Data simulator including genotype, phenotype, pedigree, selection and reproduction in R. It simulates most of reproduction process of animals or plants and provides data for GS (Genomic Selection), GWAS (Genome-Wide Association Study), and Breeding. For ADI model, please see Kao C and Zeng Z (2002) <doi:10.1093/genetics/160.3.1243>. For build.cov, please see B. D. Ripley (1987) <ISBN:9780470009604>.

"The Soil Texture Wizard" is a set of R functions designed to produce texture triangles (also called texture plots, texture diagrams, texture ternary plots), classify and transform soil textures data. These functions virtually allows to plot any soil texture triangle (classification) into any triangle geometry (isosceles, right-angled triangles, etc.). This set of function is expected to be useful to people using soil textures data from different soil texture classification or different particle size systems. Many (> 15) texture triangles from all around the world are predefined in the package. A simple text based graphical user interface is provided: soiltexture_gui().

This package provides a Rstudio addin to download, merge and upload Rstudio settings and keymaps, essentially syncing them at will. It uses Google Drive as a cloud storage to keep the settings and keymaps files.

This is a core implementation of SAS procedures for linear models - GLM, REG, ANOVA, TTEST, FREQ, and UNIVARIATE. Some R packages provide Type II and Type III SS. However, the results of nested and complex designs are often different from those of SAS'. Different results do not necessarily mean incorrectness. However, many want the same results as SAS'. This package aims to achieve that. Reference: Littell RC, Stroup WW, Freund RJ (2002, ISBN:0-471-22174-0).

This package provides a set of functions is provided for 1) the stratum lengths analysis along a chosen direction, 2) fast estimation of continuous lag spatial Markov chains model parameters and probability computing (also for large data sets), 3) transition probability maps and transiograms drawing, 4) simulation methods for categorical random fields. More details on the methodology are discussed in Sartore (2013) <doi:10.32614/RJ-2013-022> and Sartore et al. (2016) <doi:10.1016/j.cageo.2016.06.001>.

Variable selection techniques are essential tools for model selection and estimation in high-dimensional statistical models. Through this publicly available package, we provide a unified environment to carry out variable selection using iterative sure independence screening (SIS) (Fan and Lv (2008)<doi:10.1111/j.1467-9868.2008.00674.x>) and all of its variants in generalized linear models (Fan and Song (2009)<doi:10.1214/10-AOS798>) and the Cox proportional hazards model (Fan, Feng and Wu (2010)<doi:10.1214/10-IMSCOLL606>).

Newly developed methods for the estimation of several probabilities in an illness-death model. The package can be used to obtain nonparametric and semiparametric estimates for: transition probabilities, occupation probabilities, cumulative incidence function and the sojourn time distributions. Additionally, it is possible to fit proportional hazards regression models in each transition of the Illness-Death Model. Several auxiliary functions are also provided which can be used for marginal estimation of the survival functions.

Routines for a collection of screen-and-clean type variable selection procedures, including UPS and GS.

This package implements a generative model that uses a spike-and-slab like prior distribution obtained by multiplying a deterministic binary vector. Such a model allows an EM algorithm, optimizing a type-II log-likelihood.

An implementation of local and global statistical complexity measures (aka Information Theory Quantifiers, ITQ) for time series analysis based on ordinal statistics (Bandt and Pompe (2002) <DOI:10.1103/PhysRevLett.88.174102>). Several distance measures that operate on ordinal pattern distributions, auxiliary functions for ordinal pattern analysis, and generating functions for stochastic and deterministic-chaotic processes for ITQ testing are provided.

Supplementary tools for evaluating and validating partial least squares structural equation models estimated with seminr'. Provides methods for predictive model assessment, importance-performance analysis with necessary condition testing, overfitting diagnostics, measurement model verification, mediator contribution analysis, unobserved heterogeneity detection via latent class and prediction-oriented segmentation, and congruence coefficient testing. All functions accept estimated seminr model objects and return results with print, summary, and plot methods.

Take screenshots from R command and locate an image position.

Toolbox containing a variety of spectral clustering tools functions. Among the tools available are the hierarchical spectral clustering algorithm, the Shi and Malik clustering algorithm, the Perona and Freeman algorithm, the non-normalized clustering, the Von Luxburg algorithm, the Partition Around Medoids clustering algorithm, a multi-level clustering algorithm, recursive clustering and the fast method for all clustering algorithm. As well as other tools needed to run these algorithms or useful for unsupervised spectral clustering. This toolbox aims to gather the main tools for unsupervised spectral classification. See <http://mawenzi.univ-littoral.fr/> for more information and documentation.

Sometimes it is handy to be able to view an image file on an R graphics device. This package just does that. Currently it supports PNG files.

This package provides functions that calculate appropriate sample sizes for one-sample t-tests, two-sample t-tests, and F-tests for microarray experiments based on desired power while controlling for false discovery rates. For all tests, the standard deviations (variances) among genes can be assumed fixed or random. This is also true for effect sizes among genes in one-sample and two sample experiments. Functions also output a chart of power versus sample size, a table of power at different sample sizes, and a table of critical test values at different sample sizes.

