This package implements the multivariate adaptive shrinkage (mash) method of Urbut et al (2019) <DOI:10.1038/s41588-018-0268-8> for estimating and testing large numbers of effects in many conditions (or many outcomes). Mash takes an empirical Bayes approach to testing and effect estimation; it estimates patterns of similarity among conditions, then exploits these patterns to improve accuracy of the effect estimates. The core linear algebra is implemented in C++ for fast model fitting and posterior computation.

Multivariate Analysis methods and data sets used in John Marden's book Multivariate Statistics: Old School (2015) <ISBN:978-1456538835>. This also serves as a companion package for the STAT 571: Multivariate Analysis course offered by the Department of Statistics at the University of Illinois at Urbana-Champaign ('UIUC').

The method m:Explorer associates a given list of target genes (e.g. those involved in a biological process) to gene regulators such as transcription factors. Transcription factors that bind DNA near significantly many target genes or correlate with target genes in transcriptional (microarray or RNAseq data) are selected. Selection of candidate master regulators is carried out using multinomial regression models, likelihood ratio tests and multiple testing correction. Reference: m:Explorer: multinomial regression models reveal positive and negative regulators of longevity in yeast quiescence. Juri Reimand, Anu Aun, Jaak Vilo, Juan M Vaquerizas, Juhan Sedman and Nicholas M Luscombe. Genome Biology (2012) 13:R55 <doi:10.1186/gb-2012-13-6-r55>.

This package provides methods for quality control and exploratory analysis of surface water quality data collected in Massachusetts, USA. Functions are developed to facilitate data formatting for the Water Quality Exchange Network <https://www.epa.gov/waterdata/water-quality-data-upload-wqx> and reporting of data quality objectives to state agencies. Quality control methods are from Massachusetts Department of Environmental Protection (2020) <https://www.mass.gov/orgs/massachusetts-department-of-environmental-protection>.

Comprehensive toolkit for Environmental Phillips Curve analysis featuring multidimensional instrumental variable creation, transfer entropy causal discovery, network analysis, and state-of-the-art econometric methods. Implements geographic, technological, migration, geopolitical, financial, and natural risk instruments with robust diagnostics and visualization. Provides 24 different instrumental variable approaches with empirical validation. Methods based on Phillips (1958) <doi:10.1111/j.1468-0335.1958.tb00003.x>, transfer entropy by Schreiber (2000) <doi:10.1103/PhysRevLett.85.461>, and weak instrument tests by Stock and Yogo (2005) <doi:10.1017/CBO9780511614491.006>.

Generate the optimal maximin distance, minimax distance (only for low dimensions), and maximum projection designs within the class of Latin hypercube designs efficiently for computer experiments. Generate Pareto front optimal designs for each two of the three criteria and all the three criteria within the class of Latin hypercube designs efficiently. Provide criterion computing functions. References of this package can be found in Morris, M. D. and Mitchell, T. J. (1995) <doi:10.1016/0378-3758(94)00035-T>, Lu Lu and Christine M. Anderson-CookTimothy J. Robinson (2011) <doi:10.1198/Tech.2011.10087>, Joseph, V. R., Gul, E., and Ba, S. (2015) <doi:10.1093/biomet/asv002>.

Implementation of Warnes & Raftery's MCGibbsit run-length and convergence diagnostic for a set of (not-necessarily independent) Markov Chain Monte Carlo (MCMC) samplers. It combines the quantile estimate error-bounding approach of the Raftery and Lewis MCMC run length diagnostic `gibbsit` with the between verses within chain approach of the Gelman and Rubin MCMC convergence diagnostic.

Regression models can be fitted for multiple outcomes simultaneously. This package computes estimates of parameters across fitted models and returns the matrix of asymptotic covariance. Various applications of this package, including CUPED (Controlled Experiments Utilizing Pre-Experiment Data), multiple comparison adjustment, are illustrated.

This is a implementation of design methods for multi-state reliability demonstration tests (MSRDT) with failure count data, which is associated with the work from the published paper "Multi-state Reliability Demonstration Tests" by Suiyao Chen et al. (2017) <doi:10.1080/08982112.2017.1314493>. It implements two types of MSRDT, multiple periods (MP) and multiple failure modes (MFM). For MP, two different scenarios with criteria on cumulative periods (Cum) or separate periods (Sep) are implemented respectively. It also provides the implementation of conventional design method, namely binomial tests for failure count data.

Estimate genetic linkage maps for markers on a single chromosome (or in a single linkage group) from pairwise recombination fractions or intermarker distances using weighted metric multidimensional scaling. The methods are suitable for autotetraploid as well as diploid populations. Options for assessing the fit to a known map are also provided. Methods are discussed in detail in Preedy and Hackett (2016) <doi:10.1007/s00122-016-2761-8>.

Multivariate distribution derived from a Bernoulli mixed model under a marginal approach, incorporating a non-normal random intercept whose distribution is assumed to follow a generalized log-gamma (GLG) specification under a particular parameter setting. Estimation is performed by maximizing the log-likelihood using numerical optimization techniques (Lizandra C. Fabio, Vanessa Barros, Cristian Lobos, Jalmar M. F. Carrasco, Marginal multivariate approach: A novel strategy for handling correlated binary outcomes, 2025, under submission).

