Package: CLRtools 0.1.1

Brenda Contla Hernández

CLRtools: Diagnostic Tools for Logistic and Conditional Logistic Regression

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.

Authors:Brenda Contla Hernández [aut, cre], Matthieu Vignes [ctb], Chris Compton [ctb]

CLRtools_0.1.1.tar.gz
CLRtools_0.1.1.zip(r-4.7)CLRtools_0.1.1.zip(r-4.6)CLRtools_0.1.1.zip(r-4.5)
CLRtools_0.1.1.tgz(r-4.6-any)CLRtools_0.1.1.tgz(r-4.5-any)
CLRtools_0.1.1.tar.gz(r-4.7-any)CLRtools_0.1.1.tar.gz(r-4.6-any)
CLRtools_0.1.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
CLRtools/json (API)

# Install 'CLRtools' in R:
install.packages('CLRtools', repos = c('https://brendacontla.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/brendacontla/clrtools/issues

Datasets:

On CRAN:

Conda:

4.65 score 505 downloads 25 exports 132 dependencies

Last updated from:095d2d61e4. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK259
source / vignettesOK485
linux-release-x86_64OK273
macos-release-arm64OK235
macos-oldrel-arm64OK233
windows-develOK180
windows-releaseOK189
windows-oldrelOK209
wasm-releaseOK192

Exports:check_coef_changecheck_coef_significantcheck_interactionscoeff.ORcompare_bayesmcompare_bayesm_by_predictorcompare_models_looconfidence.intervalcov.patternscutpointsdelta.coefficientdiagnostic_bayesdiagnosticplots_classdiscordant.pairsDRtestlogit_prob_plotosius_rojekr_measuresrcv_measuresresiduals_clogresiduals_logisticstukels_testsummarize_resultsunivariable.clogmodelsunivariable.models

Dependencies:abindbackportsbayesplotBHbootbroomcallrcarcarDatacaretcheckmateclasscliclockcodetoolscolorspacecorrplotcowplotcpp11data.tableDerivdescdiagramdigestdistributionaldoBydplyre1071farverforeachforecastFormulafracdifffuturefuture.applygenericsggplot2ggpubrggrepelggridgesggsciggsignifglobalsgluegowergridExtragtablehardhatinlineipredisobanditeratorsKernSmoothlabelinglatticelavalifecyclelistenvlme4lmtestloolubridatemagrittrMASSMatrixMatrixModelsmatrixStatsmgcvminqaModelMetricsmodelrnlmenloptrnnetnumDerivotelparallellypatchworkpbkrtestpillarpkgbuildpkgconfigplyrpolynomposteriorpROCprocessxprodlimprogressrproxypspurrrquantregQuickJSRR6rbibutilsRColorBrewerRcppRcppArmadilloRcppEigenRcppParallelRdpackrecipesreformulasreshape2rlangrpartrstanrstatixS7scalesshapeSparseMsparsevctrsSQUAREMStanHeadersstringistringrsurvivaltensorAtibbletidyrtidyselecttimechangetimeDatetzdburcautf8vctrsviridisLitewithrzoo

Bayesian_Logistic_regression
Causal Diagram | Bayesian Analysis | Weight | Direct effect | Checking priors | Posterior distribution | Total effect | Conclusion
Rendered fromBayesian_Logistic_regression.Rmdusingknitr::rmarkdown

Last update: 2026-03-13
Started: 2026-01-30

Conditional_Logistic_Regression
Step 1. Fit univariable models | Step 2: Fit Initial multivariate Model and Refine Predictors | Step 3: Assess Potential Confounding | Step 4: Reassess Excluded Variables | Step 5: Assess Linearity of Continuous Predictors with Respect to the Logit | Step 6: Assess Interaction Terms | Step 7: Perform Model Assessment
Rendered fromConditional_Logistic_Regression.Rmdusingknitr::rmarkdown

Last update: 2026-01-30
Started: 2026-01-30

Logistic_Regression
Step 1. Fit univariable models | Step 2: Fit Initial multivariable Model and Refine Predictors | Step 3: Assess Potential Confounding | Step 4: Reassess Excluded Variables | Step 5: Assess Linearity of Continuous Predictors with Respect to the Logit | Step 6: Assess Interaction Terms | Step 7: Perform Model Assessment | Measure of goodness of fit | Classification tables | $R^2$ measures | Logistic regression diagnostics
Rendered fromLogistic_Regression.Rmdusingknitr::rmarkdown

Last update: 2026-01-30
Started: 2026-01-30

Readme and manuals

Help Manual

Help pageTopics
Assess Coefficient Change After Variable Removalcheck_coef_change
Check Significance of Excluded Variablescheck_coef_significant
Check Pairwise Interactions in Logistic Regressioncheck_interactions
Compute Odds Ratios for Logistic and Conditional Logistic Regression Modelscoeff.OR
Posterior Predictive Check for Multiple Bayesian Modelscompare_bayesm
Compare Bayesian Models by Predictor Using Posterior Predictive Simulationscompare_bayesm_by_predictor
Compare Bayesian Models Using PSIS-LOOcompare_models_loo
Compute Wald-Based Confidence Intervals for Logit and Predicted Probabilityconfidence.interval
Extract Unique Covariate Patterns from a Logistic Regression Modelcov.patterns
Table with Sensitivity and Specificity at Different Cutpointscutpoints
Delta-beta hat percentage: Change in Coefficients when Adding a Variabledelta.coefficient
Generate MCMC Diagnostic Plots for a Bayesian Modeldiagnostic_bayes
Diagnostic Plots for Model Discriminationdiagnosticplots_class
Count Discordant Pairs in Matched Case-Control Datadiscordant.pairs
Deviance Residuals Test (HL Test)DRtest
GLOW11M datasetglow11m
GLOW500 datasetglow500
Plot Predicted Probabilities from a Logistic Modellogit_prob_plot
Osius and Rojek Goodness-of-Fit Test for Logistic Regressionosius_rojek
Model Fit Evaluation: R^2-like Measures for Logistic Regression Models with J=nr_measures
Model Fit Evaluation: R^2-like Measures for Logistic Regression Models with J < nrcv_measures
Model Diagnostic for Conditional Logistic Regressionresiduals_clog
Model Diagnostic for Logistic Regression Modelsresiduals_logistic
Stukel’s Test for Logistic Regression Model Fitstukels_test
Summarize Bayesian Logistic Regression Model Resultssummarize_results
Univariable Conditional Logistic Regression Modelsunivariable.clogmodels
Univariable Logistic Regression Summary Tableunivariable.models