This package provides a probability tree allows to compute probabilities of complex events, such as genotype probabilities in intermediate generations of inbreeding through recurrent self-fertilization (selfing). This package implements functionality to compute probability trees for two- and three-marker genotypes in the F2 to F7 selfing generations. The conditional probabilities are derived automatically and in symbolic form. The package also provides functionality to extract and evaluate the relevant probabilities.

It is a single cell active pathway analysis tool based on the graph neural network (F. Scarselli (2009) <doi:10.1109/TNN.2008.2005605>; Thomas N. Kipf (2017) <arXiv:1609.02907v4>) to construct the gene-cell association network, infer pathway activity scores from different single cell modalities data, integrate multiple modality data on the same cells into one pathway activity score matrix, identify cell phenotype activated gene modules and parse association networks of gene modules under multiple cell phenotype. In addition, abundant visualization programs are provided to display the results.

Trains neural networks (multilayer perceptrons with one hidden layer) for bi- or multi-class classification.

This package provides functions for analyzing stocks or other investments. Main features are loading and aligning historical data for ticker symbols, calculating performance metrics for individual funds or portfolios (e.g. annualized growth, maximum drawdown, Sharpe/Sortino ratio), and creating graphs. C++ code is used to improve processing speed where possible.

Simulate multivariate correlated data given nonparametric marginals and their joint structure characterized by a Pearson or Spearman correlation matrix. The simulator engages the problem from a purely computational perspective. It assumes no statistical models such as copulas or parametric distributions, and can approximate the target correlations regardless of theoretical feasibility. The algorithm integrates and advances the Iman-Conover (1982) approach <doi:10.1080/03610918208812265> and the Ruscio-Kaczetow iteration (2008) <doi:10.1080/00273170802285693>. Package functions are carefully implemented in C++ for squeezing computing speed, suitable for large input in a manycore environment. Precision of the approximation and computing speed both substantially outperform various CRAN packages to date. Benchmarks are detailed in function examples. A simple heuristic algorithm is additionally designed to optimize the joint distribution in the post-simulation stage. The heuristic demonstrated good potential of achieving the same level of precision of approximation without the enhanced Iman-Conover-Ruscio-Kaczetow. The package contains a copy of Permuted Congruential Generator.

This package implements a suite of shrinkage estimators for multivariate linear regression to improve estimation stability and predictive accuracy. Provides methods including the Stein estimator, Diagonal Shrinkage, the general Shrinkage estimator (solving a Sylvester equation), and Slab Regression (Simple and Generalized). These methods address Stein's paradox by introducing structured bias to reduce variance without requiring cross-validation, except for Shrinkage Ridge Regression where the intensity is chosen by minimizing an explicit Mean Squared Error (MSE) criterion. Methods are based on paper <https://openaccess.city.ac.uk/id/eprint/35005/>.

Many complex diseases are known to be affected by the interactions between genetic variants and environmental exposures beyond the main genetic and environmental effects. Existing Bayesian methods for gene-environment (GÃ E) interaction studies are challenged by the high-dimensional nature of the study and the complexity of environmental influences. We have developed a novel and powerful semi-parametric Bayesian variable selection method that can accommodate linear and nonlinear GÃ E interactions simultaneously (Ren et al. (2020) <doi:10.1002/sim.8434>). Furthermore, the proposed method can conduct structural identification by distinguishing nonlinear interactions from main effects only case within Bayesian framework. Spike-and-slab priors are incorporated on both individual and group level to shrink coefficients corresponding to irrelevant main and interaction effects to zero exactly. The Markov chain Monte Carlo algorithms of the proposed and alternative methods are efficiently implemented in C++.

Testing of soil for the contents of organic carbon, and available macro- and micro-nutrients is a crucial part of soil fertility assessment. This package computes some routinely tested soil properties viz. organic carbon (C), total nitrogen (N), available N, mineral N, available phosphorus (P), available potassium (K), available iron (Fe), available zinc (Zn), available manganese (Mn), available copper (Cu), and available nickel (Ni) in soil based on laboratory analysis data obtained by most commonly followed protocols. Besides, it can also draw standard curves based on absorption/emission vs. concentration data, and give out unknown concentrations from absorption/emission readings.

