{ "_id": "6a269dad24555f66ed520476", "Package": "robCompositions", "Type": "Package", "Title": "Compositional Data Analysis", "Version": "2.6.0", "Date": "2026-06-05", "VignetteBuilder": "knitr", "Authors@R": "c(\nperson(\"Matthias\", \"Templ\", email=\"matthias.templ@gmail.com\", role=c(\"aut\", \"cre\"), comment=c(ORCID=\"0000-0002-8638-5276\")),\nperson(\"Karel\", \"Hron\", email=\"karel.hron@upol.cz\", role=\"aut\", comment = c(ORCID=\"0000-0002-1847-6598\")),\nperson(\"Peter\", \"Filzmoser\", email = \"peter.filzmoser@tuwien.ac.at\", role=\"aut\", comment = c(ORCID=\"0000-0002-8014-4682\")),\nperson(\"Kamila\", \"Facevicova\", email=\"kamila.facevicova@gmail.com\", role=\"ctb\"),\nperson(\"Petra\", \"Kynclova\", email=\"kynclova.petra@gmail.com\", role=\"ctb\"),\nperson(\"Jan\", \"Walach\", email =\"walach.jan@gmail.com\", role=\"ctb\"),\nperson(\"Veronika\", \"Pintar\", email =\"vroni.pintar@gmx.at\", role=\"ctb\"),\nperson(\"Jiajia\", \"Chen\", email =\"chenjiajia0401@163.com\", role=\"ctb\"),\nperson(\"Dominika\", \"Miksova\", email =\"miksovadominika1@gmail.com\", role=\"ctb\"),\nperson(\"Bernhard\", \"Meindl\", email =\"bernhard.meindl@gmail.com\", role=\"ctb\"),\nperson(\"Alessandra\", \"Menafoglio\", email = \"alessandra.menafoglio@polimi.it\", role=\"ctb\", comment = c(ORCID=\"0000-0003-0682-6412\")),\nperson(\"Alessia\", \"Di Blasi\", email = \"alessia.diblasi@mail.polimi.it\", role=\"ctb\"),\nperson(\"Federico\", \"Pavone\", email = \"federico.pavone@mail.polimi.it\", role=\"ctb\"),\nperson(\"Nikola\", \"Stefelova\", email = \"Nikola.Stefelova@seznam.cz\", role=\"ctb\"),\nperson(\"Gianluca\", \"Zeni\", email = \"gianluca.zeni@mail.polimi.it\", role=\"ctb\"),\nperson(\"Roman\", \"Wiederkehr\", email = \"wiedemeier.roman@gmail.com\", role=\"ctb\"))", "Maintainer": "Matthias Templ ", "Description": "Methods for analysis of compositional data including\nrobust methods (), imputation of\nmissing values (), methods to\nreplace rounded zeros (,\n,\n), count zeros\n(), methods to deal with\nessential zeros (), (robust)\noutlier detection for compositional data, (robust) principal\ncomponent analysis for compositional data, (robust) factor\nanalysis for compositional data, (robust) discriminant analysis\nfor compositional data (Fisher rule), robust regression with\ncompositional predictors, functional data analysis\n() and p-splines\n(), contingency\n() and compositional tables\n(, ,\n) and (robust)\nAnderson-Darling normality tests for compositional data as well\nas popular log-ratio transformations (addLR, cenLR, isomLR, and\ntheir inverse transformations). 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"title": "Austrian presidential election data", "topics": [ "electionATbp" ] }, { "page": "employment", "title": "employment in different countries by gender and status.", "topics": [ "employment" ] }, { "page": "employment_df", "title": "Employment in different countries by gender and status.", "topics": [ "employment_df" ] }, { "page": "employment2", "title": "Employment in different countries by Sex, Age, Contract, Value", "topics": [ "employment2" ] }, { "page": "expenditures", "title": "synthetic household expenditures toy data set", "topics": [ "expenditures" ] }, { "page": "expendituresEU", "title": "mean consumption expenditures data.", "topics": [ "expendituresEU" ] }, { "page": "fBPUpChi_PLS", "title": "Title", "topics": [ "fBPUpChi_PLS" ] }, { "page": "fcenLR", "title": "fcenLR transformation (functional)", "topics": [ "fcenLR" ] }, { "page": "fcenLRinv", "title": "Inverse of fcenLR transformations (functional)", "topics": [ "fcenLRinv" ] }, { "page": "fcenLRp", "title": 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