Offers a set of functions to easily make predictions for univariate time series. autoTS is a wrapper of existing functions of the forecast and prophet packages, harmonising their outputs in tidy dataframes and using default values for each. The core function getBestModel() allows the user to effortlessly benchmark seven algorithms along with a bagged estimator to identify which one performs the best for a given time series.

Fetch Australian Taxation Office (ATO) Taxation Statistics and related datasets via the data.gov.au Comprehensive Knowledge Archive Network ('CKAN') API <https://data.gov.au/data/api/3/>. Provides tidy access to individual, company, superannuation, goods and services tax (GST), fringe benefits tax (FBT), Voluntary Tax Transparency Code (VTTC), Pay As You Go (PAYG) withholding, charity, excise, and Corporate Tax Transparency data, plus Petroleum Resource Rent Tax, Medicare Levy Surcharge, and fuel tax credit aggregates. Includes reproducibility helpers (snapshot pinning, SHA-256 cache integrity, session manifest, optional Zenodo deposit), classification crosswalks (ANZSIC 2006 to 2020, ANZSCO 2013 to 2021), panel harmonisation, reconciliation against Final Budget Outcome totals, and real-terms and per-capita helpers backed by bundled Australian Bureau of Statistics (ABS) Consumer Price Index and Estimated Resident Population series. Bridges to the taxstats 2 per cent microdata sample via column-schema mapping. Data is published by the Australian Taxation Office under Creative Commons Attribution 2.5 Australia or 3.0 Australia licences (dataset-dependent).

Semi-distributed Precipitation-Runoff Modeling based on airGR package models integrating human infrastructures and their managements.

This package provides tools for Bayesian parameter estimation of adsorption isotherm models using Markov Chain Monte Carlo (MCMC) methods. This package enables users to fit non-linear and linear adsorption isotherm modelsâ Freundlich, Langmuir, and Temkinâ within a probabilistic framework, capturing uncertainty and parameter correlations. It provides posterior summaries, 95% credible intervals, convergence diagnostics (Gelman-Rubin), and visualizations through trace and density plots. With this R package, researchers can rigorously analyze adsorption behavior in environmental and chemical systems using robust Bayesian inference. For more details, see Gilks et al. (1995) <doi:10.1201/b14835>, and Gamerman & Lopes (2006) <doi:10.1201/9781482296426>.

Wraps the Ace editor in a HTML widget. The Ace editor has support for many languages. It can be opened in the viewer pane of RStudio', and this provides a second source editor.

This package provides novel nonparametric tests, APCSSA and APCSSM', for interaction in two-way ANOVA designs with balanced replications using all possible comparisons. These statistics extend previous methods, allow greater flexibility, and demonstrate higher power in detecting interactions for non-normal data. The package includes optimized functions for computing these test statistics, generating interaction plots, and simulating their null distributions. The companion package APCinteractionData is available on GitHub <https://github.com/tranbaokhue/APCinteractionData>. Methods are described and compared empirically in Tran, Wagaman, Nguyen, Jacobson, and Hartlaub (2024) <doi:10.48550/arXiv.2410.04700>.

Adaptive Rejection Sampling, Original version.

This package provides a small, dependency-light toolkit for talking to token-authenticated REST APIs. It manages authentication tokens in process environment variables (never written to disk), builds requests with configurable authentication and pagination strategies, and retrieves paginated data either one page at a time or in chunks combined into a single tibble. The design is API-agnostic: a single apifetch_api profile describes an endpoint together with how it authenticates and paginates, so the same verbs work across different services.

Bindings to libarchive <http://www.libarchive.org> the Multi-format archive and compression library. Offers R connections and direct extraction for many archive formats including tar', ZIP', 7-zip', RAR', CAB and compression formats including gzip', bzip2', compress', lzma and xz'.

This package provides a toolbox for programming Clinical Data Standards Interchange Consortium (CDISC) compliant Analysis Data Model (ADaM) datasets in R. ADaM datasets are a mandatory part of any New Drug or Biologics License Application submitted to the United States Food and Drug Administration (FDA). Analysis derivations are implemented in accordance with the "Analysis Data Model Implementation Guide" (CDISC Analysis Data Model Team, 2021, <https://www.cdisc.org/standards/foundational/adam>). The package is an extension package of the admiral package focusing on the metabolism therapeutic area.

