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alookr (version 0.5.0)

Model Classifier for Binary Classification

Description

A collection of tools that support data splitting, predictive modeling, and model evaluation. A typical function is to split a dataset into a training dataset and a test dataset. Then compare the data distribution of the two datasets. Another feature is to support the development of predictive models and to compare the performance of several predictive models, helping to select the best model.

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Version

Install

install.packages('alookr')

Monthly Downloads

896

Version

0.5.0

License

GPL-2

Maintainer

Choonghyun Ryu

Last Published

January 8th, 2026

Functions in alookr (0.5.0)

plot_performance

Visualization for ROC curve
sampling_target

Extract the data to fit the model
run_performance

Apply calculate performance metrics for model evaluation
compare_diag

Diagnosis of train set and test set of split_df object
compare_plot

Comparison plot of train set and test set
cleanse.split_df

Cleansing the dataset for classification modeling
matthews

Compute Matthews Correlation Coefficient
compare_target_category

Comparison of categorical variables of train set and test set
cleanse.data.frame

Cleansing the dataset for classification modeling
compare_target_numeric

Comparison of numerical variables of train set and test set
compare_performance

Compare model performance
extract_set

Extract train/test dataset
performance_metric

Calculate metrics for model evaluation
split_by

Split Data into Train and Test Set
run_models

Fit binary classification model
run_predict

Predict binary classification model
treatment_corr

Diagnosis and removal of highly correlated variables
summary.split_df

Summarizing split_df information
plot_cutoff

Visualization for cut-off selection