Title: Algorithms for Automatically Fitting MFA Models
Description: Provides methods for fitting the Mixture of Factor Analyzers (MFA) model automatically.
The MFA model is a mixture model where each sub-population is assumed to follow the Factor Analysis model. The Factor Analysis (FA) model is a latent variable model which assumes that observations are normally distributed, but imposes constraints on their covariance matrix. The MFA model contains two hyperparameters; g (the number of components in the mixture) and q (the number of factors in each component Factor Analysis model). Usually, the Expectation-Maximisation algorithm would be used to fit the MFA model, but this requires g and q to be known. This package treats g and q as unknowns and provides several methods which infer these values with as little input from the user as possible.
Imports: abind, MASS, Matrix, Rfast, expm, stats, utils, Rdpack,

Title: Improving MrP with Ensemble Learning
Description: A tool that improves the prediction performance of multilevel
regression with post-stratification (MrP) by combining a number of machine
learning methods. For information on the method, please refer to Broniecki,
Wüest, Leemann (2020) ''Improving Multilevel Regression with
Post-Stratification Through Machine Learning (autoMrP)'' forthcoming in
'Journal of Politics'. Final pre-print version:
1.5.1), EBMAforecast (>= 1.0.0), foreach (>= 1.5.0), doParallel
Philipp Broniecki [aut, cre] (<https://orcid.org/0000-0001-9214-4404>),

Description: Performs Rasch analysis (semi-)automatically, which has been shown to be comparable with the standard Rasch analysis (Feri Wijayanto et al. (2021) <doi:10.1111/bmsp.12218>, Feri Wijayanto et al. (2022) <https://psyarxiv.com/erxuh/>).
Imports: doParallel, foreach, Rcpp, lavaan
Suggests: testthat (>= 3.0.0), knitr, rmarkdown, markdown
Packaged: 2022-02-25 06:31:28 UTC; F.Wijayanto

Description: Performs Rasch analysis (semi-)automatically, which has been shown to be comparable with the standard Rasch analysis (Feri Wijayanto et al. (2021) <doi:10.1111/bmsp.12218>, Feri Wijayanto et al. (2022) <doi:10.3758/s13428-022-01947-9>, Feri Wijayanto et al. (2022) <doi:10.1177/01466216221125178>).
Packaged: 2022-10-19 15:26:07 UTC; F.Wijayanto