This package provides a framework to generating random variates from arbitrary multivariate copulae, while concentrating on (bivariate) extreme value copulae. Particularly useful if the multivariate copulae are not available in closed form. Detailed discussion of the methodologies used can be found in Tajvidi and Turlach (2018) <doi:10.1111/anzs.12209>.

Web front end for your R functions producing plots or tables. If you have a function or set of related functions, you can make them available over the internet through a web browser. This is the same motivation as the shiny package, but note that the development of shinylight is not in any way linked to that of shiny (beyond the use of the httpuv package). You might prefer shinylight to shiny if you want a lighter weight deployment with easier horizontal scaling, or if you want to develop your front end yourself in JavaScript and HTML just using a lightweight remote procedure call interface to your R code on the server.

This package provides tools to convert from specific formats to more general forms of spatial data. Using tables to store the actual entities present in spatial data provides flexibility, and the functions here deliberately minimize the level of interpretation applied, leaving that for specific applications. Includes support for simple features, round-trip for Spatial classes and long-form tables, analogous to ggplot2::fortify'. There is also a more normal form representation that decomposes simple features and their kin to tables of objects, parts, and unique coordinates.

This package provides a comprehensive framework for quantifying the fundamental thermodynamic parameters of adsorption reactionsâ changes in the standard Gibbs free energy (delta G), enthalpy (delta H), and entropy (delta S)â is essential for understanding the spontaneity, heat effects, and molecular ordering associated with sorption processes. By analysing temperature-dependent equilibrium data, thermodynamic interpretation expands adsorption studies beyond conventional isotherm fitting, offering deeper insight into underlying mechanisms and surfaceâ solute interactions. Such an approach typically involves evaluating equilibrium coefficients across multiple temperatures and non-temperature treatments, deriving thermodynamic parameters using established thermodynamic relationships, and determining delta G as a temperature-specific indicator of adsorption favourability. This analytical pathway is widely applicable across environmental science, soil science, chemistry, materials science, and engineering, where reliable assessment of sorption behaviour is critical for examining contaminant retention, nutrient dynamics, and the behaviour of natural and engineered surfaces. By focusing specifically on thermodynamic inference, this framework complements existing adsorption isotherm-fitting packages such as âAdIsMFâ <https://CRAN.R-project.org/package=AdIsMF> <doi:10.32614/CRAN.package.AdIsMF>, and strengthens the scientific basis for interpreting adsorption energetics in both research and applied contexts. Details can be found in Roy et al. (2025) <doi:10.1007/s11270-025-07963-7>.

This package provides a modular and extendable approach to extract (micro)saccades from gaze samples via an ensemble of methods. Although there is an agreement about a general definition of a saccade, the more specific details are harder to agree upon. Therefore, there are numerous algorithms that extract saccades based on various heuristics, which differ in the assumptions about velocity, acceleration, etc. The package uses three methods (Engbert and Kliegl (2003) <doi:10.1016/S0042-6989(03)00084-1>, Otero-Millan et al. (2014)<doi:10.1167/14.2.18>, and Nyström and Holmqvist (2010) <doi:10.3758/BRM.42.1.188>) to label individual samples and then applies a majority vote approach to identify saccades. The package includes three methods but can be extended via custom functions. It also uses a modular approach to compute velocity and acceleration from noisy samples. Finally, you can obtain methods votes per gaze sample instead of saccades.

Artificial selection through selective breeding is an efficient way to induce changes in traits of interest in experimental populations. This package (sra) provides a set of tools to analyse artificial-selection response datasets. The data typically feature for several generations the average value of a trait in a population, the variance of the trait, the population size and the average value of the parents that were chosen to breed. Sra implements two families of models aiming at describing the dynamics of the genetic architecture of the trait during the selection response. The first family relies on purely descriptive (phenomenological) models, based on an autoregressive framework. The second family provides different mechanistic models, accounting e.g. for inbreeding, mutations, genetic and environmental canalization, or epistasis. The parameters underlying the dynamics of the time series are estimated by maximum likelihood. The sra package thus provides (i) a wrapper for the R functions mle() and optim() aiming at fitting in a convenient way a predetermined set of models, and (ii) some functions to plot and analyze the output of the models.

Applies re-sampled kernel density method to detect vote fraud. It estimates the proportion of coarse vote-shares in the observed data relative to the null hypothesis of no fraud.

