This package provides a simple, configurable, provider-agnostic OAuth 2.0 and OpenID Connect (OIDC) authentication framework for shiny applications using S7 classes. Defines providers, clients, and tokens, as well as various supporting functions and a shiny module. Features include cross-site request forgery (CSRF) protection, state encryption, Proof Key for Code Exchange (PKCE) handling, validation of OIDC identity tokens (nonces, signatures, claims), automatic user info retrieval, asynchronous flows, and hooks for audit logging.

Uses simulations from generalized linear mixed-effects models to incorporate random effects across multiple sources and levels of variation, and a dispersion parameter to account for overdispersion and capture unexplained variability. Covers design scenarios for both short-term and long-term trials evaluating the impact of single or combined vector control interventions. Methods build on Kipingu et al. (2025) <doi:10.1186/s12936-025-05454-y> and Johnson et al. (2015) <doi:10.1111/2041-210X.12306>.

This package provides a collection of functions for estimating spatial regimes, aggregations of neighboring spatial units that are homogeneous in functional terms. The term spatial regime, therefore, should not be understood as a synonym for cluster. More precisely, the term cluster does not presuppose any functional relationship between the variables considered, while the term regime is linked to a regressive relationship underlying the spatial process.

This package provides a toolkit for Partially Observed Markov Decision Processes (POMDP). Provides bindings to C++ libraries implementing the algorithm SARSOP (Successive Approximations of the Reachable Space under Optimal Policies) and described in Kurniawati et al (2008), <doi:10.15607/RSS.2008.IV.009>. This package also provides a high-level interface for generating, solving and simulating POMDP problems and their solutions.

This package provides a framework to generating random variates from arbitrary multivariate copulae, while concentrating on (bivariate) extreme value copulae. Particularly useful if the multivariate copulae are not available in closed form. Detailed discussion of the methodologies used can be found in Tajvidi and Turlach (2018) <doi:10.1111/anzs.12209>.

Generates artificial point patterns marked by their spatial and temporal signatures. The resulting point cloud may exhibit inherent interactions between both signatures. The simulation integrates microsimulation (Holm, E., (2017)<doi:10.1002/9781118786352.wbieg0320>) and agent-based models (Bonabeau, E., (2002)<doi:10.1073/pnas.082080899>), beginning with the configuration of movement characteristics for the specified agents (referred to as walkers') and their interactions within the simulation environment. These interactions (Quaglietta, L. and Porto, M., (2019)<doi:10.1186/s40462-019-0154-8>) result in specific spatiotemporal patterns that can be visualized, analyzed, and used for various analytical purposes. Given the growing scarcity of detailed spatiotemporal data across many domains, this package provides an alternative data source for applications in social and life sciences.

It leverages the network-like architecture of scientific models together with software quality metrics to identify chains of function calls that are more prone to generating and propagating errors. It operates on tbl_graph objects representing call dependencies between functions (callers and callees) and computes risk scores for individual functions and for paths (sequences of function calls) based on cyclomatic complexity, in-degree and betweenness centrality. The package supports variance-based uncertainty and sensitivity analyses after Puy et al. (2022) <doi:10.18637/jss.v102.i05> to assess how risk scores change under alternative risk definitions.

This package provides a framework for performing simulations such as those common in methodological statistics papers. The design principles of this package are described in greater depth in Bien, J. (2016) "The simulator: An Engine to Streamline Simulations," which is available at <arXiv:1607.00021>.

This package provides a set of plotting methods for simmer trajectories and simulations.

Fast, wavelet-based Empirical Bayes shrinkage methods for signal denoising, including smoothing Poisson-distributed data and Gaussian-distributed data with possibly heteroskedastic error. The algorithms implement the methods described Z. Xing, P. Carbonetto & M. Stephens (2021) <https://jmlr.org/papers/v22/19-042.html>.

Estimate necessary sample sizes for comparing the location of data from two groups or categories when the distribution of the data is skewed. The package offers a non-parametric method for a Wilcoxon Mann-Whitney test of location shift as well as methods for several generalized linear models, for instance, Gamma regression.

