This package provides functions and tools for analysing consumer demand with the Almost Ideal Demand System (AIDS) suggested by Deaton and Muellbauer (1980).

Color palettes inspired by the works of Mexican painters and muralists. The package includes functions that return vectors of colors and also functions to use color and fill scales in ggplot2 visualizations.

Computes the prime implicants or a minimal disjunctive normal form for a logic expression presented by a truth table or a logic tree. Has been particularly developed for logic expressions resulting from a logic regression analysis, i.e. logic expressions typically consisting of up to 16 literals, where the prime implicants are typically composed of a maximum of 4 or 5 literals.

This package provides the biggest amount of statistical measures in the whole R world. Includes measures of regression, (multiclass) classification and multilabel classification. The measures come mainly from the mlr package and were programed by several mlr developers.

Routines for assessing multivariate normality. Implements three Wald's type chi-squared tests; non-parametric Anderson-Darling and Cramer-von Mises tests; Doornik-Hansen test, Royston test and Henze-Zirkler test.

Exploratory data analysis and manipulation functions for multi- label data sets along with an interactive Shiny application to ease their use.

This package provides the ability to perform "Marginal Mediation"--mediation wherein the indirect and direct effects are in terms of the average marginal effects (Bartus, 2005, <https://EconPapers.repec.org/RePEc:tsj:stataj:v:5:y:2005:i:3:p:309-329>). The style of the average marginal effects stems from Thomas Leeper's work on the "margins" package. This framework allows the use of categorical mediators and outcomes with little change in interpretation from the continuous mediators/outcomes. See <doi:10.13140/RG.2.2.18465.92001> for more details on the method.

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'.

Computation of various confidence intervals (Altman et al. (2000), ISBN:978-0-727-91375-3; Hedderich and Sachs (2018), ISBN:978-3-662-56657-2) including bootstrapped versions (Davison and Hinkley (1997), ISBN:978-0-511-80284-3) as well as Hsu (Hedderich and Sachs (2018), ISBN:978-3-662-56657-2), permutation (Janssen (1997), <doi:10.1016/S0167-7152(97)00043-6>), bootstrap (Davison and Hinkley (1997), ISBN:978-0-511-80284-3), intersection-union (Sozu et al. (2015), ISBN:978-3-319-22005-5) and multiple imputation (Barnard and Rubin (1999), <doi:10.1093/biomet/86.4.948>) t-test; furthermore, computation of intersection-union z-test as well as multiple imputation Wilcoxon tests. Graphical visualizations: volcano plot, Bland-Altman plots (Bland and Altman (1986), <doi:10.1016/S0140-6736(86)90837-8>; Shieh (2018), <doi:10.1186/s12874-018-0505-y>), mean difference plot (Boehning et al. (2008), <doi:10.1177/0962280207081867>), plot of test statistic for permutation and bootstrap tests as well as objects of class htest.

Computes densities, probabilities, and random deviates of the Matrix Normal (Pocuca et al. (2019) <doi:10.48550/arXiv.1910.02859>). Also includes simple but useful matrix functions. See the vignette for more information.

Query, extract, and plot genealogical data from The Mathematics Genealogy Project <https://mathgenealogy.org/>. Data is gathered from the WebSocket server run by the geneagrapher-core project <https://github.com/davidalber/geneagrapher-core>.

The microplot function writes a set of R graphics files to be used as microplots (sparklines) in tables in either LaTeX', HTML', Word', or Excel files. For LaTeX', we provide methods for the Hmisc::latex() generic function to construct latex tabular environments which include the graphs. These can be used directly with the operating system pdflatex or latex command, or by using one of Sweave', knitr', rmarkdown', or Emacs org-mode as an intermediary. For MS Word', the msWord() function uses the flextable package to construct Word tables which include the graphs. There are several distinct approaches for constructing HTML files. The simplest is to use the msWord() function with argument filetype="html". Alternatively, use either Emacs org-mode or the htmlTable::htmlTable() function to construct an HTML file containing tables which include the graphs. See the documentation for our as.htmlimg() function. For Excel use on Windows', the file examples/irisExcel.xls includes VBA code which brings the individual panels into individual cells in the spreadsheet. Examples in the examples and demo subdirectories are shown with lattice graphics, ggplot2 graphics, and base graphics. Examples for LaTeX include Sweave (both LaTeX'-style and Noweb'-style), knitr', emacs org-mode', and rmarkdown input files and their pdf output files. Examples for HTML include org-mode and Rmd input files and their webarchive HTML output files. In addition, the as.orgtable() function can display a data.frame in an org-mode document. The examples for MS Word (with either filetype="docx" or filetype="html") work with all operating systems. The package does not require the installation of LaTeX or MS Word to be able to write .tex or .docx files.

Run flexible mediation analyses using natural effect models as described in Lange, Vansteelandt and Bekaert (2012) <DOI:10.1093/aje/kwr525>, Vansteelandt, Bekaert and Lange (2012) <DOI:10.1515/2161-962X.1014> and Loeys, Moerkerke, De Smet, Buysse, Steen and Vansteelandt (2013) <DOI:10.1080/00273171.2013.832132>.

Data class for increased interoperability working with spatial-temporal data together with corresponding functions and methods (conversions, basic calculations and basic data manipulation). The class distinguishes between spatial, temporal and other dimensions to facilitate the development and interoperability of tools build for it. Additional features are name-based addressing of data and internal consistency checks (e.g. checking for the right data order in calculations).

