An analytic framework for the calculation of norm- and criterion-referenced academic growth estimates using large scale, longitudinal education assessment data as developed in Betebenner (2009) <doi:10.1111/j.1745-3992.2009.00161.x>.

Data sets utilized by the 'SGP' package as exemplars for users to conduct their own student growth percentiles (SGP) analyses.

A 'ggplot2' theme and colour palettes to create accessible
    data visualisations in the Scottish Government.

Regularized version of partial least square approaches
 providing sparse, group, and sparse group versions of partial
 least square regression models (Liquet, B., Lafaye de Micheaux, P.,
 Hejblum B., Thiebaut, R. (2016) <doi:10.1093/bioinformatics/btv535>).
 Version of PLS Discriminant analysis is also provided.

Fits the regularization path of regression models
    (linear and logistic) with additively combined penalty terms. All
    possible combinations with Least Absolute Shrinkage and Selection Operator (LASSO), 
    Smoothly Clipped Absolute Deviation (SCAD), Minimax Concave Penalty (MCP) and 
    Exponential Penalty (EP) are supported. This includes Sparse Group LASSO (SGL), 
    Sparse Group SCAD (SGS), Sparse Group MCP (SGM) and Sparse Group EP (SGE).
    For more information, see Buch, G., Schulz, A., Schmidtmann, I., Strauch, K., & Wild, P. S. (2024) <doi:10.1002/bimj.202200334>.

Computation of second-generation p-values as described in Blume et al. (2018) <doi:10.1371/journal.pone.0188299> and Blume et al. (2019) <doi:10.1080/00031305.2018.1537893>. There are additional functions which provide power and type I error calculations, create graphs (particularly suited for large-scale inference usage), and a function to estimate false discovery rates based on second-generation p-value inference.

Sample Generation by Replacement simulations (SGR; Lombardi & Pastore, 2014; Pastore & Lombardi, 2014). The package can be used to perform fake data analysis according to the sample generation by replacement approach. It includes functions for making simple inferences about discrete/ordinal fake data. The package allows to study the implications of fake data for empirical results.