Assists for presentation and visualization of data from the Norwegian Health Quality Registries following the standardization based on the requirement specified by the National Service for Health Quality Registries. This requirement can be accessed from (<https://www.kvalitetsregistre.no/resultater-til-publisering-pa-nett>). Unfortunately the website is only available in Norwegian.

Slow Feature Analysis (SFA), ported to R based on matlab implementations of SFA: SFA toolkit 1.0 by Pietro Berkes and SFA toolkit 2.8 by Wolfgang Konen.

Interface to the ZeroMQ lightweight messaging kernel (see <https://zeromq.org/> for more information).

Encode network data as strings of printable ASCII characters. Implemented functions include encoding and decoding adjacency matrices, edgelists, igraph, and network objects to/from formats graph6', sparse6', and digraph6'. The formats and methods are described in McKay, B.D. and Piperno, A (2014) <doi:10.1016/j.jsc.2013.09.003>.

This package provides access to and analysis of data from "The Red Book of Endemic Plants of Peru" (León, B., Roque, J., Ulloa, C., Jorgensen, P.M., Pitman, N., Cano, A. 2006) <doi:10.15381/rpb.v13i2.1782>. This package offers comprehensive taxonomic, geographic, and conservation information about Peru's endemic plant species. It includes functions to verify species inclusion, obtain updated taxonomic details, and explore the dataset.

Annotate text with entities and the relations between them. Annotate areas of interest in images with your labels. Providing htmlwidgets bindings to the recogito <https://github.com/recogito/recogito-js> and annotorious <https://github.com/recogito/annotorious> libraries.

R Commander plug-in for repeated-measures and mixed-design ('split-plot') ANOVA. It adds a new menu entry for repeated measures that allows to deal with up to three within-subject factors and optionally with one or several between-subject factors. It also provides supplementary options to oneWayAnova() and multiWayAnova() functions, such as choice of ANOVA type, display of effect sizes and post hoc analysis for multiWayAnova().

Updates values within csv format data files using a custom, User-built csv format lookup file. Based on data.table package.

Toolbox that provides a streamlined, end-to-end workflow for propensity score analysis in generating real-world evidence from real-world data. The package covers the full analytic pipeline - from estimating propensity scores via logistic regression, to calculating weights or creating a matched cohort, to generating publication-ready Table 1s with standardized mean differences and weighted balance diagnostics. It also estimates incidence rates, rate differences and rate ratios, hazard ratios, risks, risk ratios, and risk differences, including competing-risk methods and optionally stratified hazard models. Many functions can write formatted Excel reports with method documentation, making results immediately shareable with collaborators and stakeholders. Methods are based on Rosenbaum and Rubin (1983) <doi:10.1093/biomet/70.1.41>, Austin (2011) <doi:10.1080/00273171.2011.568786>, and Desai et al. (2017) <doi:10.1097/EDE.0000000000000595>.

This package implements Raise Regression as an inference-preserving alternative to Ridge Regression for combating multicollinearity in linear models, including the classical single-variable Raise Regression, the Simultaneous Raise Regression (SRR) based on QR decomposition and the Sequential Variance Inflation Factor (SVIF) of Jacob and Varadharajan (2022) <doi:10.1007/s11135-022-01557-9>, and the original raise parameter selection strategy of Jacob and Varadharajan (2023) <doi:10.13189/ms.2023.110106>. Also implements Robust Raise Regression for data contaminated by outliers, with exact finite-sample inference (sandwich standard errors, Wald tests, Satterthwaite-corrected degrees of freedom) obtained by down-weighting observations using Stahel-Donoho projection outlyingness and Tukey's biweight function. Provides ordinary and robust Ridge Regression (Hoerl and Kennard, 1970, <doi:10.1080/00401706.1970.10488634>), ordinary and robust Liu Regression (Liu, 1993, <doi:10.1080/03610929308831027>), with the robust variants of both based on the MM-estimates of Yohai (1987, <doi:10.1214/aos/1176350366>) and, for Liu Regression specifically, the biasing-parameter derivation of Filzmoser and Kurnaz (2018) <doi:10.1080/03610918.2016.1271889>. Also provides the classical Variance Inflation Factor (VIF) and Condition Number (Belsley, 1991) computed from the correlation matrix of the predictors, and the Robust Variance Inflation Factor (RVIF) and robust Condition Number of Jacob and Varadharajan (2024, Sankhya B, <doi:10.1007/s13571-024-00342-y>), which use the same projection outlyingness and biweight down-weighting scheme to obtain a weighted correlation matrix that resists the influence of outliers. A flexible scaleDat() function supports classical (mean and standard deviation), robust weighted (Stahel-Donoho and Tukey biweight), median and Median Absolute Deviation Normalized (MADN, the median absolute deviation scaled by 1.4826 to estimate the standard deviation under normality), and min-max scaling. Diagnostic and goodness-of-fit plots, and the standard influence-diagnostic suite (Cook's distance, DFBETAS and COVRATIO regression diagnostics) and heteroskedasticity tests (via the lmtest and car packages) analogous to those for objects of class lm', are provided for the exact, unbiased Raise Regression fit.

Extension to REddyProc that allows reading data from netCDF files.

An implementation of the RaCE-NMA (Rank-Clustered Estimation for Network Meta-Analysis) model for post-hoc clustering of treatments or interventions by rank in network meta-analysis data. Functions for model estimation, assessment, and displaying results are provided. For more details, see Pearce and Zhou (2025) <doi:10.1017/rsm.2025.10049>.

