Access and manage the application programming interface (API) of the Armed Conflict Location & Event Data Project (ACLED) at <https://acleddata.com/>. The package makes it easy to retrieve a user-defined sample (or all of the available data) of ACLED, enabling a seamless integration of regular data updates into the research work flow. It requires a minimal number of dependencies. See the package's README file for a note on replicability when drawing on ACLED data. When using this package, you acknowledge that you have read ACLED's terms and conditions of use, and that you agree with their attribution requirements.

This package provides a very fast and robust interface to ArcGIS Geocoding Services'. Provides capabilities for reverse geocoding, finding address candidates, character-by-character search autosuggestion, and batch geocoding. The public ArcGIS World Geocoder is accessible for free use via arcgisgeocode for all services except batch geocoding. arcgisgeocode also integrates with arcgisutils to provide access to custom locators or private ArcGIS World Geocoder hosted on ArcGIS Enterprise'. Learn more in the Geocode service API reference <https://developers.arcgis.com/rest/geocode/api-reference/overview-world-geocoding-service.htm>.

Self-Attention algorithm helper functions and demonstration vignettes of increasing depth on how to construct the Self-Attention algorithm, this is based on Vaswani et al. (2017) <doi:10.48550/arXiv.1706.03762>, Dan Jurafsky and James H. Martin (2022, ISBN:978-0131873216) <https://web.stanford.edu/~jurafsky/slp3/> "Speech and Language Processing (3rd ed.)" and Alex Graves (2020) <https://www.youtube.com/watch?v=AIiwuClvH6k> "Attention and Memory in Deep Learning".

This package provides ready-to-use datasets from the Korean National Assembly (assemblies 20 through 22, 2016-2026) for teaching quantitative methods in political science. Includes legislator metadata, bill proposals, roll call votes, asset declarations, and policy seminar records. Designed as a Korean politics counterpart to packages like palmerpenguins', enabling students to practice regression, panel data analysis, text analysis, and network analysis with real legislative data. Roll call vote data and spatial voting models are described in Poole and Rosenthal (1985) <doi:10.2307/2111172>. Legislative data is sourced from the Korean National Assembly Open API.

This package provides a few functions aim to provide a statistic tool for three purposes. First, simulate kin pairs data based on the assumption that every trait is affected by genetic effects (A), common environmental effects (C) and unique environmental effects (E).Second, use kin pairs data to fit an ACE model and get model fit output.Third, calculate power of A estimate given a specific condition. For the mechanisms of power calculation, we suggest to check Visscher(2004)<doi:10.1375/twin.7.5.505>.

Compute the R-squared measure under the accelerated failure time (AFT) models proposed in Chan et. al. (2018) <doi:10.1080/03610918.2016.1177072>.

Manage storage in Microsoft's Azure cloud: <https://azure.microsoft.com/en-us/products/category/storage/>. On the admin side, AzureStor includes features to create, modify and delete storage accounts. On the client side, it includes an interface to blob storage, file storage, and Azure Data Lake Storage Gen2': upload and download files and blobs; list containers and files/blobs; create containers; and so on. Authenticated access to storage is supported, via either a shared access key or a shared access signature (SAS). Part of the AzureR family of packages.

Implementation of gene-level rare variant association tests targeting allelic series: genes where increasingly deleterious mutations have increasingly large phenotypic effects. The COding-variant Allelic Series Test (COAST) operates on the benign missense variants (BMVs), deleterious missense variants (DMVs), and protein truncating variants (PTVs) within a gene. COAST uses a set of adjustable weights that tailor the test towards rejecting the null hypothesis for genes where the average magnitude of effect increases monotonically from BMVs to DMVs to PTVs. See McCaw ZR, Oâ Dushlaine C, Somineni H, Bereket M, Klein C, Karaletsos T, Casale FP, Koller D, Soare TW. (2023) "An allelic series rare variant association test for candidate gene discovery" <doi:10.1016/j.ajhg.2023.07.001>.

Estimate and plot confounder-adjusted survival curves using either Direct Adjustment', Direct Adjustment with Pseudo-Values', various forms of Inverse Probability of Treatment Weighting', two forms of Augmented Inverse Probability of Treatment Weighting', Empirical Likelihood Estimation or Targeted Maximum Likelihood Estimation'. Also includes a significance test for the difference between two adjusted survival curves and the calculation of adjusted restricted mean survival times. Additionally enables the user to estimate and plot cause-specific confounder-adjusted cumulative incidence functions in the competing risks setting using the same methods (with some exceptions). For details, see Denz et. al (2023) <doi:10.1002/sim.9681>.

