Analysis of experimental results and automatic report generation in both interactive HTML and LaTeX. This package ships with a rich interface for data modeling and built in functions for the rapid application of statistical tests and generation of common plots and tables with publish-ready quality.

This package provides functions for estimating plant pathogen parameters from access period (AP) experiments. Separate functions are implemented for semi-persistently transmitted (SPT) and persistently transmitted (PT) pathogens. The common AP experiment exposes insect cohorts to infected source plants, healthy test plants, and intermediate plants (for PT pathogens). The package allows estimation of acquisition and inoculation rates during feeding, recovery rates, and latent progression rates (for PT pathogens). Additional functions support inference of epidemic risk from pathogen and local parameters, and also simulate AP experiment data. The functions implement probability models for epidemiological analysis, as derived in Donnelly et al. (2025), <doi:10.32942/X29K9P>. These models were originally implemented in the EpiPv GitHub package.

Estimates coefficients of extended LASSO penalized linear regression and generalized linear models. Currently lasso and elastic net penalized linear regression and generalized linear models are considered. This package currently utilizes an accurate approximation of L1 penalty and then a modified Jacobi algorithm to estimate the coefficients. There is provision for plotting of the solutions and predictions of coefficients at given values of lambda. This package also contains functions for cross validation to select a suitable lambda value given the data. Also provides a function for estimation in fused lasso penalized linear regression. For more details, see Mandal, B. N.(2014). Computational methods for L1 penalized GLM model fitting, unpublished report submitted to Macquarie University, NSW, Australia.

Easily export R graphs and statistical output to Microsoft Office / LibreOffice', Latex and HTML Documents, using sensible defaults that result in publication-quality output with simple, straightforward commands. Output to Microsoft Office is in editable DrawingML vector format for graphs, and can use corporate template documents for styling. This enables the production of standardized reports and also allows for manual tidy-up of the layout of R graphs in Powerpoint before final publication. Export of graphs is flexible, and functions enable the currently showing R graph or the currently showing R stats object to be exported, but also allow the graphical or tabular output to be passed as objects. The package relies on package officer for export to Office documents,and output files are also fully compatible with LibreOffice'. Base R', ggplot2 and lattice plots are supported, as well as a wide variety of R stats objects, via wrappers to xtable(), broom::tidy() and stargazer(), including aov(), lm(), glm(), lme(), glmnet() and coxph() as well as matrices and data frames and many more...

Simplifies some complicated and labor intensive processes involved in exploring and explaining data. Allows you to quickly and efficiently visualize the interaction between variables and simplifies the process of discovering covariation in your data. Also includes some convenience features designed to remove as much redundant typing as possible.

Set of wrappers for the ncdf4 package to simplify and extend its reading/writing capabilities into/from multidimensional R arrays.

This package provides a set of functions to estimate capture probabilities and densities from multipass pass removal data.

This package implements likelihood-based evidence ratios for unified reporting in classical statistical testing. The package reports effect estimates, uncertainty intervals, and likelihood ratios on the log 10 scale derived from a single statistical model. It applies to standard normal mean tests, contingency tables, and regression coefficients, and provides a direct evidential measure while retaining classical error guarantees. For the Evidence Ratio Reporting Standard see Lawless (2026) <doi:10.5281/zenodo.18261076>.

Create causal models for use in epidemiological studies, including sufficient-component cause models as introduced by Rothman (1976) <doi:10.1093/oxfordjournals.aje.a112335>.

This cointegration based Time Delay Neural Network Model hybrid model allows the researcher to make use of the information extracted by the cointegrating vector as an input in the neural network model.

Event dataset repository including both real-life and artificial event logs. They can be used in combination with functionalities provided by the bupaR packages. Janssenswillen et al. (2020) <http://ceur-ws.org/Vol-2703/paperTD7.pdf>.

This package provides a tool to draw samples from a Empirical Likelihood Bayesian posterior of parameters using Hamiltonian Monte Carlo.

