This package provides tools for analysing multivariate time series with wavelets. This includes: simulation of a multivariate locally stationary wavelet (mvLSW) process from a multivariate evolutionary wavelet spectrum (mvEWS); estimation of the mvEWS, local coherence and local partial coherence. See Park, Eckley and Ombao (2014) <doi:10.1109/TSP.2014.2343937> for details.

This package provides a latent variable model based on factor analytic and mixture of experts models, designed to infer food intake from multiple biomarkers data. The model is framed within a Bayesian hierarchical framework, which provides flexibility to adapt to different biomarker distributions and facilitates inference on food intake from biomarker data alone, along with the associated uncertainty. Details are in D'Angelo, et al. (2020) <arXiv:2006.02995>.

Handling the microclimatic data in R. The myClim workflow begins at the reading data primary from microclimatic dataloggers, but can be also reading of meteorological station data from files. Cleaning time step, time zone settings and metadata collecting is the next step of the work flow. With myClim tools one can crop, join, downscale, and convert microclimatic data formats, sort them into localities, request descriptive characteristics and compute microclimatic variables. Handy plotting functions are provided with smart defaults.

This GUI for the mi package walks the user through the steps of multiple imputation and the analysis of completed data.

Multivariate Time Series (MTS) is a general package for analyzing multivariate linear time series and estimating multivariate volatility models. It also handles factor models, constrained factor models, asymptotic principal component analysis commonly used in finance and econometrics, and principal volatility component analysis. (a) For the multivariate linear time series analysis, the package performs model specification, estimation, model checking, and prediction for many widely used models, including vector AR models, vector MA models, vector ARMA models, seasonal vector ARMA models, VAR models with exogenous variables, multivariate regression models with time series errors, augmented VAR models, and Error-correction VAR models for co-integrated time series. For model specification, the package performs structural specification to overcome the difficulties of identifiability of VARMA models. The methods used for structural specification include Kronecker indices and Scalar Component Models. (b) For multivariate volatility modeling, the MTS package handles several commonly used models, including multivariate exponentially weighted moving-average volatility, Cholesky decomposition volatility models, dynamic conditional correlation (DCC) models, copula-based volatility models, and low-dimensional BEKK models. The package also considers multiple tests for conditional heteroscedasticity, including rank-based statistics. (c) Finally, the MTS package also performs forecasting using diffusion index , transfer function analysis, Bayesian estimation of VAR models, and multivariate time series analysis with missing values.Users can also use the package to simulate VARMA models, to compute impulse response functions of a fitted VARMA model, and to calculate theoretical cross-covariance matrices of a given VARMA model.

Chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) is the premier technology for profiling genome-wide localization of chromatin-binding proteins, including transcription factors and histones with various modifications. This package provides a robust method for normalizing ChIP-seq signals across individual samples or groups of samples. It also designs a self-contained system of statistical models for calling differential ChIP-seq signals between two or more biological conditions as well as for calling hypervariable ChIP-seq signals across samples. Refer to Tu et al. (2021) <doi:10.1101/gr.262675.120> and Chen et al. (2022) <doi:10.1186/s13059-022-02627-9> for associated statistical details.

User-friendly package for reporting replicability-analysis methods, affixed to meta-analyses summary. The replicability-analysis output provides an assessment of the investigated intervention, where it offers quantification of effect replicability and assessment of the consistency of findings. - Replicability-analysis for fixed-effects and random-effect meta analysis: - r(u)-value; - lower bounds on the number of studies with replicated positive and\or negative effect; - Allows detecting inconsistency of signals; - forest plots with the summary of replicability analysis results; - Allows Replicability-analysis with or without the common-effect assumption.

This package provides tools specifically designed for analyzing longitudinal microbiome data. This tool integrates seven functional modules, providing a systematic framework for microbiome time-series analysis. For more details on inferences involving interspecies interactions see Fisher (2014) <doi:10.1371/journal.pone.0102451>. Details on this package are also described in an unpublished manuscript.

Identify and rank CpG DNA methylation conservation along the human genome. Specifically it includes bootstrapping methods to provide ranking which should adjust for the differences in length as without it short regions tend to get higher conservation scores.

This package implements a computational framework to predict microbial community-based metabolic profiles with O2PLS model. It provides procedures of model training and prediction. Paired microbiome and metabolome data are needed for modeling, and the trained model can be applied to predict metabolites of analogous environments using new microbial feature abundances.

This package provides a series of functions to implement association of covariance for detecting differential co-expression (ACDC), a novel approach for detection of differential co-expression that simultaneously accommodates multiple phenotypes or exposures with binary, ordinal, or continuous data types. Users can use the default method which identifies modules by Partition or may supply their own modules. Also included are functions to choose an information loss criterion (ILC) for Partition using OmicS-data-based Complex trait Analysis (OSCA) and Genome-wide Complex trait Analysis (GCTA). The manuscript describing these methods is as follows: Queen K, Nguyen MN, Gilliland F, Chun S, Raby BA, Millstein J. "ACDC: a general approach for detecting phenotype or exposure associated co-expression" (2023) <doi:10.3389/fmed.2023.1118824>.

Finds the Maximum Likelihood (ML) Estimate of the mean vector and variance-covariance matrix for multivariate normal data with missing values.

