Nonparametric estimation and inference for natural direct and indirect effects by Chan, Imai, Yam and Zhang (2016) <arXiv:1601.03501>.

Clustering in metagenomics is the process of grouping of microbial contigs in species specific bins. This package contains functions that extract genomic features from metagenome data, find the number of clusters for that given data and find the best clustering algorithm for binning.

For a given test market find the best control markets using time series matching and analyze the impact of an intervention. The intervention could be a marketing event or some other local business tactic that is being tested. The workflow implemented in the Market Matching package utilizes dynamic time warping (the dtw package) to do the matching and the CausalImpact package to analyze the causal impact. In fact, this package can be considered a "workflow wrapper" for those two packages. In addition, if you don't have a chosen set of test markets to match, the Market Matching package can provide suggested test/control market pairs and pseudo prospective power analysis (measuring causal impact at fake interventions).

Perform calculations for the WHO International Reference Reagents for the microbiome. Using strain, species or genera abundance tables generated through analysis of 16S ribosomal RNA sequencing or shotgun sequencing which included a reference reagent. This package will calculate measures of sensitivity, False positive relative abundance, diversity, and similarity based on mean average abundances with respect to the reference reagent.

Basic functions for microbial sequence data analysis. The idea is to use generic R data structures as much as possible, making R data wrangling possible also for sequence data.

This package performs stability analysis of multi-environment trial data using parametric and non-parametric methods. Parametric methods includes Additive Main Effects and Multiplicative Interaction (AMMI) analysis by Gauch (2013) <doi:10.2135/cropsci2013.04.0241>, Ecovalence by Wricke (1965), Genotype plus Genotype-Environment (GGE) biplot analysis by Yan & Kang (2003) <doi:10.1201/9781420040371>, geometric adaptability index by Mohammadi & Amri (2008) <doi:10.1007/s10681-007-9600-6>, joint regression analysis by Eberhart & Russel (1966) <doi:10.2135/cropsci1966.0011183X000600010011x>, genotypic confidence index by Annicchiarico (1992), Murakami & Cruz's (2004) method, power law residuals (POLAR) statistics by Doring et al. (2015) <doi:10.1016/j.fcr.2015.08.005>, scale-adjusted coefficient of variation by Doring & Reckling (2018) <doi:10.1016/j.eja.2018.06.007>, stability variance by Shukla (1972) <doi:10.1038/hdy.1972.87>, weighted average of absolute scores by Olivoto et al. (2019a) <doi:10.2134/agronj2019.03.0220>, and multi-trait stability index by Olivoto et al. (2019b) <doi:10.2134/agronj2019.03.0221>. Non-parametric methods includes superiority index by Lin & Binns (1988) <doi:10.4141/cjps88-018>, nonparametric measures of phenotypic stability by Huehn (1990) <doi:10.1007/BF00024241>, TOP third statistic by Fox et al. (1990) <doi:10.1007/BF00040364>. Functions for computing biometrical analysis such as path analysis, canonical correlation, partial correlation, clustering analysis, and tools for inspecting, manipulating, summarizing and plotting typical multi-environment trial data are also provided.

This package provides a general framework for clinical trial simulations based on the Clinical Scenario Evaluation (CSE) approach. The package supports a broad class of data models (including clinical trials with continuous, binary, survival-type and count-type endpoints as well as multivariate outcomes that are based on combinations of different endpoints), analysis strategies and commonly used evaluation criteria.

This package implements random number generation, plotting, and estimation algorithms for the two-parameter one-sided and two-sided M-Wright (Mainardi-Wright) family. The M-Wright distributions naturally generalize the widely used one-sided (Airy and half-normal or half-Gaussian) and symmetric (Airy and Gaussian or normal) models. These are widely studied in time-fractional differential equations. References: Cahoy and Minkabo (2017) <doi:10.3233/MAS-170388>; Cahoy (2012) <doi:10.1007/s00180-011-0269-x>; Cahoy (2012) <doi:10.1080/03610926.2010.543299>; Cahoy (2011); Mainardi, Mura, and Pagnini (2010) <doi:10.1155/2010/104505>.

Fits multivariate (Brownian Motion, Early Burst, ACDC, Ornstein-Uhlenbeck and Shifts) models of continuous traits evolution on trees and time series. mvMORPH also proposes high-dimensional multivariate comparative tools (linear models using Generalized Least Squares and multivariate tests) based on penalized likelihood. See Clavel et al. (2015) <DOI:10.1111/2041-210X.12420>, Clavel et al. (2019) <DOI:10.1093/sysbio/syy045>, and Clavel & Morlon (2020) <DOI:10.1093/sysbio/syaa010>.

