Description: Tools for the fitting and cross validation of exact conditional logistic regression models with lasso and elastic net penalties. Uses cyclic coordinate descent and warm starts to compute the entire path efficiently.

Title: Fitting Exact Conditional Logistic Regression with Lasso and

Title: Lasso Estimation of Conditional Logistic Regression Models for
Author: Marius Kwemou <marius.kwemou@gmail.com> and Marta Avalos
Description: Fit a sequence of conditional logistic regression models with lasso, for small to large sized samples.

Title: Lasso Estimation of Conditional Logistic Regression Models

Title: Sparse Conditional Logistic Regression for Matched Studies
Author: Marta Avalos, Helene Pouyes, Marius Kwemou and Binbin Xu
Description: Fit a sequence of conditional logistic regression models with lasso, for small to large sized samples. Avalos, M., Pouyes, H., Grandvalet, Y., Orriols, L., & Lagarde, E. (2015) <doi:10.1186/1471-2105-16-S6-S1>.

Author: Haolun Shi [aut, cre], Guosheng Yin [aut]
Description: A set of functions to fit a boosting conditional logit model.

Title: Composite Likelihood Inference for Spatial Ordinal Data with
person("Pingping", "Wang", email = "catherinewpp@126.com", role = "aut"),
person("Dipankar", "Bandyopadhyay", email = "bandyopd@gmail.com", role = "ctb"),
person("Yincai", "Tang", email = "yctang@stat.ecnu.edu.cn", role= "ctb"))
Description: Composite likelihood parameter estimate and asymptotic covariance matrix are calculated for the spatial ordinal data with replications, where spatial ordinal response with covariate and both spatial exponential covariance within subject and independent and identically distributed measurement error.  Parametric bootstrapping is used to estimate the asymptotic standard error and covariance matrix.

Description: Composite likelihood parameter estimate and asymptotic covariance matrix are calculated for the spatial ordinal data with replications, where spatial ordinal response with covariate and both spatial exponential covariance within subject and independent and identically distributed measurement error.  Parameter estimation can be performed by either solving the gradient function or maximizing composite log-likelihood. Parametric bootstrapping is used to estimate the Godambe information matrix and hence the asymptotic standard error and covariance matrix with parallel processing option.
doParallel (>= 1.0.11), foreach (>= 1.2.0), utils, stats,