regressoR v1.1.8

0

Monthly downloads

0th

Percentile

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.

Readme

regressoR

CRAN status

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.

Installation

We are working to upload the package to CRAN, at the moment the available version is the development version from GitHub.

# install.packages("devtools")
devtools::install_github("promidat/regressor")

Start regressoR

```` library(regressoR) init_regressor()

Functions in regressoR

Name Description
boosting_prediction boosting_prediction
as_string_c as_string_c
clean_report clean_report
categorical_distribution categorical_distribution
code_NA code_NA
boosting_importance_plot boosting_importance_plot
boosting_model boosting_model
categorical_summary categorical_summary
calibrate_boosting calibrate_boosting
code_deactivate code_deactivate
code_field code_field
disp_models disp_models
exe exe
coef_lambda coef_lambda
new_col new_col
models_mode models_mode
extract_code extract_code
dt_model dt_model
code_transf code_transf
chunk chunk
code_load code_load
combine_names combine_names
colnames_empty colnames_empty
numerical_distribution numerical_distribution
rl_prediction rl_prediction
gg_color_hue gg_color_hue
def_code_num def_code_num
default_calc_normal default_calc_normal
numerical_summary numerical_summary
importance_plot_rf importance_plot_rf
dt_plot dt_plot
code_summary code_summary
default_disp default_disp
options_regressor options_regressor
error_plot error_plot
nn_prediction nn_prediction
labelInput labelInput
dt_prediction dt_prediction
error_variables error_variables
len_report len_report
normal_default normal_default
rlr_model rlr_model
comparative_table comparative_table
correlations_plot correlations_plot
rlr_prediction rlr_prediction
plot_RMSE plot_RMSE
plot_coef_lambda plot_coef_lambda
svm_model svm_model
summary_indices summary_indices
partition_code partition_code
order_report order_report
rf_prediction rf_prediction
pairs_power pairs_power
rf_model rf_model
disjunctive_data disjunctive_data
rlr_type rlr_type
var_categorical var_categorical
render_table_data render_table_data
init_regressor This function will start regressoR
radioButtonsTr radioButtonsTr
plot_var_pred_rd plot_var_pred_rd
validate_pn_data validate_pn_data
infoBoxPROMiDAT infoBoxPROMiDAT
render_index_table render_index_table
get_env_report get_env_report
def_code_cat def_code_cat
cor_model cor_model
general_indices general_indices
fisher_calc fisher_calc
kkn_model kkn_model
plot_pred_rd plot_pred_rd
new_report new_report
inputRadio inputRadio
get_report get_report
insert_report insert_report
new_section_report new_section_report
kkn_prediction kkn_prediction
plot_real_prediction plot_real_prediction
nn_plot nn_plot
nn_model nn_model
rl_coeff rl_coeff
remove_report_elem remove_report_elem
translate translate
tb_predic tb_predic
rd_model rd_model
rd_type rd_type
rd_prediction rd_prediction
word_report word_report
rl_model rl_model
var_numerical var_numerical
svm_prediction svm_prediction
tabsOptions tabsOptions
No Results!

Last month downloads

Details

Include our badge in your README

[![Rdoc](http://www.rdocumentation.org/badges/version/regressoR)](http://www.rdocumentation.org/packages/regressoR)