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 a complement to all editions of *Modern Data Science with R* (ISBN: 978-0367191498, publisher URL: <https://www.routledge.com/Modern-Data-Science-with-R/Baumer-Kaplan-Horton/p/book/9780367191498>). This package contains data and code to complete exercises and reproduce examples from the text. It also facilitates connections to the SQL database server used in the book. All editions of the book are supported by this package.

Tests of comparison of two or more survival curves. Allows for comparison of more than two survival curves whether the proportional hazards hypothesis is verified or not.

Generate a monochrome palette from a starting colour for a specified number of colours. The package can also be used to display colour palettes in the plot window, with or without hex codes and colour labels.

Compute case-wise and cluster-wise derivative for mixed effects models with respect to fixed effects parameter, random effect (co)variances, and residual variance. This material is partially based on work supported by the National Science Foundation under Grant Number 1460719.

This package provides a Shiny application to estimate the sample size required for a metabolomic experiment to achieve a desired statistical power. Estimation is possible with or without available data from a pilot study.

Assessment of inconsistency in meta-analysis by calculating the Decision Inconsistency index (DI) and the Across-Studies Inconsistency (ASI) index. These indices quantify inconsistency taking into account outcome-level decision thresholds.

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.

Grey model is commonly used in time series forecasting when statistical assumptions are violated with a limited number of data points. The minimum number of data points required to fit a grey model is four observations. This package fits Grey model of First order and One Variable, i.e., GM (1,1) for multivariate time series data and returns the parameters of the model, model evaluation criteria and h-step ahead forecast values for each of the time series variables. For method details see, Akay, D. and Atak, M. (2007) <DOI:10.1016/j.energy.2006.11.014>, Hsu, L. and Wang, C. (2007).<DOI:10.1016/j.techfore.2006.02.005>.

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>.

Compute bootstrap confidence intervals for the adjusted Schnabel and Schumacher-Eschmeyer multi-visit mark-recapture estimators based on Dettloff (2023) <doi:10.1016/j.fishres.2023.106756>.

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.

An R-Shiny module containing a "markdownInput". This input allows the user to write some markdown code and to preview the result. This input has been inspired by the "comment" window of <https://github.com/>.

An easy-to-use IMAP client that provides tools for message searching, selective fetching of message attributes, mailbox management, attachment extraction, and several other IMAP features, paving the way for e-mail data analysis in R.

This package provides the users with the ability to quickly create linked micromap plots for a collection of geographic areas. Linked micromap plots are visualizations of geo-referenced data that link statistical graphics to an organized series of small maps or graphic images. The Help description contains examples of how to use the micromapST function. Contained in this package are border group datasets to support creating linked micromap plots for the 50 U.S. states and District of Columbia (51 areas), the U. S. 20 Seer Registries, the 105 counties in the state of Kansas, the 62 counties of New York, the 24 counties of Maryland, the 29 counties of Utah, the 32 administrative areas in China, the 218 administrative areas in the UK and Ireland (for testing only), the 25 districts in the city of Seoul South Korea, and the 52 counties on the Africa continent. A border group dataset contains the boundaries related to the data level areas, a second layer boundaries, a top or third layer boundary, a parameter list of run options, and a cross indexing table between area names, abbreviations, numeric identification and alias matching strings for the specific geographic area. By specifying a border group, the package create linked micromap plots for any geographic region. The user can create and provide their own border group dataset for any area beyond the areas contained within the package with the BuildBorderGroup function. In April of 2022, it was announced that maptools', rgdal', and rgeos R packages would be retired in middle to end of 2023 and removed from the CRAN libraries. The BuildBorderGroup function was dependent on these packages. micromapST functions were not impacted by the retired R packages. Upgrading of BuildBorderGroup function was completed and released with version 3.0.0 on August 10, 2023 using the sf R package. References: Carr and Pickle, Chapman and Hall/CRC, Visualizing Data Patterns with Micromaps, CRC Press, 2010. Pickle, Pearson, and Carr (2015), micromapST: Exploring and Communicating Geospatial Patterns in US State Data., Journal of Statistical Software, 63(3), 1-25., <https://www.jstatsoft.org/v63/i03/>. Copyrighted 2013, 2014, 2015, 2016, 2022, 2023, 2024, and 2025 by Carr, Pearson and Pickle.

