Analysis of experimental multi-parent populations to detect regions of the genome (called quantitative trait loci, QTLs) influencing phenotypic traits measured in unique and multiple environments. The population must be composed of crosses between a set of at least three parents (e.g. factorial design, diallel', or nested association mapping). The functions cover data processing, QTL detection, and results visualization. The implemented methodology is described in Garin, Wimmer, Mezmouk, Malosetti and van Eeuwijk (2017) <doi:10.1007/s00122-017-2923-3>, in Garin, Malosetti and van Eeuwijk (2020) <doi: 10.1007/s00122-020-03621-0>, and in Garin, Diallo, Tekete, Thera, ..., and Rami (2024) <doi: 10.1093/genetics/iyae003>.

This package provides a data generator of multivariate non-normal data in R. It combines two different methods to generate non-normal data, one with user-specified multivariate skewness and kurtosis (more details can be found in the paper: Qu, Liu, & Zhang, 2019 <doi:10.3758/s13428-019-01291-5>), and the other with the given marginal skewness and kurtosis. The latter one is the widely-used Vale and Maurelli's method. It also contains a function to calculate univariate and multivariate (Mardia's Test) skew and kurtosis.

Enable operationalized evaluation of disease outcomes in multiple sclerosis. â MSoutcomesâ requires longitudinally recorded clinical data structured in long format. The package is based on the research developed at Clinical Outcomes Research unit (CORe), University of Melbourne and Neuroimmunology Centre, Royal Melbourne Hospital. Kalincik et al. (2015) <doi:10.1093/brain/awv258>. Lorscheider et al. (2016) <doi:10.1093/brain/aww173>. Sharmin et al. (2022) <doi:10.1111/ene.15406>. Dzau et al. (2023) <doi:10.1136/jnnp-2023-331748>.

Persistent interface to Macaulay2 <https://www.macaulay2.com> and front-end tools facilitating its use in the R ecosystem. For details see Kahle et. al. (2020) <doi:10.18637/jss.v093.i09>.

Quantification is a prominent machine learning task that has received an increasing amount of attention in the last years. The objective is to predict the class distribution of a data sample. This package is a collection of machine learning algorithms for class distribution estimation. This package include algorithms from different paradigms of quantification. These methods are described in the paper: A. Maletzke, W. Hassan, D. dos Reis, and G. Batista. The importance of the test set size in quantification assessment. In Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI20, pages 2640â 2646, 2020. <doi:10.24963/ijcai.2020/366>.

The goal of mammalcol is to provide easy access to a meticulously structured dataset of Colombian mammal species in R. The 2025 update includes comprehensive, detailed species accounts, and distribution information.

Allows to perform the multivariate version of the Diebold-Mariano test for equal predictive ability of multiple forecast comparison. Main reference: Mariano, R.S., Preve, D. (2012) <doi:10.1016/j.jeconom.2012.01.014>.

This package provides a simple informative powerful test (mvnTest()) for multivariate normality proposed by Zhou and Shao (2014) <doi:10.1080/02664763.2013.839637>, which combines kurtosis with Shapiro-Wilk test that is easy for biomedical researchers to understand and easy to implement in all dimensions. This package also contains some other multivariate normality tests including Fattorini's FA test (faTest()), Mardia's skewness and kurtosis test (mardia()), Henze-Zirkler's test (mhz()), Bowman and Shenton's test (msk()), Roystonâ s H test (msw()), and Villasenor-Alva and Gonzalez-Estrada's test (msw()). Empirical power calculation functions for these tests are also provided. In addition, this package includes some functions to generate several types of multivariate distributions mentioned in Zhou and Shao (2014).

The modified Adult Treatment Panel -III guidelines (ATP-III) proposed by American Heart Association (AHA) and National Heart, Lung and Blood Institute (NHLBI) are used widely for the clinical diagnosis of Metabolic Syndrome. The AHA-NHLBI criteria advise using parameters such as waist circumference (WC), systolic blood pressure (SBP), diastolic blood pressure (DBP), fasting plasma glucose (FPG), triglycerides (TG) and high-density lipoprotein cholesterol (HDLC) for diagnosis of metabolic syndrome. Each parameter has to be interpreted based on the proposed cut-offs, making the diagnosis slightly complex and error-prone. This package is developed by incorporating the modified ATP-III guidelines, and it will aid in the easy and quick diagnosis of metabolic syndrome in busy healthcare settings and also for research purposes. The modified ATP-III-AHA-NHLBI criteria for the diagnosis is described by Grundy et al ., (2005) <doi:10.1161/CIRCULATIONAHA.105.169404>.

