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.

Provides functions to format and summarise already computed 
    outputs from commonly used statistical and psychometric functions into 
    compact, single-row tables and simple graphs, with utilities to export 
    results to CSV, Word, and Excel formats. The package does not implement 
    new statistical methods or estimation procedures; instead, it organises 
    and presents results obtained from existing packages such as 'psych', 
    'stats', 'gtsummary', and 'lavaan' to streamline reporting workflows in 
    clinical and psychological research.

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>.

Generalized additive models under shape
        constraints on the component functions of the linear predictor.       
        Models can include multiple shape-constrained (univariate
        and bivariate) and unconstrained terms. Routines of the 
        package 'mgcv' are used to set up the model matrix, print, 
        and plot the results. Multiple smoothing parameter 
        estimation by the Generalized Cross Validation or similar.
        See Pya and Wood (2015) <doi:10.1007/s11222-013-9448-7> 
        for an overview. A broad selection of shape-constrained 
        smoothers, linear functionals of smooths with shape constraints,
        and Gaussian models with AR1 residuals.