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], Diego Jimenez A. [ctb, prg]

regressoR_3.0.2.tar.gz
regressoR_3.0.2.tar.gz(r-4.5-noble)regressoR_3.0.2.tar.gz(r-4.4-noble)
regressoR_3.0.2.tgz(r-4.4-emscripten)regressoR_3.0.2.tgz(r-4.3-emscripten)
regressoR.pdf |regressoR.html
regressoR/json (API)
NEWS

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

Peer review:

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

1.30 score 2 stars 267 downloads 29 exports 159 dependencies

Last updated 1 years agofrom:2e687cea7d. Checks:OK: 1 NOTE: 1. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 23 2024
R-4.5-linuxNOTEOct 23 2024

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:adaadabagattemptbackportsbase64encbitopsbroombslibcachemcaretcaToolscellrangerclasscliclockcodetoolscolorspacecolourpickercommonmarkconfigConsRankcorrplotcountrycodecpp11crayoncrosstalkdata.tableDerivdiagramdigestdoParalleldplyrDTe1071echarts4revaluatefansifarverfastmapfontawesomeforeachfreshfsfuturefuture.applygbmgenericsggplot2glmnetglobalsgluegolemgowergplotsgtablegtoolshardhatherehighrhmshtmltoolshtmlwidgetshttpuvigraphipredisobanditeratorsjquerylibjsonliteKernSmoothkknnknitrlabelinglaterlatticelavalazyevallifecyclelistenvloadeRlubridatemagrittrMASSMatrixmemoisemgcvmimeminiUIModelMetricsmunsellneuralnetnlmennetnumDerivparallellypillarpkgconfigplsplyrprettyunitspROCprodlimprogressprogressrpromisesproxypurrrR6randomForestrappdirsRColorBrewerRcppRcppEigenreadxlrecipesrematchreshape2rglrlangrlistrmarkdownROCRrpartrpart.plotrprojrootrstudioapisassscalesshapeshinyshinyAceshinycustomloadershinydashboardshinydashboardPlusshinyjssourcetoolsSQUAREMstringistringrsurvivaltibbletidyrtidyselecttimechangetimeDatetinytextraineRtzdbutf8vctrsviridisLitewaiterwithrwritexlxfunxgboostXMLxtableyaml

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