This package provides functions for cost-optimal control charts with a focus on health care applications. Compared to assumptions in traditional control chart theory, here, we allow random shift sizes, random repair and random sampling times. The package focuses on X-bar charts with a sample size of 1 (representing the monitoring of a single patient at a time). The methods are described in Zempleni et al. (2004) <doi:10.1002/asmb.521>, Dobi and Zempleni (2019) <doi:10.1002/qre.2518> and Dobi and Zempleni (2019) <http://ac.inf.elte.hu/Vol_049_2019/129_49.pdf>.

Advanced methods for a valuable quantitative environmental risk assessment using Bayesian inference of survival Data with toxicokinetics toxicodynamics (TKTD) models. Among others, it facilitates Bayesian inference of the general unified threshold model of survival (GUTS). See models description in Jager et al. (2011) <doi:10.1021/es103092a> and implementation using Bayesian inference in Baudrot and Charles (2019) <doi:10.1038/s41598-019-47698-0>.

This package provides methods for analyzing and using quartets displayed on a collection of gene trees, primarily to make inferences about the species tree or network under the multi-species coalescent model. These include quartet hypothesis tests for the model, as developed by Mitchell et al. (2019) <doi:10.1214/19-EJS1576>, simplex plots of quartet concordance factors as presented by Allman et al. (2020) <doi:10.1101/2020.02.13.948083>, species tree inference methods based on quartet distances of Rhodes (2019) <doi:10.1109/TCBB.2019.2917204> and Yourdkhani and Rhodes (2019) <doi:10.1007/s11538-020-00773-4>, the NANUQ algorithm for inference of level-1 species networks of Allman et al. (2019) <doi:10.1186/s13015-019-0159-2>, the TINNIK algorithm for inference of the tree of blobs of an arbitrary network of Allman et al.(2022) <doi:10.1007/s00285-022-01838-9>, and NANUQ+ routines for resolving multifurcations in the tree of blobs to cycles as in Allman et al.(2024) (forthcoming). Software announcement by Rhodes et al. (2020) <doi:10.1093/bioinformatics/btaa868>.

Extends the mlr3 ecosystem to functional analysis by adding support for irregular and regular functional data as defined in the tf package. The package provides PipeOps for preprocessing functional columns and for extracting scalar features, thereby allowing standard machine learning algorithms to be applied afterwards. Available operations include simple functional features such as the mean or maximum, smoothing, interpolation, flattening, and functional PCA'.

Get map data frames for the Indian subcontinent with different region levels (e.g., district, state). The package also offers convenience functions for plotting choropleths, visualizing spatial data, and handling state/district codes.

An implementation of the additive (Gurevitch et al., 2000 <doi:10.1086/303337>) and multiplicative (Lajeunesse, 2011 <doi:10.1890/11-0423.1>) factorial null models for multiple stressor data (Burgess et al., 2021 <doi:10.1101/2021.07.21.453207>). Effect sizes are able to be calculated for either null model, and subsequently classified into one of four different interaction classifications (e.g., antagonistic or synergistic interactions). Analyses can be conducted on data for single experiments through to large meta-analytical datasets. Minimal input (or statistical knowledge) is required, with any output easily understood. Summary figures are also able to be easily generated.

An efficient implementation of the MCPMod (Multiple Comparisons and Modeling) method to support a simulation-based design and analysis of dose-finding trials with normally distributed, binary and count endpoints (Bretz et al. (2005) <doi:10.1111/j.1541-0420.2005.00344.x>).

This package provides essential tools for the pre-processing techniques of matching and weighting multiply imputed datasets. The package includes functions for matching within and across multiply imputed datasets using various methods, estimating weights for units in the imputed datasets using multiple weighting methods, calculating causal effect estimates in each matched or weighted dataset using parametric or non-parametric statistical models, and pooling the resulting estimates according to Rubin's rules (please see <https://journal.r-project.org/archive/2021/RJ-2021-073/> for more details).

Create minimal, responsive, and style-agnostic HTML documents with the lightweight CSS frameworks such as sakura', Water.css', and spcss'. Powerful features include table of contents floating as a sidebar, folding codes and results, and more.

