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 provides a comprehensive suite for assessing multivariate normality using six statistical tests (Mardia, Henzeâ Zirkler, Henzeâ Wagner, Royston, Doornikâ Hansen, Energy). Also includes univariate diagnostics, bivariate density visualization, robust outlier detection, power transformations (e.g., Boxâ Cox, Yeoâ Johnson), and imputation strategies ("mean", "median", "mice") for handling missing data. Bootstrap resampling is supported for selected tests to improve p-value accuracy in small samples. Diagnostic plots are available via both ggplot2 and interactive plotly visualizations. See Korkmaz et al. (2014) <https://journal.r-project.org/archive/2014-2/korkmaz-goksuluk-zararsiz.pdf>.

Various reliability analysis methods for rare event inference (computing failure probability and quantile from model/function outputs).

Developed for model-based clustering using the finite mixtures of skewed sub-Gaussian stable distributions developed by Teimouri (2022) <arXiv:2205.14067> and estimating parameters of the symmetric stable distribution within the Bayesian framework.

An R implementation of the python program Metabolomics Peak Analysis Computational Tool ('MPACT') (Robert M. Samples, Sara P. Puckett, and Marcy J. Balunas (2023) <doi:10.1021/acs.analchem.2c04632>). Filters in the package serve to address common errors in tandem mass spectrometry preprocessing, including: (1) isotopic patterns that are incorrectly split during preprocessing, (2) features present in solvent blanks due to carryover between samples, (3) features whose abundance is greater than user-defined abundance threshold in a specific group of samples, for example media blanks, (4) ions that are inconsistent between technical replicates, and (5) in-source fragment ions created during ionization before fragmentation in the tandem mass spectrometry workflow.

Mass measurement corrections and uncertainties using calibration data, as recommended by EURAMET's guideline No. 18 (2015) ISBN:978-3-942992-40-4 . The package provides classes, functions, and methods for storing information contained in calibration certificates and converting balance readings to both conventional mass and real mass. For the latter, the Magnitude of the Air Buoyancy Correction factor employs models (such as the CIMP-2007 formula revised by Picard, Davis, Gläser, and Fujii (2008) <doi:10.1088/0026-1394/45/2/004>) to estimate the local air density using measured environmental conditions.

Topological data analysis (TDA) is a method of data analysis that uses techniques from topology to analyze high-dimensional data. Here we implement Mapper, an algorithm from this area developed by Singh, Mémoli and Carlsson (2007) which generalizes the concept of a Reeb graph <https://en.wikipedia.org/wiki/Reeb_graph>.

Enables you to create accessible modal dialogs, with confidence and with minimal configuration.

This package provides a computational method developed for model-based analysis of alternative polyadenylation (APA) using 3 end-linked reads. It accurately assigns 3 RNA-seq reads to polyA sites through statistical modeling, and generates multiple statistics for APA analysis. Please also see Li WV, Zheng D, Wang R, Tian B (2021) <doi:10.1186/s13059-021-02429-5>.

There is an increasing interest in investigating how the compositions of microbial communities are associated with human health and disease. In this package, we present a novel global testing method called aMiSPU, that is highly adaptive and thus high powered across various scenarios, alleviating the issue with the choice of a phylogenetic distance. Our simulations and real data analysis demonstrated that aMiSPU test was often more powerful than several competing methods while correctly controlling type I error rates.

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>).

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.

The Markowitz criterion is a multicriteria decision-making method that stands out in risk and uncertainty analysis in contexts where probabilities are known. This approach represents an evolution of Pascal's criterion by incorporating the dimension of variability. In this framework, the expected value reflects the anticipated return, while the standard deviation serves as a measure of risk. The markowitz package provides a practical and accessible tool for implementing this method, enabling researchers and professionals to perform analyses without complex calculations. Thus, the package facilitates the application of the Markowitz criterion. More details on the method can be found in Octave Jokung-Nguéna (2001, ISBN 2100055372).

Calculate different metrics based on aquatic macroinvertebrate density data (individuals per square meter) to assess water quality (Prat N et al. 2009).

Visualise admixture as pie charts on a projected map, admixture as traditional structure barplots or facet barplots, and scatter plots from genotype principal components analysis. A shiny app allows users to create admixture maps interactively. Jenkins TL (2024) <doi:10.1111/1755-0998.13943>.

This package provides a collection of function to solve multiple criteria optimization problems using genetic algorithms (NSGA-II). Also included is a collection of test functions.

Wrapper for minepy implementation of Maximal Information-based Nonparametric Exploration statistics (MIC and MINE family). Detailed information of the ANSI C implementation of minepy can be found at <http://minepy.readthedocs.io/en/latest>.

This package provides a comprehensive framework for calculating unbiased distances in datasets containing mixed-type variables (numerical and categorical). The package implements a general formulation that ensures multivariate additivity and commensurability, meaning that variables contribute equally to the overall distance regardless of their type, scale, or distribution. Supports multiple distance measures including Gower's distance, Euclidean distance, Manhattan distance, and various categorical variable distances such as simple matching, Eskin, occurrence frequency, and association-based distances. Provides tools for variable scaling (standard deviation, range, robust range, and principal component scaling), and handles both independent and association-based category dissimilarities. Implements methods to correct for biases that typically arise from different variable types, distributions, and number of categories. Particularly useful for cluster analysis, data visualization, and other distance-based methods when working with mixed data. Methods based on van de Velden et al. (2024) <doi:10.48550/arXiv.2411.00429> "Unbiased mixed variables distance".

Development, simulation testing, and implementation of management procedures for fisheries (see Carruthers & Hordyk (2018) <doi:10.1111/2041-210X.13081>).

This package performs matrix skew-t parameter estimation, Gallaugher and McNicholas (2017) <doi: 10.1002/sta4.143>.

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>.

Quickly make tables of descriptive statistics (i.e., counts, means, confidence intervals) for continuous variables. This package is designed to work in a Tidyverse pipeline, and consideration has been given to get results from R to Microsoft Word ® with minimal pain.

Matching algorithm based on network-flow structure. Users are able to modify the emphasis on three different optimization goals: two different distance measures and the number of treated units left unmatched. The method is proposed by Pimentel and Kelz (2019) <doi:10.1080/01621459.2020.1720693>. The rrelaxiv package, which provides an alternative solver for the underlying network flow problems, carries an academic license and is not available on CRAN, but may be downloaded from Github at <https://github.com/josherrickson/rrelaxiv/>.

Likelihood-based estimation of conditional transformation models via the most likely transformation approach described in Hothorn et al. (2018) <DOI:10.1111/sjos.12291> and Hothorn (2020) <DOI:10.18637/jss.v092.i01>. Shift-scale (Siegfried et al, 2023, <DOI:10.1080/00031305.2023.2203177>) and multivariate (Klein et al, 2022, <DOI:10.1111/sjos.12501>) transformation models are part of this package. A package vignette is available from <DOI:10.32614/CRAN.package.mlt.docreg> and more convenient user interfaces to many models from <DOI:10.32614/CRAN.package.tram>.