Package: regressoR 3.0.2

Oldemar Rodriguez

regressoR: Regression Data Analysis System

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.

Authors:Oldemar Rodriguez [aut, cre], Andres Navarro D. [ctb, prg], Diego Jimenez A. [ctb, prg], Ariel Arroyo S. [ctb, prg], Joseline Quiros M. [ctb, prg]

regressoR_3.0.2.tar.gz
regressoR_3.0.2.zip(r-4.7-any)regressoR_3.0.2.zip(r-4.6-any)regressoR_3.0.2.zip(r-4.5-any)
regressoR_3.0.2.tgz(r-4.6-any)regressoR_3.0.2.tgz(r-4.5-any)
regressoR_3.0.2.tar.gz(r-4.7-any)regressoR_3.0.2.tar.gz(r-4.6-any)
regressoR_3.0.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
regressoR/json (API)

# Install 'regressoR' in R:
install.packages('regressoR', repos = c('https://promidat.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/promidat/predictor/issues

On CRAN:

Conda:

1.48 score 1 stars 538 downloads 29 exports 154 dependencies

Last updated from:a111c08c36. Checks:7 NOTE, 2 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-develNOTE240
source / vignettesOK242
linux-releaseNOTE196
macos-releaseNOTE155
macos-oldrelNOTE149
windows-develNOTE138
windows-releaseNOTE137
windows-oldrelNOTE141
wasm-releaseOK142

Exports:as_string_cboosting_importance_plotcalibrate_boostingcoef_lambdadatos.disyuntivosdisp_modelsdt_plote_coeff_landae_JSe_posib_lambdaexeextract_codegeneral_indicesimportance_plot_rfnn_plotpairs_powerplot_pred_rdplot_real_predictionplot_RMSEplot_var_pred_rdrd_modelrd_predictionrd_typerl_coeffrlr_modelrlr_predictionrlr_typerun_appsummary_indices

Dependencies:adabagattemptbackportsbase64encbitopsbroombslibcachemcaretcaToolscellrangerclasscliclockcodetoolscolourpickercommonmarkconfigConsRankcorrplotcountrycodecpp11crayoncrosstalkdata.tableDerivdiagramdigestdoParalleldplyrDTe1071echarts4revaluatefarverfastmapfontawesomeforeachfreshfsfuturefuture.applygbmgenericsggplot2glmnetglobalsgluegolemgowergplotsgtablegtoolshardhathighrhmshtmltoolshtmlwidgetshttpuvigraphipredisobanditeratorsjquerylibjsonliteKernSmoothkknnknitrlabelinglaterlatticelavalazyevallifecyclelistenvloadeRlubridatemagrittrMASSMatrixmemoisemimeminiUIModelMetricsneuralnetnlmennetnumDerivotelparallellypillarpkgconfigplsplyrprettyunitspROCprodlimprogressprogressrpromisesproxypurrrR6randomForestrappdirsRColorBrewerRcppRcppEigenreadxlrecipesrematchreshape2rlangrlistrmarkdownROCRrpartrpart.plotrstudioapiS7sassscalesshapeshinyshinyAceshinycustomloadershinydashboardshinydashboardPlusshinyjssourcetoolssparsevctrsSQUAREMstringistringrsurvivaltibbletidyrtidyselecttimechangetimeDatetinytextraineRtzdbutf8vctrsviridisLitewaiterwithrwritexlxfunxgboostXMLxtableyaml

Readme and manuals

Help Manual

Help pageTopics
The application server-sideapp_server
as_string_cas_string_c
boosting_importance_plotboosting_importance_plot
calibrate_boostingcalibrate_boosting
coef_lambdacoef_lambda
Create disjunctive columns to a data.frame.datos.disyuntivos
disp_modelsdisp_models
dt_plotdt_plot
e_coeff_landae_coeff_landa
Eval character vectors to JS codee_JS
e_posib_lambdae_posib_lambda
exeexe
extract_codeextract_code
general_indicesgeneral_indices
importance_plot_rfimportance_plot_rf
nn_plotnn_plot
pairs_powerpairs_power
plot_pred_rdplot_pred_rd
plot_real_predictionplot_real_prediction
plot_RMSEplot_RMSE
plot_var_pred_rdplot_var_pred_rd
rd_modelrd_model
rd_predictionrd_prediction
rd_typerd_type
rl_coeffrl_coeff
rlr_modelrlr_model
rlr_predictionrlr_prediction
rlr_typerlr_type
Run the Shiny Applicationrun_app
summary_indicessummary_indices