Package: GammaFrailty 0.1.0

GammaFrailty: Gamma Frailty Regression Models with Multiple Baseline Distributions

Implements univariate gamma frailty regression models for survival data with six different baseline distributions: the Arvind distribution (Pandey et al., 2024), the Lindley distribution (Lindley, 1958), the Linear Failure Rate distribution (Bain, 1974), the Power Xgamma distribution (Tyagi et al., 2022), the Modified Topp-Leone distribution (Singh et al., 2025), and the Power Failure Rate distribution (Mugdadi, 2005). The package supports uncensored (complete) and censored data (right, left, interval, and progressive censoring) with and without covariates. It provides maximum likelihood estimation, standard errors, confidence intervals, t-statistics, p-values, Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), a bootstrap approximation of the Widely Applicable Information Criterion (WAIC), k-fold cross-validation, variance inflation factors, R-squared, adjusted R-squared, Mean Squared Error (MSE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), an overall model F-test, frailty variance estimation, survival probabilities at user-specified time points, median survival, expected survival within a fixed window, risk predictions, marginal predictions, martingale and deviance residuals, standardized and studentized residuals, leverage values, Cook's distance, Difference in Fits (DFFITS), Difference in Betas (DFBETAS), and a comprehensive suite of diagnostic and survival plots including Kaplan-Meier overlays and coefficient forest plots. Random number generation is available for each baseline distribution and the full frailty model, and a simulation study function evaluates parameter recovery across sample sizes and censoring scenarios. References are Lindley (1958) <doi:10.1111/j.2517-6161.1958.tb00278.x>, Mugdadi (2005) <doi:10.1016/j.amc.2004.09.064>, Bain (1974) <doi:10.1080/00401706.1974.10489237>, Singh, Tyagi, Singh, and Tyagi (2025) <https://ph02.tci-thaijo.org/index.php/thaistat/article/view/257215>, Pandey, Singh, Tyagi, and Tyagi (2024) <https://ssca.org.in/journal.html>, and Tyagi, Kumar, Pandey, Saha, and Bagariya (2022) <https://ijsreg.com/>.

Authors:Shikhar Tyagi [aut, cre]

GammaFrailty_0.1.0.tar.gz
GammaFrailty_0.1.0.zip(r-4.7)GammaFrailty_0.1.0.zip(r-4.6)GammaFrailty_0.1.0.zip(r-4.5)
GammaFrailty_0.1.0.tgz(r-4.6-any)GammaFrailty_0.1.0.tgz(r-4.5-any)
GammaFrailty_0.1.0.tar.gz(r-4.7-any)GammaFrailty_0.1.0.tar.gz(r-4.6-any)
GammaFrailty_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html
card.svg |card.png
GammaFrailty/json (API)

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

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

2.00 score 3 scripts 34 exports 11 dependencies

Last updated from:6d176dd1c5. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK162
source / vignettesOK208
linux-release-x86_64OK183
macos-release-arm64OK195
macos-oldrel-arm64OK249
windows-develOK98
windows-releaseOK99
windows-oldrelOK101
wasm-releaseOK103

Exports:baseline_hazardbootstrap_waiccompare_modelscv_frailtydiagnostics_tablefit_gamma_frailtyforecast_frailtygamma_frailtygamma_frailty_functionsinfluence_frailtyloglik_gamma_frailtyplot_allplot_baselineplot_coef_forestplot_dfbetasplot_leverageplot_qq_residualsplot_residuals_fittedplot_residuals_leverageplot_scale_locationplot_survival_kmpredict_frailtyr_arvindr_gamma_frailtyr_lfrr_lindleyr_mtlr_pfrr_pxgresiduals_frailtyrisk_predictsimulate_mle_performancesimulation_studysurvival_at

Dependencies:digestgenericslatticeMASSMatrixmaxLikmiscToolsnumDerivsandwichsurvivalzoo

GammaFrailty: Gamma Frailty Regression Models with Multiple Baseline Distributions

Rendered fromGammaFrailty.Rmdusingknitr::rmarkdownon Jun 19 2026.

Last update: 2026-06-18
Started: 2026-06-18

Readme and manuals

Help Manual

Help pageTopics
Baseline Cumulative Hazard and Hazard Functionsbaseline_hazard
Bootstrap WAIC for the Gamma Frailty Modelbootstrap_waic
Compare Multiple Gamma Frailty Modelscompare_models
K-Fold Cross-Validation for the Gamma Frailty Modelcv_frailty
Summary Diagnostics Tablediagnostics_table
Fit a Gamma Frailty Regression Modelfit_gamma_frailty
Forecast Survival Curvesforecast_frailty
Gamma Frailty Regression Model (Formula Interface)gamma_frailty
Gamma Frailty Model Functionsgamma_frailty_functions
Influence Diagnostics for the Gamma Frailty Modelinfluence_frailty
Log-Likelihood for the Gamma Frailty Modelloglik_gamma_frailty
Plot All Diagnosticsplot_all
Plot Baseline Distribution Functionsplot_baseline
Coefficient Forest Plotplot_coef_forest
DFBETAS Dot Plotplot_dfbetas
Leverage Histogramplot_leverage
Q-Q Plot of Cox-Snell Residualsplot_qq_residuals
Residuals vs Fitted Values Plotplot_residuals_fitted
Residuals vs Leverage Plotplot_residuals_leverage
Scale-Location Plotplot_scale_location
Kaplan-Meier vs Model-Based Survival Plotplot_survival_km
Predictions from a Gamma Frailty Modelpredict_frailty
Random samples from the Arvind distributionr_arvind
Generate Random Survival Times from the Gamma Frailty Modelr_gamma_frailty
Random samples from the Linear Failure Rate distributionr_lfr
Random samples from the Lindley distributionr_lindley
Random samples from the Modified Topp-Leone distributionr_mtl
Random samples from the Power Failure Rate distributionr_pfr
Random samples from the Power Xgamma distributionr_pxg
Residuals for the Gamma Frailty Modelresiduals_frailty
Risk Prediction for New Subjectsrisk_predict
Quick Simulation Performance Checksimulate_mle_performance
Monte Carlo Simulation Study for Gamma Frailty Modelssimulation_study
Survival Probability at Specified Time Pointssurvival_at