Description: Provides functions to compute Generalized Likelihood Ratio Tests (GLRT) also known as Likelihood Ratio Tests (LRT) and Rao's score tests of simple
See Wilks (1938) <doi:10.1214/aoms/1177732360> for the LRT chi-square approximation.
Imports: stats, parameters (>= 0.1.0), MASS
Suggests: testthat (>= 2.1.0), lme4, nlme, datasets, nnet, survival,
Packaged: 2020-08-03 22:17:34 UTC; andre.gillibert

Title: Graph-Constrained Regularization for Sparse Generalized Linear
Description: 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.

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

Title: Fit GLM with LEP-Based Penalized Maximum Likelihood
Author: Canhong Wen, Hao Lin, Xueqin Wang
Packaged: 2019-03-08 08:55:54 UTC; quanshijief

Title: Generalized Linear Mixed Models via Monte Carlo Likelihood
Description: 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.
Depends: R (>= 3.1.1), trust, mvtnorm, Matrix
Packaged: 2015-02-11 21:41:35 UTC; christina

Packaged: 2015-02-25 20:54:42 UTC; christina

Packaged: 2015-04-28 18:34:34 UTC; christina

Packaged: 2015-07-24 16:08:36 UTC; christina

Packaged: 2015-02-11 02:30:53 UTC; christina

Packaged: 2016-08-12 19:49:25 UTC; christina

Packaged: 2016-08-17 18:39:21 UTC; christina

Packaged: 2017-08-15 22:59:01 UTC; christina

Packaged: 2017-11-07 14:49:52 UTC; christina

Authors@R: c(person("Christina", "Knudson", role = c("aut", "cre"),
email = "knud8583@stthomas.edu"), person("Charles J.", "Geyer", role = "ctb",
Depends: R (>= 3.1.1), trust, mvtnorm, Matrix, digest