'
'gaussian_mechanism.R' 'laplace_mechanism.R'
'sensitivity_sampler.R'
'AllClasses.R' 'AllGenerics.R' 'annotations.R' 'augment.R'
'biplot.R' 'bootstrap.R' 'ca.R' 'coerce.R' 'data.R'
'dimensio-defunct.R' 'dimensio-deprecated.R'
'dimensio-internal.R' 'dimensio-package.R' 'export.R'
'get_contributions.R' 'get_coordinates.R' 'get_correlations.R'
'get_cos2.R' 'get_data.R' 'get_distances.R' 'get_eigenvalues.R'
'get_inertia.R' 'get_variance.R' 'mca.R' 'mutators.R' 'pca.R'
'pcoa.R' 'plot.R' 'predict.R' 'reexport.R' 'screeplot.R'
'show.R' 'subset.R' 'summary.R' 'svd.R' 'tidy.R' 'tools.R'
'viz_contributions.R' 'viz_coordinates.R' 'viz_cos2.R'
'viz_ellipse.R' 'viz_hull.R' 'wrap_ellipses.R' 'wrap_hull.R'
'zzz.R'
'autoencoder.R' 'misc.R' 'dimRedData-class.R' 'dataSets.R'
'dimRedMethod-class.R' 'dimRedResult-class.R' 'diffmap.R'
'dimRed.R' 'drr.R' 'embed.R' 'fastica.R' 'get_info.R'
'graph_embed.R' 'hlle.R' 'isomap.R' 'kpca.R' 'l1pca.R' 'leim.R'
'lle.R' 'loe.R' 'mds.R' 'mixColorSpaces.R' 'nmds.R' 'nnmf.R'
'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
'DIscBIO-classes.R' 'DIscBIO-generic-ClassVectoringDT.R'
'DIscBIO-generic-ClustDiffGenes.R' 'DIscBIO-generic-Clustexp.R'
'DIscBIO-generic-DEGanalysis.R'
'DIscBIO-generic-DEGanalysis2clust.R'
'DIscBIO-generic-Exprmclust.R'
'DIscBIO-generic-FinalPreprocessing.R'
'DIscBIO-generic-FindOutliers.R'
'DIscBIO-generic-NoiseFiltering.R'
'DIscBIO-generic-Normalizedata.R'
'DIscBIO-generic-PCAplotSymbols.R'
'DIscBIO-generic-PlotmclustMB.R'
'DIscBIO-generic-clusteringOrder.R'
'DIscBIO-generic-clustheatmap.R' 'DIscBIO-generic-comptSNE.R'
'DIscBIO-generic-plotExptSNE.R' 'DIscBIO-generic-plotGap.R'
'DIscBIO-generic-plotLabelstSNE.R'
'DIscBIO-generic-plotOrderTsne.R'
'DIscBIO-generic-plotSilhouette.R'
'DIscBIO-generic-plotSymbolstSNE.R'
'DIscBIO-generic-plottSNE.R'
'DIscBIO-generic-pseudoTimeOrdering.R' 'J48DT.R' 'J48DTeval.R'
'Jaccard.R' 'NetAnalysis.R' 'Networking.R' 'PPI.R'
'PlotMBpca.R' 'RpartDT.R' 'RpartEVAL.R' 'VolcanoPlot.R'
'bootstrap.R' 'calc_pcareduceres.R' 'cross.val.R'
'customConverters.R' 'datasets.R'
'internal-functions-samr-adapted.R' 'internal-functions.R'
"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' 'serialize-bit64.R' 'transactions.R'
'use-dittodb.R' 'utils.R' 'vctrs_s3_register.R'
'headers.R' 'diversity.R' 'div_conf_colour.R'
'div_fake_team.R' 'div_ci_median.R' 'div_paygap.R'
'div_parse_paygap.R' 'div_round_paygap.R' 'div_gauge_plot.R'
'div_plot_paygap_distribution.R' 'div_add_median_label.R'
'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
'artigos.R' 'banca.R' 'colaboracao_tecnica.R' 'disciplina.R'
'trab_anais.R' 'tecnica.R' 'pvecd.R' 'projeto.R'
'producao_associada.R' 'premio.R' 'posdoc.R' 'orientacao.R'
'livros.R' 'linhas_pesquisa.R' 'encode_xml2.R' 'dola.R'
'DOOR.R' 'door_summary.R' 'calc_doorprob.R' 'check.HLP.R'
'check.homog.R' 'create.U.R' 'create.Z.ZF.R' 'data.R'
'door_barplot.R' 'var_pi.R' 'door_ci.R' 'door_test.R'
'forest_plot.R' 'mph.fit.R' 'mph.summary.R' 'num.deriv.fct.R'
'partial_credit_analysis.R' 'partial_credit_plots.R'
'doPar.R' 'doParabar-package.R' 'helpers.R' 'logo.R'
'registerDoParabar.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_ssm.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'
'checkInputs.R' 'DataInput.R' 'downscale.R' 'OptimisePars.R'
'OptimiseParsGNB.R' 'OptimiseParsFNB.R' 'OptimiseParsLogis.R'
'OptimiseParsThomas.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' 'zzz.R'
'Data.R' 'Dowser.R' 'Clones.R' 'Classes.R' 'Plotting.R'
'Germlines.R' 'Statistics.R' 'TreeFunctions.R' 'zzz.R'
'AUCtest.R' 'BioradCNV.R' 'White.R' 'adpcr2panel.R'
'adpcr2ppp.R' 'binarize.R' 'classes.R' 'bind_dpcr.R' 'bioamp.R'
'calc_breaks.R' 'calc_coordinates.R' 'calc_lambda.R'
'compare_dens.R' 'count_test-class.R' 'create_dpcr.R'
'dPCRmethyl.R' 'ddpcRquant.R' 'df2dpcr.R' 'dpcR-package.R'
'dpcReport_gui.R' 'dpcr2df.R' 'dpcr_calculator.R'
'dpcr_density.R' 'dpcr_density_gui.R' 'dpcr_density_table.R'
'extract_run.R' 'fit_adpcr.R' 'fl.R' 'get_k_n.R' 'hsm.R'
'limit_cq.R' 'many_peaks.R' 'modlist.R' 'moments.R' 'num2int.R'
'pds.R' 'pds_raw.R' 'plot_distr.R' 'plot_panel.R'
'plot_vf_circ.R' 'plot_vic_fam.R' 'print_summary.R' 'qdpcr.R'
'qpcr2pp.R' 'qpcr_analyser.R' 'read_amp.R' 'read_dpcr.R'
'rename_dpcr.R' 'rtadpcr.R' 'safe_efficiency.R' 'show_dpcr.R'
'sim_adpcr.R' 'sim_ddpcr_bkm.R' 'sim_dpcr.R' 'simulations.R'
'six_panels.R' 'summary_dpcr.R' 'test_counts.R'
'test_counts_gui.R' 'test_panel.R' 'test_peaks.R'
'test_pooled.R' 'valid_amp.R' 'y_val_conf.R'
'DrugExposureDiagnostics-package.R' 'checkDaysSupply.R'
'checkDrugDose.R' 'checkDrugExposureDuration.R'
'checkDrugQuantity.R' 'checkDrugRoutes.R' 'checkDrugSig.R'
'checkDrugSourceConcepts.R' 'checkDrugType.R'
'checkDrugsMissing.R' 'checkTimeBetween.R'
'checkVerbatimEndDate.R' 'shinyModule.R' 'dataPlotPanel.R'
'executeChecks.R' 'getDrugRecords.R' 'globals.R'
'ingredientDescendantsInDb.R' 'metaDataPanel.R'
'mockDrugExposure.R' 'obscureCounts.R' 'runBenchmark.R'
'shiny.R' 'shinyApp.R' 'summariseChecks.R' 'utils-pipe.R'
'utils.R'
'util_fun.R' 'num_util_fun.R' 'text_util_fun.R'
'time_util_fun.R' 'color_fun.R' 'data.R' 'data_fun.R'
'text_fun.R' 'time_fun.R' 'num_fun.R' 'theme_fun.R'
'plot_fun.R' 'start.R'
'DSObject.R' 'DSConnection.R' 'DSDriver.R' 'DSI-package.R'
'DSLoginBuilder.R' 'DSResult.R' 'datashield.aggregate.R'
'datashield.assign.R' 'datashield.connections.R'
'datashield.errorMessages.R' 'datashield.errors.R'
'datashield.list.R' 'datashield.login.R' 'datashield.logout.R'
'datashield.status.R' 'datashield.symbol.R'
'datashield.workspace.R' 'utils.R'
'AICc.R' 'generic.functions.R' 'Density.Summary.R' 'Density.R'
'Population.Description.R' 'Detectability.R' 'Population.R'
'Survey.R' 'DS.Analysis.R' 'Simulation.R' 'ClassConstructors.R'
'Simulation.Summary.R' 'Survey.LT.R' 'Survey.PT.R'
'accumulate.PP.results.R' 'accumulate.warnings.R'
'add.covariate.values.R' 'add.summary.results.R'
'calc.perp.dists.R' 'calc.rad.dists.R'
'calculate.scale.param.R' 'check.covariates.R'
'check.simulation.R' 'check.transects.R'
'create.results.arrays.R' 'description.summary.R'
'dsims-package.R' 'generate.pop.D.R' 'generate.pop.N.R'
'get.covered.area.lines.R' 'get.covered.area.points.R'
'get.density.surface.R' 'message.handler.R'
'modify.strata.for.analysis.R' 'process.dist.shapes.R'
'read.line.transects.R' 'read.point.transects.R'
'read.seg.transects.R' 'run.simulation.R' 'rztpois.R'
'save.sim.results.R' 'simulate.detections.R'
'simulation.consistency.check.R' 'single.sim.loop.R'
'store.ddf.results.R' 'store.dht.results.R'
'DSLiteDriver.R' 'DSLiteConnection.R' 'DSLiteResult.R'
'DSLiteServer.R' 'ast.R' 'data.cnsim.R' 'data.dasim.R'
'data.discordant.R' 'data.survival.R' 'data.testing.dataset.R'
'defaultDSConfiguration.R' 'getDSLiteData.R' 'lex-yacc.R'
'setupCNSIMTest.R' 'setupDASIMTest.R' 'setupDATASETTest.R'
'setupDISCORDANTTest.R' 'setupDSLiteServer.R'
'setupSURVIVALTest.R' 'testParse.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'
'mat.mult.R' 'point.coords.as.dataframe.R' 'run.coverage.R'
'write.transects.R'
'treatmentClasses.R' 'Ahat.R' 'interactive.R'
'inputProcessing.R' 'utils.R' 'weights.R' 'sandwich.R' 'gest.R'
'dwols.R' 'bootstrapStep.R' 'dtrProcedure.R' 'DTRreg.R'
'DWsurv.R' 'chooseM.R' 'data_generate.R' 's3-methods.R'
'varest.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' 'RD-helpers.R' 'S4-Distmat.R'
'S4-DistmatLowerTriangular.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'
'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'
'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'
RcppExports.R ToolFunctions.R ModelFunctions.R GenomeClass.R
MutationMatrixClass.R SelectionClass.R PopulationClass.R
MetapopulationClass.R UserFunctions.R
'EBMAforecast-package.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'
'DiscrepancyPrior-IndLTPrior-class.R'
'DiscrepancyPrior-IndSTPrior-class.R'
'DiscrepancyPrior-ShaSTPrior-class.R'
'DiscrepancyPrior-TruthPrior-class.R' 'EcoEnsemble-package.R'
'EnsemblePrior-class.R' 'EnsembleData-class.R'
'EnsembleFit-class.R' 'EnsembleSample-class.R'
'Prior_sampling.R' 'RcppExports.R' 'data.R' 'fit_ensemble.R'
'fit_ensemble_withdrivers.R' 'generate_simulator_stan_data.R'
'generate_simulator_stan_data_withdrivers.R'
'get_stan_outputs.R' 'get_stan_outputs_withdrivers.R'
'plot_functions.R' 'plot_functions_withdrivers.R'
'prior_functions.R' 'stanmodels.R' 'validation_functions.R'
'validation_functions_withdrivers.R'
'validation_prior_functions.R'
'aggregate_risk.R' 'calc_exposure.R' 'calc_sensitivity.R'
'create_template_exposure.R' 'create_template_sensitivity.R'
'data.R' 'model_exposure.R' 'model_sensitivity.R'
'plot_heatmap.R' 'plot_radar.R' 'risk.R' 'status.R'
'vulnerability.R'
'ECOTOXr-package.r' 'cas_handlers.r' 'database_access.r'
'helpers.r' 'imports.r' 'init.r' 'online.r' 'process_date.r'
'process_unit.r' 'process_numeric.r' 'wrappers.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'
'CD.R' 'CDF.R' 'check_python_libraries.R' 'data.bfi.R'
'data.datasets.R' 'data.scaler.R' 'DNN_predictor.R'
'EFAhclust.R' 'EFAindex.R' 'EFAkmeans.R' 'EFAvote.R' 'EKC.R'
'EFAscreet.R' 'EFAsim.data.R' 'extractor.feature.DNN.R'
'extractor.feature.FF.R' 'factor.analysis.R' 'FF.R' 'GenData.R'
'get.runs.R' 'Hull.R' 'KGC.R' 'load.R' 'model.xgb.R'
'normalizor.R' 'PA.R' 'ParamHelpers.R' 'plot.R' 'print.R'
'RcppExports.R' 'af.softmax.R' 'utils.R' 'zzz.R'
'BatchCorrection.R' 'ELISAplate.R' 'ELISAtools_IO.R'
'ELISAtools.R' 'Regression.R'
'utils-S7.R' 'types.R' 'ellmer-package.R' 'tools-def.R'
'content.R' 'provider.R' 'as-json.R' 'batch-chat.R'
'chat-structured.R' 'turns.R' 'chat-tools.R' 'utils-coro.R'
'chat.R' 'content-image.R' 'content-pdf.R' 'content-replay.R'
'deprecated.R' 'httr2.R' 'import-standalone-obj-type.R'
'import-standalone-purrr.R' 'import-standalone-types-check.R'
'interpolate.R' 'live.R' 'parallel-chat.R' 'params.R'
'provider-anthropic.R' 'provider-any.R' 'provider-aws.R'
'provider-openai.R' 'provider-azure.R' 'provider-google.R'
'provider-cloudflare.R' 'provider-databricks.R'
'provider-deepseek.R' 'provider-github.R'
'provider-google-upload.R' 'provider-groq.R'
'provider-huggingface.R' 'provider-mistral.R'
'provider-ollama.R' 'provider-openrouter.R'
'provider-perplexity.R' 'provider-portkey.R'
'provider-snowflake-cortex.R' 'provider-snowflake.R'
'provider-vllm.R' 'tokens.R' 'tools-def-auto.R'
'utils-callbacks.R' 'utils-cat.R' 'utils-merge.R'
'utils-prettytime.R' 'utils.R' 'zzz.R'
'hspec.R' 'harrival.R' 'utilities.R' 'hmoment.R' 'hllf.R'
'hfit.R' 'hgfit.R' 'hreal.R' 'hsim.R' 'script.R' 'tzexp.R'
'zzz.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'
'auth.R' 'avail_endpoints.R' 'cache.R' 'check.R' 'constants.R'
'covidcast.R' 'endpoints.R' 'epidatacall.R' 'epidatr-package.R'
'model.R' 'request.R' 'utils-pipe.R' 'utils.R'
'RcppExports.R' 'betabinom.R' 'data.R' 'utils.R'
'mle-factory.R' 'epiphy.R' 'intensity-classes.R'
'distr-fitting.R' 'indices.R' 'mapcomp.R' 'power-law.R'
'sadie.R' 'spatial-hier.R'
'DiseaseModel.R' 'RcppExports.R' 'SimulationHandler.R'
'aspatial_siri_seasons.R' 'check_aspatial_siri_inputs.R'
'check_simulator_inputs.R' 'data.R' 'disease_dispersal.r'
'disease_results.R' 'disease_simulator.R'
'disease_transformation.R' 'disease_transitions.R'
'epizootic-package.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'
'erpm-package.R' 'functions_utility.R'
'functions_Metropolis.R' 'functions_burninthining.R'
'functions_change_statistics.R' 'functions_estimate.R'
'functions_exactcalculations.R'
'functions_exchange_algorithm.R' 'functions_loglikelihood.R'
'functions_output.R' 'functions_phase1.R' 'functions_phase2.R'
'functions_phase3.R' 'functions_statistics.R'
'functions_visualisation.R' 'outcomeObjects.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'
RcppExports.R const.R helpers.R core_import.R core_search.R
extras.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'
'aaa.R' 'FDboost-package.R' 'FDboost.R' 'baselearners.R'
'baselearnersX.R' 'bootstrapCIs.R' 'clr_functions.R'
'constrainedX.R' 'crossvalidation.R' 'factorize.R'
'FDboostLSS.R' 'hmatrix.R' 'methods.R' 'stabsel.R'
'utilityFunctions.R'
'AttachMessage.R' 'RcppExports.R' 'close_to_lreturn.R'
'lin_filters.R' 'hessCalc.R' 'arma-farima-wrappers.R'
'format_applier_ts.R' 'ts_split_train_and_test.R'
'snorm-distribution-functions.R'
'sstd-distribution-functions.R' 'sged-distribution-functions.R'
'sald-distribution-functions.R' 'base_sim_functions.R'
'density_selectors.R' 'generics.R'
'input_checkers_egarch_spec.R' 'input_checkers_mean_spec.R'
'input_checkers_nonpar_spec.R' 'class-mean_spec.R'
'class-locpol_spec.R' 'class-fEGarch_fit.R'
'class-egarch-spec.R' 'fitting-function.R' 'sim-functions.R'
'nonparametric-step.R' 'setup-estim.R'
'general_garch_fitting.R' 'aparchfit.R' 'gjrgarchfit.R'
'tgarchfit.R' 'garchfit.R' 'fiaparchfit.R' 'figjrgarchfit.R'
'fitgarchfit.R' 'figarchfit.R' 'garch_estim.R' 'varescalc.R'
'datasets.R' 'fEGarch-package.R' 'rugarch-wrappers.R'
'class-fEGarch_forecast.R' 'forecasting-functions.R'
'fEGarch_fit-plot.R' 'class-fEGarch_risk.R'
'ufRisk-functions.R' 'reexport-pipe.R' 'test-functions.R'
'class-fEGarch_distr_est.R' 'distr_est.R' 'popular-methods.R'
'fhir_body.R' 'fhir_resource.R' 'fhir_url.R' 'fhir_bundle.R'
'fhir_bundle_list.R' 'build_resources.R' 'design.R'
'download_resources.R' 'fhir_xpath_expression.R'
'fhir_columns.R' 'fhir_resource_type.R'
'fhir_table_description.R' 'fhir_design.R' 'fhir_style.R'
'fhir_table_list.R' 'fhir_tree.R' 'flatten_resources.R'
'miscellaneous.R' 'multiple_entries.R' 'sample_resources.R'
'loggers.R' 'aaa.R' 'HandlerStack.R' 'Fire.R' 'PromiseStack.R'
'additional_type_checks.R' 'fake_request.R' 'fiery-package.R'
'import-standalone-obj-type.R'
'import-standalone-types-check.R' 'request_store.R' 'zzz.R'
filehash.R filehash-DB1.R filehash-RDS.R coerce.R dump.R
hash.R queue.R stack.R zzz.R
'aaa.R' 'class_score.R' 'data.R' 'desirability2.R'
'filtro-package.R' 'import-standalone-obj-type.R'
'import-standalone-types-check.R' 'misc.R' 'score-aov.R'
'utilities.R' 'score-cor.R' 'score-cross_tab.R'
'score-forest_imp.R' 'score-info_gain.R' 'score-roc_auc.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
'fml_resampler.R' 'fml_resample.R' 'fml_format_response.R'
'fml_parser.R' 'fml_bootstrap.R' 'fml_categorize.R'
'fml_example.R' 'fml_globals.R' 'fml_grids.R' 'fml_interpret.R'
'fml_train.R' 'fml_validate.R'
'flps-package.r' 'load_rstan.r' 'make_FLPSdata.r' 'run_FLPS.r'
'priors.r' 'sim_gen_measurement.r' 'sim_gen_structure.r'
'sim_gen_standata.r' 'sim_make_data.r' 'sim_utils.r'
'z_utils.r' 'z_validate.r' 'plot_function.r'
'summary_function.r' 'data_desc.r' 'zzz.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'
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'misc_functions.R' 'flight_simulation.R' 'migrate.R'
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'ExtrapolationDetector.R' 'FME.R' 'FMEPlot.R'
'NonLinearityMeasure.R' 'Partitioning.R' 'PartitioningCtree.R'
'PartitioningPlot.R' 'PartitioningRpart.R' 'Predictor.R'
'PredictorCaret.R' 'PredictorLM.R' 'PredictorMLR3.R'
'PredictorParsnip.R' 'Pruner.R' 'S3.R' 'ame.R' 'bikes.R'
'misc.R' 'zzz.R'
'RcppExports.R' 'deprecated.R' 'bary.R' 'basis.R' 'bbox.R'
'collect.R' 'components.R' 'print.R' 'crs.R' 'data-fmexample.R'
'diameter.R' 'evaluator.R' 'fem.R' 'fm.R' 'fmesher-package.R'
'fmesher.R' 'ggplot.R' 'integration.R' 'lattice_2d.R'
'lattice_Nd.R' 'list.R' 'local.R' 'manifold.R' 'mapping.R'
'matern.R' 'mesh.R' 'mesh_1d.R' 'mesh_2d.R' 'mesh_3d.R'
'mesh_assessment.R' 'nonconvex_hull.R' 'onload.R' 'plot.R'
'segm.R' 'sf_mesh.R' 'sf_utils.R' 'simplify.R' 'sp_mesh.R'
'split_lines.R' 'tensor.R' 'utils.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
'utils-not-exported.R' 'GLAD.R' 'allometry.R'
'canopy-height.R' 'chorological-maps.R' 'data.R' 'eutrees4f.R'
'forest-country.R' 'forest-inventory.R' 'forestdata-package.R'
'globals.R' 'land-cover.R' 'pathogens.R'
'data.R' 'format_decimal.R' 'format_numbers.R'
'format_power.R' 'format_text.R' 'format_units.R'
'formatdown_options.R' 'formatdown-deprecated.R'
'formatdown-package.R' 'roxygen.R' 'utils.R'
'data.R' 'format_value.R' 'matrix_form.R' 'generics.R'
'labels.R' 'mpf_exporters.R' 'package.R' 'page_size.R'
'pagination.R' 'tostring.R' 'utils.R' 'zzz.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'
'config.R' 'api_client.R' 'utils.R' 'schema.R'
'datasets_api_client.R' 'datasets.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
'data_types.R' 'internal_functions.R' 'basic_functions.R'
'construction_module.R' 'general_operations.R'
'fuzzy_numerical_operations.R' 'fuzzy_geom_set_operations.R'
'fuzzy_topological_relations.R' 'fsi_module.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' '0_summary_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' '7_plottingFunctionsStandard.R'
'8_outilsStats.R' '8_precalculated_Xfgpm_objects.R'
'Data.R' 'HashTable.R' 'Lrner.R' 'Model.R' 'PredictData.R'
'PredictLayer.R' 'PredictMetaLayer.R' 'Predicting.R' 'Target.R'
'TestData.R' 'TestLayer.R' 'TestMetaLayer.R' 'Testing.R'
'TrainData.R' 'TrainLayer.R' 'TrainMetaLayer.R' 'Training.R'
'VarSel.R' 'bestLayerLearner.R' 'cobra.R' 'createCobraPred.R'
'createDif.R' 'createLoss.R' 'createWeights.R' 'multi_omics.R'
'predict.bestLayerLearner.R' 'predict.cobra.R'
'weightedMeanLearner.R' 'predict.weightedMeanLearner.R'
'testingFunctions.R' 'trainingFunctions.R'
'options.R' 'appender.R' 'constants.R' 'layout.R' 'logger.R'
'scat.R' 'futile.logger-package.R'
'000.bquote.R' '000.import.R' '000.re-exports.R'
'010.tweakable.R' '010.utils-parallelly.R'
'backend_api-01-FutureBackend-class.R'
'backend_api-03.MultiprocessFutureBackend-class.R'
'backend_api-11.ClusterFutureBackend-class.R'
'backend_api-11.MulticoreFutureBackend-class.R'
'backend_api-11.SequentialFutureBackend-class.R'
'backend_api-13.MultisessionFutureBackend-class.R'
'backend_api-ConstantFuture-class.R'
'backend_api-Future-class.R' 'backend_api-FutureRegistry.R'
'backend_api-UniprocessFuture-class.R'
'backend_api-evalFuture.R' 'core_api-cancel.R'
'core_api-future.R' 'core_api-reset.R' 'core_api-resolved.R'
'core_api-value.R' 'delayed_api-futureAssign.R'
'delayed_api-futureOf.R' 'demo_api-mandelbrot.R'
'infix_api-01-futureAssign_OP.R' 'infix_api-02-globals_OP.R'
'infix_api-03-seed_OP.R' 'infix_api-04-stdout_OP.R'
'infix_api-05-conditions_OP.R' 'infix_api-06-lazy_OP.R'
'infix_api-07-label_OP.R' 'infix_api-08-plan_OP.R'
'infix_api-09-tweak_OP.R'
'protected_api-FutureCondition-class.R'
'protected_api-FutureGlobals-class.R'
'protected_api-FutureResult-class.R' 'protected_api-futures.R'
'protected_api-globals.R' 'protected_api-journal.R'
'protected_api-resolve.R' 'protected_api-signalConditions.R'
'testme.R' 'utils-basic.R' 'utils-conditions.R'
'utils-connections.R' 'utils-debug.R'
'utils-immediateCondition.R' 'utils-marshalling.R'
'utils-objectSize.R' 'utils-options.R' 'utils-prune_pkg_code.R'
'utils-registerClusterTypes.R' 'utils-rng_utils.R'
'utils-signalEarly.R' 'utils-stealth_sample.R'
'utils-sticky_globals.R' 'utils-tweakExpression.R'
'utils-uuid.R' 'utils-whichIndex.R' 'utils_api-backtrace.R'
'utils_api-capture_journals.R' 'utils_api-futureCall.R'
'utils_api-futureSessionInfo.R' 'utils_api-makeClusterFuture.R'
'utils_api-minifuture.R' 'utils_api-nbrOfWorkers.R'
'utils_api-plan.R' 'utils_api-plan-with.R'
'utils_api-sessionDetails.R' 'utils_api-tweak.R' 'zzz.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'
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'labels-new.R' 'labels-percent.R' 'legend-draw-tern.R'
'ggtern-constructor.R' 'gg-internal.R'
'modifications-gridExtra.R' 'onLoad.R' 'plot.R' 'plot-build.R'
'plot-construction.R' 'position-.R' 'position-nudge-tern.R'
'position-jitter-tern.R' 'save.R' 'scales-tern.R'
'strip-unapproved.R' 'tern-limits.R' 'theme.R'
'theme-arrowlength.R' 'theme-clockwise.R' 'theme-defaults.R'
'theme-elements.R' 'theme-gridsontop.R' 'theme-bordersontop.R'
'theme-legend-position.R' 'theme-latex.R' 'theme-mesh.R'
'theme-noarrows.R' 'theme-nomask.R' 'theme-novar-tern.R'
'theme-rotate.R' 'theme-showgrid.R' 'theme-showlabels.R'
'theme-ticks.R' 'theme-showtitles.R' 'theme-ticksoutside.R'
'theme-zoom.R' 'utilities.R' 'utilities-help.R'
'geom-density-tern.R' 'stat-density-tern.R' 'geom-mask.R'
'geom-Xline.R' 'geom-Xisoprop.R' 'geom-confidence-tern.R'
'stat-confidence-tern.R' 'geom-errorbarX.R'
'geom-smooth-tern.R' 'stat-smooth-tern.R' 'geom-mean-ellipse.R'
'stat-mean-ellipse.R' 'geom-interpolate-tern.R'
'stat-interpolate-tern.R' 'stat-interpolate-methods.R'
'geom-crosshair-tern.R' 'geom-point-swap.R' 'geom-hex-tern.R'
'stat-hex-tern.R' 'annotation-tern.R'
'annotation-raster-tern.R' 'geom-text-viewport.R'
'geom-label-viewport.R' 'geom-polygon-closed.R'
'geom-tri-tern.R' 'stat-tri-tern.R'
'a-legend-draw.R' 'data.R' 'geom-path-trace.R'
'geom-point-trace.R' 'ggtrace-package.R' 'grouping.R'
'utilities-ggplot2.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'
'libgit2.R' 'merge.R' 'note.R' 'odb.R' 'plot.R' 'pull.R'
'punch_card.R' 'push.R' 'reference.R' 'reflog.R' 'refspec.R'
'remote.R' 'repository.R' 'reset.R' 'revparse.R' 'sha.R'
'signature.R' 'stash.R' 'status.R' 'tag.R' 'time.R' 'tree.R'
'when.R'
'clean_data_path.R' 'data_package.R' 'datahash.R'
'display_metadata.R' 'git2rdata_package.R' 'write_vc.R'
'is_git2rdata.R' 'is_git2rmeta.R' 'list_data.R' 'meta.R'
'print.R' 'prune.R' 'read_vc.R' 'recent_commit.R' 'reexport.R'
'relabel.R' 'rename_variable.R' 'update_metadata.R'
'upgrade_data.R' 'utils.R' 'verify_vc.R'
'gitr-params.R' 'gitr-package.R' 'gitr.R' 'gitr-branch.R'
'gitr-checkout.R' 'gitr-commit.R' 'gitr-lint-commit.R'
'gitr-pr.R' 'gitr-sitrep.R' 'gitr-tag.R' 'gitr-trim-sha.R'
'gitr-utils.R' 'zsh.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' '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'
'initJava.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'
'Bipartite_Walk.R' 'Estimation_BetaSBM.R' 'Estimation_Block.R'
'Estimation_ERSBM.R' 'Get_Directed_Piece.R'
'Get_Directed_Move_p1_ed.R'
'Get_Between_Blocks_Move_beta_SBM.R' 'as_arbitrary_directed.R'
'Get_Bidirected_Piece.R' 'Get_Bidirected_Move.R'
'Get_Induced_Subgraph.R' 'Get_Within_Blocks_beta_SBM.R'
'Get_Move_Beta_SBM.R' 'Get_Next_Network.R'
'Sampling_Graph_BetaSBM.R' 'TestStatistic_BetaSBM.R'
'GoFTest_BetaSBM.R' 'Sampling_Graph_ERSBM.R'
'TestStatistic_ERSBM.R' 'GoFTest_ERSBM.R' 'zachary.R'
'api.R' 'customization.R' 'lists.R' 'chk_covr.R'
'chk_cyclocomp.R' 'chk_description.R' 'chk_lintr.R'
'chk_namespace.R' 'chk_rcmdcheck.R' 'chk_tnf.R' 'gp.R'
'my_linters.R' 'package.R' 'prep_covr.R' 'prep_cyclocomp.R'
'prep_description.R' 'prep_expressions.R' 'prep_lintr.R'
'prep_namespace.R' 'prep_rcmdcheck.R' 'print.R'
'rstudio_markers.R' 'utils.R'
'helpers.R' 'means.R' 'weights.R' 'contributions.R'
'price_indexes.R' 'geks.R' 'splice.R' 'operators.R'
'offset_prices.R' 'outliers.R' 'price_data.R'
'gpindex-package.R'
'AAA-utils.R' 'R6--aaa-utils.R' 'S3--aaa-setup.R'
'R6--definition.R' 'R6-3.1.1-types-scalars.R'
'R6-6.1-executing-requests.R' 'R6-Schema.R' 'R6z-from-json.R'
'graphql_json.R' 'R6-3.2-directives.R' 'gqlr_schema.R'
'R6-4-introspection.R' 'R6-6.2-executing-operations.R'
'R6-6.3-executing-selection-sets.R' 'R6-6.4-executing-fields.R'
'R6-7-response.R' 'R6-ErrorList.R' 'R6-ObjectHelpers.R'
'R6-Result.R' 'R6-VariableValdationHelper.R'
'S3-3.1.2.3-validation-object-type.R' 'S3-str.R'
'gqlr-package.R' 'server.R' 'upgrade_query_remove_fragments.R'
'validation-arguments.R' 'validation-input-coercion.R'
'validation-selection-set-can-merge.R' 'validation-query.R'
'zzz.R'
'grainscape-package.R' 'classes.R' 'GOC.R' 'MPG.R'
'RcppExports.R' 'grain.R' 'corridor.R' 'distance.R' 'export.R'
'extract.R' 'ggGS.R' 'grainscape-deprecated.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'
'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'
'data-deps-tf-tfp.R' 'greta_model_class.R' 'progress_bar.R'
'inference_class.R' 'samplers.R' 'optimisers.R'
'optimiser_class.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' 'write-logfiles.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'
'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'
'GRShiny-package.r' '0_shinyapp.r' 'excel_table.r'
'gen_data.r' 'model_grm.r' 'model_out_tidy.r' 'plot.r'
'word_table.r' 'zz_runAPP.r'
'gtfsio_error.R' 'assert_gtfs.R' 'assert_inputs.R' 'checks.R'
'data.R' 'export_gtfs.R' 'get_gtfs_standards.R'
'gtfs_methods.R' 'gtfs_subset.R' 'gtfsio.R' 'import_gtfs.R'
'new_gtfs.R' 'utils.R'
'data.R' 'utils.R' 'define_variance_wrapper.R'
'variance_function.R' 'define_statistic_wrapper.R'
'standard_statistic_wrapper.R' 'qvar.R'
'browser.R' 'cc.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' 'wrappers.R' 'write_xlsx.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'
'adaboost.R' 'admissibleml.R' 'aggregator.R' 'anovaglm.R'
'astfun.R' 'automl.R' 'classes.R' 'communication.R' 'config.R'
'connection.R' 'constants.R' 'coxph.R' 'coxphutils.R'
'datasets.R' 'decisiontree.R' 'deeplearning.R' 'edicts.R'
'explain.R' 'export.R' 'extendedisolationforest.R' 'frame.R'
'gam.R' 'gbm.R' 'generic.R' 'glm.R' 'glrm.R' 'grid.R'
'import.R' 'infogram.R' 'isolationforest.R'
'isotonicregression.R' 'kmeans.R' 'kvstore.R' 'locate.R'
'logging.R' 'models.R' 'modelselection.R' 'naivebayes.R'
'parse.R' 'pca.R' 'permutation_varimp.R' 'predict.R' 'psvm.R'
'randomforest.R' 'rulefit.R' 'segment.R' 'stackedensemble.R'
'svd.R' 'targetencoder.R' 'tf-idf.R' 'upliftrandomforest.R'
'w2vutils.R' 'word2vec.R' 'xgboost.R' 'zzz.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' 'banditNarm_2par_lapse.R'
'banditNarm_4par.R' 'banditNarm_delta.R'
'banditNarm_kalman_filter.R' 'banditNarm_lapse.R'
'banditNarm_lapse_decay.R' 'banditNarm_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'
'pstRT_ddm.R' 'pstRT_rlddm1.R' 'pstRT_rlddm6.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'
'E1684-data.R' 'E1690-data.R' 'E1694-data.R' 'E2696-data.R'
'IBCSG_curr-data.R' 'IBCSG_hist-data.R' 'actg019-data.R'
'actg036-data.R' 'data_checks.R' 'expfam_loglik.R'
'get_stan_data.R' 'glm_bhm.R' 'glm_bhm_lognc.R'
'glm_commensurate.R' 'mixture_loglik.R'
'glm_commensurate_lognc.R' 'glm_leap.R' 'glm_leap_lognc.R'
'glm_logml_commensurate.R' 'glm_logml_leap.R' 'glm_logml_map.R'
'glm_logml_napp.R' 'glm_logml_npp.R' 'glm_logml_post.R'
'glm_pp_lognc.R' 'glm_logml_pp.R' 'glm_napp.R'
'glm_npp_lognc.R' 'glm_npp.R' 'glm_post.R' 'glm_pp.R'
'glm_rmap.R' 'hdbayes-package.R' 'lm_npp.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' 'geom_rect2.R' 'hSet.R' 'hierarchicalSet.R'
'hierarchicalSets-package.R' 'twitter.R' 'visuals.R'
'add_shadow_2d.R' 'matrix_to_raster.R' 'write_raster.R'
'utils.R' 'hillshader.R' 'maungawhau.R' 'maungawhau_hr.R'
utils.R hima_classic.R hima_dblasso.R hima_survival.R
hima_microbiome.R hima_quantile.R hima_efficient.R hima.R
hima_data.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'
'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' 'fill.R' 'html_dependency.R' 'html_escape.R'
'html_print.R' 'htmltools-package.R' 'images.R' 'known_tags.R'
'selector.R' 'staticimports.R' 'tag_query.R' 'utils.R' 'tags.R'
'template.R'
'RcppExports.R' 'httpuv.R' 'random_port.R' 'server.R'
'staticServer.R' 'static_paths.R' 'utils.R'
'Add_boundary_points.R' 'Corrected_Tessellations.R'
'Transform_Coordinates.R' 'ScaleMat.R' 'DelaunayInfo.R'
'Delete_Outpoints.R' 'VQ_codebookSplit.R' 'clusterPlot.R'
'clustHVT.R' 'diagPlot.R' 'diagSuggestion.R' 'displayTable.R'
'edaPlots.R' 'getCellId.R' 'getCentroids.R'
'getCentroids_for_opti.R' 'getOptimalCentroids.R'
'getTransitionProbability.R' 'global.R' 'hvq.R' 'madPlot.R'
'msm_plots.R' 'msm.R' 'plotAnimatedFlowmap.R' 'plotHVT.R'
'plotModelDiagnostics.R' 'plotNovelCells.R'
'plotQuantErrorHistogram.R' 'plotStateTransition.R'
'plotZscore.R' 'reconcileTransitionProbability.R'
'removeNovelty.R' 'scoreHVT.R' 'scoreLayeredHVT.R' 'summary.R'
'trainHVT.R'
'Class-WaterLevelDataFrame.R' 'WaterLevelDataFrame-methods.R'
'data.R' 'getGaugingDataW.R' 'getPegelonlineW.R'
'hyd1d-internal.R' 'hyd1d.R' 'plotShiny.R'
'updateGaugingData.R' 'waterLevel.R' 'waterLevelFlood1.R'
'waterLevelFlood2.R' 'waterLevelFlys3.R'
'waterLevelFlys3InterpolateX.R' 'waterLevelFlys3InterpolateY.R'
'waterLevelFlys3Seq.R' 'waterLevelPegelonline.R' 'zzz.R'
'classifyToPNV.R' 'createTiles.R' 'data.R' 'flood1.R'
'flood2.R' 'flood3.R' 'flood3Points.R' 'getDEM.R' 'hydflood.R'
'hydflood-internal.R' 'hydSpatRaster.R' 'w80ToSpatial.R'
'waterDepth.R' 'zzz.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'
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-sf-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-data.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'
'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'
'data.R' 'delay_estimation.R' 'delay.R' 'delay_test.R'
'incubate-package.R' 'utils.R'
'0_inlabru_envir.R' 'bru.gof.R' 'bru.inference.R'
'bru_conversion.R' 'bru_index.R' 'bru_input.R' 'bru_sp.R'
'data.Poisson1_1D.R' 'data.Poisson2_1D.R' 'data.Poisson3_1D.R'
'data.gorillas.R' 'data.mexdolphin.R' 'data.mrsea.R'
'data.robins_subset.R' 'data.shrimp.R' 'data.toygroups.R'
'data.toypoints.R' 'deltaIC.R' 'deprecated.R' 'effect.R'
'environment.R' 'fmesher.R' 'ggplot.R' 'inla.R'
'inlabru-package.R' 'local_testthat.R' 'mappers.R'
'mapper_collect.R' 'mapper_repeat.R' 'mapper_sum.R' 'model.R'
'nlinla.R' 'plotsample.R' 'rgl.R' 'sampling.R' 'spatstat.R'
'spde.R' 'stack.R' 'track_plotting.R' 'transformation.R'
'used.R' 'utils.R'
'AgnosticMethods.R' 'AgnosticWrapper.R' 'ConnectionWeights.R'
'Convert_keras.R' 'Convert_neuralnet.R' 'Convert_torch.R'
'ConvertedModel.R' 'Converter.R' 'DeepLift.R' 'GradienBased.R'
'InterpretingLayer.R' 'InterpretingMethod.R' 'LRP.R'
'Layer_conv1d.R' 'Layer_conv2d.R' 'Layer_dense.R'
'Layer_normalization.R' 'Layer_other.R' 'Layer_pooling.R'
'innsight.R' 'utils.R' 'utils_ggplot.R' 'utils_plotly.R'
'innsight_sugar.R' 'innsight_ggplot2.R' 'innsight_plotly.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'
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 zzz.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' 'classification.R' 'item-class.R'
'item-class-methods.R' 'itempool-class.R'
'itempool-class-methods.R' 'dif.R' 'equate_stuirt.R'
'flexMIRT.R' 'response-class.R' 'generate_objects.R' 'info.R'
'ipd.R' 'irtpro.R' 'item_analysis.R' 'item_fit.R'
'kernel_smoothing.R' 'response_set-class.R' 'max_score.R'
'mean.R' 'misc.R' 'package-irt.R' 'person_fit.R'
'plot_cat_output.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'
'winsteps.R' 'zzz.R'
'ability.R' 'estimate.R' 'itemfit.R' 'nonpar.R' 'readin.R'
'scale.R' 'tracelines.R' 'utils.R' 'irtoys-package.R'
'interaction.R'
'AllGenerics.R' 'coordinates.R' 'data.R'
'isopleuros-internal.R' 'isopleuros-package.R'
'ternary_arrows.R' 'ternary_axes.R' 'ternary_box.R'
'ternary_contour.R' 'ternary_crosshairs.R' 'ternary_density.R'
'ternary_ellipse.R' 'ternary_grid.R' 'ternary_hull.R'
'ternary_image.R' 'ternary_labels.R' 'ternary_lines.R'
'ternary_mean.R' 'ternary_pairs.R' 'ternary_pca.R'
'ternary_plot.R' 'ternary_points.R' 'ternary_polygon.R'
'ternary_segments.R' 'ternary_text.R' 'ternary_title.R'
'ternary_window.R' 'triangle_phase.R' 'triangle_soil.R' 'zzz.R'
'ISOweek.R' 'ISOweekday.R' 'date2ISOweek.R' 'ISOweek2date.R'
'ISOweek-internal.R'
'addins.R' 'data.R' 'learn.R' 'object.R' 'utils.R' 'output.R'
'user.R' 'ops.R'
'jlmerclusterperm-package.R' 'aaa.R' 'utils.R'
'interop-utils.R' 'interop-utils-unexported.R' 'julia_rng.R'
'jlmer_spec.R' 'jlmer.R' 'compute_timewise_statistics.R'
'permute.R' 'permute_timewise_statistics.R'
'clusters_methods.R' 'extract_clusters.R' 'calculate_pvalue.R'
'clusterpermute.R' 'threshold_search.R' 'tidy.R' 'zzz.R'
'srr-stats-standards.R'
'data.R' 'support.R' 'check_measures.R' 'create_jointVIP.R'
'create_post_jointVIP.R' 'get_measures.R' 'get_post_measures.R'
'get_boot_measures.R' 'plot.R' 'print.R' 'summary.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'
'bootstrap.R' 'coerce.R' 'event_date.R' 'event_model.R'
'event_plot.R' 'fit.R' 'jackknife.R' 'kairos-deprecated.R'
'kairos-internal.R' 'kairos-package.R' 'mcd.R' 'mutators.R'
'plot_time.R' 'seriation_assess.R' 'seriation_average.R'
'seriation_coerce.R' 'seriation_permute.R' 'seriation_rank.R'
'seriation_refine.R' 'show.R' 'subset.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'
'change.R' 'color.R' 'compare.R' 'info.R' 'khroma-defunct.R'
'khroma-deprecated.R' 'khroma-internal.R' 'khroma-package.R'
'palettes.R' 'plot.R' 'plot_map.R' 'plot_scheme.R'
'plot_scheme_colorblind.R' 'plot_tiles.R' 'print.R'
