Code to identify functional enrichments across diverse taxa in phylogenetic tree, particularly where these taxa differ in abundance across samples in a non-random pattern. The motivation for this approach is to identify microbial functions encoded by diverse taxa that are at higher abundance in certain samples compared to others, which could indicate that such functions are broadly adaptive under certain conditions. See GitHub repository for tutorial and examples: <https://github.com/gavinmdouglas/POMS/wiki>. Citation: Gavin M. Douglas, Molly G. Hayes, Morgan G. I. Langille, Elhanan Borenstein (2022) <doi:10.1093/bioinformatics/btac655>.

This package provides a multiple testing procedure for testing several groups of hypotheses is implemented. Linear dependency among the hypotheses within the same group is modeled by using hidden Markov Models. It is noted that a smaller p value does not necessarily imply more significance due to the dependency. A typical application is to analyze genome wide association studies datasets, where SNPs from the same chromosome are treated as a group and exhibit strong linear genomic dependency. See Wei Z, Sun W, Wang K, Hakonarson H (2009) <doi:10.1093/bioinformatics/btp476> for more details.

This R package assists breeders in linking data systems with their analytic pipelines, a crucial step in digitizing breeding processes. It supports querying and retrieving phenotypic and genotypic data from systems like EBS <https://ebs.excellenceinbreeding.org/>, BMS <https://bmspro.io>, BreedBase <https://breedbase.org>, GIGWA <https://github.com/SouthGreenPlatform/Gigwa2> (using BrAPI <https://brapi.org> calls), , and Germinate <https://germinateplatform.github.io/get-germinate/>. Extra helper functions support environmental data sources, including TerraClimate <https://www.climatologylab.org/terraclimate.html> and FAO HWSDv2 <https://gaez.fao.org/pages/hwsd> soil database.

Estimate the transition diagnostic classification model (TDCM) described in Madison & Bradshaw (2018) <doi:10.1007/s11336-018-9638-5>, a longitudinal extension of the log-linear cognitive diagnosis model (LCDM) in Henson, Templin & Willse (2009) <doi:10.1007/s11336-008-9089-5>. As the LCDM subsumes many other diagnostic classification models (DCMs), many other DCMs can be estimated longitudinally via the TDCM. The TDCM package includes functions to estimate the single-group and multigroup TDCM, summarize results of interest including item parameters, growth proportions, transition probabilities, transitional reliability, attribute correlations, model fit, and growth plots.

The R package CTSV implements the CTSV approach developed by Jinge Yu and Xiangyu Luo that detects cell-type-specific spatially variable genes accounting for excess zeros. CTSV directly models sparse raw count data through a zero-inflated negative binomial regression model, incorporates cell-type proportions, and performs hypothesis testing based on R package pscl. The package outputs p-values and q-values for genes in each cell type, and CTSV is scalable to datasets with tens of thousands of genes measured on hundreds of spots. CTSV can be installed in Windows, Linux, and Mac OS.

Uncertainty quantification and inverse estimation by probabilistic generative models from the beginning of the data analysis. An example is a Fourier basis method for inverse estimation in scattering analysis of microscopy videos. It does not require specifying a certain range of Fourier bases and it substantially reduces computational cost via the generalized Schur algorithm. See the reference: Mengyang Gu, Yue He, Xubo Liu and Yimin Luo (2023), <doi:10.48550/arXiv.2309.02468>, and Tong Lin, Jinseok Lee, Matt Helgeson, Megan T Valentine, Yimin Luo, Mengyang Gu (2026), <doi:10.48550/arXiv.2605.29424>.

Model fitting and species biotic interaction network topology selection for explicit interaction community models. Explicit interaction community models are an extension of binomial linear models for joint modelling of species communities, that incorporate both the effects of species biotic interactions and the effects of missing covariates. Species interactions are modelled as direct effects of each species on each of the others, and are estimated alongside the effects of missing covariates, modelled as latent factors. The package includes a penalized maximum likelihood fitting function, and a genetic algorithm for selecting the most parsimonious species interaction network topology.

Evidence of Absence software (EoA) is a user-friendly application for estimating bird and bat fatalities at wind farms and designing search protocols. The software is particularly useful in addressing whether the number of fatalities has exceeded a given threshold and what search parameters are needed to give assurance that thresholds were not exceeded. The models are applicable even when zero carcasses have been found in searches, following Huso et al. (2015) <doi:10.1890/14-0764.1>, Dalthorp et al. (2017) <doi:10.3133/ds1055>, and Dalthorp and Huso (2015) <doi:10.3133/ofr20151227>.

