Checks function arguments, ideally for use in R packages. Uses a simple interface and produces clean, informative error messages using cli'.

This package provides functions to estimate and interpret the alpha-NOMINATE ideal point model developed in Carroll et al. (2013, <doi:10.1111/ajps.12029>). alpha-NOMINATE extends traditional spatial voting frameworks by allowing for a mixture of Gaussian and quadratic utility functions, providing flexibility in modeling political actors preferences. The package uses Markov Chain Monte Carlo (MCMC) methods for parameter estimation, supporting robust inference about individuals ideological positions and the shape of their utility functions. It also contains functions to simulate data from the model and to calculate the probability of a vote passing given the ideal points of the legislators/voters and the estimated location of the choice alternatives.

Extract and process bird sightings records from eBird (<http://ebird.org>), an online tool for recording bird observations. Public access to the full eBird database is via the eBird Basic Dataset (EBD; see <http://ebird.org/ebird/data/download> for access), a downloadable text file. This package is an interface to AWK for extracting data from the EBD based on taxonomic, spatial, or temporal filters, to produce a manageable file size that can be imported into R.

Filters animal satellite tracking data obtained from the Argos system(<https://www.argos-system.org/>), following the algorithm described in Freitas et al (2008) <doi:10.1111/j.1748-7692.2007.00180.x>. It is especially indicated for telemetry studies of marine animals, where Argos locations are predominantly of low-quality.

Package to query the Twitter Academic Research Product Track, providing access to full-archive search and other v2 API endpoints. Functions are written with academic research in mind. They provide flexibility in how the user wishes to store collected data, and encourage regular storage of data to mitigate loss when collecting large volumes of tweets. They also provide workarounds to manage and reshape the format in which data is provided on the client side.

Core methods and classes used by higher-level aroma.* packages part of the Aroma Project, e.g. aroma.affymetrix and aroma.cn'.

This package provides a color palette generator inspired by American politics, with colors ranging from blue on the left to gray in the middle and red on the right. A variety of palettes allow for a range of applications from brief discrete scales (e.g., three colors for Democrats, Independents, and Republicans) to continuous interpolated arrays including dozens of shades graded from blue (left) to red (right). This package greatly benefitted from building on the source code (with permission) from Ram and Wickham (2015).

This package implements specialized K-Nearest Neighbor (KNN) logic to address the unique challenges of spatial modeling in archipelagic environments. Standard contiguity models often leave significant portions of island nations (e.g., 20% of the Philippines) mathematically isolated. This package provides tools to ensure 100% network connectivity, neutralizing spatial bias and enabling robust econometric inference. Methodology follows Anselin (1988, ISBN:9024737354) and LeSage and Pace (2009) <doi:10.1201/9781420064254>.

This package contains functions to help create an Analysis Results Dataset. The dataset follows industry recommended structure. The dataset can be created in multiple passes, using different data frames as input. Analysis Results Datasets are used in the pharmaceutical and biotech industries to capture analysis in a common tabular data structure.

The process of resolving and updating taxon names is necessary when working with biodiversity data. APCalign uses the Australian Plant Census (APC) and the Australian Plant Name Index (APNI) to align and update plant taxon names to current, accepted standards. APCalign also supplies information about the establishment status (i.e. native or introduced) of plant taxa across different states/territories.

Enables sampling from arbitrary distributions if the log density is known up to a constant; a common situation in the context of Bayesian inference. The implemented sampling algorithm was proposed by Vihola (2012) <DOI:10.1007/s11222-011-9269-5> and achieves often a high efficiency by tuning the proposal distributions to a user defined acceptance rate.

This package provides a tool to analyse ActiGraph accelerometer data and to implement the use of the PROactive Physical Activity in COPD (chronic obstructive pulmonary disease) instruments. Once analysis is completed, the app allows to export results to .csv files and to generate a report of the measurement. All the configured inputs relevant for interpreting the results are recorded in the report. In addition to the existing R packages that are fully integrated with the app, the app uses some functions from the actigraph.sleepr package developed by Petkova (2021) <https://github.com/dipetkov/actigraph.sleepr/>.

