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MNB - Diagnostic Tools for a Multivariate Negative Binomial Model

Diagnostic tools as residual analysis, global, local and total-local influence for the multivariate model from the random intercept Poisson generalized log gamma model are available in this package. Including also, the estimation process by maximum likelihood method, for details see Fabio, L. F; Villegas, C. L.; Carrasco, J.M.F and de Castro, M. (2021) <doi:10.1080/03610926.2021.1939380>.

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3.54 score 302 downloads

HZIP - Likelihood-Based Inference for Joint Modeling of Correlated Count and Binary Outcomes with Extra Variability and Zeros

Inference approach for jointly modeling correlated count and binary outcomes. This formulation allows simultaneous modeling of zero inflation via the Bernoulli component while providing a more accurate assessment of the Hierarchical Zero-Inflated Poisson's parsimony (Lizandra C. Fabio, Jalmar M. F. Carrasco, Victor H. Lachos and Ming-Hui Chen, Likelihood-based inference for joint modeling of correlated count and binary outcomes with extra variability and zeros, 2026, under submission).

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cpp

3.18 score 6 scripts 454 downloads

EMGCR - Mixture Cure Rate Models with Flexible Link Functions via the EM Algorithm

Fits mixture cure rate models by the Expectation-Maximization (EM) algorithm. The incidence component (the probability of being uncured) accepts the logit, probit, cauchit, power logit and reversed power logit link functions, and the latency component accepts the exponential, Rayleigh, Weibull, log-normal, log-logistic and inverse Gaussian distributions. The package provides parameter estimates with standard errors, simulation of data from the model, and diagnostic tools based on residuals and simulated envelopes. The methods build on Berkson and Gage (1952) <doi:10.2307/2281318>, Dempster, Laird and Rubin (1977) <doi:10.1111/j.2517-6161.1977.tb01600.x> and Bazán, Torres-Avilés, Suzuki and Louzada (2017) <doi:10.1002/asmb.2215>.

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3.18 score 2 scripts 286 downloads

GTDL - The Generalized Time-Dependent Logistic Family

Computes the probability density, survival function, the hazard rate functions and generates random samples from the GTDL distribution given by Mackenzie, G. (1996) <doi:10.2307/2348408>. The likelihood estimates, the randomized quantile (Louzada, F., et al. (2020) <doi:10.1109/ACCESS.2020.3040525>) residuals and the normally transformed randomized survival probability (Li,L., et al. (2021) <doi:10.1002/sim.8852>) residuals are obtained for the GTDL model.

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2.70 score 3 scripts 295 downloads