'scale_colour_crameri.R' 'scale_colour_okabeito.R'
'scale_colour_picker.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
'block.R' 'cache.R' 'citation.R' 'hooks-html.R' 'plot.R'
'utils.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-string.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'
'api_choose.R' 'api_functions.R' 'api_gluestick.R'
'api_object_request.R' 'api_objects.R' 'api_slides_auth.R'
'api_utils.R' 'create_slides.R' 'generics.R' 'data.frame.R'
'flextable.R' 'flextable_borders.R'
'flextable_cell_properties.R' 'flextable_column_row_sizes.R'
'flextable_merge.R' 'flextable_paragraph_style.R'
'flextable_text_style.R' 'functions.R' 'gauth_token.R'
'get_slide_ids.R' 'helpers.R' 'images.R' 'matrix.R' 'package.R'
'run_script.R' 'thumbnail.R' 'zzz.R'
'calculate_gaps_perc.R' 'gap_fbh.R' 'distances_calculation.R'
'depths_calculation.R' 'corrected_base_heights.R'
'corrected_depth.R' 'corrected_distances.R'
'maxlad_metrics_25perc.R' 'cbh_metrics.R'
'cummLAD_breaks_metrics.R' 'plot_cbh_bpoint.R'
'plot_gaps_fbhs.R' 'plot_cbh_maxdist.R' 'plot_cbh_lastdist.R'
'plot_cbh_maxlad.R' 'plot_maxlad_metrics.R' 'renamed0_df.R'
'renamed_df.R' 'foofy.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' 'Cultivars_List.R' 'GPKGTools.R'
'tools.R' 'Methods-LandsepiParams.R' 'demo_landsepi.R'
'landsepi-package.R' 'output.R' 'runShiny.R' 'simul_landsepi.R'
'assert.R' 'citation.R' 'compute.R' 'data.R' 'formula.R'
'generics.R' 'latrend.R' 'make.R' 'matrix.R' 'method.R'
'meta-method.R' 'meta-fit.R' 'meta-fit-converged.R'
'meta-fit-rep.R' 'methodMatrix.R' 'methodAKMedoids.R'
'methodCrimCV.R' 'methodDtwclust.R' 'trajectories.R' 'model.R'
'modelApprox.R' 'modelPartition.R' 'methodFeature.R'
'methodFlexmix.R' 'methodFlexmixGBTM.R' 'methodFunFEM.R'
'methodFunction.R' 'methodLMKM.R' 'methodGCKM.R' 'methodKML.R'
'methodLcmmGMM.R' 'methodLcmmGBTM.R' 'methodMclustLLPA.R'
'methodMixAK_GLMM.R' 'methodMixTVEM.R' 'methodMixtoolsGMM.R'
'methodMixtoolsNPRM.R' 'methodRandom.R' 'methodStratify.R'
'methods.R' 'metrics.R' 'metricsInternal.R' 'metricsExternal.R'
'model-evaluation.R' 'model-summary.R' 'model-transform.R'
'modelCrimCV.R' 'modelDtwclust.R' 'modelFlexmix.R'
'modelFunFEM.R' 'modelKML.R' 'modelLMKM.R' 'modelLcmmGMM.R'
'modelLcmmGBTM.R' 'modelMclustLLPA.R' 'modelMixAK_GLMM.R'
'modelMixAK_GLMMlist.R' 'modelMixTVEM.R' 'modelMixtoolsGMM.R'
'modelMixtoolsRM.R' 'modelStratify.R'
'modelWeightedPartition.R' 'models.R' 'random.R' 'test.R'
'timing.R' 'variables.R' 'verbose.R' 'zzz.R'
'SFSQLConnection.R' 'SFSQLDriver.R' 'SFSQLResult.R'
'connect.R' 'lazysf-package.R' 'lazysf.R' 'utils-pipe.R'
'zzz.R'
'ecld-cdf-method.R' 'ecld-class.R' 'ecld-constructor.R'
'ecld-pdf-method.R' 'ecld-sd-method.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-data-config-internal.R'
'ldhmm-decode_stats_history.R' 'ldhmm-decoding.R'
'ldhmm-df2ts-method.R' 'ldhmm-forecast_prob.R'
'ldhmm-forecast_state.R' 'ldhmm-forecast_volatility.R'
'ldhmm-fred_data.R' 'ldhmm-gamma_init.R'
'ldhmm-get-data-method.R' 'ldhmm-ld_stats.R'
'ldhmm-log_forward.R' 'ldhmm-mle.R' 'ldhmm-mllk.R'
'ldhmm-n2w.R' 'ldhmm-plot_spx_vix_obs.R'
'ldhmm-pseudo_residuals.R' 'ldhmm-read-csv-by-symbol-method.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'
'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'
'coord-flex.r' 'coord-capped.r' 'dot.r' 'facet-rep-lab.r'
'facet-wrap.r' 'geom-pointline.r' 'lemon_print.r'
'geom-siderange.r' 'grob_utils.r' 'gtable_show-.r'
'guides-axis.r' 'legends.r' 'lemon.r' 'scale-symmetric.r'
'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'
'LayoutJson.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' 'RoundingHelper.R'
'contractGrid.R' 'create_LIR_project.R'
'exportInsuranceContract_xlsx.R' 'showVmGlgExamples.R'
'exportInsuranceContractExample.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'
'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' 'BuyseTest_discreteRoot.R' 'LMMstar-package.R'
'LMMstar.options.R' 'add.R' 'anova.R' 'autoplot.R'
'backtransform.R' 'baselineAdjustment.R' 'coef.R' 'confint.R'
'constrain.R' 'df.R' 'discreteRoot.R' 'doc-data.R' 'effects.R'
'estimate.R' 'findPatterns.R' 'fitted.R' 'formula.R' 'iid.R'
'information.R' 'levels.R' 'lmm.R' 'lmmCC.R' 'logLik.R'
'manifest.R' 'mlmm.R' 'model.frame.R' 'model.matrix.R'
'model.tables.R' 'moments.R' 'mt.test.R' 'multcomp.R'
'namePatterns.R' 'nobs.R' 'operator.R' 'partialCor.R' 'plot.R'
'precompute.R' 'predict.R' 'print.R' 'profile.R' 'proportion.R'
'ranef.R' 'rbind.R' 'reformat.R' 'remove.R' 'reparametrize.R'
'resample.R' 'residuals.R' 'restaureNA.R' 'sampleRem.R'
'scatterplot.R' 'score.R' 'sigma.R' 'structure-calc_Omega.R'
'structure-calc_d2Omega.R' 'structure-calc_dOmega.R'
'structure-initialization.R' 'structure-skeleton.R'
'structure-skeletonK.R' 'structure-skeletonRho.R'
'structure-skeletonSigma.R' 'structure-update.R' 'structure.R'
'summarize.R' 'summarizeNA.R' 'summary.R' 'terms.R' 'utils.R'
'vcov.R' 'weights.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' 'cprm.power.R' 'diggle.linear.power.R'
'edland.linear.power.R' 'liu.liang.linear.power.R'
'hu.mackey.thomas.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
'af.softmax.R' 'check_python_libraries.R' 'data.DAPCS.R'
'data.datasets.LSTM.R' 'data.scaler.LSTM.R' 'LSTM.R'
'extractor.feature.R' 'load.R' 'normalizor.R' 'plot.R'
'print.R' 'utils.R' 'zzz.R'
MCLogLin.R MCPower.R Plot.R PrintPower.R table_f.R
tableToData.R Renewal.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' '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-amp.R' 'callbacks-interrupt.R' 'callbacks-mixup.R'
'callbacks-monitor-metrics.R' 'callbacks-profile.R'
'callbacks-resume.R' 'callbacks-tfevents.R' 'context.R'
'losses.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' 'validators.r'
'classes.R' 'conditions.R' 'MachineShop-package.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'
'ModelFrame.R' 'ModelRecipe.R' 'ModelSpecification.R'
'TrainedInputs.R' 'TrainedModels.R' 'TrainingParams.R'
'append.R' 'calibration.R' 'case_comps.R' 'coerce.R'
'combine.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' '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' 'utils.r' 'Ops.r' 'bind.r' 'blockrep.r'
'coerce.r' 'data.r' 'det.r' 'diag.r' 'eigen.r' 'elwise.r'
'madness_pkg.r' 'matwise.r' 'max.r' 'norm.r' 'numderiv.r'
'reshape.r' 'solve.r' 'sum.r' 'sums.r' 'theta.r'
'to_objective.r' 'trace.r' 'twomoments.r' 'vcov.r' 'vec.r'
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 parties.R dedication.R
'register.R' 'calc_biodiversity_intactness_index.R'
'calc_biome.R' 'calc_burned_area.R' 'calc_carbon.R'
'calc_deforestation_drivers.R' 'calc_drought_indicator.R'
'calc_ecoregion.R' 'calc_elevation.R'
'calc_exposed_population_acled.R'
'calc_exposed_population_ucdp.R' 'calc_fatalities_acled.R'
'calc_fatalities_ucdp.R' 'calc_gsw_change.R'
'calc_gsw_occurrence.R' 'calc_gsw_recurrence.R'
'calc_gsw_seasonality.R' 'calc_gsw_time_series.R'
'calc_gsw_transitions.R' 'calc_hfp.R' 'get_resources.R'
'calc_indicators.R' 'calc_ipbes_biomes.R' 'calc_iucn.R'
'calc_key_biodiversity_areas.R' 'calc_landcover.R'
'calc_mangroves_area.R' 'calc_population_count.R'
'calc_precipitation_chelsa.R' 'calc_precipitation_chirps.R'
'calc_precipitation_wc.R' 'calc_slope.R'
'calc_soilproperties.R' 'calc_temperature_max_wc.R'
'calc_temperature_min_wc.R' 'calc_traveltime.R'
'calc_traveltime_2000.R' 'calc_treecover_area.R'
'calc_treecover_area_and_emissions.R'
'calc_treecoverloss_emissions.R' 'calc_tri.R' 'chunking.R'
'engines.R' 'get_accessibility_2000.R' 'get_acled.R'
'get_biodiversity_intactness_index.R' 'get_carbon.R'
'get_chelsa.R' 'get_chirps.R' 'get_esalandcover.R'
'get_fritz_et_al.R' 'get_gfw_emissions.R' 'get_gfw_lossyear.R'
'get_gfw_treecover.R' 'get_gmw.R' 'get_gsw.R'
'get_gsw_time_series.R' 'get_hfp.R' 'get_ipbes_biomes.R'
'get_iucn.R' 'get_key_biodiversity_areas.R' 'get_mcd64A1.R'
'get_nasa_grace.R' 'get_nasa_srtm.R' 'get_nelson_et_al.R'
'get_soilgrids.R' 'get_teow.R' 'get_ucdp_ged.R'
'get_worldclim.R' 'get_worldpop.R' 'mapme.biodiversity-pkg.R'
'portfolio.R' 'spatial-utils.R' 'utils.R'
'data.R' 'equation_base.R' 'system_base.R' 'model_logger.R'
'market_model.R' 'disequilibrium_model.R' 'diseq_basic.R'
'diseq_deterministic_adjustment.R' 'diseq_directional.R'
'diseq_stochastic_adjustment.R' 'equation_basic.R'
'equation_deterministic_adjustment.R' 'equation_directional.R'
'equation_stochastic_adjustment.R' 'equilibrium_model.R'
'system_basic.R' 'gradient_basic.R'
'system_deterministic_adjustment.R'
'gradient_deterministic_adjustment.R' 'system_directional.R'
'gradient_directional.R' 'system_equilibrium.R'
'gradient_equilibrium.R' 'system_stochastic_adjustment.R'
'gradient_stochastic_adjustment.R' 'hessian_basic.R'
'hessian_directional.R' 'likelihood_basic.R'
'likelihood_deterministic_adjustment.R'
'likelihood_directional.R' 'likelihood_equilibrium.R'
'likelihood_stochastic_adjustment.R' 'market_fit.R' 'markets.R'
'model_simulation.R'
'RcppExports.R' 'data.R' 'utils.R' 'markov_mix_utils.R'
'markov_mix.R' 'markovmix-package.R'
'gregt.R' 'varMase.R' 'GREG.R' 'IdahoPop.R' 'IdahoSamp.R'
'RcppExports.R' 'gregElasticNett.R' 'gregElasticNet.R'
'gregTree.R' 'gregtreet.R' 'htt.R' 'horvitzThompson.R'
'logisticGregElasticNett.R' 'logisticGregt.R' 'mase-package.R'
'modifiedGreg.R' 'modifiedGregt.R' 'modifiedLogisticGregt.R'
'postStratt.R' 'postStrat.R' 'ratio.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_cvg.R'
'plot_prob.R' 'sim_data.R' 'tsc.setting.R'
'AuditorFitters.R' 'MCBoost.R' 'PipelineMCBoost.R'
'PipeOpLearnerPred.R' 'PipeOpMCBoost.R' 'Predictor.R'
'ProbRange.R' 'helpers.R' 'zzz.R'
'AR1_sampler.R' 'GMRF_extension.R' 'MCMCsim.R' 'MH.R'
'tabMatrix.R' 'MatrixUtils.R' 'RcppExports.R' 'TMVN_methods.R'
'TMVN_sampler.R' 'aux_closures.R' 'cMVN_sampler.R' 'cholesky.R'
'conjugate_gradients.R' 'f_binomial.R' 'f_gamma.R'
'f_gaussian.R' 'f_multinomial.R' 'f_negbinomial.R'
'f_poisson.R' 'formulas.R' 'kronprod.R' 'mc_bart.R'
'mc_block.R' 'mc_gen.R' 'mc_gl.R' 'mc_mec.R' 'mc_offset.R'
'mc_reg.R' 'mc_vfac.R' 'mc_vreg.R' 'mcmcsae.R' 'model_eval.R'
'modelmatrix.R' 'models.R' 'parallel.R' 'plots.R'
'prediction.R' 'priors.R' 'projection.R' 'random.R'
'samplers.R' 'sbc.R' 'set_constraints.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" "mcPBequi.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"
"ktau.r"
'pkg_class.R' 'pkg_methods.R' 'autoplot.R' 'blandAltman.R'
'correlation.R' 'data.R' 'desc.R' 'mcr.R' 'outlier.R'
'package.R' 'pairedroc.R' 'qual_stat.R' 'referenceInterval.R'
'samplesize.R' 'utils.R' 'vca.R'
"mcrMisc.r" "mcLinReg.r" "mcDeming.r" "mcWDeming.r"
"mcPaBaLarge.r" "mcPaBa.r" "mcPBequi.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"
"ktau.r" "mcMDeming.r" "mcMMDeming.r" "mcNgMMDeming.r"
"mcPiMMDeming.r"
'lru.R' 'cache.R' 'getPointer.R' 'map.R' 'memo-description.r'
'setupAgent.R' 'parseBotResponse.R' 'sendPrompt.R'
'executeCode.R' 'selfcorrect.R' 'clean_code_blocks.R'
'extractInstallPkg.R' 'extractFilenames.R' 'fileHeaderPrompt.R'
'test-helper-test_argument_validation.R' 'promptContext.R'
'runCodeInResponse.R'
MetaNLP.R util.R delete_functions.R feature_selection.R
useful_functions.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
'utils.R' 'base.R' 'acide.R' 'baranyi.R' 'data.R' 'gompertz.R'
'linear.R' 'rosso.R'
'handlers-static.R' 'miniCRAN.R' 'packages.R' 'handlers-.R'
'api.R' 'microCRAN-package.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'
'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'
'dictionaries.R' 'MiesOperator.R' 'Filtor.R' 'FiltorMaybe.R'
'FiltorNull.R' 'FiltorProxy.R' 'FiltorSurrogate.R'
'FiltorSurrogateProgressive.R' 'FiltorSurrogateTournament.R'
'Mutator.R' 'MutatorCmpMaybe.R' 'MutatorDiscreteUniform.R'
'MutatorErase.R' 'MutatorGauss.R' 'MutatorMaybe.R'
'MutatorNull.R' 'MutatorProxy.R' 'MutatorSequential.R'
'ParamSetShadow.R' 'OperatorCombination.R' 'Recombinator.R'
'Selector.R' 'mies_methods.R' 'OptimizerMies.R'
'RecombinatorCmpMaybe.R' 'RecombinatorConvex.R'
'RecombinatorConvexPair.R' 'RecombinatorCrossoverNary.R'
'RecombinatorCrossoverUniform.R' 'RecombinatorMaybe.R'
'RecombinatorNull.R' 'RecombinatorProxy.R'
'RecombinatorSequential.R'
'RecombinatorSimulatedBinaryCrossover.R' 'RecombinatorSwap.R'
'Scalor.R' 'ScalorAggregate.R' 'ScalorDomcount.R'
'ScalorFixedProjections.R' 'ScalorHypervolume.R'
'ScalorNondom.R' 'ScalorOne.R' 'ScalorProxy.R'
'ScalorSingleObjective.R' 'SelectorBest.R' 'SelectorMaybe.R'
'SelectorNull.R' 'SelectorProxy.R' 'SelectorRandom.R'
'SelectorSequential.R' 'SelectorTournament.R'
'TerminatorBudget.R' 'TerminatorGenerationPerfReached.R'
'TerminatorGenerationStagnation.R' 'TerminatorGenerations.R'
'TunerMies.R' 'bibentries.R' 'repr.R' 'utils.R' 'utils_mo.R'
'zzz.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'
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
'age_mat.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'
'setExtEncounter.R' 'setReproduction.R' 'setResource.R'
'setFishing.R' 'setInitialValues.R' 'setBevertonHolt.R'
'upgrade.R' 'selectivity_funcs.R' 'pred_kernel_funcs.R'
'resource_dynamics.R' 'resource_semichemostat.R'
'resource_logistic.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' 'extension.R' 'data.R'
'RcppExports.R' 'deprecated.R' 'get_initial_n.R'
'compareParams.R' 'customFunction.R' 'manipulate_species.R'
'calibrate.R' 'match.R' 'matchGrowth.R' 'steadySingleSpecies.R'
'defaults_edition.R' 'validSpeciesParams.R'
'cast-utils.R' 'cli.R' 'databricks-utils.R' 'globals.R'
'imports.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' 'zzz.R' 'LearnerClustAffinityPropagation.R'
'LearnerClustAgnes.R' 'LearnerClustBICO.R'
'LearnerClustBIRCH.R' 'LearnerClustCMeans.R'
'LearnerClustCobweb.R' 'LearnerClustDBSCAN.R'
'LearnerClustDBSCANfpc.R' 'LearnerClustDiana.R'
'LearnerClustEM.R' 'LearnerClustFanny.R'
'LearnerClustFarthestFirst.R' 'LearnerClustFeatureless.R'
'LearnerClustHDBSCAN.R' 'LearnerClustHclust.R'
'LearnerClustKKMeans.R' 'LearnerClustKMeans.R'
'LearnerClustMclust.R' 'LearnerClustMeanShift.R'
'LearnerClustMiniBatchKMeans.R' 'LearnerClustOPTICS.R'
'LearnerClustPAM.R' 'LearnerClustSimpleKMeans.R'
'LearnerClustXMeans.R' 'MeasureClust.R' 'measures.R'
'MeasureClustInternal.R' 'PredictionClust.R'
'PredictionDataClust.R' 'TaskClust.R' 'TaskClust_ruspini.R'
'TaskClust_usarrest.R' 'as_prediction_clust.R'
'as_task_clust.R' 'bibentries.R' 'helper.R'
'zzz.R' 'PipeOpFDACor.R' 'PipeOpFDAExtract.R'
'PipeOpFDAFlatten.R' 'PipeOpFDAInterpol.R'
'PipeOpFDAScaleRange.R' 'PipeOpFDASmooth.R' 'PipeOpFPCA.R'
'TaskClassif_phoneme.R' 'TaskRegr_dti.R' 'TaskRegr_fuel.R'
'bibentries.R' 'datasets.R' 'hash_input.R'
'Filter.R' 'mlr_filters.R' 'FilterAUC.R' 'FilterAnova.R'
'FilterBoruta.R' 'FilterCMIM.R' 'FilterCarScore.R'
'FilterCarSurvScore.R' 'FilterCorrelation.R' 'FilterDISR.R'
'FilterFindCorrelation.R' 'FilterLearner.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' 'FilterUnivariateCox.R'
'FilterVariance.R' 'bibentries.R' 'flt.R' 'helper.R'
'reexports.R' 'zzz.R'
'ArchiveAsyncFSelect.R' 'ArchiveAsyncFSelectFrozen.R'
'ArchiveBatchFSelect.R' 'AutoFSelector.R'
'CallbackAsyncFSelect.R' 'CallbackBatchFSelect.R'
'ContextAsyncFSelect.R' 'ContextBatchFSelect.R'
'EnsembleFSResult.R' 'FSelectInstanceAsyncSingleCrit.R'
'FSelectInstanceAsyncMultiCrit.R'
'FSelectInstanceBatchSingleCrit.R'
'FSelectInstanceBatchMultiCrit.R' 'mlr_fselectors.R'
'FSelector.R' 'FSelectorAsync.R' 'FSelectorAsyncDesignPoints.R'
'FSelectorAsyncExhaustiveSearch.R'
'FSelectorAsyncFromOptimizerAsync.R'
'FSelectorAsyncRandomSearch.R' 'FSelectorBatch.R'
'FSelectorBatchDesignPoints.R'
'FSelectorBatchExhaustiveSearch.R'
'FSelectorBatchFromOptimizerBatch.R'
'FSelectorBatchGeneticSearch.R' 'FSelectorBatchRFE.R'
'FSelectorBatchRFECV.R' 'FSelectorBatchRandomSearch.R'
'FSelectorBatchSequential.R'
'FSelectorBatchShadowVariableSearch.R' 'ObjectiveFSelect.R'
'ObjectiveFSelectAsync.R' 'ObjectiveFSelectBatch.R'
'assertions.R' 'auto_fselector.R' 'bibentries.R'
'embedded_ensemble_fselect.R' 'ensemble_fselect.R'
'extract_inner_fselect_archives.R'
'extract_inner_fselect_results.R' 'fselect.R'
'fselect_nested.R' 'helper.R' 'mlr_callbacks.R' 'reexports.R'
'sugar.R' 'zzz.R'
'OptimizerAsyncSuccessiveHalving.R' 'aaa.R'
'OptimizerBatchSuccessiveHalving.R' 'OptimizerBatchHyperband.R'
'TunerAsyncSuccessiveHalving.R' 'TunerBatchHyperband.R'
'TunerBatchSuccessiveHalving.R' 'bibentries.R' 'helper.R'
'zzz.R'
'MeasureAbstractCi.R' 'aaa.R' 'MeasureCI.R' 'MeasureCIConZ.R'
'MeasureCICorT.R' 'MeasureCIHoldout.R' 'MeasureCIWaldCV.R'
'MeasureCiNestedCV.R' 'ResamplingNestedCV.R'
'ResamplingPairedSubsampling.R' 'bibentries.R' 'zzz.R'
'aaa.R' 'LearnerClassifCVGlmnet.R' 'LearnerClassifGlmnet.R'
'LearnerClassifKKNN.R' 'LearnerClassifLDA.R'
'LearnerClassifLogReg.R' 'LearnerClassifMultinom.R'
'LearnerClassifNaiveBayes.R' 'LearnerClassifNnet.R'
'LearnerClassifQDA.R' 'LearnerClassifRanger.R'
'LearnerClassifSVM.R' 'LearnerClassifXgboost.R'
'LearnerRegrCVGlmnet.R' 'LearnerRegrGlmnet.R'
'LearnerRegrKKNN.R' 'LearnerRegrKM.R' 'LearnerRegrLM.R'
'LearnerRegrNnet.R' 'LearnerRegrRanger.R' 'LearnerRegrSVM.R'
'LearnerRegrXgboost.R' 'bibentries.R' 'helpers.R'
'helpers_glmnet.R' 'helpers_ranger.R' 'zzz.R'
'mlr_acqfunctions.R' 'AcqFunction.R' 'AcqFunctionAEI.R'
'AcqFunctionCB.R' 'AcqFunctionEHVI.R' 'AcqFunctionEHVIGH.R'
'AcqFunctionEI.R' 'AcqFunctionEILog.R' 'AcqFunctionEIPS.R'
'AcqFunctionMean.R' 'AcqFunctionMulti.R' 'AcqFunctionPI.R'
'AcqFunctionSD.R' 'AcqFunctionSmsEgo.R'
'AcqFunctionStochasticCB.R' 'AcqFunctionStochasticEI.R'
'AcqOptimizer.R' 'mlr_input_trafos.R' 'InputTrafo.R'
'InputTrafoUnitcube.R' 'aaa.R' 'OptimizerADBO.R'
'OptimizerAsyncMbo.R' 'OptimizerMbo.R' 'mlr_output_trafos.R'
'OutputTrafo.R' 'OutputTrafoLog.R' 'OutputTrafoStandardize.R'
'mlr_result_assigners.R' 'ResultAssigner.R'
'ResultAssignerArchive.R' 'ResultAssignerSurrogate.R'
'Surrogate.R' 'SurrogateLearner.R'
'SurrogateLearnerCollection.R' 'TunerADBO.R' 'TunerAsyncMbo.R'
'TunerMbo.R' 'mlr_loop_functions.R' 'bayesopt_ego.R'
'bayesopt_emo.R' 'bayesopt_mpcl.R' 'bayesopt_parego.R'
'bayesopt_smsego.R' 'bibentries.R' 'helper.R' 'loop_function.R'
'mbo_defaults.R' 'sugar.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_gmean.R' 'binary_gpr.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' 'classif_mcc.R'
'classif_zero_one.R' 'confusion_matrix.R' 'helper.R'
'regr_ae.R' 'regr_ape.R' 'regr_bias.R' 'regr_ktau.R'
'regr_linex.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_pinball.R' 'regr_rae.R'
'regr_rmse.R' 'regr_rmsle.R' 'regr_rrse.R' 'regr_rse.R'
'regr_rsq.R' 'regr_sae.R' 'regr_se.R' 'regr_sle.R'
'regr_smape.R' 'regr_srho.R' 'regr_sse.R' 'roxygen.R'
'similarity_jaccard.R' 'similarity_phi.R' 'zzz.R'
'Dictionary.R' 'named_list.R' 'Callback.R' 'Context.R'
'as_factor.R' 'as_short_string.R' 'assert_ro_binding.R'
'calculate_hash.R' 'capitalize.R' 'cat_cli.R' 'catn.R'
'check_operators.R' 'check_packages_installed.R' 'chunk.R'
'compose.R' 'compute_mode.R' 'count_missing.R' 'crate.R'
'cross_join.R' 'dictionary_sugar.R' 'did_you_mean.R'
'distinct_values.R' 'encapsulate.R' 'enframe.R'
'extract_vars.R' 'format_bib.R' 'formulate.R' 'get_private.R'
'get_seed.R' 'has_element.R' 'ids.R' 'insert_named.R'
'invoke.R' 'is_scalar_na.R' 'keep_in_bounds.R' 'leanify.R'
'load_dataset.R' 'map_values.R' 'modify.R' 'named_vector.R'
'names2.R' 'nin.R' 'open_help.R' 'printf.R' 'probe.R'
'purrr_map.R' 'rcbind.R' 'rd_info.R' 'recycle_vector.R'
'register_namespace_callback.R' 'remove_named.R'
'reorder_vector.R' 'require_namespaces.R' 'rowwise_table.R'
'seq.R' 'set_class.R' 'set_names.R' 'set_params.R' 'shuffle.R'
'str_collapse.R' 'str_indent.R' 'str_trunc.R' 'strip_srcrefs.R'
'to_decimal.R' 'topo_sort.R' 'transpose.R' 'unnest.R'
'which_max.R' 'with_package.R' 'zzz.R'
'DataBackendRaster.R' 'DataBackendVector.R'
'LearnerClassifSpatial.R' 'LearnerRegrSpatial.R' 'TaskRegrST.R'
'TaskClassifST.R' 'TaskClassif_leipzig.R'
'as_task_classif_st.R' 'as_task_regr_st.R'
'as_task_unsupervised.R' 'data.R' 'helper.R'
'predict_spatial.R' 'zzz.R'
'CallbackSet.R' 'aaa.R' 'TorchCallback.R'
'CallbackSetCheckpoint.R' 'CallbackSetEarlyStopping.R'
'CallbackSetHistory.R' 'CallbackSetLRScheduler.R'
'CallbackSetProgress.R' 'CallbackSetTB.R'
'CallbackSetUnfreeze.R' 'ContextTorch.R' 'DataBackendLazy.R'
'utils.R' 'DataDescriptor.R' 'LearnerFTTransformer.R'
'LearnerTorch.R' 'LearnerTorchFeatureless.R'
'LearnerTorchImage.R' 'LearnerTorchMLP.R' 'task_dataset.R'
'shape.R' 'PipeOpTorchIngress.R' 'LearnerTorchModel.R'
'LearnerTorchModule.R' 'LearnerTorchTabResNet.R'
'LearnerTorchVision.R' 'ModelDescriptor.R' 'PipeOpModule.R'
'PipeOpTorch.R' 'PipeOpTaskPreprocTorch.R'
'PipeOpTorchActivation.R' 'PipeOpTorchAdaptiveAvgPool.R'
'PipeOpTorchAvgPool.R' 'PipeOpTorchBatchNorm.R'
'PipeOpTorchBlock.R' 'PipeOpTorchCallbacks.R'
'PipeOpTorchConv.R' 'PipeOpTorchConvTranspose.R'
'PipeOpTorchDropout.R' 'PipeOpTorchFTCLS.R'
'PipeOpTorchFTTransformerBlock.R' 'PipeOpTorchFn.R'
'PipeOpTorchHead.R' 'PipeOpTorchIdentity.R'
'PipeOpTorchLayerNorm.R' 'PipeOpTorchLinear.R' 'TorchLoss.R'
'PipeOpTorchLoss.R' 'PipeOpTorchMaxPool.R' 'PipeOpTorchMerge.R'
'PipeOpTorchModel.R' 'PipeOpTorchOptimizer.R'
'PipeOpTorchReshape.R' 'PipeOpTorchSoftmax.R'
'PipeOpTorchTokenizer.R' 'Select.R' 'TaskClassif_cifar.R'
'TaskClassif_lazy_iris.R' 'TaskClassif_melanoma.R'
'TaskClassif_mnist.R' 'TaskClassif_tiny_imagenet.R'
'TorchDescriptor.R' 'TorchOptimizer.R' 'bibentries.R' 'cache.R'
'lazy_tensor.R' 'learner_torch_methods.R' 'materialize.R'
'merge_graphs.R' 'multi_tensor_dataset.R' 'nn.R' 'nn_graph.R'
'paramset_torchlearner.R' 'preprocess.R' 'rd_info.R'
'with_torch_settings.R' 'zzz.R'
'ArchiveAsyncTuning.R' 'ArchiveAsyncTuningFrozen.R'
'ArchiveBatchTuning.R' 'AutoTuner.R' 'CallbackAsyncTuning.R'
'CallbackBatchTuning.R' 'ContextAsyncTuning.R'
'ContextBatchTuning.R' 'ObjectiveTuning.R'
'ObjectiveTuningAsync.R' 'ObjectiveTuningBatch.R'
'mlr_tuners.R' 'Tuner.R' 'TunerAsync.R'
'TunerAsyncDesignPoints.R' 'TunerAsyncFromOptimizerAsync.R'
'TunerAsyncGridSearch.R' 'TunerAsyncRandomSearch.R'
'TunerBatch.R' 'TunerBatchCmaes.R' 'TunerBatchDesignPoints.R'
'TunerBatchFromBatchOptimizer.R' 'TunerBatchGenSA.R'
'TunerBatchGridSearch.R' 'TunerBatchInternal.R'
'TunerBatchIrace.R' 'TunerBatchNLoptr.R'
'TunerBatchRandomSearch.R' 'TuningInstanceBatchSingleCrit.R'
'TuningInstanceAsyncMulticrit.R'
'TuningInstanceAsyncSingleCrit.R'
'TuningInstanceBatchMulticrit.R' 'TuningInstanceMultiCrit.R'
'TuningInstanceSingleCrit.R' 'as_search_space.R' 'as_tuner.R'
'assertions.R' 'auto_tuner.R' 'bibentries.R'
'extract_inner_tuning_archives.R'
'extract_inner_tuning_results.R' 'helper.R' 'mlr_callbacks.R'
'reexport.R' 'sugar.R' 'tune.R' 'tune_nested.R' 'zzz.R'
'mlr_tuning_spaces.R' 'TuningSpace.R' 'bibentries.R' 'sugar.R'
'tuning_spaces_default.R' 'tuning_spaces_rbv1.R'
'tuning_spaces_rbv2.R' 'zzz.R'
'BenchmarkResult.R' 'Filter.R' 'LearnerClassif.R'
'LearnerClassifCVGlmnet.R' 'LearnerClassifGlmnet.R'
'LearnerClassifRpart.R' 'LearnerClustHierarchical.R'
'LearnerRegr.R' 'LearnerRegrCVGlmnet.R' 'LearnerRegrGlmnet.R'
'LearnerRegrRpart.R' 'LearnerSurvCoxPH.R'
'OptimInstanceBatchSingleCrit.R' 'Prediction.R'
'PredictionClassif.R' 'PredictionClust.R' 'PredictionRegr.R'
'ResampleResult.R' 'Task.R' 'TaskClassif.R' 'TaskClust.R'
'TaskRegr.R' 'TuningInstanceBatchSingleCrit.R'
'EnsembleFSResult.R' 'as_precrec.R' 'bibentries.R' 'helper.R'
'plot_learner_prediction.R' 'reexports.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' 'RandomForestSRC.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'
'between-within.R' 'catch-routine-registration.R'
'component.R' 'cov_struct.R' 'data.R' 'empirical.R' 'fit.R'
'kenwardroger.R' 'mmrm-methods.R' 'mmrm-package.R' 'utils.R'
'residual.R' 'utils-nse.R' 'utils-formula.R' 'satterthwaite.R'
'skipping.R' 'tidiers.R' 'testing.R' 'tmb-methods.R' 'tmb.R'
'interop-emmeans.R' 'interop-parsnip.R' 'interop-car.R' 'zzz.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'
'bind_est_gof.R' 'coef_rename.R' 'config_modelsummary.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_DT.R' 'factory_dataframe.R'
'factory_flextable.R' 'factory_gt.R' 'factory_huxtable.R'
'factory_kableExtra.R' 'factory_markdown.R'
'factory_tinytable.R' 'factory_typst.R' 'fmt_factory.R'
'format_estimates.R' 'format_gof.R' 'format_msg.R'
'get_estimates.R' 'get_gof.R' 'get_vcov.R' 'glance_custom.R'
'gof_map.R' 'hush.R' 'map_estimates.R' 'map_gof.R'
'methods_did.R' 'methods_estimatr.R' 'methods_fixest.R'
'methods_lfe.R' 'methods_stats.R' 'modelplot.R'
'modelsummary.R' 'modelsummary_cbind.R' 'modelsummary_list.R'
'modelsummary_rbind.R' 'poorman.R' 'reexport.R'
'rename_statistics.R' 'sanitize_conf_level.R' 'sanitize_fmt.R'
'sanitize_gof_map.R' 'sanitize_models.R' 'sanitize_output.R'
'sanitize_shape.R' 'sanitize_statistic.R' 'sanitize_vcov.R'
'sanity_checks.R' 'settings.R' 'shape_estimates.R' 'span.R'
'stars.R' 'supported_models.R' 'themes.R' 'tidy_custom.R'
'update_modelsummary.R' 'utils_labels.R' 'utils_pad.R'
'utils_print.R' 'utils_replace.R' 'utils_stats.R'
'utils_warn.R' 'zzz.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 gam.R ggm.R greedyGLM.R
infocriteria.R initParameters.R localnulltest.R msPriorSpec.R
modelsearch.R modelSelection.R modelSelectionGLM.R mombf.R
normaliwish.R normmix.R nlpMarginal.R postMode.R rmom.R
RcppExports.R testfunction.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' 'weak.R' 'minAlgo.R' 'utils.R'
'pipeop.R' 'monad.R' 'list.R' 'maybe.R' 'monad-package.R'
'utils.R' 'zzz.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'
'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'
'custom-tol.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' 'mwrite.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'
'MSigSeg_Class.R' 'allGenerics.R' 'brkps_method.R' 'change.R'
'data.R' 'data.input_method.R' 'data.output_method.R'
'funtion_colsum_colmean.R' 'lambda_estimator.R'
'lambda_method.R' 'multi_plot.R' 'noisegen.R' 'print_method.R'
'seg.len_method.R' 'segmentation.R' 'summary_method.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'
'fetal_covid_data.R' 'IALS_data.R' 'mult.latent.reg-package.R'
'mult.reg_1level.R' 'mult.reg_2level.R' 'start.em.1level.R'
'start.em.2level.R' 'start.em.R' 'trading_data.R'
'twins_data.R'
'check_raster.R' 'classes.R' 'data.R' 'func_checks.R'
'generate_points.R' 'generate_points_checks.R' 'globals.R'
'methods.R' 'metrics_bind.R' 'metrics_corr.R'
'metrics_corr_checks.R' 'metrics_filter.R'
'metrics_filter_checks.R' 'metrics_gradient.R'
'metrics_gradient_checks.R' 'metrics_list.R' 'metrics_plots.R'
'metrics_scalogram.R' 'metrics_scalogram_checks.R' 'mland.R'
'mland_checks.R' 'mland_export_gis.R' 'mland_load.R'
'mland_metrics.R' 'mland_metrics_checks.R' 'mland_overlap.R'
'mland_overlap_checks.R' 'mland_plot.R' 'mland_plot_checks.R'
'mland_save.R' 'utils.R'
'AllGeneric.R' 'Pn_lognormalDensity.R' 'Pn_logtDensity.R'
'Rn_Gauss.R' 'Rn_floorNorm.R' 'Rn_normalDensity.R'
'Rn_tDensity.R' 'domainChecks.R' 'misc.R' 'multIntTestFunc.R'
'standardSimplex_Dirichlet.R' 'standardSimplex_exp_sum.R'
'unitBall_normGauss.R' 'unitBall_polynomial.R'
'unitCube_BFN1.R' 'unitCube_BFN2.R' 'unitCube_BFN3.R'
'unitCube_BFN4.R' 'unitCube_Genz1.R' 'unitCube_cos2.R'
'unitCube_floor.R' 'unitCube_max.R'
'unitSphere_innerProduct1.R' 'unitSphere_polynomial.R'
'Data_Source.R' 'get_internal_data.R' 'get_contribution.R'
'M_value.R' 'M.R' 'get_general_contribution.R'
'get_proportion.R' 'M_within.R' 'M_within_inv.R'
'RcppExports.R' 'globals.R' 'mutual.R' 'prepare_data.R'
'mvn.R' 'mardia.R' 'hz.R' 'hw.R' 'royston.R'
'doornik_hansen.R' 'energy.R' 'descriptives.R'
'test_univariate_normality.R' 'multivariate_diagnostic_plot.R'
'mv_outlier.R' 'univariate_diagnostic_plot.R'
'power_transform.R' 'arw_adjustment.R' 'plot.mvn.R'
'summary.mvn.R' 'impute_missing.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'
'add-cols.R' 'add-n-prop-miss.R' 'any-na-complete.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' 'impute-factor.R' 'impute-fixed.R'
'impute-median.R' 'impute-mode.R' 'impute-zero.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' 'replace_na_with.R'
'scoped-replace-with-na.R' 'set-n-prop-miss.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'
'AOI.R' 'AOImethod.R' 'CFArray.R' 'CFAuxiliaryLongLat.R'
'CFAxis.R' 'CFAxisCharacter.R' 'CFAxisDiscrete.R'
'CFAxisLatitude.R' 'CFAxisLongitude.R' 'CFAxisNumeric.R'
'CFAxisTime.R' 'CFAxisVertical.R' 'CFBounds.R'
'CFCellMeasure.R' 'CFDataset.R' 'CFGridMapping.R' 'CFLabel.R'
'NCObject.R' 'CFObject.R' 'CFResource.R' 'CFVariable.R'
'CFVariableBase.R' 'CFVariableL3b.R' 'NCDimension.R'
'NCGroup.R' 'NCUDT.R' 'NCVariable.R' 'makeCFObjects.R'
'ncdfCF-package.R' 'readCF.R' 'utils.R' 'wkt2.R' 'zzz.R'
'RcppExports.R' 'imports.r' 'graph_data.r' 'adjmat.r' 'bass.r'
'bootnet.r' 'citer_environment.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' 'helpers.R'
'agentMatrix-Class-methods.R' 'agentset-functions.R'
'function-arguments.R' 'patch-functions.R' 'plot.R'
'quickPlot.R' 'spades-functions.R' 'turtle-functions.R'
'world-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'
'RcppExports.R' 'afni_io.R' 'all_generic.R' 'all_class.R'
'array_like.R' 'axis.R' 'big_neurovec.R' 'binary_io.R'
'clustervol.R' 'common.R' 'conncomp.R' 'file_format.R'
'filebacked_neurovec.R' 'globals.R' 'index_vol.R'
'mapped_neurovec.R' 'meta_info.R' 'neuro_obj.R'
'neurohypervec.R' 'neuroim.R' 'neuroslice.R' 'neurospace.R'
'neurovec.R' 'neurovecseq.R' 'neurovol.R' 'nifti_io.R'
'niml_io.R' 'ops.R' 'resample.R' 'roi.R' 'searchlight.R'
'sparse_neurovec.R' 'spat_filter.R' 'zzz.R'
'AllClasses.R' 'AllGenerics.R' 'aggregate.R' 'barplot.R'
'bind.R' 'boxplot.R' 'chemistry.R' 'coerce.R' 'condense.R'
'covariance.R' 'data.R' 'describe.R' 'dist.R' 'graph.R'
'group.R' 'hist.R' 'margin.R' 'mean.R' 'mix.R' 'mutators.R'
'nexus-internal.R' 'nexus-package.R' 'outliers.R' 'pairs.R'
'pca.R' 'plot.R' 'quantile.R' 'reexport.R' 'replace.R'
'scale.R' 'show.R' 'simplex.R' 'split.R' 'subset.R' 'summary.R'
'transform_alr.R' 'transform_clr.R' 'transform_ilr.R'
'transform_inverse.R' 'transform_lr.R' 'transform_plr.R'
'validate.R' 'variance.R' 'variation.R' 'zzz.R'
'internalFunctions.R' 'allClasses.R' 'allGenerics.R'
'allMethods.R' 'functions.R' 'chmFromJSON.R'
'dendrogram-fixes.R' 'shaidy.R' 'ngchmshaidy.R' 'zzz.R'
'package.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_Rderivs.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 parameterTransform.R CAR.R
Laplace.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 QuadratureGrids.R zzz.R
utils.R dBetaBinom.R dCJS.R dDynOcc.R dDHMM.R dHMM.R dOcc.R
dNmixture.R dNmixtureAD.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 dmultiLocal_normal.R dbinom_vector.R
dnormalizer.R dpoisLocal_normal.R dbinomLocal_normalPlateau.R
dbinomLocal_exp.R dpoisppAC.R dpoisppDetection_normal.R
dpoisppLocalDetection_normal.R getLocalObjects.R
getMidPointNodes.R getWindowCoords.R getSparseY.R
getHomeRangeArea.R localTrapCalculations.R
makeConstantNimbleFunction.R marginalVoidProbIntegrand.R
marginalVoidProbNumIntegration.R scaleCoordsToHabitatGrid.R
dcatState1Alive1Dead.R dcatState1Alive2Dead.R
dcatState2Alive2Dead.R sampler_categorical_general.R
calculateDensity.R zzz.R
AuxiliaryFilter.R BootstrapFilter.R EnKFilter.R IF2Filter.R
LiuWestFilter.R MCMCSamplers.R resamplers.R utils.R zzz.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' 'vignetteFunctions.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'
'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'
'classes.R' 'fish.R' 'fish_raw.R' 'get_posterior.R' 'gof.R'
'occumbData.R' 'occumb.R' 'plot.R' 'predict.R' 'show.R'
'summary.R' 'utility.R' 'utils.R'
'idfocean.R' 'idfeez.R' 'data.R' 'dotplot.R' 'idfland.R'
'idfcode.R' 'idfport.R' 'sixtytoten.R'
'RcppExports.R' 'aaa-odbc-data-type.R' 'connection-pane.R'
'dbi-connection.R' 'odbc-connection.R' 'db.R' 'dbi-driver.R'
'dbi-result.R' 'dbi-table.R' 'dbi.R' 'driver-access.R'
'driver-bigquery.R' 'driver-databricks.R' 'driver-db2.R'
'driver-hana.R' 'driver-hive.R' 'driver-impala.R'
'driver-mysql.R' 'driver-netezza.R' 'driver-oracle.R'
'driver-postgres.R' 'driver-redshift.R' 'driver-snowflake.R'
'driver-spark.R' 'driver-sql-server.R' 'driver-sqlite.R'
'driver-teradata.R' 'driver-vertica.R'
'import-standalone-obj-type.R'
'import-standalone-types-check.R' 'odbc-config.R'
'odbc-data-sources.R' 'odbc-drivers.R' 'odbc-package.R'
'odbc.R' 'utils.R' 'zzz.R'
'ODMeansSampleData.R' 'ODMeansTaxiData.R' 'first_hierarchy.R'
'second_hierarchy.R' 'odmeans.R' 'odmeans_graph.R'
