Choi and Hall Style Data Sharpening

Functions for use in perturbing data prior to use of nonparametric smoothers and clustering.

Functions for Multi-Dimensional Analysis

Multi-Dimensional Analysis (MDA) is an adaptation of factor analysis developed by Douglas Biber (1992) . Its most common use is to describe language as it varies by genre, register, and use. This package contains functions for carrying out the calculations needed to describe and plot MDA results: dimension scores, dimension means, and factor loadings.

Generalized Additive Latent and Mixed Models

Estimates generalized additive latent and mixed models using maximum marginal likelihood, as defined in Sorensen et al. (2023) , which is an extension of Rabe-Hesketh and Skrondal (2004)'s unifying framework for multilevel latent variable modeling . Efficient computation is done using sparse matrix methods, Laplace approximation, and automatic differentiation. The framework includes generalized multilevel models with heteroscedastic residuals, mixed response types, factor loadings, smoothing splines, crossed random effects, and combinations thereof. Syntax for model formulation is close to 'lme4' (Bates et al. (2015) ) and 'PLmixed' (Rockwood and Jeon (2019) ).

Transforming and Harmonizing CHMS Variables

Harmonizes variables from the Canadian Health Measures Survey (CHMS) across cycles 1-6 (2007-2019), producing consistent, analysis-ready variables for use with CHMS data. Recoding is data-driven through metadata tables and applied with recodeflow::rec_with_table() from the 'recodeflow' package. The recoding approach builds on sjmisc::rec() from the 'sjmisc' package (Ludecke 2018) .

Design of Experiments and Factorial Plans Utilities

A number of functions to create and analyze factorial plans according to the Design of Experiments (DoE) approach, with the addition of some utility function to perform some statistical analyses. DoE approach follows the approach in "Design and Analysis of Experiments" by Douglas C. Montgomery (2019, ISBN:978-1-119-49244-3). The package also provides utilities used in the course "Analysis of Data and Statistics" at the University of Trento, Italy.

A Crew Launcher Plugin for AWS Batch

In computationally demanding analysis projects, statisticians and data scientists asynchronously deploy long-running tasks to distributed systems, ranging from traditional clusters to cloud services. The 'crew.aws.batch' package extends the 'mirai'-powered 'crew' package with a worker launcher plugin for AWS Batch. Inspiration also comes from packages 'mirai' by Gao (2023) < https://github.com/r-lib/mirai>, 'future' by Bengtsson (2021) , 'rrq' by FitzJohn and Ashton (2023) < https://github.com/mrc-ide/rrq>, 'clustermq' by Schubert (2019) ), and 'batchtools' by Lang, Bischl, and Surmann (2017). .

Similarity-Based Segmentation of Multidimensional Signals

A dynamic programming solution to segmentation based on maximization of arbitrary similarity measures within segments. The general idea, theory and this implementation are described in Machne, Murray & Stadler (2017) . In addition to the core algorithm, the package provides time-series processing and clustering functions as described in the publication. These are generally applicable where a `k-means` clustering yields meaningful results, and have been specifically developed for clustering of the Discrete Fourier Transform of periodic gene expression data (`circadian' or `yeast metabolic oscillations'). This clustering approach is outlined in the supplemental material of Machne & Murray (2012) ), and here is used as a basis of segment similarity measures. Notably, the time-series processing and clustering functions can also be used as stand-alone tools, independent of segmentation, e.g., for transcriptome data already mapped to genes.

Asymptotic Classification Theory for Cognitive Diagnosis

Cluster analysis for cognitive diagnosis based on the Asymptotic Classification Theory (Chiu, Douglas & Li, 2009; ). Given the sample statistic of sum-scores, cluster analysis techniques can be used to classify examinees into latent classes based on their attribute patterns. In addition to the algorithms used to classify data, three labeling approaches are proposed to label clusters so that examinees' attribute profiles can be obtained.

Crew Launcher Plugins for Traditional High-Performance Computing Clusters

In computationally demanding analysis projects, statisticians and data scientists asynchronously deploy long-running tasks to distributed systems, ranging from traditional clusters to cloud services. The 'crew.cluster' package extends the 'mirai'-powered 'crew' package with worker launcher plugins for traditional high-performance computing systems. Inspiration also comes from packages 'mirai' by Gao (2023) < https://github.com/r-lib/mirai>, 'future' by Bengtsson (2021) , 'rrq' by FitzJohn and Ashton (2023) < https://github.com/mrc-ide/rrq>, 'clustermq' by Schubert (2019) ), and 'batchtools' by Lang, Bischl, and Surmann (2017). .

Geographic and Taxonomic Occurrence R-Based Scrubbing

Streamlines downloading and cleaning biodiversity data from Integrated Digitized Biocollections (iDigBio) and the Global Biodiversity Information Facility (GBIF).