Generates Muller plot from parental/genealogy/phylogeny information and population/abundance/frequency dynamics data. Muller plots are plots which combine information about succession of different OTUs (genotypes, phenotypes, species, ...) and information about dynamics of their abundances (populations or frequencies) over time. They are powerful and fascinating tools to visualize evolutionary dynamics. They may be employed also in study of diversity and its dynamics, i.e. how diversity emerges and how changes over time. They are called Muller plots in honor of Hermann Joseph Muller which used them to explain his idea of Muller's ratchet (Muller, 1932, American Naturalist). A big difference between Muller plots and normal box plots of abundances is that a Muller plot depicts not only the relative abundances but also succession of OTUs based on their genealogy/phylogeny/parental relation. In a Muller plot, horizontal axis is time/generations and vertical axis represents relative abundances of OTUs at the corresponding times/generations. Different OTUs are usually shown with polygons with different colors and each OTU originates somewhere in the middle of its parent area in order to illustrate their succession in evolutionary process. To generate a Muller plot one needs the genealogy/phylogeny/parental relation of OTUs and their abundances over time. MullerPlot package has the tools to generate Muller plots which clearly depict the origin of successors of OTUs.

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

Evolutionary black box optimization algorithms building on the bbotk package. miesmuschel offers both ready-to-use optimization algorithms, as well as their fundamental building blocks that can be used to manually construct specialized optimization loops. The Mixed Integer Evolution Strategies as described by Li et al. (2013) <doi:10.1162/EVCO_a_00059> can be implemented, as well as the multi-objective optimization algorithms NSGA-II by Deb, Pratap, Agarwal, and Meyarivan (2002) <doi:10.1109/4235.996017>.

This package provides tools for systematic comparison of data frames, offering functionality to identify, quantify, and extract differences. Provides functions with user-friendly and interactive console output for immediate analysis, while also offering options to export differences as structured data frames that can be easily integrated into existing workflows.

This package provides functions for measuring population divergence from genotypic data.

Builds and interprets multi-response machine learning models using tidymodels syntax. Users can supply a tidy model, and mrIML automates the process of fitting multiple response models to multivariate data and applying interpretable machine learning techniques across them. For more details see Fountain-Jones (2021) <doi:10.1111/1755-0998.13495> and Fountain-Jones et al. (2024) <doi:10.22541/au.172676147.77148600/v1>.

This is the very popular mine sweeper game! The game requires you to find out tiles that contain mines through clues from unmasking neighboring tiles. Each tile that does not contain a mine shows the number of mines in its adjacent tiles. If you unmask all tiles that do not contain mines, you win the game; if you unmask any tile that contains a mine, you lose the game. For further game instructions, please run `help(run_game)` and check details. This game runs in X11-compatible devices with `grDevices::x11()`.

An interface to the Microsoft 365 (formerly known as Office 365') suite of cloud services, building on the framework supplied by the AzureGraph package. Enables access from R to data stored in Teams', SharePoint Online and OneDrive', including the ability to list drive folder contents, upload and download files, send messages, and retrieve data lists. Also provides a full-featured Outlook email client, with the ability to send emails and manage emails and mail folders.

Straightforward and detailed evaluation of machine learning models. MLeval can produce receiver operating characteristic (ROC) curves, precision-recall (PR) curves, calibration curves, and PR gain curves. MLeval accepts a data frame of class probabilities and ground truth labels, or, it can automatically interpret the Caret train function results from repeated cross validation, then select the best model and analyse the results. MLeval produces a range of evaluation metrics with confidence intervals.

This package provides tools for the integration, visualisation, and modelling of spatial epidemiological data using the method described in Azeez, A., & Noel, C. (2025). Predictive Modelling and Spatial Distribution of Pancreatic Cancer in Africa Using Machine Learning-Based Spatial Model <doi:10.5281/zenodo.16529986> and <doi:10.5281/zenodo.16529016>. It facilitates the analysis of geographic health data by combining modern spatial mapping tools with advanced machine learning (ML) algorithms. mlspatial enables users to import and pre-process shapefile and associated demographic or disease incidence data, generate richly annotated thematic maps, and apply predictive models, including Random Forest, XGBoost', and Support Vector Regression, to identify spatial patterns and risk factors. It is suited for spatial epidemiologists, public health researchers, and GIS analysts aiming to uncover hidden geographic patterns in health-related outcomes and inform evidence-based interventions.

Multiple imputation using XGBoost', subsampling, and predictive mean matching as described in Deng and Lumley (2024) <doi:10.1080/10618600.2023.2252501>. The package supports various types of variables, offers flexible settings, and enables saving an imputation model to impute new data. Data processing and memory usage have been optimised to speed up the imputation process.

This package provides graph-constrained regression methods in which regularization parameters are selected automatically via estimation of equivalent Linear Mixed Model formulation. riPEER (ridgified Partially Empirical Eigenvectors for Regression) method employs a penalty term being a linear combination of graph-originated and ridge-originated penalty terms, whose two regularization parameters are ML estimators from corresponding Linear Mixed Model solution; a graph-originated penalty term allows imposing similarity between coefficients based on graph information given whereas additional ridge-originated penalty term facilitates parameters estimation: it reduces computational issues arising from singularity in a graph-originated penalty matrix and yields plausible results in situations when graph information is not informative. riPEERc (ridgified Partially Empirical Eigenvectors for Regression with constant) method utilizes addition of a diagonal matrix multiplied by a predefined (small) scalar to handle the non-invertibility of a graph Laplacian matrix. vrPEER (variable reducted PEER) method performs variable-reduction procedure to handle the non-invertibility of a graph Laplacian matrix.