Meta-package for statistical and machine learning with a unified interface for model fitting, prediction, performance assessment, and presentation of results. Approaches for model fitting and prediction of numerical, categorical, or censored time-to-event outcomes include traditional regression models, regularization methods, tree-based methods, support vector machines, neural networks, ensembles, data preprocessing, filtering, and model tuning and selection. Performance metrics are provided for model assessment and can be estimated with independent test sets, split sampling, cross-validation, or bootstrap resampling. Resample estimation can be executed in parallel for faster processing and nested in cases of model tuning and selection. Modeling results can be summarized with descriptive statistics; calibration curves; variable importance; partial dependence plots; confusion matrices; and ROC, lift, and other performance curves.

This package provides a likelihood-based approach to modeling species distributions using presence-only data. In contrast to the popular software program MAXENT, this approach yields estimates of the probability of occurrence, which is a natural descriptor of a species distribution.

Several functions can be used to analyze neuroimaging data using multivariate methods based on the msma package. The functions used in the book entitled "Multivariate Analysis for Neuroimaging Data" (2021, ISBN-13: 978-0367255329) are contained.

Create dummy variables from categorical data. This package can convert categorical data (factor and ordered) into dummy variables and handle multiple columns simultaneously. This package enables to select whether a dummy variable for base group is included (for principal component analysis/factor analysis) or excluded (for regression analysis) by an option. makedummies function accepts data.frame', matrix', and tbl (tibble) class (by tibble package). matrix class data is automatically converted to data.frame class.

Determines single or multiple modes (most frequent values). Checks if missing values make this impossible, and returns NA in this case. Dependency-free source code. See Franzese and Iuliano (2019) <doi:10.1016/B978-0-12-809633-8.20354-3>.

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>.

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 support data cleaning, evaluation, and description, developed for integration with Maelstrom Research software tools. madshapR provides functions primarily to evaluate and manipulate datasets and data dictionaries in preparation for data harmonization with the package Rmonize and to facilitate integration and transfer between RStudio servers and secure Opal environments. madshapR functions can be used independently but are optimized in conjunction with â Rmonizeâ functions for streamlined and coherent harmonization processing.

The number of biological databases is growing rapidly, but different databases use different IDs to refer to the same biological entity. The inconsistency in IDs impedes the integration of various types of biological data. To resolve the problem, we developed MantaID', a data-driven, machine-learning based approach that automates identifying IDs on a large scale. The MantaID model's prediction accuracy was proven to be 99%, and it correctly and effectively predicted 100,000 ID entries within two minutes. MantaID supports the discovery and exploitation of ID patterns from large quantities of databases. (e.g., up to 542 biological databases). An easy-to-use freely available open-source software R package, a user-friendly web application, and API were also developed for MantaID to improve applicability. To our knowledge, MantaID is the first tool that enables an automatic, quick, accurate, and comprehensive identification of large quantities of IDs, and can therefore be used as a starting point to facilitate the complex assimilation and aggregation of biological data across diverse databases.

Multi-Dimensional Analysis (MDA) is an adaptation of factor analysis developed by Douglas Biber (1992) <doi:10.1007/BF00136979>. Its most common use is to describe language as it varies by genre, register, and use. This package contains functions for carrying out the calculations needed to describe and plot MDA results: dimension scores, dimension means, and factor loadings.

This package provides functionality for estimating cross-sectional network structures representing partial correlations while accounting for missing data. Networks are estimated via neighborhood selection or regularization, with model selection guided by information criteria. Missing data can be handled primarily via multiple imputation or a maximum likelihood-based approach, as demonstrated by Nehler and Schultze (2025a) <doi:10.31234/osf.io/qpj35> and Nehler and Schultze (2025b) <doi:10.1080/00273171.2025.2503833>. Deletion-based approaches are also available but play a secondary role.

Allows practitioners and researchers a wholesale approach for deriving magnitude-based inferences from raw data. A major goal of mbir is to programmatically detect appropriate statistical tests to run in lieu of relying on practitioners to determine correct stepwise procedures independently.

Extract, transform and load MITRE standards. This package gives you an approach to cybersecurity data sets. All data sets are build on runtime downloading raw data from MITRE public services. MITRE <https://www.mitre.org/> is a government-funded research organization based in Bedford and McLean. Current version includes most used standards as data frames. It also provide a list of nodes and edges with all relationships.

This package provides a framework to factorise electromyography (EMG) data. Tools are provided for raw data pre-processing, non negative matrix factorisation, classification of factorised data and plotting of obtained outcomes. In particular, reading from ASCII files is supported, along with wide-used filtering approaches to process EMG data. All steps include one or more sensible defaults that aim at simplifying the workflow. Yet, all functions are largely tunable at need. Example data sets are included.

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