Set of tools for descriptive analysis of metaproteomics data generated from high-throughput mass spectrometry instruments. These tools allow to cluster peptides and proteins abundance, expressed as spectral counts, and to manipulate them in groups of metaproteins. This information can be represented using multiple visualization functions to portray the global metaproteome landscape and to differentiate samples or conditions, in terms of abundance of metaproteins, taxonomic levels and/or functional annotation. The provided tools allow to implement flexible analytical pipelines that can be easily applied to studies interested in metaproteomics analysis.

This package provides sampling and density functions for matrix variate normal, t, and inverted t distributions; ML estimation for matrix variate normal and t distributions using the EM algorithm, including some restrictions on the parameters; and classification by linear and quadratic discriminant analysis for matrix variate normal and t distributions described in Thompson et al. (2019) <doi:10.1080/10618600.2019.1696208>. Performs clustering with matrix variate normal and t mixture models.

Multiscale moving sum procedure for the detection of changes in expectation in univariate sequences. References - Multiscale change point detection via gradual bandwidth adjustment in moving sum processes (2021+), Tijana Levajkovic and Michael Messer.

Simple tools to perform mixture optimization based on the desirability package by Max Kuhn. It also provides a plot routine using ggplot2 and patchwork'.

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.

It is a hybrid spatial model that combines the strength of two widely used regression models, MARS (Multivariate Adaptive Regression Splines) and GWR (Geographically Weighted Regression) to provide an effective approach for predicting a response variable at unknown locations. The MARS model is used in the first step of the development of a hybrid model to identify the most important predictor variables that assist in predicting the response variable. For method details see, Friedman, J.H. (1991). <DOI:10.1214/aos/1176347963>.The GWR model is then used to predict the response variable at testing locations based on these selected variables that account for spatial variations in the relationships between the variables. This hybrid model can improve the accuracy of the predictions compared to using an individual model alone.This developed hybrid spatial model can be useful particularly in cases where the relationship between the response variable and predictor variables is complex and non-linear, and varies across locations.

Fit growth curves to various known microbial growth models automatically to estimate growth parameters. Growth curves can be plotted with their uncertainty band. Growth models are: modified Gompertz model (Zwietering et al. (1990) <doi:10.1128/aem.56.6.1875-1881.1990>), Baranyi model (Baranyi and Roberts (1994) <doi:10.1016/0168-1605%2894%2990157-0>), Rosso model (Rosso et al. (1993) <doi:10.1006/jtbi.1993.1099>) and linear model (Dantigny (2005) <doi:10.1016/j.ijfoodmicro.2004.10.013>).

Spatio-temporal multivariate occupancy models can handle multiple species in occupancy models. This method for fitting such models is described in Hepler and Erhardt (2021) "A spatiotemporal model for multivariate occupancy data".

This package provides a hybrid of the K-means algorithm and a Majorization-Minimization method to introduce a robust clustering. The reference paper is: Julien Mairal, (2015) <doi:10.1137/140957639>. The two most important functions in package MajKMeans are cluster_km() and cluster_MajKm(). cluster_km() clusters data without Majorization-Minimization and cluster_MajKm() clusters data with Majorization-Minimization method. Both of these functions calculate the sum of squares (SS) of clustering.

This package provides tools to help visualize Major League Baseball analysis in ggplot2 and gt'. You provide team/player information and mlbplotR will transform that information into team colors, logos, or player headshots for graphics.

Three main functions about analyzing massive data (missing observations are allowed) considered from multiple layers of categories are demonstrated. Flexible and diverse applications of the function parameters make the data analyses powerful.

Two pipelines are provided to study microbial turnover along a gradient, including the beta diversity and microbial abundance change. The betaturn class consists of the steps of community dissimilarity matrix generation, matrix conversion, differential test and visualization. The workflow of taxaturn class includes the taxonomic abundance calculation, abundance transformation, abundance change summary, statistical analysis and visualization. Multiple statistical approaches can contribute to the analysis of microbial turnover.

Additional documentation, a package vignette and regression tests for package mlt.

Fits a Bayesian Regression Model for multivariate count data. This model assumes that the data is distributed according to the Conway-Maxwell-Poisson distribution, and for each response variable it is associate different covariates. This model allows to account for correlations between the counts by using latent effects based on the Chib and Winkelmann (2001) <http://www.jstor.org/stable/1392277> proposal.