'core_properties.R' 'custom_properties.R' 'defunct.R'
'dev-utils.R' 'docx_add.R' 'docx_comments.R' 'docx_cursor.R'
'docx_part.R' 'docx_replace.R' 'docx_section.R'
'docx_settings.R' 'empty_content.R' 'formatting_properties.R'
'fortify_docx.R' 'fortify_pptx.R' 'knitr_utils.R' 'officer.R'
'ooxml.R' 'ooxml_block_objects.R' 'ooxml_run_objects.R'
'openxml_content_type.R' 'openxml_document.R' 'pack_folder.R'
'ph_location.R' 'post-proc.R' 'ppt_class_dir_collection.R'
'ppt_classes.R' 'ppt_notes.R' 'ppt_ph_dedupe_layout.R'
'ppt_ph_manipulate.R' 'ppt_ph_rename_layout.R'
'ppt_ph_with_methods.R' 'pptx_informations.R'
'pptx_layout_helper.R' 'pptx_matrix.R' 'utils.R'
'pptx_slide_manip.R' 'read_docx.R' 'read_docx_styles.R'
'read_pptx.R' 'read_xlsx.R' 'relationship.R' 'rtf.R'
'shape_properties.R' 'shorcuts.R' 'docx_append_context.R'
'utils-xml.R' 'deprecated.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' 'spatial.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'
'API-CPAC.R' 'API-Local.R' 'API-Remote.R' 'Utils.R'
'ClassModel.R' 'ClassModelRun.R' 'ClassModelRunSet.R'
'ClassWorkset.R' 'RunOptions.R' 'Scenario.R'
'oms-Administrative.R' 'oms-DownloadModels.R'
'oms-ModelExtras.R' 'oms-ModelMetadata.R'
'oms-ModelRunResults.R' 'oms-ModelWorksetMetadata.R'
'oms-ModelingTaskMetadata.R' 'oms-ReadMicrodata.R'
'oms-ReadOutputTables.R' 'oms-ReadParameters.R'
'oms-RunModels.R' 'oms-Services.R' 'oms-UpdateModelProfile.R'
'oms-UpdateModelRuns.R' 'oms-UpdateModelWorkset.R'
'oms-UpdateModelingTasks.R' 'oms-UploadModels.R'
'oms-UserFiles.R' 'oms-UserSettings.R' 'openmpp-package.R'
'zzz.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' 'globals.R' 'gmMain.r'
'gridlist.R' 'import.r' 'measures.r' 'onair.r' 'openrepgrid.r'
'perturbate.R' 'preferred_poles.R' 'repgrid.r'
'repgrid-basicops.r' 'repgrid-constructs.r'
'repgrid-elements.r' 'repgrid-output.r' 'repgrid-plots.r'
'repgrid-ratings.r' 'resampling.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'
'setWindowSize.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.r Display.r PicoVR.r PhoneHMD.r ImoVifa.r dbTocd.r
data-RtDbUnits.r data-RtSigmaUnits.r SimYes.r SimNo.r
SimHenson.r SimGaussian.r zest.r mocs.r fourTwo.r
full_threshold.r pix2deg.r QUESTP.r KTPsi.r Compass.r
Octopus900.r Octopus600.r OPI-package.r SimHensonRT.r
KowaAP7000Client.r MAIA.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'
'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'
'set_mw.R' 'get_mw.R' 'apply_multiwave.R' 'matwgt_sim.R'
'merge_strata.R' 'multiwave_diagram.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' 'MCFSolutions.R'
'Ops.optmatch.dlist.R' 'Optmatch-package.R' 'RcppExports.R'
'abs.optmatch.dlist.R' 'boxplotMethods.R' 'caliper.R'
'complementarySlackness.R' 'data.R' 'deprecated.R'
'distUnion.R' 'edgelist.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'
'optmatchS3.R' 'pairmatch.R' 'print.optmatch.R'
'print.optmatch.dlist.R' 'scores.R' 'solve_reg_fm_prob.R'
'solver.R' 'strata.R' 'stratumStructure.R' 'summary.ism.R'
'summary.optmatch.R' 'utilities.R' 'zzz.R'
'zzzDistanceSpecification.R'
'aaa-.r' 'biplot-key.r' 'biplot.r' 'coord-scaffold.r' 'data.r'
'dplyr-verbs.r' 'utils.r' 'ord-recoverers.r'
'ord-augmentation.r' 'ord-conference.r' 'ord-tbl.r' 'fun-lda.r'
'fun-lra.r' 'fun-wrap.r' 'geom-interpolation.r' 'geom-origin.r'
'geom-utils.r' 'layer-utils.r' 'methods-base-eigen.r'
'methods-base-svd.r' 'methods-mass-correspondence.r'
'methods-mass-lda.r' 'methods-mass-mca.r' 'methods-ordr-lra.r'
'methods-stats-cancor.r' 'methods-stats-cmds.r'
'methods-stats-factanal.r' 'methods-stats-kmeans.r'
'methods-stats-lm.r' 'methods-stats-prcomp.r'
'methods-stats-princomp.r' 'ord-annotation.r' 'ord-format.r'
'ord-negation.r' 'ord-plot.r' 'ord-supplementation.r'
'ord-tidiers.r' 'ordinate.r' 'ordr.r' 'stat-matrix.r'
'stat-projection.r' 'stat-utils.r' 'theme-scaffold.r'
'zzz-biplot-geoms.r' 'zzz-biplot-stats.r' 'zzz.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'
'RcppExports.R' 'osqp-package.R' 'sparse.R' 'solve.R' 'osqp.R'
'params.R'
'anolis.R' 'print.R' 'package.R' 'ouchtree.R' 'ape2ouch.R'
'bootstrap.R' 'update.R' 'simulate.R' 'plot.R' 'summary.R'
'logLik.R' 'coef.R' 'rmvnorm.R' 'glssoln.R' 'hansen.R'
'brown.R' 'as_data_frame.R' 'bimac.R' 'geospiza.R' 'paint.R'
data.R PaleoPopModel.R PaleoPopResults.R PaleoRegion.R
paleopop_simulator.R paleopop-package.R region_subset.R
'panelPomp-package.R' 'aaa.R' 'contacts.R' 'generics.R'
'get_col_row.R' 'panel_loglik.R' 'panel_logmeanexp.R'
'panelPomp.R' 'panelPomp_methods.R' 'params.R' 'pfilter.R'
'pfilter_methods.R' 'mif2.R' 'mif2_methods.R' 'panelGompertz.R'
'panelGompertzLikelihood.R' 'panelMeasles.R'
'panelRandomWalk.R' 'panel_designs.R' 'plot.R' 'simulate.R'
'twentycities.R' 'uk_measles.R'
'helpers.R' 'labels.R' 'summarize.R' 'plot.R' 'prettify.R'
'toLatex.R'
'TaskState.R' 'Options.R' 'Helper.R' 'Exception.R'
'Specification.R' 'BackendService.R' 'Backend.R'
'AsyncBackend.R' 'SyncBackend.R' 'BackendFactory.R' 'Bar.R'
'ModernBar.R' 'BasicBar.R' 'BarFactory.R' 'Context.R'
'ProgressTrackingContext.R' 'ContextFactory.R' 'SessionState.R'
'Warning.R' 'UserApiConsumer.R' 'exports.R' 'logo.R'
'parabar-package.R'
'Condition.R' 'Design.R' 'Domain.R' 'Domain_methods.R'
'NoDefault.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' 'generate_design_grid.R'
'generate_design_lhs.R' 'generate_design_random.R'
'generate_design_sobol.R' 'helper.R' 'ps.R' 'ps_replicate.R'
'ps_union.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
pspaceChecks.R pspaceSupplements.R pspaceMisc.R
pspaceWatershed.R pspaceDecay.R pspaceClasses.R
pspaceGenerics.R pspaceMethods.R pspacePlots.R
Patterns-package.R global.R datasets.R omics_array.R
omics_network.R omics_array-omics_network.R omics_predict.R
'eventbridge_service.R' 'eventbridge_interfaces.R'
'eventbridge_operations.R' 'eventbridgepipes_service.R'
'eventbridgepipes_interfaces.R' 'eventbridgepipes_operations.R'
'eventbridgescheduler_service.R'
'eventbridgescheduler_interfaces.R'
'eventbridgescheduler_operations.R' 'locationservice_service.R'
'locationservice_interfaces.R' 'locationservice_operations.R'
'mq_service.R' 'mq_interfaces.R' 'mq_operations.R'
'mwaa_service.R' 'mwaa_interfaces.R' 'mwaa_operations.R'
'reexports_paws.common.R' 'resourceexplorer_service.R'
'resourceexplorer_interfaces.R' 'resourceexplorer_operations.R'
'schemas_service.R' 'schemas_interfaces.R'
'schemas_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'
'RcppExports.R' 'util.R' 'cache.R' 'struct.R' 'handlers.R'
'iniutil.R' 'config.R' 'logging.R' 'dateutil.R'
'credential_sso.R' 'credential_sts.R' 'url.R' 'net.R'
'credential_providers.R' 'credentials.R' 'client.R' 'convert.R'
'service.R' 'custom_dynamodb.R' 'custom_rds.R' 'head_bucket.R'
'http_status.R' 'error.R' 'custom_s3.R' 'handlers_core.R'
'handlers_ec2query.R' 'handlers_jsonrpc.R' 'handlers_query.R'
'handlers_rest.R' 'handlers_restjson.R' 'tags.R' 'xmlutil.R'
'handlers_restxml.R' 'handlers_stream.R' 'idempotency.R'
'jsonutil.R' 'mock_bindings.R' 'onLoad.R' 'paginate.R'
'populate.R' 'populateutil.R' 'queryutil.R' 'request.R'
'retry.R' 'service_parameter_helper.R' 'signer_v4.R'
'signer_s3.R' 'signer_s3v4.R' 'signer_v1.R' 'signer_v2.R'
'time.R'
'apprunner_service.R' 'apprunner_interfaces.R'
'apprunner_operations.R' 'batch_service.R' 'batch_interfaces.R'
'batch_operations.R' 'braket_service.R' 'braket_interfaces.R'
'braket_operations.R' 'computeoptimizer_service.R'
'computeoptimizer_interfaces.R' 'computeoptimizer_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' 'ecrpublic_service.R'
'ecrpublic_interfaces.R' 'ecrpublic_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' 'emrcontainers_service.R'
'emrcontainers_interfaces.R' 'emrcontainers_operations.R'
'emrserverless_service.R' 'emrserverless_interfaces.R'
'emrserverless_operations.R' 'imagebuilder_service.R'
'imagebuilder_interfaces.R' 'imagebuilder_operations.R'
'lambda_service.R' 'lambda_interfaces.R' 'lambda_operations.R'
'lightsail_service.R' 'lightsail_interfaces.R'
'lightsail_operations.R' 'proton_service.R'
'proton_interfaces.R' 'proton_operations.R'
'reexports_paws.common.R'
'serverlessapplicationrepository_service.R'
'serverlessapplicationrepository_interfaces.R'
'serverlessapplicationrepository_operations.R'
'billing_service.R' 'billing_interfaces.R'
'billing_operations.R' 'billingconductor_service.R'
'billingconductor_interfaces.R' 'billingconductor_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' 'marketplacecatalog_service.R'
'marketplacecatalog_interfaces.R'
'marketplacecatalog_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'
'paymentcryptographycontrolplane_service.R'
'paymentcryptographycontrolplane_interfaces.R'
'paymentcryptographycontrolplane_operations.R'
'paymentcryptographydataplane_service.R'
'paymentcryptographydataplane_interfaces.R'
'paymentcryptographydataplane_operations.R' 'pricing_service.R'
'pricing_interfaces.R' 'pricing_operations.R'
'reexports_paws.common.R' 'savingsplans_service.R'
'savingsplans_interfaces.R' 'savingsplans_operations.R'
'connect_service.R' 'connect_interfaces.R'
'connect_operations.R' 'connectcampaignservice_service.R'
'connectcampaignservice_interfaces.R'
'connectcampaignservice_operations.R'
'connectcampaignservicev2_service.R'
'connectcampaignservicev2_interfaces.R'
'connectcampaignservicev2_operations.R'
'connectcases_service.R' 'connectcases_interfaces.R'
'connectcases_operations.R' 'connectcontactlens_service.R'
'connectcontactlens_interfaces.R'
'connectcontactlens_operations.R'
'connectparticipant_service.R'
'connectparticipant_interfaces.R'
'connectparticipant_operations.R'
'connectwisdomservice_service.R'
'connectwisdomservice_interfaces.R'
'connectwisdomservice_operations.R'
'customerprofiles_service.R' 'customerprofiles_interfaces.R'
'customerprofiles_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'
'pinpointsmsvoicev2_service.R'
'pinpointsmsvoicev2_interfaces.R'
'pinpointsmsvoicev2_operations.R' 'reexports_paws.common.R'
'ses_service.R' 'ses_interfaces.R' 'ses_operations.R'
'sesv2_service.R' 'sesv2_interfaces.R' 'sesv2_operations.R'
'dax_service.R' 'dax_interfaces.R' 'dax_operations.R'
'docdb_service.R' 'docdb_interfaces.R' 'docdb_operations.R'
'docdbelastic_service.R' 'docdbelastic_interfaces.R'
'docdbelastic_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'
'keyspaces_service.R' 'keyspaces_interfaces.R'
'keyspaces_operations.R' 'lakeformation_service.R'
'lakeformation_interfaces.R' 'lakeformation_operations.R'
'memorydb_service.R' 'memorydb_interfaces.R'
'memorydb_operations.R' 'neptune_service.R'
'neptune_interfaces.R' 'neptune_operations.R'
'neptunedata_service.R' 'neptunedata_interfaces.R'
'neptunedata_operations.R' 'qldb_service.R' 'qldb_interfaces.R'
'qldb_operations.R' 'qldbsession_service.R'
'qldbsession_interfaces.R' 'qldbsession_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' 'redshiftdataapiservice_service.R'
'redshiftdataapiservice_interfaces.R'
'redshiftdataapiservice_operations.R'
'redshiftserverless_service.R'
'redshiftserverless_interfaces.R'
'redshiftserverless_operations.R' 'reexports_paws.common.R'
'simpledb_service.R' 'simpledb_interfaces.R'
'simpledb_operations.R' 'timestreamquery_service.R'
'timestreamquery_interfaces.R' 'timestreamquery_operations.R'
'timestreamwrite_service.R' 'timestreamwrite_interfaces.R'
'timestreamwrite_operations.R'
'cloud9_service.R' 'cloud9_interfaces.R' 'cloud9_operations.R'
'cloudcontrolapi_service.R' 'cloudcontrolapi_interfaces.R'
'cloudcontrolapi_operations.R' 'codeartifact_service.R'
'codeartifact_interfaces.R' 'codeartifact_operations.R'
'codebuild_service.R' 'codebuild_interfaces.R'
'codebuild_operations.R' 'codecatalyst_service.R'
'codecatalyst_interfaces.R' 'codecatalyst_operations.R'
'codecommit_service.R' 'codecommit_interfaces.R'
'codecommit_operations.R' 'codeconnections_service.R'
'codeconnections_interfaces.R' 'codeconnections_operations.R'
'codedeploy_service.R' 'codedeploy_interfaces.R'
'codedeploy_operations.R' 'codeguruprofiler_service.R'
'codeguruprofiler_interfaces.R' 'codeguruprofiler_operations.R'
'codegurureviewer_service.R' 'codegurureviewer_interfaces.R'
'codegurureviewer_operations.R' 'codegurusecurity_service.R'
'codegurusecurity_interfaces.R' 'codegurusecurity_operations.R'
'codepipeline_service.R' 'codepipeline_interfaces.R'
'codepipeline_operations.R' 'codestarconnections_service.R'
'codestarconnections_interfaces.R'
'codestarconnections_operations.R'
'codestarnotifications_service.R'
'codestarnotifications_interfaces.R'
'codestarnotifications_operations.R' 'devopsguru_service.R'
'devopsguru_interfaces.R' 'devopsguru_operations.R'
'drs_service.R' 'drs_interfaces.R' 'drs_operations.R'
'fis_service.R' 'fis_interfaces.R' 'fis_operations.R'
'reexports_paws.common.R' 'wellarchitected_service.R'
'wellarchitected_interfaces.R' 'wellarchitected_operations.R'
'xray_service.R' 'xray_interfaces.R' 'xray_operations.R'
'appstream_service.R' 'appstream_interfaces.R'
'appstream_operations.R' 'chatbot_service.R'
'chatbot_interfaces.R' 'chatbot_operations.R'
'ivschat_service.R' 'ivschat_interfaces.R'
'ivschat_operations.R' 'reexports_paws.common.R'
'workdocs_service.R' 'workdocs_interfaces.R'
'workdocs_operations.R' 'workmail_service.R'
'workmail_interfaces.R' 'workmail_operations.R'
'workmailmessageflow_service.R'
'workmailmessageflow_interfaces.R'
'workmailmessageflow_operations.R' 'workspaces_service.R'
'workspaces_interfaces.R' 'workspaces_operations.R'
'workspacesweb_service.R' 'workspacesweb_interfaces.R'
'workspacesweb_operations.R'
'backup_service.R' 'backup_interfaces.R' 'backup_operations.R'
'dlm_service.R' 'dlm_interfaces.R' 'dlm_operations.R'
'ebs_service.R' 'ebs_interfaces.R' 'ebs_operations.R'
'efs_service.R' 'efs_interfaces.R' 'efs_operations.R'
'finspacedata_service.R' 'finspacedata_interfaces.R'
'finspacedata_operations.R' 'fsx_service.R' 'fsx_interfaces.R'
'fsx_operations.R' 'glacier_service.R' 'glacier_interfaces.R'
'glacier_operations.R' 'omics_service.R' 'omics_interfaces.R'
'omics_operations.R' 'recyclebin_service.R'
'recyclebin_interfaces.R' 'recyclebin_operations.R'
'reexports_paws.common.R' 's3_service.R' 's3_operations.R'
's3_custom.R' 's3_interfaces.R' 's3control_service.R'
's3control_interfaces.R' 's3control_operations.R'
's3outposts_service.R' 's3outposts_interfaces.R'
's3outposts_operations.R' 's3tables_service.R'
's3tables_interfaces.R' 's3tables_operations.R'
'storagegateway_service.R' 'storagegateway_interfaces.R'
'storagegateway_operations.R'
'odpbc.R' 'odpbc_binom.R' 'pbcc.R' 'plot.pbcc.R'
'summary.pbcc.R'
'SEAex.R' 'pbox.R' 'coefAll2.R' 'copula_families.R'
'deltaCI.R' 'final_pbox.R' 'fit_copula.R' 'fit_copula_pbox.R'
'fit_dist_pbox.R' 'fun_stats.R' 'gen_scenario.R' 'grid_pbox.R'
'make_pbox.R' 'match_maker.R' 'modify_pbox.R' 'param_dev.R'
'perProb.R' 'perturbate_params.R' 'probCI.R' 'q_parser.R'
'qpbox.R' 'scenario_pbox.R' 'set_pbox.R' 'show.R'
'stats_calc.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'
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
'Administration.R' 'AdministrationConstraints.R' 'Fim.R'
'PFIMProject.R' 'Optimization.R' 'PGBOAlgorithm.R'
'PSOAlgorithm.R' 'SimplexAlgorithm.R' 'FedorovWynnAlgorithm.R'
'MultiplicativeAlgorithm.R' 'Model.R' 'Arm.R' 'BayesianFim.R'
'ModelError.R' 'Combined1.R' 'Constant.R' 'Design.R'
'Distribution.R' 'Evaluation.R' 'IndividualFim.R'
'LibraryOfModels.R' 'LibraryOfPDModels.R' 'LibraryOfPKModels.R'
'LogNormal.R' 'ModelODE.R' 'ModelAnalytic.R' 'ModelInfusion.R'
'ModelAnalyticInfusion.R' 'ModelAnalyticInfusionSteadyState.R'
'ModelAnalyticSteadyState.R' 'ModelODEBolus.R'
'ModelODEDoseInEquations.R' 'ModelODEDoseNotInEquations.R'
'ModelODEInfusion.R' 'ModelODEInfusionDoseInEquation.R'
'ModelParameter.R' 'Normal.R' 'PFIM-package.R'
'PopulationFim.R' 'Proportional.R' 'SamplingTimeConstraints.R'
'SamplingTimes.R' 'zzz.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'
'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' 'ZIPLNfit-class.R' 'ZIPLN.R'
'ZIPLNfit-S3methods.R' 'ZIPLNnetwork.R' 'barents.R'
'import_utils.R' 'mollusk.R' 'oaks.R' 'plot_utils.R' 'scRNA.R'
'trichoptera.R' 'utils-pipe.R' 'utils-zipln.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'
'plspm.R' 'auxiliar.R' 'check_arguments.R'
'check_specifications.R' 'get_alpha.R' 'get_ave.R'
'get_boots.R' 'get_dummies.R' 'get_effects.R' 'get_generals.R'
'get_gof.R' 'get_inner_summary.R' 'get_manifests.R'
'get_metric.R' 'get_nom_scale.R' 'get_num_scale.R'
'get_ord_scale.R' 'get_path_scheme.R' 'get_paths.R'
'get_plsr1.R' 'get_PLSRdoubleQ.R' 'get_rank.R' 'get_rho.R'
'get_scores.R' 'get_treated_data.R' 'get_unidim.R'
'get_weights.R' 'get_weights_nonmetric.R' 'innerplot.R'
'outerplot.R' 'plot.plspm.R' 'rescale.R' 'summary_plspm.R'
'test_manifest_scaling.R' 'test_null_weights.R'
'unidimensionality.R' 'test_factors.R' 'russett-data.R'
'plspm.fit.R' 'plspm.groups.R' 'test_dataset.R' 'get_GQI.R'
'get_locals_test.R' 'get_scaled_data.R' 'it.reb.R'
'local.models.R' 'print.rebus.R' 'rebus.pls.R' 'rebus.test.R'
'res.clus.R' 'get_PLSR.R' 'get_PLSR_NA.R' 'quantiplot.R'
'plspm-package.R'
'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-package.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' 'href.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' 'subset.R'
'templates.R' 'terms.R' 'token_stream.R' 'tooltips.R'
'tree_structure.R' 'trim.R' 'type.R' 'use.R' 'utils.R' 'view.R'
'weigh.R' 'zzz.R'
'AAA_check_installed.R' 'AAA_pomdp-package.R' 'AAA_shorten.R'
'Cliff_walking.R' 'DynaMaze.R' 'POMDP.R' 'MDP.R'
'MDP_policy_functions.R' 'Maze.R' 'POMDP_file_examples.R'
'RcppExports.R' 'RussianTiger.R' 'Tiger.R' 'Windy_gridworld.R'
'accessors.R' 'accessors_reward.R' 'accessors_trans_obs.R'
'actions.R' 'add_policy.R' 'check_and_fix_MDP.R' 'colors.R'
'estimate_belief_for_nodes.R' 'foreach_helper.R' 'gridworld.R'
'make_partially_observable.R' 'optimal_action.R'
'plot_belief_space.R' 'plot_policy_graph.R' 'policy.R'
'policy_graph.R' 'print.text.R' 'projection.R' 'queue.R'
'reachable_and_absorbing.R' 'read_write_POMDP.R'
'read_write_pomdp_solve.R' 'regret.R' 'reward.R'
'round_stochchastic.R' 'sample_belief_space.R' 'simulate_MDP.R'
'simulate_POMDP.R' 'solve_MDP.R' 'solve_POMDP.R'
'solve_SARSOP.R' 'stack.R' 'transition_graph.R'
'update_belief.R' 'value_function.R' 'which_max_random.R'
'package.R' 'pstop.R' 'undefined.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' 'dinit_spec.R' 'templates.R' 'builder.R'
'pomp.R' 'probe.R' 'abc.R' 'accumulators.R' 'melt.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' 'bake.R' 'basic_components.R'
'basic_probes.R' 'betabinom.R' 'blowflies.R' 'bsflu.R'
'bsplines.R' 'childhood.R' 'coef.R' 'conc.R' 'concat.R'
'cond_logLik.R' 'covmat.R' 'dacca.R' 'design.R' 'ebola.R'
'eff_sample_size.R' 'elementary_algorithms.R' 'emeasure_spec.R'
'estimation_algorithms.R' 'eulermultinom.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' 'obs.R' 'ou2.R' 'parmat.R'
'parus.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' 'wquant.R'
'RcppExports.R' 'covariance_importance.R' 'prior-class.R'
'initial-class.R' 'model-class.R' 'fit-class.R'
'pompp-package.R' 'pompp.R'
'RcppExports.R' 'default_garch_spec.R' 'S4_classes.R'
'datadoc_and_rcpp.R' 'dvine_ordering.R' 'risk_measures.R'
'rcondvinecop.R' 'estimate_dependence_and_risk.R'
'estimate_marginal_models.R' 'estimate_risk_roll.R' 'utils.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'
'TransformationObjects.R' 'TransformationYeoJohnson.R'
'TransformationBoxCox.R' 'AccessorsMutatorsLambda.R'
'AccessorsMutatorsScale.R' 'AccessorsMutatorsShift.R'
'AccessorsTransformationMethod.R' 'Checks.R' 'FindParameters.R'
'GoodnessOfFit.R' 'ParameterEstimators.R'
'ParameterEstimatorEDF.R' 'ParameterEstimatorMLE.R'
'ParameterEstimatorRaymaekers.R'
'ParameterEstimatorSkewnessKurtosis.R' 'PlotQQPlot.R'
'PlotResidualPlot.R' 'PlotUtilities.R'
'TransformationSkeleton.R' 'Utilities.R' 'WeightingFunctions.R'
'WeightingFunctionParameters.R' 'power.transform-package.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'
'data.R' 'prais-package.R' 'prais_winsten.R' 'predict.prais.R'
'print.prais.R' 'summary.prais.R' 'print.summary.prais.R'
'pw_transform.R' 'vcovHC.R' 'vcovPC.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'
'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'
'bound_prediction.R' 'cal-apply-binary.R' 'cal-apply-impl.R'
'cal-apply-multi.R' 'cal-apply-regression.R' 'cal-apply.R'
'cal-estimate-beta.R' 'cal-estimate-isotonic.R'
'cal-estimate-linear.R' 'cal-estimate-logistic.R'
'cal-estimate-multinom.R' 'cal-estimate-utils.R'
'cal-estimate-none.R' 'cal-pkg-check.R' 'cal-plot-breaks.R'
'cal-plot-logistic.R' 'cal-plot-regression.R'
'cal-plot-utils.R' 'cal-plot-windowed.R' 'cal-utils.R'
'cal-validate.R' 'class-pred.R' 'conformal_infer.R'
'conformal_infer_cv.R' 'conformal_infer_quantile.R'
'conformal_infer_split.R' 'data.R'
'import-standalone-obj-type.R'
'import-standalone-types-check.R' 'make_class_pred.R'
'printing.R' 'probably-package.R' 'reexports.R'
'threshold_perf.R' 'utils.R' 'vctrs-compat.R' 'zzz.R'
'RcppExports.R' 'create_base_precedence.R' 'custom.R'
'deprecated.R' 'dotted_chart.R' 'dotted_char_plotly_i.R'
'dotted_chart_helpers.R' 'export_graph.R' 'export_map.R'
'frequency.R' 'get_meta_data.R' 'layout.R' 'lined_chart.R'
'lined_chart_helpers.R' 'lined_chart_plotly_i.R'
'performance.R' 'precedence_matrix.R'
'precedence_matrix.plot.R' 'processMapOutput.R' 'process_map.R'
'process_matrix.R' 'processmapR.R' 'renderProcessMap.R'
'render_map.R' 'resource_map.R' 'resource_matrix.R'
'trace_explorer.R' 'utils.R' 'utils_animateR.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'
'ProjectTemplate-package.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.project.rstudio.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' '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'
'StudySpecification.R' 'StudySpecificationAccessors.R'
'SandwichLayer.R' 'block_center_residuals.R'
'SandwichLayerVariance.R' 'StudySpecificationConverters.R'
'StudySpecificationStructure.R' 'StudySpecificationUtilities.R'
'WeightedStudySpecification.R' 'areg.center.R' 'confint_lm.R'
'teeMod.R' 'as.lmitt.R' 'as_data_frame.R' 'assigned.R'
'bread.R' 'c_weightedStudySpecification.R' 'cov_adj.R' 'data.R'
'dichotomy.R' 'expand.model.frame_tee.R' 'get_cov_adj.R'
'get_data_from_model.R' 'get_spec.R' 'glmrobMethods.R'
'lmitt.R' 'lmrob_methods.R' 'merge_preserve_order.R'
'propertee_package.R' 'specification_table.R'
'summary.StudySpecification.R' 'summary.teeMod.R' 'update_by.R'
'validWeights.R' 'weights_internal.R' 'weights_exported.R'
'RcppExports.R' 'import.R' 'loading_functions.R'
'post_functions.R' 'core_functions.R' 'datasets.R' 'example.R'
'helper_functions.R' 'linking_functions.R'
'parameterization_functions.R' 'plot_functions.R'
'preanalysis_functions.r' 'runshiny.R'
'ProTrackR2-package.R' 'cell.R' 'command.R' 'cpp11.R' 'data.R'
'demo.R' 'helpers.R' 'instrument.R' 'io.R' 'mod_info.R'
'modplug.R' 'note.R' 'pattern.R' 'render.R' 's3.R' 'samples.R'
'select_ops.R' 'validate.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'
'zzz.R' 'add_cov.R' 'add_time.R' 'add_mcmc.R' 'apply_mcmc.R'
'simu_cov.R' 'match_cov.R' 'simu_time.R' 'run_mcmc.R'
'get_summary.R' 'utils.R'
'generics.R' 'prior_class.R' 'covariate_class.R'
'add_covariates.R' 'prior_normal.R' 'treatment_class.R'
'borrowing_class.R' 'outcome_class.R' 'analysis_class.R'
'borrowing_details.R' 'borrowing_full.R'
'borrowing_hierarchical_commensurate.R' 'borrowing_none.R'
'cast_mat_to_long_pem.R' 'check_data_matrix_has_columns.R'
'cmdstan.R' 'create_analysis_obj.R' 'create_data_matrix.R'
'treatment_details.R' 'sim_treatment_list.R' 'helpers.R'
'outcome_bin_logistic.R' 'prior_exponential.R'
'outcome_surv_weibull_ph.R' 'outcome_surv_exponential.R'
'sim_outcome_list.R' 'sim_borrowing_list.R'
'sim_covariate_list.R' 'sim_data_list.R' 'simulation_class.R'
'create_simulation_obj.R' 'data.R'
'get_prior_string_covariates.R' 'prior_half_cauchy.R'
'outcome_cont_normal.R' 'outcome_surv_pem.R'
'load_and_interpolate_stan_model.R' 'load_stan_file.R'
'make_analysis_object_list.R' 'mcmc_sample.R'
'mcmc_simulation_result.R' 'package.R'
'prepare_stan_data_inputs.R' 'prior_bernoulli.R' 'prior_beta.R'
'prior_cauchy.R' 'prior_gamma.R' 'prior_half_normal.R'
'prior_poisson.R' 'sim_covariates.R' 'sim_estimate_bias.R'
'sim_estimate_effect_variance.R' 'sim_estimate_mse.R'
'sim_is_null_effect_covered.R' 'sim_is_true_effect_covered.R'
'sim_samplesize.R' 'simulate_data_baseline.R' 'simulate_data.R'
'simvar_class.R' 'trim_data_matrix.R' 'uniform_prior.R' 'zzz.R'
'MetSSS.R' 'MetSSS_model.R' 'methods.R' 'misc.R' 'classes.R'
'composite.R' 'onLoad.R' 'predictions.R'
PStrata-package.R PSFormula.R PStrataInfo.R survival.R prior.R
make_standata.R PSObject.R make_stancode.R PSSample.R PStrata.R
PSOutcome.R sim_data_normal.R sim_data_Cox.R PSContrast.R
'bayesfun.R' 'data.R' 'fake_data.R' 'logit_funs.R'
'particlefilter.R' 'pttstability_man.R' 'smapping_functions.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'
'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' 'pubtator3_function.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' 'utils.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' 'cnqgate.R'
'cqgate.R' 'export2qiskit.R' 'measure.R' 'phase_estimation.R'
'plot-qstate.R' 'qft.R' 'qsimulatR-package.R' 'swapgate.R'
'RcppExports.R' 'characteristicPolynomial.R' 'creation.R'
'evaluation.R' 'groebner.R' 'integrateOnSimplex.R' 'internal.R'
'qspray.R' 'qsprayDivision.R' 'queries.R' 'show.R'
'symmetricPolynomials.R' 'transformation.R' 'yacas.R'
'CppLcpFinder-class.R' 'CppNode-class.R' 'CppQuadtree-class.R'
'classes.R' 'generics.R' 'as_data_frame.R' 'as_foreign.R'
'as_raster.R' 'as_vector.R' 'copy.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' 'dr.R' 'llqrcv.R' 'llqr.R' 'cqs.R'
'RcppExports.R' 'tokenizers.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_chunk.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-package.R' 'quanteda_options.R'
'spacyr-methods.R' 'stopwords.R' 'summary.R' 'textmodel.R'
'textplot.R' 'texts.R' 'textstat.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_restore.R' 'tokens_sample.R'
'tokens_segment.R' 'tokens_select.R' 'tokens_split.R'
'tokens_subset.R' 'tokens_trim.R' 'tokens_xptr.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_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'
'queryBuilder-package.R' 'utils.R' 'operators.R'
'conditions.R' 'config.R' 'rules_and_groups.R'
'parse_results.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'
'quest_functions.R' 'psymet_functions.R'
'describes_functions.R' 'diary_functions.R' 'mia_functions.R'
'environment.R' 'plotting-classes.R' 'plotting-colours.R'
'plotting-helpers.R' 'plotting-other.R' 'plotting.R'
'quickPlot-classes.R' 'quickPlot-package.R' 'testing-helpers.R'
'zzz.R'
'ancestry_categories.R' 'cc.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' 'delay.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'
'parse_estimate.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'
'warnings.R' 'write_xlsx.R'
'QurvE-package.R' 'control_functions.R' 'data_parsers.R'
'dose-response-analysis.R' 'fluorescence_plots.R'
'fluorescence_summaries.R' 'fluorescence_workflows.R'
'group_tables.R' 'growth_plots.R' 'growth_summaries.R'
'growth_workflows.R' 'linear_fits.R' 'nonparametric_fits.R'
'parametric_fits.R' 'utils.R' 'report_functions.R'
'shiny_app_functions.R'
'CDM.R' 'IndexBeta.R' 'IndexPriority.R' 'IndexPVAF.R'
'IndexR2.R' 'MLR.R' 'MLRlasso.R' 'Mmatrix.R' 'QvalBeta.R'
'QvalGDI.R' 'QvalHull.R' 'QvalMLRB.R' 'QvalWald.R'
'QvalIndex.R' 'QvalValidation.R' 'RcppExports.R' 'Rmatrix.R'
'S3Extract.R' 'S3Plot.R' 'S3Print.R' 'S3Summary.R' 'S3Update.R'
'sim.Q.R' 'sim.MQ.R' 'sim.data.R' 'convex.R' 'fit.R'
'Wald.test.R' 'utils.R' 'zzz.R'
'location.R' 'generics.R' 'element.R' 'slide.R'
'presentation.R' 'methods.R' 'utils.R' 'zzz.R'
'R6causal-package.R' 'R6causal_utils.R' 'R6causal.R'
'R6causal_examples.R' 'R6causal_counterfactual.R'
'R6causal_functions.R' 'R6causal_linear.R'
'aem.R' 'areasink.R' 'constant.R' 'flow-variables.R'
'linesink.R' 'tracelines.R' 'plot.R' 'state-variables.R'
'uniformflow.R' 'utils.R' 'well.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' 'pbrPar.R' 'rarPar.R' 'randSeq.R'
'rarSeq.R' 'GSD-Bias.R' 'abcdPar.R' 'abcdSeq.R'
'getParameters.R' 'getExpectation.R' 'survEndp.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' 'maccombo.R'
'mpPar.R' 'mpSeq.R' 'pbrSeq.R' 'probUnDes.R' 'rtbdSeq.R'
'rpbrSeq.R' 'randomBlockSeq.R' 'randomizeROverview.R'
'randomizeRPackage.R' 'rpbrPar.R' 'tbdPar.R' 'rtbdPar.R'
'saveAssess.R' 'saveRand.R' 'stratifiedAnalysis.R' 'tbdSeq.R'
'udPar.R' 'udSeq.R'
'RcppExports.R' 'raptr-internal.R' 'generics.R'
'DemandPoints.R' 'misc.R' 'PlanningUnitPoints.R'
'AttributeSpace.R' 'AttributeSpaces.R' 'GurobiOpts.R'
'ManualOpts.R' 'calcSpeciesAverageInPus.R' 'calcBoundaryData.R'
'RapData.R' 'RapReliableOpts.R' 'RapResults.R'
'RapUnreliableOpts.R' 'RapUnsolved.R' 'RapSolved.R'
'convert2PolySet.R' 'data.R' 'deprecated.R' 'package.R' 'rap.R'
'rrap.proportion.held.R' 'sim.pus.R' 'sim.space.R'
'sim.species.R' 'urap.proportion.held.R' 'zzz.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' 'deprecated.R'
'brar_select_au_binary.r' 'brar_select_au_known_var.r'
'brar_select_au_unknown_var.r' 'convert_gamma_to_chisq.r'
'convert_chisq_to_gamma.r' 'update_par_nichisq.r'
'pgreater_beta.r' 'pgreater_normal.r' 'pgreater_NIX.r'
'pmax_beta.r' 'pmax_normal.r' 'pmax_NIX.r' 'sim_brar_binary.r'
'sim_brar_known_var.r' 'sim_brar_unknown_var.r'
'flgi_cut_off_binary.r' 'flgi_cut_off_known_var.r'
'flgi_cut_off_unknown_var.r' 'Gittins.r' 'sim_flgi_binary.r'
'sim_flgi_known_var.r' 'sim_flgi_unknown_var.r'
'dabcd_max_power.r' 'dabcd_min_var.r' 'sim_RPTW.r'
'sim_A_optimal_known_var.r' 'sim_A_optimal_unknown_var.r'
'sim_Aa_optimal_known_var.r' 'sim_Aa_optimal_unknown_var.r'
'sim_dabcd_max_power.r' 'sim_dabcd_min_var.r'
'sim_RSIHR_optimal_known_var.r'
'sim_RSIHR_optimal_unknown_var.r' 'functions.r' 'RAR-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'
'RcppExports.R' 'creation.R' 'evaluation.R' 'internal.R'
'ratioOfQsprays.R' 'queries.R' 'show.R' 'transformation.R'
'types.R' 'fields.R' 'registry.R' 'template.R' 'meta.R'
'read-marketdata.R' 'fetch-marketdata.R'
'download-marketdata.R' 'scraper-futures.R'
'scraper-cotahist.R' 'scraper-yc.R' 'scraper-indexes.R'
'readers.R' 'downloaders.R' 'rb3-package.R' 'zzz.R'
'rbcb-package.R' 'rbcb_get.R' 'sgs.R' 'datasets.R' 'addins.R'
'rbcb_get_series.R' 'currency_helpers.R' 'utils.R'
'rbcb_get_currencies.R' 'odata-expectativas.R' 'odata_ptax.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'
'RcmdrPlugin.KMggplot2-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'
'classes.r' 'estimate_feature_score.r' 'estimate_nkr.r'
'correct_gc_bias.r' 'RCNA.r' 'data.R'
'CompadreDB.R' 'Rcompadre-package.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_elementwise_apply.R' 'mpm_mean.R' 'mpm_methods.R'
'mpm_sd.R' 'mpm_median.R' 'string_representation.R' 'zzz.R'
'RcppExports.R' 'TROLLv3_climatedaytime12.R'
'TROLLv3_daytimevar.R' 'TROLLv3_input.R' 'TROLLv3_output.R'
'TROLLv3_pointcloud.R' 'TROLLv3_species.R' 'zzz.R'
'TROLLversion.R' 'trollsim.R' 'load_output.R' 'troll.R'
'autogif.R' 'get_chm.trollsim.R' 'trollstack.R'
'autoplot.troll.R' 'utils-pipe.R' 'generate_climate.R'
'generate_lidar.R' 'generate_parameters.R'
'get_forest.trollsim.R' 'get_log.trollsim.R' 'load_stack.R'
'load_sim.trollsim.R' 'print.trollsim.R' 'rcontroll-package.R'
'rcontroll.R' 'stack.R' 'update_parameters.trollsim.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
'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'
'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'
'C4_Ready4Module.R' 'C4_Ready4Private.R' 'C4_Ready4Public.R'
'fn_add.R' 'fn_bind.R' 'fn_get.R' 'fn_make.R' 'fn_print.R'
'fn_remove.R' 'fn_rowbind.R' 'fn_transform.R' 'fn_update.R'
'fn_write.R' 'grp_generics.R' 'imp_fns.R' 'imp_mthds.R'
'mthd_authorSlot.R' 'mthd_characterizeSlot.R'
'mthd_depictSlot.R' 'mthd_enhanceSlot.R' 'mthd_exhibitSlot.R'
'mthd_ingestSlot.R' 'mthd_investigateSlot.R'
'mthd_manufactureSlot.R' 'mthd_metamorphoseSlot.R'
'mthd_procureSlot.R' 'mthd_prognosticateSlot.R'
'mthd_ratifySlot.R' 'mthd_reckonSlot.R' 'mthd_renewSlot.R'
'mthd_shareSlot.R' 'pkg_ready4.R' 'ready4-package.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-package.R' 'show.R' 'simEvent.R' 'summary.R' 'zzz.R'
'REDCapCAST-package.R' 'utils.r' 'process_user_input.r'
