Solves a least squares system Ax~=b (dim(A)=(m,n) with m >= n) with a precondition matrix B: BAx=Bb (dim(B)=(n,m)). Implemented method is based on GMRES (Saad, Youcef; Schultz, Martin H. (1986). "GMRES: A Generalized Minimal Residual Algorithm for Solving Nonsymmetric Linear Systems" <doi:10.1137/0907058>) with callback functions, i.e. no explicit A, B or b are required.

Identify and visualize individuals with unusual association patterns of genetics and geography using the approach of Chang and Schmid (2023) <doi:10.1101/2023.04.06.535838>. It detects potential outliers that violate the isolation-by-distance assumption using the K-nearest neighbor approach. You can obtain a table of outliers with statistics and visualize unusual geo-genetic patterns on a geographical map. This is useful for landscape genomics studies to discover individuals with unusual geography and genetics associations from a large biological sample.

Companion package for the manual guide-R : Guide pour lâ analyse de données dâ enquêtes avec R available at <https://larmarange.github.io/guide-R/>. guideR implements miscellaneous functions introduced in guide-R to facilitate statistical analysis and manipulation of survey data.

This package provides a post-estimation method for categorical response models (CRM). Inputs from objects of class serp(), clm(), polr(), multinom(), mlogit(), vglm() and glm() are currently supported. Available tests include the Hosmer-Lemeshow tests for the binary, multinomial and ordinal logistic regression; the Lipsitz and the Pulkstenis-Robinson tests for the ordinal models. The proportional odds, adjacent-category, and constrained continuation-ratio models are particularly supported at ordinal level. Tests for the proportional odds assumptions in ordinal models are also possible with the Brant and the Likelihood-Ratio tests. Moreover, several summary measures of predictive strength (Pseudo R-squared), and some useful error metrics, including, the brier score, misclassification rate and logloss are also available for the binary, multinomial and ordinal models. Ugba, E. R. and Gertheiss, J. (2018) <http://www.statmod.org/workshops_archive_proceedings_2018.html>.

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 >>.

Application of multi-site models for daily precipitation and temperature data. This package is designed for an application to 105 precipitation and 26 temperature gauges located in Switzerland. It applies fitting procedures and provides weather generators described in the following references: - Evin, G., A.-C. Favre, and B. Hingray. (2018) <doi:10.5194/hess-22-655-2018>. - Evin, G., A.-C. Favre, and B. Hingray. (2018) <doi:10.1007/s00704-018-2404-x>.

This is a wrapper for the command line tool googler', which can be found at the following URL: <https://github.com/jarun/googler>.

Reads annual and quarterly financial reports from companies traded at B3, the Brazilian exchange <https://www.b3.com.br/>. All data is downloaded and imported from CVM's public ftp site <https://dados.cvm.gov.br/dados/CIA_ABERTA/>.

Gaussian processes are flexible distributions to model functional data. Whilst theoretically appealing, they are computationally cumbersome except for small datasets. This package implements two methods for scaling Gaussian process inference in Stan'. First, a sparse approximation of the likelihood that is generally applicable and, second, an exact method for regularly spaced data modeled by stationary kernels using fast Fourier methods. Utility functions are provided to compile and fit Stan models using the cmdstanr interface. References: Hoffmann and Onnela (2025) <doi:10.18637/jss.v112.i02>.

An interactive document on the topic of goodness of fit analysis using rmarkdown and shiny packages. Runtime examples are provided in the package function as well as at <https://predanalyticssessions1.shinyapps.io/ChiSquareGOF/>.

Provision of classes and methods for estimating generalized orthogonal GARCH models. This is an alternative approach to CC-GARCH models in the context of multivariate volatility modeling.

An implementation of the generalized graded unfolding model (GGUM) in R, see Roberts, Donoghue, and Laughlin (2000) <doi:10.1177/01466216000241001>). It allows to simulate data sets based on the GGUM. It fits the GGUM and the GUM, and it retrieves item and person parameter estimates. Several plotting functions are available (item and test information functions; item and test characteristic curves; item category response curves). Additionally, there are some functions that facilitate the communication between R and GGUM2004'. Finally, a model-fit checking utility, MODFIT(), is also available.

Maximum likelihood estimation under relational models, with or without the overall effect.

It provides a custom ggplot2 geom to add day/night patterns to plots. It visually distinguishes daytime and nighttime periods. It is useful for visualizing data that spans multiple days and for highlighting diurnal patterns.

Create stunning network experiences powered by the G6 graph visualisation engine JavaScript library <https://g6.antv.antgroup.com/en>. In shiny mode, modify your graph directly from the server function to dynamically interact with nodes and edges. Select your favorite layout among 20 choices. 15 behaviors are available such as interactive edge creation, collapse-expand and brush select. 17 plugins designed to improve the user experience such as a mini-map, toolbars and grid lines. Customise the look and feel of your graph with comprehensive options for nodes, edges and more.

This package provides a simple way to interact with and extract data from the official Google Knowledge Graph API <https://developers.google.com/knowledge-graph/>.

For plant physiologists, converts conductance (e.g. stomatal conductance) to different units: m/s, mol/m^2/s, and umol/m^2/s/Pa.

Estimates grid type bivariate copula functions, calculates some association measures and provides several copula graphics.

Integrates game theory and ecological theory to construct social-ecological models that simulate the management of populations and stakeholder actions. These models build off of a previously developed management strategy evaluation (MSE) framework to simulate all aspects of management: population dynamics, manager observation of populations, manager decision making, and stakeholder responses to management decisions. The newly developed generalised management strategy evaluation (GMSE) framework uses genetic algorithms to mimic the decision-making process of managers and stakeholders under conditions of change, uncertainty, and conflict. Simulations can be run using gmse(), gmse_apply(), and gmse_gui() functions.

Uses simple Bayesian conjugate prior update rules to calculate the win probability of each option, value remaining in the test, and percent lift over the baseline for various marketing objectives. References: Fink, Daniel (1997) "A Compendium of Conjugate Priors" <https://www.johndcook.com/CompendiumOfConjugatePriors.pdf>. Stucchio, Chris (2015) "Bayesian A/B Testing at VWO" <https://vwo.com/downloads/VWO_SmartStats_technical_whitepaper.pdf>.

Gene and Region Counting of Mutations (GARCOM) package computes mutation (or alleles) counts per gene per individuals based on gene annotation or genomic base pair boundaries. It comes with features to accept data formats in plink(.raw) and VCF. It provides users flexibility to extract and filter individuals, mutations and genes of interest.

This package implements a variant of the Self-Organizing Map (SOM) algorithm designed for mixed-attribute datasets. Similarity between observations is computed using the Gower distance, and categorical prototypes are updated via heuristic strategies (weighted mode and multinomial sampling). Provides functions for model fitting, mapping, visualization (U-Matrix and component planes), and evaluation, making SOM applicable to heterogeneous real-world data. For methodological details see Sáez and Salas (2026) <doi:10.1007/s41060-025-00941-6>.

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.

Likelihood-based boosting approaches for generalized mixed models are provided.