Implementations of canonical associative learning models,
    with tools to run experiment simulations, estimate model parameters,
    and compare model representations. Experiments and results are
    represented using S4 classes and methods.

Provides a Shiny application to conduct comprehensive analysis of latent means including the examination of group equivalency, propensity score analysis, 
 measurement invariance analysis, and assessment of latent mean differences of 
 equivalent groups with invariant data. Group equivalency and propensity score analyses are implemented using the 'MatchIt' package [Ho et al. (2011) <doi:10.18637/jss.v042.i08>], 
 ensuring robust control for covariates. Structural equation modeling and invariance testing rely heavily on the 'lavaan' package [Rosseel (2012) <doi:10.18637/jss.v048.i02>], 
 providing a flexible and powerful modeling framework. The application also integrates modified functions from Hammack-Brown et al. (2021) <doi:10.1002/hrdq.21452> 
 to support factor ratio testing and the list-and-delete procedure.

Implements methods for querying data from CalPASS using its API.
 CalPASS Plus.  MMAP API V1. <https://mmap.calpassplus.org/docs/index.html>.

Generates the calibration simplex (a generalization of the reliability diagram) for three-category probability forecasts, as proposed by Wilks (2013) <doi:10.1175/WAF-D-13-00027.1>.

Record and generate a 'gif' of your 'R' sessions plots. When creating 
    a visualization, there is inevitably iteration and refinement that occurs. 
    Automatically save the plots made to a specified directory, previewing them 
    as they would be saved. Then combine all plots generated into a 'gif'
    to show the plot refinement over time.

A tool for causal meta-analysis. This package implements the aggregation formulas and inference methods proposed in Berenfeld et al. (2025) <doi:10.48550/arXiv.2505.20168>. Users can input aggregated data across multiple studies and compute causally meaningful aggregated effects of their choice (risk difference, risk ratio, odds ratio, etc) under user-specified population weighting. The built-in function camea() allows to obtain precise variance estimates for these effects and to compare the latter to a classical meta-analysis aggregate, the random effect model, as implemented in the 'metafor' package <https://CRAN.R-project.org/package=metafor>.