'REDCap_split.r' 'as_factor.R' 'as_logical.R' 'doc2dd.R'
'ds2dd_detailed.R' 'easy_redcap.R' 'export_redcap_instrument.R'
'fct_drop.R' 'html_styling.R' 'mtcars_redcap.R'
'read_redcap_instrument.R' 'read_redcap_tables.R'
'redcap_wider.R' 'redcapcast_data.R' 'redcapcast_meta.R'
'shiny_cast.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' 'content-data.R' 'COVID19-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' 'reexports.R'
'report-package.R' 'report.BFBayesFactor.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.loo.R' 'report.compare_performance.R'
'report.coxph.R' 'report.data.frame.R' 'report.default.R'
'report.estimate_contrasts.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_htest_chi2.R'
'report_htest_cor.R' 'report_htest_fisher.R'
'report_htest_friedman.R' 'report_htest_kruskal.R'
'report_htest_ttest.R' 'report_htest_wilcox.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_s.R' 'report_sample.R' 'report_statistics.R'
'report_table.R' 'utils_error_message.R' 'utils_grouped_df.R'
'utils_misspelled_variables.R' 'zzz.R'
'helper.R' 'main.R' 'globals.R' 'data.R' 'lblCode.R'
'rm_compactsum.R' 'rm_uvsum2.R' 'autoreg.R' 'autosum.R'
'getVarLevels.R' 'rm_mvsum2.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' 'zzz_onload.r'
'DBI.R' 'cache-helpers.R' 'cache-internals.R' 'robustDigest.R'
'cache.R' 'cacheGeo.R' 'checksums.R' 'cloud.R' 'convertPaths.R'
'copy.R' 'download.R' 'messages.R' 'exportedMethods.R' 'gis.R'
'helpers.R' 'listNamed.R' 'objectSize.R' 'options.R'
'packages.R' 'paths.R' 'pipe.R' 'postProcess.R'
'postProcessTo.R' 'preProcess.R' 'prepInputs.R'
'reproducible-deprecated.R' 'reproducible-package.R' 'search.R'
'showCacheEtc.R' 'spatialObjects-class.R' 'terra-migration.R'
'zzz.R'
'CRAN.R' 'Require-helpers.R' 'Require-package.R' 'messages.R'
'Require2.R' 'RequireOptions.R' 'envs.R' 'extract.R'
'helpers.R' 'pak.R' 'pkgDep.R' 'pkgDep3.R' 'pkgSnapshot.R'
'setLibPaths.R' 'setup.R' 'zzz.R'
'RcppExports.R' 'fit_util.R' 'distribution_class.R' 'zzz.R'
'interval.R' 'aaa.R' 'blended_transition.R'
'callback_adaptive_lr.R' 'callback_debug_dist_gradients.R'
'check_lengths.R' 'compiler.R' 'dist_bdegp.R' 'dist_beta.R'
'dist_binomial.R' 'fit_blended.R' 'dist_blended.R'
'dist_dirac.R' 'dist_discrete.R' 'dist_empirical.R'
'fit_erlang_mixture.R' 'dist_erlangmix.R' 'dist_exponential.R'
'dist_gamma.R' 'gpd.R' 'dist_genpareto.R' 'dist_lognormal.R'
'fit_mixture.R' 'dist_mixture.R' 'dist_negbinomial.R'
'dist_normal.R' 'pareto.R' 'dist_pareto.R' 'dist_poisson.R'
'dist_translate.R' 'dist_trunc.R' 'dist_uniform.R'
'dist_weibull.R' 'distribution_generics.R'
'distribution_methods.R' 'flatten_params.R' 'integrate.R'
'plot_distributions.R' 'prob_report.R' 'reservr-package.R'
'softmax.R' 'tf_compile.R' 'tf_compile_loss.R' 'tf_constants.R'
'tf_fit.R' 'tf_initialise.R' 'tf_util.R'
'trunc_erlangmix_init.R' 'trunc_obs.R' 'truncate_claims.R'
'weighted_stats.R'
'internal_functions.R' 'summary.resi.R' 'resi.R' 'resi_pe.R'
'Anova.resi.R' 'data.R' 'chisq2S.R' 'f2S.R' 't2S.R' 'z2S.R'
'Rsq2S.R' 'S2Rsq.R' 'S2d.R' 'S2fsq.R' 'S2z.R' 'S2chisq.R'
'd2S.R' 'fsq2S.R' 'plot.R' 'ggplot.R' 'print.R' 'omnibus.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
'internal.R' 'add_available_areas_constraint.R'
'add_compactness_constraint.R' 'add_components_constraint.R'
'add_connected_constraint.R' 'add_locked_out_constraint.R'
'add_min_iic_constraint.R' 'add_min_mesh_constraint.R'
'add_nb_patches_constraint.R' 'add_no_new_patch_constraint.R'
'add_restorable_constraint.R' 'add_settings.R'
'is_java_available.R' 'package.R' 'preprocessing.R'
'restopt_component.R' 'restopt_problem.R' 'restopt_solution.R'
'set_max_iic_objective.R' 'set_max_mesh_objective.R'
'set_max_nb_pus_objective.R' 'set_max_restore_objective.R'
'set_min_nb_patches_objective.R' 'set_min_nb_pus_objective.R'
'set_min_restore_objective.R' 'set_no_objective.R' 'solve.R'
'terra_io.R' 'terra_utils.R' 'utils-pipe.R' 'zzz.R'
'adf_est.R' 'adf_gof.R' 'adf_properties.R' 'bootstrap.R'
'composite_likelihood.R' 'heffernantawn.R'
'marginal_transform.R' 'min_projection_hill.R' 'rc_exp.R'
'rc_est.R' 'rc_gof.R' 'rc_uncertainty.R'
'ReturnCurves-package.R' 'airdata.R' 'runShiny.R' 'marggpd.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'
'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
'zzz.R' 'cry_demo.R' 'dp_demo.R' 'getCIF.R' 'align.R'
'select.R' 'rgl.cry-package.R'
gspaceChecks.R gspaceValidation.R gspaceSupplements.R
gspaceMisc.R gspacePlots.R gspaceClasses.R gspaceGenerics.R
gspaceMethods.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_crisk.R' 'plot_plane.R' 'plot_tree.R'
'plot_mosaic.R' 'data.R' 'read_data.R' 'riskyr_class.R'
'riskyr_sims.R' 'FFTrees_riskyr.R' 'start_riskyr.R'
readJSON.R asVars.R isValid.R json.R simpleHandler.R stream.R
'logging.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' 'textMetadata.R'
'timer.R' 'train_algo.R' 'utils.R' 'escalation_rule.R'
'rl_dnn_config.R' 'rl_config_set.R' 'compute_rl_scenarios.R'
'learn_escalation_rule.R' 'setup_python.R' 'zzz.R'
'simulate_one_trial.R'
'timer.R' 'train_algo.R' 'utils.R' 'allocation_rule.R'
'generate_setup_code.R' 'rl_dnn_config.R' 'rl_config_set.R'
'learn_allocation_rule.R' 'setup_python.R' 'zzz.R'
'simulate_one_trial.R' 'adjust_significance_level.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
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 cluster_distance.R data.R read_maczek.R
'MariaDBConnection.R' 'MariaDBDriver.R' 'MariaDBResult.R'
'RMariaDB-package.R' 'coerce.R' 'compatRowNames.R' 'connect.R'
'cpp11.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'
'dbQuoteLiteral_MySQLConnection.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' 'query.R' 'quote-mysql.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'
'AllClasses.R' 'associate.R' 'crowding_distance.R' 'data.R'
'generate_reference_points.R' 'geneticoperator.R'
'get_fixed_rowsum_integer_matrix.R' 'miscfun.R' 'AllGenerics.R'
'niching.R' 'non_dominated_fronts.R' 'nsga.R' 'nsga2.R'
'nsga3.R' 'nsgaControl.R' 'performance_metrics.R'
'reference_point_multi_layer.R' 'rmoo.R' 'rmoo_func.R'
'sharing.R' 'update_points.R' 'zzz.R'
'Limits.R' 'CovEst.R' 'CovTest.R' 'GeneralMP.R'
'GeneralWishartMax.R' 'SignalPlusNoise.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'
'rnaCrosslinkOO.R' 'rnaCrosslinkDataSet.R'
'clusterrnaCrosslink.R'
'clusterrnaCrosslinkMethodsAndHelpers.R'
'commonHelpersAndMethods.R' 'commonStatsAndPlots.R'
'foldrnaCrosslink.R' 'foldrnaCrosslinkMethodsAndHelpers.R'
'genericMethods.R' 'rnaCrosslinkDataSetMethodsAndHelpers.R'
'rnaCrosslinkOO-package.R' 'rnaCrosslinkQC.R'
'auth.R' 'helpers.R' 'rnassqs-package.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'
'aurnmf.r' 'gaurnmf.r' 'giqpm.r' 'murnmf.r' 'rnnmf-package.r'
'CLUSexample.R' 'buildSimList.R' 'buildSnapRoads.R'
'demoScen.R' 'getClosestRoad.R' 'getDistFromSource.R'
'getGraph.R' 'weightFunctions.R' 'getLandingsFromTarget.R'
'lcpList.R' 'mstList.R' 'pathsToLines.R' 'projectRoads.R'
'rasterToLineSegments.R' 'shortestPaths.R' 'plotRoads.R'
'rasterizeLine.R' 'prepExData.R' 'roads-package.R'
'dem_example.R'
robis.R util.R area.R checklist.R dataset.R dna.R map.R mof.R
node.R occurrence.R taxon.R
'RcppExports.R' 'checkTypes.R' 'regressionInit.R'
'initRoboBayes.R' 'verifyAnalysisSettings.R' 'roboBayes.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'
'generateAnovaDatasets.R' 'generateMixedEffectDatasets.R'
'generateSensitivityCurveDatasets.R' 'manageDatasets.R'
'fitDatasets.R' 'processFit.R' 'processFile.R'
'simulationStudies.R' 'asymptoticEfficiency.R' 'emmeans.R'
'LR.R' 'multishift.R' 'M.MSH.sampler.R' 'CFTP.R'
'ROCFTP.MMS.R'
'ROCket.R' 'generics.R' 'auc.R' 'mwu.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'
'round.R' 'ieee-754.R' 'utils.R' 'reround-to-fraction.R'
'round-ceil-floor.R' 'reround.R' 'rounding-bias.R' 'unround.R'
'utils-pipe.R'
'RcppExports.R' 'f_logger.R' 'class_dictionary.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_core_plot.R' 'class_design.R' 'f_object_r_code.R'
'f_analysis_base.R' 'class_analysis_dataset.R'
'class_analysis_stage_results.R' 'class_analysis_results.R'
'f_design_general_utilities.R' 'class_time.R'
'class_design_set.R' 'class_design_plan.R'
'class_design_power_and_asn.R' 'class_event_probabilities.R'
'f_simulation_base_counts.R' 'f_simulation_utilities.R'
'f_simulation_base_survival.R' 'class_simulation_results.R'
'class_performance_score.R' 'class_summary.R' 'data.R'
'f_analysis_base_means.R' 'f_analysis_base_rates.R'
'f_analysis_base_survival.R'
'f_analysis_boundary_recalculation.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_as251.R'
'f_core_output_formats.R' 'f_design_fisher_combination_test.R'
'f_design_group_sequential.R' 'f_design_plan_counts.R'
'f_design_plan_means.R' 'f_design_plan_plot.R'
'f_design_plan_rates.R' 'f_design_plan_survival.R'
'f_design_plan_utilities.R' 'f_quality_assurance.R'
'f_simulation_base_means.R' 'f_simulation_base_rates.R'
'f_simulation_calc_subjects_function.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'
'f_simulation_performance_score.R' 'f_simulation_plot.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'
'GOFmetrics.R' 'aaa.R' 'RPhosFate.R' 'RcppExports.R'
'assertives.R' 'gettersSetters.R' 'input.R' 'interface.R'
'utils.R' 'zzz.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'
'Redshift.R' 'cpp11.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' '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' 'PrestoSession.R'
'PrestoConnection.R' 'PrestoQuery.R' 'PrestoResult.R'
'RPresto-package.R' 'chunk.R' 'create.dummy.tables.R' 'cte.R'
'dbAppendTable.R' 'dbClearResult.R' 'dbConnect.R'
'dbCreateTable.R' 'dbCreateTableAs.R' 'dbDataType.R'
'dbDisconnect.R' 'dbExistsTable.R' 'dbFetch.R' 'dbGetInfo.R'
'dbGetQuery.R' 'dbGetRowCount.R' 'dbGetRowsAffected.R'
'dbGetStatement.R' 'dbHasCompleted.R' 'dbIsValid.R'
'dbListFields.R' 'dbListTables.R' 'dbQuoteIdentifier.R'
'dbQuoteLiteral.R' 'dbReadTable.R' 'dbRemoveTable.R'
'dbRenameTable.R' 'dbSendQuery.R' 'dbUnloadDriver.R'
'dbWriteTable.R' 'dbplyr-db.R' 'dbplyr-sql.R' 'dbplyr-src.R'
'default.R' 'fetch.R' 'presto.field.R'
'presto.field_utilities.R' 'request_headers.R'
'sqlCreateTable.R' 'sqlCreateTableAs.R' 'zzz.R'
'rqti.R' 'QtiMetadata.R' 'ModalFeedback.R' 'AssessmentItem.R'
'AssessmentSection.R' 'AssessmentTest.R' 'AssessmentTestOpal.R'
'Choice.R' 'CorrectFeedback.R' 'MatchTable.R' 'DirectedPair.R'
'Entry.R' 'Essay.R' 'Gap.R' 'InlineChoice.R' 'LMS.R'
'MultipleChoice.R' 'MultipleChoiceTable.R' 'NumericGap.R'
'OneInColTable.R' 'OneInRowTable.R' 'Opal.R' 'Ordering.R'
'SingleChoice.R' 'TextGap.R' 'TextGapOpal.R' 'WrongFeedback.R'
'character.R' 'extract_results.R' 'helpers.R'
'knit_functions.R' 'object_builder.R' 'qti_task.R' 'qti_test.R'
'response_processing.R' 'rqti_project.R' 'section_builder.R'
'utils-pipe.R' 'zzz.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' 'dem.R' 'study_area.R'
'add2rtoi.R' 'api_dataspace.R' 'api_lpdaac.R' 'api_usgs.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' '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 readDatamlx.R pkbuild.R pkpopini.R
whichPKmodel.R
utils.R pminmax.R Context-class.R DocCollection-class.R
Expression-class.R Facets-class.R FieldInfo-class.R
FieldType-class.R Promise-class.R SolrExpression-class.R
SolrQuery-class.R SolrSchema-class.R SolrCore-class.R
SolrResult-class.R SolrSummary-class.R Solr-class.R
SolrList-class.R SolrFrame-class.R SolrPromise-class.R
GroupedSolrFrame-class.R test.R zzz.R
'SQLiteConnection.R' 'SQLKeywords_SQLiteConnection.R'
'SQLiteDriver.R' 'SQLite.R' 'SQLiteResult.R' 'coerce.R'
'compatRowNames.R' 'copy.R' 'cpp11.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_Id.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'
'import-standalone-check_suggested.R'
'import-standalone-purrr.R' 'initExtension.R' 'initRegExp.R'
'isSQLKeyword_SQLiteConnection_character.R'
'make.db.names_SQLiteConnection_character.R' 'pkgconfig.R'
'show_SQLiteConnection.R' 'sqlData_SQLiteConnection.R'
'table.R' 'transactions.R' 'utils.R' 'version.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
apiTools.R apiManager.R utils.R data.R RsSimulx-deprecated.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-package.R' 'RcppExports.R' 'S4VM.R' 'SVM.R'
'SelfLearning.R' 'TSVM.R' 'USMLeastSquaresClassifier.R'
'WellSVM.R' 'scaleMatrix.R' 'svmd.R' 'svmlin.R'
'testdata-data.R'
'cql2-expr-funs.R' 'cql2-types.R' 'parse-utils.R'
'cql2-core.R' 'cql2-json.R' 'cql2-text.R' 'cql2-adv_comp.R'
'cql2-funs.R' 'cql2-env.R' 'cql2-utils.R' 'assets-utils.R'
'assets-funs.R' 'check-utils.R' 'conformance-query.R'
'collections-funs.R' 'collections-query.R' 'deprec-funs.R'
'doc-funs.R' 'ext_filter.R' 'ext_query.R' 'extensions.R'
'geom-funs.R' 'items-funs.R' 'items-utils.R' 'items-query.R'
'message-utils.R' 'preview-utils.R' 'print.R' 'query-funs.R'
'queryables-query.R' 'request.R' 'signatures.R' 'stac-query.R'
'search-query.R' 'stac-funs.R' 'static-funs.R' 'url-utils.R'
'utils.R' 'rstac.R' 'rstac-funs.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' 'backup.R'
'chart.R' 'chartCriteria.R' 'chartData.R' 'chartDisagg.R'
'chartErrorBar.R' 'chartId.R' 'chartInclude.R'
'chartOptionsFont.R' 'chartOptionsFormat.R'
'chartOptionsLegend.R' 'chartOptionsXAxis.R'
'chartOptionsYAxis.R' 'command.R' 'condaFilepath.R'
'createCondaEnv.R' 'datasheet.R' 'datasheetSpatRaster.R'
'dateModified.R' 'delete.R' 'deleteLibrary.R' 'dependency.R'
'description.R' 'filepath.R' 'folder.R' 'folderId.R'
'ignoreDependencies.R' 'info.R' 'installConda.R'
'installPackage.R' 'internalHelpers.R' 'viewProfile.R'
'packages.R' 'name.R' 'scenarioId.R' 'projectId.R'
'sqlStatement.R' 'scenario.R' 'project.R' 'ssimLibrary.R'
'session.R' 'internalWrappers.R' 'mergeDependencies.R'
'owner.R' 'parentId.R' 'print.R' 'printCmd.R' 'readOnly.R'
'removePackage.R' 'rsyncrosim.R' 'run.R' 'runLog.R'
'saveDatasheet.R' 'signin.R' 'signout.R' 'silent.R'
'ssimEnvironment.R' 'uninstallPackage.R' 'useConda.R'
'version.R'
'00tabletrees.R' 'Viewer.R' 'argument_conventions.R'
'as_html.R' 'utils.R' 'colby_constructors.R'
'compare_rtables.R' 'format_rcell.R' 'indent.R'
'make_subset_expr.R' 'custom_split_funs.R'
'default_split_funs.R' 'make_split_fun.R' 'summary.R'
'package.R' 'tree_accessors.R' 'tt_afun_utils.R' 'tt_as_df.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' 'validate_table_struct.R'
'zzz_constants.R'
'add_flextable_separators.R' 'as_flextable.R'
'export_as_docx.R' 'package.R' 'theme_defaults.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
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
'Construct.R' 'Plot.R' 'SetOperations.R' 'RVenn.R'
'Enrichment.R' 'Pairs.R' 'Heatmap.R'
'methods_rxylib.R' 'rxylib-package.R' 'RcppExports.R'
'read_xyData.R' 'convert_xy2TKA.R'
'zzz.R' 'utils.R' 's3filesystem_class.R' 'file_system.R'
'file_system_async.R' 'reexport_fs.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'
'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' 'SaemixData-methods.R'
'SaemixData-methods_covariates.R' 'SaemixModel.R' 'SaemixRes.R'
'SaemixObject.R' 'backward.R' 'compute_LL.R' 'forward.R'
'func_FIM.R' 'func_aux.R' 'func_bootstrap.R' 'func_compare.R'
'func_discreteVPC.R' 'func_distcond.R' 'func_estimParam.R'
'func_exploreData.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-data.R' 'internal-functions.R'
'internal-utils.R' 'internal-validation.R' 'locate.R' 'map.R'
'mortality.R' 'pairwise.R' 'rasterize.R' 'samc.R' 'survival.R'
'zzz.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 velocity.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' 'utils_shiny.R'
'Request.R' 'RequestResult.R' 'Rule.R' 'URL.R'
'request_fcts.R' 'Scheduler.R' 'package.R' 'rule_fcts.R'
'imports.R' 'biomarker.R' 'SHUM.R' 'EHUM.R' 'ULBA.R' 'SCOR.R'
'SCOR-package.R' 'optimized_HUM.R' 'youden_points.R'
'YoudenBoxPlot.R'
'is-numeric-like.R' 'import-reexport.R' 'utils.R'
'mapper-function-helpers.R' 'audit-cols-minimal.R' 'audit.R'
'baseline-consistency-tests.R' 'before-inside-parens.R'
'function-factory-helpers.R' 'round-ceil-floor.R' 'round.R'
'reround.R' 'unround.R' 'sd-binary.R' 'decimal-places.R'
'debit-table.R' 'debit.R' 'grim.R' 'function-map.R' 'grimmer.R'
'grimmer-map.R' 'duplicate-detect.R' 'debit-map.R'
'restore-zeros.R' 'seq-decimal.R' 'manage-extra-cols.R'
'grim-map.R' 'data-doc.R' 'data-frame-predicates.R'
'seq-predicates.R' 'function-map-seq.R' 'debit-map-seq.R'
'disperse.R' 'function-map-total-n.R' 'debit-map-total-n.R'
'debit-plot.R' 'duplicate-count-colpair.R' 'duplicate-count.R'
'grim-granularity.R' 'grim-map-debug.R' 'grim-map-seq-debug.R'
'grim-map-seq.R' 'grim-map-total-n.R' 'grim-plot.R'
'grim-stats.R' 'grimmer-map-seq.R' 'grimmer-map-total-n.R'
'grimmer-rsprite2.R' 'metadata.R' 'method-audit-seq.R'
'method-audit-total-n.R' 'method-debit-map.R' 'method-detect.R'
'method-dup-count-colpair.R' 'method-dup-count.R'
'method-grim-map.R' 'method-grim-sequence.R'
'method-grimmer-map.R' 'method-tally.R' 'odds-ratio-map.R'
'reround-to-fraction.R' 'reverse-map-seq.R'
'reverse-map-total-n.R' 'rivets-perfect-mean-sd.R'
'rivets-plot-cols.R' 'rivets-plot-lines.R' 'rivets-t-test.R'
'rivets_new.R' 'rounding-bias.R' 'row-to-colnames.R'
'scrutiny-package.R' 'seq-disperse.R' 'seq-length.R'
'split-by-parens.R' 'subset-superset.R' 'utils-pipe.R'
'utils-tidy-eval.R' 'write-doc-audit.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'
'createDat.R' 'createNewID.R' 'dataGen.r' 'dataSets.R'
'dRisk.R' 'dRiskRMD.R' 'dUtility.R' 'freqCalc.r'
'globalRecode.R' 'groupAndRename.R' 'GUIfunctions.R'
'indivRisk.R' 'infoLoss.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' 'recordSwap.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' 'combineCV.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-Maxnet.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'
'auxiliary_functions.R' 'chirps.R' 'data.R' 'diagrams.R'
'ICPAC_temp.R' 'ncdf_to_dt.R' 'plotting.R' 'quantiles.R'
'scores.R' 'seasonal.R' 'SeaVal-package.R' 'spatial_grids.R'
'upscaling.R' 'utils.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'
'auxFUN.R' 'data.R' 'datesToDoY.R' 'fill_initialgap_MOD13Q1.R'
'getDist_phenoParam.R' 'getSpiralPlot.R' 'get_metadata_years.R'
'global_min_max.R' 'local_min_max.R' 'ndvi_derivatives.R'
'phenopar.R' 'phenopar_polygon.R' 'plot.R' 'sephora-class.R'
'sephora-methods.R' 'sephora-package.R' 'vecFromData.R'
'vecToMatrix.R'
'AAA_check_installed.R' 'AAA_color_palette.R' 'AAA_defaults.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'
'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'
'lines_and_ordered_data.R' 'lle.R' 'permute.R'
'register_DendSer.R' 'register_GA.R' 'register_optics.R'
'register_smacof.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.dist.R'
'seriate.matrix.R' 'seriate.array.R' 'seriate.data.frame.R'
'seriate.table.R' 'seriate_AOE.R' 'seriate_ARSA_Branch-Bound.R'
'seriate_TSP.R' 'seriate_BEA.R' 'seriate_BK.R' 'seriate_CA.R'
'seriate_GSA.R' 'seriate_HC.R' 'seriate_LLE.R' 'seriate_MDS.R'
'seriate_Mean.R' 'seriate_PCA.R' 'seriate_QAP.R'
'seriate_R2E.R' 'seriate_SGD.R' 'seriate_SPIN.R'
'seriate_VAT.R' 'seriate_best.R' 'seriate_enumerate.R'
'seriate_heatmap.R' 'seriate_identity.R' 'seriate_random.R'
'seriate_reverse.R' 'seriate_spectral.R' 'seriate_vegan.R'
'uniscale.R'
'data.R' 'mseir_model.R' 'sir_basic_model.R'
'sir_static_model.R' 'sir_subpops_model.R'
'fractional_polynomial_models.R' 'polynomial_models.R'
'utils.R' 'compare_models.R' 'correct_prevalence.R'
'weibull_model.R' 'nonparametric.R' 'semiparametric_models.R'
'mixture_model.R' 'hierarchical_bayesian_model.R' 'serosv.R'
'stanmodels.R' 'plots.R' 'compute_ci.R'
'RcppExports.R' 'reexports.R' 'generics.R' 'clustering.R'
'visualization.R' 'convenience.R' 'data.R'
'differential_expression.R' 'dimensional_reduction.R'
'integration.R' 'zzz.R' 'integration5.R' 'mixscape.R'
'objects.R' 'preprocessing.R' 'preprocessing5.R' 'roxygen.R'
'sketching.R' 'tree.R' 'utilities.R'
'RcppExports.R' 'zzz.R' 'generics.R' 'keymixin.R' 'graph.R'
'default.R' 'assay.R' 'logmap.R' 'layers.R' 'assay5.R'
'centroids.R' 'command.R' 'compliance.R' 'data.R' 'jackstraw.R'
'dimreduc.R' 'segmentation.R' 'molecules.R' 'spatial.R' 'fov.R'
'neighbor.R' 'seurat.R' 'sparse.R' 'utils.R'
'RcppExports.R' 'init.R' 'import-standalone-s3-register.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' 'sf-package.R' 'defunct.R' 'z_range.R'
'm_range.R' 'shift_longitude.R' 'make_grid.R' 's2.R' 'terra.R'
'geos-overlayng.R' 'break_antimeridian.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' 'overoptimism.r' 'sr_bias.r' 'tests.r'
'unified.r'
'Shazam.R' 'Core.R' 'RegionDefinitions.R' 'Baseline.R'
'ConvertNumbering.R' 'MutationProfiling.R' 'Deprecated.R'
'DistToNearest.R' 'MutationDefinitions.R' 'RegionsExtend.R'
'Shmulate.R' 'TargetingModels.R' 'kedd.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' 'busy-indicators-spinners.R'
'busy-indicators.R' 'cache-utils.R' 'deprecated.R' 'devmode.R'
'diagnose.R' 'extended-task.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' 'staticimports.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_jqueryui.R' 'version_selectize.R' 'version_strftime.R'
'viewer.R'
'shinyCohortBuilder-package.R' 'ui_utils.R' 'control_utils.R'
'actions.R' 'renders.R' 'filter_discrete.R' 'filter_range.R'
'filter_date_range.R' 'filter_discrete_text.R'
'filter_multi_discrete.R' 'filter_query.R' 'app.R' 'cb_layer.R'
'source.R' 'source_tblist.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'
'shinyloadtest-package.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'
'shinyQueryBuilder-package.R' 'query_builder_input.R'
'filters.R' 'operators.R' 'query_from_data.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-timeout.R'
'app-driver-upload-file.R' 'app-driver-variant.R'
'app-driver-wait.R' 'app-driver-window.R' 'app-driver.R'
'chromote-methods.R' 'compare-screenshot-threshold.R' 'cpp11.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'
'shinytest2-package.R' 'test-app.R' 'use.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'
'biomass.R' 'data.R' 'utils-not-exported.R'
'dendrometry-summary.R' 'dendrometry.R' 'globals.R'
'inventory.R' 'lidar.R' 'volume.R' 'zzz.R'
'C-generator.R' 'check_arguments.R' 'init.R' 'valid.R'
'classes.R' 'SimInf_model.R' 'SEIR.R' 'SIR.R' 'SIS.R' 'SISe.R'
'SISe3.R' 'SISe3_sp.R' 'SISe_sp.R' 'SimInf-package.R'
'SimInf.R' 'SimInf_events.R' 'SimInf_indiv_events.R' 'run.R'
'density_ratio.R' 'abc.R' 'degree.R' 'distance.R'
'distributions.R' 'edge_properties.R' 'match_compartments.R'
'mparse.R' 'n.R' 'openmp.R' 'package_skeleton.R' 'pfilter.R'
'plot.R' 'prevalence.R' 'print.R' 'punchcard.R' 'trajectory.R'
'u0.R' 'v0.R'
'0classes.R' 'addKnownMargins.R' 'addWeights.r' 'calibPop.R'
'calibSample.R' 'calibVars.R' 'contingencyWt.R' 'correctHeap.R'
'crossValidation.R' 'fitGPD.R' 'getBreaks.R' 'getCat.R' 'ipu.r'
'meanWt.R' 'printFunctions.R' 'quantileWt.R' 'sampHH.R'
'RcppExports.R' 'silcTools.R' 'silcTools2.R' 'simAnnealingDT.R'
'simCategorical.R' 'simComponents.R' 'simContinuous.R'
'simEUSILC.R' 'simGPD.R' 'simInitSpatial.R' 'simple_dis.R'
'simPop-package.R' 'simRelation.R' 'simStructure.R'
'spBwplot.R' 'spBwplotStats.R' 'spCdf.R' 'spCdfplot.R'
'spMosaic.R' 'spPredict.R' 'spSample.R' 'spTable.R'
'specifyInput.R' 'sprague.R' 'tableWt.R' 'utility.R' 'utils.R'
'whipple.R' 'dataSets.R' 'zzz.R'
'evals-class.R' 'output-class.R' 'component-class.R'
'model-class.R' 'draws-class.R' 'reference-classes.R'
'simulation-class.R' 'add-to-simulation.R' 'aggregator-class.R'
'utils.R' 'create.R' 'draws.R' 'evaluate.R' 'metric-class.R'
'method-class.R' 'examples.R' 'extended-method-class.R'
'get-from-simulation.R' 'import_from.R' 'load.R' 'manage.R'
'method-extension-class.R' 'methods.R' 'models.R'
'parallel-draws.R' 'parallel-methods.R' 'parallel.R'
'plot_eval.R' 'plot_eval_by.R' 'plot_evals.R' 'tables.R'
'zzz.R'
'windowed_sindy.R' 'sindy.R' 'features.R'
'finite_differences.R' 'finite_difference.R'
'lorenzattractor.R'
'deprecated.R' 'dplyr.R' 'stats.R' 'skim_with.R'
'get_skimmers.R' 'package.R' 'reshape.R' 'sfl.R' 'skim.R'
'skim_obj.R' 'skim_print.R' 'skimr-package.R' 'summary.R'
'utils.R' 'vctrs.R'
'arithmetic.R' 'block.R' 'conditions.R' 'hop-common.R'
'hop-index-common.R' 'hop-index.R' 'hop-index2.R' 'hop.R'
'hop2.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'
'utils.R' 'logs.R' 'code_build.R' 'builder.R' 'config.R'
'logging.R' 'sm_role.R' 'main.R' 'zzz.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-package.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
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 elide.R sp2Mondrian.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
'module-dependencies-class.R' 'misc-methods.R' 'environment.R'
'helpers.R' 'simList-class.R' 'times.R' 'simList-accessors.R'
'Plots.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-param-check.R'
'module-repository.R' 'module-template.R' 'moduleCoverage.R'
'moduleMetadata.R' 'objectSynonyms.R' 'options.R' 'paths.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'
'AllClasses.R' 'AllGenerics.R' 'SpatialDDLS.R' 'dnnModel.R'
'evalMetrics.R' 'interGradientsDL.R' 'loadData.R'
'plotSpatialCoor.R' 'simMixedSpots.R' 'simSingleCell.R'
'spatialClustering.R' 'utils.R'
'spatPomp_class.R' 'abf.R' 'abfir.R' 'arma_benchmark.R'
'get_covariate_names.R' 'as_data_frame.R' 'bm.R' 'bm2.R'
'bm2_kalman_logLik.R' 'bm_kalman_logLik.R' 'bpfilter.R'
'city_data_UK.R' 'iter_filter.R' 'safecall.R' 'pstop.R'
'undefined.R' 'spatPomp.R' 'ibpf.R' 'listie.R' 'conc.R'
'concat.R' 'dunit_measure.R' 'enkf.R' 'eunit_measure.R' 'gbm.R'
'girf.R' 'he10.R' 'he10coordinates.R' 'he10demography.R'
'he10measles.R' 'he10mle.R' 'ienkf.R' 'igirf.R' 'init.R'
'iubf.R' 'loglik.R' 'lorenz.R' 'measles.R' 'measles2.R'
'measlesUK.R' 'munit_measure.R' 'package.R' 'param_formats.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
'create_neig.r' 'extract_extrema.r' 'extrema_irr.r'
'mean_enveloppe.r' 'spemd.r'
'utils.R' 'spikeAndSlab.R' 'terms.R' 'ssGAMDesign.R'
'spikeSlabGAM.R' 'bugs.R' 'plot.R' 'predict.R' 'summary.R'
Swrappers.R peaks.R bits.per.integer.R doTest.R muS2RC.R
showStructure.R sigseriesS3.R sigseriesS4.R splus2R_is.R
splus2R_pkg.R
'SRTaffine.R' 'SRTcount.R' 'SRTfit.R' 'SRTmodels.R'
'SRTsim_class.R' 'body.R' 'compareSRT.R' 'createSRT.R' 'data.R'
'globals.R' 'reGenCountshiny.R' 'runapp.R' 'server.R'
'shiny2srt.R' 'sidebar.R' 'simSRTLocs.R' 'subsetSRT.R'
'SRTcci.R' 'ui.R' 'utilies_func.R' 'visualize_gene.R'
'visualize_metrics.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'
'optim.thresh.R' 'evaluate.R' 'modelling.R'
'ensemble_modelling.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'
'utils-pipe.R' 'zzz.R'
cte.R ExternalPackageInterface.R ToolsFct.R Interpolation.R
RegularInverse.R stableCF.R CFbasedMoment.R WeightingMatrix.R
eCFfirstZero.R tSchemes.R MultiDimIntegral.R InitialGuess.R
KoutParamsEstim.R MLParamsEstim.R GMMParamsEstim.R
CgmmParamsEstim.R OutputFileManip.R CheckPoint.R BestT_Class.R
Estim_Class.R Estim.R Simulation.R BestT.R
'create_generic.R' 'data.R' 'fun_table_per_dis.R'
'fun_write_monthly.R' 'fungraph.R' 'fungraph_glasseel.R'
'funschema.R' 'funstat.R' 'funtable.R' 'ref_choice.R'
'ref_coe.R' 'ref_dc.R' 'ref_df.R' 'ref_env.R' 'ref_horodate.R'
'ref_list.R' 'ref_par.R' 'ref_parqual.R' 'ref_parquan.R'
'ref_stage.R' 'ref_taxa.R' 'ref_textbox.R' 'ref_timestep.R'
'ref_timestep_daily.R' 'ref_year.R' 'report_annual.R'
'report_dc.R' 'report_df.R' 'utilities.R' 'report_env.R'
'report_ge_weight.R' 'report_ope.R' 'report_mig.R'
'report_sample_char.R' 'report_mig_char.R' 'report_mig_mult.R'
'report_mig_env.R' 'report_mig_interannual.R'
'report_sea_age.R' 'report_silver_eel.R' 'report_species.R'
'setAs.R' 'stacomi.R' 'stacomiR-package.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'
'aggregate.R' 'cubble.R' 'datasets.R' 'dimensions.R'
'extract.R' 'factors.R' 'geom.R' 'gt.R' 'init.R' 'intervals.R'
'mdim.R' 'mosaic.R' 'ncdf.R' 'ncproxy.R' 'OpenStreetMap.R'
'ops.R' 'plot.R' 'prcomp.R' 'proxy.R' 'rasterize.R' 'raster.R'
'read.R' 'rotate.R' 'sample.R' 'sf.R' 'spacetime.R'
'spatstat.R' 'sp.R' 'stars.R' 'subset.R' 'tidyverse.R' 'tile.R'
'transform.R' 'values.R' 'warp.R' 'write.R' 'xts.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_progress.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'
'NRepeat.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' 'snap_points_to_lines.R'
'space_restriction.R' 'stm.R' 'stp_learner.R'
'helpers.R' 'algorithms_1sided.R' 'algorithms_2sided.R'
'algorithms_of_other_authors.R' 'data.R' 'opt.R'
'stratallo-package.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-package.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'
'stylest2_select_vocab.R' 'stylest2_fit.R'
'stylest2_predict.R' 'data.R' 'stylest2.R'
'SUMO.R' 'compute_means_vars.R' 'demo_multiomics_analysis.R'
'divide_vector.R' 'divide_features_one.R'
'divide_features_two.R' 'divide_samples.R'
'divide_samples_alternative.R' 'feature_selection_one.R'
'feature_selection_two.R' 'globals.R' 'plot_factor.R'
'plot_simData.R' 'plot_weights.R' 'simulateMultiOmics.R'
'simulate_twoOmicsData.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'
'RcppExports.R' 'approx_evdsi.R' 'evdsi.R' 'internal.R'
'approx_near_optimal_survey_scheme.R'
'approx_optimal_survey_scheme.R' 'cluster.R' 'data.R' 'ilp.R'
'env_div_survey_scheme.R' 'evdci.R' 'feasible_survey_schemes.R'
'fit_hglm_occupancy_models.R' 'fit_xgb_occupancy_models.R'
'geo_cov_survey_scheme.R' 'greedy_heuristic_optimization.R'
'n_states.R' 'optimal_survey_scheme.R' 'package.R'
'prior_probability_matrix.R' 'relative_site_richness_scores.R'
'relative_site_uncertainty_scores.R' 'simulate_feature_data.R'
'simulate_site_data.R' 'validate.R' 'weighted_survey_scheme.R'
'zzz.R'
'betaEst.R' 'dataset.R' 'local_kernel.R' 'scoreNVCF.R'
'scoreLVCF.R' 'scoreHalf.R' 'scoreFull.R' 'preprocessInputs.R'
'kernelFixed.R' 'kernelAuto.R' 'fullKernel.R' 'halfKernel.R'
'lastValue.R' 'nearValue.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'
'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'
'JacobiPolynomial.R' 'RcppExports.R' 'creation.R'
'evaluation.R' 'internal.R' 'symbolicQspray.R' 'queries.R'
'show.R' 'symmetricPolynomials.R' 'transformation.R'
'RcppExports.R' 'basic-getinfo.R' 'classes.R' 'basic.R'
'codegen.R' 'double_visitor.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'
'coinertia.partial.syncsa.R' 'coinertia.syncsa.R'
'cor.matrix.R' 'cor.matrix.bf.R' 'cor.matrix.partial.R'
'cor.matrix.rao.R' 'matmult.syncsa.R' 'matrix.p.R' 'matrix.t.R'
'matrix.w.transformation.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.bf.R'
'pro.matrix.partial.R' 'pro.matrix.rao.R' 'procrustes.syncsa.R'
'procrustes.partial.syncsa.R' 'rao.diversity.R' 'rv.matrix.R'
'rv.matrix.bf.R' 'rv.matrix.partial.R' 'rv.matrix.rao.R'
'startup.R' 'syncsa.obs.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-joins.R'
'UTILS-misc.R' 'UTILS-nest_expr.R' 'UTILS-tidyselect.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'
'AllClasses.R' 'AllGenerics.R' 'coerce.R' 'data.R'
'diversity.R' 'diversity_alpha.R' 'diversity_beta.R'
'diversity_test.R' 'matrigraph.R' 'mutators.R' 'occurrence.R'
'plot_bertin.R' 'plot_diceleraas.R' 'plot_diversity.R'
'plot_ford.R' 'plot_heatmap.R' 'plot_matrix.R' 'plot_rank.R'
'plot_spot.R' 'rarefaction.R' 'reexport.R' 'resample.R'
'seriograph.R' 'show.R' 'similarity.R' 'statistics.R'
'subset.R' 'tabula-deprecated.R' 'tabula-internal.R'
'tabula-package.R' 'validate.R' 'zzz.R'
utils.R saving_and_loading_data.R graph.R tad.R data.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' 'levels.R' 'prune_levels.R'
'deprecated-functions.R' 'id_generator.R' 'replace_x.R'
'insert_rows.R' 'dissect_name.R' 'clean_strings.R'
'taxlist-class.R' 'matched_names-class.R' 'clean.R'
'coerce-methods.R' 'taxon_views.R' 'count_taxa.R'
'taxon_names.R' 'taxon_relations.R' 'add_concept.R'
'taxon_traits.R' 'accepted_name.R' 'get_children.R'
'merge_to_parent.R' 'merge_taxa.R' 'Extract.R' 'subset.R'
'backup_object.R' 'summary.R' 'df2taxlist.R' 'tv2taxlist.R'
'tax2traits.R' 'match_names.R' 'print_name.R' 'indented_list.R'
'Easplist-data.R' 'taxlist-package.R' 'taxlist2df.R'
'parents.R'
'TealAppDriver.R' 'checkmate.R' 'dummy_functions.R'
'include_css_js.R' 'modules.R' 'init.R'
'landing_popup_module.R' 'module_bookmark_manager.R'
'module_data_summary.R' 'module_filter_data.R'
'module_filter_manager.R' 'module_init_data.R'
'module_nested_tabs.R' 'module_session_info.R'
'module_snapshot_manager.R' 'module_teal.R'
