A toolbox for modeling manifest and latent group differences and moderation effects in various statistical network models.

An API package for the Members' Name Information Service operated 
    by the UK parliament. Documentation for the API itself can be found 
    here: <http://data.parliament.uk/membersdataplatform/default.aspx>.

Density evaluation and random number generation for the Matrix-Normal Inverse-Wishart (MNIW) distribution, as well as the the Matrix-Normal, Matrix-T, Wishart, and Inverse-Wishart distributions.  Core calculations are implemented in a portable (header-only) C++ library, with matrix manipulations using the 'Eigen' library for linear algebra.  Also provided is a Gibbs sampler for Bayesian inference on a random-effects model with multivariate normal observations.

Conducts moderated nonlinear factor analysis (e.g., Curran et al., 2014,
    <doi:10.1080/00273171.2014.889594>). 
    Regularization methods are implemented for assessing non-invariant items. 
    Currently, the package includes dichotomous items and unidimensional
    item response models. Extensions will be included in future package
    versions.

Functions to easily return simulated predicted probabilities and
    first differences for multinomial logit models. It takes a specified 
    scenario and a multinomial model to predict probabilities with a set of
    coefficients, drawn from a simulated sampling distribution. The simulated 
    predictions allow for meaningful plots with means and confidence intervals.
    The methodological approach is based on the principles laid out by King,
    Tomz, and Wittenberg (2000) <doi:10.2307/2669316> and Hanmer and Ozan Kalkan 
    (2016) <doi:10.1111/j.1540-5907.2012.00602.x>.

An interactive presentation on  the topic of Multinomial Logistic Regression. It is helpful to those who want to learn Multinomial Logistic Regression quickly and get a hands on experience. The presentation has a template for solving problems on Multinomial Logistic Regression. Runtime examples are provided in the package function as well as at  <https://jarvisatharva.shinyapps.io/MultinomPresentation>.