Pseudo-random number generation for 11 multivariate distributions: Normal, t, Uniform, Bernoulli, Hypergeometric, Beta (Dirichlet), Multinomial, Dirichlet-Multinomial, Laplace, Wishart, and Inverted Wishart. The details of the method are explained in Demirtas (2004) <DOI:10.22237/jmasm/1099268340>.

Flexible and informed regression with Multiple Change Points. mcp can infer change points in means, variances, autocorrelation structure, and any combination of these, as well as the parameters of the segments in between. All parameters are estimated with uncertainty and prediction intervals are supported - also near the change points. mcp supports hypothesis testing via Savage-Dickey density ratio, posterior contrasts, and cross-validation. mcp is described in Lindeløv (submitted) <doi:10.31219/osf.io/fzqxv> and generalizes the approach described in Carlin, Gelfand, & Smith (1992) <doi:10.2307/2347570> and Stephens (1994) <doi:10.2307/2986119>.

This package provides a framework which should improve reproducibility and transparency in data processing. It provides functionality such as automatic meta data creation and management, rudimentary quality management, data caching, work-flow management and data aggregation. * The title is a wish not a promise. By no means we expect this package to deliver everything what is needed to achieve full reproducibility and transparency, but we believe that it supports efforts in this direction.

This package provides functions for creating designs for mixture experiments, making ternary contour plots, and making mixture effect plots.

Computes densities, probabilities, and random deviates of the Matrix Normal (Pocuca et al. (2019) <doi:10.48550/arXiv.1910.02859>). Also includes simple but useful matrix functions. See the vignette for more information.

This package implements the MST-kNN clustering algorithm which was proposed by Inostroza-Ponta, M. (2008) <https://trove.nla.gov.au/work/28729389?selectedversion=NBD44634158>.

Fits the neighboring models of a fitted structural equation model and assesses the model uncertainty of the fitted model based on BIC posterior probabilities, using the method presented in Wu, Cheung, and Leung (2020) <doi:10.1080/00273171.2019.1574546>.

This package provides functions and datasets from Hilbe, J.M., and Robinson, A.P. 2013. Methods of Statistical Model Estimation. Chapman & Hall / CRC.

Estimates the multivariate skew-t and nested models, as described in the articles Liseo, B., Parisi, A. (2013). Bayesian inference for the multivariate skew-normal model: a population Monte Carlo approach. Comput. Statist. Data Anal. <doi:10.1016/j.csda.2013.02.007> and in Parisi, A., Liseo, B. (2017). Objective Bayesian analysis for the multivariate skew-t model. Statistical Methods & Applications <doi: 10.1007/s10260-017-0404-0>.

Metadynamics is a state of the art biomolecular simulation technique. Plumed Tribello, G.A. et al. (2014) <doi:10.1016/j.cpc.2013.09.018> program makes it possible to perform metadynamics using various simulation codes. The results of metadynamics done in Plumed can be analyzed by metadynminer'. The package metadynminer reads 1D and 2D metadynamics hills files from Plumed package. It uses a fast algorithm by Hosek, P. and Spiwok, V. (2016) <doi:10.1016/j.cpc.2015.08.037> to calculate a free energy surface from hills. Minima can be located and plotted on the free energy surface. Transition states can be analyzed by Nudged Elastic Band method by Henkelman, G. and Jonsson, H. (2000) <doi:10.1063/1.1323224>. Free energy surfaces, minima and transition paths can be plotted to produce publication quality images.

This package provides a container for data used by the mapindia package. The data used by mapindia has been extracted into this package so that the file size of the mapindia package can be reduced considerably. The data in this package will be updated when latest data is available.

Process OpenPose human body keypoints for computer vision, including data structuring and user-defined linear transformations for standardization. It optionally, includes metadata extraction from filenames in the UCLA NewsScape archive.

Fits Semiparametric Promotion Time Cure Models, taking into account (using a corrected score approach or the SIMEX algorithm) or not the measurement error in the covariates, using a backfitting approach to maximize the likelihood.

