Description: It estimates the parameters of a censored or missing data in spatio-temporal models using the SAEM algorithm (Delyon et al., 1999). This algorithm is a stochastic approximation of the widely used EM algorithm and an important tool for models in which the E-step does not have an analytic form. Besides the expressions obtained to estimate the parameters to the proposed model, we include the calculations for the observed information matrix using the method developed by Louis (1982). To examine the performance of the fitted model, case-deletion measure are provided.
Imports: Rcpp, stats, utils, mvtnorm, tmvtnorm, MCMCglmm, ggplot2,
Author: Larissa A. Matos [aut, cre] (<https://orcid.org/0000-0002-2635-0901>),
Katherine L. Valeriano [aut] (<https://orcid.org/0000-0001-6388-4753>),
Maintainer: Larissa A. Matos <larissa.amatos@gmail.com>

Title: Provide the Stepwise Procedure for Beta Regression Models
Description: Starting from a Beta Regression Model, it provides a stepwise
procedure to select the linear predictor.

Title: Stepwise Procedure for Beta Regression Models

Title: Stepwise Procedure for Beta, Beta-Binomial and Negative Binomial
Description: Starting from a Regression Model, it provides a stepwise
Imports: glue, stats, betareg, combinat, aod, MASS

Description: Stepwise regression analysis for variable selection can be used to get the best candidate final regression model in univariate or multivariate regression analysis with the 'forward' and 'stepwise' steps. Procedure uses Akaike information criterion, the small-sample-size corrected version of Akaike information criterion, Bayesian information criterion, Hannan and Quinn information criterion, the corrected form of Hannan and Quinn information criterion, Schwarz criterion and significance levels as selection criteria, where the significance levels for entry and for stay are set to 0.15 as default. Multicollinearity detection in regression model are performed by checking tolerance value, which is set to 1e-7 as default. Continuous variables nested within class effect are also considered in this package.