Allows the user to create graphs with multiple layers. The user can also modify the layers, the nodes, and the edges. The graph can also be visualized. Zaynab Hammoud and Frank Kramer (2018) <doi:10.3390/genes9110519>. More about multilayered graphs and their usage can be found in our review paper: Zaynab Hammoud and Frank Kramer (2020) <doi:10.1186/s41044-020-00046-0>.

Constructs trees for multivariate survival data using marginal and frailty models. Grows, prunes, and selects the best-sized tree.

Maximum entropy density based dependent data bootstrap. An algorithm is provided to create a population of time series (ensemble) without assuming stationarity. The reference paper (Vinod, H.D., 2004 <DOI:10.1016/j.jempfin.2003.06.002>) explains how the algorithm satisfies the ergodic theorem and the central limit theorem.

This package contains the Markov cluster algorithm (MCL) for identifying clusters in networks and graphs. The algorithm simulates random walks on a (n x n) matrix as the adjacency matrix of a graph. It alternates an expansion step and an inflation step until an equilibrium state is reached.

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>.

An increasingly important source of health-related bibliographic content are preprints - preliminary versions of research articles that have yet to undergo peer review. The two preprint repositories most relevant to health-related sciences are medRxiv <https://www.medrxiv.org/> and bioRxiv, both of which are operated by the Cold Spring Harbor Laboratory. medrxivr provides programmatic access to the Cold Spring Harbour Laboratory (CSHL) API <https://api.biorxiv.org/>, allowing users to easily download medRxiv and bioRxiv preprint metadata (e.g. title, abstract, publication date, author list, etc) into R. medrxivr also provides functions to search the downloaded preprint records using regular expressions and Boolean logic, as well as helper functions that allow users to export their search results to a .BIB file for easy import to a reference manager and to download the full-text PDFs of preprints matching their search criteria.

This package contains a dataset of morphological and structural features of Medicinal LEAves (MedLEA)'. The features of each species is recorded by manually viewing the medicinal plant repository available at (<http://www.instituteofayurveda.org/plants/>). You can also download repository of leaf images of 1099 medicinal plants in Sri Lanka.

We introduce a generalized factor model designed to jointly analyze high-dimensional multi-modality data from multiple studies by extracting study-shared and specified factors. Our factor models account for heterogeneous noises and overdispersion among modality variables with augmented covariates. We propose an efficient and speedy variational estimation procedure for estimating model parameters, along with a novel criterion for selecting the optimal number of factors. More details can be referred to Liu et al. (2025) <doi:10.48550/arXiv.2507.09889>.

Hypothesis testing of the parameters of multivariate normal distributions, including the testing of a single mean vector, two mean vectors, multiple mean vectors, a single covariance matrix, multiple covariance matrices, a mean and a covariance matrix simultaneously, and the testing of independence of multivariate normal random vectors. Huixuan, Gao (2005, ISBN:9787301078587), "Applied Multivariate Statistical Analysis".

Get map data frames for the Indian subcontinent with different region levels (e.g., district, state). The package also offers convenience functions for plotting choropleths, visualizing spatial data, and handling state/district codes.

Explore and retrieve marine spatial data from the Marine Regions Gazetteer <https://marineregions.org/gazetteer.php?p=webservices> and the Marine Regions Data Products <https://marineregions.org/webservices.php>.

Inference of a multi-states birth-death model from a phylogeny, comprising a number of states N, birth and death rates for each state and on which edges each state appears. Inference is done using a hybrid approach: states are progressively added in a greedy approach. For a fixed number of states N the best model is selected via maximum likelihood. Reference: J. Barido-Sottani, T. G. Vaughan and T. Stadler (2018) <doi:10.1098/rsif.2018.0512>.

This package creates sophisticated models of training data and validates the models with an independent test set, cross validation, or Out Of Bag (OOB) predictions on the training data. Create graphs and tables of the model validation results. Applies these models to GIS .img files of predictors to create detailed prediction surfaces. Handles large predictor files for map making, by reading in the .img files in chunks, and output to the .txt file the prediction for each data chunk, before reading the next chunk of data.

Compute the multiple Grubbs-Beck low-outlier test on positively distributed data and utilities for noninterpretive U.S. Geological Survey annual peak-streamflow data processing discussed in Cohn et al. (2013) and England et al. (2017) <doi:10.3133/tm4B5>. Other utilities for working with peak streamflow are provided.

Calculate morphine milligram equivalents (MME) for opioid dose comparison using standardized methods. Can directly call the NIH HEAL MME Online Calculator <https://research-mme.wakehealth.edu/api> API or replicate API calculations on the user's local machine from the comfort of R'. Creation of the NIH HEAL MME Online Calculator and the MME calculations implemented in this package are described in Adams MCB, Sward KA, Perkins ML, Hurley RW (2025) <doi:10.1097/j.pain.0000000000003529>.

This package provides functions for calculating the point and interval estimates of the natural indirect effect (NIE), total effect (TE), and mediation proportion (MP), based on the product approach. We perform the methods considered in Cheng, Spiegelman, and Li (2021) Estimating the natural indirect effect and the mediation proportion via the product method.

Highly variable gene selection methods, including popular public available methods, and also the mixture of multiple highly variable gene selection methods, <https://github.com/RuzhangZhao/mixhvg>. Reference: <doi:10.1101/2024.08.25.608519>.

This package provides a function for plotting multivariate time series data.

This package provides TOML representations of packages needed that must be installed to run a project. Includes functions to specify detailed installation functions, validate files, and to use a given file as the requirements for a project. Handles package installations, when necessary, via pak'.

This package provides compact tools for missing-data analysis, including artificial amputation, chained single and multiple imputation, statistical and machine-learning-based imputation methods, diagnostic evaluation, and post-imputation pooling.

Analyses the stability and structural behaviour of export and import patterns across multiple countries using a Markov chain modelling framework. Constructs transition probability matrices to quantify changes in trade shares between successive periods, thereby capturing persistence, structural shifts, and inter-country interdependence in trade performance. By iteratively generating expected trade distributions over time, the approach facilitates assessment of stability, long-run equilibrium tendencies, and comparative dynamics in longitudinal trade data, providing a rigorous tool for empirical analysis of exportâ import behaviour. Methodological foundations follow standard Markov chain theory as described in Gagniuc (2017) <Doi:10.1002/9781119387596>.

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.

It contains six common multi-category classification accuracy evaluation measures. All of these measures could be found in Li and Ming (2019) <doi:10.1002/sim.8103>. Specifically, Hypervolume Under Manifold (HUM), described in Li and Fine (2008) <doi:10.1093/biostatistics/kxm050>. Correct Classification Percentage (CCP), Integrated Discrimination Improvement (IDI), Net Reclassification Improvement (NRI), R-Squared Value (RSQ), described in Li, Jiang and Fine (2013) <doi:10.1093/biostatistics/kxs047>. Polytomous Discrimination Index (PDI), described in Van Calster et al. (2012) <doi:10.1007/s10654-012-9733-3>. Li et al. (2018) <doi:10.1177/0962280217692830>. PDI with variance estimation using Dover et al. (2021) <doi:10.1002/sim.9187>. We described all these above measures and our mcca package in Li, Gao and D'Agostino (2019) <doi:10.1002/sim.8103>.

Stand-alone HTTP capable R-package repository, that fully supports R's install.packages() and available.packages(). It also contains API endpoints for end-users to add/update packages. This package can supplement miniCRAN', which has functions for maintaining a local (partial) copy of CRAN'. Current version is bare-minimum without any access-control or much security.