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mixor - Mixed-Effects Ordinal Regression Analysis

Provides the function 'mixor' for fitting a mixed-effects ordinal and binary response models and associated methods for printing, summarizing, extracting estimated coefficients and variance-covariance matrix, and estimating contrasts for the fitted models.

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fortranglibc

4.26 score 36 scripts 266 downloads

hdcuremodels - High-Dimensional Cure Models

Provides functions for fitting various penalized parametric and semi-parametric mixture cure models with different penalty functions, testing for a significant cure fraction, and testing for sufficient follow-up as described in Fu et al (2022)<doi:10.1002/sim.9513> and Archer et al (2024)<doi:10.1186/s13045-024-01553-6>. False discovery rate controlled variable selection is provided using model-X knock-offs.

Last updated

4.08 score 7 scripts 351 downloads

ordinalgmifs - Ordinal Regression for High-Dimensional Data

Provides a function for fitting cumulative link, adjacent category, forward and backward continuation ratio, and stereotype ordinal response models when the number of parameters exceeds the sample size, using the the generalized monotone incremental forward stagewise method.

Last updated

3.90 score 16 scripts 269 downloads

glmpathcr - Fit a Penalized Continuation Ratio Model for Predicting an Ordinal Response

Provides a function for fitting a penalized constrained continuation ratio model using the glmpath algorithm and methods for extracting coefficient estimates, predicted class, class probabilities, and plots.

Last updated

3.74 score 11 scripts 293 downloads

countgmifs - Discrete Response Regression for High-Dimensional Data

Provides a function for fitting Poisson and negative binomial regression models when the number of parameters exceeds the sample size, using the the generalized monotone incremental forward stagewise method.

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3.70 score 1 scripts 312 downloads

ordinalbayes - Bayesian Ordinal Regression for High-Dimensional Data

Provides a function for fitting various penalized Bayesian cumulative link ordinal response models when the number of parameters exceeds the sample size. These models have been described in Zhang and Archer (2021) <doi:10.1186/s12859-021-04432-w>.

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jagscpp

3.70 score 1 stars 2 scripts 328 downloads

glmnetcr - Fit a Penalized Constrained Continuation Ratio Model for Predicting an Ordinal Response

Penalized methods are useful for fitting over-parameterized models. This package includes functions for restructuring an ordinal response dataset for fitting continuation ratio models for datasets where the number of covariates exceeds the sample size or when there is collinearity among the covariates. The 'glmnet' fitting algorithm is used to fit the continuation ratio model after data restructuring.

Last updated

3.48 score 1 dependents 344 downloads

hdcuremodels - High-Dimensional Cure Models

Provides functions for fitting various penalized parametric and semi-parametric mixture cure models with different penalty functions, testing for a significant cure fraction, and testing for sufficient follow-up as described in Fu et al (2022)<doi:10.1002/sim.9513> and Archer et al (2024)<doi:10.1186/s13045-024-01553-6>. False discovery rate controlled variable selection is provided using model-X knock-offs.

Last updated

3.18 score 7 scripts 382 downloads