The diffUTR package provides a uniform interface and plotting functions for limma/edgeR/DEXSeq -powered differential bin/exon usage. It includes in addition an improved version of the limma::diffSplice method. Most importantly, diffUTR further extends the application of these frameworks to differential UTR usage analysis using poly-A site databases.

metaCCA performs multivariate analysis of a single or multiple GWAS based on univariate regression coefficients. It allows multivariate representation of both phenotype and genotype. metaCCA extends the statistical technique of canonical correlation analysis to the setting where original individual-level records are not available, and employs a covariance shrinkage algorithm to achieve robustness.

Estimation of large Vector AutoRegressive (VAR), Vector AutoRegressive with Exogenous Variables X (VARX) and Vector AutoRegressive Moving Average (VARMA) Models with Structured Lasso Penalties, see Nicholson, Wilms, Bien and Matteson (2020) <https://jmlr.org/papers/v21/19-777.html> and Wilms, Basu, Bien and Matteson (2021) <doi:10.1080/01621459.2021.1942013>.

Boosting Regression Quantiles is a component-wise boosting algorithm, that embeds all boosting steps in the well-established framework of quantile regression. It is initialized with the corresponding quantile, uses a quantile-specific learning rate, and uses quantile regression as its base learner. The package implements this algorithm and allows cross-validation and stability selection.

Fast fitting of Stable Isotope Mixing Models in R. Allows for the inclusion of covariates. Also has built-in summary functions and plot functions which allow for the creation of isospace plots. Variational Bayes is used to fit these models, methods as described in: Tran et al., (2021) <doi:10.48550/arXiv.2103.01327>.

Gain access to the Spark Catalog API making use of the sparklyr API. Catalog <https://spark.apache.org/docs/2.4.3/api/java/org/apache/spark/sql/catalog/Catalog.html> is the interface for managing a metastore (aka metadata catalog) of relational entities (e.g. database(s), tables, functions, table columns and temporary views).

Phone numbers are often represented as strings because there is no obvious and suitable native representation for them. This leads to high memory use and a lack of standard representation. The package provides integer representation of Australian phone numbers with optional raw vector calling code. The package name is an extension of au and ph'.

This package provides a user-friendly wrapper for fitting Distributed Single-Index Models with Multiple Links ('SIMML'). It estimates a linear combination (single-index) of baseline covariates X and models the treatment-specific outcome y via treatment-specific, non-parametrically estimated link functions. This allows flexible modelling of complex relationships between covariates, treatments and outcomes.

The main functions are emmreml', and emmremlMultiKernel'. emmreml solves a mixed model with known covariance structure using the EMMA algorithm. emmremlMultiKernel is a wrapper for emmreml to handle multiple random components with known covariance structures. The function emmremlMultivariate solves a multivariate gaussian mixed model with known covariance structure using the ECM algorithm.

This package provides a high-performance implementation of redundancy analysis (RDA) in C++ using Armadillo and OpenMP'. Supports standard and partial RDA, centering, scaling, overall and axis-wise permutation tests, biplot visualization, score extraction, and prediction. Designed for large ecological, genomic, and other multivariate data sets where computational speed and memory efficiency are required.

This package provides functions for computing fixed-b critical values and conducting robust inference procedures for time series data with unknown correlation structures. Implements long-run variance estimators using various kernel functions and lugsail transformations for improved finite-sample properties as described by Kurtz-Garcia and Flegal (2026) <doi:10.48550/arXiv.2606.17369>.

This package provides tools for efficient processing of large, whole genome genotype data sets in variant call format (VCF). It includes several functions to calculate commonly used population genomic metrics and a method for reference panel free genotype imputation, which is described in the preprint Gurke & Mayer (2024) <doi:10.22541/au.172515591.10119928/v1>.

This package provides a secure and configurable interface to the Hawkin Dynamics API for accessing athlete performance data, tests, and metadata. The package supports profile-based authentication with secure credential storage via the operating system keychain, automatic access token refresh, and region-aware API routing. Designed for reproducible analysis, data synchronization workflows, and production deployment.

