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CLRtools (version 0.1.0)

Diagnostic Tools for Logistic and Conditional Logistic Regression

Description

Provides tools for fitting, assessing, and comparing logistic and conditional logistic regression models. Includes residual diagnostics and goodness of fit measures for model development and evaluation in matched case control studies.

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Version

Install

install.packages('CLRtools')

Version

0.1.0

License

GPL-3

Maintainer

Brenda Contla Hernández

Last Published

January 29th, 2026

Functions in CLRtools (0.1.0)

glow500

GLOW500 dataset
diagnostic_bayes

Generate MCMC Diagnostic Plots for a Bayesian Model
rcv_measures

Model Fit Evaluation: R^2-like Measures for Logistic Regression Models with J < n
r_measures

Model Fit Evaluation: R^2-like Measures for Logistic Regression Models with J=n
check_coef_significant

Check Significance of Excluded Variables
summarize_results

Summarize Bayesian Logistic Regression Model Results
stukels_test

Stukel’s Test for Logistic Regression Model Fit
CLRtools-package

CLRtools: Diagnostic Tools for Logistic and Conditional Logistic Regression
diagnosticplots_class

Diagnostic Plots for Model Discrimination
DRtest

Deviance Residuals Test (HL Test)
discordant.pairs

Count Discordant Pairs in Matched Case-Control Data
cutpoints

Table with Sensitivity and Specificity at Different Cutpoints
residuals_clog

Model Diagnostic for Conditional Logistic Regression
osius_rojek

Osius and Rojek Goodness-of-Fit Test for Logistic Regression
residuals_logistic

Model Diagnostic for Logistic Regression Models
univariable.clogmodels

Univariable Conditional Logistic Regression Models
logit_prob_plot

Plot Predicted Probabilities from a Logistic Model
cov.patterns

Extract Unique Covariate Patterns from a Logistic Regression Model
univariable.models

Univariable Logistic Regression Summary Table
compare_bayesm

Posterior Predictive Check for Multiple Bayesian Models
coeff.OR

Compute Odds Ratios for Logistic and Conditional Logistic Regression Models
compare_models_loo

Compare Bayesian Models Using PSIS-LOO
confidence.interval

Compute Wald-Based Confidence Intervals for Logit and Predicted Probability
check_coef_change

Assess Coefficient Change After Variable Removal
compare_bayesm_by_predictor

Compare Bayesian Models by Predictor Using Posterior Predictive Simulations
check_interactions

Check Pairwise Interactions in Logistic Regression
delta.coefficient

Delta-beta hat percentage: Change in Coefficients when Adding a Variable
glow11m

GLOW11M dataset