GLRT P-Values in Generalized Linear Models
Provides functions to compute Generalized Likelihood Ratio Tests (GLRT) and Rao's score tests of simple
and complex contrasts of Generalized Linear Models (GLMs). It provides the same interface as summary.glm(), adding GLRT P-values,
less biased than Wald's P-values and consistent with profile-likelihood confidence interval generated by confint().
base, stats, parameters (>= 0.1.0), MASS
testthat (>= 2.1.0), lme4, nlme, datasets

Provides functions to compute Generalized Likelihood Ratio Tests (GLRT) also known as Likelihood Ratio Tests (LRT) and Rao's score tests of simple
and complex contrasts of Generalized Linear Models (GLMs). It provides the same interface as summary.glm(), adding GLRT P-values,
less biased than Wald's P-values and consistent with profile-likelihood confidence interval generated by confint().
See Wilks (1938) <doi:10.1214/aoms/1177732360> for the LRT chi-square approximation.
See Rao (1948) <doi:10.1017/S0305004100023987> for Rao's score test.
See Wald (1943) <doi:10.2307/1990256> for Wald's test.
testthat (>= 2.1.0), lme4, nlme, datasets, nnet, survival,
lmerTest, mgcv, gam, multcomp

Graph-Constrained Regularization for Sparse Generalized Linear
Models
We propose to use sparse regression model to achieve variable selection while accounting for graph-constraints among coefficients. Different linear combination of a sparsity penalty(L1) and a smoothness(MCP) penalty has been used, which induces both sparsity of the solution and certain smoothness on the linear coefficients.

Fit GLM with LEP-based penalized maximum likelihood
Efficient algorithms for fitting regularization paths for
linear or logistic regression models penalized by LEP.

Fit GLM with LEP-Based Penalized Maximum Likelihood

Generalized Linear Mixed Models via Monte Carlo Likelihood
Approximation
Approximates the likelihood of a generalized linear mixed model using Monte Carlo likelihood approximation. Then maximizes the likelihood approximation to return maximum likelihood estimates, observed Fisher information, and other model information.