The goal of rFIA is to increase the accessibility and use of the United States Forest Services (USFS) Forest Inventory and Analysis (FIA) Database by providing a user-friendly, open source toolkit to easily query and analyze FIA Data. Designed to accommodate a wide range of potential user objectives, rFIA simplifies the estimation of forest variables from the FIA Database and allows all R users (experts and newcomers alike) to unlock the flexibility inherent to the Enhanced FIA design. Specifically, rFIA improves accessibility to the spatial-temporal estimation capacity of the FIA Database by producing space-time indexed summaries of forest variables within user-defined population boundaries. Direct integration with other popular R packages (e.g., dplyr', tidyr', and sf') facilitates efficient space-time query and data summary, and supports common data representations and API design. The package implements design-based estimation procedures outlined by Bechtold & Patterson (2005) <doi:10.2737/SRS-GTR-80>, and has been validated against estimates and sampling errors produced by FIA EVALIDator'. Current development is focused on the implementation of spatially-enabled model-assisted and model-based estimators to improve population, change, and ratio estimates.

Helps users in quickly visualizing risk-of-bias assessments performed as part of a systematic review. It allows users to create weighted bar-plots of the distribution of risk-of-bias judgments within each bias domain, in addition to traffic-light plots of the specific domain-level judgments for each study. The resulting figures are of publication quality and are formatted according the risk-of-bias assessment tool use to perform the assessments. Currently, the supported tools are ROB2.0 (for randomized controlled trials; Sterne et al (2019) <doi:10.1136/bmj.l4898>), ROBINS-I (for non-randomised studies of interventions; Sterne et al (2016) <doi:10.1136/bmj.i4919>), and QUADAS-2 (for diagnostic accuracy studies; Whiting et al (2011) <doi:10.7326/0003-4819-155-8-201110180-00009>).

R-based access to mass-spectrometry (MS) data. While many packages exist to process MS data, many of these make it difficult to access the underlying mass-to-charge ratio (m/z), intensity, and retention time of the files themselves. This package is designed to format MS data in a tidy fashion and allows the user perform the plotting and analysis.

This package provides functions to access data from the Strava v3 API <https://developers.strava.com/>.

Finds a robust instrumental variables estimator using a high breakdown point S-estimator of multivariate location and scatter matrix.

This package performs the Joint and Individual Variation Explained (JIVE) decomposition on a list of data sets when the data share a dimension, returning low-rank matrices that capture the joint and individual structure of the data [O'Connell, MJ and Lock, EF (2016) <doi:10.1093/bioinformatics/btw324>]. It provides two methods of rank selection when the rank is unknown, a permutation test and a Bayesian Information Criterion (BIC) selection algorithm. Also included in the package are three plotting functions for visualizing the variance attributed to each data source: a bar plot that shows the percentages of the variability attributable to joint and individual structure, a heatmap that shows the structure of the variability, and principal component plots.

Makes documents containing plots and tables from a table of R codes. Can make "HTML", "pdf('LaTex')", "docx('MS Word')" and "pptx('MS Powerpoint')" documents with or without R code. In the package, modularized shiny app codes are provided. These modules are intended for reuse across applications.

Accessible and flexible implementation of three ecoacoustic indices that are less commonly available in existing R frameworks: Background Noise, Soundscape Power and Soundscape Saturation. The functions were design to accommodate a variety of sampling designs. Users can tailor calculations by specifying spectrogram time bin size, amplitude thresholds and normality tests. By simplifying computation and standardizing reproducible methods, the package aims to support ecoacoustics studies. For more details about the indices read Towsey (2017) <https://eprints.qut.edu.au/110634/> and Burivalova (2017) <doi:10.1111/cobi.12968>.

Pattern matching, extraction, replacement and other string processing operations using Google's RE2 <https://github.com/google/re2> regular-expression engine. Consistent interface (similar to stringr'). RE2 uses finite-automata based techniques, and offers a fast and safe alternative to backtracking regular-expression engines like those used in stringr', stringi and other PCRE implementations.

Client for the Ocean Biodiversity Information System (<https://obis.org>).

This package provides functions to analyse DNA fragment samples (i.e. derived from RFLP-analysis) and standalone BLAST report files (i.e. DNA sequence analysis).

This package provides tools for randomization-based inference. Current focus is on the d^2 omnibus test of differences of means following Hansen and Bowers (2008) <doi:10.1214/08-STS254> . This test is useful for assessing balance in matched observational studies or for analysis of outcomes in block-randomized experiments.

Allows for production of Czekanowski's Diagrams with clusters. See K. Bartoszek, A. Vasterlund (2020) <doi:10.2478/bile-2020-0008> and K. Bartoszek, Y. Luo (2023) <doi:10.14708/ma.v51i2.7259>. The suggested FuzzyDBScan package (which allows for fuzzy clustering) can be obtained from <https://github.com/henrifnk/FuzzyDBScan/> (or from CRAN's Archive <https://cran.r-project.org/src/contrib/Archive/FuzzyDBScan/>).