In many agricultural, engineering, industrial, post-harvest and processing experiments, the number of factor level changes and hence the total number of changes is of serious concern as such experiments may consists of hard-to-change factors where it is physically very difficult to change levels of some factors or sometime such experiments may require normalization time to obtain adequate operating condition. For this reason, run orders that offer the minimum number of factor level changes and at the same time minimize the possible influence of systematic trend effects on the experimentation have been sought. Factorial designs with minimum changes in factors level may be preferred for such situations as these minimally changed run orders will minimize the cost of the experiments. This technique can be employed to any half replicate of two level factorial run order where the number of factors are greater than two. For method details see, Bhowmik, A., Varghese, E., Jaggi, S. and Varghese, C. (2017) <doi:10.1080/03610926.2016.1152490>. This package generates all possible minimally changed two-level half-fractional factorial designs for different experimental setups along with various statistical criteria to measure the performance of these designs through a user-friendly interface. It consist of the function minimal.2halfFFD() which launches the application interface.

Maximum likelihood Gaussian process modeling for univariate and multi-dimensional outputs with diagnostic plots following Santner et al (2003) <doi:10.1007/978-1-4757-3799-8>. Contact the maintainer for a package version that includes sensitivity analysis.

It implements a new procedure of variable selection in the context of redundancy between explanatory variables, which holds true with high dimensional data (Grimonprez et al. (2023) <doi:10.18637/jss.v106.i03>).

Generation of synthetic data from a real dataset using the combination of rank normal inverse transformation with the calculation of correlation matrix <doi:10.1055/a-2048-7692>. Completely artificial data may be generated through the use of Generalized Lambda Distribution and Generalized Poisson Distribution <doi:10.1201/9781420038040>. Quantitative, binary, ordinal categorical, and survival data may be simulated. Functionalities are offered to generate synthetic data sets according to user's needs.

The aim of the package is two-fold: (i) To implement the MMD method for attribution of individuals to sources using the Hamming distance between multilocus genotypes. (ii) To select informative genetic markers based on information theory concepts (entropy, mutual information and redundancy). The package implements the functions introduced by Perez-Reche, F. J., Rotariu, O., Lopes, B. S., Forbes, K. J. and Strachan, N. J. C. Mining whole genome sequence data to efficiently attribute individuals to source populations. Scientific Reports 10, 12124 (2020) <doi:10.1038/s41598-020-68740-6>. See more details and examples in the README file.

Quantitative RT-PCR data are analyzed using generalized linear mixed models based on lognormal-Poisson error distribution, fitted using MCMC. Control genes are not required but can be incorporated as Bayesian priors or, when template abundances correlate with conditions, as trackers of global effects (common to all genes). The package also implements a lognormal model for higher-abundance data and a "classic" model involving multi-gene normalization on a by-sample basis. Several plotting functions are included to extract and visualize results. The detailed tutorial is available here: <https://matzlab.weebly.com/uploads/7/6/2/2/76229469/mcmc.qpcr.tutorial.v1.2.4.pdf>.

Enables you to create accessible modal dialogs, with confidence and with minimal configuration.

Supports the generation of parallelogram, equilateral triangle, regular hexagon, isosceles trapezoid, Koch snowflake, hexaflake', Sierpinski triangle, Sierpinski carpet and Sierpinski trapezoid mazes via TurtleGraphics'. Mazes are generated by the recursive method: the domain is divided into sub-domains in which mazes are generated, then dividing lines with holes are drawn between them, see J. Buck, Recursive Division, <http://weblog.jamisbuck.org/2011/1/12/maze-generation-recursive-division-algorithm>.

This package provides a collection of functions to do some statistical inferences. On estimation, it has the function to get the method of moments estimates, the sampling interval. In terms of testing it has function of doing most powerful test.

Create tile grid maps, which are like choropleth maps except each region is represented with equal visual space.

An implementation of the iterative proportional fitting (IPFP), maximum likelihood, minimum chi-square and weighted least squares procedures for updating a N-dimensional array with respect to given target marginal distributions (which, in turn can be multidimensional). The package also provides an application of the IPFP to simulate multivariate Bernoulli distributions.

To determine the number of quantitative assays needed for a sample of data using pooled testing methods, which include mini-pooling (MP), MP with algorithm (MPA), and marker-assisted MPA (mMPA). To estimate the number of assays needed, the package also provides a tool to conduct Monte Carlo (MC) to simulate different orders in which the sample would be collected to form pools. Using MC avoids the dependence of the estimated number of assays on any specific ordering of the samples to form pools.

This package provides methods for extracting results from mixed-effect model objects fit with the lme4 package. Allows construction of prediction intervals efficiently from large scale linear and generalized linear mixed-effects models. This method draws from the simulation framework used in the Gelman and Hill (2007) textbook: Data Analysis Using Regression and Multilevel/Hierarchical Models.