Smoothing signals and computing their derivatives is a common
 requirement in signal processing workflows. Savitzky-Golay filters are a
 established method able to do both (Savitzky and Golay, 1964 <doi:10.1021/ac60214a047>).
 This package implements one dimensional Savitzky-Golay filters that can be applied to
 vectors and matrices (either row-wise or column-wise).
 Vectorization and memory allocations have been profiled to reduce computational
 fingerprint. Short filter lengths are implemented in the direct space, while
 longer filters are implemented in frequency space, using a Fast Fourier
 Transform (FFT).

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.