Perform a supervised data analysis on a database through a 'shiny' graphical interface. It includes methods such as linear regression, penalized regression, k-nearest neighbors, decision trees, ada boosting, extreme gradient boosting, random forest, neural networks, deep learning and support vector machines.
Version: 4.0.4 Depends: R (≥ 4.1) Imports: DT (≥ 0.27), gbm (≥ 2.2.2), pls (≥ 2.8-1), dplyr (≥ 1.1.0), psych (≥ 2.4.6), shiny (≥ 1.7.4), golem (≥ 0.3.5), rlang (≥ 1.0.6), glmnet (≥ 4.1-6), loadeR (≥ 1.1.3), shinyjs (≥ 2.1.0), traineR (≥ 2.2.0), shinyAce (≥ 0.4.2), echarts4r (≥ 0.4.4), htmltools (≥ 0.5.4), rpart.plot (≥ 3.1.1), shinydashboard (≥ 0.7.2), shinycustomloader (≥ 0.9.0), shinydashboardPlus (≥ 2.0.3) Published: 2025-05-28 DOI: 10.32614/CRAN.package.regressoR Author: Oldemar Rodriguez [aut, cre], Andres Navarro D. [ctb, prg], Diego Jimenez A. [ctb, prg], Ariel Arroyo S. [ctb, prg], Joseline Quiros M. [ctb, prg] Maintainer: Oldemar Rodriguez <oldemar.rodriguez at ucr.ac.cr> BugReports: https://github.com/PROMiDAT/predictoR/issues License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] URL: https://promidat.website/ NeedsCompilation: no Materials: NEWS CRAN checks: regressoR results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=regressoR to link to this page.
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