This package provides a set of tools to facilitate data sonification and handle the musicXML format <https://usermanuals.musicxml.com/MusicXML/Content/XS-MusicXML.htm>. Several classes are defined for basic musical objects such as note pitch, note duration, note, measure and score. Moreover, sonification utilities functions are provided, e.g. to map data into musical attributes such as pitch, loudness or duration. A typical sonification workflow hence looks like: get data; map them to musical attributes; create and write the musicXML score, which can then be further processed using specialized music software (e.g. MuseScore', GuitarPro', etc.). Examples can be found in the blog <https://globxblog.github.io/>, the presentation by Renard and Le Bescond (2022, <https://hal.science/hal-03710340v1>) or the poster by Renard et al. (2023, <https://hal.inrae.fr/hal-04388845v1>).

This package provides pipe-style interface for data.table'. Package preserves all data.table features without significant impact on performance. let and take functions are simplified interfaces for most common data manipulation tasks. For example, you can write take(mtcars, mean(mpg), by = am) for aggregation or let(mtcars, hp_wt = hp/wt, hp_wt_mpg = hp_wt/mpg) for modification. Use take_if/let_if for conditional aggregation/modification. Additionally there are some conveniences such as automatic data.frame conversion to data.table'.

The base apply function and its variants, as well as the related functions in the plyr package, typically apply user-defined functions to a single argument (or a list of vectorized arguments in the case of mapply). The multiApply package extends this paradigm with its only function, Apply, which efficiently applies functions taking one or a list of multiple unidimensional or multidimensional arrays (or combinations thereof) as input. The input arrays can have different numbers of dimensions as well as different dimension lengths, and the applied function can return one or a list of unidimensional or multidimensional arrays as output. This saves development time by preventing the R user from writing often error-prone and memory-inefficient loops dealing with multiple complex arrays. Also, a remarkable feature of Apply is the transparent use of multi-core through its parameter ncores'. In contrast to the base apply function, this package suggests the use of target dimensions as opposite to the margins for specifying the dimensions relevant to the function to be applied.

To perform main effect matrix factor model (MEFM) estimation for a given matrix time series as described in Lam and Cen (2024) <doi:10.48550/arXiv.2406.00128>. Estimation of traditional matrix factor models is also supported. Supplementary functions for testing MEFM over factor models are included.

This package provides tools that extend the functionality of the RODBC package to work with Microsoft SQL Server databases. Makes it easier to browse the database and examine individual tables and views.

Takes QC signal for each day and normalize metabolomic data that has been acquired in a certain period of time. At least three QC per day are required.

Supports the generation of parallelogram, equilateral triangle, regular hexagon, isosceles trapezoid, Koch snowflake, hexaflake', Sierpinski triangle, Sierpinski carpet and Sierpinski trapezoid mazes via TurtleGraphics'. Mazes are generated by the recursive method: the domain is divided into sub-domains in which mazes are generated, then dividing lines with holes are drawn between them, see J. Buck, Recursive Division, <http://weblog.jamisbuck.org/2011/1/12/maze-generation-recursive-division-algorithm>.

Simultaneously estimates sparse regression coefficients and response network structure in multivariate models with missing data. Unlike traditional approaches requiring imputation, handles missingness natively through unbiased estimating equations (MCAR/MAR compatible). Employs dual L1 regularization with automated selection via cross-validation or information criteria. Includes parallel computation, warm starts, adaptive grids, publication-ready visualizations, and prediction methods. Ideal for genomics, neuroimaging, and multi-trait studies with incomplete high-dimensional outcomes. See Zeng et al. (2025) <doi:10.48550/arXiv.2507.05990>.

Developed for computing the probability density function, computing the cumulative distribution function, computing the quantile function, random generation, drawing q-q plot, and estimating the parameters of 24 G-family of statistical distributions via the maximum product spacing approach introduced in <https://www.jstor.org/stable/2345411>. The set of families contains: beta G distribution, beta exponential G distribution, beta extended G distribution, exponentiated G distribution, exponentiated exponential Poisson G distribution, exponentiated generalized G distribution, exponentiated Kumaraswamy G distribution, gamma type I G distribution, gamma type II G distribution, gamma uniform G distribution, gamma-X generated of log-logistic family of G distribution, gamma-X family of modified beta exponential G distribution, geometric exponential Poisson G distribution, generalized beta G distribution, generalized transmuted G distribution, Kumaraswamy G distribution, log gamma type I G distribution, log gamma type II G distribution, Marshall Olkin G distribution, Marshall Olkin Kumaraswamy G distribution, modified beta G distribution, odd log-logistic G distribution, truncated-exponential skew-symmetric G distribution, and Weibull G distribution.

This package contains a collection of datasets for working with machine learning tasks. It will contain datasets for supervised machine learning Jiang (2020)<doi:10.1016/j.beth.2020.05.002> and will include datasets for classification and regression. The aim of this package is to use data generated around health and other domains.