This package provides an extension of the shadow-test approach to computerized adaptive testing (CAT) implemented in the TestDesign package for the assessment framework involving multiple tests administered periodically throughout the year. This framework is referred to as the Multiple Administrations Adaptive Testing (MAAT) and supports multiple item pools vertically scaled and multiple phases (stages) of CAT within each test. Between phases and tests, transitioning from one item pool (and associated constraints) to another is allowed as deemed necessary to enhance the quality of measurement.

The following methods are implemented to evaluate how sensitive the results of a meta-analysis are to potential bias in meta-analysis and to support Schwarzer et al. (2015) <DOI:10.1007/978-3-319-21416-0>, Chapter 5 Small-Study Effects in Meta-Analysis': - Copas selection model described in Copas & Shi (2001) <DOI:10.1177/096228020101000402>; - limit meta-analysis by Rücker et al. (2011) <DOI:10.1093/biostatistics/kxq046>; - upper bound for outcome reporting bias by Copas & Jackson (2004) <DOI:10.1111/j.0006-341X.2004.00161.x>; - imputation methods for missing binary data by Gamble & Hollis (2005) <DOI:10.1016/j.jclinepi.2004.09.013> and Higgins et al. (2008) <DOI:10.1177/1740774508091600>; - LFK index test and Doi plot by Furuya-Kanamori et al. (2018) <DOI:10.1097/XEB.0000000000000141>.

This package provides tools for computing Monte Carlo standard errors (MCSE) in Markov chain Monte Carlo (MCMC) settings (survey in <doi:10.1201/b10905>, Chapter 7). MCSE computation for expectation and quantile estimators is supported as well as multivariate estimations. The package also provides functions for computing effective sample size and for plotting Monte Carlo estimates versus sample size.

An implementation of a taxonomy of models of restricted diffusion in biological tissues parametrized by the tissue geometry (axis, diameter, density, etc.). This is primarily used in the context of diffusion magnetic resonance (MR) imaging to model the MR signal attenuation in the presence of diffusion gradients. The goal is to provide tools to simulate the MR signal attenuation predicted by these models under different experimental conditions. The package feeds a companion shiny app available at <https://midi-pastrami.apps.math.cnrs.fr> that serves as a graphical interface to the models and tools provided by the package. Models currently available are the ones in Neuman (1974) <doi:10.1063/1.1680931>, Van Gelderen et al. (1994) <doi:10.1006/jmrb.1994.1038>, Stanisz et al. (1997) <doi:10.1002/mrm.1910370115>, Soderman & Jonsson (1995) <doi:10.1006/jmra.1995.0014> and Callaghan (1995) <doi:10.1006/jmra.1995.1055>.

Procedures to simulate, estimate and diagnose MGARCH processes of BEKK and multivariate GJR (bivariate asymmetric GARCH model) specification.

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>.

Facilitate tasks typically encountered during metabolomics data analysis including data import, filtering, missing value imputation (Stacklies et al. (2007) <doi:10.1093/bioinformatics/btm069>, Stekhoven et al. (2012) <doi:10.1093/bioinformatics/btr597>, Tibshirani et al. (2017) <doi:10.18129/B9.BIOC.IMPUTE>, Troyanskaya et al. (2001) <doi:10.1093/bioinformatics/17.6.520>), normalization (Bolstad et al. (2003) <doi:10.1093/bioinformatics/19.2.185>, Dieterle et al. (2006) <doi:10.1021/ac051632c>, Zhao et al. (2020) <doi:10.1038/s41598-020-72664-6>) transformation, centering and scaling (Van Den Berg et al. (2006) <doi:10.1186/1471-2164-7-142>) as well as statistical tests and plotting. metamorphr introduces a tidy (Wickham et al. (2019) <doi:10.21105/joss.01686>) format for metabolomics data and is designed to make it easier to build elaborate analysis workflows and to integrate them with tidyverse packages including dplyr and ggplot2'.

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 implements model-robust standardization for cluster-randomized trials (CRTs). Provides functions that standardize user-specified regression models to estimate marginal treatment effects. The targets include the cluster-average and individual-average treatment effects, with utilities for variance estimation and example simulation datasets. Methods are described in Li, Tong, Fang, Cheng, Kahan, and Wang (2025) <doi:10.1002/sim.70270>.