We have designed this package to address experimental scenarios involving multiple covariates. It focuses on construction of Optimal Covariate Designs (OCDs), checking space filling property of the developed design. The primary objective of the package is to generate OCDs using four methods viz., M array method, Juxtapose method, Orthogonal Integer Array and Hadamard method. The package also evaluates space filling properties of both the base design and OCDs using the MaxPro criterion, providing a meaningful basis for comparison. In addition, it includes tool to visualize the spread offered by the design points in the form of scatterplot, which help users to assess distribution and coverage of design points.

Generate common data forms for complex data suitable for conversions and transmission by decomposition as paths or primitives. Paths are sequentially-linked records, primitives are basic atomic elements and both can model many forms and be grouped into hierarchical structures. The universal models SC0 (structural) and SC (labelled, relational) are composed of edges and can represent any hierarchical form. Specialist models PATH', ARC and TRI provide the most common intermediate forms used for converting from one form to another. The methods are inspired by the simplicial complex <https://en.wikipedia.org/wiki/Simplicial_complex> and provide intermediate forms that relate spatial data structures to this mathematical construct.

Basic and model-based soil physical analyses.

This package provides basic functions that support an implementation of multi-profile case (Case 3) best-worst scaling (BWS). Case 3 BWS is a question-based survey method to elicit people's preferences for attribute levels. Case 3 BWS constructs various combinations of attribute levels (profiles) and then asks respondents to select the best and worst profiles in each choice set. A main function creates a dataset for the analysis from the choice sets and the responses to the questions. For details on Case 3 BWS, refer to Louviere et al. (2015) <doi:10.1017/CBO9781107337855>.

This package provides two methods for segmentation and joint segmentation/clustering of bivariate time-series. Originally intended for ecological segmentation (home-range and behavioural modes) but easily applied on other series, the package also provides tools for analysing outputs from R packages moveHMM and marcher'. The segmentation method is a bivariate extension of Lavielle's method available in adehabitatLT (Lavielle, 1999 <doi:10.1016/S0304-4149(99)00023-X> and 2005 <doi:10.1016/j.sigpro.2005.01.012>). This method rely on dynamic programming for efficient segmentation. The segmentation/clustering method alternates steps of dynamic programming with an Expectation-Maximization algorithm. This is an extension of Picard et al (2007) <doi:10.1111/j.1541-0420.2006.00729.x> method (formerly available in cghseg package) to the bivariate case. The method is fully described in Patin et al (2018) <doi:10.1101/444794>.

Estimate the receiver operating characteristic (ROC) curve, area under the curve (AUC) and optimal cut-off points for individual classification taking into account complex sampling designs when working with complex survey data. Methods implemented in this package are described in: A. Iparragirre, I. Barrio, I. Arostegui (2024) <doi:10.1002/sta4.635>; A. Iparragirre, I. Barrio, J. Aramendi, I. Arostegui (2022) <doi:10.2436/20.8080.02.121>; A. Iparragirre, I. Barrio (2024) <doi:10.1007/978-3-031-65723-8_7>.

This package provides functionality to fit a zero-inflated estimator for small area estimation. This estimator is a combines a linear mixed effects regression model and a logistic mixed effects regression model via a two-stage modeling approach. The estimator's mean squared error is estimated via a parametric bootstrap method. Chandra and others (2012, <doi:10.1080/03610918.2011.598991>) introduce and describe this estimator and mean squared error estimator. White and others (2024+, <doi:10.48550/arXiv.2402.03263>) describe the applicability of this estimator to estimation of forest attributes and further assess the estimator's properties.

Computes the trimmed-k mean by removing the k smallest and k largest values from a numeric vector. Created for STAT 5400 at the University of Iowa.

This package provides a toolkit for stratified medicine, subgroup identification, and precision medicine. Current tools include (1) filtering models (reduce covariate space), (2) patient-level estimate models (counterfactual patient-level quantities, such as the conditional average treatment effect), (3) subgroup identification models (find subsets of patients with similar treatment effects), and (4) treatment effect estimation and inference (for the overall population and discovered subgroups). These tools can be customized and are directly used in PRISM (patient response identifiers for stratified medicine; Jemielita and Mehrotra 2019 <arXiv:1912.03337>. This package is in beta and will be continually updated.