This package provides a collection of helper functions for analyzing Second Primary Cancer data, including functions to reshape data, to calculate patient states and analyze cancer incidence.

Constructs and analyzes optimal row-column designs for mixed-level factorial experiments under square and rectangular field layouts. For square field layouts, the package implements direct common-factor constructions by first forming two component treatment arrays, one for each factor or super-factor, and then combining them through a symbolic cell-wise product following Gopinath, Parsad and Mandal (2018) <doi:10.1080/03610926.2017.1376091>. For rectangular field layouts, the package constructs designs by extracting a balanced principal block from a mixed-level block design, treating it as the principal column, taking the complete treatment set as the principal row, and generating the full row-column design by cyclic modular development. The package also includes repair utilities for improving disconnected or partially connected row-column designs through bounded treatment-swap searches while preserving the row-column layout structure. The package provides diagnostic tools for connectedness, orthogonal factorial structure, balance, estimability, and selected optimality criteria for row-column designs.

The tools for MicroRNA Set Enrichment Analysis can identify risk pathways(or prior gene sets) regulated by microRNA set in the context of microRNA expression data. (1) This package constructs a correlation profile of microRNA and pathways by the hypergeometric statistic test. The gene sets of pathways derived from the three public databases (Kyoto Encyclopedia of Genes and Genomes ('KEGG'); Reactome'; Biocarta') and the target gene sets of microRNA are provided by four databases('TarBaseV6.0'; mir2Disease'; miRecords'; miRTarBase';). (2) This package can quantify the change of correlation between microRNA for each pathway(or prior gene set) based on a microRNA expression data with cases and controls. (3) This package uses the weighted Kolmogorov-Smirnov statistic to calculate an enrichment score (ES) of a microRNA set that co-regulate to a pathway , which reflects the degree to which a given pathway is associated with the specific phenotype. (4) This package can provide the visualization of the results.

This package provides a simple in-memory, LRU cache that can be wrapped around any function to memoize it. The cache is keyed on a hash of the input data (using digest') or on pointer equivalence. Also includes a generic hashmap object that can key on any object type.

This package implements the multicorrelation coefficient for p-variables as described in Cankaya (2023). The package provides a numerically stable implementation using logarithmic transformations and a log-sum-exp approach to reduce numerical overflow and underflow when calculations involve a large number of variables.

Conducts and visualizes propensity score analysis for multilevel, or clustered data. Bryer & Pruzek (2011) <doi:10.1080/00273171.2011.636693>.

Generate central composite designs (CCD)with full as well as fractional factorial points (half replicate) and Box Behnken designs (BBD) with minimally changed run sequence.

The main functions perform mixed models analysis by least squares or REML by adding the function r() to formulas of lm() and glm(). A collection of text-book statistics for higher education is also included, e.g. modifications of the functions lm(), glm() and associated summaries from the package stats'.

This package provides functions used for graphing in multivariate contexts. These functions are designed to support produce reasonable graphs with minimal input of graphing parameters. The motivation for these functions was to support students learning multivariate concepts and R - there may be other functions and packages better-suited to practical data analysis. For details about the ellipse methods see Johnson and Wichern (2007, ISBN:9780131877153).

Easy implementation of the MABAC multi-criteria decision method, that was introduced by PamuÄ ar and Ä iroviÄ in the work entitled: "The selection of transport and handling resources in logistics centers using Multi-Attributive Border Approximation area Comparison (MABAC)" - <doi:10.1016/j.eswa.2014.11.057> - which aimed to choose implements for logistics centers. This package receives data, preferably in a spreadsheet, reads it and applies the mathematical algorithms inherent to the MABAC method to generate a ranking with the optimal solution according to the established criteria, weights and type of criteria. The data will be normalized, weighted by the weights, the border area will be determined, the distances to this border area will be calculated and finally a ranking with the optimal option will be generated.

Give access to MUI X Tree View components, which lets users navigate hierarchical lists of data with nested levels that can be expanded and collapsed.

Econometric analysis of multiple-input-multiple-output production technologies with ray-based input distance functions as suggested by Price and Henningsen (2023): "A Ray-Based Input Distance Function to Model Zero-Valued Output Quantities: Derivation and an Empirical Application", Journal of Productivity Analysis 60, p. 179-188, <doi:10.1007/s11123-023-00684-1>.

This package provides a metadata structure for clinical data analysis and reporting based on Analysis Data Model (ADaM) datasets. The package simplifies clinical analysis and reporting tool development by defining standardized inputs, outputs, and workflow. The package can be used to create analysis and reporting planning grid, mock table, and validated analysis and reporting results based on consistent inputs.

An R interface to version 0.3 of the ROPTLIB optimization library (see <https://www.math.fsu.edu/~whuang2/> for more information). Optimize real-valued functions over manifolds such as Stiefel, Grassmann, and symmetric positive definite matrices. For details see Martin et al. (2020) <doi:10.18637/jss.v093.i01>. Note that the optional ldr package used in some of this package's examples can be obtained from either the article <doi:10.18637/jss.v061.i03> or from the ldr package <https://cran.r-project.org/package=ldr>.