Formalizes spatial support at scale for ecological and geographical analysis. Given points and support polygons, classifies points as "core" (inside original support) or "halo" (inside scaled support but outside original), pruning all others. The default scale produces equal core and halo areas - a geometrically derived choice requiring no tuning. An optional mask enforces hard boundaries such as coastlines. Political borders are treated as soft boundaries with no ecological meaning.

This package provides ANOCVA (ANalysis Of Cluster VAriability), a non-parametric statistical test to compare clustering structures with applications in functional magnetic resonance imaging data (fMRI). The ANOCVA allows us to compare the clustering structure of multiple groups simultaneously and also to identify features that contribute to the differential clustering.

Helper functions for working with Regional Ocean Modeling System ROMS output. See <https://www.myroms.org/> for more information about ROMS'.

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.

This package provides new_partialised() and new_composed(), which extend partial() and compose() functions of purrr to make it easier to extract and replace arguments and functions. It also has additional adverbial functions.

Easily estimate the introduction rates of alien species given first records data. It specializes in addressing the role of sampling on the pattern of discoveries, thus providing better estimates than using Generalized Linear Models which assume perfect immediate detection of newly introduced species.

Extraction, preparation, visualisation and analysis of TERN AusPlots ecosystem monitoring data. Direct access to plot-based data on vegetation and soils across Australia, including physical sample barcode numbers. Simple function calls extract the data and merge them into species occurrence matrices for downstream analysis, or calculate things like basal area and fractional cover. TERN AusPlots is a national field plot-based ecosystem surveillance monitoring method and dataset for Australia. The data have been collected across a national network of plots and transects by the Terrestrial Ecosystem Research Network (TERN - <https://www.tern.org.au>), an Australian Government NCRIS-enabled project, and its Ecosystem Surveillance platform (<https://www.tern.org.au/tern-land-observatory/ecosystem-surveillance-and-environmental-monitoring/>).

This package provides WHO 2007 References for School-age Children and Adolescents (5 to 19 years) (z-scores) with confidence intervals and standard errors around the prevalence estimates, taking into account complex sample designs. More information on the methods is available online: <https://www.who.int/tools/growth-reference-data-for-5to19-years>.

This package provides the data sets used to build the ArchaeoPhases vignettes. The data sets were formerly distributed with ArchaeoPhases', however they exceed current CRAN policy for package size.

Functionality to add, delete, read and update table records from your AppSheet apps, using the official API <https://api.appsheet.com/>.

Argument parsing for R scripts, with support for long and short Unix-style options including option clustering, positional arguments including those of variable length, and multiple usage patterns which may take different subsets of options.

Fit linear, logistic and Cox models regularized with L0, lasso (L1), elastic-net (L1 and L2), or net (L1 and Laplacian) penalty, and their adaptive forms, such as adaptive lasso / elastic-net and net adjusting for signs of linked coefficients. It solves the L0 penalty problem by simultaneously selecting regularization parameters and performing hard-thresholding or selecting the number of non-zeros. This augmented and penalized minimization method provides an approximation solution to the L0 penalty problem, but runs as fast as L1 regularization. The package uses a one-step coordinate descent algorithm and runs extremely fast by taking into account the sparsity structure of coefficients. It can handle very high dimensional data and has superior selection performance.

This package implements a constrained version of hierarchical agglomerative clustering, in which each observation is associated to a position, and only adjacent clusters can be merged. Typical application fields in bioinformatics include Genome-Wide Association Studies or Hi-C data analysis, where the similarity between items is a decreasing function of their genomic distance. Taking advantage of this feature, the implemented algorithm is time and memory efficient. This algorithm is described in Ambroise et al (2019) <doi:10.1186/s13015-019-0157-4>.

Sets the alpha level for coefficients in a regression model as a decreasing function of the sample size through the use of Jeffreys Approximate Bayes factor. You tell alphaN() your sample size, and it tells you to which value you must lower alpha to avoid Lindley's Paradox. For details, see Wulff and Taylor (2024) <doi:10.1177/14761270231214429>.

This package provides a collection of efficient functions for working with individual ages and corresponding intervals. These include functions for conversion from an age to an interval, aggregation of ages with associated counts in to intervals and the splitting of interval counts based on specified age distributions.