Computationally efficient functions to provide direct likelihood-based inference for partially-observed multivariate birth-death processes. Such processes range from a simple Yule model to the complex susceptible-infectious-removed model in disease dynamics. Efficient likelihood evaluation facilitates maximum likelihood estimation and Bayesian inference.

R functions for the estimation and eigen-decomposition of multivariate autoregressive models.

This package implements FUSE (Functional Segmentation of DNA methylation data), a data-driven method for identifying spatially coherent DNA methylation segments from whole-genome bisulfite sequencing (WGBS) count data. The method performs hierarchical clustering of CpG sites based on methylated and unmethylated read counts across multiple samples and determines the optimal number of segments using an information criterion (AIC or BIC). Resulting segments represent regions with homogeneous methylation profiles across the input cohort while allowing sample-specific methylation levels. The package provides functions for clustering, model selection, tree cutting, segment-level summarization, and visualization. Input can be supplied as count matrices or extracted directly from BSseq and methrix objects.

Three algorithms for estimating a Markov random field structure.Two of them are an exact version and a simulated annealing version of a penalized maximum conditional likelihood method similar to the Bayesian Information Criterion. These algorithm are described in Frondana (2016) <doi:10.11606/T.45.2018.tde-02022018-151123>.The third one is a greedy algorithm, described in Bresler (2015) <doi:10.1145/2746539.2746631).

The multispatial convergent cross mapping algorithm can be used as a test for causal associations between pairs of processes represented by time series. This is a combination of convergent cross mapping (CCM), described in Sugihara et al., 2012, Science, 338, 496-500, and dew-drop regression, described in Hsieh et al., 2008, American Naturalist, 171, 71â 80. The algorithm allows CCM to be implemented on data that are not from a single long time series. Instead, data can come from many short time series, which are stitched together using bootstrapping.

An approach to identify microbiome biomarker for time to event data by discovering microbiome for predicting survival and classifying subjects into risk groups. Classifiers are constructed as a linear combination of important microbiome and treatment effects if necessary. Several methods were implemented to estimate the microbiome risk score such as the LASSO method by Robert Tibshirani (1998) <doi:10.1002/(SICI)1097-0258(19970228)16:4%3C385::AID-SIM380%3E3.0.CO;2-3>, Elastic net approach by Hui Zou and Trevor Hastie (2005) <doi:10.1111/j.1467-9868.2005.00503.x>, supervised principle component analysis of Wold Svante et al. (1987) <doi:10.1016/0169-7439(87)80084-9>, and supervised partial least squares analysis by Inge S. Helland <https://www.jstor.org/stable/4616159>. Sensitivity analysis on the quantile used for the classification can also be accessed to check the deviation of the classification group based on the quantile specified. Large scale cross validation can be performed in order to investigate the mostly selected microbiome and for internal validation. During the evaluation process, validation is accessed using the hazard ratios (HR) distribution of the test set and inference is mainly based on resampling and permutations technique.

An implementation of the expectation conditional maximization (ECM) algorithm for matrix-variate variance gamma (MVVG) and normal-inverse Gaussian (MVNIG) linear models. These models are designed for settings of multivariate analysis with clustered non-uniform observations and correlated responses. The package includes fitting and prediction functions for both models, and an example dataset from a periodontal on Gullah-speaking African Americans, with responses in gaad_res', and covariates in gaad_cov'. For more details on the matrix-variate distributions used, see Gallaugher & McNicholas (2019) <doi:10.1016/j.spl.2018.08.012>.

An RStudio Addin wrapper for the mergen package. This package employs artificial intelligence to convert data analysis questions into executable code, explanations, and algorithms. This package makes it easier to use Large Language Models in your development environment by providing a chat-like interface, while also allowing you to inspect and execute the returned code.

Extended tools for analyzing telemetry data using generalized hidden Markov models. Features of momentuHMM (pronounced ``momentum'') include data pre-processing and visualization, fitting HMMs to location and auxiliary biotelemetry or environmental data, biased and correlated random walk movement models, hierarchical HMMs, multiple imputation for incorporating location measurement error and missing data, user-specified design matrices and constraints for covariate modelling of parameters, random effects, decoding of the state process, visualization of fitted models, model checking and selection, and simulation. See McClintock and Michelot (2018) <doi:10.1111/2041-210X.12995>.

This package provides a framework for analyzing broth microdilution assays in various 96-well plate designs, visualizing results and providing descriptive and (simple) inferential statistics (i.e. summary statistics and sign test). The functions are designed to add metadata to 8 x 12 tables of absorption values, creating a tidy data frame. Users can choose between clean-up procedures via function parameters (which covers most cases) or user prompts (in cases with complex experimental designs). Users can also choose between two validation methods, i.e. exclusion of absorbance values above a certain threshold or manual exclusion of samples. A function for visual inspection of samples with their absorption values over time for certain group combinations helps with the decision. In addition, the package includes functions to subtract the background absorption (usually at time T0) and to calculate the growth performance compared to a baseline. Samples can be visually inspected with their absorption values displayed across time points for specific group combinations. Core functions of this package (i.e. background subtraction, sample validation and statistics) were inspired by the manual calculations that were applied in Tewes and Muller (2020) <doi:10.1038/s41598-020-67600-7>.