Mining informative genes with certain biological meanings are important for clinical diagnosis of disease and discovery of disease mechanisms in plants and animals. This process involves identification of relevant genes and removal of redundant genes as much as possible from a whole gene set. This package selects the informative genes related to a specific trait using gene expression dataset. These trait specific genes are considered as informative genes. This package returns the informative gene set from the high dimensional gene expression data using a combination of methods SVM and MRMR (for feature selection) with bootstrapping procedure.

Allows to map species richness and endemism based on stacked species distribution models (SSDM). Individuals SDMs can be created using a single or multiple algorithms (ensemble SDMs). For each species, an SDM can yield a habitat suitability map, a binary map, a between-algorithm variance map, and can assess variable importance, algorithm accuracy, and between- algorithm correlation. Methods to stack individual SDMs include summing individual probabilities and thresholding then summing. Thresholding can be based on a specific evaluation metric or by drawing repeatedly from a Bernoulli distribution. The SSDM package also provides a user-friendly interface.

The sdrt() function is designed for estimating subspaces for Sufficient Dimension Reduction (SDR) in time series, with a specific focus on the Time Series Central Mean subspace (TS-CMS). The package employs the Fourier transformation method proposed by Samadi and De Alwis (2023) <doi:10.48550/arXiv.2312.02110> and the Nadaraya-Watson kernel smoother method proposed by Park et al. (2009) <doi:10.1198/jcgs.2009.08076> for estimating the TS-CMS. The package provides tools for estimating distances between subspaces and includes functions for selecting model parameters using the Fourier transformation method.

An easy to use implementation of routine structural missing data diagnostics with functions to visualize the proportions of missing observations, investigate missing data patterns and conduct various empirical missing data diagnostic tests. Reference: Weberpals J, Raman SR, Shaw PA, Lee H, Hammill BG, Toh S, Connolly JG, Dandreo KJ, Tian F, Liu W, Li J, Hernández-Muñoz JJ, Glynn RJ, Desai RJ. smdi: an R package to perform structural missing data investigations on partially observed confounders in real-world evidence studies. JAMIA Open. 2024 Jan 31;7(1):ooae008. <doi:10.1093/jamiaopen/ooae008>.

Comprehensive set of tools for analyzing and manipulating functional data with non-uniform lengths. This package addresses two common scenarios in functional data analysis: Variable Domain Data, where the observation domain differs across samples, and Partially Observed Data, where observations are incomplete over the domain of interest. VDPO enhances the flexibility and applicability of functional data analysis in R'. See Amaro et al. (2024) <doi:10.48550/arXiv.2401.05839>, Hernandez-Amaro et al. (2025) <doi:10.48550/arXiv.2510.26917>, and Hernandez-Amaro et al. (2026) <doi:10.48550/arXiv.2605.03633>.

This package provides interface to the Copernicus Data Space Ecosystem API <https://dataspace.copernicus.eu/analyse/apis>, mainly for searching the catalog of available data from Copernicus Sentinel missions and obtaining the images for just the area of interest based on selected spectral bands. The package uses the Sentinel Hub REST API interface <https://dataspace.copernicus.eu/analyse/apis/sentinel-hub> that provides access to various satellite imagery archives. It allows you to access raw satellite data, rendered images, statistical analysis, and other features. This package is in no way officially related to or endorsed by Copernicus.

Solves multivariate least squares (MLS) problems subject to constraints on the coefficients, e.g., non-negativity, orthogonality, equality, inequality, monotonicity, unimodality, smoothness, etc. Includes flexible functions for solving MLS problems subject to user-specified equality and/or inequality constraints, as well as a wrapper function that implements 24 common constraint options. Also does k-fold or generalized cross-validation to tune constraint options for MLS problems. See ten Berge (1993, ISBN:9789066950832) for an overview of MLS problems, and see Goldfarb and Idnani (1983) <doi:10.1007/BF02591962> for a discussion of the underlying quadratic programming algorithm.

This package provides tools for factor analysis in high-dimensional settings under copula-based factor models. It includes functions to simulate factor-model data with copula-distributed idiosyncratic errors (e.g., Clayton, Gumbel, Frank, Student t and Gaussian copulas) and to perform diagnostic tests such as the Kaiser-Meyer-Olkin measure and Bartlett's test of sphericity. Estimation routines include principal component based factor analysis, projected principal component analysis, and principal orthogonal complement thresholding for large covariance matrix estimation. The philosophy of the package is described in Guo G. (2023) <doi:10.1007/s00180-022-01270-z>.