Automatic open data acquisition from resources of IGN ('Institut National de Information Geographique et forestiere') (<https://www.ign.fr/>). Available datasets include various types of raster and vector data, such as digital elevation models, state borders, spatial databases, cadastral parcels, and more. happign also provide access to API Carto (<https://apicarto.ign.fr/api/doc/>).

Dieses R-Paket stellt Zusatzmaterial in Form von Daten, Funktionen und R-Hilfe-Seiten für den Herausgeberband Breit, S. und Schreiner, C. (Hrsg.). (2016). "Large-Scale Assessment mit R: Methodische Grundlagen der österreichischen Bildungsstandardüberprüfung." Wien: facultas. (ISBN: 978-3-7089-1343-8, <https://www.iqs.gv.at/themen/bildungsforschung/publikationen/veroeffentlichte-publikationen>) zur Verfügung.

This package provides validation, harmonization, descriptive analysis, compositional analysis, transparent risk components, grouped cross-validation, visualization, and predictive modelling tools for freshwater microplastic datasets. The package includes synthetic demonstration data conforming to the LIMPID-India data model and emphasizes explicit units, provenance, percentage closure, non-imputation of missing environmental covariates, and leakage-aware model evaluation.

This package provides functionality to produce graphs of sampling distributions of test statistics from a variety of common statistical tests. With only a few keystrokes, the user can conduct a hypothesis test and visualize the test statistic and corresponding p-value through the shading of its sampling distribution. Initially created for statistics at Middlebury College.

This package provides methods and functions to analyze the quantitative or qualitative performance for diagnostic assays, and outliers detection, reader precision and reference range are discussed. Most of the methods and algorithms refer to CLSI (Clinical & Laboratory Standards Institute) recommendations and NMPA (National Medical Products Administration) guidelines. In additional, relevant plots are constructed by ggplot2'.

Fit and compare nonlinear mixed-effects models in differential equations with flexible dosing information commonly seen in pharmacokinetics and pharmacodynamics (Almquist, Leander, and Jirstrand 2015 <doi:10.1007/s10928-015-9409-1>). Differential equation solving is by compiled C code provided in the rxode2 package (Wang, Hallow, and James 2015 <doi:10.1002/psp4.12052>).

This package implements univariate continuous probability distributions and associated model diagnostics based on the Lindley, Logistic, Half-Cauchy, Half-Logistic, and Poisson families. Provides functions for probability density, cumulative distribution, quantile, and hazard evaluation, random variate generation, and diagnostic procedures including Q-Q and P-P plots, goodness-of-fit tests, and model selection criteria.

Predicting the structure of a graph including new nodes and edges using a time series of graphs. Flux balance analysis, a linear and integer programming technique used in biochemistry is used with time series prediction methods to predict the graph structure at a future time point Kandanaarachchi (2025) <doi:10.48550/arXiv.2507.05806>.

The openFDA API facilitates access to Federal Drug Agency (FDA) data on drugs, devices, foodstuffs, tobacco, and more with httr2'. This package makes the API easily accessible, returning objects which the user can convert to JSON data and parse. Kass-Hout TA, Xu Z, Mohebbi M et al. (2016) <doi:10.1093/jamia/ocv153>.

Efficient algorithm for solving PU (Positive and Unlabeled) problem in low or high dimensional setting with lasso or group lasso penalty. The algorithm uses Maximization-Minorization and (block) coordinate descent. Sparse calculation and parallel computing are supported for the computational speed-up. See Hyebin Song, Garvesh Raskutti (2018) <doi:10.48550/arXiv.1711.08129>.

Visualizes feature influence by projecting data into 2D via PCA and adjusting points toward sector centers weighted by importances. Supports linear models and optionally tree-based models via SHAP values from the fastshap package. For tree-based models, please install fastshap manually from the CRAN archive: <https://cran.r-project.org/src/contrib/Archive/fastshap/>.