c(person(given = "Lorena", family = "León",role = c("aut", "cre"), email = "ylorenaleonv@gmail.com"),
Packaged: 2021-01-12 22:07:45 UTC; leonvelasco

Packaged: 2021-03-05 18:44:51 UTC; leonvelasco

Packaged: 2021-03-06 09:53:20 UTC; leonvelasco

Description: In statistical modeling, there is a wide variety of regression models for categorical dependent variables (nominal or ordinal data); yet, there is no software embracing all these models together in a uniform and generalized format. Following the methodology proposed by Peyhardi, Trottier, and Guédon (2015) <doi:10.1093/biomet/asv042>, we introduce 'GLMcat', an R package to estimate generalized linear models implemented under the unified specification (r, F, Z). Where r represents the ratio of probabilities (reference, cumulative, adjacent, or sequential), F the cumulative cdf function for the linkage, and Z, the design matrix.
Packaged: 2021-05-09 18:26:06 UTC; leonvelasco

Packaged: 2021-05-13 20:39:40 UTC; leonvelasco

Packaged: 2021-09-23 14:38:33 UTC; leonvelasco

Suggests: knitr, rmarkdown, testthat (>= 3.0.0), dplyr, ggplot2,
Packaged: 2022-08-16 19:41:24 UTC; leonvelasco

Title: Data and Code to Accompany Generalized Linear Models, 2nd
Authors@R: c(person("Jeff", "Gill", email="jgill@american.edu", role="aut"), person("Michelle", "Torres", email="smtorres@wustl.edu", role=c("aut", "cre")), person("Simon", "Heuberger", email="sh6943a@american.edu", role="aut"))
Description: Contains all the data and functions used in Generalized
Linear Models, 2nd edition, by Jeff Gill and Michelle Torres. Examples to create
all models, tables, and plots are included for each data set.
Imports: MASS, pBrackets, nnet, effects, AER, pscl, foreign, Matrix,
Packaged: 2019-07-19 01:05:52 UTC; michelletorres

Title: Generalized Linear Model Data Sets
Description: Data sets from the book Generalized Linear Models with Examples in R by Dunn and Smyth.
Author: Peter K. Dunn [cre,aut], Gordon K. Smyth [aut]
Maintainer: Peter K. Dunn <pdunn2@usc.edu.au>

Title: Graphical Least Square Estimation
Description: The Graphical Least Square Estimation 'GLSE' package consists of several functions. The first function (called GLSE()), which is the main function in this package, is for estimating linear regression via graphical models, especially when the number of observations is smaller than the number of variables. This function gives unbiased estimated regression models under some conditions on the predictors. Moreover, this function aims to deal with studies where all covariates need to be kept in the model (such as portfolio optimisation). The second function provides an estimation of the bootstrap standard errors of the 'GLSE' parameters. The third function generates data from multivariate normal distribution for regression purposes, and the fourth function generates data from multivariate t-distribution for the same purpose. Generating an undirected decomposable graph is carried out by the fifth function and its  decomposability is tested in the sixth function. The seventh function gives a perfect sequence of the cliques and separators of the given decomposable graph. Finally, the last function gives the plot of the decomposale graph. The package supports both serial and parallel computations for estimating the graph.The 'GLSE' package is an implementation of the method introduced in Aldahmani, S., and Dai, H. (2015) <doi:10.5539/ijsp.v4n3p61>.
Imports: gRbase, igraph, mvtnorm, parallel, stats
Packaged: 2018-11-12 12:48:37 UTC; saldahmani