Enhances the R Optimization Infrastructure ('ROI') package by registering the SYMPHONY open-source solver from the COIN-OR suite. It allows for solving mixed integer linear programming (MILP) problems as well as all variants/combinations of LP, IP.

This package performs wood cell anatomical data analyses on spatially explicit xylem (tracheids) datasets derived from thin sections of woody tissue. The package includes functions for visualisation, detection and alignment of continuous tracheid radial file (defined as rows) and individual tracheid position within an annual ring of coniferous species. This package is designed to be used with elaborate cell output, e.g. as provided with ROXAS (von Arx & Carrer, 2014 <doi:10.1016/j.dendro.2013.12.001>). The package has been validated for Picea abies, Larix Siberica, Pinus cembra and Pinus sylvestris.

Utilities to access Integrated Food Security Phase Classification (IPC) and Cadre Harmonisé (CH) food security data. Wrapper functions are available for all of the IPC-CH Public API (<https://docs.api.ipcinfo.org>) simplified and advanced endpoints to easily download the data in a clean and tidy format.

This package provides realistic synthetic example datasets for the R4SUB (R for Regulatory Submission) ecosystem. Includes a pharma study evidence table, ADaM (Analysis Data Model) and SDTM (Study Data Tabulation Model) metadata following CDISC (Clinical Data Interchange Standards Consortium) conventions (<https://www.cdisc.org>), traceability mappings, a risk register based on ICH (International Council for Harmonisation) Q9 quality risk management principles (<https://www.ich.org/page/quality-guidelines>), and regulatory indicator definitions. Designed for demos, vignettes, and package testing.

Microbenchmarks for determining the run time performance of aspects of the R programming environment and packages relevant to high-performance computation. The benchmarks are divided into three categories: dense matrix linear algebra kernels, sparse matrix linear algebra kernels, and machine learning functionality.

HydroBudget is a spatially distributed groundwater recharge model that computes a superficial water budget on grid cells with outputs aggregated into monthly time steps. It was developed as an accessible and computationally affordable model to simulate groundwater recharge over large areas (thousands of km2, regional-scale watersheds) and for long time periods (decades), in cold and humid climates. Model algorithms are based on the research of Dubois, E. et al. (2021a) <doi:10.5683/SP3/EUDV3H> and Dubois, E. et al. (2021b) <doi:10.5194/hess-25-6567-2021>.

This package provides utilities for processing and analyzing dyadic data collected using a round-robin design, in which each person in a group rates or interacts with every other person on at least one variable. Data manipulation functions prepare datasets for dyadic data analysis by creating the actor and partner dummy variables required by the social relations model (SRM). Analysis functions implement the SRM using multilevel modeling via a custom nlme covariance class ('pdSRM'), following the approach of Snijders and Kenny (1999) <doi:10.1111/j.1475-6811.1999.tb00204.x> and Knight and Humphrey (2019) <doi:10.1037/0000115-019>. The package estimates group, actor, partner, and relationship variance components along with generalized and dyadic reciprocity correlations, and supports both null and fixed-effects models.

This package provides a framework for Bayesian meta-analysis, including model estimation, prior specification, model comparison, prediction, summaries, visualizations, and diagnostics. The package fits single and model-averaged meta-analytic, meta-regression, multilevel, publication bias adjusted, and generalized linear mixed models The model-averaged meta-analytic models combine competing models based on their predictive performance, weight inference by posterior model probabilities, and test model components using Bayes factors (e.g., effect vs. no effect; Bartoš et al., 2022, <doi:10.1002/jrsm.1594>; Maier, Bartoš & Wagenmakers, 2022, <doi:10.1037/met0000405>; Bartoš et al., 2025, <doi:10.1037/met0000737>). Users can specify flexible prior distributions for effect sizes, heterogeneity, publication bias (including selection models and PET-PEESE), and moderators.

Download the latest data from the Australian Prudential Regulation Authority <https://www.apra.gov.au/> and import it into R as a tidy data frame.

Estimation of Bayes and local Bayes false discovery rates for replicability analysis (Heller & Yekutieli, 2014 <doi:10.1214/13-AOAS697> ; Heller at al., 2015 <doi: 10.1093/bioinformatics/btu434>).

Extend Rasch and Item Response Theory (IRT) analyses by providing tools for post-processing the output from five major IRT packages (i.e., eRm', psychotools', ltm', mirt', and TAM'). The current version provides the plotPIccc() function, which extracts from the return object of the originating package all information required to draw an extended Person-Item-Map (PIccc), showing any combination of * category characteristic curves (CCCs), * threshold characteristic curves (TCCs), * item characteristic curves (ICCs), * category information functions (CIFs), * item information functions (IIFs), * test information function (TIF), and the * standard error curve (S.E.). for uni- and multidimensional models (as far as supported by each package). It allows for selecting dimensions, items, and categories to plot and offers numerous options to adapt the output. The return object contains all calculated values for further processing.