This package provides methods to detect differential item functioning (DIF) in dichotomous, polytomous, and continuous items, using both classical and modern approaches. These include Mantel-Haenszel procedures, logistic regression (including ordinal models), and regularization-based methods such as LASSO. Uniform and non-uniform DIF effects can be detected, and some methods support multiple focal groups. The package also provides tools for anchor purification, rest score matching, effect size estimation, and DIF simulation. See Magis, Beland, Tuerlinckx, and De Boeck (2010, Behavior Research Methods, 42, 847â 862, <doi:10.3758/BRM.42.3.847>) for a general overview.

Fit model for datasets with easy-to-interpret Gaussian process modeling, predict responses for new inputs. The input variables of the datasets can be quantitative, qualitative/categorical or mixed. The output variable of the datasets is a scalar (quantitative). The optimization of the likelihood function can be chosen by the users (see the documentation of EzGP_fit()). The modeling method is published in "EzGP: Easy-to-Interpret Gaussian Process Models for Computer Experiments with Both Quantitative and Qualitative Factors" by Qian Xiao, Abhyuday Mandal, C. Devon Lin, and Xinwei Deng (2022) <doi:10.1137/19M1288462>.

This package provides a method to integrate molecular profiles of cancer patients (gene copy number and mRNA abundance) to identify candidate gain of function alterations. These candidate alterations can be subsequently further tested to discover cancer driver alterations. Briefly, this method tests of genomic correlates of mRNA dysregulation and prioritise those where DNA gains/amplifications are associated with elevated mRNA expression of the same gene. For details see, Haider S et al. (2016) "Genomic alterations underlie a pan-cancer metabolic shift associated with tumour hypoxia", Genome Biology, <https://pubmed.ncbi.nlm.nih.gov/27358048/>.

An implementation of the Invariance Partial Pruning (IVPP) approach described in Du, X., Johnson, S. U., Epskamp, S. (2025) The Invariance Partial Pruning Approach to The Network Comparison in Longitudinal Data. IVPP is a two-step method that first test for global network structural difference with invariance test and then inspect specific edge difference with partial pruning. The package also allows you to compute centrality measures and use radar chart to plot. Analysis of bridge centralities by community pairs is also possible (e.g., the bridge strength from depression to anxiety, and from depression to panic disorder).

This package provides a unified and consistent S3 interface for training and predicting with a variety of machine learning models in R. The package wraps popular algorithms (e.g., from glmnet', lightgbm', ranger', e1071', and caret') under a common workflow based on simple wrap_*() and predict() functions, allowing users to switch between models without changing their code structure. It supports both classification and regression tasks and facilitates rapid experimentation, benchmarking, and comparison of models. By abstracting away package-specific APIs while preserving flexibility in parameter specification, the package streamlines machine learning workflows and promotes reproducibility.

This package implements the Segment Profile Extraction via Pattern Analysis method for row-mean-centered multivariate data. Core capabilities include SVD-based row-isometric biplot construction, bias-corrected and accelerated, and percentile bootstrap confidence intervals for domain coordinates and per-person direction cosines, Procrustes alignment of bootstrap replicates across planes, parallel analysis for dimensionality selection, and segment profile reconstruction in planes defined by pairs of singular dimensions. A synthetic Woodcock-Johnson IV look-alike dataset is provided for examples and testing. The method is described in Kim and Grochowalski (2019) <doi:10.1007/s00357-018-9277-7>.

Structurally guided sampling (SGS) approaches for airborne laser scanning (ALS; LIDAR). Primary functions provide means to generate data-driven stratifications & methods for allocating samples. Intermediate functions for calculating and extracting important information about input covariates and samples are also included. Processing outcomes are intended to help forest and environmental management practitioners better optimize field sample placement as well as assess and augment existing sample networks in the context of data distributions and conditions. ALS data is the primary intended use case, however any rasterized remote sensing data can be used, enabling data-driven stratifications and sampling approaches.

This package provides tools for the analysis of epidemiological and surveillance data. It contains functions for directly and indirectly adjusting measures of disease frequency, quantifying measures of association on the basis of single or multiple strata of count data presented in a contingency table, computation of confidence intervals around incidence risk and incidence rate estimates and sample size calculations for cross-sectional, case-control and cohort studies. Surveillance tools include functions to calculate an appropriate sample size for 1- and 2-stage representative freedom surveys, functions to estimate surveillance system sensitivity and functions to support scenario tree modelling analyses.