Use multi-state splitting to apply Adaptive-Dynamic PCA (ADPCA) to data generated from a continuous-time multivariate industrial or natural process. Employ PCA-based dimension reduction to extract linear combinations of relevant features, reducing computational burdens. For a description of ADPCA, see <doi:10.1007/s00477-016-1246-2>, the 2016 paper from Kazor et al. The multi-state application of ADPCA is from a manuscript under current revision entitled "Multi-State Multivariate Statistical Process Control" by Odom, Newhart, Cath, and Hering, and is expected to appear in Q1 of 2018.

This package provides sample data sets that are used in statistics and data science courses at the Münster School of Business. The datasets refer to different business topics but also other domains, e.g. sports, traffic, etc.

It can be used to create/encode molecular "license-plates" from sequences and to also decode the "license-plates" back to sequences. While initially created for transfer RNA-derived small fragments (tRFs), this tool can be used for any genomic sequences including but not limited to: tRFs, microRNAs, etc. The detailed information can reference to Pliatsika V, Loher P, Telonis AG, Rigoutsos I (2016) <doi:10.1093/bioinformatics/btw194>. It can also be used to annotate tRFs. The detailed information can reference to Loher P, Telonis AG, Rigoutsos I (2017) <doi:10.1038/srep41184>.

This package provides a new approach to detect change points based on smoothing and multiple testing, which is for long data sequence modeled as piecewise constant functions plus stationary Gaussian noise, see Dan Cheng and Armin Schwartzman (2015) <arXiv:1504.06384>.

Estimates monotone regression and variance functions in a nonparametric model, based on Dette, Holger, Neumeyer, and Pilz (2006) <doi:10.3150/bj/1151525131>.

This package provides a tool for computing probabilities and other quantities that are relevant in selecting performance criteria for discrete trial training. The main function, miebl(), computes Bayesian and frequentist probabilities and bounds for each of n possible performance criterion choices when attempting to determine a student's true mastery level by counting their number of successful attempts at displaying learning among n trials. The reporting function miebl_re() takes output from miebl() and prepares it into a brief report for a specific criterion. miebl_cp() combines 2 to 5 distributions of true mastery level given performance criterion in one plot for comparison. Ramos (2025) <doi:10.1007/s40617-025-01058-9>.

Implementation of imputation techniques based on locally stationary wavelet time series forecasting methods from Wilson, R. E. et al. (2021) <doi:10.1007/s11222-021-09998-2>.

Estimates average treatment effects at the cutoff based on sharp regression discontinuity designs (RDD) and multiple imputation regression discontinuity designs (MIRDD). It provides diagnostic tools for RDD by comparing results with those from MIRDD, as proposed in Takahashi (2023) <doi:10.1080/03610918.2021.1960374>. The package includes datasets from Takahashi (2023) and Takahashi (2026) <doi:10.1016/j.softx.2026.102707>.

Converts results from the manymome package, presented in Cheung and Cheung (2024) <doi:10.3758/s13428-023-02224-z>, to publication-ready tables.

Calculate various indices, like Crude Migration Rate, different Gini indices or the Coefficient of Variation among others, to show the (un)equality of migration.

This package provides a new way to predict time series using the marginal distribution table in the absence of the significance of traditional models.

Mine metrics on common places on the web through the power of their APIs (application programming interfaces). It also helps make the data in a format that is easily used for a dashboard or other purposes. There is an associated dashboard template and tutorials that are underdevelopment that help you fully utilize metricminer'.

Most multilevel methodologies can only model macro-micro multilevel situations in an unbiased way, wherein group-level predictors (e.g., city temperature) are used to predict an individual-level outcome variable (e.g., citizen personality). In contrast, this R package enables researchers to model micro-macro situations, wherein individual-level (micro) predictors (and other group-level predictors) are used to predict a group-level (macro) outcome variable in an unbiased way.

Mixed model-based genome-wide association analysis that accommodate population membership information, variance adjustment, and correlated responses.

Estimation of k-Order time-varying Mixed Graphical Models and mixed VAR(p) models via elastic-net regularized neighborhood regression. For details see Haslbeck & Waldorp (2020) <doi:10.18637/jss.v093.i08>.

Computation of various Markovian models for categorical data including homogeneous Markov chains of any order, MTD models, Hidden Markov models, and Double Chain Markov Models.

This package provides a lavaan'-like syntax for OpenMx models. The syntax supports definition variables, bounds, and parameter transformations. This allows for latent growth curve models with person-specific measurement occasions, moderated nonlinear factor analysis and much more.

This package provides tools for predicting moonlight intensity on the ground based on the position of the moon, atmospheric conditions, and other factors. Provides functions to calculate moonlight intensity and related statistics for ecological and behavioral research, offering more accurate estimates than simple moon phase calculations. The underlying model is described in Smielak (2023) <doi:10.1007/s00265-022-03287-2>.

Monte Carlo simulation is a stochastic method computing trajectories of photons in media. Surface backscattering is performing calculations in semi-infinite media and summarizing photon flux leaving the surface. This simulation is modeling the optical measurement of diffuse reflectance using an incident light beam. The semi-infinite media is considered to have flat surface. Media, typically biological tissue, is described by four optical parameters: absorption coefficient, scattering coefficient, anisotropy factor, refractive index. The media is assumed to be homogeneous. Computational parameters of the simulation include: number of photons, radius of incident light beam, lowest photon energy threshold, intensity profile (halo) radius, spatial resolution of intensity profile. You can find more information and validation in the Open Access paper. Laszlo Baranyai (2020) <doi:10.1016/j.mex.2020.100958>.

Estimate Multidimensional Poverty Indices disaggregated by population subgroups based on the Alkire and Foster method (2011) <doi:10.1016/j.jpubeco.2010.11.006>. This includes the calculation of standard errors and confidence intervals. Other partial indices such as incidence, intensity and indicator-specific measures as well as intertemporal changes analysis can also be estimated. The standard errors and confidence intervals are calculated considering the complex survey design.

Providing the kubernetes-like class ManagedCloudProvider as a child class of the CloudProvider class in the DockerParallel package. The class is able to manage the cloud instance made by the non-kubernetes cloud service. For creating a provider for the non-kubernetes cloud service, the developer needs to define a reference class inherited from ManagedCloudProvider and define the method for the generics runDockerWorkerContainers(), getDockerWorkerStatus() and killDockerWorkerContainers(). For more information, please see the vignette in this package and <https://CRAN.R-project.org/package=DockerParallel>.

Mitteroecker & Gunz (2009) <doi:10.1007/s11692-009-9055-x> describe how geometric morphometric methods allow researchers to quantify the size and shape of physical biological structures. We provide tools to extend geometric morphometric principles to the study of non-physical structures, hormone profiles, as outlined in Ehrlich et al (2021) <doi:10.1002/ajpa.24514>. Easily transform daily measures into multivariate landmark-based data. Includes custom functions to apply multivariate methods for data exploration as well as hypothesis testing. Also includes shiny web app to streamline data exploration. Developed to study menstrual cycle hormones but functions have been generalized and should be applicable to any biomarker over any time period.

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>.

Download data from the Ada and Archibald MacLeish Field Station in Whately, MA. The Ada and Archibald MacLeish Field Station is a 260-acre patchwork of forest and farmland located in West Whately, MA that provides opportunities for faculty and students to pursue environmental research, outdoor education, and low-impact recreation (see <https://www.smith.edu/discover-smith/smith-action/sustainable-smith/macleish-field-station> for more information). This package contains weather data over several years, and spatial data on various man-made and natural structures.