Implementation in R of the alpha-shape of a finite set of points in the three-dimensional space. The alpha-shape generalizes the convex hull and allows to recover the shape of non-convex and even non-connected sets in 3D, given a random sample of points taken into it. Besides the computation of the alpha-shape, this package provides users with functions to compute the volume of the alpha-shape, identify the connected components and facilitate the three-dimensional graphical visualization of the estimated set.

This package provides functions to perform the fitting of an adaptive mixture of Student-t distributions to a target density through its kernel function as described in Ardia et al. (2009) <doi:10.18637/jss.v029.i03>. The mixture approximation can then be used as the importance density in importance sampling or as the candidate density in the Metropolis-Hastings algorithm to obtain quantities of interest for the target density itself.

Uses Auth0 API (see <https://auth0.com> for more information) to use a simple authentication system. It provides tools to log in and out a shiny application using social networks or a list of e-mails.

This package provides a collection of datasets on the Alone survival TV series in tidy format. Included in the package are 4 datasets detailing the survivors, their loadouts, episode details and season information.

Accumulated Local Effects (ALE) were initially developed as a model-agnostic approach for global explanations of the results of black-box machine learning algorithms. ALE has a key advantage over other approaches like partial dependency plots (PDP) and SHapley Additive exPlanations (SHAP): its values represent a clean functional decomposition of the model. As such, ALE values are not affected by the presence or absence of interactions among variables in a mode. Moreover, its computation is relatively rapid. This package reimplements the algorithms for calculating ALE data and develops highly interpretable visualizations for plotting these ALE values. It also extends the original ALE concept to add bootstrap-based confidence intervals and ALE-based statistics that can be used for statistical inference. For more details, see Okoli, Chitu. 2023. â Statistical Inference Using Machine Learning and Classical Techniques Based on Accumulated Local Effects (ALE).â arXiv. <doi:10.48550/arXiv.2310.09877>.

This package implements several exact methods for allocating optimal sample sizes when designing stratified samples. These methods are discussed in Wright (2012) <doi:10.1080/00031305.2012.733679> and Wright (2017) <doi:10.1016/j.spl.2017.04.026>.

This package provides functions to simplify and standardise antimicrobial resistance (AMR) data analysis and to work with microbial and antimicrobial properties by using evidence-based methods, as described in <doi:10.18637/jss.v104.i03>.

This package provides a comprehensive set of tools for descriptive statistics, graphical data exploration, outlier detection, homoscedasticity testing, and multiple comparison procedures. Includes manual implementations of Levene's test, Bartlett's test, and the Fligner-Killeen test, as well as post hoc comparison methods such as Tukey, Scheffé, Games-Howell, Brunner-Munzel, and others. This version introduces two new procedures: the Jonckheere-Terpstra trend test and the Jarque-Bera test with Glinskiy's (2024) correction. Designed for use in teaching, applied statistical analysis, and reproducible research. Additionally you can find a post hoc Test Planner, which helps you to make a decision on which procedure is most suitable.

Estimating and analyzing auto regressive integrated moving average (ARIMA) models. The primary function in this package is arima(), which fits an ARIMA model to univariate time series data using a random restart algorithm. This approach frequently leads to models that have model likelihood greater than or equal to that of the likelihood obtained by fitting the same model using the arima() function from the stats package. This package enables proper optimization of model likelihoods, which is a necessary condition for performing likelihood ratio tests. This package relies heavily on the source code of the arima() function of the stats package. For more information, please see Jesse Wheeler and Edward L. Ionides (2025) <doi:10.1371/journal.pone.0333993>.

Flagger to detect acute kidney injury (AKI) in a patient dataset.

Lite interface for finding locations of addresses or businesses around the world using the ArcGIS REST API service <https://developers.arcgis.com/rest/geocode/api-reference/overview-world-geocoding-service.htm>. Address text can be converted to location candidates and a location can be converted into an address. No API key required.

Fits random effects models for multi-level/high-cardinality factors using credibility theory (Buhlmann-Straub for single-level, Jewell for hierarchical structures), GLM extensions following Ohlsson (2008) <doi:10.1080/03461230701878612>, or Tweedie generalized linear mixed models. Provides functions for model fitting, visualization, and prediction. See Campo, B.D.C. and Antonio, K. (2023) <doi:10.1080/03461238.2022.2161413>.