Fits generalized linear models where the parameters are subject to linear constraints. The model is specified by giving a symbolic description of the linear predictor, a description of the error distribution, and a matrix of constraints on the parameters.

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.

Provides Generalized Inferences based on exact distributions and exact probability statements for mixed effect models, provided by such papers as Weerahandi and Yu (2020) <doi:10.1186/s40488-020-00105-w> under the widely used Compound Symmetric Covariance structure. The package returns the estimation of the coefficients in random and fixed part of the mixed models by generalized inference.

Recursive partitioning based on (generalized) linear mixed models
    (GLMMs) combining lmer()/glmer() from 'lme4' and lmtree()/glmtree() from 
    'partykit'. The fitting algorithm is described in more detail in Fokkema,
    Smits, Zeileis, Hothorn & Kelderman (2018; <DOI:10.3758/s13428-017-0971-x>).

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.