Three Shiny apps are provided that introduce Harvest Control Rules (HCR) for fisheries management. Introduction to HCRs provides a simple overview to how HCRs work. Users are able to select their own HCR and step through its performance, year by year. Biological variability and estimation uncertainty are introduced. Measuring performance builds on the previous app and introduces the idea of using performance indicators to measure HCR performance. Comparing performance allows multiple HCRs to be created and tested, and their performance compared so that the preferred HCR can be selected.

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 Division 293, Petroleum Resource Rent Tax, Medicare Levy Surcharge, fuel tax credits, compliance, and Working Holiday Maker 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).

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 image of the amino acid transform on the protein level is drawn, and the automatic routing of the functional elements such as the domain and the mutation site is completed.

This package provides a tool to obtain activity counts, originally a translation of the python package agcounts <https://github.com/actigraph/agcounts>. This tool allows the processing of data from any accelerometer brand, with a more flexible approach to handle different sampling frequencies.

This package provides a function to calculate multiple performance metrics for actual and predicted values. In total eight metrics will be calculated for particular actual and predicted series. Helps to describe a Statistical model's performance in predicting a data. Also helps to compare various models performance. The metrics are Root Mean Squared Error (RMSE), Relative Root Mean Squared Error (RRMSE), Mean absolute Error (MAE), Mean absolute percentage error (MAPE), Mean Absolute Scaled Error (MASE), Nash-Sutcliffe Efficiency (NSE), Willmottâ s Index (WI), and Legates and McCabe Index (LME). Among them, first five are expected to be lesser whereas, the last three are greater the better. More details can be found from Garai and Paul (2023) <doi:10.1016/j.iswa.2023.200202> and Garai et al. (2024) <doi:10.1007/s11063-024-11552-w>.

An integrated set of functions for building, analyzing, and visualizing Analytic Hierarchy Process (AHP) models, designed to support structured decision-making in consultancy, policy analysis, and research (Bose 2022 <doi:10.1002/mcda.1784>; Bose 2023 <doi:10.1002/mcda.1821>). In addition to tools for assessing and improving the consistency of pairwise comparison matrices (PCMs), the package supports full-hierarchy weight computation, intuitive tree-based visualization, sensitivity analysis, along with convenient PCM generation from user preferences.

R and C++ functions to perform exact and approximate optimal transport. All C++ methods can be linked to other R packages via their header files.

This package provides tools for downloading hourly averages, daily maximums and minimums from each of the pollution, wind, and temperature measuring stations or geographic zones in the Mexico City metro area. The package also includes the locations of each of the stations and zones. See <http://aire.cdmx.gob.mx/> for more information.

Machine learning based package to predict anti-angiogenic peptides using heterogeneous sequence descriptors. AntAngioCOOL exploits five descriptor types of a peptide of interest to do prediction including: pseudo amino acid composition, k-mer composition, k-mer composition (reduced alphabet), physico-chemical profile and atomic profile. According to the obtained results, AntAngioCOOL reached to a satisfactory performance in anti-angiogenic peptide prediction on a benchmark non-redundant independent test dataset.

Computationally efficient method to estimate orthant probabilities of high-dimensional Gaussian vectors. Further implements a function to compute conservative estimates of excursion sets under Gaussian random field priors.

Programming vaccine specific Clinical Data Interchange Standards Consortium (CDISC) compliant Analysis Data Model (ADaM) datasets in R'. Flat model is followed as per Center for Biologics Evaluation and Research (CBER) guidelines for creating vaccine specific domains. 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/adamig-v1-3-release-package>). The package is an extension package of the admiral package.

The at-Risk (aR) approach is based on a two-step parametric estimation procedure that allows to forecast the full conditional distribution of an economic variable at a given horizon, as a function of a set of factors. These density forecasts are then be used to produce coherent forecasts for any downside risk measure, e.g., value-at-risk, expected shortfall, downside entropy. Initially introduced by Adrian et al. (2019) <doi:10.1257/aer.20161923> to reveal the vulnerability of economic growth to financial conditions, the aR approach is currently extensively used by international financial institutions to provide Value-at-Risk (VaR) type forecasts for GDP growth (Growth-at-Risk) or inflation (Inflation-at-Risk). This package provides methods for estimating these models. Datasets for the US and the Eurozone are available to allow testing of the Adrian et al. (2019) model. This package constitutes a useful toolbox (data and functions) for private practitioners, scholars as well as policymakers.

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.

You can use this package to create custom pipeline badges in a standard svg format. This is useful for a company to use internally, where it may not be possible to create badges through external providers. This project was inspired by the anybadge library in python.

Allows you to connect to an Alfresco content management repository and interact with its contents using simple and intuitive functions. You will be able to establish a connection session to the Alfresco repository, read and upload content and manage folder hierarchies. For more details on the Alfresco content management repository see <https://www.alfresco.com/ecm-software/document-management>.

