The qda() function from package MASS is extended to calculate a weighted linear (LDA) and quadratic discriminant analysis (QDA) by changing the group variances and group means based on cell-wise uncertainties. The uncertainties can be derived e.g. through relative errors for each individual measurement (cell), not only row-wise or column-wise uncertainties. The method can be applied compositional data (e.g. portions of substances, concentrations) and non-compositional data.

This package provides a wrapped LASSO approach by integrating an ensemble learning strategy to help select efficient, stable, and high confidential variables from omics-based data. Using a bagging strategy in combination of a parametric method or inflection point search method for cut-off threshold determination. This package can integrate and vote variables generated from multiple LASSO models to determine the optimal candidates. Luo H, Zhao Q, et al (2020) <doi:10.1126/scitranslmed.aax7533> for more details.

This package provides a visualization for characterizing subgroups defined by a decision tree structure. The visualization simplifies the ability to interpret individual pathways to subgroups; each sub-plot describes the distribution of observations within individual terminal nodes and percentile ranges for the associated inner nodes.

The vcfpp.h (<https://github.com/Zilong-Li/vcfpp>) provides an easy-to-use C++ API of htslib', offering full functionality for manipulating Variant Call Format (VCF) files. The vcfppR package serves as the R bindings of the vcfpp.h library, enabling rapid processing of both compressed and uncompressed VCF files. Explore a range of powerful features for efficient VCF data manipulation.

This is a sparklyr extension integrating VariantSpark and R. VariantSpark is a framework based on scala and spark to analyze genome datasets, see <https://bioinformatics.csiro.au/>. It was tested on datasets with 3000 samples each one containing 80 million features in either unsupervised clustering approaches and supervised applications, like classification and regression. The genome datasets are usually writing in VCF, a specific text file format used in bioinformatics for storing gene sequence variations. So, VariantSpark is a great tool for genome research, because it is able to read VCF files, run analyses and return the output in a spark data frame.

This package provides a Shiny application and functions for visual exploration of hierarchical clustering with numeric datasets. Allows users to iterative set hyperparameters, select features and evaluate results through various plots and computation of evaluation criteria.

Designed to help the user to determine the sensitivity of an proposed causal effect to unconsidered common causes. Users can create visualizations of sensitivity, effect sizes, and determine which pattern of effects would support a causal claim for between group differences. Number needed to treat formula from Kraemer H.C. & Kupfer D.J. (2006) <doi:10.1016/j.biopsych.2005.09.014>.

This package provides a set of visual input controls for Shiny apps to facilitate filtering across multiple outputs.

Many bioacoustic data workflows rely on manual review (i.e., validation) of a subset of call files to provide information to statistical models that account for misclassification by automated algorithms. Because manual review can be prohibitively expensive, simulation can be a valuable tool to aid the design of studies that use validation. This package provides user-friendly functions to reduce the programming burden of simulation studies that compare validation sampling designs. Simulations assume the count-detection model, which is a realistic model for bioacoustic data, especially for bats. For more information, see Oram et al. (2025) <doi:10.1214/25-AOAS2096>.

Bayesian variable selection using shrinkage priors to identify significant variables in high-dimensional datasets. The package includes methods for determining the number of significant variables through innovative clustering techniques of posterior distributions, specifically utilizing the 2-Means and Sequential 2-Means (S2M) approaches. The package aims to simplify the variable selection process with minimal tuning required in statistical analysis.

This package provides methods to calculate diagnostics for multicollinearity among predictors in a linear or generalized linear model. It also provides methods to visualize those diagnostics following Friendly & Kwan (2009), "Whereâ s Waldo: Visualizing Collinearity Diagnostics", <doi:10.1198/tast.2009.0012>. These include better tabular presentation of collinearity diagnostics that highlight the important numbers, a semi-graphic tableplot of the diagnostics to make warning and danger levels more salient, and a "collinearity biplot" of the smallest dimensions of predictor space, where collinearity is most apparent.

In order to make it easy to use variance reduction algorithms for any simulation, this framework can help you. We propose user friendly and easy to extend framework. Antithetic Variates, Inner Control Variates, Outer Control Variates and Importance Sampling algorithms are available in the framework. User can write its own simulation function and use the Variance Reduction techniques in this package to obtain more efficient simulations. An implementation of Asian Option simulation is already available within the package. See Kemal Dinçer Dingeç & Wolfgang Hörmann (2012) <doi:10.1016/j.ejor.2012.03.046>.

