Fit Conway-Maxwell Poisson (COM-Poisson or CMP) regression models
    to count data (Sellers & Shmueli, 2010) <doi:10.1214/09-AOAS306>. The
    package provides functions for model estimation, dispersion testing, and
    diagnostics. Zero-inflated CMP regression (Sellers & Raim, 2016)
    <doi:10.1016/j.csda.2016.01.007> is also supported.

The reliability of assessment tools is a crucial aspect of monitoring student performance in various educational settings. It ensures that the assessment outcomes accurately reflect a student's true level of performance. However, when assessments are combined, determining composite reliability can be challenging, especially for naturalistic and unbalanced datasets. This package provides an easy-to-use solution for calculating composite reliability for different assessment types. It allows for the inclusion of weight per assessment type and produces extensive G- and D-study results with graphical interpretations. Overall, our approach enhances the reliability of composite assessments, making it suitable for various education contexts.

The reliability of assessment tools is a crucial aspect of monitoring student performance in various educational settings. It ensures that the assessment outcomes accurately reflect a student's true level of performance. However, when assessments are combined, determining composite reliability can be challenging, especially for naturalistic and unbalanced datasets in nested design as is often the case for Workplace-Based Assessments. This package is designed to estimate composite reliability in nested designs using multivariate generalizability theory and enhance the analysis of assessment data. The package allows for the inclusion of weight per assessment type and produces extensive G- and D-study results with graphical interpretations, and options to find the set of weights that maximizes the composite reliability or minimizes the standard error of measurement (SEM).

Provides composable optimization strategies for maximum likelihood
    estimation (MLE). Solvers are first-class functions that combine via
    sequential chaining, parallel racing, and random restarts. Implements
    gradient ascent, Newton-Raphson, quasi-Newton (BFGS), and derivative-free
    methods with support for constrained optimization and tracing. Returns
    'mle' objects compatible with 'algebraic.mle' for downstream analysis.
    Methods based on Nocedal J, Wright SJ (2006) "Numerical Optimization"
    <doi:10.1007/978-0-387-40065-5>.