GEE solver for correlated nominal or ordinal multinomial responses
    using a local odds ratios parameterization.

Provides a multi action button for usage in 'shiny'
    applications.

Covers several approaches to ANOVA (Analysis of Variance), specifically studying a balanced two-factor fixed-fixed ANOVA design. It consists of four sections. The first section uses a dynamic scheme to indicate which possible alternatives to follow depending on the fulfillment of the assumptions of the model. It also presents an analysis on the fulfillment of the assumptions of linearity, homoscedasticity, normality, and independence in the residuals of the model, as well as dynamic statistical graphs on the residuals of the model. The second section presents an analysis with a non-parametric approach of Kruskal Wallis. After Kruskal Wallis, a Post-Hoc analysis of multiple comparisons on the medians of the treatments is carried out. The third section presents a classical parametric ANOVA. Following classical ANOVA, a post-hoc analysis of multiple comparisons on the medians of the treatments, factor levels by Dunn's test, and statistical graphs for the treatments and factor levels are shown. Additionally, a post-hoc analysis of multiple comparisons on the means of the treatments is done. The fourth section presents an analysis of variance under a Bayesian approach. In this section, interactive statistical graphs are presented on the posterior distributions of treatments, factor levels, and a convergence analysis of the estimated parameters, using MCMC (Markov Chain Monte Carlo). These results are displayed in an interactive glossy panel which allows modification of the test arguments, contains interactive statistical plots, and presents automatic conclusions depending on the fulfillment of the assumptions of the balanced two-factor fixed ANOVA model.

The base apply function and its variants, as well as the related
    functions in the 'plyr' package, typically apply user-defined functions to a
    single argument (or a list of vectorized arguments in the case of mapply). The
    'multiApply' package extends this paradigm with its only function, Apply, which
    efficiently applies functions taking one or a list of multiple unidimensional
    or multidimensional arrays (or combinations thereof) as input. The input
    arrays can have different numbers of dimensions as well as different dimension
    lengths, and the applied function can return one or a list of unidimensional or
    multidimensional arrays as output. This saves development time by preventing the
    R user from writing often error-prone and memory-inefficient loops dealing with
    multiple complex arrays. Also, a remarkable feature of Apply is the transparent
    use of multi-core through its parameter 'ncores'. In contrast to the base apply
    function, this package suggests the use of 'target dimensions' as opposite
    to the 'margins' for specifying the dimensions relevant to the function to be
    applied.