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:
regressoR_3.0.2.tar.gz
regressoR_3.0.2.zip(r-4.5)regressoR_3.0.2.zip(r-4.4)regressoR_3.0.2.zip(r-4.3)
regressoR_3.0.2.tgz(r-4.4-any)regressoR_3.0.2.tgz(r-4.3-any)
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://promidat.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/promidat/predictor/issues
Last updated 1 years agofrom:a111c08c36. Checks:OK: 3 NOTE: 4. Indexed: no.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Oct 31 2024 |
| R-4.5-win | NOTE | Oct 31 2024 |
| R-4.5-linux | NOTE | Oct 31 2024 |
| R-4.4-win | NOTE | Oct 31 2024 |
| R-4.4-mac | NOTE | Oct 31 2024 |
| R-4.3-win | OK | Oct 31 2024 |
| R-4.3-mac | OK | Oct 31 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 page | Topics |
|---|---|
| The application server-side | app_server |
| as_string_c | as_string_c |
| boosting_importance_plot | boosting_importance_plot |
| calibrate_boosting | calibrate_boosting |
| coef_lambda | coef_lambda |
| Create disjunctive columns to a data.frame. | datos.disyuntivos |
| disp_models | disp_models |
| dt_plot | dt_plot |
| e_coeff_landa | e_coeff_landa |
| Eval character vectors to JS code | e_JS |
| e_posib_lambda | e_posib_lambda |
| exe | exe |
| extract_code | extract_code |
| general_indices | general_indices |
| importance_plot_rf | importance_plot_rf |
| nn_plot | nn_plot |
| pairs_power | pairs_power |
| plot_pred_rd | plot_pred_rd |
| plot_real_prediction | plot_real_prediction |
| plot_RMSE | plot_RMSE |
| plot_var_pred_rd | plot_var_pred_rd |
| rd_model | rd_model |
| rd_prediction | rd_prediction |
| rd_type | rd_type |
| rl_coeff | rl_coeff |
| rlr_model | rlr_model |
| rlr_prediction | rlr_prediction |
| rlr_type | rlr_type |
| Run the Shiny Application | run_app |
| summary_indices | summary_indices |