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;.

It leverages the network-like architecture of scientific models together with software quality metrics to identify chains of function calls that are more prone to generating and propagating errors. It operates on tbl_graph objects representing call dependencies between functions (callers and callees) and computes risk scores for individual functions and for paths (sequences of function calls) based on cyclomatic complexity, in-degree and betweenness centrality. The package supports variance-based uncertainty and sensitivity analyses after Puy et al. (2022) &lt;<a href="https://doi.org/10.18637%2Fjss.v102.i05" target="_top">doi:10.18637/jss.v102.i05</a>&gt; to assess how risk scores change under alternative risk definitions.

Provides a native R implementation for encoding and decoding 'sixel' graphics (&lt;<a href="https://vt100.net/docs/vt3xx-gp/chapter14.html" target="_top">https://vt100.net/docs/vt3xx-gp/chapter14.html</a>&gt;), and a dedicated 'sixel' graphics device that allows plots to be rendered directly within compatible terminal emulators.

For the problem of indirect treatment comparison with limited subject-level data, this package provides tools for model-based standardisation with several different computation approaches. See Remiro‐Azócar A, Heath A, Baio G (2022) "Parametric G‐computation for compatible indirect treatment comparisons with limited individual patient data", Res. Synth. Methods, 1–31. ISSN 1759-2879, &lt;<a href="https://doi.org/10.1002%2Fjrsm.1565" target="_top">doi:10.1002/jrsm.1565</a>&gt;.

Implements the non-asymptotically valid and asymptotically exact confidence intervals in two cases: estimation of the mean, and estimation of (a linear combination of) the coefficients in a linear regression model, following (Derumigny, Girard and Guyonvarch, 2025) &lt;<a href="https://doi.org/10.48550%2FarXiv.2507.16776" target="_top">doi:10.48550/arXiv.2507.16776</a>&gt;.

Implements likelihood-based evidence ratios for unified reporting in classical statistical testing. The package reports effect estimates, uncertainty intervals, and likelihood ratios on the log 10 scale derived from a single statistical model. It applies to standard normal mean tests, contingency tables, and regression coefficients, and provides a direct evidential measure while retaining classical error guarantees. For the Evidence Ratio Reporting Standard see Lawless (2026) &lt;<a href="https://doi.org/10.5281%2Fzenodo.18261076" target="_top">doi:10.5281/zenodo.18261076</a>&gt;.

Provides simple functions to convert between color names and hexadecimal color codes using an extensive database of over 32,000 colors. Includes all 657 R built-in colors plus the comprehensive color-names database. The package supports bidirectional conversion with backward compatibility, prioritizing R colors when available.

A modular framework for standardized analysis of thermal imaging data in animal experimentation. The package integrates thermographic data import (FLIR, raw, CSV), automated region of interest (ROI) segmentation based on 'EBImage' (Pau et al., 2010 &lt;<a href="https://doi.org/10.1093%2Fbioinformatics%2Fbtq046" target="_top">doi:10.1093/bioinformatics/btq046</a>&gt;), interactive ROI refinement, and high-throughput batch processing.

Methods for detecting and visualizing cladogenic shifts in multivariate trait data on phylogenies. Implements penalized-likelihood multivariate generalized least squares models, enabling analyses of high-dimensional trait datasets and large trees via searchOptimalConfiguration(). Includes a greedy step-wise shift-search algorithm following approaches developed in Smith et al. (2023) &lt;<a href="https://doi.org/10.1111%2Fnph.19099" target="_top">doi:10.1111/nph.19099</a>&gt; and Berv et al. (2024) &lt;<a href="https://doi.org/10.1126%2Fsciadv.adp0114" target="_top">doi:10.1126/sciadv.adp0114</a>&gt;. Methods build on multivariate GLS approaches described in Clavel et al. (2019) &lt;<a href="https://doi.org/10.1093%2Fsysbio%2Fsyy045" target="_top">doi:10.1093/sysbio/syy045</a>&gt; and implemented in the mvgls() function from the 'mvMORPH' package. Documentation and vignettes are available at &lt;<a href="https://jakeberv.com/bifrost/" target="_top">https://jakeberv.com/bifrost/</a>&gt;, including the introductory vignette at &lt;<a href="https://jakeberv.com/bifrost/articles/jaw-shape-vignette.html" target="_top">https://jakeberv.com/bifrost/articles/jaw-shape-vignette.html</a>&gt;.

Converts R scripts (.R) into 'Quarto' markdown documents (.qmd) with automatic formatting. Recognizes 'RStudio' code sections, preserves comments as narrative text, extracts metadata from special comments, and provides both programmatic functions and an interactive 'RStudio' add-in for easy conversion.

https://cran.r-project.org/package=Open.Visualization.Academy

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