Multivariate Adaptive Regression Spline (MARS) based Support Vector Regression (SVR) hybrid model is combined Machine learning hybrid approach which selects important variables using MARS and then fits SVR on the extracted important variables.

Generate a stream of pseudo-random numbers generated using the MLS Junk Generator algorithm. Functions exist to generate single pseudo-random numbers as well as a vector, data frame, or matrix of pseudo-random numbers.

Implementation of Matched Wake Analysis (mwa) for studying causal relationships in spatiotemporal event data, introduced by Schutte and Donnay (2014) <doi:10.1016/j.polgeo.2014.03.001>.

Suite of interactive functions and helpers for selecting and editing geospatial data.

Multivariate estimation and testing, currently a package for testing parametric data. To deal with parametric data, various multivariate normality tests and outlier detection are performed and visualized using the ggplot2 package. Homogeneity tests for covariance matrices are also possible, as well as the Hotelling's T-square test and the multivariate analysis of variance test. We are exploring additional tests and visualization techniques, such as profile analysis and randomized complete block design, to be made available in the future and making them easily accessible to users.

An implementation of several machine learning algorithms for multivariate time series. The package includes functions allowing the execution of clustering, classification or outlier detection methods, among others. It also incorporates a collection of multivariate time series datasets which can be used to analyse the performance of new proposed algorithms. Some of these datasets are stored in GitHub data packages ueadata1 to ueadata8'. To access these data packages, run install.packages(c('ueadata1', ueadata2', ueadata3', ueadata4', ueadata5', ueadata6', ueadata7', ueadata8'), repos='<https://anloor7.github.io/drat/>')'. The installation takes a couple of minutes but we strongly encourage the users to do it if they want to have available all datasets of mlmts. Practitioners from a broad variety of fields could benefit from the general framework provided by mlmts'.

This package provides functions to calculate the minimum and maximum possible values of Cronbach's alpha when item-level missing data are present. Cronbach's alpha (Cronbach, 1951 <doi:10.1007/BF02310555>) is one of the most widely used measures of internal consistency in the social, behavioral, and medical sciences (Bland & Altman, 1997 <doi:10.1136/bmj.314.7080.572>; Tavakol & Dennick, 2011 <doi:10.5116/ijme.4dfb.8dfd>). However, conventional implementations assume complete data, and listwise deletion is often applied when missingness occurs, which can lead to biased or overly optimistic reliability estimates (Enders, 2003 <doi:10.1037/1082-989X.8.3.322>). This package implements computational strategies including enumeration, Monte Carlo sampling, and optimization algorithms (e.g., Genetic Algorithm, Differential Evolution, Sequential Least Squares Programming) to obtain sharp lower and upper bounds of Cronbach's alpha under arbitrary missing data patterns. The approach is motivated by Manski's partial identification framework and pessimistic bounding ideas from optimization literature.

Models and predicts multiple output features in single random forest considering the linear relation among the output features, see details in Rahman et al (2017)<doi:10.1093/bioinformatics/btw765>.

The 1001 time series from the M-competition (Makridakis et al. 1982) <DOI:10.1002/for.3980010202> and the 3003 time series from the IJF-M3 competition (Makridakis and Hibon, 2000) <DOI:10.1016/S0169-2070(00)00057-1>.

This package contains the datasets for use with the book Salvan, Sartori and Pace (2020, ISBN:978-88-470-4002-1) "Modelli Lineari Generalizzati".

In the omics data association studies, it is common to conduct the p-value corrections to control the false significance. Beyond the P-value corrections, E-value is recently studied to facilitate multiple testing correction based on V. Vovk and R. Wang (2021) <doi:10.1214/20-AOS2020>. This package provides E-value calculation for DNA methylation data and RNA-seq data. Currently, five data formats are supported: DNA methylation levels using DMR detection tools (BiSeq, DMRfinder, MethylKit, Metilene and other DNA methylation tools) and RNA-seq data. The relevant references are listed below: Katja Hebestreit and Hans-Ulrich Klein (2022) <doi:10.18129/B9.bioc.BiSeq>; Altuna Akalin et.al (2012) <doi:10.18129/B9.bioc.methylKit>.