This package is devoted to analyzing high-throughput data (e.g. gene expression microarray, DNA methylation microarray, RNA-seq) from complex tissues. Current functionalities include

1. detect cell-type specific or cross-cell type differential signals

2. tree-based differential analysis

3. improve variable selection in reference-free deconvolution

4. partial reference-free deconvolution with prior knowledge.

ADAPT carries out differential abundance analysis for microbiome metagenomics data in phyloseq format. It has two innovations. One is to treat zero counts as left censored and use Tobit models for log count ratios. The other is an innovative way to find non-differentially abundant taxa as reference, then use the reference taxa to find the differentially abundant ones.

Exon-intron split analysis (EISA) uses ordinary RNA-seq data to measure changes in mature RNA and pre-mRNA reads across different experimental conditions to quantify transcriptional and post-transcriptional regulation of gene expression. For details see Gaidatzis et al., Nat Biotechnol 2015. doi: 10.1038/nbt.3269. eisaR implements the major steps of EISA in R.

This package is designed to model gene detection pattern of scRNA-seq through a binary factor analysis model. This model allows user to pass into a cell level covariate matrix X and gene level covariate matrix Q to account for nuisance variance(e.g batch effect), and it will output a low dimensional embedding matrix for downstream analysis.

This package provides functions to convert origin-destination data, represented as straight desire lines in the sf Simple Features class system, into JSON files that can be directly imported into A/B Street <https://www.abstreet.org>, a free and open source tool for simulating urban transport systems and scenarios of change <doi:10.1007/s10109-020-00342-2>.

Finds the k nearest neighbours in a dataset of specified points, adding the option to wrap certain variables on a torus. The user chooses the algorithm to use to find the nearest neighbours. Two such algorithms, provided by the packages RANN <https://cran.r-project.org/package=RANN>, and nabor <https://cran.r-project.org/package=nabor>, are suggested.

Parses financial condition and performance data (Call Reports) for institutions in the United States Farm Credit System. Contains functions for downloading files from the Farm Credit Administration (FCA) Call Report archive website and reading the files into tidy data frame format. The archive website can be found at <https://www.fca.gov/bank-oversight/call-report-data-for-download>.

Generate commonly used plots in the field of design of experiments using ggplot2'. ggDoE currently supports the following plots: alias matrix, box cox transformation, boxplots, lambda plot, regression diagnostic plots, half normal plots, main and interaction effect plots for factorial designs, contour plots for response surface methodology, Pareto plot, and two dimensional projections of a latin hypercube design.

An EM algorithm, Karl et al. (2013) <doi:10.1016/j.csda.2012.10.004>, is used to estimate the generalized, variable, and complete persistence models, Mariano et al. (2010) <doi:10.3102/1076998609346967>. These are multiple-membership linear mixed models with teachers modeled as "G-side" effects and students modeled with either "G-side" or "R-side" effects.

Simulate an inhomogeneous self-exciting process (IHSEP), or Hawkes process, with a given (possibly time-varying) baseline intensity and an excitation function. Calculate the likelihood of an IHSEP with given baseline intensity and excitation functions for an (increasing) sequence of event times. Calculate the point process residuals (integral transforms of the original event times). Calculate the mean intensity process.

This package implements a nonparametric maximum likelihood method for assessing potentially time-varying vaccine efficacy (VE) against SARS-CoV-2 infection under staggered enrollment and time-varying community transmission, allowing crossover of placebo volunteers to the vaccine arm. Lin, D. Y., Gu, Y., Zeng, D., Janes, H. E., and Gilbert, P. B. (2021) <doi:10.1093/cid/ciab630>.

This package provides an efficient implementation of univariate local polynomial kernel density estimators that can handle bounded and discrete data. See Geenens (2014) <doi:10.48550/arXiv.1303.4121>, Geenens and Wang (2018) <doi:10.48550/arXiv.1602.04862>, Nagler (2018a) <doi:10.48550/arXiv.1704.07457>, Nagler (2018b) <doi:10.48550/arXiv.1705.05431>.