This package provides functions that facilitate the use of accepted taxonomic nomenclature, collection of functional trait data, and assignment of functional group classifications to phytoplankton species. Possible classifications include Morpho-functional group (MFG; Salmaso et al. 2015 <doi:10.1111/fwb.12520>) and CSR (Reynolds 1988; Functional morphology and the adaptive strategies of phytoplankton. In C.D. Sandgren (ed). Growth and reproductive strategies of freshwater phytoplankton, 388-433. Cambridge University Press, New York). Versions 2.0.0 and later includes new functions for querying the algaebase online taxonomic database (www.algaebase.org), however these functions require a valid API key that must be acquired from the algaebase administrators. Note that none of the algaeClassify authors are affiliated with algaebase in any way. Taxonomic names can also be checked against a variety of taxonomic databases using the Global Names Resolver service via its API (<https://resolver.globalnames.org/api>). In addition, currently accepted and outdated synonyms, and higher taxonomy, can be extracted for lists of species from the ITIS database via its JSON web service API. The algaeClassify package is a product of the GEISHA (Global Evaluation of the Impacts of Storms on freshwater Habitat and Structure of phytoplankton Assemblages), funded by CESAB (Centre for Synthesis and Analysis of Biodiversity) and the U.S. Geological Survey John Wesley Powell Center for Synthesis and Analysis, with data and other support provided by members of GLEON (Global Lake Ecology Observation Network). DISCLAIMER: This software has been approved for release by the U.S. Geological Survey (USGS). Although the software has been subjected to rigorous review, the USGS reserves the right to update the software as needed pursuant to further analysis and review. No warranty, expressed or implied, is made by the USGS or the U.S. Government as to the functionality of the software and related material nor shall the fact of release constitute any such warranty. Furthermore, the software is released on condition that neither the USGS nor the U.S. Government shall be held liable for any damages resulting from its authorized or unauthorized use.

This package provides tools and functions to efficiently create datasets used in pharmacometric analysis. Additional functionality is added to create documentation and prepare files for submission and quality control purposes.

This package provides a set of functions to access the ARDECO (Annual Regional Database of the European Commission) data directly from the official ARDECO public repository through the exploitation of the ARDECO APIs. The APIs are completely transparent to the user and the provided functions provide a direct access to the ARDECO data. The ARDECO database is a collection of variables related to demography, employment, labour market, domestic product, capital formation. Each variable can be exposed in one or more units of measure as well as refers to total values plus additional dimensions like economic sectors, gender, age classes. Data can be also aggregated at country level according to the tercet classes as defined by EUROSTAT. The description of the ARDECO database can be found at the following URL <https://territorial.ec.europa.eu/ardeco>.

This package provides a routine to partial out factors with many levels during the optimization of the log-likelihood function of the corresponding generalized linear model (glm). The package is based on the algorithm described in Stammann (2018) <doi:10.48550/arXiv.1707.01815> and is restricted to glm's that are based on maximum likelihood estimation and nonlinear. It also offers an efficient algorithm to recover estimates of the fixed effects in a post-estimation routine and includes robust and multi-way clustered standard errors. Further the package provides analytical bias corrections for binary choice models derived by Fernandez-Val and Weidner (2016) <doi:10.1016/j.jeconom.2015.12.014> and Hinz, Stammann, and Wanner (2020) <doi:10.48550/arXiv.2004.12655>.

This package provides automated visual inference of residual plots using computer vision models, facilitating diagnostic checks for classical normal linear regression models.

It calculates the Air Pollution Tolerance Index (APTI) of plant species using biochemical parameters such as chlorophyll content, leaf extract pH, relative water content, and ascorbic acid content. It helps in identifying tolerant species for greenbelt development and pollution mitigation studies. It includes a shiny app for interactive APTI calculation and visualisation. For method details see, Sahu et al. (2020).<DOI:10.1007/s42452-020-3120-6>.

This package provides functions to simulate data sets from hierarchical ecological models, including all the simulations described in the two volume publication Applied Hierarchical Modeling in Ecology: Analysis of distribution, abundance and species richness in R and BUGS by Marc Kéry and Andy Royle: volume 1 (2016, ISBN: 978-0-12-801378-6) and volume 2 (2021, ISBN: 978-0-12-809585-0), <https://www.mbr-pwrc.usgs.gov/pubanalysis/keryroylebook/>. It also has all the utility functions and data sets needed to replicate the analyses shown in the books.

This package provides a collection of tools that support data splitting, predictive modeling, and model evaluation. A typical function is to split a dataset into a training dataset and a test dataset. Then compare the data distribution of the two datasets. Another feature is to support the development of predictive models and to compare the performance of several predictive models, helping to select the best model.