Make the compiled Java modules of the Amazon Web Services ('AWS') SDK available to be used in downstream R packages interacting with AWS'. See <https://aws.amazon.com/sdk-for-java> for more information on the AWS SDK for Java.

Fitting, simulation, and inference for antedependence models for longitudinal data, as described in Zimmerman and Nunez-Anton (2009, ISBN:9781420011074). Supports integer-valued antedependence (INAD) models for count data with thinning operators (binomial, Poisson, negative binomial) and flexible innovation distributions (Poisson, Bell, negative binomial), categorical antedependence models for discrete-state longitudinal outcomes, and Gaussian antedependence (AD) models for continuous data. Implements maximum likelihood estimation via time-separable optimization and block coordinate descent, with confidence intervals based on Louis identity and profile likelihood.

Forced-choice (FC) response has gained increasing popularity and interest for its resistance to faking when well-designed (Cao & Drasgow, 2019 <doi:10.1037/apl0000414>). To established well-designed FC scales, typically each item within a block should measure different trait and have similar level of social desirability (Zhang et al., 2020 <doi:10.1177/1094428119836486>). Recent study also suggests the importance of high inter-item agreement of social desirability between items within a block (Pavlov et al., 2021 <doi:10.31234/osf.io/hmnrc>). In addition to this, FC developers may also need to maximize factor loading differences (Brown & Maydeu-Olivares, 2011 <doi:10.1177/0013164410375112>) or minimize item location differences (Cao & Drasgow, 2019 <doi:10.1037/apl0000414>) depending on scoring models. Decision of which items should be assigned to the same block, also called as item pairing, is thus critical to the quality of an FC test. Because such pairing process often requires researchers to meet multiple objectives, manual pairing becomes impractical or even not feasible once the number of latent traits and/or number of items per elevates. To address these problems, autoFC is developed as a automatic and efficient tool for facilitating the automatic construction of FC tests (Li et al., 2022 <doi:10.1177/01466216211051726>), essentially exempting users from the burden of manual item pairing. Given characteristics of each item (and item responses), FC measures can be constructed either automatically based on user-defined pairing criteria and weights, or based on exact specifications of each block (i.e., blueprint; see Li et al., 2025 <doi:10.1177/10944281241229784>). Users can also generate simulated responses based on the Thurstonian Item Response Theory model (Brown & Maydeu-Olivares, 2011 <doi:10.1177/0013164410375112>) and predict trait scores of simulated/actual respondents based on an estimated model.

This package provides tools for the automatic stratification of survey populations using clustering and optimization techniques. The package assists researchers and survey practitioners in constructing homogeneous strata to improve the efficiency and precision of survey estimates. Functions are provided for generating strata, evaluating stratification quality, summarizing stratified populations, and visualizing stratification results. These tools support the design and implementation of efficient survey sampling strategies. The package utilizes standard statistical methods from survey sampling, clustering, and optimization for automatic stratification. Methods are described in Cochran (1977, ISBN:9780471162405) and Lohr (2021, ISBN:9780367354556).

This package provides a novel parametrization of log transformation and a shift parameter to automate the transformation process are proposed in R package AutoTransQF based on Feng et al. (2016). Please read Feng et al. (2016) <doi:10.1002/sta4.104> for more details of the method.

The AHP method (Analytic Hierarchy Process) is a multi-criteria decision-making method addressing choice and outranking problems. The method enables to perform the analysis of alternatives in each type of criterion and then provides a global performance of each alternative in the decision context. The main difference of this package is the possibility of evaluating the alternatives using quantitative data, by numerical representation, and qualitative data, using the Saaty scale, providing preference relation between variables by a pairwise evaluation.

