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>.

Algorithms for fitting scaled sparse linear regression and estimating precision matrices.

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.

A collection of procedures for analysing, visualising, 
  and managing single-case data. These include regression models 
  (multilevel, multivariate, bayesian), between case standardised mean difference, 
  overlap indices ('PND', 'PEM', 'PAND', 'PET', 'tau-u', 'IRD', 'baseline corrected tau', 
  'CDC'), and randomization tests. Data preparation functions support outlier 
  detection, handling missing values, scaling, and custom transformations. 
  An export function helps to generate html, word, and latex tables in a 
  publication friendly style. A shiny app allows to use scan in a graphical 
  user interface.
  More details can be found in the online book 'Analyzing single-case data with 
  R and scan', Juergen Wilbert (2025)
  <https://jazznbass.github.io/scan-Book/>.