'module_teal_data.R' 'module_teal_lockfile.R'
'module_teal_with_splash.R' 'module_transform_data.R'
'reporter_previewer_module.R' 'show_rcode_modal.R' 'tdata.R'
'teal.R' 'teal_data_module.R' 'teal_data_module-eval_code.R'
'teal_data_module-within.R' 'teal_data_utils.R'
'teal_modifiers.R' 'teal_reporter.R' 'teal_slices-store.R'
'teal_slices.R' 'teal_transform_module.R' 'utils.R'
'validate_inputs.R' 'validations.R' 'zzz.R'
'qenv-c.R' 'qenv-class.R' 'qenv-errors.R' 'qenv-concat.R'
'qenv-constructor.R' 'qenv-eval_code.R' 'qenv-extract.R'
'qenv-get_code.R' 'qenv-get_env.R' 'qenv-get_messages.r'
'qenv-get_outputs.R' 'qenv-get_var.R' 'qenv-get_warnings.R'
'qenv-join.R' 'qenv-length.R' 'qenv-show.R' 'qenv-within.R'
'teal.code-package.R' 'utils-get_code_dependency.R' 'utils.R'
'cdisc_data.R' 'data.R' 'formatters_var_labels.R'
'deprecated.R' 'dummy_function.R' 'join_key.R' 'join_keys-c.R'
'join_keys-extract.R' 'join_keys-names.R' 'join_keys-parents.R'
'join_keys-print.R' 'join_keys-utils.R' 'join_keys.R'
'teal.data.R' 'teal_data-class.R' 'teal_data-constructor.R'
'teal_data-extract.R' 'teal_data-get_code.R'
'teal_data-names.R' 'teal_data-show.R' 'topological_sort.R'
'verify.R' 'zzz.R'
'allauc.R' 'attenuation.R' 'averageandfilterexprs.R'
'countna.R' 'genesECDF.R' 'kneeid.R' 'meandifference.R'
'plotauc.R' 'plotecdf.R' 'plothistoknee.R' 'plotmetagenes.R'
'plotmulti.R' 'preprocessing-blacklisthighmap-utils.R'
'preprocessing-blacklisthighmap.R'
'preprocessing-createtablescores.R'
'preprocessing-makewindows.R' 'preprocessing-retrieveanno.R'
'preprocessing.R' 'tepr.R' 'universegroup.R' 'utils.R'
'utils.R' 'assert_data.R' 'covariance_plot.R' 'fit_mmrm.R'
'formula.R' 'g_mmrm.R' 'labels.R' 'lsmeans.R' 'subgroups.R'
'tabulate_mmrm.R' 'tern.mmrm-package.R'
'chk-filter.R' 'chk.R' 'comparison.R' 'deprec-chk.R'
'deprec-expect.R' 'deprec-reporter.R' 'expectation.R'
'expect-generic.R' 'expect-make.R' 'expect-chk.R'
'expect-conditional.R' 'expect-data.R' 'expect-datacomp.R'
'expect-exclusive.R' 'expect-labels.R' 'expect-proportion.R'
'expect-unique.R' 'expect_depends.R' 'reporter-excel.R'
'reporter-zzz.R' 'testdat-package.R' 'utils.R' 'zzz.R'
'RcppExports.R' 'import.R' 'extensions.R' 'item_class.R'
'calc_prob_functions.r' 'calc_escore_functions.r'
'calc_location_functions.r' 'calc_fisher_functions.r'
'calc_loglikelihood_functions.r' 'calc_jacobian_functions.r'
'calc_hessian_functions.r' 'sim_resp_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'
'calculate_adaptivity_measures.r' 'constraint_functions.R'
'cpp_calc_documents.r' 'cpp_core_documents.r'
'cpp_theta_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'
'simulation_data_cache_class.r'
'simulation_data_cache_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' 'is_rectangular_matrix.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'
'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'
'approx.R' 'bibentries.R' 'brackets.R' 'calculus.R'
'convert-construct-utils.R' 'convert.R' 'depth.R' 'evaluate.R'
'fwise.R' 'globals.R' 'graphics.R' 'interpolate.R' 'ops.R'
'math.R' 'methods.R' 'print-format.R' 'rebase.R' 'rng.R'
'smooth.R' 'soft-impute-svd.R' 'summarize.R' 'tf-package.R'
'tfb-fpc.R' 'tfb-spline.R' 'tfb-class.R' 'tfd-class.R'
'tf-s4.R' 'tfb-fpc-utils.R' 'tfb-spline-utils.R' 'utils.R'
'vctrs-cast.R' 'vctrs-ptype2.R' 'where.R' 'zoom.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' 'knitr.R' 'lattice.R' 'onLoad.R' 'thematic-package.R'
'thematic-save-plot.R' 'thematic.R' 'utils.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"
'tidyllm-package.R' 'utilites.R' 'embedding_helpers.R'
'LLMMessage.R' 'APIProvider.R' 'llm_message.R' 'llm_verbs.R'
'media.R' 'message_retrieval.R' 'perform_api_requests.R'
'rate_limits.R' 'tidyllm_schema.R' 'tools.R' 'api_openai.R'
'api_azure_openai.R' 'api_groq.R' 'api_mistral.R'
'api_perplexity.R' 'api_deepseek.R' 'api_voyage.R'
'api_ollama.R' 'api_claude.R' 'api_gemini.R' 'pdfbatch.R'
'zzz.R'
'compat.R' 'query.R' 'join.R' 'quote.R' 'remove.R' 'replace.R'
'show_dplyr.R' 'unscope.R'
'lens.R' 'verbs.R' 'base-lenses.R' 'dataframe-lenses.R'
'tinylens-package.R' 'zzz.R'
'KM.R' 'miscfunc.R' 'martingale.R' 'augment.R' 'ipw.R'
'ness.R' 'onestep.R' 'print.tLagInterim.R' 'verifyInputs.R'
'tLagInterim.R' 'tLagInterimData.R' 'type_Mean.R'
'type_OddsRatio.R' 'type_RiskDiff.R' 'type_RiskRatio.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'
'biber150_ice_gb.R' 'biber150_spokenBNC1994.R'
'biber150_spokenBNC2014.R' 'brown_metadata.R' 'disp.R'
'disp_DA.R' 'disp_DKL.R' 'disp_DP.R' 'disp_R.R' 'disp_S.R'
'dispersion_min_max_functions.R' 'ice_metadata.R'
'spokenBNC1994_metadata.R' 'spokenBNC2014_metadata.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'
'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' 'CalcEuroLotteryPnL.R'
'CalcSetForLifePnL.R' 'CalcUKLotteryPnL.R'
'CalcUKThunderBallPnL.R' 'Chebyshev_distance.R' 'Collateral.R'
'Commodity.R' 'Credit.R' 'CrossSampleEntropy.R' 'Curve.R'
'DynamicBeta.R' 'Equity.R' 'EuroJackpotResults.R'
'EuroLotteryAllCombinations.R' 'EuroLotteryBacktesting.R'
'EuroMillionsResults.R' 'FX.R' 'GetTradeDetails.R'
'HashTable.R' 'InformationAdjustedBeta.R'
'InformationAdjustedCorr.R' 'NormXASampEn.R' 'Other.R'
'OuterJoinMerge.R' 'ParseTrades.R' 'SampleEntropy.R'
'SelectDerivatives.R' 'SetForLifeBacktesting.R'
'SetForLifeExample.R' 'SetForLifeResults.R'
'UKLotteryBacktesting.R' 'UKLotteryExample.R'
'UKLotteryResults.R' 'UKThunderBallBacktesting.R'
'UKThunderBallExample.R' 'UKThunderBallResults.R'
'VariationOfInformation.R' 'capped_fibonacci_seq.R' 'cf.R'
'ci.R' 'eurojackpotExample.R' 'euromillionsExample.R'
'globals.R' 'martingale_strategy_calculator.R' 'onLoad.R'
'roulette_pl_calculator_Labouchere.R'
'roulette_pl_calculator_dalembert.R'
'roulette_pl_calculator_fibonacci.R'
'roulette_pl_calculator_martingale.R'
'roulette_pl_calculator_specific_number.R' 'tce.R' 'top5.R'
'waci.R'
Class-Tracks.R Tracks-methods.R generalize.R stcube.R stplot.R
difftrack.R compare-methods.R rtracks.R Trackstat.R
'aaa.R' 'assert.R' 'class-location.R' 'class-text.R'
'class-translator.R' 'find-source-in-exprs.R' 'find-source.R'
'flat.R' 'hash.R' 'language.R' 'normalize.R' 'serialize.R'
'text-io.R' 'translator-io.R' 'transltr-package.R'
'utils-format-vector.R' 'utils-map.R' 'utils-nullish-op.R'
'utils-stop.R' 'utils-strings.R' 'uuid.R' 'zzz.R'
'CDMInterface.R' 'TreatmentPatterns-package.R'
'TreatmentPatternsResults.R' 'attrition.R' 'computePathways.R'
'constructPathways.R' 'createSankeyDiagram.R'
'createSunburstPlot.R' 'executeTreatmentPatterns.R' 'export.R'
'exportPatientLevel.R' 'getResultsDataModelSpecification.R'
'ggSunburst.R' 'plotEventDuration.R'
RcppExports.R metrics.R medTree.R treespace.R findGroves.R
plotGroves.R plotTreeDiff.R servers.R transmissionTrees.R
data.R makeCollapsedTree.R relatedTreeDist.R simulateIndTree.R
tipsMRCAdepths.R treeConcordance.R
'RcppExports.R' 'calculate_weights.R' 'data.R'
'data_extension.R' 'data_manipulation.R' 'data_preparation.R'
'data_simulation.R' 'data_utils.R' 'expand_trials.R'
'generics.R' 'initiators.R' 'lr_utils.R' 'te_model_fitter.R'
'te_outcome_model.R' 'te_datastore.R' 'te_weights.R'
'te_data.R' 'te_expansion.R' 'trial_sequence.R' 'modelling.R'
'package.R' 'predict.R' 'robust.R' 'sampling.R'
'te_datastore_csv.R' 'te_datastore_duckdb.R' 'te_parsnip.R'
'te_stats_glm_logit.R' 'utils.R' 'weighting.R'
'cdfe.R' 'dtriangle.R' 'fit-plots.R' 'ltriangle.r'
'mle-utils.R' 'mle.R' 'mom.R' 'ptriangle.r' 'qtriangle.R'
'rtriangle.r'
'basic_objects.R' 'armacopula.R' 'sarmacopula.R'
'dvinecopula.R' 'dvinecopula2.R' 'dvinecopula3.R'
'fitting_basic.R' 'margins.R' 'full_models.R' 'vtransforms.R'
'fitting_vtscopula.R' 'helper_vtarma.R' 'data.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.r'
'RcppExports.R' 'convert_date_to_numeric.R' 'init.R'
'plot_cost_matrix.R' 'twdtw.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'
'kfold.R' 'occuTTD.R' 'multinomPois.R' 'occuRN.R' 'pcount.R'
'loglik.R' 'mb_chisq.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' 'util.R' 'util_color.R'
'util_plot.R' 'color_fun_1.R' 'color_fun_2.R' 'plot_box.R'
'plot_box_calls.R' 'plot_text.R' 'plot_text_calls.R'
'plot_themes.R' 'plot_kn.R' 'plot_demo.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' 'unmarkedFrame.R'
'unmarkedFit.R' 'utils.R' 'getDesign.R' 'fitted.R'
'residuals.R' 'update.R' 'simulate.R' 'nonparboot.R' 'getP.R'
'ranef.R' 'posteriorSamples.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' 'parboot.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' 'nmixTTD.R' 'gdistremoval.R'
'plotEffects.R' 'mixedModelTools.R' 'power.R' 'predict.R'
'IDS.R' 'goccu.R' 'occuCOP.R' 'occuComm.R'
'deprecated_sim_power.R' 'RcppExports.R' 'zzz.R'
'utils.R' 'count.R' 'extendr-wrappers.R' 'performance.R'
'horns.R' 'generate.R' 'horns-analyze.R' 'plot.R'
'read-write.R'
'utils.R' 'pipe.R' 'rawrd.R' 'formulas.R' 'checklist.R'
'components.R' 'call.R' 'firmly.R' 'validate.R' 'scope.R'
'checkers.R' 'valaddin-package.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'
'model_gen.R' 'refit.R' 'varband_cv.R' 'varband_path.R'
'RcppExports.R' 'misc.R' 'utils.R'
'data.R' 'dina.R' 'dino.R' 'hm_dcm.R' 'mc_dina.R' 'satu_dcm.R'
'variationalDCM.R' 'summary.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' 'df2coverconvert.R'
'new_relation.R' 'relation2header.R' 'cover_trans.R' 'clean.R'
'coerce-methods.R' 'add_releves.R' 'header.R' 'Extract.R'
'veg_relation.R' 'vegtable_stat.R' 'df2vegtable.R'
'used_synonyms.R' 'taxa2samples.R' 'subset.R' 'names.R'
'tv2vegtable.R' 'crosstable.R' 'veg_aggregate.R'
'write_juice.R' 'layers2samples.R' 'make_cocktail.R'
'summary.R' 'count_taxa.R' 'trait_stats.R' 'update_det.R'
'veg_diversity.R' 'aspect_conv-data.R' 'braun_blanquet-data.R'
'dune_veg-data.R' 'Kenya_veg-data.R' 'Wetlands-data.R'
'FileComparator.R' 'BinaryFileComparator.R' 'Config.R'
'Debugger.R' 'TxtFileComparator.R' 'FileComparatorFactory.R'
'ImgFileComparator.R' 'PdfFileComparator.R'
'TxtWithImagesComparator.R' 'RtfFileComparator.R'
'list_files.R' 'list_folder_files.R' 'run_example.R'
'utils-pipe.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'
zzz.R data.R generics.R utility.R classes.R
VirtualArray-base.R VirtualArray-xattrib.R
VirtualArray-subset.R VirtualArray-replace.R
VirtualArray-combine.R XArray-base.R XArray-xattrib.R
XArray-combine.R RasterArray-base.R RasterArray-xattrib.R
RasterArray-groupgen.R RasterArray-replace.R
RasterArray-combine.R RasterArray-rast.R SfcArray-base.R
SfcArray-replace.R SfcArray-sf.R SfArray-base.R
SfArray-replace.R conversions.R
'visa.R' 'Spectra-class.R' 'cm.nsr.R' 'cm.rbd3.R' 'cm.sr.R'
'data-specdf.R' 'data-speclib.R' 'find.bestBands.R'
'ggplot-method.R' 'ggplot.cm.R' 'ggplot.lmfit.R'
'ggplot.spectra.R' 'ndvi2.R' 'nsr.R' 'plt.2dcm.R'
'plt.3dcm_best.R' 'spectra.R' 'sr.R' 'wavelength.R'
'globals.R' 'adjusted_coef.R' 'apa.R' 'big_donut.R'
'calculate_d.R' 'd_to_f2.R' 'd_to_nnt.R' 'd_to_r.R'
'estimate_d.R' 'estimate_r.R' 'noncentral_t.R' 'other_to_d.R'
'probability_superiority.R' 'proportion_overlap.R'
'runExample.R' 'visualize_c.R' 'visualize_c_map.R'
'visualize_effects.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'
'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'
'data.R' 'nam.R' 'plot.R' 'wcep-package.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' 'connection.R' 'db.R' 'defer-exit.R'
'standalone-defer.R' 'defer.R' 'devices.R' 'local_.R' 'with_.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' 'wrap.R' 'sink.R' 'tempfile.R' 'timezone.R'
'torture.R' 'utils.R' 'with.R'
'CPN_27.R' 'CPN_120.R' 'tab_upload_data.R' 'tab_estimations.R'
'tab_estimate_participants.R' 'tab_property_simulator.R'
'tab_pa_data.R' 'tab_pa_values.R' 'tab_cluster_image.R'
'tab_cosine_distance.R' 'tab_t_c_a_calculator.R' 'PLT_ui.R'
'fun_generate_norms.R' 'fun_estimate_participant.R'
'fun_property_simulator.R' 'fun_pa_function.R'
'fun_threshold_graph.R' 'fun_cluster_image_function.R'
'fun_tca_calculator.R' 'PLT_server.R' 'PLT_app.R'
'animate.R' 'banner.R' 'broadcast.R' 'clipboard.R'
'editable.R' 'fit-screen.R' 'freezeframe.R' 'panelset.R'
'progress-bar.R' 'scribble.R' 'share_again.R' 'slide-tone.R'
'styles.R' 'search.R' 'tachyons.R' 'tile-view.R' 'use_logo.R'
'utils.R' 'webcam.R' 'xaringanExtra-package.R'
'evalGene.R' 'scaling.R' 'selectGene.R'
'selectGeneBenchmark.R' 'timer.R' 'DeJongF4.R' 'Parabola2D.R'
'newXOR.R' 'newTSP.R' 'xegaSelectGene-package.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' 'XiMpLe-package.R'
'gen_tag_functions.R' 'parseXMLTree.R' 'pasteXMLTag.R'
'provide_file.R' 'zzz_is_get_utils.R'
'zzz_is_get_utils_deprecated.R'
as_base_r.R xlr_table.R xlr_to_workbook.R
build_multiple_response_table.R build_table.R write_xlsx.R
xlr_numeric.R xlr_integer.R xlr_vector.R xlr_percent.R
xlr_format.R openxlsx_utils.R xlr_doc.R error_utils.R
create_table_of_contents.R build_question_block_table.R
table_utils.R data.R is_xlr_type.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' 'format.R' 'import-standalone-obj-type.R'
'import-standalone-purrr.R' 'import-standalone-types-check.R'
'init.R' 'nodeset_apply.R' 'paths.R' 'utils.R' 'xml2-package.R'
'xml_attr.R' 'xml_children.R' 'xml_document.R' 'xml_find.R'
'xml_missing.R' 'xml_modify.R' 'xml_name.R' 'xml_namespaces.R'
'xml_node.R' 'xml_nodeset.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' 'calcDefCapital.R' 'calcEADRegulatory.R'
'calcEffectiveMaturity.R' 'calcKVA.R' 'xVACalculator.R'
'xVACalculatorExample.R' 'onLoad.R' 'IS_ELIGIBLE_CCY.R'
'IS_IG.R' 'LoadSupervisoryCVAData.R' 'calcCVACapital.R'
'imports.R' 'classes.R' 'as.R' 'print.R' 'read_rmd.R'
'render_rmd.R' 'update.R' 'write_rmd.R'
'aaa-metrics.R' 'import-standalone-types-check.R' 'aaa-new.R'
'aaa.R' 'check-metric.R' 'class-accuracy.R'
'class-bal_accuracy.R' 'class-detection_prevalence.R'
'class-f_meas.R' 'class-j_index.R' 'class-kap.R' 'class-mcc.R'
'class-npv.R' 'class-ppv.R' 'class-precision.R'
'class-recall.R' 'class-sens.R' 'class-spec.R' 'conf_mat.R'
'data.R' 'deprecated-prob_helpers.R' 'deprecated-template.R'
'estimator-helpers.R' 'event-level.R' 'fair-aaa.R'
'fair-demographic_parity.R' 'fair-equal_opportunity.R'
'fair-equalized_odds.R' 'import-standalone-obj-type.R'
'import-standalone-survival.R' 'metric-tweak.R' 'misc.R'
'missings.R' 'num-ccc.R' 'num-huber_loss.R' 'num-iic.R'
'num-mae.R' 'num-mape.R' 'num-mase.R' 'num-mpe.R' 'num-msd.R'
'num-poisson_log_loss.R' 'num-pseudo_huber_loss.R' 'num-rmse.R'
'num-rpd.R' 'num-rpiq.R' 'num-rsq.R' 'num-rsq_trad.R'
'num-smape.R' 'prob-average_precision.R'
'prob-binary-thresholds.R' 'prob-brier_class.R'
'prob-classification_cost.R' 'prob-gain_capture.R'
'prob-gain_curve.R' 'prob-helpers.R' 'prob-lift_curve.R'
'prob-mn_log_loss.R' 'prob-pr_auc.R' 'prob-pr_curve.R'
'prob-roc_auc.R' 'prob-roc_aunp.R' 'prob-roc_aunu.R'
'prob-roc_curve.R' 'reexports.R' 'surv-brier_survival.R'
'surv-brier_survival_integrated.R'
'surv-concordance_survival.R' 'surv-roc_auc_survival.R'
'surv-roc_curve_survival.R' 'template.R' 'validation.R'
'yardstick-package.R'
Please report bugs and other issues to
<kevin.egan@durham.ac.uk>.
Wilfried Thuiller <wilfried.thuiller@univ-grenoble-alpes.fr>,
Maya Gueguen <maya.gueguen@univ-grenoble-alpes.fr>, Helene
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<marielaure.delignettemuller@vetagro-sup.fr> or Christophe
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Bug report mailing list <ismrp@grp.ism.ac.jp>
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Facebook, Inc. and created by Yann Collet; and code derived
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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.
Original python appdirs module copyright (c) 2010
ActiveState Software Inc. R port copyright Hadley Wickham,
RStudio. See file LICENSE for details.
United States Environmental Protection Agency
Trustees of University of Pennsylvania owns the copyright of
the package unless otherwise stated. Zhengjia Wang owns the
copyright of all the low-level functions included in
'R/common.R', 'R/fastmap2', 'R/fastqueue2.R', 'R/filesys.R',
'R/fst.R', 'R/json.R', 'R/os_info.R', 'R/parallel.R',
'R/progress.R', 'R/simplelocker.R', 'R/yaml.R', and all the
template files under 'inst/rave-pipelines' and
'inst/rave-modules', these files are licensed under 'MIT'.
Karim Rahim (author of R package 'fftwtools', licensed under
'GPL-2' or later) is the original author of 'src/ffts.h' and
'src/ffts.cpp'. Prerau's Lab wrote the original
'R/multitaper.R', licensed under 'MIT'. Marcus Geelnard wrote
the source code of 'TinyThread' library ('MIT' license) located
at 'inst/include/tthread'. Stefan Schlager wrote the original
code that converts R objects to 'vcg' (see 'src/vcgCommon.h',
licensed under 'GPL-2' or later). Visual Computing Lab is the
copyright holder of 'vcglib' source code (see 'src/vcglib',
licensed under GPL-2 or later).
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.
The included 'stb_rect_pack.h' header (v1.01) is Copyright
(c) 2017 Sean Barrett and licensed under the MIT license. See
COPYRIGHTS file for more details.
(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.
(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
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(License: Simplified BSD). The matio io routines have been
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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
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'nanoflann' library is copyright Jose Luis Blanco. See file
COPYRIGHT for details.
Institut national de recherche pour l’agriculture,
l’alimentation et l’environnement (INRAE)
The data in star_groj0422.32 have been kindly provided by
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respective documentation.
(C) 2014 Coherent Logic Limited; ALL RIGHTS RESERVED.
Authors listed above, PostgreSQL Global Development Group,
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For the rpyANTs package - Zhengjia Wang; For ANTs -
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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.
F. Hoffmann-La Roche AG, Pattern Institute, Atorus Research
LLC and Transition Technologies Science sp. z o.o.
(c) 2014, Pioneer Hi-Bred International, Inc.
We have used the following software and made minimal
necessary changes: tabix, Heng Li <lh3@live.co.uk> (MIT
license), SQLite (Public Domain), Zstandard (BSD 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.
This package includes code from the 'span-lite' library
owned by Martin Moene under Boost Software License 1.0. This
package includes code from the 'ankerl' library owned by Martin
Leitner-Ankerl under MIT License. This package contains data
derived from Adaptive Biotechnologies "ImmuneCODE" dataset
under Creative Commons Attribution 4.0.
The code in 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
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Copyright details for stochtree's C++ dependencies, which
are vendored along with the core stochtree source code, are
detailed in inst/COPYRIGHTS
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
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'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
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Underlying C++ code copyright 2018 Chris Whidden.
Copyright 2012. The Regents of the University of California
(Regents). All Rights Reserved.
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This software is in the public domain because it contains
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Geological Survey, an agency of the United States Department of
Interior. For more information, see the official USGS copyright
policy at
https://www.usgs.gov/visual-id/credit_usgs.html#copyright
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> and 'RUtreebalance' by
Robert Noble <doi:10.5281/zenodo.5873857>.
All code is Copyright (C) Michael Hahsler and Kurt Hornik.
2019 Conservation Science Partners, Inc.
See the file COPYRIGHTS for copyright details
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This software is in the public domain because it contains
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Copyright © 2024 Merck & Co., Inc., Rahway, NJ, USA and its
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See the file COPYRIGHTS for various htslib copyright details
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
This software is in the public domain because it contains
materials that originally came from the United States Bureau of
Reclamation, an agency of the United States Department of
Interior.
2009-2022 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.
This package includes code from the 'xxhash' written Yann
Collet. See file 'inst/LICENSE-xxhash' for copyright
information of the original library.
(C) 2022 John W. Emerson and Walton Green
The included 'yyjson' code is Copyright (c) 2020 YaoYuan.
See 'COPYRIGHTS' for LICENSE for included code.
Provides the conditional Nelson-Aalen and Aalen-Johansen estimators. The methods are based on Bladt & Furrer (2023), in preparation.
Implements a simple version of multivariate matching using a propensity score, near-exact matching, near-fine balance, and robust Mahalanobis distance matching (Rosenbaum 2020 <doi:10.1146/annurev-statistics-031219-041058>).  You specify the variables, and the program does everything else.
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.
Provides a suite of functions for analyzing sequences of events. Users can generate and code sequences based on predefined rules, with a special focus on the identification of sequences coded as 'ABA' (when one element appears, followed by a different one, and then followed by the first). Additionally, the package offers the ability to calculate the length of consecutive 'ABA'-coded sequences sharing common elements. The methods implemented in this package are based on the work by Ziembowicz, K., Rychwalska, A., & Nowak, A. (2022). <doi:10.1177/10464964221118674>.
Strings are abbreviated to at least "minlength" characters, such that they remain unique 
    (if they were). The abbreviations should be recognisable.
Implements several ABC algorithms for
        performing parameter estimation, model selection, and goodness-of-fit.
        Cross-validation tools are also available for measuring the
        accuracy of ABC estimates, and to calculate the
        misclassification probabilities of different models.
Contains data which are used by functions of the 'abc' package.
It aims to identify candidate genes that are “differentially
    methylated” between cases and controls. It applies Student’s t-test and delta beta analysis to
    identify candidate genes containing multiple “CpG sites”.
For a given data set, the package provides a novel method of computing precise limits to acquire subsets which are easily interpreted. Closely related to the Lorenz curve, the ABC curve visualizes the data by graphically representing the cumulative distribution function. Based on an ABC analysis the algorithm calculates, with the help of the ABC curve, the optimal limits by exploiting the mathematical properties pertaining to distribution of analyzed items. The data containing positive values is divided into three disjoint subsets A, B and C, with subset A comprising very profitable values, i.e. largest data values ("the important few"), subset B comprising values where the yield equals to the effort required to obtain it, and the subset C comprising of non-profitable values, i.e., the smallest data sets ("the trivial many"). Package is based on "Computed ABC Analysis for rational Selection of most informative Variables in multivariate Data", PLoS One. Ultsch. A., Lotsch J. (2015) <DOI:10.1371/journal.pone.0129767>.
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.
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.
    See Nunes and Prangle (2015) <doi:10.32614/RJ-2015-030> and Rodrigues, Prangle and Sisson (2018) <doi:10.1016/j.csda.2018.04.004>.
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>.
Detect several types of aberrant behavior, including answer
    copying, answer similarity, change point, nonparametric misfit, parametric
    misfit, preknowledge, rapid guessing, and test tampering.
Extremely efficient toolkit for solving the best subset selection problem <https://www.jmlr.org/papers/v23/21-1060.html>. 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 <doi:10.1287/ijoc.2022.1241> and sure independence screening <doi:10.1111/j.1467-9868.2008.00674.x> are also provided.
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.
Assess whether and how a specific continuous or categorical exposure affects the outcome of interest through one- or multi-dimensional mediators using an  adaptive bootstrap (AB) approach. The AB method allows to make inference for composite null hypotheses of no mediation effect, providing valid type I error control and thus optimizes statistical power. For more technical details, refer to He, Song and Xu (2024) <doi:10.1093/jrsssb/qkad129>.
Combine multidimensional arrays into a single array.
  This is a generalization of 'cbind' and 'rbind'.  Works with
  vectors, matrices, and higher-dimensional arrays (aka tensors).
  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 has a set of datasets commonly used in
    our book.
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.
Implements the Arellano-Bond estimation method combined with LASSO for dynamic linear panel models. See Chernozhukov et al. (2024) "Arellano-Bond LASSO Estimator for Dynamic Linear Panel Models". arXiv preprint <doi:10.48550/arXiv.2402.00584>.
A high-performance, flexible and extensible framework to
    develop continuous-time agent based models. Its high performance
    allows it to simulate millions of agents efficiently. Agents are
    defined by their states (arbitrary R lists). The events are handled in
    chronological order. This avoids the multi-event interaction problem
    in a time step of discrete-time simulations, and gives precise
    outcomes. The states are modified by provided or user-defined events.
    The framework provides a flexible and customizable implementation of
    state transitions (either spontaneous or caused by agent
    interactions), making the framework suitable to apply to epidemiology
    and ecology, e.g., to model life history stages, competition and
    cooperation, and disease and information spread. The agent
    interactions are flexible and extensible. The framework provides
    random mixing and network interactions, and supports multi-level
    mixing patterns.  It can be easily extended to other interactions such
    as inter- and intra-households (or workplaces and schools) by
    subclassing an R6 class. It can be used to study the effect of
    age-specific, group-specific, and contact- specific intervention
    strategies, and complex interactions between individual behavior and
    population dynamics. This modeling concept can also be used in
    business, economical and political models. As a generic event based
    framework, it can be applied to many other fields. More information
    about the implementation and examples can be found at
    <https://github.com/junlingm/ABM>.
Tools to perform model selection alongside estimation under Linear, Logistic, Negative binomial, Quantile, and Skew-Normal regression. Under the spike-and-slab method, a probability for each possible model is estimated with the posterior mean, credibility interval, and standard deviation of coefficients and parameters under the most probable model.
The 'abn' R package facilitates Bayesian network analysis, a
    probabilistic graphical model that derives from empirical data a
    directed acyclic graph (DAG). This DAG describes the dependency
    structure between random variables. The R package 'abn' provides
    routines to help determine optimal Bayesian network models for a given
    data set. These models are used to identify statistical dependencies
    in messy, complex data. Their additive formulation is equivalent to
    multivariate generalised linear modelling, including mixed models with
    independent and identically distributed (iid) random effects. The core
    functionality of the 'abn' package revolves around model selection,
    also known as structure discovery. It supports both exact and
    heuristic structure learning algorithms and does not restrict the data
    distribution of parent-child combinations, providing flexibility in
    model creation and analysis. The 'abn' package uses Laplace
    approximations for metric estimation and includes wrappers to the
    'INLA' package. It also employs 'JAGS' for data simulation purposes.
    For more resources and information, visit the 'abn' website.
Contains the functions to implement the methodology and considerations laid out by Marks et al. in the manuscript Measuring Abnormality in High Dimensional Spaces: Applications in Biomechanical Gait Analysis. As of 2/27/2018 this paper has been submitted and is under scientific review. Using high-dimensional datasets to measure a subject’s overall level of abnormality as compared to a reference population is often needed in outcomes research. Utilizing applications in instrumented gait analysis, that article demonstrates how using data that is inherently non-independent to measure overall abnormality may bias results. A methodology is introduced to address this bias to accurately measure overall abnormality in high dimensional spaces. While this methodology is in line with previous literature, it differs in two major ways. Advantageously, it can be applied to datasets in which the number of observations is less than the number of features/variables, and it can be abstracted to practically any number of domains or dimensions. After applying the proposed methodology to the original data, the researcher is left with a set of uncorrelated variables (i.e. principal components) with which overall abnormality can be measured without bias. Different considerations are discussed in that article in deciding the appropriate number of principal components to keep and the aggregate distance measure to utilize.
Performs angle-based outlier detection on a given dataframe. Three methods are available, a full but slow implementation using all the data that has cubic complexity, a fully randomized one which is way more efficient and another using k-nearest neighbours. These algorithms are specially well suited for high dimensional data outlier detection.
An implementation of the Abnormal Blood Profile Score (ABPS,
    part of the Athlete Biological Passport program of the World Anti-Doping
    Agency), which combines several blood parameters into a single score in
    order to detect blood doping (Sottas et al. (2006)
    <doi:10.2202/1557-4679.1011>). The package also contains functions to
    calculate other scores used in anti-doping programs, such as the
    OFF-score (Gore et al. (2003) <http://www.haematologica.org/content/88/3/333>),
    as well as example data.
This toolkit implements a numerical solution algorithm 
    to invert a quality of life measure from observed data. Unlike
    the traditional Rosen-Roback measure, this measure accounts for
    mobility frictions—generated by idiosyncratic tastes and local
    ties — and trade frictions — generated by trade costs and
    non-tradable services, thereby reducing non-classical
    measurement error. The QoL measure is based on Ahlfeldt, Bald,
    Roth, Seidel (2024)
    <https://econpapers.repec.org/RePEc:boc:bocode:s459382>
    "Measuring Quality of Life under Spatial Frictions". When using
    this programme or the toolkit in your work, please cite the paper.
Wraps the 'Abseil' 'C++' library for use by R packages.
    Original files are from <https://github.com/abseil/abseil-cpp>.
    Patches are located at
    <https://github.com/doccstat/abseil-r/tree/main/local/patches>.
A variable selection method using B-Splines in multivariate nOnparametric Regression models Based on partial dErivatives Regularization (ABSORBER) implements a novel variable selection method in a nonlinear multivariate model using B-splines. For further details we refer the reader to the paper Savino, M. E. and Lévy-Leduc, C. (2024), <https://hal.science/hal-04434820>.
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.
