Provides a robust and reproducible pipeline for extracting, cleaning, and analyzing athlete performance data generated by 'VALD' 'ForceDecks' systems. The package supports batch-oriented data processing for large datasets, standardized data transformation workflows, and visualization utilities for sports science research and performance monitoring. It is designed to facilitate reproducible analysis across multiple sports with comprehensive documentation and error handling.

Download and import agricultural data from the Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES) &lt;<a href="https://www.agriculture.gov.au/abares" target="_top">https://www.agriculture.gov.au/abares</a>&gt; and Australian Bureau of Statistics (ABS) &lt;<a href="https://www.abs.gov.au" target="_top">https://www.abs.gov.au</a>&gt;. Data types serviced include spreadsheets, comma separated value (CSV) files, geospatial data including shape files and geotiffs covering topics including broadacre crops, livestock, soil data, commodities and more. Unifies field names and formats for data interoperability making analysis easier by standardising names between data formats. Also simplifies importing geospatial data as well as correcting issues in the geospatial data upon import.

A unified latent class modeling framework that encompasses both latent class analysis (LCA) and latent profile analysis (LPA), offering a one-stop solution for latent class modeling. It implements state-of-the-art parameter estimation methods, including the expectation–maximization (EM) algorithm, neural network estimation (NNE; requires users to have 'Python' and its dependent libraries installed on their computer), and integration with 'Mplus' (requires users to have 'Mplus' installed on their computer). In addition, it provides commonly used model fit indices such as the Akaike information criterion (AIC) and Bayesian information criterion (BIC), as well as classification accuracy measures such as entropy. The package also includes fully functional likelihood ratio tests (LRT) and bootstrap likelihood ratio tests (BLRT) to facilitate model comparison, along with bootstrap-based and observed information matrix-based standard error estimation. Furthermore, it supports the standard three-step approach for LCA, LPA, and latent transition analysis (LTA) with covariates, enabling detailed covariate analysis. Finally, it includes several user-friendly auxiliary functions to enhance interactive usability.

Package that accesses the 'Lichess' API (&lt;<a href="https://lichess.org/api" target="_top">https://lichess.org/api</a>&gt;). Supports both authenticated and unauthenticated requests. Basic functionality for game and player analysis.

Provides adaptive trend estimation, cycle detection, Fourier harmonic selection, bootstrap confidence intervals, change-point detection, and rolling-origin forecasting. Supports LOESS (Locally Estimated Scatterplot Smoothing), GAM (Generalized Additive Model), and GAMM (Generalized Additive Mixed Model), and automatically handles irregular sampling using the Lomb–Scargle periodogram. Methods implemented in this package are described in Cleveland et al. (1990) &lt;<a href="https://doi.org/10.2307%2F2289548" target="_top">doi:10.2307/2289548</a>&gt;, Wood (2017) &lt;<a href="https://doi.org/10.1201%2F9781315370279" target="_top">doi:10.1201/9781315370279</a>&gt;, and Scargle (1982) &lt;<a href="https://doi.org/10.1086%2F160554" target="_top">doi:10.1086/160554</a>&gt;.

https://cran.r-project.org/package=bertopicr

Built on graph theory and the high-performance 'data.table' framework, this package provides a comprehensive suite of tools for tidying, pruning, and visualizing animal pedigrees. By modeling pedigrees as directed acyclic graphs using 'igraph', it ensures robust loop detection, efficient generation assignment, and sophisticated hierarchical layouts. Key features include standardizing pedigree formats, flexible ancestry tracing, and generating legible vector-based PDF graphs. A unique compaction algorithm enables the visualization of massive pedigrees (e.g., in aquaculture selective breeding population) by grouping full-sib families, maintaining structural clarity without overcrowding.

Implementation of custom 'tidymodels' metrics for multi-class prediction models with a single negative class. Currently are implemented macro-average sensitivity and specificity as in Mortaz, Ebrahim (2020) "Imbalance accuracy metric for model selection in multi-class imbalance classification problems” &lt;<a href="https://doi.org/10.1016%2Fj.knosys.2020.106490" target="_top">doi:10.1016/j.knosys.2020.106490</a>&gt; and a generalized weighted Youden index as in Li, D.L., Shen F., Yin Y., Peng J.X and Chen P.Y. (2013) “Weighted Youden index and its two-independent-sample comparison based on weighted sensitivity and specificity” &lt;<a href="https://doi.org/10.3760%2Fcma.j.issn.0366-6999.20123102" target="_top">doi:10.3760/cma.j.issn.0366-6999.20123102</a>&gt;.

Provides specialized visualization tools for Single-Case Experimental Design (SCED) research using 'ggplot2'. SCED studies are a crucial methodology in behavioral and educational research where individual participants serve as their own controls through carefully designed experimental phases. This package extends 'ggplot2' to create publication-ready graphics with professional phase change lines, support for multiple baseline designs, and styling functions that follow SCED visualization conventions. Key functions include adding phase change demarcation lines to existing plots and formatting axes with broken axis appearance commonly used in single-case research.

Calculate the likelihood ratio test p-value and likelihood confidence intervals for misspecified Cox models, as described in Shao and Guo (2025) &lt;<a href="https://doi.org/10.48550%2FarXiv.2508.11851" target="_top">doi:10.48550/arXiv.2508.11851</a>&gt;.