Rogue ("wildcard") taxa are leaves with uncertain phylogenetic position. Their position may vary from tree to tree under inference methods that yield a tree set (e.g. bootstrapping, Bayesian tree searches, maximum parsimony). The presence of rogue taxa in a tree set can potentially remove all information from a consensus tree. The information content of a consensus tree - a function of its resolution and branch support values - can often be increased by removing rogue taxa. Rogue provides an explicitly information-theoretic approach to rogue detection (Smith 2022) <doi:10.1093/sysbio/syab099>, and an interface to RogueNaRok (Aberer et al. 2013) <doi:10.1093/sysbio/sys078>.

Much as roxygen2 allows one to document functions in the same file as the function itself, roxut allows one to write the unit tests in the same file as the function. Once processed, the unit tests are moved to the appropriate directory. Currently supports testthat and tinytest frameworks. The roxygen2 package provides much of the infrastructure.

Sundry discrete probability distributions and helper functions.

Fits Bayesian probit models for binary, multinomial, ordered, and ranked choices in cross-sectional and panel data. Correlated or uncorrelated normal and log-normal random coefficients, finite mixtures, sparse finite mixtures, and Dirichlet process mixtures describe preference heterogeneity. Multiple Gibbs chains produce posterior draws for diagnostics and choice prediction. Empirical model data can be supplied as a data frame or simulated from the requested specification. For an overarching treatment of the methodology, see Oelschlaeger (2026) <https://pub.uni-bielefeld.de/record/3014719>. The latent-class model is described in Oelschlaeger and Bauer (2021) <https://trid.trb.org/view/1759753>.

Iterative least cost path and minimum spanning tree methods for projecting forest road networks. The methods connect a set of target points to an existing road network using igraph <https://igraph.org> to identify least cost routes. The cost of constructing a road segment between adjacent pixels is determined by a user supplied weight raster and a weight function; options include the average of adjacent weight raster values, and a function of the elevation differences between adjacent cells that penalizes steep grades. These road network projection methods are intended for integration into R workflows and modelling frameworks used for forecasting forest change, and can be applied over multiple time-steps without rebuilding a graph at each time-step.

Implementation of corrected two-sample tests. A corrected version of the Pearson and Kendall correlation tests, the Mann-Whitney (Wilcoxon) rank sum test, the Wilcoxon signed rank test and a variance test are implemented. The package also proposes a test for the median and an independence test between two continuous variables of Kolmogorov-Smirnov's type. All these corrected tests are asymptotically calibrated in the sense that the probability of rejection under the null hypothesis is asymptotically equal to the level of the test. See <doi:10.48550/arXiv.2211.08784> for more details on the statistical tests.

Compute spatially explicit land-use metrics for stream survey sites in GRASS GIS and R as an open-source implementation of IDW-PLUS (Inverse Distance Weighted Percent Land Use for Streams). The package includes functions for preprocessing digital elevation and streams data, and one function to compute all the spatially explicit land use metrics described in Peterson et al. (2011) <doi:10.1111/j.1365-2427.2010.02507.x> and previously implemented by Peterson and Pearse (2017) <doi:10.1111/1752-1688.12558> in ArcGIS-Python as IDW-PLUS.

This package provides a general-purpose optimisation engine that supports i) Monte Carlo optimisation with Metropolis criterion [Metropolis et al. (1953) <doi:10.1063/1.1699114>, Hastings (1970) <doi:10.1093/biomet/57.1.97>] and Acceptance Ratio Simulated Annealing [Kirkpatrick et al. (1983) <doi:10.1126/science.220.4598.671>, Ä erný (1985) <doi:10.1007/BF00940812>] on multiple cores, and ii) Acceptance Ratio Replica Exchange Monte Carlo Optimisation. In each case, the system pseudo-temperature is dynamically adjusted such that the observed acceptance ratio is kept near to the desired (fixed or changing) acceptance ratio.

Fetches NCBI data (RefSeq <https://www.ncbi.nlm.nih.gov/refseq/> database) and provides an environment to extract information at the level of gene, mRNA or protein accessions.

An easy way to get started with Generative Adversarial Nets (GAN) in R. The GAN algorithm was initially described by Goodfellow et al. 2014 <https://proceedings.neurips.cc/paper/2014/file/5ca3e9b122f61f8f06494c97b1afccf3-Paper.pdf>. A GAN can be used to learn the joint distribution of complex data by comparison. A GAN consists of two neural networks a Generator and a Discriminator, where the two neural networks play an adversarial minimax game. Built-in GAN models make the training of GANs in R possible in one line and make it easy to experiment with different design choices (e.g. different network architectures, value functions, optimizers). The built-in GAN models work with tabular data (e.g. to produce synthetic data) and image data. Methods to post-process the output of GAN models to enhance the quality of samples are available.

Plots the Receiver Operating Characteristics Surface for high-throughput class-skewed data, calculates the Volume under the Surface (VUS) and the FDR-Controlled Area Under the Curve (FCAUC), and conducts tests to compare two ROC surfaces. Computes eROC curve and the corresponding AUC for imperfect reference standard.

This package provides Rcpp bindings for cpptimer', a simple tic-toc timer class for benchmarking C++ code <https://github.com/BerriJ/cpptimer>. It's not just simple, it's blazing fast! This sleek tic-toc timer class supports overlapping timers as well as OpenMP parallelism <https://www.openmp.org/>. It boasts a nanosecond-level time resolution. We did not find any overhead of the timer itself at this resolution. Results (with summary statistics) are automatically passed back to R as a data frame.