Computes the solution path for generalized lasso problems. Important use cases are the fused lasso over an arbitrary graph, and trend fitting of any given polynomial order. Specialized implementations for the latter two subproblems are given to improve stability and speed. See Taylor Arnold and Ryan Tibshirani (2016) <doi:10.1080/10618600.2015.1008638>.

Trace plots and convergence diagnostics for Markov Chain Monte Carlo (MCMC) algorithms on highly multivariate or unordered spaces. Methods outlined in a forthcoming paper.

The Geocoordinate Validation Service (GVS) runs checks of coordinates in latitude/longitude format. It returns annotated coordinates with additional flags and metadata that can be used in data cleaning. Additionally, the package has functions related to attribution and metadata information. More information can be found at <https://github.com/ojalaquellueva/gvs/tree/master/api>.

This package provides tools for the generalized logistic distribution (Type I, also known as skew-logistic distribution), encompassing basic distribution functions (p, q, d, r, score), maximum likelihood estimation, and structural change methods.

Providing publication-ready graphs for Multiple sequence alignment. Moreover, it provides a unique solution for visualizing the multiple sequence alignment without the need to do the alignment in each run which is a big limitation in other available packages.

This package provides a simple API for downloading and reading xml data directly from Lattes <http://lattes.cnpq.br/>.

Streamline the creation of common charts by taking care of a lot of data preprocessing and plot customization for the user. Provides a high-level interface to create plots using ggplot2'.

Geoms for placing arrowheads at multiple points along a segment, not just at the end; position function to shift starts and ends of arrows to avoid exactly intersecting points.

Efficient computation of likelihoods in design-based choice response time models, including the Decision Diffusion Model, is supported. The package enables rapid evaluation of likelihood functions for both single- and multi-subject models across trial-level data. It also offers fast initialisation of starting parameters for genetic sampling with many Markov chains, facilitating estimation in complex models typically found in experimental psychology and behavioural science. These optimisations help reduce computational overhead in large-scale model fitting tasks.

Quantitative genetics tool supporting the modelling of multivariate genetic variance structures in quantitative data. It allows fitting different models through multivariate genetic-relationship-matrix (GRM) structural equation modelling (SEM) in unrelated individuals, using a maximum likelihood approach. Specifically, it combines genome-wide genotyping information, as captured by GRMs, with twin-research-based SEM techniques, St Pourcain et al. (2017) <doi:10.1016/j.biopsych.2017.09.020>, Shapland et al. (2020) <doi:10.1101/2020.08.14.251199>.

The function plotLRT() draws pairwise graphical model checks for the Rasch Model (RM; Rasch, 1960), the Partial Credit Model(PCM; Masters, 1982), and the Rating Scale Model (RSM; Andrich, 1978) using the output object of eRm::LRtest(). The function cLRT() provides a conditional Likelihood Ratio Test (Andersen, 1973), using the routines of psychotools'. Users may choose to plot the threshold parameters, the cumulative thresholds, the average thresholds per item, or the person parameters. Extended coloring options allow for automated item-wise or threshold-wise coloring. For multi-group splits, all pairwise group comparisons are drawn automatically. For more details see Andersen (1973) <doi:10.1007/BF02291180>, Andrich (1978) <doi:10.1007/BF02293814>, Masters (1982) <doi:10.1007/BF02296272> and Rasch (1960, ISBN:9780598554512).

Data sets used in the book "R Graphics Cookbook" by Winston Chang, published by O'Reilly Media.

Plot glycans following the Symbol Nomenclature for Glycans (SNFG) using ggplot2'. SNFG provides a standardized visual representation of glycan structures.

This package provides a fully parameterized Generalized Wendland covariance function for use in Gaussian process models, as well as multiple methods for approximating it via covariance interpolation. The available methods are linear interpolation, polynomial interpolation, and cubic spline interpolation. Moreno Bevilacqua and Reinhard Furrer and Tarik Faouzi and Emilio Porcu (2019) <url:<https://projecteuclid.org/journalArticle/Download?urlId=10.1214%2F17-AOS1652 >>. Moreno Bevilacqua and Christian Caamaño-Carrillo and Emilio Porcu (2022) <doi:10.48550/arXiv.2008.02904>. Reinhard Furrer and Roman Flury and Florian Gerber (2022) <url:<https://CRAN.R-project.org/package=spam >>.

Plot brain atlases as interactive 3D meshes using Three.js via htmlwidgets', or render publication-quality static images through rgl and rayshader'. A pipe-friendly API lets you map data onto brain regions, control camera angles, toggle region edges, overlay glass brains, and snapshot or ray-trace the result. Additional atlases are available through the ggsegverse r-universe. Mowinckel & Vidal-Piñeiro (2020) <doi:10.1177/2515245920928009>.

Fits gastric emptying time series from MRI or scintigraphic measurements using nonlinear mixed-model population fits with nlme and Bayesian methods with Stan; computes derived parameters such as t50 and AUC.

This package provides tools to set up, train, store, load, investigate and analyze generative neural networks. In particular, functionality for generative moment matching networks is provided.

This package provides tools to build and work with bilateral generalized-mean price indexes (and by extension quantity indexes), and indexes composed of generalized-mean indexes (e.g., superlative quadratic-mean indexes, GEKS). Covers the core mathematical machinery for making bilateral price indexes, computing price relatives, detecting outliers, and decomposing indexes, with wrappers for all common (and many uncommon) index-number formulas. Implements and extends many of the methods in Balk (2008, <doi:10.1017/CBO9780511720758>), von der Lippe (2007, <doi:10.3726/978-3-653-01120-3>), and the CPI manual (2020, <doi:10.5089/9781484354841.069>).

This package provides a novel PRS model is introduced to enhance the prediction accuracy by utilising GxE effects. This package performs Genome Wide Association Studies (GWAS) and Genome Wide Environment Interaction Studies (GWEIS) using a discovery dataset. The package has the ability to obtain polygenic risk scores (PRSs) for a target sample. Finally it predicts the risk values of each individual in the target sample. Users have the choice of using existing models (Li et al., 2015) <doi:10.1093/annonc/mdu565>, (Pandis et al., 2013) <doi:10.1093/ejo/cjt054>, (Peyrot et al., 2018) <doi:10.1016/j.biopsych.2017.09.009> and (Song et al., 2022) <doi:10.1038/s41467-022-32407-9>, as well as newly proposed models for genomic risk prediction (refer to the URL for more details).

Informal implementation of some algorithms from Graph Theory and Combinatorial Optimization which arise in the subject "Graphs and Network Optimization" from first course of the EUPLA degree of Data Engineering in Industrial Processes.

Reconstruction of muscle fibers from image stacks using textural analysis. Includes functions for tracking, smoothing, cleaning, plotting and exporting muscle fibers. Also calculates basic fiber properties (e.g., length and curvature).

Using simple input, this package creates plots of gene models. Users can create plots of alternatively spliced gene variants and the positions of mutations and other gene features.

This package provides methods for calculating gradient surface metrics for continuous analysis of landscape features.

This package provides Gaussian process (GP) regression tools for social science inference problems. GPs combine flexible nonparametric regression with principled uncertainty quantification: rather than committing to a single model fit, the posterior reflects lesser knowledge at the edge of or beyond the observed data, where other approaches become highly model-dependent. The package reduces user-chosen hyperparameters from three to zero and supplies convenience functions for regression discontinuity (gp_rdd()), interrupted time-series (gp_its()), and general GP fitting (gpss(), gp_train(), gp_predict()). Methods are described in Cho, Kim, and Hazlett (2026) <doi:10.1017/pan.2026.10032>.