Replicates vectors using ALTREP (Alternative Representations for R Objects), avoiding unnecessary memory allocation. When a vector is repeated many times, only a reference to the original data is stored rather than copying the full expanded replicates into memory. The expanded data is only materialised if it is modified, making repeated vectors cheap to create and pass around. This is particularly useful when working with large repeated sequences, such as replicated index vectors, simulation inputs, or repeated reference values in data pipelines.

Fits variance-guided linear regression models that provide an alternative to ordinary least squares (OLS) for general linear-model design matrices, including ANOVA-style encodings. The methods use an iteratively reweighted least squares estimator or an iteratively reweighted lasso estimator and implement the global linear mean-variance model from the associated 2026 Statistics in Medicine article <doi:10.1002/sim.70632>. Under the assumptions in that paper, the estimator matches the homoscedastic baseline in population predictive quasi-risk when variance is constant and improves on it when the variance depends on covariates. The grouping-based nonlinear prediction extension from Section 3 is available in the development version on GitHub.

Make it easy to use vue in R with helper dependency functions and examples.

New wavelet methodology (vector wavelet coherence) (Oygur, T., Unal, G, 2020 <doi:10.1007/s40435-020-00706-y>) to handle dynamic co-movements of multivariate time series via extending multiple and quadruple wavelet coherence methodologies. This package can be used to perform multiple wavelet coherence, quadruple wavelet coherence, and n-dimensional vector wavelet coherence analyses.

Fits the extended Vasicek single-factor credit loss model where the probability of default depends on macroeconomic covariates. Maximum likelihood estimates of all parameters, including asset value correlation, are obtained via closed-form probit-transformed OLS regression; see Mayorov (2026) <doi:10.2139/ssrn.6506378> for derivation.

Uses large language models to create poems about R packages. Currently contains the roses() function to make "roses are red, ..." style poems and the prompt() function to only assemble the prompt without submitting it to the model.

This package implements wild bootstrap tests for autocorrelation in Vector Autoregressive (VAR) models based on Ahlgren and Catani (2016) <doi:10.1007/s00362-016-0744-0>, a combined Lagrange Multiplier (LM) test for Autoregressive Conditional Heteroskedasticity (ARCH) in VAR models from Catani and Ahlgren (2016) <doi:10.1016/j.ecosta.2016.10.006>, and bootstrap-based methods for determining the cointegration rank from Cavaliere, Rahbek, and Taylor (2012) <doi:10.3982/ECTA9099> and Cavaliere, Rahbek, and Taylor (2014) <doi:10.1080/07474938.2013.825175>.

Facilitates modeling species ecological niches and geographic distributions based on occurrences and environments that have a vertical as well as horizontal component, and projecting models into three-dimensional geographic space. Working in three dimensions is useful in an aquatic context when the organisms one wishes to model can be found across a wide range of depths in the water column. The package also contains functions to automatically generate marine training model training regions using machine learning, and interpolate and smooth patchily sampled environmental rasters using thin plate splines. Davis Rabosky AR, Cox CL, Rabosky DL, Title PO, Holmes IA, Feldman A, McGuire JA (2016) <doi:10.1038/ncomms11484>. Nychka D, Furrer R, Paige J, Sain S (2021) <doi:10.5065/D6W957CT>. Pateiro-Lopez B, Rodriguez-Casal A (2022) <https://CRAN.R-project.org/package=alphahull>.

Simulates and evaluates stochastic scenarios of death and lapse events in life reinsurance contracts with profit commissions. The methodology builds on materials published by the Institute of Actuaries of Japan <https://www.actuaries.jp/examin/textbook/pdf/modeling.pdf>. A paper describing the detailed algorithms will be published by the author within a few months after the initial release of this package.

Generates interactive plots for analysing and visualising three-class high dimensional data. It is particularly suited to visualising differences in continuous attributes such as gene/protein/biomarker expression levels between three groups. Differential gene/biomarker expression analysis between two classes is typically shown as a volcano plot. However, with three groups this type of visualisation is particularly difficult to interpret. This package generates 3D volcano plots and 3-way polar plots for easier interpretation of three-class data.

Mixed type vectors are useful for combining semantically similar classes. Some examples of semantically related classes include time across different granularities (e.g. daily, monthly, annual) and probability distributions (e.g. Normal, Uniform, Poisson). These groups of vector types typically share common statistical operations which vary in results with the attributes of each vector. The vecvec data structure facilitates efficient storage and computation across multiple vectors within the same object.

Automatically generates HTML variable documentation including variable names, labels, classes, value labels (if applicable), value ranges, and summary statistics. See the vignette "vtable" for a package overview.