This package provides methods for Geographically Weighted Regression with spatial autocorrelation (Geniaux and Martinetti 2017) <doi:10.1016/j.regsciurbeco.2017.04.001>. Implements Multiscale Geographically Weighted Regression with Top-Down Scale approaches (Geniaux 2026) <doi:10.1007/s10109-025-00481-4>.

Sometimes data for analysis are obtained using more convenient or less expensive means yielding "surrogate" variables for what could be obtained more accurately, albeit with less convenience; or less conveniently or at more expense yielding "reference" variables, thought of as being measured without error. Analysis of the surrogate variables measured with error generally yields biased estimates when the objective is to make inference about the reference variables. Often it is thought that ignoring the measurement error in surrogate variables only biases effects toward the null hypothesis, but this need not be the case. Measurement errors may bias parameter estimates either toward or away from the null hypothesis. If one has a data set with surrogate variable data from the full sample, and also reference variable data from a randomly selected subsample, then one can assess the bias introduced by measurement error in parameter estimation, and use this information to derive improved estimates based upon all available data. Formulaically these estimates based upon the reference variables from the validation subsample combined with the surrogate variables from the whole sample can be interpreted as starting with the estimate from reference variables in the validation subsample, and "augmenting" this with additional information from the surrogate variables. This suggests the term "augmented" estimate. The meerva package calculates these augmented estimates in the regression setting when there is a randomly selected subsample with both surrogate and reference variables. Measurement errors may be differential or non-differential, in any or all predictors (simultaneously) as well as outcome. The augmented estimates derive, in part, from the multivariate correlation between regression model parameter estimates from the reference variables and the surrogate variables, both from the validation subset. Because the validation subsample is chosen at random any biases imposed by measurement error, whether non-differential or differential, are reflected in this correlation and these correlations can be used to derive estimates for the reference variables using data from the whole sample. The main functions in the package are meerva.fit which calculates estimates for a dataset, and meerva.sim.block which simulates multiple datasets as described by the user, and analyzes these datasets, storing the regression coefficient estimates for inspection. The augmented estimates, as well as how measurement error may arise in practice, is described in more detail by Kremers WK (2021) <arXiv:2106.14063> and is an extension of the works by Chen Y-H, Chen H. (2000) <doi:10.1111/1467-9868.00243>, Chen Y-H. (2002) <doi:10.1111/1467-9868.00324>, Wang X, Wang Q (2015) <doi:10.1016/j.jmva.2015.05.017> and Tong J, Huang J, Chubak J, et al. (2020) <doi:10.1093/jamia/ocz180>.

This package provides a set of tools to perform multiple versions of the Mobility Oriented-Parity metric. This multivariate analysis helps to characterize levels of dissimilarity between a set of conditions of reference and another set of conditions of interest. If predictive models are transferred to conditions different from those over which models were calibrated (trained), this metric helps to identify transfer conditions that differ substantially from those of calibration. These tools are implemented following principles proposed in Owens et al. (2013) <doi:10.1016/j.ecolmodel.2013.04.011>, and expanded to obtain more detailed results that aid in interpretation as in Cobos et al. (2024) <doi:10.21425/fob.17.132916>.

The midasml package implements estimation and prediction methods for high-dimensional mixed-frequency (MIDAS) time-series and panel data regression models. The regularized MIDAS models are estimated using orthogonal (e.g. Legendre) polynomials and sparse-group LASSO (sg-LASSO) estimator. For more information on the midasml approach see Babii, Ghysels, and Striaukas (2021, JBES forthcoming) <doi:10.1080/07350015.2021.1899933>. The package is equipped with the fast implementation of the sg-LASSO estimator by means of proximal block coordinate descent. High-dimensional mixed frequency time-series data can also be easily manipulated with functions provided in the package.

Package to carry out merged block randomization (Van der Pas (2019), <doi:10.1177/1740774519827957>), a restricted randomization method designed for small clinical trials (at most 100 subjects) or trials with small strata, for example in multicentre trials. It can be used for more than two groups or unequal randomization ratios.

This package provides functions to calculate hazard and survival function of Multi-Stage Clonal Expansion Models used in cancer epidemiology. For the Two-Stage Clonal Expansion Model an exact solution is implemented assuming piecewise constant parameters, see Heidenreich, Luebeck, Moolgavkar (1997) <doi:10.1111/j.1539-6924.1997.tb00878.x>. Numerical solutions are provided for its extensions, see also Little, Vineis, Li (2008) <doi:10.1016/j.jtbi.2008.05.027>.