Survival analysis is employed to model the time it takes for events to occur. Survival model examines the relationship between survival and one or more predictors, usually termed covariates in the survival-analysis literature. To this end, Cox-proportional (Cox-PH) hazard rate model introduced in a seminal paper by Cox (1972) <doi:10.1111/j.2517-6161.1972.tb00899.x>, is a broadly applicable and the most widely used method of survival analysis. This package can be used to estimate the effect of fixed and time-dependent covariates and also to compute the survival probabilities of the lactation of dairy animal. This package has been developed using algorithm of Klein and  Moeschberger (2003) <doi:10.1007/b97377>.
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.
Functionality to allow users to easily colour plots with the colour
    palettes of various academic institutions.
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.
We present a statistical method for imputing missing values in accelerometer data. The methodology includes both parametric and semi-parametric multiple imputation under the zero-inflated Poisson lognormal model. It also offers several functions to preprocess accelerometer data before imputation. These include detecting wear and non-wear time, selecting valid days and subjects, and generating plots.
Estimate the Šesták–Berggren kinetic model (degradation model) from experimental data.
  A closed-form (analytic) solution to the degradation model is implemented as a non-linear fit,
  allowing for the extrapolation of the degradation of a drug product - both in time and 
  temperature. Parametric bootstrap, with kinetic parameters drawn from the multivariate
  t-distribution, and analytical formulae (the delta method) are available options to calculate
  the confidence and prediction intervals.
  The results (modelling, extrapolations and statistical intervals) can be visualised
  with multiple plots. The examples illustrate the accelerated stability modelling in drugs and
  vaccines development.
Allows clinicians to predict the rate and severity of future acute exacerbation in Chronic Obstructive Pulmonary Disease (COPD) patients, based on the clinical prediction models published in Adibi et al. (2020) <doi:10.1016/S2213-2600(19)30397-2> and Safari et al. (2022) <doi:10.1016/j.eclinm.2022.101574>.
Provides functionality for creating and
	evaluating acceptance sampling plans. Sampling plans can be single,
	double or multiple.
Provides a function that implements the acceptance-rejection method in an optimized manner to generate pseudo-random observations for discrete or continuous random variables. Proposed by von Neumann J. (1951), <https://mcnp.lanl.gov/pdf_files/>, the function is optimized to work in parallel on Unix-based operating systems and performs well on Windows systems. The acceptance-rejection method implemented optimizes the probability of generating observations from the desired random variable, by simply providing the probability function or probability density function, in the discrete and continuous cases, respectively. Implementation is based on references CASELLA, George at al. (2004) <https://www.jstor.org/stable/4356322>,  NEAL, Radford M. (2003) <https://www.jstor.org/stable/3448413> and Bishop, Christopher M. (2006, ISBN: 978-0387310732).
A set of fast and convenient functions to help conducting
    accessibility analyses. Given a pre-computed travel cost matrix and a
    land use dataset (containing the location of jobs, healthcare and
    population, for example), the package allows one to calculate
    accessibility levels and accessibility poverty and inequality. The
    package covers the majority of the most commonly used accessibility
    measures (such as cumulative opportunities, gravity-based and floating
    catchment areas methods), as well as the most frequently used
    inequality and poverty metrics (such as the Palma ratio, the
    concentration and Theil indices and the FGT family of measures).
Provides functions to produce accessible 'HTML' slides, 'HTML', 
    'Word' and 'PDF' documents from input 'R markdown' files. Accessible 'PDF' 
    files are produced only on a 'Windows' Operating System. One aspect of 
    accessibility is providing a headings structure that is recognised by a 
    screen reader, providing a navigational tool for a blind or 
    partially-sighted person. A key aim is to produce documents of different 
    formats easily from each of a collection of 'R markdown' source files. 
    Input 'R markdown' files are rendered using the render() function from the 
    'rmarkdown' package <https://cran.r-project.org/package=rmarkdown>. A 'zip' 
    file containing multiple output files can be produced from one function 
    call. A user-supplied template 'Word' document can be used to determine the 
    formatting of an output 'Word' document. Accessible 'PDF' files are 
    produced from 'Word' documents using 'OfficeToPDF' 
    <https://github.com/cognidox/OfficeToPDF>. A convenience function, 
    install_otp() is provided to install this software. The option to print 
    'HTML' output to (non-accessible) 'PDF' files is also available.
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.
Participant recruitment for medical research is challenging. Slow accrual leads to delays 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 the researcher's experience with previous trials and data from the current study, providing reliable predictions on accrual rate for clinical studies. For more details and background on these methodologies, see the publications of Byron, Stephen and Susan (2008) <doi:10.1002/sim.3128>, and Yu et al. (2015) <doi:10.1002/sim.6359>. In this R package, Bayesian accrual prediction functions are presented, 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.
Provides tools for designing and analyzing Acceptance Sampling plans.
    Supports both Attributes Sampling (Binomial and Poisson distributions) and 
    Variables Sampling (Normal and Beta distributions), enabling quality control 
    for fractional and compositional data. Uses nonlinear programming for sampling 
    plan optimization, minimizing sample size while controlling producer's and 
    consumer's risks. Operating Characteristic curves are available for plan visualization.
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>.
Estimate group aggregates, where one can set user-defined conditions
   that each group of records must satisfy to be suitable for aggregation. If
   a group of records is not suitable, it is expanded using a collapsing scheme
   defined by the user. A paper on this package was published in the Journal
   of Statistical Software <doi:10.18637/jss.v112.i04>.
Interact with the Attentional Control Data Collection (ACDC).
  Connect to the database via connect_to_db(), set filter arguments via add_argument()
  and query the database via query_db().
The functions are designed to calculate the most widely-used county-level variables in 
  agricultural production or agricultural-climatic and weather analyses. To operate some functions 
  in this package needs download of the bulk PRISM raster. See the examples, testing versions and 
  more details from: <https://github.com/ysd2004/acdcR>.
Provides tools for autoregressive conditional duration (ACD, Engle and Russell, 1998) models. Functions to create trade, price, or volume durations from transaction data, perform diurnal adjustments, fit various ACD models, and test them.
A function for estimating factor models. Give factor-adjusted statistics, 
  factor-adjusted mean estimation (one-sample test) or factor-adjusted mean difference 
  estimation (two-sample test).
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.
La libreria 'ACEP' contiene funciones especificas para
    desarrollar analisis computacional de eventos de protesta. Asimismo,
    contiene base de datos con colecciones de notas sobre protestas y
    diccionarios de palabras conflictivas. Coleccion de diccionarios que
    reune diccionarios de diferentes origenes.  The 'ACEP' library
    contains specific functions to perform computational analysis of
    protest events. It also contains a database with collections of notes
    on protests and dictionaries of conflicting words. Collection of
    dictionaries that brings together dictionaries from different sources.
Two nonparametric methods for multiple regression transform selection are provided.
  The first, Alternating 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 Harrell's "Regression Modeling Strategies" in the Springer Series in Statistics.
  A permutation independence test is included from [Holzmann, H., Klar, B. 2025. "Lancaster correlation - a new dependence measure
  linked to maximum correlation". Scandinavian Journal of Statistics.
   52(1):145-169 <doi:10.1111/sjos.12733>].
'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>.
A few functions aim to provide a statistic tool for three purposes. First, simulate kin pairs data based on the assumption that every trait is affected by genetic effects (A), common environmental effects (C) and unique environmental effects (E).Second, use kin pairs data to fit an ACE model and get model fit output.Third, calculate power of A estimate given a specific condition. For the mechanisms of power calculation, we suggest to check Visscher(2004)<doi:10.1375/twin.7.5.505>.
Twin models that are able to estimate the dynamic behaviour of the variance components in the classical twin models with respect to age using B-splines and P-splines.
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>.
Automated Characterization of Health Information at Large-Scale 
  Longitudinal Evidence Systems. Creates a descriptive statistics summary for 
  an Observational Medical Outcomes Partnership Common Data Model standardized 
  data source. This package includes functions for executing summary queries on 
  the specified data source and exporting reporting content for use across a 
  variety of Observational Health Data Sciences and Informatics community 
  applications.
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.
Tools working with data from ACLED (Armed Conflict Location and Event Data). Functions include simplified access to ACLED's API (<https://apidocs.acleddata.com/>), methods for keeping local versions of ACLED data up-to-date, and functions for common ACLED data transformations.
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, contrary to higher dimensions when all derivatives are linearly approximated. Several non-archimax families (normal, FGM, Plackett) are provided as well.
Exclusion-based parentage assignment is essential for studies in biodiversity conservation and breeding programs - Kang Huang, Rui Mi, Derek W Dunn, Tongcheng Wang, Baoguo Li, (2018), <doi:10.1534/genetics.118.301592>. The tool compares multilocus genotype data of potential parents and offspring, identifying likely parentage relationships while accounting for genotyping errors, missing data, and duplicate genotypes. 'acoRn' includes two algorithms: one generates synthetic genotype data based on user-defined parameters, while the other analyzes existing genotype data to identify parentage patterns. The package is versatile, applicable to diverse organisms, and offers clear visual outputs, making it a valuable resource for researchers.
Analysis of count data exhibiting autoregressive properties, using the Autoregressive Conditional Poisson model (ACP(p,q)) proposed by Heinen (2003).
A Tool for Semi-Automating the Statistical Disclosure Control of Research Outputs.
A tool for generating acronyms and initialisms from arbitrary text input.
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.
Allow user to run the Adaptive Correlated Spike and Slab (ACSS) algorithm, corresponding INdependent Spike and Slab (INSS) algorithm, and Giannone, Lenza and Primiceri (GLP) algorithm with adaptive burn-in. 
            All of the three algorithms are used to fit high dimensional data set with either sparse structure, or dense structure with smaller contributions from all predictors. 
            The state-of-the-art GLP algorithm is in Giannone, D., Lenza, M., & Primiceri, G. E. (2021, ISBN:978-92-899-4542-4) 
            "Economic predictions with big data: The illusion of sparsity".
            The two new algorithms, ACSS algorithm and INSS algorithm, and the discussion on their performance can be seen in 
            Yang, Z., Khare, K., & Michailidis, G. (2024, preprint) "Bayesian methodology for adaptive sparsity and shrinkage in regression".
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.
The 'actfts' package provides tools for performing autocorrelation analysis of time series data. It includes functions to compute and visualize the autocorrelation function (ACF) and the partial autocorrelation function (PACF). Additionally, it performs the Dickey-Fuller, KPSS, and Phillips-Perron unit root tests to assess the stationarity of time series. Theoretical foundations are based on Box and Cox (1964) <doi:10.1111/j.2517-6161.1964.tb00553.x>, Box and Jenkins (1976) <isbn:978-0-8162-1234-2>, and Box and Pierce (1970) <doi:10.1080/01621459.1970.10481180>. Statistical methods are also drawn from Kolmogorov (1933) <doi:10.1007/BF00993594>, Kwiatkowski et al. (1992) <doi:10.1016/0304-4076(92)90104-Y>, and Ljung and Box (1978) <doi:10.1093/biomet/65.2.297>. The package integrates functions from 'forecast' (Hyndman & Khandakar, 2008) <https://CRAN.R-project.org/package=forecast>, 'tseries' (Trapletti & Hornik, 2020) <https://CRAN.R-project.org/package=tseries>, 'xts' (Ryan & Ulrich, 2020) <https://CRAN.R-project.org/package=xts>, and 'stats' (R Core Team, 2023) <https://stat.ethz.ch/R-manual/R-devel/library/stats/html/00Index.html>. Additionally, it provides visualization tools via 'plotly' (Sievert, 2020) <https://CRAN.R-project.org/package=plotly> and 'reactable' (Glaz, 2023) <https://CRAN.R-project.org/package=reactable>. The package also incorporates macroeconomic datasets from the U.S. Bureau of Economic Analysis: Disposable Personal Income (DPI) <https://fred.stlouisfed.org/series/DPI>, Gross Domestic Product (GDP) <https://fred.stlouisfed.org/series/GDP>, and Personal Consumption Expenditures (PCEC) <https://fred.stlouisfed.org/series/PCEC>.
A tool to obtain activity counts, originally a translation of the 
  'python' package 'agcounts' <https://github.com/actigraph/agcounts>. This tool
  allows the processing of data from any accelerometer brand, with a more flexible 
  approach to handle different sampling frequencies.
Provides sleep duration estimates using a Pruned Dynamic
  Programming (PDP) algorithm that efficiently identifies
  change-points. PDP applied to physical activity data can identify
  transitions from wakefulness to sleep and vice versa. Baek, Jonggyu, Banker,
  Margaret, Jansen, Erica C., She, Xichen, Peterson, Karen E., Pitchford,
  E. Andrew, Song, Peter X. K. (2021) An Efficient Segmentation Algorithm to
  Estimate Sleep Duration from Actigraphy Data <doi:10.1007/s12561-021-09309-3>.
A tool to analyse 'ActiGraph' accelerometer data and to implement 
    the use of the PROactive Physical Activity in COPD (chronic obstructive pulmonary disease) instruments. Once analysis
    is completed, the app allows to export results to .csv files and to generate
    a report of the measurement. All the configured inputs relevant for interpreting
    the results are recorded in the report. In addition to the existing 'R' packages 
    that are fully integrated with the app, the app uses some functions from the 
    'actigraph.sleepr' package developed by Petkova (2021) <https://github.com/dipetkov/actigraph.sleepr/>.
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>.
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. (JCGS, 2021) <doi:10.48550/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. Genes can be integrated using directional constraints that reflect how the input datasets are expected interact with one another. 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 recent version of the package is part of the following publication: Directional integration and pathway enrichment analysis for multi-omics data. Slobodyanyuk M^, Bahcheli AT^, Klein ZP, Bayati M, Strug LJ, Reimand J. Nature Communications (2024) <doi:10.1038/s41467-024-49986-4>.
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>.
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.
Contains data and functions that can be used to make 
  actuarial life tables. Each function adds a column to the inputted dataset for
  each intermediate calculation between mortality rate and life expectancy. Users can 
  run any of our functions to complete the life table until that step, or run
  lifetable() to output a full life table that can be customized to remove optional columns. 
  Methods for creating lifetables are as described in Zedstatistics (2021) <https://www.youtube.com/watch?v=Dfe59glNXAQ>.
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. Main reference: <doi:10.18637/jss.v025.i07>.
  Implementation of the Feller-Pareto family of distributions:
  <doi:10.18637/jss.v103.i06>.
Using this package, you can fit a random effects model using either the hierarchical credibility model, a combination of the hierarchical credibility model with a generalized linear model or a Tweedie generalized linear mixed model. See Campo, B.D.C. and Antonio, K. (2023) <doi:10.1080/03461238.2022.2161413>.
It computes two frequently applied actuarial measures, the expected shortfall and the value at risk. Seven well-known classical distributions in connection to the Bell generalized family are used as follows: Bell-exponential distribution, Bell-extended exponential distribution, Bell-Weibull distribution, Bell-extended Weibull distribution, Bell-Lomax distribution, Bell-Burr-12 distribution, and Bell-Burr-X distribution. Related works include:
     a) Fayomi, A., Tahir, M. H., Algarni, A., Imran, M., & Jamal, F. (2022). "A new useful exponential model with applications to quality control and 
        actuarial data". Computational Intelligence and Neuroscience, 2022. <doi:10.1155/2022/2489998>.
     b) Alsadat, N., Imran, M., Tahir, M. H., Jamal, F., Ahmad, H., & Elgarhy, M. (2023). "Compounded Bell-G class of statistical models with applications to COVID-19 and actuarial data". Open Physics, 21(1), 20220242. <doi:10.1515/phys-2022-0242>.
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.
Experience studies are used by actuaries to explore historical
    experience across blocks of business and to inform assumption setting
    activities. This package provides functions for preparing data, creating 
    studies, visualizing results, and beginning assumption development. 
    Experience study methods, including exposure calculations, are described in:
    Atkinson & McGarry (2016) "Experience Study Calculations" 
    <https://www.soa.org/49378a/globalassets/assets/files/research/experience-study-calculations.pdf>.
    The limited fluctuation credibility method used by the 'exp_stats()'
    function is described in: Herzog (1999, ISBN:1-56698-374-6) 
    "Introduction to Credibility Theory".
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. 
	Version 5.0 includes the Boosting and Bagging algorithms for label ranking (Albano, Sciandra
	and Plaia, 2023).
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.
Runs projections of groups of matrix projection models (MPMs),
             allowing density dependence mechanisms to work across MPMs. This
             package was developed to run both adaptive dynamics simulations
             such as pairwise and multiple invasibility analyses, and community
             projections in which species are represented by MPMs. All forms of
             MPMs are allowed, including integral projection models (IPMs). Also
             includes individual-based modeling (IBM) versions of these.
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. See Courtois et al <doi:10.1186/s12874-021-01450-3>.
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.
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 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.
Contains functions carrying out adaptive procedures using mixed scaling approach to establish bioequivalence for in-vitro permeation test (IVPT) data. Currently, the package provides procedures based on parallel replicate design and balanced data, according to the U.S. Food and Drug Administration's "Draft Guidance on Acyclovir" <https://www.accessdata.fda.gov/drugsatfda_docs/psg/Acyclovir_topical%20cream_RLD%2021478_RV12-16.pdf>. Potvin et al. (2008) <doi:10.1002/pst.294> provides the basis for our adaptive design (see Method B). For a comprehensive overview of the method, refer to Lim et al. (2023) <doi:10.1002/pst.2333>. This package reflects the views of the authors and should not be construed to represent the views or policies of the U.S. Food and Drug Administration.
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 (multi-arm, multi-stage) 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/>), 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>.
Adaptive smoothing functions for estimating the blood
    oxygenation level dependent (BOLD) effect by using functional Magnetic
    Resonance Imaging (fMRI) data, based on adaptive Gauss Markov random
    fields, for real as well as simulated data. The implemented models make 
    use of efficient Markov Chain Monte Carlo methods. Implemented methods 
    are based on the research developed by A. Brezger, L. Fahrmeir, A. 
    Hennerfeind (2007) <https://www.jstor.org/stable/4626770>.
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.
A wrapper for 'ada-url', a 'WHATWG' compliant and fast URL parser written in modern 'C++'. Also contains auxiliary functions such as a public suffix extractor.
A number of functions to create and analyze factorial plans according to the Design of Experiments (DoE) approach, with the addition of some utility function to perform some statistical analyses. DoE approach follows the approach in "Design and Analysis of Experiments" by Douglas C. Montgomery (2019, ISBN:978-1-119-49244-3). The package also provides utilities used in the course "Analysis of Data and Statistics" at the University of Trento, Italy.
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. (2023) <doi:10.1007/s00180-022-01223-6>.
Provides a developer-facing interface to 'Arrow' Database
  Connectivity ('ADBC') for the purposes of driver development, driver
  testing, and building high-level database interfaces for users. 'ADBC'
  <https://arrow.apache.org/adbc/> is an API standard for database access
  libraries that uses 'Arrow' for result sets and query parameters.
Provides a developer-facing interface to the 'Arrow' Database
  Connectivity ('ADBC') 'PostgreSQL' driver for the purposes of building high-level
  database interfaces for users. 'ADBC' <https://arrow.apache.org/adbc/> is
  an API standard for database access libraries that uses 'Arrow' for result
  sets and query parameters.
Provides a developer-facing interface to the 'Arrow' Database
  Connectivity ('ADBC') 'SQLite' driver for the purposes of building high-level
  database interfaces for users. 'ADBC' <https://arrow.apache.org/adbc/> is
  an API standard for database access libraries that uses 'Arrow' for result
  sets and query parameters.
In order to make Arrow Database Connectivity ('ADBC' <https://arrow.apache.org/adbc/>) accessible from R, an interface compliant with the 'DBI' package is provided, using driver back-ends that are implemented in the 'adbcdrivermanager' framework. This enables interacting with database systems using the Arrow data format, thereby offering an efficient alternative to 'ODBC' for analytical applications.
Calculates some antecedent discharge conditions useful in
    water quality modeling. Includes methods for calculating flow
    anomalies, base flow, and smooth discounted flows from daily flow
    measurements. Antecedent discharge algorithms are described and
    reviewed in Zhang and Ball (2017) <doi:10.1016/j.jhydrol.2016.12.052>.
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.4784245>. '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>.
Implementation of the Tukey, Mandel, Johnson-Graybill, LBI, Tusell
    and modified Tukey non-additivity tests.
Methods for fitting identity-link GLMs and GAMs to discrete data,
    using EM-type algorithms with more stable convergence properties than standard methods.
Accelerated destructive degradation tests (ADDT) are often used to collect necessary data for assessing the long-term properties of polymeric materials. Based on the collected data, a thermal index (TI) is estimated. The TI can be useful for material rating and comparison. This package implements the traditional method based on the least-squares method, the parametric method based on maximum likelihood estimation, and the semiparametric method based on spline methods, and the corresponding methods for estimating TI for polymeric materials. The traditional approach is a two-step approach that is currently used in industrial standards, while the parametric method is widely used in the statistical literature. The semiparametric method is newly developed. Both the parametric and semiparametric approaches allow one to do statistical inference such as quantifying uncertainties in estimation, hypothesis testing, and predictions. Publicly available datasets are provided illustrations. More details can be found in Jin et al. (2017).
Tools for multivariate data analysis. Several methods are provided for the analysis (i.e., ordination) of one-table (e.g., principal component analysis, correspondence analysis), two-table (e.g., coinertia analysis, redundancy analysis), three-table (e.g., RLQ analysis) and K-table (e.g., STATIS, multiple coinertia analysis). The philosophy of the package is described in Dray and Dufour (2007) <doi:10.18637/jss.v022.i04>.
A Tcl/Tk GUI for some basic functions in the 'ade4' package.
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.
Extremely efficient procedures for fitting the entire group lasso and group elastic net regularization path for GLMs, multinomial, the Cox model and multi-task Gaussian models. Similar to the R package 'glmnet' in scope of models, and in computational speed.  This package provides  R bindings to the C++ code underlying the corresponding Python package 'adelie'. These bindings offer a general purpose group elastic net solver, 
    a wide range of matrix classes that can exploit special structure 
    to allow large-scale inputs, and an assortment of 
    generalized linear model classes for fitting various types of data. 
    The package is an implementation of Yang, J. and Hastie, T. (2024) <doi:10.48550/arXiv.2405.08631>.
Multivariate tools to analyze comparative data, i.e. a phylogeny and some traits measured for each taxa. The package contains functions to represent comparative data, compute phylogenetic proximities, perform multivariate analysis with phylogenetic constraints and test for the presence of phylogenetic autocorrelation. The package is described in Jombart et al (2010) <doi:10.1093/bioinformatics/btq292>.
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:978-84-8476-833-3).
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>.
Methods to evaluate the performance characteristics of
  various point and interval estimators for optimal adaptive two-stage designs as described
  in Meis et al. (2024) <doi:10.1002/sim.10020>.
  Specifically, this package is written to work with trial designs created by the 'adoptr' package
  (Kunzmann et al. (2021) <doi:10.18637/jss.v098.i09>; Pilz et al. (2021) <doi:10.1002/sim.8953>)).
  Apart from the a priori evaluation of performance characteristics, this package also allows for the
  evaluation of the implemented estimators on real datasets, and it implements methods
  to calculate p-values.
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. The 'adfExplorer' package enables you to
    establish R connections to files on such
    virtual DOS-formatted disks, which can be use to
    read from and write to those files.
Collect  your data on digital marketing campaigns from Adform Ads using the 'Windsor.ai' API <https://windsor.ai/api-fields/>.
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>.
The Langmuir and Freundlich adsorption isotherms are pivotal in characterizing adsorption processes, essential across various scientific disciplines. Proper interpretation of adsorption isotherms involves robust fitting of data to the models, accurate estimation of parameters, and efficiency evaluation of the models, both in linear and non-linear forms. For researchers and practitioners in the fields of chemistry, environmental science, soil science, and engineering, a comprehensive package that satisfies all these requirements would be ideal for accurate and efficient analysis of adsorption data, precise model selection and validation for rigorous scientific inquiry and real-world applications. Details can be found in Langmuir (1918) <doi:10.1021/ja02242a004> and Giles (1973) <doi:10.1111/j.1478-4408.1973.tb03158.x>.
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) <doi: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. For bootstrap 
             procedures, mean and CI bootstrap values of sensitivity, specificity, 
             crossing point between specificity and specificity as well as AUC 
             and AUCPR can be evaluated.
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.
Estimate and plot confounder-adjusted survival curves using
    either 'Direct Adjustment', 'Direct Adjustment with Pseudo-Values',
    various forms of 'Inverse Probability of Treatment Weighting', two
    forms of 'Augmented Inverse Probability of Treatment Weighting',
    'Empirical Likelihood Estimation' or 'Targeted Maximum Likelihood Estimation'.
	Also includes a significance test for the difference
    between two adjusted survival curves and the calculation of adjusted
    restricted mean survival times.  Additionally enables the user to
    estimate and plot cause-specific confounder-adjusted cumulative
    incidence functions in the competing risks setting using the same
    methods (with some exceptions).
	For details, see Denz et. al (2023) <doi:10.1002/sim.9681>.
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>.
Loss reserving generally focuses on identifying a single model that can generate superior predictive performance. However, different loss reserving models specialise in capturing different aspects of loss data.
    This is recognised in practice in the sense that results from different models are often considered, and sometimes combined. For instance, actuaries may take a weighted average of the prediction outcomes from
    various loss reserving models, often based on subjective assessments.
        This package allows for the use of a systematic framework to objectively combine (i.e. ensemble) multiple stochastic loss reserving models such that the strengths offered by different models can be utilised effectively. Our
    framework is developed in Avanzi et al. (2023). Firstly, our criteria model combination considers the full distributional properties of the ensemble and not just the central
    estimate - which is of particular importance in the reserving context. Secondly, our framework is that it is tailored for the features inherent to reserving data. These include, for instance, accident, development,
    calendar, and claim maturity effects. Crucially, the relative importance and scarcity of data across accident periods renders the problem distinct from the traditional ensemble techniques in statistical learning.
        Our framework is illustrated with a complex synthetic dataset. In the results, the optimised ensemble outperforms both (i) traditional model selection strategies, and (ii) an equally weighted ensemble. In
    particular, the improvement occurs not only with central estimates but also relevant quantiles, such as the 75th percentile of reserves (typically of interest to both insurers and regulators).
    Reference: Avanzi B, Li Y, Wong B, Xian A (2023) "Ensemble distributional forecasting for insurance loss reserving" <doi:10.48550/arXiv.2206.08541>.
A toolbox for programming Clinical Data Interchange Standards
    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>).
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.
Utility functions to check data, variables and conditions for
    functions used in 'admiral' and 'admiral' extension packages.
    Additional utility helper functions to assist developers with
    maintaining documentation, testing and general upkeep of 'admiral' and
    'admiral' extension packages.
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>).  The package is
    an extension package of the 'admiral' package focusing on the
    metabolism therapeutic area.
Programming oncology specific Clinical Data Interchange Standards 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>).
    The package is an extension package of the 'admiral' package.
Aids the programming of Clinical Data Standards Interchange
    Consortium (CDISC) compliant Ophthalmology 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 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>). The package is
    an extension package of the 'admiral' package for pediatric clinical
    trials.
Programming vaccine specific Clinical Data Interchange
    Standards Consortium (CDISC) compliant Analysis Data Model (ADaM)
    datasets in 'R'. Flat model is followed as per 
    Center for Biologics Evaluation and Research (CBER) guidelines for
    creating vaccine specific domains. 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>).
    The package is an extension package of 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 some more flexible alternatives to the base functions 'with()'
    and 'within()'.
    SOP simplification functions in this package use related minimization from
    package 'QCA', which is recommended to be installed 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 techniques 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>, and Milhaud, Pommeret, Salhi, Vandekerkhove
    (2024) <doi:10.3150/23-BEJ1593>. 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-sample contexts to test the unknown component density (see
    Milhaud, Pommeret, Salhi and Vandekerkhove (2022)
    <doi:10.1016/j.jspi.2021.05.010>, and Milhaud, Pommeret, Salhi,
    Vandekerkhove (2024) <doi:10.3150/23-BEJ1593>). Finally, clustering of
    unknown mixture components is also feasible in a K-sample setting (see
    Milhaud, Pommeret, Salhi, Vandekerkhove (2024) 
    <https://jmlr.org/papers/v25/23-0914.html>).
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>.
Estimate age-depth models from stratigraphic and sedimentological data,
    and transform data
    between the time and stratigraphic domain.
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 implemented objective criteria or custom objectives.
    Optimization under uncertainty and conditional (given stage-one outcome) 
    constraints are supported.
    See Pilz et al. (2019) <doi:10.1002/sim.8291> and 
    Kunzmann et al. (2021) <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.
The empirical cumulative average deviation function introduced by the author is utilized to
             develop both Ad- and Ud-plots. The Ad-plot can identify symmetry, skewness, and outliers of the
             data distribution, including anomalies. The Ud-plot created by slightly modifying Ad-plot is
             exceptional in assessing normality, outperforming normal QQ-plot, normal PP-plot, and their 
             derivations. The d-value that quantifies the degree of proximity between the Ud-plot and the graph
             of the estimated normal density function helps guide to make decisions on confirmation of 
             normality. Full description of this methodology can be found in the article by Wijesuriya (2025) 
             <doi:10.1080/03610926.2024.2440583>.
Obtain overlapping clustering models for object-by-variable data
        matrices using the Additive Profile Clustering (ADPROCLUS) method. 
        Also contains the low dimensional ADPROCLUS method 
        for simultaneous dimension reduction and overlapping clustering. 
        For reference see Depril, Van Mechelen, Mirkin (2008) 
        <doi:10.1016/j.csda.2008.04.014> and Depril, Van Mechelen, Wilderjans 
        (2012) <doi:10.1007/s00357-012-9112-5>.
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.
Model adsorption behavior using classical isotherms, including Langmuir,
    Freundlich, Brunauer–Emmett–Teller (BET), and Temkin models. The package supports
    parameter estimation through both linearized and non-linear fitting techniques and
    generates high-quality plots for model diagnostics. It is intended for environmental
    scientists, chemists, and researchers working on adsorption phenomena in soils,
    water treatment, and material sciences. Functions are compatible with base 'R' and
    'ggplot2' for visualization.
Provides tools for classical parameter estimation of adsorption isotherm models, including both 
  linear and nonlinear forms of the Freundlich, Langmuir, and Temkin isotherms. This package allows 
  users to fit these models to experimental data, providing parameter estimates along with fit statistics 
  such as Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). Error metrics are 
  computed to evaluate model performance, and the package produces model fit plots with bootstrapped 95% 
  confidence intervals. Additionally, it generates residual plots for diagnostic assessment of the models. 
  Researchers and engineers in material science, environmental engineering, and chemical engineering can 
  rigorously analyze adsorption behavior in their systems using this straightforward, non-Bayesian approach. 
  For more details, see Harding (1907) <doi:10.2307/2987516>.
Provides cross-validation tools for adsorption isotherm models, supporting both linear and non-linear forms. 
    Current methods cover commonly used isotherms including the Freundlich, Langmuir, and Temkin models. 
    This package implements K-fold and leave-one-out cross-validation (LOOCV) with optional clustering-based
    fold assignment to preserve underlying data structures during validation. Model predictive performance is assessed 
    using mean squared error (MSE), with optional graphical visualization of fold-wise MSEs to support intuitive evaluation of model accuracy. 
    This package is intended to facilitate rigorous model validation in adsorption studies and aid researchers in selecting robust isotherm models.
    For more details, see Montgomery et al. (2012) <isbn: 978-0-470-54281-1>, Lumumba et al. (2024) <doi:10.11648/j.ajtas.20241305.13>, and Yates et al. (2022) <doi:10.1002/ecm.1557>.
Provides tools for Bayesian parameter estimation of adsorption isotherm models using Markov Chain Monte Carlo (MCMC) methods. 
  This package enables users to fit non-linear and linear adsorption isotherm models—Freundlich, Langmuir, and Temkin—within a 
  probabilistic framework, capturing uncertainty and parameter correlations. It provides posterior summaries, 95% credible intervals, 
  convergence diagnostics (Gelman-Rubin), and visualizations through trace and density plots. With this R package, researchers can 
  rigorously analyze adsorption behavior in environmental and chemical systems using robust Bayesian inference. For more details, 
  see Gilks et al. (1995) <doi:10.1201/b14835>, and Gamerman & Lopes (2006) <doi:10.1201/9781482296426>.
Analyzes autocorrelation and partial autocorrelation using surrogate methods and 
  bootstrapping, and computes the acceleration constants for the vectorized moving block 
  bootstrap provided by this package. It generates percentile, bias-corrected, and accelerated 
  intervals and estimates partial autocorrelations using Durbin-Levinson. This package 
  calculates the autocorrelation power spectrum, computes cross-correlations between two 
  time series, computes bandwidth for any time series, and performs autocorrelation frequency 
  analysis. It also calculates the periodicity of a time series.
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.
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).
Provides new_partialised() and new_composed(), which extend 
    partial() and compose() functions of 'purrr' to make it easier to extract 
    and replace arguments and functions. It also has additional adverbial
    functions.
An application for analysis of Adverse Events, as described in Chen, et al., (2023) <doi:10.3390/cancers15092521>.
             The required data for the application includes demographics, follow up, adverse event, drug administration and optional tumor measurement data.
             The app can produce swimmers plots of adverse events, Kaplan-Meier plots and Cox Proportional Hazards model results
             for the association of adverse event biomarkers and overall survival and progression free survival.
             The adverse event biomarkers include occurrence of grade 3, low grade (1-2), and treatment related adverse events.
             Plots and tables of results are downloadable.
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. In addition to this, we also provide a simple way (Jones and Hulme, 1996) to grid the irregularly-spaced data points onto regular latitude-longitude grids by averaging all stations in grid-boxes.
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.
Facilitates access to the data from the Atlas do Estado Brasileiro
    (<https://www.ipea.gov.br/atlasestado/>), maintained by the Instituto de
    Pesquisa Econômica Aplicada (Ipea). It allows users to search for specific
    series, list series or themes, and download data when available.
A powerful tool for automating the early detection of disease
             outbreaks in time series data. 'aeddo' employs advanced statistical 
             methods, including hierarchical models, in an innovative manner to 
             effectively characterize outbreak signals. It is particularly useful 
             for epidemiologists, public health professionals, and researchers 
             seeking to identify and respond to disease outbreaks in a timely 
             fashion. For a detailed reference on hierarchical models, consult 
             Henrik Madsen and Poul Thyregod's book (2011), ISBN: 9781420091557.
Package to incorporate change point analysis in ARIMA forecasting.
A powerful tool for automating the early detection of seasonal epidemic
             onsets in time series data. It offers the ability to estimate growth rates
             across consecutive time intervals, calculate the sum of cases (SoC) within
             those intervals, and estimate seasonal onsets within user defined seasons.
             With use of a disease-specific threshold it also offers the possibility to
             estimate seasonal onset of epidemics.
             Additionally it offers the ability to estimate burden levels for seasons
             based on historical data. It is aimed towards epidemiologists,
             public health professionals, and researchers seeking to identify and respond
             to seasonal epidemics in a timely fashion.
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>.
Facilitate the analysis of data related to aquatic ecology, specifically the establishment of carbon budget. 
  Currently, the package allows the below analysis.
  (i) the calculation of greenhouse gas flux based on data obtained from trace gas analyzer using the method described in Lin et al. (2024).
  (ii) the calculation of Dissolved Oxygen (DO) metabolism based on data obtained from dissolved oxygen data logger using the method described in Staehr et al. (2010). 
  Yong et al. (2024) <doi:10.5194/bg-21-5247-2024>.
  Staehr et al. (2010) <doi:10.4319/lom.2010.8.0628>.
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. The asymmetric
             exponential power distribution studied here is a special case of that introduced
             by Dongming and Zinde-Walsh (2009) <doi:10.1016/j.jeconom.2008.09.038>.
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. <doi:10.1007/978-0-387-77318-6>
             (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>.
Colour palettes and a 'ggplot2' theme to follow the UK Government
    Analysis Function best practice guidance for producing data visualisations,
    available at
    <https://analysisfunction.civilservice.gov.uk/policy-store/data-visualisation-charts/>.
    Includes continuous and discrete colour and fill scales, as well as a
    'ggplot2' theme.
Government Analysis Function recommended colours for use in
    charts on gov.uk to help meet accessibility guidance.
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).
We aim to deal with data with measurement error in the response and misclassification censoring status under an AFT model. This package primarily contains three functions, which are used to generate artificial data, correction for error-prone data and estimate the functional covariates for an AFT model.
Dilate, permute, project, reflect, rotate, shear, and translate 2D and 3D points.  Supports parallel projections including oblique projections such as the cabinet projection as well as axonometric projections such as the isometric projection.  Use 'grid's "affine transformation" feature to render illustrated flat surfaces.
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 affluence line. Some simple errors are fixed and it works with new version of Spatial Statistics packaged.
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>.
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.
An R API providing access to a relational database with macroeconomic data for Africa. 
             The database contains >700 macroeconomic time series from mostly international sources, 
             grouped into 50 macroeconomic and development-related topics. Series are carefully selected
             on the basis of data coverage for Africa, frequency, and relevance to the macro-development context. 
             The project is part of the 'Kiel Institute Africa Initiative' 
             <https://www.ifw-kiel.de/institute/initiatives/kiel-institute-africa-initiative/>, 
             which, amongst other things, aims to develop a parsimonious database with highly relevant indicators 
             to monitor macroeconomic developments in Africa, accessible through a fast API and a web-based platform
             at <https://africamonitor.ifw-kiel.de/>. 
             The database is maintained at the Kiel Institute for the World Economy <https://www.ifw-kiel.de/>.
Generate spreadsheet publications that follow best practice
    guidance from the UK government's Analysis Function, available at 
    <https://analysisfunction.civilservice.gov.uk/policy-store/releasing-statistics-in-spreadsheets/>,
    with a focus on accessibility. See also the 'Python' package 'gptables'.
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>.
Compute the R-squared measure under the accelerated failure time (AFT) models proposed in Chan et. al. (2018) <doi:10.1080/03610918.2016.1177072>.
Implements several basic algorithms for estimating regression parameters for semiparametric accelerated failure time (AFT) model. The main methods are: Jin rank-based method (Jin (2003) <doi:10.1093/biomet/90.2.341>), Heller’s estimating method (Heller (2012) <doi:10.1198/016214506000001257>), Polynomial smoothed Gehan function method (Chung (2013) <doi:10.1007/s11222-012-9333-9>), Buckley-James method (Buckley (1979) <doi:10.2307/2335161>) and Jin`s improved least squares method (Jin (2006) <doi:10.1093/biomet/93.1.147>). This package can be used for modeling right-censored data and for comparing different estimation algorithms.
A collection of model checking methods for semiparametric accelerated 
    failure time (AFT) models under the rank-based approach. For the (computational)
    efficiency, Gehan's weight is used. It provides functions to verify whether the 
    observed data fit the specific model assumptions such as a functional form of 
    each covariate, a link function, and an omnibus test. The p-value offered in this 
    package is based on the Kolmogorov-type supremum test and the variance of the 
    proposed test statistics is estimated through the re-sampling method. Furthermore, 
    a graphical technique to compare the shape of the observed residual to a number of 
    the approximated realizations is provided.
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).
Calculate 'ActiGraph' counts from the X, Y, and Z axes of a triaxial 
    accelerometer. This work was inspired by Neishabouri et al. who published the 
    article "Quantification of Acceleration as Activity Counts in 'ActiGraph' Wearables" 
    on February 24, 2022. The link to the article (<https://pubmed.ncbi.nlm.nih.gov/35831446>) 
    and 'python' implementation of this code (<https://github.com/actigraph/agcounts>).
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.
Implements the Age Band Decomposition (ABD) method for 
 standardizing tree ring width data while preserving both low and high frequency 
 variability. Unlike traditional detrending approaches that can distort long term 
 growth trends, ABD decomposes ring width series into multiple age classes, detrends 
 each class separately, and then recombines them to create standardized chronologies. 
 This approach improves the detection of growth signals linked to past climatic and 
 environmental factors, making it particularly valuable for dendroecological and 
 dendroclimatological studies. The package provides functions to perform ABD-based 
 standardization, compare results with other common methods (e.g., BAI, C method, RCS), 
 and facilitate the interpretation of growth patterns under current and future climate 
 variability.
Pair of simple convenience functions to convert a vector of birth dates to age and age distributions. These functions may be helpful when related age and custom age distributions are desired given a vector of birth dates.
An R wrapper for 'agena.ai' <https://www.agena.ai> which provides users capabilities to work with 'agena.ai' using the R environment. Users can create Bayesian network models from scratch or import existing models in R and export to 'agena.ai' cloud or local API for calculations. Note: running calculations requires a valid 'agena.ai' API license (past the initial trial period of the local API).
Automate the modelling of age-structured population data
  using survey data, grid population estimates and urban-rural extents.
Provides a collection of efficient functions for working with
  individual ages and corresponding intervals. These include functions for
  conversion from an age to an interval, aggregation of ages with associated
  counts in to intervals and the splitting of interval counts based on
  specified age distributions.
Implements the Agnostic Fay-Herriot model, an extension of 
    the traditional small area model. In place of normal sampling errors, the 
    sampling error distribution is estimated with a Gaussian process to 
    accommodate a broader class of distributions. This flexibility is most 
    useful in the presence of bounded, multi-modal, or heavily skewed sampling 
    errors.