Calculates concentration and dispersion in ordered rating scales. It implements various measures of concentration and dispersion to describe what researchers variably call agreement, concentration, consensus, dispersion, or polarization among respondents in ordered data. It also implements other related measures to classify distributions. In addition to a generic city-block based concentration measure and a generic dispersion measure, the package implements various measures, including van der Eijk's (2001) <DOI: 10.1023/A:1010374114305> measure of agreement A, measures of concentration by Leik, Tatsle and Wierman, Blair and Lacy, Kvalseth, Berry and Mielke, Reardon, and Garcia-Montalvo and Reynal-Querol. Furthermore, the package provides an implementation of Galtungs AJUS-system to classify distributions, as well as a function to identify the position of multiple modes.

Analyses of frequencies can be performed using an alternative test based on the G statistic. The test has similar type-I error rates and power as the chi-square test. However, it is based on a total statistic that can be decomposed in an additive fashion into interaction effects, main effects, simple effects, contrast effects, etc., mimicking precisely the logic of ANOVA. We call this set of tools ANOFA (Analysis of Frequency data) to highlight its similarities with ANOVA. This framework also renders plots of frequencies along with confidence intervals. Finally, effect sizes and planning statistical power are easily done under this framework. The ANOFA is a tool that assesses the significance of effects instead of the significance of parameters; as such, it is more intuitive to most researchers than alternative approaches based on generalized linear models. See Laurencelle and Cousineau (2023) <doi:10.20982/tqmp.19.2.p173>.

This package provides functions to compute various clinical scores used in healthcare. These include the Charlson Comorbidity Index (CCI), predicting 10-year survival in patients with multiple comorbidities; the EPICES score, an individual indicator of precariousness considering its multidimensional nature; the MELD score for chronic liver disease severity; the Alternative Fistula Risk Score (a-FRS) for postoperative pancreatic fistula risk; and the Distal Pancreatectomy Fistula Risk Score (D-FRS) for risk following distal pancreatectomy. For detailed methodology, refer to Charlson et al. (1987) <doi:10.1016/0021-9681(87)90171-8> , Sass et al. (2006) <doi:10.1007/s10332-006-0131-5>, Kamath et al. (2001) <doi:10.1053/jhep.2001.22172>, Kim et al. (2008) <doi:10.1056/NEJMoa0801209> Kim et al. (2021) <doi:10.1053/j.gastro.2021.08.050>, Mungroop et al. (2019) <doi:10.1097/SLA.0000000000002620>, and de Pastena et al. (2023) <doi:10.1097/SLA.0000000000005497>..

Another implementation of object-orientation in R. It provides syntactic sugar for the S4 class system and two alternative new implementations. One is an experimental version built around S4 and the other one makes it more convenient to work with lists as objects.

Evaluates land suitability for different crops production. The package is based on the Food and Agriculture Organization (FAO) and the International Rice Research Institute (IRRI) methodology for land evaluation. Development of ALUES is inspired by similar tool for land evaluation, Land Use Suitability Evaluation Tool (LUSET). The package uses fuzzy logic approach to evaluate land suitability of a particular area based on inputs such as rainfall, temperature, topography, and soil properties. The membership functions used for fuzzy modeling are the following: Triangular, Trapezoidal and Gaussian. The methods for computing the overall suitability of a particular area are also included, and these are the Minimum, Maximum and Average. Finally, ALUES is a highly optimized library with core algorithms written in C++.

All animal behaviour occurs sequentially. The package has a number of functions to format sequence data from different sources, to analyse sequential behaviour and communication in animals. It also has functions to plot the data and to calculate the entropy of sequences.

Manage dependencies during package development. This can retrieve all dependencies that are used in ".R" files in the "R/" directory, in ".Rmd" files in "vignettes/" directory and in roxygen2 documentation of functions. There is a function to update the "DESCRIPTION" file of your package with CRAN packages or any other remote package. All functions to retrieve dependencies of ".R" scripts and ".Rmd" or ".qmd" files can be used independently of a package development.

This package provides a tidy interface to the open data API of the Legislative Assembly of the State of Pernambuco, Brazil ('ALEPE', <https://dadosabertos.alepe.pe.gov.br>). Retrieve data on representatives, staff, positions, departments, remuneration, contracts, procurement processes, and legislative propositions as tibbles with clean names and parsed column types. Requests are cached locally and retried with exponential backoff; network failures are handled gracefully.