This package provides a complete rewrite and reimagining of bakR (see Vock et al. (2025) <doi:10.1371/journal.pcbi.1013179>). Designed to support a wide array of analyses of nucleotide recoding RNA-seq (NR-seq) datasets of any type, including TimeLapse-seq/SLAM-seq/TUC-seq, Start-TimeLapse-seq (STL-seq), TT-TimeLapse-seq (TT-TL-seq), and subcellular NR-seq. EZbakR extends standard NR-seq standard NR-seq mutational modeling to support multi-label analyses (e.g., 4sU and 6sG dual labeling), and implements an improved hierarchical model to better account for transcript-to-transcript variance in metabolic label incorporation. EZbakR also generalized dynamical systems modeling of NR-seq data to support analyses of premature mRNA processing and flow between subcellular compartments. Finally, EZbakR implements flexible and well-powered comparative analyses of all estimated parameters via design matrix-specified generalized linear modeling.

Implementation of the Edge Selection Algorithm for undirected graph selection. The least angle regression-based algorithm selects edges of an undirected graph based on the projection of the current residuals on the two dimensional edge-planes. The algorithm selects symmetric adjacency matrix, which many other regression-based undirected graph selection procedures cannot do.

This package provides a set of tools to perform Ecological Niche Modeling with presence-absence data. It includes algorithms for data partitioning, model fitting, calibration, evaluation, selection, and prediction. Other functions help to explore signals of ecological niche using univariate and multivariate analyses, and model features such as variable response curves and variable importance. Unique characteristics of this package are the ability to exclude models with concave quadratic responses, and the option to clamp model predictions to specific variables. These tools are implemented following principles proposed in Cobos et al., (2022) <doi:10.17161/bi.v17i.15985>, Cobos et al., (2019) <doi:10.7717/peerj.6281>, and Peterson et al., (2008) <doi:10.1016/j.ecolmodel.2007.11.008>.

Two classifiers for open set recognition and novelty detection based on extreme value theory. The first classifier is based on the generalized Pareto distribution (GPD) and the second classifier is based on the generalized extreme value (GEV) distribution. For details, see Vignotto, E., & Engelke, S. (2018) <arXiv:1808.09902>.

Measurement and partitioning of diversity, based on Tsallis entropy, following Marcon and Herault (2015) <doi:10.18637/jss.v067.i08>. entropart provides functions to calculate alpha, beta and gamma diversity of communities, including phylogenetic and functional diversity. Estimation-bias corrections are available.

Modular implementation of the Differential Evolution algorithm for experimenting with different types of operators.

This package provides a comprehensive toolkit for single-cell annotation with the CellMarker2.0 database (see Xia Li, Peng Wang, Yunpeng Zhang (2023) <doi: 10.1093/nar/gkac947>). Streamlines biological label assignment in single-cell RNA-seq data and facilitates transcriptomic analysis, including preparation of TCGA<https://portal.gdc.cancer.gov/> and GEO<https://www.ncbi.nlm.nih.gov/geo/> datasets, differential expression analysis and visualization of enrichment analysis results. Additional utility functions support various bioinformatics workflows. See Wei Cui (2024) <doi: 10.1101/2024.09.14.609619> for more details.

This package provides a plotting package for climate science and services. Provides a set of functions for visualizing climate data, including maps, time series, scorecards and other diagnostics. Some functions are adapted and extended from the s2dv and CSTools packages (Manubens et al. (2018) <doi:10.1016/j.envsoft.2018.01.018>; Pérez-Zanón et al. (2022) <doi:10.5194/gmd-15-6115-2022>), with more consistent and integrated functionalities.

Description: Application of empirical mode decomposition based support vector regression model for nonlinear and non stationary univariate time series forecasting. For method details see (i) Choudhury (2019) <http://krishi.icar.gov.in/jspui/handle/123456789/44873>; (ii) Das (2020) <http://krishi.icar.gov.in/jspui/handle/123456789/43174>; (iii) Das (2023) <http://krishi.icar.gov.in/jspui/handle/123456789/77772>.

This package provides basic distribution functions for a mixture model of a Gaussian and exponential distribution.

Training and prediction functions are provided for the Extreme Learning Machine algorithm (ELM). The ELM use a Single Hidden Layer Feedforward Neural Network (SLFN) with random generated weights and no gradient-based backpropagation. The training time is very short and the online version allows to update the model using small chunk of the training set at each iteration. The only parameter to tune is the hidden layer size and the learning function.

Initially designed to distribute code for estimating the Gaussian graphical model with Lasso regularization, also known as the graphical lasso (glasso), using an Expectation-Maximization (EM) algorithm based on work by Städler and Bühlmann (2012) <doi:10.1007/s11222-010-9219-7>. As a byproduct, code for estimating means and covariances (or the precision matrix) under a multivariate normal (Gaussian) distribution is also available.