The 'agghoo' procedure is an alternative to usual cross-validation.
    Instead of choosing the best model trained on V subsamples, it determines
    a winner model for each subsample, and then aggregates the V outputs.
    For the details, see "Aggregated hold-out" by Guillaume Maillard,
    Sylvain Arlot, Matthieu Lerasle (2021) <arXiv:1909.04890>
    published in Journal of Machine Learning Research 22(20):1--55.
The use of structured elicitation to inform decision making has grown dramatically in recent decades, however, judgements from multiple experts must be aggregated into a single estimate. Empirical evidence suggests that mathematical aggregation provides more reliable estimates than enforcing behavioural consensus on group estimates. 'aggreCAT' provides state-of-the-art mathematical aggregation methods for elicitation data including those defined in Hanea, A. et al. (2021) <doi:10.1371/journal.pone.0256919>. The package also provides functions to visualise and evaluate the performance of your aggregated estimates on validation data.
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>].
Nonparametric data-driven approach to discovering heterogeneous subgroups in a selection-on-observables framework. 
    'aggTrees' allows researchers to assess whether there exists relevant heterogeneity in treatment effects by generating a sequence of optimal groupings, 
    one for each level of granularity. For each grouping, we obtain point estimation and inference about the group average treatment effects. 
    Please reference the use as Di Francesco (2022) <doi:10.2139/ssrn.4304256>.
Provides several methods for aggregating probabilistic forecasts. You have a group of
    people who have made probabilistic forecasts for the same event. You want to take advantage of
    the "wisdom of the crowd" and combine these forecasts in some sensible way. This package
    provides implementations of several strategies, including geometric mean of odds, an extremized
    aggregate (Neyman, Roughgarden (2021) <doi:10.1145/3490486.3538243>), and "high-density trimmed
    mean" (Powell et al. (2022) <doi:10.1037/dec0000191>).
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.
Power and associated functions useful in prospective planning and monitoring of a clinical trial when a recurrent event endpoint is to be assessed by the robust Andersen-Gill model, see Lin, Wei, Yang, and Ying (2010) <doi:10.1111/1467-9868.00259>. The equations developed in Ingel and Jahn-Eimermacher (2014) <doi:10.1002/bimj.201300090> and their consequences are employed.
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 (2021), <doi:10.1111/ajae.12155>)).
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.
Utilities designed to make the analysis of field trials easier and
 more accessible for everyone working in plant breeding. It provides a simple
 and intuitive interface for conducting single and multi-environmental trial
 analysis, with minimal coding required. Whether you're a beginner or an
 experienced user, 'agriutilities' will help you quickly and easily carry out
 complex analyses with confidence. With built-in functions for fitting Linear
 Mixed Models, 'agriutilities' is the ideal choice for anyone who wants to save
 time and focus on interpreting their results.
 Some of the functions require the R package 'asreml' for the 'ASReml' software,
 this can be obtained upon purchase from 'VSN' international <https://vsni.co.uk/software/asreml-r/>.
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.
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). Shimizu, G. D., Marubayashi, R. Y. P., Goncalves, L. S. A. (2025) <doi:10.4025/actasciagron.v47i1.73889>.
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.
Construct time series for Germany's municipalities (Gemeinden) and districts (Kreise) using a annual crosswalk constructed by the Federal Office for Building and Regional Planning (BBSR).
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.
Create and evaluate models using 'tidymodels' and 'h2o' <https://h2o.ai/>. The
    package enables users to specify 'h2o' as an engine for several
    modeling methods.
'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.
Provides functions to simulate data sets from hierarchical ecological models, including all the simulations described in the two volume publication 'Applied Hierarchical Modeling in Ecology: Analysis of distribution, abundance and species richness in R and BUGS' by Marc Kéry and Andy Royle: volume 1 (2016, ISBN: 978-0-12-801378-6) and volume 2 (2021, ISBN: 978-0-12-809585-0), <https://www.mbr-pwrc.usgs.gov/pubanalysis/keryroylebook/>. It also has all the utility functions and data sets needed to replicate the analyses shown in the books.
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.
Implements the Analytic Hierarchy Process (AHP) method using Gaussian normalization (AHPGaussian) to derive the relative weights of the criteria and alternatives. It also includes functions for visualizing the results and generating graphical outputs. Method as described in: dos Santos, Marcos (2021) <doi:10.13033/ijahp.v13i1.833>.
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.
An integrated set of functions for building, analyzing, and visualizing 
 Analytic Hierarchy Process (AHP) models, designed to support structured decision-making
 in consultancy, policy analysis, and research (Bose 2022 <doi:10.1002/mcda.1784>; 
 Bose 2023 <doi:10.1002/mcda.1821>). In addition to tools for assessing and improving the 
 consistency of pairwise comparison matrices (PCMs), the package supports 
 full-hierarchy weight computation, intuitive tree-based visualization, sensitivity 
 analysis, along with convenient PCM generation from user preferences.
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>.
Compute a tree level hierarchy, judgment matrix, consistency index and ratio, priority vectors, hierarchic synthesis and rank. Based on the book entitled "Models, Methods, Concepts and Applications of the Analytic Hierarchy Process" by Saaty and Vargas (2012, ISBN 978-1-4614-3597-6).
Flexible parametric Accelerated Hazards (AH) regression models in overall and relative survival frameworks with 13 distinct Baseline Distributions. The AH Model can also be applied to lifetime data with crossed survival curves. Any user-defined parametric distribution can be fitted, given at least an R function defining the cumulative hazard and hazard rate functions. See Chen and Wang (2000) <doi:10.1080/01621459.2000.10474236>, and Lee (2015) <doi:10.1007/s10985-015-9349-5> for more details.
Estimates continuous time weights for performing causal survival
             analysis. For instance, weighted Nelson-Aalen or Kaplan-Meier
             estimates can be given a causal interpretation. See Ryalen, Stensrud, 
             and Røysland (2019) <doi:10.1007/s10985-019-09468-y> and Ryalen (2019)
             <https://www.duo.uio.no/handle/10852/70353>
             for theory and examples.
An interface for data processing, building models, predicting values and analysing outcomes. Fitting Linear Models, Robust Fitting of Linear Models, k-Nearest Neighbor Classification, 1-Nearest Neighbor Classification, and Conditional Inference Trees are available.
A set of tests for compositional pathologies. Tests for coherence of correlations with aIc.coherent() as suggested by (Erb et al. (2020) <doi:10.1016/j.acags.2020.100026>),  compositional dominance of distance with aIc.dominant(), compositional perturbation invariance with aIc.perturb() as suggested by (Aitchison (1992) <doi:10.1007/BF00891269>) and singularity of the covariation matrix with aIc.singular(). Currently tests five data transformations: prop, clr, TMM, TMMwsp, and RLE from the R packages 'ALDEx2', 'edgeR' and 'DESeq2' (Fernandes et al (2014) <doi:10.1186/2049-2618-2-15>, Anders et al. (2013)<doi:10.1038/nprot.2013.099>).
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.
Provides tools for model selection and model averaging of PerMANOVA 
    models using Akaike Information Criterion corrected for small sample sizes 
    (AICc) and Information Theoretic criteria principles. The package is built 
    around the PERMANOVA analysis from the 'vegan' package and provides a 
    streamlined workflow for generating and comparing models, obtaining model 
    weights, and summarizing results using model averaging approaches.  The 
    methods implemented in this package are based on the practical information-
    theoretic approach described by Burnham, K. P. and Anderson, D. R. (2002) 
    (<doi:10.1007/b97636>).
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.
Provides tools for the quantitative analysis of axon integrity in microscopy images. 
  It implements image pre-processing, adaptive thresholding, feature extraction, and support vector machine-based classification to compute indices such as the Axon Integrity Index (AII) and Degeneration Index (DI).
  The package is designed for reproducible and automated analysis in neuroscience research.
In social and educational settings, the use of Artificial
    Intelligence (AI) is a challenging task. Relevant data is often only
    available in handwritten forms, or the use of data is restricted by
    privacy policies. This often leads to small data sets. Furthermore, in
    the educational and social sciences, data is often unbalanced in terms
    of frequencies. To support educators as well as educational and social
    researchers in using the potentials of AI for their work, this package
    provides a unified interface for neural nets in 'PyTorch' to deal with
    natural language problems. In addition, the package ships with a shiny
    app, providing a graphical user interface.  This allows the usage of
    AI for people without skills in writing python/R scripts.  The tools
    integrate existing mathematical and statistical methods for dealing
    with small data sets via pseudo-labeling (e.g. Cascante-Bonilla et al.
    (2020) <doi:10.48550/arXiv.2001.06001>) and imbalanced data via the
    creation of synthetic cases (e.g.  Islam et al. (2012)
    <doi:10.1016/j.asoc.2021.108288>).  Performance evaluation of AI is
    connected to measures from content analysis which educational and
    social researchers are generally more familiar with (e.g. Berding &
    Pargmann (2022) <doi:10.30819/5581>, Gwet (2014)
    <ISBN:978-0-9708062-8-4>, Krippendorff (2019)
    <doi:10.4135/9781071878781>). Estimation of energy consumption and CO2
    emissions during model training is done with the 'python' library
    'codecarbon'.  Finally, all objects created with this package allow to
    share trained AI models with other people.
Functions are provided to read and convert AIFF audio files to WAVE (WAV) format.  This supports, for example, use of the 'tuneR' package, which does not currently handle AIFF files.  The AIFF file format is defined in  <https://web.archive.org/web/20080125221040/http://www.borg.com/~jglatt/tech/aiff.htm> and   <https://www.mmsp.ece.mcgill.ca/Documents/AudioFormats/AIFF/Docs/AIFF-1.3.pdf> .
Provides statistical methods for analyzing experimental 
    evaluation of the causal impacts of algorithmic recommendations 
    on human decisions developed by Imai, Jiang, Greiner, Halen, and 
    Shin (2023) <doi:10.1093/jrsssa/qnad010> and Ben-Michael, Greiner,  
    Huang, Imai, Jiang, and Shin (2024) <doi:10.48550/arXiv.2403.12108>.
    The data used for this paper, and made available here, are interim, 
    based on only half of the observations in the study and (for those 
    observations) only half of the study follow-up period. We use them 
    only to illustrate methods, not to draw substantive conclusions.
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.
A toolkit for archaeological time series and time intervals.
    This package provides a system of classes and methods to represent and
    work with archaeological time series and time intervals. Dates are
    represented as "rata die" and can be converted to (virtually) any
    calendar defined by Reingold and Dershowitz (2018)
    <doi:10.1017/9781107415058>. This packages offers a simple API that
    can be used by other specialized packages.
The 'AIPW' package 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). AIPW: An R Package for Augmented Inverse Probability Weighted Estimation of Average Causal Effects. American Journal of Epidemiology. <doi:10.1093/aje/kwab207>". Visit: <https://yqzhong7.github.io/AIPW/> for more information.
An R console utility that lets you ask R related questions to the
    'OpenAI' large language model. It can answer how-to questions by  
    providing code, and what-is questions by explaining what given code does.
    You must provision your own key for the 'OpenAI' API 
    <https://platform.openai.com/docs/api-reference>.
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.
Model that assesses daily exposure to air pollution, which considers daily population mobility on a geographical scale and the spatial and temporal variability of pollutant concentrations, in addition to traditional parameters such as exposure time and pollutant concentration.
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.
Sample of hydro-meteorological datasets extracted from the 'CAMELS-FR' French database <https://hal.inrae.fr/hal-03687235>. It provides metadata and catchment-scale aggregated hydro-meteorological time series on a pool of French catchments for use by the 'airGR' packages.
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 Modeling 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://document.airnow.gov/technical-assistance-document-for-the-reporting-of-daily-air-quailty.pdf>.
Retrieve air quality data via the 'AirNow'
    <https://www.airnow.gov/> API.
Airport problems, introduced by Littlechild and Owen (1973)
    <https://www.jstor.org/stable/2629727>, are cost allocation 
    problems where agents share the cost of a facility (or service) based on 
    their ordered needs. Valid allocations must satisfy no-subsidy constraints, 
    meaning that no group of agents contributes more than the highest cost of 
    its members (i.e., no agent is allowed to subsidize another). A rule is a 
    mechanism that selects an allocation vector for a given problem. This 
    package computes several rules proposed in the literature, including both 
    standard rules and their variants, such as weighted versions, rules for 
    clones, and rules based on the agents’ hierarchy order. These rules can be 
    applied to various problems of interest, including the allocation of 
    liabilities and the maintenance of irrigation systems, among others.  
    Moreover, the package provides functions for graphical representation, 
    enabling users to visually compare the outcomes produced by each rule, 
    or to display the no-subsidy set. In addition, it includes four datasets 
    illustrating different applications and examples of airport problems. For a 
    more detailed explanation of all concepts, see Thomson (2024)
    <doi:10.1016/j.mathsocsci.2024.03.007>.
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>.
Implements two complementary high-dimensional feature screening
    methods, Adaptive Iterative Ridge High-dimensional Ordinary Least-squares
    Projection (Air-HOLP, suitable when the number of predictors p is greater
    than or equal to the sample size n) and Adaptive Iterative Ridge Ordinary
    Least Squares (Air-OLS, for n greater than p). Also  provides helper
    functions to generate compound-symmetry and AR(1) correlated data, plus
    a unified Air() front end and a summary method. For methodological details
    see Joudah, Muller and Zhu (2025) <doi:10.1007/s11222-025-10599-6>.
Plots simulation results of clinical trials. Its main feature is allowing users to simultaneously investigate the impact of several simulation input dimensions through dynamic filtering of the simulation results. A more detailed description of the app can be found in Meyer et al. <DOI:10.1016/j.softx.2023.101347> or the vignettes on 'GitHub'.
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.
Provides functions to conduct title and abstract screening in systematic reviews using large language models, such as the Generative Pre-trained Transformer (GPT) models from 'OpenAI' <https://platform.openai.com/>. These functions can enhance the quality of title and abstract screenings while reducing the total screening time significantly. In addition, the package includes tools for quality assessment of title and abstract screenings, as described in Vembye, Christensen, Mølgaard, and Schytt (2025) <DOI:10.1037/met0000769>.
Nonparametric estimation of additive isotonic covariate effects for proportional hazards model.
Uncertainty quantification and inverse estimation by probabilistic generative models from the beginning of the data analysis. An example is a Fourier basis method for inverse estimation in scattering analysis of microscopy videos. It does not require specifying a certain range of Fourier bases and it substantially reduces computational cost via the generalized Schur algorithm. See the reference: Mengyang Gu, Yue He, Xubo Liu and Yimin Luo (2023), <doi:10.48550/arXiv.2309.02468>.
A thin wrapper around the 'ajv' JSON validation package for
    JavaScript. See <http://epoberezkin.github.io/ajv/> for details.
Create aliases for other R names or arbitrarily complex R expressions. Accessing the alias acts as-if the aliased expression were invoked instead, and continuously reflects the current value of that expression: updates to the original expression will be reflected in the alias; and updates to the alias will automatically be reflected in the original expression.
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.
  Please note that most of the functions are now also covered in package
  interp, which is a re-implementation from scratch under a free license.
Implements the alternating k-means biclustering algorithm in Fraiman and Li (2020) <arXiv:2009.04550>.
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>.
Utility functions to download and process data produced by the ALARM Project,
    including 2020 redistricting files Kenny and McCartan (2021) 
    <https://alarm-redist.org/posts/2021-08-10-census-2020/> and the 50-State 
    Redistricting Simulations of McCartan, Kenny, Simko, Garcia, Wang, Wu, 
    Kuriwaki, and Imai (2022) <doi:10.7910/DVN/SLCD3E>. The package extends 
    the data introduced in McCartan, Kenny, Simko, Garcia, Wang, Wu, Kuriwaki,
    and Imai (2022) <doi:10.1038/s41597-022-01808-2> to also include states with 
    only a single district.
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.
Provides tools for estimating length-based indicators from length frequency data to assess fish stock status and manage fisheries sustainably. Implements methods from Cope and Punt (2009) <doi:10.1577/C08-025.1> for data-limited stock assessment and Froese (2004) <doi:10.1111/j.1467-2979.2004.00144.x> for detecting overfishing using simple indicators. Key functions include:
    FrequencyTable(): Calculate the frequency table from the collected and also the extract the length frequency data from the frequency table with the upper length_range. A numeric value specifying the bin width for class intervals. If not provided, the bin width is automatically calculated using Sturges (1926) <doi:10.1080/01621459.1926.10502161> formula.
    CalPar(): Calculates various lengths used in fish stock assessment as biological length indicators such as asymptotic length (Linf), maximum length (Lmax), length at sexual maturity (Lm), and optimal length (Lopt).
    FishPar(): Calculates length-based indicators (LBIs) proposed by Froese (2004) <doi:10.1111/j.1467-2979.2004.00144.x> such as the percentage of mature fish (Pmat), percentage of optimal length fish (Popt), percentage of mega spawners (Pmega), and the sum of these as Pobj. This function also estimates confidence intervals for different lengths, visualizes length frequency distributions, and provides data frames containing calculated values.
    FishSS(): Makes decisions based on input from Cope and Punt (2009) <doi:10.1577/C08-025.1> and parameters calculated by FishPar() (e.g., Pobj, Pmat, Popt, LM_ratio) to determine stock status as target spawning biomass (TSB40) and limit spawning biomass (LSB25).
    LWR(): Fits and visualizes length-weight relationships using linear regression, with options for log-transformation and customizable plotting.
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>.
Contains data from an observational study concerning possible effects of light daily alcohol consumption on survival and on HDL cholesterol.  It also replicates various simple analyses in Rosenbaum (2025a) <doi:10.1080/09332480.2025.2473291>.  Finally, it includes new R code in wgtRankCef() that implements and replicates a new method for constructing evidence factors in observational block designs.
Interface package for 'sala', the spatial network analysis library
    from the 'depthmapX' software application. The R parts of the code are based
    on the 'rdepthmap' package. Allows for the analysis of urban and
    building-scale networks and provides metrics and methods usually found
    within the Space Syntax domain. Methods in this package are described by K.
    Al-Sayed, A. Turner, B. Hillier, S. Iida and A. Penn (2014) "Space Syntax
    methodology", and also by A. Turner (2004)
    <https://discovery.ucl.ac.uk/id/eprint/2651> "Depthmap 4: a researcher's
    handbook".
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.
An implementation of the ALFAM2 dynamic emission model for ammonia volatilization from field-applied animal slurry (manure with dry matter below about 15%). The model can be used to predict cumulative emission and emission rate of ammonia following field application of slurry. Predictions may be useful for emission inventory calculations, fertilizer management, assessment of mitigation strategies, or research aimed at understanding ammonia emission. Default parameter sets include effects of application method, slurry composition, and weather. The model structure is based on a simplified representation of the physical-chemical slurry-soil-atmosphere system. See Hafner et al. (2018) <doi:10.1016/j.atmosenv.2018.11.034> for information on the model and Hafner et al. (2019) <doi:10.1016/j.agrformet.2017.11.027> for more on the measurement data used for parameter development.
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.
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).
Easily estimate the introduction rates of alien species given first records data.
    It specializes in addressing the role of sampling on the pattern of discoveries, 
    thus providing better estimates than using Generalized Linear Models which assume perfect 
    immediate detection of newly introduced species.
A dynamic time warping (DTW) algorithm for stratigraphic alignment,
    translated into R from the original published 'MATLAB' code by Hay et al. (2019)
    <doi:10.1130/G46019.1>. The DTW algorithm incorporates two geologically relevant
    parameters (g and edge) for augmenting the typical DTW cost matrix, allowing
    for a range of sedimentologic and chronologic conditions to be explored, as 
    well as the generation of an alignment library (as opposed to a single alignment
    solution). The g parameter relates to the relative sediment accumulation rate
    between the two time series records,  while the edge parameter relates to the 
    amount of total shared time between the records. Note that this algorithm is
    used for all DTW alignments in the Align Shiny application, detailed in Hagen
    et al. (in review).
Allows for multiple group item response theory alignment a la 'Mplus' to be applied to lists of single-group models estimated in 'lavaan' or 'mirt'. Allows item sets that are overlapping but not identical, facilitating alignment in secondary data analysis where not all items may be shared across assessments.
A lightweight, dependency-free toolbox for pre-processing XY
    data from experimental methods (i.e. any signal that can be measured
    along a continuous variable). This package provides methods for
    baseline estimation and correction, smoothing, normalization,
    integration and peaks detection. Baseline correction methods includes
    polynomial fitting as described in Lieber and Mahadevan-Jansen (2003)
    <doi:10.1366/000370203322554518>, Rolling Ball algorithm after Kneen
    and Annegarn (1996) <doi:10.1016/0168-583X(95)00908-6>, SNIP algorithm
    after Ryan et al. (1988) <doi:10.1016/0168-583X(88)90063-8>, 4S Peak
    Filling after Liland (2015) <doi:10.1016/j.mex.2015.02.009> and more.
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; targeted for use with large datasets and databases containing multiple samples of each individual (a common situation in conservation genetics, 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 package, please cite the original publication in Molecular Ecology Resources (Galpern et al., 2012), the details for which can be generated using citation('allelematch'). For a complete vignette, please access via the Data S1 Supplementary documentation and tutorials (PDF) located at <doi:10.1111/j.1755-0998.2012.03137.x>.
Simulate the effect of management or demography on allele retention and inbreeding accumulation in bottlenecked populations of animals with overlapping generations.
Methods (<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.
Implementation of gene-level rare variant association tests targeting allelic series: genes where increasingly deleterious mutations have increasingly large phenotypic effects. The COding-variant Allelic Series Test (COAST) operates on the benign missense variants (BMVs), deleterious missense variants (DMVs), and protein truncating variants (PTVs) within a gene. COAST uses a set of adjustable weights that tailor the test towards rejecting the null hypothesis for genes where the average magnitude of effect increases monotonically from BMVs to DMVs to PTVs. See McCaw ZR, O’Dushlaine C, Somineni H, Bereket M, Klein C, Karaletsos T, Casale FP, Koller D, Soare TW. (2023) "An allelic series rare variant association test for candidate gene discovery" <doi:10.1016/j.ajhg.2023.07.001>.
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 (2007) <doi:10.1177/1536867X0700700203> ), 
    '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 Cox proportional hazards regression: all_cox().
Provides a function to calculate multiple performance metrics for actual and predicted values. In total eight metrics will be calculated for particular actual and predicted series. Helps to describe a Statistical model's performance in predicting a data. Also helps to compare various models' performance. The metrics are Root Mean Squared Error (RMSE), Relative Root Mean Squared Error (RRMSE), Mean absolute Error (MAE), Mean absolute percentage error (MAPE), Mean Absolute Scaled Error (MASE), Nash-Sutcliffe Efficiency (NSE), Willmott’s Index (WI), and Legates and McCabe Index (LME). Among them, first five are expected to be lesser whereas, the last three are greater the better. More details can be found from Garai and Paul (2023) <doi:10.1016/j.iswa.2023.200202> and Garai et al. (2024) <doi:10.1007/s11063-024-11552-w>.
Evaluates acute lymphoblastic leukemia maintenance therapy practice at patient and cohort level.
Streamline use of the 'All of Us' Researcher Workbench (<https://www.researchallofus.org/data-tools/workbench/>)with tools to extract and manipulate data from the 'All of Us' database. Increase interoperability with the Observational Health Data Science and Informatics ('OHDSI') tool stack by decreasing reliance of 'All of Us' tools and allowing for cohort creation via 'Atlas'. Improve reproducible and transparent research using 'All of Us'.
Examples of datasets on allometry, the study of the relationship of biological traits to body size. This package contains the dataset of morphological measurement taken from 113 maritime earwigs (Anisolabis maritima) by Matsuzawa and Konuma (2025) <doi:10.1093/biolinnean/blaf031>.
Implementation of the technique of Lleonart et al. 
    (2000) <doi:10.1006/jtbi.2000.2043> to scale body measurements 
    that exhibit an allometric growth. This procedure is a theoretical 
    generalization of the technique used by Thorpe 
    (1975) <doi:10.1111/j.1095-8312.1975.tb00732.x> and
    Thorpe (1976) <doi:10.1111/j.1469-185X.1976.tb01063.x>.
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, International Journal of Molecular Sciences 23:4574, <doi:10.3390/ijms23094574>. 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.
Provides statistical tools to analyze heterogeneous effects of rare variants within genes that are associated with multiple traits. The package implements methods for assessing pleiotropic effects and identifying allelic heterogeneity, which can be useful in large-scale genetic studies. Methods include likelihood-based statistical tests to assess these effects. For more details, see Lu et al. (2024) <doi:10.1101/2024.10.01.614806>.
Creating alluvial diagrams (also known as parallel sets plots) for multivariate
  and time series-like data.
Provides tools for defining recurrence rules and recurrence
    sets. 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 sets. A full holiday and
    calendar interface is also provided that can generate holidays within
    a particular year, can detect if a date is a holiday, can respect
    holiday observance rules, and allows for custom holidays.
A collection of datasets on the Alone survival TV series in tidy format. 
  Included in the package are 4 datasets detailing the survivors, their loadouts,
  episode details and season information.
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.
Creates all leave-one-out models and produces predictions for test samples.
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 nonlinear. 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 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 the alpha-adjustment correction from "Benjamini, Y., & Hochberg, Y. (1995) <doi:10.1111/j.2517-6161.1995.tb02031.x> Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal statistical society: series B (Methodological), 57(1), 289-300". For researchers interested in using the exact mathematical formulas and procedures as used in the original paper.
Provides algorithms for frequency-based pairing of alpha-beta T
  cell receptors.
Calculate confidence intervals for alpha and standardized
    alpha using asymptotic theory or the studentized bootstrap, with or
    without transformations. Supports the asymptotic distribution-free
    method of Maydeu-Olivares, et al. (2007)
    <doi:10.1037/1082-989X.12.2.157>, the pseudo-elliptical method of Yuan
    & Bentler (2002) <doi:10.1007/BF02294845>, and the normal method of
    van Zyl et al. (1999) <doi:10.1007/BF02296146>, for both coefficient
    alpha and standardized alpha.
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.
Sets the alpha level for coefficients in a regression model
    as a decreasing function of the sample size through the use of
    Jeffreys' Approximate Bayes factor. You tell alphaN() your sample
    size, and it tells you to which value you must lower alpha to avoid
    Lindley's Paradox. For details, see Wulff and Taylor (2024)
    <doi:10.1177/14761270231214429>.
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.
Quantile regression with fixed effects solves longitudinal data, considering the individual intercepts as fixed effects. The parametric set of this type of problem used to be huge. Thus penalized methods such as Lasso are currently applied. Adaptive Lasso presents oracle proprieties, which include Gaussianity and correct model selection. Bayesian information criteria (BIC) estimates the optimal tuning parameter lambda. Plot tools are also available.
Datasets to Accompany S. Weisberg (2014, ISBN: 978-1-118-38608-8), 
 "Applied Linear Regression," 4th edition.  Many data files 
 in this package are included in the `alr3` package as well, so only one of them 
 should be used.
Alternating least squares is often used to resolve
        components contributing to data with a bilinear structure; the
        basic technique may be extended to alternating constrained
        least squares.  Commonly applied constraints include
        unimodality, non-negativity, and normalization of components.
        Several data matrices may be decomposed simultaneously by
        assuming that one of the two matrices in the bilinear
        decomposition is shared between datasets.
Using of the accelerated line search algorithm  for simultaneously diagonalize a set of symmetric positive definite matrices.
Functions 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'.
This is a simple and powerful package to create, render, preview, 
    and deploy documentation websites for 'R' packages. It is a lightweight and 
    flexible alternative to 'pkgdown', with support for many documentation 
    generators, including 'Quarto', 'Docute', 'Docsify', and 'MkDocs'.
Inference of protein complex states from quantitative proteomics data. The package takes information on known stable protein interactions (i.e. protein components of the same complex) and assesses how protein quantitative ratios change between different conditions. It reports protein pairs for which relative protein quantities to each other have been significantly altered in the tested condition.
Alternative and fast algorithms for the analysis of receiver operating 
  characteristics curves (ROC curves) as described in Thomas et al. (2017) 
  <doi:10.1186/s41512-017-0017-y> and Thomas et al. (2023) <doi:10.1016/j.ajogmf.2023.101110>.
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 and penalization methods that are robust to outliers
   (Lin et al., 2017 <doi:10.1111/biom.12543>;
    Wang et al., 2022 <doi:10.1002/sim.9261>);
 - 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 combining standardized mean differences and odds ratios
   (Jing et al., 2023 <doi:10.1080/10543406.2022.2105345>);
 - 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 (2020) <DOI:10.1525/collabra.343>.
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++.
Functions are designed to facilitate access to and utility with large scale, publicly available environmental data in R. The package contains functions for downloading raw data files from web URLs (download_data()), processing the raw data files into clean spatial objects (process_covariates()), and extracting values from the spatial data objects at point and polygon locations (calculate_covariates()). These functions call a series of source-specific functions which are tailored to each data sources/datasets particular URL structure, data format, and spatial/temporal resolution. The functions are tested, versioned, and open source and open access. For sum_edc() method details, see Messier, Akita, and Serre (2012) <doi:10.1021/es203152a>.
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.
Build and control interactive 2D and 3D maps with 'R/Shiny'.  Lean set of powerful commands wrapping native calls to 'AMap' <https://lbs.amap.com/api/jsapi-v2/summary/>.
    Deliver rich mapping functionality with minimal overhead.
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>.
Collect  your data on digital marketing campaigns from Amazon Ads using the 'Windsor.ai' API <https://windsor.ai/api-fields/>.
Collect  your data on digital marketing campaigns from Amazon S3 using the 'Windsor.ai' API <https://windsor.ai/api-fields/>.
Collect  your data on digital marketing campaigns from Amazon Sp using the 'Windsor.ai' API <https://windsor.ai/api-fields/>.
'Amber' is a server application for capturing electronic data records.
    Rich forms are used to collect data. This 'Amber' client allows
    to perform data extraction for reporting or data transfer at persistent location
    purposes.
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 perturbation 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.
Simulation and estimation tools for 
    various types of ambit processes, including trawl processes and weighted
    trawl processes.
Accompanies the book "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 
    <https://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.
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.
A collection of lightweight functions that can be used to
    determine the computing environment in which your code is running.
    This includes operating systems, continuous integration (CI)
    environments, containers, and more.
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.
Fast tool to calculate the Adjusted Market Inefficiency Measure following Tran & Leirvik (2019) <doi:10.1016/j.frl.2019.03.004>. This tool provides rolling window estimates of the Adjusted Market Inefficiency Measure for multiple instruments simultaneously.
Implements the Adaptive Multiple Importance Sampling (AMIS) algorithm, as described by Retkute et al. (2021, <doi:10.1214/21-AOAS1486>), to estimate key epidemiological parameters by combining outputs from a geostatistical model of infectious diseases (such as prevalence, incidence, or relative risk) with a disease transmission model. Utilising the resulting posterior distributions, the package enables forward projections at the local level.
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.
Computes various stability parameters from Additive Main Effects
    and Multiplicative Interaction (AMMI) analysis results such as  Modified
    AMMI Stability Value (MASV), Sums of the Absolute Value of the Interaction
    Principal Component Scores (SIPC), Sum Across Environments of
    Genotype-Environment Interaction Modelled by AMMI (AMGE), Sum Across 
    Environments of Absolute Value of Genotype-Environment Interaction Modelled
    by AMMI (AV_(AMGE)), AMMI Stability Index (ASI), Modified ASI (MASI), AMMI
    Based Stability Parameter (ASTAB), Annicchiarico's D Parameter (DA), Zhang's
    D Parameter (DZ), Averages of the Squared Eigenvector Values (EV), Stability
    Measure Based on Fitted AMMI Model (FA), Absolute Value of the Relative
    Contribution of IPCs to the Interaction (Za). Further calculates the
    Simultaneous Selection Index for Yield and Stability from the computed
    stability parameters. See the vignette for complete list of citations for
    the methods implemented.
Helps enable adaptive management by codifying knowledge in the
    form of models generated from numerous analyses and data sets. Facilitates
    this process by storing all models and data sets in a single object that can
    be updated and saved, thus tracking changes in knowledge through time. A shiny
    application called AM Model Manager (modelMgr()) enables the use of these
    functions via a GUI.
Provides a function to calculate the concentration of un-ionized ammonia in the total ammonia in aqueous solution using the pH and temperature values.
Automated generation, running, and interpretation of moderated nonlinear factor analysis models for obtaining scores from observed variables, 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.
Fits tractable fully parametric odds-based regression models for survival data, including proportional odds (PO), accelerated failure time (AFT), accelerated odds (AO), and General Odds (GO) models in overall survival frameworks. Given at least an R function specifying the survivor, hazard rate and cumulative distribution functions, any user-defined parametric distribution can be fitted. We applied and evaluated a minimum of seventeen (17) various baseline distributions that can handle different failure rate shapes for each of the four different proposed odds-based regression models. For more information see Bennet et al., (1983) <doi:10.1002/sim.4780020223>, and Muse et al., (2022) <doi:10.1016/j.aej.2022.01.033>.
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, as described in <doi:10.18637/jss.v104.i03>.
Automates regression testing of package 'allelematch'. Over
    2500 tests covers all functions in 'allelematch', reproduces the
    examples from the documentation and includes negative tests. The
    implementation is based on 'testthat'.
Provides functions to compute various clinical scores used in healthcare.
            These include the Charlson Comorbidity Index (CCI), predicting 10-year survival in patients with multiple comorbidities; 
            the EPICES score, an individual indicator of precariousness considering its multidimensional nature;
            the MELD score for chronic liver disease severity; the Alternative Fistula Risk Score (a-FRS) for postoperative pancreatic fistula risk;
            and the Distal Pancreatectomy Fistula Risk Score (D-FRS) for risk following distal pancreatectomy.
            For detailed methodology, refer to Charlson et al. (1987) <doi:10.1016/0021-9681(87)90171-8> ,
            Sass et al. (2006) <doi:10.1007/s10332-006-0131-5>, 
            Kamath et al. (2001) <doi:10.1053/jhep.2001.22172>,
            Kim et al. (2008) <doi:10.1056/NEJMoa0801209>
            Kim et al. (2021) <doi:10.1053/j.gastro.2021.08.050>,
            Mungroop et al. (2019) <doi:10.1097/SLA.0000000000002620>,
            and de Pastena et al. (2023) <doi:10.1097/SLA.0000000000005497>..
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.
Creates interactive Venn diagrams using the 'amCharts5' library for 
    'JavaScript'. They can be used directly from the R console, from 'RStudio', 
    in 'shiny' applications, and in 'rmarkdown' documents.
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.
A free software for a fast and easy analysis of 1:1 molecular interaction studies. 
   This package is suitable for a high-throughput data analysis. 
   Both the online app and the package are completely open source. 
   You provide a table of sensogram, tell 'anabel' which method to use,
   and it takes care of all fitting details.
   The first two releases of 'anabel' were created and implemented as in 
   (<doi:10.1177/1177932218821383>, <doi:10.1093/database/baz101>).
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 comprehensive set of tools for descriptive statistics,
    graphical data exploration, outlier detection, homoscedasticity testing, and
    multiple comparison procedures. Includes manual implementations of Levene's test,
    Bartlett's test, and the Fligner-Killeen test, as well as post hoc comparison
    methods such as Tukey, Scheffé, Games-Howell, Brunner-Munzel, and others.
    This version introduces two new procedures: the Jonckheere-Terpstra trend test
    and the Jarque-Bera test with Glinskiy's (2024) correction. Designed for use in
    teaching, applied statistical analysis, and reproducible research.
Provides a set of functions for interacting with the 'DigitalOcean'
    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.
A quick and effective data exploration toolkit. It provides essential features, including a descriptive statistics table for a quick overview of your dataset, interactive distribution plots to visualize variable patterns, Principal Component Analysis for dimensionality reduction and feature analysis, missing value imputation methods, and correlation analysis.
Functions for I/O, visualisation and analysis of functional Magnetic Resonance Imaging (fMRI) datasets stored in the ANALYZE or 'NIFTI' format. Note that the latest version of 'XQuartz' seems to be necessary under MacOS.
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.
Simple radiocarbon calibration and chronological analysis.
    This package allows the calibration of radiocarbon ages and modern
    carbon fraction values using multiple calibration curves. It allows
    the calculation of highest density region intervals and credible
    intervals. The package also provides tools for visualising results and
    estimating statistical summaries.
Enables gene regulatory network (GRN) analysis on single cell clusters,
    using the GRN analysis software 'ANANSE', Xu et al.(2021) <doi:10.1093/nar/gkab598>.
    Export data from 'Seurat' objects, for GRN analysis by 'ANANSE' 
    implemented in 'snakemake'. Finally, incorporate results for visualization 
    and interpretation.
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.
Causal discovery in linear structural equation models (Schultheiss, and Bühlmann (2023) <doi:10.1093/biomet/asad008>) and vector autoregressive models (Schultheiss, Ulmer, and Bühlmann (2025) <doi:10.1515/jci-2024-0011>) with explicit error control for false discovery, at least asymptotically.
Construct language-aware lists. Make "and"-separated and
    "or"-separated lists that automatically conform to the user's language
    settings.
Visualisation of multidimensional data through different Andrews curves: 
   Andrews, D. F. (1972) Plots of High-Dimensional Data. Biometrics, 28(1), 125-136. <doi:10.2307/2528964>.
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.
Opens and imports log files from Angstrom Engineering Thermal Evaporator
    and extracts basic characteristics, such as base pressure, time of the evaporation. 
    It can visualize the deposition observables for review.
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).
All animal behaviour occurs sequentially. The package has a number of functions to format sequence data from different sources, to analyse sequential behaviour and communication in animals. It also has functions to plot the data and to calculate the entropy of sequences.
Implements a web-based graphics device for animated visualisations.
  Modelled on the 'base' syntax, it extends the 'base' graphics functions to
  support frame-by-frame animation and keyframes animation.
  The target use cases are real-time animated visualisations, including agent-based
  models, dynamical systems, and animated diagrams.
  The generated visualisations can be deployed as GIF images / MP4 videos, as
  'Shiny' apps (with interactivity) or as HTML documents through embedding into
  R Markdown documents.
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.
Create an interactive visualization to be used for communication purposes. Providing the function for preparing, plotting, and animating the data.
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.
Obtain network structures from animal GPS telemetry observations and 
    statistically analyse them to assess their adequacy for social network analysis. Methods include 
    pre-network data permutations, bootstrapping techniques to obtain confidence intervals 
    for global and node-level network metrics, and correlation and regression analysis of the local network metrics.
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.
Implements persistent row and column annotations for R matrices. The annotations associated with rows and columns are preserved after subsetting, transposition, and various other matrix-specific operations. Intended use case is for storing and manipulating genomic datasets which typically consist of a matrix of measurements (like gene expression values) as well as annotations about rows (i.e. genomic locations) and annotations about columns (i.e. meta-data about collected samples). But 'annmatrix' objects are also expected to be useful in various other contexts.
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.
Provides non-invasive annotation of package load calls 
    such as \code{library()}, \code{p_load()}, and \code{require()} so that we can have an idea of what 
    the packages we are loading are meant for.
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>.
Provides functions to create image annotations through polygon outlining. Annotator has the same function as 'graphics::locator()' but achieves its purpose through drawing, rather than multiple mouse clicks. It is based on the 'htmlwidgets' package and 'fabric.js' JavaScript library <http://fabricjs.com/>.
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.
Analyses of frequencies can be performed using an alternative test based 
    on the G statistic. The test has similar type-I error rates and power as 
    the chi-square test. However, it is based on a total statistic that can be 
    decomposed in an additive fashion into interaction effects, main effects, simple 
    effects, contrast effects, etc., mimicking precisely the logic of ANOVA. We call 
    this set of tools 'ANOFA' (Analysis of Frequency data) to highlight its 
    similarities with ANOVA. This framework also renders plots of frequencies 
    along with confidence intervals. Finally, effect sizes and planning statistical 
    power are easily done under this framework. The ANOFA is a tool that assesses 
    the significance of effects instead of the significance of parameters; as such, 
    it is more intuitive to most researchers than alternative approaches based on 
    generalized linear models. 
    See Laurencelle and Cousineau (2023) <doi:10.20982/tqmp.19.2.p173>.
The tools in this package are intended to help researchers assess multiple treatment-covariate interactions with data from a parallel-group randomized controlled clinical trial. The methods implemented in the package were proposed in Kovalchik, Varadhan and Weiss (2013) <doi: 10.1002/sim.5881>.
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) Fisch, Eckley, and Fearnhead (2022) <doi:10.1002/sam.11586>, Multi-Variate Collective And Point Anomaly (MVCAPA) Fisch, Eckley, and Fearnhead (2021) <doi:10.1080/10618600.2021.1987257>, Proportion Adaptive Segment Selection (PASS) Jeng, Cai, and Li (2012) <doi:10.1093/biomet/ass059>, and Bayesian Abnormal Region Detector (BARD) Bardwell and Fearnhead (2015) <doi:10.1214/16-BA998>. These methods are for the detection of anomalies in time series data. Further information regarding the use of this package along with detailed examples can be found in Fisch, Grose, Eckley, Fearnhead, and Bardwell (2024) <doi:10.18637/jss.v110.i01>.
Compute an anomaly score for multivariate time series based on the k-nearest neighbors algorithm. Different computations of distances between time series are provided.