Multisite causal mediation analysis using the methods proposed by Qin and Hong (2017) <doi:10.3102/1076998617694879>, Qin, Hong, Deutsch, and Bein (2019) <doi:10.1111/rssa.12446>, and Qin, Deutsch, and Hong (2021) <doi:10.1002/pam.22268>. It enables causal mediation analysis in multisite trials, in which individuals are assigned to a treatment or a control group at each site. It allows for estimation and hypothesis testing for not only the population average but also the between-site variance of direct and indirect effects transmitted through one single mediator or two concurrent (conditionally independent) mediators. This strategy conveniently relaxes the assumption of no treatment-by-mediator interaction while greatly simplifying the outcome model specification without invoking strong distributional assumptions. This package also provides a function that can further incorporate a sample weight and a nonresponse weight for multisite causal mediation analysis in the presence of complex sample and survey designs and non-random nonresponse, to enhance both the internal validity and external validity. The package also provides a weighting-based balance checking function for assessing the remaining overt bias.

Learning, manipulation and evaluation of mixtures of truncated basis functions (MoTBFs), which include mixtures of polynomials (MOPs) and mixtures of truncated exponentials (MTEs). MoTBFs are a flexible framework for modelling hybrid Bayesian networks (I. Pérez-Bernabé, A. Salmerón, H. Langseth (2015) <doi:10.1007/978-3-319-20807-7_36>; H. Langseth, T.D. Nielsen, I. Pérez-Bernabé, A. Salmerón (2014) <doi:10.1016/j.ijar.2013.09.012>; I. Pérez-Bernabé, A. Fernández, R. Rumà , A. Salmerón (2016) <doi:10.1007/s10618-015-0429-7>). The package provides functionality for learning univariate, multivariate and conditional densities, with the possibility of incorporating prior knowledge. Structural learning of hybrid Bayesian networks is also provided. A set of useful tools is provided, including plotting, printing and likelihood evaluation. This package makes use of S3 objects, with two new classes called motbf and jointmotbf'.

Quantify exposure-outcome causal effects with adjustment for multiple biases. The functions can simultaneously adjust for any combination of uncontrolled confounding, exposure/outcome misclassification, and selection bias. The underlying method generalizes the combination of inverse probability of selection weighting with predictive value weighting. Simultaneous multi-bias analysis can be used to enhance the validity and transparency of real-world evidence obtained from observational, longitudinal studies. Based on the work from Paul Brendel, Aracelis Torres, and Onyebuchi Arah (2023) <doi:10.1093/ije/dyad001>.

Calculates the Most Probable Number (MPN) to quantify the concentration (density) of microbes in serial dilutions of a laboratory sample (described in Jarvis, 2010 <doi:10.1111/j.1365-2672.2010.04792.x>). Also calculates the Aerobic Plate Count (APC) for similar microbial enumeration experiments.

This package provides a framework package aimed to provide standardized computational environment for specialist work via object classes to represent the data coded by samples, taxa and segments (i.e. subpopulations, repeated measures). It supports easy processing of the data along with cross tabulation and relational data tables for samples and taxa. An object of class `mefa is a project specific compendium of the data and can be easily used in further analyses. Methods are provided for extraction, aggregation, conversion, plotting, summary and reporting of `mefa objects. Reports can be generated in plain text or LaTeX format. Vignette contains worked examples.

This package provides functionality to generate compound optimal designs for targeting the multiple experimental objectives directly, ensuring that the full set of research questions is answered as economically as possible. Designs can be found using point or coordinate exchange algorithms combining estimation, inference and lack-of-fit criteria that account for model inadequacy. Details and examples are given by Koutra et al. (2024) <doi:10.48550/arXiv.2412.17158>.

Convenience functions for multivariate MCMC using univariate samplers including: slice sampler with stepout and shrinkage (Neal (2003) <DOI:10.1214/aos/1056562461>), adaptive rejection sampler (Gilks and Wild (1992) <DOI:10.2307/2347565>), adaptive rejection Metropolis (Gilks et al (1995) <DOI:10.2307/2986138>), and univariate Metropolis with Gaussian proposal.

This package provides a method to impute the missingness in categorical data. Details see the paper <doi:10.4310/SII.2020.v13.n1.a2>.

This package implements multi-factor curve analysis for grouped data in R', replicating and extending the functionality of the the Stata ado mfcurve (Krähmer, 2023) <https://ideas.repec.org/c/boc/bocode/s459224.html>. Related to the idea of specification curve analysis (Simonsohn, Simmons, and Nelson, 2020) <doi:10.1038/s41562-020-0912-z>. Includes data preprocessing, statistical testing, and visualization of results with confidence intervals.

Datasets and wrapper functions for tidyverse-friendly introductory linear regression, used in "Statistical Inference via Data Science: A ModernDive into R and the Tidyverse" available at <https://moderndive.com/>.