Screen for and analyze non-linear sparse direct effects in the presence of unobserved confounding using the spectral deconfounding techniques (Ä evid, Bühlmann, and Meinshausen (2020)<jmlr.org/papers/v21/19-545.html>, Guo, Ä evid, and Bühlmann (2022) <doi:10.1214/21-AOS2152>). These methods have been shown to be a good estimate for the true direct effect if we observe many covariates, e.g., high-dimensional settings, and we have fairly dense confounding. Even if the assumptions are violated, it seems like there is not much to lose, and the deconfounded models will, in general, estimate a function closer to the true one than classical least squares optimization. SDModels provides functions SDAM() for Spectrally Deconfounded Additive Models (Scheidegger, Guo, and Bühlmann (2025) <doi:10.1145/3711116>) and SDForest() for Spectrally Deconfounded Random Forests (Ulmer, Scheidegger, and Bühlmann (2025) <doi:10.1080/10618600.2025.2569602>).

This package provides a general framework for performing sparse functional clustering as originally described in Floriello and Vitelli (2017) <doi:10.1016/j.jmva.2016.10.008>, with the possibility of jointly handling data misalignment (see Vitelli, 2019, <doi:10.48550/arXiv.1912.00687>).

This package provides a general framework to perform statistical inference of each gene pair and global inference of whole-scale gene pairs in gene networks using the well known Gaussian graphical model (GGM) in a time-efficient manner. We focus on the high-dimensional settings where p (the number of genes) is allowed to be far larger than n (the number of subjects). Four main approaches are supported in this package: (1) the bivariate nodewise scaled Lasso (Ren et al (2015) <doi:10.1214/14-AOS1286>) (2) the de-sparsified nodewise scaled Lasso (Jankova and van de Geer (2017) <doi:10.1007/s11749-016-0503-5>) (3) the de-sparsified graphical Lasso (Jankova and van de Geer (2015) <doi:10.1214/15-EJS1031>) (4) the GGM estimation with false discovery rate control (FDR) using scaled Lasso or Lasso (Liu (2013) <doi:10.1214/13-AOS1169>). Windows users should install Rtools before the installation of this package.

Adds support for R startup configuration via .Renviron.d and .Rprofile.d directories in addition to .Renviron and .Rprofile files. This makes it possible to keep private / secret environment variables separate from other environment variables. It also makes it easier to share specific startup settings by simply copying a file to a directory.

This package provides a collection of functions which (i) assess the quality of variable subsets as surrogates for a full data set, in either an exploratory data analysis or in the context of a multivariate linear model, and (ii) search for subsets which are optimal under various criteria. Theoretical support for the heuristic search methods and exploratory data analysis criteria is in Cadima, Cerdeira, Minhoto (2003, <doi:10.1016/j.csda.2003.11.001>). Theoretical support for the leap and bounds algorithm and the criteria for the general multivariate linear model is in Duarte Silva (2001, <doi:10.1006/jmva.2000.1920>). There is a package vignette "subselect", which includes additional references.

Estimate attribute values for one set of polygons from values measured on a different, misaligned set. Provides area-weighted areal interpolation and a dasymetric method that distributes values across a point layer (such as parcel centroids). Count (extensive) measures are total-preserving; rate (intensive) measures use area-weighted means.

This package provides functions that simplify calls to the Skilljar API. See <https://api.skilljar.com/docs/> for documentation on the Skilljar API. This package is not supported by Skilljar'.

This package provides a modular simulation engine for a wide range of stochastic processes. Provides exact and approximate simulation methods for Poisson processes (homogeneous and inhomogeneous), Brownian motion (standard, drifted, and bridge), discrete- and continuous-time Markov chains, birth-death processes, the Yule pure-birth process, infinitesimal generator matrix utilities, Markovian queuing systems (M/M/1, M/M/c, M/M/c/K) with exact steady-state statistics, Levy processes (gamma, normal inverse Gaussian, variance-gamma, alpha-stable), Merton jump-diffusion models, Hawkes self-exciting processes, geometric Brownian motion, and Ornstein-Uhlenbeck mean-reverting diffusions. Includes variance reduction techniques (antithetic variates, control variates, importance sampling, stratified sampling), parallel simulation via the future framework, rare-event simulation (cross-entropy and multilevel splitting), path visualisation, and summary statistics. Methods are based on Glasserman (2003) <doi:10.1007/978-0-387-21617-1>, Asmussen & Glynn (2007) <doi:10.1007/978-0-387-69033-9>, Norris (1997) <doi:10.1017/CBO9780511810633>, and Kleinrock (1975, ISBN:0471491101).

This package provides methods for generating, exploring and executing seamless Phase II-III designs of Lai, Lavori and Shih using generalized likelihood ratio statistics. Includes pdf and source files that describe the entire R implementation with the relevant mathematical details.