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regressoR  

Regression Data Analysis System
View on CRAN: Click here


Download and install regressoR package within the R console
Install from CRAN:
install.packages("regressoR")

Install from Github:
library("remotes")
install_github("cran/regressoR")

Install by package version:
library("remotes")
install_version("regressoR", "4.0.3")



Attach the package and use:
library("regressoR")
Maintained by
Oldemar Rodriguez
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2019-06-11
Latest Update: 2023-06-29
Description:
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.
How to cite:
Oldemar Rodriguez (2019). regressoR: Regression Data Analysis System. R package version 4.0.3, https://cran.r-project.org/web/packages/regressoR. Accessed 03 Feb. 2025.
Previous versions and publish date:
1.1.7 (2019-06-11 19:20), 1.1.8 (2019-09-19 06:30), 1.1.9 (2020-07-01 19:00), 1.2.1 (2021-04-06 19:40), 2.0.0 (2021-09-23 23:20), 2.0.1 (2021-10-13 16:42), 3.0.0 (2023-04-04 01:10), 3.0.2 (2023-06-29 18:40)
Other packages that cited regressoR R package
View regressoR citation profile
Other R packages that regressoR depends, imports, suggests or enhances
Complete documentation for regressoR
Functions, R codes and Examples using the regressoR R package
Some associated functions: app_server . as_string_c . boosting_importance_plot . calibrate_boosting . coef_lambda . datos.disyuntivos . disp_models . dt_plot . e_JS . e_coeff_landa . e_posib_lambda . exe . extract_code . general_indices . importance_plot_rf . nn_plot . pairs_power . plot_RMSE . plot_pred_rd . plot_real_prediction . plot_var_pred_rd . rd_model . rd_prediction . rd_type . rl_coeff . rlr_model . rlr_prediction . rlr_type . run_app . summary_indices . 
Some associated R codes: app_config.R . app_server.R . app_ui.R . cv_utils.R . general_plots.R . generate_code_utils.R . golem_utils_server.R . golem_utils_ui.R . indices_utils.R . mod_Predictive_Power.R . mod_Predictive_Power_utils.R . mod_boosting.R . mod_boosting_fct.R . mod_boosting_utils.R . mod_comparacion.R . mod_cross_validation.R . mod_cv_boosting.R . mod_cv_dt.R . mod_cv_knn.R . mod_cv_rd.R . mod_cv_rf.R . mod_cv_rl.R . mod_cv_rlr.R . mod_cv_svm.R . mod_dimension_reduction.R . mod_dimension_reduction_utils.R . mod_ind_nuevos.R . mod_ind_nuevos_utils.R . mod_information_page.R . mod_knn.R . mod_knn_utils.R . mod_l_regression.R . mod_l_regression_utils.R . mod_neural_net.R . mod_neural_net_utils.R . mod_penalized_l_r.R . mod_penalized_l_r_utils.R . mod_r_forest.R . mod_r_forest_fct.R . mod_r_forest_utils.R . mod_regression_trees.R . mod_regression_trees_utils.R . mod_svm.R . mod_varerr.R . regressoR.R . run_app.R . string_manipulation.R . utils_global.R . utils_inputs.R . utils_outputs.R .  Full regressoR package functions and examples
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