A HTML widget that shows differences between files (text,
    images, and data frames).

Statistical hypothesis testing of pattern heterogeneity
    via differences in underlying distributions across multiple
    contingency tables. Five tests are included: the comparative
    chi-squared test (Song et al. 2014) <doi:10.1093/nar/gku086>
    (Zhang et al. 2015) <doi:10.1093/nar/gkv358>, the Sharma-Song
    test (Sharma et al. 2021) <doi:10.1093/bioinformatics/btab240>, 
    the heterogeneity test, the marginal-change test (Sharma et al.
    2020) <doi:10.1145/3388440.3412485>, and the strength test
    (Sharma et al. 2020) <doi:10.1145/3388440.3412485>. Under the
    null hypothesis that row and column variables are statistically
    independent and joint distributions are equal, their test
    statistics all follow an asymptotically chi-squared distribution.
    A comprehensive type analysis categorizes the relation among the
    contingency tables into type null, 0, 1, and 2 (Sharma et al.
    2020) <doi:10.1145/3388440.3412485>. They can identify
    heterogeneous patterns that differ in either the first order
    (marginal) or the second order (differential departure from
    independence). Second-order differences reveal more
    fundamental changes than first-order differences across
    heterogeneous patterns.

Performs DIFlasso as proposed by Tutz and Schauberger (2015) <doi:10.1007/s11336-013-9377-6>, a method to detect DIF (Differential Item Functioning) in Rasch Models. It can handle settings with many variables and also metric variables.

Bayesian factor models are effective tools for dimension reduction. This is especially applicable to multivariate large-scale datasets. It allows researchers to understand the latent factors of the data which are the linear or non-linear combination of the variables. Dynamic Intrinsic Conditional Autocorrelative Priors (ICAR) Spatiotemporal Factor Models 'DIFM' package provides function to run Markov Chain Monte Carlo (MCMC), evaluation methods and visual plots from Shin and Ferreira (2023)<doi:10.1016/j.spasta.2023.100763>. Our method is a class of Bayesian factor model which can account for spatial and temporal correlations. By incorporating these correlations, the model can capture specific behaviors and provide predictions.