Provides the function 'mixor' for fitting a mixed-effects ordinal and binary response models and associated methods for printing, summarizing, extracting estimated coefficients and variance-covariance matrix, and estimating contrasts for the fitted models.

Fits multiple variable mixtures of various parametric proportional hazard models using the EM-Algorithm. Proportionality restrictions can be imposed on the latent groups and/or on the variables. Several survival distributions can be specified. Missing values and censored values are allowed. Independence is assumed over the single variables.

Fits mixed Poisson regression models (Poisson-Inverse Gaussian or Negative-Binomial) on data sets with response variables being count data. The models can have varying precision parameter, where a linear regression structure (through a link function) is assumed to hold on the precision parameter. The Expectation-Maximization algorithm for both these models (Poisson Inverse Gaussian and Negative Binomial) is an important contribution of this package. Another important feature of this package is the set of functions to perform global and local influence analysis. See Barreto-Souza and Simas (2016) <doi:10.1007/s11222-015-9601-6> for further details.

A comprehensive, simulation-based toolkit for power and sample-size
    analysis for linear and generalized linear mixed-effects models (LMMs and
    GLMMs). Supports Gaussian, binomial, Poisson, and negative binomial families
    via 'lme4'; Wald and likelihood-ratio tests; multi-parameter sensitivity
    grids; power curves and minimum sample-size solvers; parallel evaluation with
    deterministic seeds; and full reproducibility (manifests, result bundling,
    and export to CSV/JSON). Delivers thorough diagnostics per run (failure rate,
    singular-fit rate, effective N) and publication-ready summary tables.
    References: Bates et al. (2015) "Fitting Linear Mixed-Effects Models Using
    lme4" <doi:10.18637/jss.v067.i01>; Green and MacLeod (2016) "SIMR: an R
    package for power analysis of generalized linear mixed models by simulation"
    <doi:10.1111/2041-210X.12504>.