Extends the SelectBoost approach to Generalized Additive Models for Location, Scale and Shape (GAMLSS). Implements bootstrap stability-selection across parameter-specific formulas (mu, sigma, nu, tau) via gamlss::stepGAIC(). Includes optional standardization of predictors and helper functions for corrected AIC calculation. More details can be found in Bertrand and Maumy (2024) <https://hal.science/hal-05352041> that highlights correlation-aware resampling to improve variable selection for GAMLSS and quantile regression when predictors are numerous and highly correlated.

This package provides functionality for image processing and shape analysis in the context of reconstructed medical images generated by deep learning-based methods or standard image processing algorithms and produced from different medical imaging types, such as X-ray, Computational Tomography (CT), Magnetic Resonance Imaging (MRI), and pathology imaging. Specifically, offers tools to segment regions of interest and to extract quantitative shape descriptors for applications in signal processing, statistical analysis and modeling, and machine learning.

Decode raw SYNOP (surface synoptic observations) messages into data frames, extracting data from Sections 0, 1, and 3, including temperature, dew point, pressure, wind, clouds, and precipitation. Available functions to download SYNOP messages from Ogimet <https://www.ogimet.com/> if needed. The decoding logic follows the specifications defined in the World Meteorological Organization (2019) "Manual on Codes, Volume I.1 (WMO-No. 306)".

Runs SQL statements on in-memory data frames within a temporary in-memory duckdb data base.

Apache Drill is a low-latency distributed query engine designed to enable data exploration and analysis on both relational and non-relational data stores, scaling to petabytes of data. Methods are provided that enable working with Apache Drill instances via the REST API, DBI methods and using dplyr'/'dbplyr idioms. Helper functions are included to facilitate using official Drill Docker images/containers.

This package provides a tool for producing synthetic versions of microdata containing confidential information so that they are safe to be released to users for exploratory analysis. The key objective of generating synthetic data is to replace sensitive original values with synthetic ones causing minimal distortion of the statistical information contained in the data set. Variables, which can be categorical or continuous, are synthesised one-by-one using sequential modelling. Replacements are generated by drawing from conditional distributions fitted to the original data using parametric or classification and regression trees models. Data are synthesised via the function syn() which can be largely automated, if default settings are used, or with methods defined by the user. Optional parameters can be used to influence the disclosure risk and the analytical quality of the synthesised data. For a description of the implemented method see Nowok, Raab and Dibben (2016) <doi:10.18637/jss.v074.i11>. Functions to assess identity and attribute disclosure for the original and for the synthetic data are included in the package, and their use is illustrated in a vignette on disclosure (Practical Privacy Metrics for Synthetic Data).

Print function signatures and find overly complicated code.

An implementation of sensitivity analysis for phylogenetic comparative methods. The package is an umbrella of statistical and graphical methods that estimate and report different types of uncertainty in PCM: (i) Species Sampling uncertainty (sample size; influential species and clades). (ii) Phylogenetic uncertainty (different topologies and/or branch lengths). (iii) Data uncertainty (intraspecific variation and measurement error).

This package provides a socket server allows to connect clients to R.

This package provides a small collection of data on graduate statistics programs from the United States.

This package provides some easy-to-use functions to interpolate species range based on species occurrences and to estimate centers of biodiversity.

This package provides a unified framework for the simultaneous testing of differential expression, variability, and skewness of genes using RNA-Seq data. The framework adopts a compositional data analysis approach for modelling RNA-Seq count data, applies the centered log-ratio transformation to obtain continuous variables, and uses the skew-normal distribution for statistical inference. Methods are described in Li and Khang (bioRxiv preprint, 2024, version 3) <doi:10.1101/2024.04.09.588804>.

This package performs the Scott & Knott (1974) clustering algorithm as a multiple comparison method in the Analysis of Variance context, for both balanced and unbalanced <doi:10.1590/1984-70332017v17n1a1> designs. Accepts input from formula', aov', lm', aovlist', and lmerMod objects.