Provides functions to estimate and interpret the alpha-NOMINATE ideal point model developed in Carroll et al. (2013, <doi:10.1111/ajps.12029>). alpha-NOMINATE extends traditional spatial voting frameworks by allowing for a mixture of Gaussian and quadratic utility functions, providing flexibility in modeling political actors' preferences. The package uses Markov Chain Monte Carlo (MCMC) methods for parameter estimation, supporting robust inference about individuals' ideological positions and the shape of their utility functions. It also contains functions to simulate data from the model and to calculate the probability of a vote passing given the ideal points of the legislators/voters and the estimated location of the choice alternatives.
Analysis of moderation (ANOMO) method conceptualizes
    the difference and equivalence tests as a moderation problem to 
    test the difference and equivalence of 
    two means (or two effects in two studies).
Analyses of Proportions can be performed on the Anscombe (arcsine-related) transformed  
    data. The 'ANOPA' package can analyze proportions obtained from up to 
    four factors. The factors can be within-subject or between-subject or a mix 
    of within- and between-subject. The main, omnibus analysis can be followed by
    additive decompositions into interaction effects, main effects, simple 
    effects, contrast effects, etc., mimicking precisely the logic of ANOVA. For
    that reason, we call this set of tools 'ANOPA' (Analysis of Proportion
    using Anscombe transform) to highlight its similarities with ANOVA. 
    The 'ANOPA' framework also allows plots of proportions easy to obtain 
    along with confidence intervals. Finally, effect sizes and planning statistical 
    power are easily done under this framework. Only particularity, the 'ANOPA' computes  
    F statistics which have an infinite degree of freedom on the denominator. 
    See Laurencelle and Cousineau (2023) <doi:10.3389/fpsyg.2022.1045436>.
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>.
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.
Functions and data to accompany the 5th edition of the book "Applied Nonparametric Statistical Methods" (4th edition: Sprent & Smeeton, 2024, ISBN:158488701X), the revisions from the 4th edition including a move from describing the output from a miscellany of statistical software packages to using R. While the output from many of the functions can also be obtained using a range of other R functions, this package provides functions in a unified setting and give output using both p-values and confidence intervals, exemplifying the book's approach of treating p-values as a guide to statistical importance and not an end product in their own right. Please note that in creating the ANSM5 package we do not claim to have produced software which is necessarily the most computationally efficient nor the most comprehensive.
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>.
The generated wealth of immune repertoire sequencing data
    requires software to investigate and quantify inter- and
    intra-antibody repertoire evolution to uncover how B cells evolve
    during immune responses. Here, we present 'AntibodyForests', a software
    to investigate and quantify inter- and intra-antibody repertoire
    evolution.
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.
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).
Data from the anxiety and confinement study from Alvarado-Aravena et al. (2022) <doi:10.3390/bs12100398>.
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.
An iterative process that optimizes a function by alternately 
    performing restricted optimization over parameter subsets. Instead of joint 
    optimization, it breaks the optimization problem down into simpler 
    sub-problems. This approach can make optimization feasible when joint 
    optimization is too difficult.
To address the violation of the assumption of normally distributed variables, researchers frequently employ bootstrapping. Building upon established packages for R (Sigmann et al. (2024) <doi:10.32614/CRAN.package.afex>, Lenth (2024) <doi:10.32614/CRAN.package.emmeans>), we provide bootstrapping functions to approximate a normal distribution of the parameter estimates for between-subject, within-subject, and mixed one-way and two-way ANOVA.
Provides a set of functions to analyse
        overdispersed counts or proportions. Most of the methods are
        already available elsewhere but are scattered in different
        packages. The proposed functions should be considered as
        complements to more sophisticated methods such as generalized
        estimating equations (GEE) or generalized linear mixed effect
        models (GLMM).
Provides functions to analyse overdispersed counts or proportions. These functions should be considered as complements to more sophisticated methods such as generalized estimating equations (GEE) or generalized linear mixed effect models (GLMM). aods3 is an S3 re-implementation of the deprecated S4 package aod.
Another implementation of object-orientation in R. It provides
    syntactic sugar for the S4 class system and two alternative new
    implementations. One is an experimental version built around S4
    and the other one makes it more convenient to work with lists as objects.
Download data from the 'Access to Opportunities Project (AOP)'. The 
             'aopdata' package brings annual estimates of access to employment, 
             health, education and social assistance services by transport mode, 
             as well as data  on the spatial distribution of population, jobs, 
             health care, schools and social assistance facilities at a fine 
             spatial resolution for all cities included in the project. More 
             info on the 'AOP' website <https://www.ipea.gov.br/acessooportunidades/en/>.
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.
Fit, interpret, and compute predictions with oblique random
    forests. Includes support for partial dependence, variable importance,
    passing customized functions for variable importance and identification
    of linear combinations of features. Methods for the oblique random 
    survival forest are described in Jaeger et al., (2023)
    <DOI:10.1080/10618600.2023.2231048>.
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.
Functions for processing and analyzing survey data from the All of 
    Us Social Determinants of Health (AOUSDOH) program, including tools for 
    calculating health and well-being scores, recoding variables, and simplifying 
    survey data analysis. For more details see - Koleck TA, Dreisbach C, Zhang C, 
    Grayson S, Lor M, Deng Z, Conway A, Higgins PDR, Bakken S (2024) 
    <doi:10.1093/jamia/ocae214>.
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.
Visualize clonal expansion via circle-packing. 'APackOfTheClones' extends 'scRepertoire' to produce a publication-ready visualization of clonal expansion at a single cell resolution, by representing expanded clones as differently sized circles. The method was originally implemented by Murray Christian and Ben Murrell in the following immunology study: Ma et al. (2021) <doi:10.1126/sciimmunol.abg6356>.
A common task faced by researchers is the creation of APA style
    (i.e., American Psychological Association style) tables from statistical
    output. In R a large number of function calls are often needed to obtain all of
    the desired information for a single APA style table. As well, the process of
    manually creating APA style tables in a word processor is prone to transcription
    errors. This package creates Word files (.doc files) containing APA style tables
    for several types of analyses. Using this package minimizes transcription errors
    and reduces the number commands needed by the user.
Create APA style text from analyses for use within R Markdown documents. Descriptive statistics, confidence intervals, and cell sizes are reported.
Functions for age-period-cohort analysis. 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. 2-sample analysis is possible. Mixed frequency data are possible. 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.
The process of resolving taxon names is necessary when working with biodiversity data. 'APCalign' uses the Australian Plant Census (APC) and the Australian Plant Name Index (APNI) to align and update plant taxon names to current, accepted standards. 'APCalign' also supplies information about the established status of plant taxa across different states/territories.
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).  The pair
  correlation function describes the spatial distribution of objects, e.g.
  random, aggregated or regularly spaced. 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>.
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).
Simplifies aspects of linear regression analysis, particularly simultaneous inference. Additionally, supports "A Progressive Introduction to Linear Models" by Joshua French (<https://jfrench.github.io/LinearRegression/>).
Parentage assignment package.
    Parentage assignment is performed based on observed average Mendelian transmission probability distributions or Exclusion.
    The main functions of this package are the function APIS_2n(), APIS_3n() and launch_APIShiny(), which perform parentage assignment.
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>.
Provides methods for the processing of liquid chromatography-mass spectrometry (LC/MS) based metabolomics data, including adaptive tolerance level searching, non-parametric intensity grouping, the use of run filter to preserve weak signals, model-based estimation of peak intensities, and peak detection based on existing knowledge. Related references include Yu et al. (2009) <doi:10.1093/bioinformatics/btp291>, Liu et al. (2020) <doi:10.1038/s41598-020-70850-0>, Yu et al. (2014) <doi:10.1093/bioinformatics/btu430>, Yu et al. (2013) <doi:10.1021/pr301053d>.
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. Inspired by the 'Method 2' in 'ggtree' (G Yu (2018) <doi:10.1093/molbev/msy194>), 'aplot' provides utilities to aligns associated subplots to a main plot at different sides (left, right, top and bottom) with axes exactly matched.
Many complex plots are actually composite plots, such as 'oncoplot', 'funkyheatmap', 'upsetplot', etc. We can produce subplots using 'ggplot2' and combine them to create composite plots using 'aplot'. In this way, it is easy to customize these complex plots, by adding, deleting or modifying subplots in the final plot. This package provides a set of utilities to help users to create subplots and complex plots.
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.
In panel data settings, specifies set of candidate models, fits them to data from pre-treatment validation periods, and selects model as average over candidate models, weighting each by posterior probability of being most robust given its differential average prediction errors in pre-treatment validation periods. Subsequent estimation and inference of causal effect's bounds accounts for both model and sampling uncertainty, and calculates the robustness changepoint value at which bounds go from excluding to including 0. The package also includes a range of diagnostic plots, such as those illustrating models' differential average prediction errors and the posterior distribution of which model is most robust.
Automated methods to assemble population PK (pharmacokinetic) and
    PKPD (pharmacodynamic) datasets for analysis in 'NONMEM' (non-linear mixed effects
    modeling) by Bauer (2019) <doi:10.1002/psp4.12404>. The package includes functions
    to build datasets from SDTM (study data tabulation module)
    <https://www.cdisc.org/standards/foundational/sdtm>, ADaM (analysis dataset
    module) <https://www.cdisc.org/standards/foundational/adam>, or other dataset
    formats. The package will combine population datasets, add covariates, and
    create documentation to support regulatory submission and internal communication.
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.
Finds, prioritizes and deletes erroneous taxa in a phylogenetic tree. This package calculates scores for taxa in a tree. Higher score means the taxon is more erroneous. If the score is zero for a taxon, the taxon is not erroneous. This package also can remove all erroneous taxa automatically by iterating score calculation and pruning taxa with the highest score.
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.
Programmatic interface to the NASA Application for Extracting and Exploring Analysis Ready Samples services (AppEEARS; <https://appeears.earthdatacloud.nasa.gov/>).
 The package provides easy access to analysis ready earth observation data in R.
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.
Collect  your data on digital marketing campaigns from Apple Search Ads using the 'Windsor.ai' API <https://windsor.ai/api-fields/>.
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 Gadaleta
    et al (2016) <doi:10.4018/IJQSPR.2016010102> for an overview of
    applicability domains.
A few functions and several data set for the Springer book 'Applied Predictive Modeling'.
Simple and transparent parsing of genotype/dosage data
    from an input Variant Call Format (VCF) file, matching of genotype
    coordinates to the component Single Nucleotide Polymorphisms (SNPs)
    of an existing polygenic score (PGS), and application of SNP weights
    to dosages for the calculation of a polygenic score for each individual
    in accordance with the additive weighted sum of dosages model. Methods
    are designed in reference to best practices described by
    Collister, Liu, and Clifton (2022) <doi:10.3389/fgene.2022.818574>.
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.
Convert populations into integer number of seats for legislative 
    bodies. Implements apportionment methods used historically and currently in the
    United States for reapportionment after the Census, as described in 
    <https://www.census.gov/history/www/reference/apportionment/methods_of_apportionment.html>.
With 'appRiori' <doi:10.1177/25152459241293110>, users upload the research variables and the app guides them to the best set of comparisons fitting the hypotheses, for both main and interaction effects. Through a graphical explanation and empirical examples on reproducible data, it is shown that it is possible to understand both the logic behind the planned comparisons and the way to interpret them when a model is tested.
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 can be linked 
  to other R packages via their header files.
Collect  your data on digital marketing campaigns from 'Appsflyer' using the 'Windsor.ai' API <https://windsor.ai/api-fields/>.
Functionality to add, delete, read and update table
    records from your 'AppSheet' apps, using the official API <https://api.appsheet.com/>.
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.
Implements the scenario analysis proposed by Antolin-Diaz, 
    Petrella and Rubio-Ramirez (2021) 
    "Structural scenario analysis with SVARs" <doi:10.1016/j.jmoneco.2020.06.001>.
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/>).
The transmission between two time-series prices is assessed. It contains several functions for linear and nonlinear threshold co-integration, and furthermore, symmetric and asymmetric error correction models.
It calculates the Air Pollution Tolerance Index (APTI) of plant species using biochemical parameters such as chlorophyll content, leaf extract pH, relative water content, and ascorbic acid content. It helps in identifying tolerant species for greenbelt development and pollution mitigation studies. It includes a 'shiny' app for interactive APTI calculation and visualisation. For method details see, Sahu et al. (2020).<DOI:10.1007/s42452-020-3120-6>.
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).
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.app/>.
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 to detect and quantify change events (Ropkins & Tate, 2021,
            <doi:10.1016/j.scitotenv.2020.142374>).
Display air quality model output and monitoring data using scatterplots, grids, and legends.
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.
A varied array of mathematical derivations from various titrimetric and colorimetric methods for analyzing water quality parameters were condensed and integrated for the better physicochemical analysis. It is indispensable for managing any aquatic ecosystem, including aquaculture facilities. By substituting titrant and spectrophotometric absorbance readings, accurate determination of the concentrations of critical parameters such as Dissolved Oxygen, Free Carbon Dioxide, Total Alkalinity, Water Hardness, Hydrogen Sulfide, Total Ammonia Nitrogen, Nitrite, Nitrate, Chlorinity, Salinity, Inorganic Phosphate, and Transparency can be facilitated APHA(2017,ISBN:9780875532875).
Computes and integrates daily potential evapotranspiration (PET) 
    and a soil water balance model. It allows users to estimate and predict the
    wet season calendar, including onset, cessation, and duration, based
    on an agroclimatic approach for a specified period. This functionality
    helps in managing agricultural water resources more effectively. For
    detailed methodologies, users can refer to Allen et al. (1998,
    ISBN:92-5-104219-5); Allen (2005, ISBN:9780784408056); Doorenbos and
    Pruitt (1975, ISBN:9251002797); Guo et al. (2016)
    <doi:10.1016/j.envsoft.2015.12.019>; Hargreaves and Samani (1985)
    <doi:10.13031/2013.26773>; Priestley and Taylor (1972)
    <https://journals.ametsoc.org/view/journals/apme/18/7/1520-0450_1979_018_0898_tptema_2_0_co_2.xml>.
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 functions proposed in this package allows to evaluate the process of measurement of the chemical components of water numerically or graphically. TSSS(), ICHS and datacheck() functions are useful to control the quality of measurements of chemical components of a sample of water. If one or more measurements include an error, the generated graph will indicate it with a position of the point that represents the sample outside the confidence interval. The function CI() allows to evaluate the possibility of contamination of a water sample after being obtained. Validation() is a function that allows to calculate the quality parameters of a technique for the measurement of a chemical component.
Estimate aquatic species life history using robust techniques.
    This package supports users undertaking two types of analysis: 1) Growth from
    length-at-age data, and 2) maturity analyses for length and/or age data.
    Maturity analyses are performed using generalised linear model approaches incorporating
    either a binomial or quasibinomial distribution.
    Growth modelling is performed using the multimodel approach presented by
    Smart et al. (2016) "Multimodel approaches in shark and ray growth studies:
    strengths, weaknesses and the future" <doi:10.1111/faf.12154>.
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) algorithm for association rule classification.
    The package, also described in Hahsler et al. (2019) <doi:10.32614/RJ-2019-048>,
    contains several convenience methods that allow to automatically
    set CBA parameters (minimum confidence, minimum support) and it also natively
    handles numeric attributes by integrating a pre-discretization step.
    The rule generation phase is handled by the 'arules' package. 
    To further decrease the size of the CBA models produced by the 'arc' package, postprocessing by the 
    'qCBA' package is suggested.
It fits a univariate left, right, or interval censored linear regression model
    with autoregressive errors, considering the normal or the Student-t distribution for the 
    innovations. It provides estimates and standard errors of the parameters, predicts 
    future observations, and supports missing values on the dependent variable.
    References used for this package:
    Schumacher, F. L., Lachos, V. H., & Dey, D. K. (2017). Censored regression models with 
    autoregressive errors: A likelihood-based perspective. Canadian Journal of Statistics,
    45(4), 375-392 <doi:10.1002/cjs.11338>.
    Schumacher, F. L., Lachos, V. H., Vilca-Labra, F. E., & Castro, L. M. (2018). Influence 
    diagnostics for censored regression models with autoregressive errors. Australian & New 
    Zealand Journal of Statistics, 60(2), 209-229 <doi:10.1111/anzs.12229>.
    Valeriano, K. A., Schumacher, F. L., Galarza, C. E., & Matos, L. A. (2024). Censored 
    autoregressive regression models with Student‐t innovations. Canadian Journal of Statistics, 
    52(3), 804-828 <doi:10.1002/cjs.11804>.
Lite interface for finding locations of addresses or
    businesses around the world using the 'ArcGIS' REST API service
    <https://developers.arcgis.com/rest/geocode/api-reference/overview-world-geocoding-service.htm>.
    Address text can be converted to location candidates and a location
    can be converted into an address. No API key required.
Provides easy installation and loading of core ArcGIS
    location services packages 'arcgislayers', 'arcgisutils',
    'arcgisgeocode', and 'arcgisplaces'. Enabling developers to interact
    with spatial data and services from 'ArcGIS Online', 'ArcGIS
    Enterprise', and 'ArcGIS Platform'. Learn more about the 'arcgis'
    meta-package at <https://developers.arcgis.com/r-bridge/>.
A very fast and robust interface to ArcGIS 'Geocoding
    Services'. Provides capabilities for reverse geocoding, finding
    address candidates, character-by-character search autosuggestion, and
    batch geocoding. The public 'ArcGIS World Geocoder' is accessible for
    free use via 'arcgisgeocode' for all services except batch geocoding.
    'arcgisgeocode' also integrates with 'arcgisutils' to provide access
    to custom locators or private 'ArcGIS World Geocoder' hosted on
    'ArcGIS Enterprise'. Learn more in the 'Geocode service' API reference
    <https://developers.arcgis.com/rest/geocode/api-reference/overview-world-geocoding-service.htm>.
Enables users of 'ArcGIS Enterprise', 'ArcGIS Online', or
    'ArcGIS Platform' to read, write, publish, or manage vector and raster
    data via ArcGIS location services REST API endpoints
    <https://developers.arcgis.com/rest/>.
The ArcGIS 'Places service' is a ready-to-use location
    service that can search for businesses and geographic locations around
    the world. It allows you to find, locate, and discover detailed
    information about each place. Query for places near a point, within a
    bounding box, filter based on categories, or provide search text.
    'arcgisplaces' integrates with 'sf' for out of the box compatibility
    with other spatial libraries. Learn more in the 'Places service' API reference <https://developers.arcgis.com/rest/places/>.
Developer oriented utility functions designed to be used as
    the building blocks of R packages that work with ArcGIS Location
    Services. It provides functionality for authorization, Esri JSON
    construction and parsing, as well as other utilities pertaining to
    geometry and Esri type conversions. To support 'ArcGIS Pro' users,
    authorization can be done via 'arcgisbinding'. Installation
    instructions for 'arcgisbinding' can be found at
    <https://developers.arcgis.com/r-bridge/installation/>.
Statistical analysis of archaeological dates and groups of
    dates. This package allows to post-process Markov Chain Monte Carlo
    (MCMC) simulations from 'ChronoModel' <https://chronomodel.com/>,
    'Oxcal' <https://c14.arch.ox.ac.uk/oxcal.html> or 'BCal'
    <https://bcal.shef.ac.uk/>. It provides functions for the study of
    rhythms of the long term from the posterior distribution of a series
    of dates (tempo and activity plot). It also allows the estimation and
    visualization of time ranges from the posterior distribution of groups
    of dates (e.g. duration, transition and hiatus between successive
    phases) as described in Philippe and Vibet (2020)
    <doi:10.18637/jss.v093.c01>.
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 spatial units in archaeology from the relationships between refitting fragmented objects scattered in these units (e.g. stratigraphic layers). Graphs 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. Empirical datasets are provided as examples. Documentation about 'archeofrag' is provided by the vignette included in this package, by the accompanying scientific papers: Plutniak (2021, Journal of Archaeological Science, <doi:10.1016/j.jas.2021.105501>) and Plutniak (2022, Journal of Open Source Software, <doi:10.21105/joss.04335>). This package is complemented by a companion GUI application available at <https://analytics.huma-num.fr/Sebastien.Plutniak/archeofrag/>.
A 'Shiny' application to access the functionalities and datasets of the 'archeofrag' package for spatial analysis in archaeology from refitting data. Quick and seamless exploration of archaeological refitting datasets, focusing on physical refits only. Features include: built-in documentation and convenient workflow, plot generation and exports, exploration of spatial units merging solutions, simulation of archaeological site formation processes, support for parallel computing, R code generation to re-execute simulations and ensure reproducibility, code generation for the 'openMOLE' model exploration software. A demonstration of the app is available at <https://analytics.huma-num.fr/Sebastien.Plutniak/archeofrag/>.
An R 'Shiny' application for visual and statistical exploration and web communication of archaeological spatial data, either remains or sites. It offers interactive 3D and 2D visualisations (cross sections and maps of remains, timeline of the work made in a site) which can be exported in SVG and HTML formats. It performs simple spatial statistics (convex hull, regression surfaces, 2D kernel density estimation) and allows exporting data to other online applications for more complex methods. 'archeoViz' can be used offline locally or deployed on a server, either with interactive input of data or with a static data set. Example is provided at <https://analytics.huma-num.fr/archeoviz/en>.
Performs archetypal analysis by using Principal Convex Hull Analysis 
    under a full control of all algorithmic parameters. 
    It contains a set of functions for determining the initial solution, the optimal 
    algorithmic parameters and the optimal number of archetypes.
    Post run tools are also available for the assessment of the derived solution. 
    Morup, M., Hansen, LK (2012) <doi:10.1016/j.neucom.2011.06.033>.
    Hochbaum, DS, Shmoys, DB (1985) <doi:10.1287/moor.10.2.180>.
    Eddy, WF (1977) <doi:10.1145/355759.355768>.
    Barber, CB, Dobkin, DP, Huhdanpaa, HT (1996) <doi:10.1145/235815.235821>.    
    Christopoulos, DT (2016) <doi:10.2139/ssrn.3043076>.    
    Falk, A., Becker, A., Dohmen, T., Enke, B., Huffman, D., Sunde, U. (2018), <doi:10.1093/qje/qjy013>. 
    Christopoulos, DT (2015) <doi:10.1016/j.jastp.2015.03.009> .  
    Murari, A., Peluso, E., Cianfrani, Gaudio, F., Lungaroni, M., (2019), <doi:10.3390/e21040394>.
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.
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>.
Stepwise Uncertainty Reduction criterion and algorithm for sequentially learning a Gaussian Process Classifier as described in Menz et al. (2025).
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.
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>.
Fast processing of ArcGIS FeatureCollection protocol buffers in R.
  It is designed to work seamlessly with 'httr2' and integrates with 'sf'.
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.
An interface to the 'ArcGIS' 'arcpy' and 'arcgis' 'python' API
    <https://pro.arcgis.com/en/pro-app/latest/arcpy/get-started/arcgis-api-for-python.htm>.
    Provides various tools for installing and configuring a 'Conda' environment
    for accessing 'ArcGIS' geoprocessing functions. Helper functions for
    manipulating and converting 'ArcGIS' objects from R are also provided.
Provides functions to process minute level actigraphy-measured activity counts data and extract commonly used physical activity volume and fragmentation metrics.
Performs requests to the 'Arctos' API to download data. Provides a
    set of builder classes for performing complex requests, as well as a set of
    simple functions for automating many common requests and workflows. More
    information about 'Arctos' can be found in 
    Cicero et al. (2024) <doi:10.1371/journal.pone.0296478> or on their website
    <https://arctosdb.org/>.
Fits a linear-binomial model using a modified Newton-type 
    algorithm for solving the maximum likelihood 
    estimation problem under linear box constraints. Similar methods are 
    described in Wagenpfeil, Schöpe and Bekhit (2025, ISBN:9783111341972) 
    "Estimation of adjusted relative risks in log-binomial regression 
    using the BSW algorithm". In: Mau, Mukhin, Wang and Xu (Eds.), 
    Biokybernetika. De Gruyter, Berlin, pp. 665–676.
Autoregressive-based decomposition of a time series based on the approach in West (1997). Particular cases include the extraction of trend and seasonal components.
A set of functions to access the 'ARDECO' (Annual Regional Database
    of the European Commission) data directly from the official ARDECO public
    repository through the exploitation of the 'ARDECO' APIs.
    The APIs are completely transparent to the user and the provided functions
    provide a direct access to the 'ARDECO' data.
    The 'ARDECO' database is a collection of variables related to demography,
    employment, labour market, domestic product, capital formation.
    Each variable can be exposed in one or more units of measure as well as
    refers to total values plus additional dimensions like economic sectors, gender,
    age classes. Data can be also aggregated at country level according
    to the tercet classes as defined by EUROSTAT.
    The description of the 'ARDECO' database can be found at the following URL
    <https://territorial.ec.europa.eu/ardeco>.
Creates complex autoregressive distributed lag (ARDL) models and 
    constructs the underlying unrestricted and restricted error correction 
    model (ECM) automatically, just by providing the order. 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. The validity and the accuracy of this package have been verified 
    by successfully replicating the results of Pesaran et al. (2001) in 
    Natsiopoulos and Tzeremes (2022) <doi:10.1002/jae.2919>.
Estimate the linear and nonlinear autoregressive distributed lag (ARDL & NARDL) models and the corresponding error correction models, and test for longrun and short-run asymmetric. The general-to-specific approach is also available in estimating the ARDL and NARDL models. The Pesaran, Shin & Smith (2001) (<doi:10.1002/jae.616>) bounds test for level relationships is also provided. The 'ardl.nardl' package also performs short-run and longrun symmetric restrictions available at Shin et al. (2014) <doi:10.1007/978-1-4899-8008-3_9> and their corresponding tests.
Contains functions to help create an Analysis Results Dataset.
    The dataset follows industry recommended structure.  The dataset can be 
    created in multiple passes, using different data frames as input.  Analysis
    Results Datasets are used in the pharmaceutical and biotech industries
    to capture analysis in a common tabular data structure.
Calculate the area of triangles and polygons using the shoelace 
 formula. Area may be signed, taking into account path orientation, or unsigned, 
 ignoring path orientation. The shoelace formula is described at 
 <https://en.wikipedia.org/wiki/Shoelace_formula>.
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.
Many relevant applications in the environmental and socioeconomic 
    sciences use areal data, such as biodiversity checklists, agricultural statistics, 
    or socioeconomic surveys. For applications that surpass the spatial, temporal or 
    thematic scope of any single data source, data must be integrated from several 
    heterogeneous sources. Inconsistent concepts, definitions, or messy data tables 
    make this a tedious and error-prone process. 'arealDB' tackles those problems and 
    helps the user to integrate a harmonised databases of areal data. Read the paper
    at Ehrmann, Seppelt & Meyer (2020) <doi:10.1016/j.envsoft.2020.104799>.
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.
Adversarial random forests (ARFs) recursively partition data into 
    fully factorized leaves, where features are jointly independent. The 
    procedure is iterative, with alternating rounds of generation and 
    discrimination. Data becomes increasingly realistic at each round, until 
    original and synthetic samples can no longer be reliably distinguished. 
    This is useful for several unsupervised learning tasks, such as density
    estimation and data synthesis. Methods for both are implemented in this
    package. ARFs naturally handle unstructured data with mixed continuous and 
    categorical covariates. They inherit many of the benefits of random forests, 
    including speed, flexibility, and solid performance with default parameters. 
    For details, see Watson et al. (2023) <https://proceedings.mlr.press/v206/watson23a.html>.
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.
Provides functions to access data from the 'ArgentinaDatos API' and the 'REST Countries API'
    related to Argentina's exchange rates, inflation, political figures, holidays, and general
    country-level indicators. Additionally, the package includes curated datasets related to Argentina,
    covering topics such as economic indicators, biodiversity, agriculture, human rights, genetic data,
    and consumer prices. The package supports research and analysis focused on Argentina by integrating
    open APIs with high-quality datasets from various domains. For more details on the 'ArgentinaDatos API',
    see <https://argentinadatos.com/> and for 'REST Countries', see <https://restcountries.com/>.
Provides functions to retrieve information from Web Feature Service (WFS) and Web Map Service (WMS) layers
    from various Argentine organizations and import them into R for further analysis.
    WFS and WMS are standardized protocols for serving georeferenced map data over the internet.
    For more information on these services, see <https://www.ogc.org/publications/standard/wfs/> and <https://www.ogc.org/publications/standard/wms/>.
Provides methods to construct frequentist confidence sets with valid marginal 
    coverage for identifying the population-level argmin or argmax based on IID data. 
    For instance, given an n by p loss matrix—where n is the sample size and p is the 
    number of models—the CS.argmin() method produces a discrete confidence set that contains 
    the model with the minimal (best) expected risk with desired probability. The argmin.HT() 
    method helps check if a specific model should be included in such a confidence set. The main
    implemented method is proposed by Tianyu Zhang, Hao Lee and Jing Lei (2024) 
    "Winners with confidence: Discrete argmin inference with an application to model selection".
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.
Create awesome 'Bootstrap 4' dashboards powered by 'Argon'.
R wrapper around the argon HTML library.
    More at <https://demos.creative-tim.com/argon-design-system/>.
Comprehensive set of tools for performing system identification of both linear and nonlinear dynamical systems directly from data. The Automatic Regression for Governing Equations (ARGOS) simplifies the complex task of constructing mathematical models of dynamical systems from observed input and output data, supporting various types of systems, including those described by ordinary differential equations. It employs optimal numerical derivatives for enhanced accuracy and employs formal variable selection techniques to help identify the most relevant variables, thereby enabling the development of predictive models for system behavior analysis.
Filters animal satellite tracking data obtained from the Argos system(<https://www.argos-system.org/>), following the algorithm described in Freitas et al (2008) <doi:10.1111/j.1748-7692.2007.00180.x>. It is especially indicated for telemetry studies of marine animals, where Argos locations are predominantly of low-quality.
A command line parser to
    be used with 'Rscript' to write "#!" shebang scripts that gracefully
    accept positional and optional arguments and automatically generate usage.
Cross-platform command-line argument parser written purely in R
    with no external dependencies. It is useful with the Rscript
    front-end and facilitates turning an R script into an executable script.
Random variate generation, density, CDF and quantile function 
  for the Argus distribution. Especially, it includes for random variate generation a
  flexible inversion method that is also fast in the varying parameter case.
  A Ratio-of-Uniforms method is provided as second alternative.
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), as described in Sundqvist et al. <doi:10.1007/s00180-022-01230-7> 
    and simple Chi-square distance since version 1.0.0.
Describes a series first. After that does time series analysis using one hybrid model and two specially structured Machine Learning (ML) (Artificial Neural Network or ANN and Support Vector Regression or SVR) models. More information can be obtained from Paul and Garai (2022) <doi:10.1007/s41096-022-00128-3>.
Estimating and analyzing auto regressive integrated moving average
    (ARIMA) models. The primary function in this package is arima(), which fits 
    an ARIMA model to univariate time series data using a random restart
    algorithm. This approach frequently leads to models that have model 
    likelihood greater than or equal to that of the likelihood obtained by 
    fitting the same model using the arima() function from the 'stats' package. 
    This package enables proper optimization of model likelihoods, which is a 
    necessary condition for performing likelihood ratio tests. This package 
    relies heavily on the source code of the arima() function of the 'stats' 
    package. For more information, please see Jesse Wheeler and Edward L. 
    Ionides (2023) <doi:10.48550/arXiv.2310.01198>.
Testing, Implementation, and Forecasting of the ARIMA-ANN hybrid model. The ARIMA-ANN hybrid model combines the distinct strengths of the Auto-Regressive Integrated Moving Average (ARIMA) model and the Artificial Neural Network (ANN) model for time series forecasting.For method details see Zhang, GP (2003) <doi:10.1016/S0925-2312(01)00702-0>.
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 dependency-free collection of simple functions for cleaning
    rectangular data. This package allows to detect, count and replace
    values or discard rows/columns using a predicate function. In
    addition, it provides tools to check conditions and return informative
    error messages.
Implements an innovative approach to community detection in social networks using Association Rules Learning. The package provides tools for processing graph and rules objects, generating association rules, and detecting communities based on node interactions. Designed to facilitate advanced research in Social Network Analysis, this package leverages association rules learning for enhanced community detection. This approach is described in El-Moussaoui et al. (2021) <doi:10.1007/978-3-030-66840-2_3>.
Functions to accompany A. Gelman and J. Hill, Data Analysis Using Regression and Multilevel/Hierarchical Models, Cambridge University Press, 2007.
The real-life time series data are hardly pure linear or nonlinear. Merging a linear time series model like the autoregressive moving average (ARMA) model with a nonlinear neural network model such as the Long Short-Term Memory (LSTM) model can be used as a hybrid model for more accurate modeling purposes. Both the autoregressive integrated moving average (ARIMA) and autoregressive fractionally integrated moving average (ARFIMA) models can be implemented. Details can be found in Box et al. (2015, ISBN: 978-1-118-67502-1) and Hochreiter and Schmidhuber (1997) <doi:10.1162/neco.1997.9.8.1735>.
Providing ways to estimate the value of European stock options 
    given historical stock price data. It includes functions for calculating option values 
    based on autoregressive–moving-average (ARMA) models and generates information about these models. 
    This package is make to be easy to understand and for financial analysis capabilities.
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.
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>).
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 of this implementation is a trade-off for universality: user defined
  functions cannot be used with this package. However, about 20 most currently employed
  functions are available for usage. They can be divided in three types:
  reducing functions (like mean(), sum() etc., giving a scalar when applied to a vector),
  mapping function (like normalise(), cumsum() etc., giving a vector of the same length
  as the input vector) and finally, vector reducing function (like diff() which produces
  result vector of a length different from the length of input vector).
  Optional or mandatory additional arguments required by some functions
  (e.g. norm type for norm()) can be
  passed as named arguments in '...'.
Some convenient functions to work with arrays.
Argument parsing for R scripts, with support for long and short
    Unix-style options including option clustering, positional arguments
    including those of variable length, and multiple usage patterns which may
    take different subsets of options.
'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.
The 'ggarrow' package is a 'ggplot2' extension that plots
    a variety of different arrow segments with many options to customize. 
    The 'arrowheadr' package makes it easy to create custom arrowheads 
    and fins within the parameters that 'ggarrow' functions expect. It 
    has preset arrowheads and a collection of functions to create and 
    transform data for customizing arrows.
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>.
Provides a unified and straightforward interface for performing a variety of meta-analysis methods directly from user data. Users can input a data frame, specify key parameters, and effortlessly execute and compare multiple common meta-analytic models. Designed for immediate usability, the package facilitates transparent, reproducible research without manual implementation of each analytical method. Ideal for researchers aiming for efficiency and reproducibility, it streamlines workflows from data preparation to results interpretation.
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>.
Create data that displays generative art when mapped into a 'ggplot2' plot. Functionality includes specialized data frame creation for geometric shapes, tools that define artistic color palettes, tools for geometrically transforming data, and other miscellaneous tools that are helpful when using 'ggplot2' for generative art.
Provides algorithms for creating artworks in the 'ggplot2' language that incorporate some form of randomness.
Adaptive and Robust Transfer Learning (ART) is a flexible framework for transfer learning that integrates information from auxiliary data sources to improve model performance on primary tasks. It is designed to be robust against negative transfer by including the non-transfer model in the candidate pool, ensuring stable performance even when auxiliary datasets are less informative. See the paper, Wang, Wu, and Ye (2023) <doi:10.1002/sta4.582>.
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. 
  Hahsler et al (2019) <doi:10.32614/RJ-2019-048>.
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>.
Parse Autonomous Recording Unit (ARU) data and for sub-sampling recordings. 
    Extract Metadata from your recordings, select a subset of recordings for 
    interpretation, and prepare files for processing on the 
    'WildTrax' <https://wildtrax.ca/> platform. Read and process metadata 
    from recordings collected using the SongMeter and BAR-LT types of ARUs.
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.
Understanding morphological variation is an important task in many applications. Recent studies in computational biology have focused on developing computational tools for the task of sub-image selection which aims at identifying structural features that best describe the variation between classes of shapes. A major part in assessing the utility of these approaches is to demonstrate their performance on both simulated and real datasets. However, when creating a model for shape statistics, real data can be difficult to access and the sample sizes for these data are often small due to them being expensive to collect. Meanwhile, the landscape of current shape simulation methods has been mostly limited to approaches that use black-box inference---making it difficult to systematically assess the power and calibration of sub-image models. In this R package, we introduce the alpha-shape sampler: a probabilistic framework for simulating realistic 2D and 3D shapes based on probability distributions which can be learned from real data or explicitly stated by the user. The 'ashapesampler' package supports two mechanisms for sampling shapes in two and three dimensions. The first, empirically sampling based on an existing data set, was highlighted in the original main text of the paper. The second, probabilistic sampling from a known distribution, is the computational implementation of the theory derived in that paper. Work based on Winn-Nunez et al. (2024) <doi:10.1101/2024.01.09.574919>.
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>] or Bayer, Fay, and Graubard, 2023 <doi:10.48550/arXiv.2205.13494>). 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.
A chat package connecting to API endpoints by 'OpenAI' 
  (<https://platform.openai.com/>) to answer questions (about R).
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 <https://www.coseal.net/aslib/> 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>.
Performs linear regression with respect to a data-driven convex loss function that is chosen to minimize the asymptotic covariance of the resulting M-estimator. The convex loss function is estimated in 5 steps: (1) form an initial OLS (ordinary least squares) or LAD (least absolute deviation) estimate of the regression coefficients; (2) use the resulting residuals to obtain a kernel estimator of the error density; (3) estimate the score function of the errors by differentiating the logarithm of the kernel density estimate; (4) compute the L2 projection of the estimated score function onto the set of decreasing functions; (5) take a negative antiderivative of the projected score function estimate. Newton's method (with Hessian modification) is then used to minimize the convex empirical risk function. Further details of the method are given in Feng et al. (2024) <doi:10.48550/arXiv.2403.16688>.
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).
Provides functions to accompany the book "Applied Statistical Modeling for Ecologists" by Marc Kéry and Kenneth F. Kellner (2024, ISBN: 9780443137150). Included are functions for simulating and customizing the datasets used for the example models in each chapter, summarizing output from model fitting engines, and running custom Markov Chain Monte Carlo.
Adaptive Sparse Multi-block Partial Least Square, a supervised algorithm, is an extension of the Sparse Multi-block Partial Least Square, which allows different quantiles to be used in different blocks of different partial least square components to decide the proportion of features to be retained. The best combinations of quantiles can be chosen from a set of user-defined quantiles combinations by cross-validation. By doing this, it enables us to do the feature selection for different blocks, and the selected features can then be further used to predict the outcome. For example, in biomedical applications, clinical covariates plus different types of omics data such as microbiome, metabolome, mRNA data, methylation data, copy number variation data might be predictive for patients outcome such as survival time or response to therapy. Different types of data could be put in different blocks and along with survival time to fit the model. The fitted model can then be used to predict the survival for the new samples with the corresponding clinical covariates and omics data. In addition, Adaptive Sparse Multi-block Partial Least Square Discriminant Analysis is also included, which extends Adaptive Sparse Multi-block Partial Least Square for classifying the categorical outcome.
A wrapper for machine learning (ML) methods to select among a portfolio of algorithms based on the value of a key performance indicator (KPI). A number of features is used to adjust a model to predict the value of the KPI for each algorithm, then, for a new value of the features the KPI is estimated and the algorithm with the best one is chosen. To learn it can use the regression methods in 'caret' package or a custom function defined by the user. Several graphics available to analyze the results obtained. This library has been used in Ghaddar et al. (2023) <doi:10.1287/ijoc.2022.0090>).
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.
A collection of functions for computing centrographic
        statistics (e.g., standard distance, standard deviation
        ellipse, standard deviation box) for observations taken at
        point locations. Separate plotting functions have been
        developed for each measure. Users interested in writing results
        to ESRI shapefiles can do so by using results from 'aspace'
        functions as inputs to the convert.to.shapefile() and
        write.shapefile() functions in the 'shapefiles' library. We intend to
        provide 'terra' integration for geographic data in a future release.
        The 'aspace' package was originally conceived to aid in the analysis of
        spatial patterns of travel behaviour (see Buliung and Remmel 2008
        <doi:10.1007/s10109-008-0063-7>).
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.
Perform one-dimensional spline regression with automatic knot selection.
      This package uses a penalized approach to select the most relevant knots.
      B-splines of any degree can be fitted. More details in 'Goepp et al. (2018)',
      "Spline Regression with Automatic Knot Selection", <arXiv:1808.01770>.
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. Procedures are available for choosing models that 
  conform to the hierarchy or marginality principle, for fitting and choosing between 
  two-dimensional spatial models using correlation, natural cubic smoothing spline and 
  P-spline models. A history of the fitting of a sequence of models is kept in a data frame. 
  Also used to compute functions and contrasts of, to investigate differences between and 
  to plot predictions obtained using any model fitting function. The content  falls into 
  the following natural groupings: (i) Data, (ii) Model modification functions, (iii) Model 
  selection and description functions, (iv) Model diagnostics and simulation functions, 
  (v) Prediction production and presentation functions, (vi) Response transformation 
  functions, (vii) Object manipulation functions, and (viii) Miscellaneous functions 
  (for further details see 'asremlPlus-package' in help). The 'asreml' package provides a 
  computationally efficient algorithm for fitting a wide range of linear mixed models using 
  Residual Maximum Likelihood. It is a commercial package and a license for it can be 
  purchased from 'VSNi' <https://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/>.