Simple Component Analysis (SCA) often provides much more
   interpretable components than Principal Components (PCA) while still
   representing much of the variability in the data.

Calculates graph theoretic scagnostics. Scagnostics describe various measures of interest for pairs of variables, based on their appearance on a scatterplot.  They are useful tool for discovering interesting or unusual scatterplots from a scatterplot matrix, without having to look at every individual plot.

Estimates unit-level and population-level parameters from a hierarchical model in marketing applications. The package includes:
  Hierarchical Linear Models with a mixture of normals prior and covariates,
  Hierarchical Multinomial Logits with a mixture of normals prior and covariates,
  Hierarchical Multinomial Logits with a Dirichlet Process prior and covariates. For more details, see Bumbaca, F. (Rico), Misra, S., & Rossi, P. E. (2020) <doi:10.1177/0022243720952410> "Scalable Target Marketing: Distributed Markov Chain Monte Carlo for Bayesian Hierarchical Models". Journal of Marketing Research, 57(6), 999-1018.

Scale alignment is a new procedure for rescaling dimensions of 
    between-items multidimensional Rasch family models so that dimensions scores
    can be compared directly (Feuerstahler & Wilson, 2019; under review) 
	<doi:10.1111/jedm.12209>. This package includes functions for implementing 
    delta-dimensional alignment (DDA) and logistic regression alignment (LRA) 
    for dichotomous or polytomous data. This function also includes a wrapper 
	for models fit using the 'TAM' package.

Perform a 'probabilistic' linkage of two data files using a scaling procedure using the methods described in Goldstein, H., Harron, K. and Cortina-Borja, M. (2017) <doi:10.1002/sim.7287>.

Graphical scales map data to aesthetics, and provide methods
    for automatically determining breaks and labels for axes and legends.

A scalable Gibbs sampling implementation for high dimensional Bayesian regression with the continuous spike-and-slab prior. Niloy Biswas, Lester Mackey and Xiao-Li Meng, "Scalable Spike-and-Slab" (2022) <arXiv:2204.01668>.