Publication-ready regional gene locus plots similar to those produced by the web interface LocusZoom <https://my.locuszoom.org>, but running locally in R. Genetic or genomic data with gene annotation tracks are plotted via R base graphics, ggplot2 or plotly', allowing flexibility and easy customisation including laying out multiple locus plots on the same page. It uses the LDlink API <https://ldlink.nih.gov/?tab=apiaccess> to query linkage disequilibrium data from the 1000 Genomes Project and can overlay this on plots <doi:10.1093/bioadv/vbaf006>.

This is for code management functions, NLP tools, a Monty Hall simulator, and for implementing my own variable reduction technique called Feed Reduction. The Feed Reduction technique is not yet published, but is merely a tool for implementing a series of binary neural networks meant for reducing data into N dimensions, where N is the number of possible values of the response variable.

Location and scale hypothesis testing using the LePage test and variants of its as proposed by Hussain A. and Tsagris M. (2025), <doi:10.48550/arXiv.2509.19126>.

This package provides R bindings to llama.cpp for running large language models locally, with optional GPU acceleration via ggmlR'. Supports text generation, embeddings, chat-based workflows, tool calling, and multimodal (vision) inference. Includes OpenAI'- and Anthropic'-compatible HTTP servers for serving local models, along with device selection and multi-GPU support.

Determining consensus seriations for binary incidence matrices, using a two-step process of Procrustes-fit correspondence analysis for heuristic selection of partial seriations and iterative regression to establish a single consensus. Contains the Lakhesis Calculator, a graphical platform for identifying seriated sequences. Collins-Elliott (2026) "Lakhesis: Consensus Seriation via Iterative Regression of Partial Rankings for Binary Data" <doi:10.1080/02664763.2026.2672564>.

Aids in learning statistical functions incorporating the result of calculus done with each function and how they are obtained, that is, which equation and variables are used. Also for all these equations and their related variables detailed explanations and interactive exercises are also included. All these characteristics allow to the package user to improve the learning of statistics basics by means of their use.

This package provides a collection of parametric and nonparametric methods for the analysis of survival data, described in <doi:10.32614/RJ-2025-034>. Parametric families implemented include Gompertz-Makeham, exponential and generalized Pareto models and extended models. The package includes an implementation of the nonparametric maximum likelihood estimator for arbitrary truncation and censoring pattern based on Turnbull (1976) <doi:10.1111/j.2517-6161.1976.tb01597.x>, along with graphical goodness-of-fit diagnostics. Parametric models for positive random variables and peaks over threshold models based on extreme value theory are described in Rootzén and Zholud (2017) <doi:10.1007/s10687-017-0305-5>; Belzile et al. (2021) <doi:10.1098/rsos.202097> and Belzile et al. (2022) <doi:10.1146/annurev-statistics-040120-025426>.

Read, register and compare point sets from single molecule localization microscopy.

This package provides a collection of colour palettes inspired by some of our dearest butterfly species. This package provides continuous and categorical palettes, including some colour blind friendly options.

This package provides extensions for packages leaflet & mapdeck', many of which are used by package mapview'. Focus is on functionality readily available in Geographic Information Systems such as Quantum GIS'. Includes functions to display coordinates of mouse pointer position, query image values via mouse pointer and zoom-to-layer buttons. Additionally, provides a feature type agnostic function to add points, lines, polygons to a map.

In Latent Space Item Response Models, subjects and items are embedded in a multidimensional Euclidean latent space. As such, interactions among persons, items, and person-item combinations can be revealed that are unmodelled in more conventional item response theory models. This package implements the methods from Molenaar & Jeon (in press) and can be used to fit Latent Space Item Response Models to data using joint maximum likelihood estimation. The package can handle binary data, ordinal data, and data with mixed scales. The package incorporates facilities for data simulation, rotation of the latent space, and K-fold cross-validation to select the number of dimensions of the latent space.

Converts output from latent variable model tools into publication-ready path diagrams and model schematics. lavaan fit objects and parameter tables are supported as a primary workflow, with graph adapters for objects from blavaan', lavaan.mi', semPlot', mirt', eRm', OpenMx', psych', poLCA', mclust', flexmix', lcmm', tidyLPA', and MplusAutomation workflows when those packages are available. Supports structural equation and confirmatory factor analysis diagrams, multilevel structural equation models, growth models, higher-order factor models, latent class and profile models, item response theory models, and common mixture outputs through a unified graph grammar with model-aware defaults, geometry diagnostics, layout quality scoring, automatic layout selection, customizable publication styles, RStudio preview, SVG/PDF/PNG export, TikZ output, and reproducible publication bundles. A local Shiny editor supports mouse dragging of nodes and coefficient labels, synchronized figure downloads, and reusable editing state.

Clustering or classification of longitudinal data based on a mixture of multivariate t or Gaussian distributions with a Cholesky-decomposed covariance structure. Details in McNicholas and Murphy (2010) <doi:10.1002/cjs.10047> and McNicholas and Subedi (2012) <doi:10.1016/j.jspi.2011.11.026>.

