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 years ago

4.61 score 1 packages 27 scripts 247 downloads

hdcuremodels - Penalized Mixture Cure Models for High-Dimensional Data

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 2 months ago

4.00 score 5 scripts 101 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 years ago

3.81 score 13 scripts 255 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 2 years ago

3.70 score 6 scripts 225 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.

Last updated 3 years ago

3.70 score 1 scripts 152 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>.

Last updated 3 years ago

3.70 score 1 stars 1 scripts 152 downloads