To decompose symmetric matrices such as brain connectivity matrices so that one can extract sparse latent component matrices and also estimate mixing coefficients, a blind source separation (BSS) method named LOCUS was proposed in Wang and Guo (2023) <arXiv:2008.08915>. For brain connectivity matrices, the outputs correspond to sparse latent connectivity traits and individual-level trait loadings.

The maybe type represents the possibility of some value or nothing. It is often used instead of throwing an error or returning `NULL`. The advantage of using a maybe type over `NULL` is that it is both composable and requires the developer to explicitly acknowledge the potential absence of a value, helping to avoid the existence of unexpected behaviour.

Fit Bayesian Dynamic Generalized Additive Models to multivariate observations. Users can build nonlinear State-Space models that can incorporate semiparametric effects in observation and process components, using a wide range of observation families. Estimation is performed using Markov Chain Monte Carlo with Hamiltonian Monte Carlo in the software Stan'. References: Clark & Wells (2023) <doi:10.1111/2041-210X.13974>.

Identifies the optimal number of clusters by calculating the similarity between two clustering methods at the same number of clusters using the corrected indices of Rand and Jaccard as described in Albatineh and Niewiadomska-Bugaj (2011). The number of clusters at which the index attain its maximum more frequently is a candidate for being the optimal number of clusters.

Imputation for both missing covariates and censored observations (optional) for survival data with missing covariates by the nearest neighbor based multiple imputation algorithm as described in Hsu et al. (2006) <doi:10.1002/sim.2452>, and Hsu and Yu (2018) <doi: 10.1177/0962280218772592>. Note that the current version can only impute for a situation with one missing covariate.

This package provides utility functions and objects for Extreme Value Analysis. These include probability functions with their exact derivatives w.r.t. the parameters that can be used for estimation and inference, even with censored observations. The transformations exchanging the two parameterizations of Peaks Over Threshold (POT) models: Poisson-GP and Point-Process are also provided with their derivatives.

The purpose of this library is to to call different optimization solvers (such as Gonzalez Rodriguez et al. (2022) <doi:10.1007/s10898-022-01229-w>, Tawarmalani and Sahinidis (2005) <doi:10.1007/s10107-005-0581-8>, and Byrd et al. (2006) <doi:10.1007/0-387-30065-1_4>) to solve problems given by a standard nl file.

Optimal group-sequential designs minimise some function of the expected and maximum sample size whilst controlling the type I error rate and power at a specified level. OptGS provides functions to quickly search for near-optimal group-sequential designs for normally distributed outcomes. The methods used are described in Wason, JMS (2015) <doi:10.18637/jss.v066.i02>.

Implementation of the modified skew discrete Laplace (SDL) regression model. The package provides a set of functions for a complete analysis of integer-valued data, where the dependent variable is assumed to follow a modified SDL distribution. This regression model is useful for the analysis of integer-valued data and experimental studies in which paired discrete observations are collected.

This package provides crop yield and meteorological data for Ontario, Canada. Includes functions for fitting and predicting data using spatio-temporal models, as well as tools for visualizing the results. The package builds upon existing R packages, including copula (Hofert et al., 2025) <doi:10.32614/CRAN.package.copula>, and bsts (Scott, 2024) <doi:10.32614/CRAN.package.bsts>.

An integrated set of extensions to the ergm package to analyze and simulate network evolution based on exponential-family random graph models (ERGM). tergm is a part of the statnet suite of packages for network analysis. See Krivitsky and Handcock (2014) <doi:10.1111/rssb.12014> and Carnegie, Krivitsky, Hunter, and Goodreau (2015) <doi:10.1080/10618600.2014.903087>.

This package provides a toolbox to assist with statistical analysis of animal trajectories. It provides simple access to algorithms for calculating and assessing a variety of characteristics such as speed and acceleration, as well as multiple measures of straightness or tortuosity. Some support is provided for 3-dimensional trajectories. McLean & Skowron Volponi (2018) <doi:10.1111/eth.12739>.