Calculates fetch (open water distance) and wave exposure metrics for lake sampling points. Downloads lake boundaries from OpenStreetMap', calculates directional fetch using a ray-casting approach, and optionally integrates National Hydrography Dataset ('NHD') data <https://www.usgs.gov/national-hydrography> for hydrological context including outlet and inlet locations. Can estimate lake depth from surface area using empirical relationships, and integrate historical weather data for cumulative wave energy calculations. Includes an optional interactive shiny application for visualization.

This package provides a framework for lazy computation on large sparse matrices. Enables lazy evaluation of normalized data matrices, preserving sparsity throughout operations without materializing dense intermediate objects. Implements statistical algorithms including LSQR for sparse least squares as described in Paige and Saunders (1982) <doi:10.1145/355984.355989> and partial singular value decomposition via the augmented implicitly restarted Lanczos bidiagonalization algorithm of Baglama and Reichel (2005) <doi:10.1137/04060593X>.

This package provides a set of functions to locate some programs available on the user machine. It can locate Node.js', npm', LibreOffice', Microsoft Word', Microsoft PowerPoint', Microsoft Excel', Python', pip', Mozilla Firefox', Google Chrome', Air and Pandoc'. The user can test the availability of a program, optionally with a required version, and call it with the functions system2() or system(). This allows the use of a single function to retrieve the path to a program regardless of the operating system and its configuration.

This package provides a dataset containing several color naming conventions established by multiple sources, along with associated color metadata. The package also provides related helper functions for mapping among the different Lego color naming conventions and between Lego colors, hex colors, and R color names, making it easy to convert any color palette to one based on existing Lego colors while keeping as close to the original color palette as possible. The functions use nearest color matching based on Euclidean distance in RGB space. Naming conventions for color mapping include those from BrickLink (<https://www.bricklink.com>), The Lego Group (<https://www.lego.com>), LDraw (<https://www.ldraw.org/>), and Peeron (<http://www.peeron.com/>).

This package provides fast and scalable Gibbs sampling algorithms for Bayesian Lasso regression model in high-dimensional settings. The package implements efficient partially collapsed and nested Gibbs samplers for Bayesian Lasso, with a focus on computational efficiency when the number of predictors is large relative to the sample size. Methods are described at Davoudabadi and Ormerod (2026) <https://github.com/MJDavoudabadi/LassoHiDFastGibbs>.

This package implements the multivariate classification methods of Alexander Alexandrovich Lyubishchev (1890-1972), as described in his 1943 manuscript Programma obshchey sistematiki Lyubishchev (1943) <https://www.zin.ru/animalia/coleoptera/rus/lyubis05.htm> and published in Lubischew (1962) <https://www.jstor.org/stable/2527894>. Provides divergence_coefficient() for measuring separation between groups on continuous features, scatter_ellipse() for fitting covariance ellipses per class, transgression() for detecting ellipse overlap, and classify() for Bayesian posterior classification. These methods predate and are more general than the binary-character similarity coefficients of Sokal and Sneath (1963) that appear in other R packages.

Local Mean Decomposition is an iterative and self-adaptive approach for demodulating, processing, and analyzing multi-component amplitude modulated and frequency modulated signals. This R package is based on the approach suggested by Smith (2005) <doi:10.1098/rsif.2005.0058> and the Python library PyLMD'.

Bayesian Survival models via the mixture of Log-Normal distribution extends the well-known survival models and accommodates different behaviour over time and considers higher censored survival times. The proposal combines mixture distributions Fruhwirth-Schnatter(2006) <doi:10.1007/s11336-009-9121-4>, and data augmentation techniques Tanner and Wong (1987) <doi:10.1080/01621459.1987.10478458>.

Summarizes and visualizes categorical data extracted during literature reviews and evidence syntheses, starting from a data frame with one row per study. Generates publication-ready bar, stacked bar, histogram, waffle, donut, treemap, alluvial, trend, co-occurrence, UpSet', tree, and study-by-criteria matrix figures, together with world maps and formatted summary tables. Plot functions return standard ggplot2 objects that can be further customized, and an interactive Shiny application is included for building figures without writing code. Aims to help researchers report study characteristics consistently across many publications.

Detects near-duplicate images across dataset splits using perceptual hashing, reports the resulting train/validation/test contamination, and produces a corrected, leak-free split assignment. Intended for machine learning researchers who need to verify that image classification splits do not share near-duplicate samples across partitions before reporting model metrics.

This package performs approximate GP regression for large computer experiments and spatial datasets. The approximation is based on finding small local designs for prediction (independently) at particular inputs. OpenMP and SNOW parallelization are supported for prediction over a vast out-of-sample testing set; GPU acceleration is also supported for an important subroutine. OpenMP and GPU features may require special compilation. An interface to lower-level (full) GP inference and prediction is provided. Wrapper routines for blackbox optimization under mixed equality and inequality constraints via an augmented Lagrangian scheme, and for large scale computer model calibration, are also provided. For details and tutorial, see Gramacy (2016 <doi:10.18637/jss.v072.i01>.