QuantLib bindings are provided for R using Rcpp via an updated variant of the header-only Quantuccia project (put together initially by Peter Caspers) offering an essential subset of QuantLib (and now maintained separately for the calendaring subset). See the included file AUTHORS for a full list of contributors to both QuantLib and Quantuccia'. Note that this package provided an initial viability proof, current work is done (via approximately quarterly releases tracking QuantLib') in the smaller package qlcal which is generally preferred.

This package provides methods from Yeh, Rice, and Dubin (2022) <doi:10.1080/00031305.2021.1967781> for comparing two continuously updated probabilistic forecasts under squared (Brier) loss: pointwise loss and variance, a global delta test (Monte Carlo p-values), simulation designs, and a naive pointwise band plot.

The RcppClassic package provides a deprecated C++ library which facilitates the integration of R and C++. New projects should use the new Rcpp API in the Rcpp package.

The RMM fits Revenue Management Models using the RDE(Robust Demand Estimation) method introduced in the paper by <doi:10.2139/ssrn.3598259>, one of the customer choice-based Revenue Management Model. Furthermore, it is possible to select a multinomial model as well as a conditional logit model as a model of RDE.

Extends roxygen2 to support @meta tags for documenting testthat test cases and function specifications. Includes a custom JUnit reporter that exports test metadata as XML properties and validation functions to ensure all exported functions and tests contain required tags. Designed for traceability between requirements and tests in regulated industries such as pharma and finance.

An algorithm is proposed to estimate regression kink model proposed by the paper, Lixiong Yang and Jen-Je Su (2018) <doi:10.1016/j.jimonfin.2018.06.002>.

Implementation of the Robust Gauss-Newton (RGN) algorithm, designed for solving optimization problems with a sum of least squares objective function. For algorithm details please refer to Qin et. al. (2018) <doi:10.1029/2017WR022488>.

Mixture Composer <https://github.com/modal-inria/MixtComp> is a project to build mixture models with heterogeneous data sets and partially missing data management. It includes models for real, categorical, counting, functional and ranking data. This package contains the minimal R interface of the C++ MixtComp library.

This package provides the CppAD C++ header library for automatic differentiation, for use by R packages via LinkingTo. Headers are vendored with CRAN-safe defaults and R-safe error handling that does not call std::cerr or std::exit. The two final components of the version number are the CppAD release number.

Draw maps using the javascript library roughjs'. This allows to draw sketchy, hand-drawn-like maps.

This package provides functionality to read files containing observations which consist of arbitrary key/value pairs.

This package provides methods for multiway data analysis by means of Parafac and Tucker 3 models. Robust versions (Engelen and Hubert (2011) <doi:10.1016/j.aca.2011.04.043>) and versions for compositional data are also provided (Gallo (2015) <doi:10.1080/03610926.2013.798664>, Di Palma et al. (2018) <doi:10.1080/02664763.2017.1381669>). Several optimization methods alternative to ALS are available (Simonacci and Gallo (2019) <doi:10.1016/j.chemolab.2019.103822>, Simonacci and Gallo (2020) <doi:10.1007/s00500-019-04320-9>).

Cloth Simulation Filter (CSF) is an airborne LiDAR (Light Detection and Ranging) ground points filtering algorithm which is based on cloth simulation. It tries to simulate the interactions between the cloth nodes and the corresponding LiDAR points, the locations of the cloth nodes can be determined to generate an approximation of the ground surface <https://www.mdpi.com/2072-4292/8/6/501/htm>.

This package provides an interface from R to the autodiff library <https://autodiff.github.io/>, a modern header-only C++ library for automatic differentiation. Unlike numerical differentiation, automatic differentiation computes derivatives of functions to machine precision without truncation error, using either forward or reverse mode. The autodiff header files are shipped with this package so that other R packages can use them by including Rcppautodiff in the LinkingTo field of their DESCRIPTION file. Example programs demonstrate computing derivatives of single-variable and multi-variable functions, gradient vectors, Jacobian matrices and derivatives with respect to parameters, using Rcpp and RcppEigen'.

Implemented are an ANOVA-type test statistic for testing hypotheses formulated in Mann-Whitney-type effects in nonparametric factorial designs. Statistical inference is based on a wild or a sample-specific bootstrap approach as described in Dobler et al. (2019) <doi:10.1007/s10463-019-00717-3>'. The unweighted treatment effects considered do not depend on sample sizes and allow for transitive ordering. The package thus provides an extension of the univariate rankFD package to multivariate data.

Rapidly estimates tree-topology from large allele frequency data using Root Distances Method, under a Brownian Motion Model. See Peng et al. (2021) <doi:10.1016/j.ympev.2021.107142>.

The mixed integer programming library MIPLIB (see <http://miplib.zib.de/>) is commonly used to compare the performance of mixed integer optimization solvers. This package provides functions to access MIPLIB from the R Optimization Infrastructure ('ROI'). More information about MIPLIB can be found in the paper by Koch et al. available at <http://mpc.zib.de/index.php/MPC/article/viewFile/56/28>. The README.md file illustrates how to use this package.

This package provides tools to derive species-level phylogenies from large synthesis mega-trees for a wide range of taxonomic groups, including plants, birds, mammals, amphibians, reptiles, fish, bees, butterflies, and sharks. When a queried species is absent from the mega-tree, it is grafted onto the tree using one of two placement strategies: attachment at the basal node of the most closely related genus or family ('at_basal_node'), or random attachment below that basal node with probability proportional to branch length ('random_below_basal'). See Li (2023) <doi:10.1111/ecog.06643> for details. Multiple species from a genus not represented in the mega-tree are placed as a polytomy to preserve clade coherence. The package interfaces with the megatrees data package, which bundles or downloads on demand curated mega-trees. Users can also provide their own mega-trees.

Modified Poisson, logistic and least-squares regression analyses for binary outcomes of Zou (2004) <doi:10.1093/aje/kwh090>, Noma (2026)<doi:10.1016/j.spl.2026.110698>, and Cheung (2007) <doi:10.1093/aje/kwm223> have been standard multivariate analysis methods to estimate risk ratio and risk difference in clinical and epidemiological studies. This R package involves an easy-to-handle function to implement these analyses by simple commands. Missing data analysis tools (multiple imputation) are also involved. In addition, recent studies have shown the ordinary robust variance estimator possibly has serious bias under small or moderate sample size situations for these methods. This package also provides computational tools to calculate alternative accurate confidence intervals.

Fits the robust Bayesian Copas (RBC) selection model of Bai et al. (2020) <arXiv:2005.02930> for correcting and quantifying publication bias in univariate meta-analysis. Also fits standard random effects meta-analysis and the Copas-like selection model of Ning et al. (2017) <doi:10.1093/biostatistics/kxx004>.

R parallel implementation of Local Outlier Factor(LOF) which uses multiple CPUs to significantly speed up the LOF computation for large datasets. (Note: The overall performance depends on the computers especially the number of the cores).It also supports multiple k values to be calculated in parallel, as well as various distance measures in addition to the default Euclidean distance.

An exact finite-sample test for whether two groups share a covariance matrix, the omnibus form of the differential-network question. Under the Gaussian null the likelihood-ratio statistic has a distribution given by the real Jacobi ensemble that is free of the unknown common covariance, so a single Monte-Carlo calibration at the identity serves every covariance with no estimate of the nuisance covariance; this is the property that survives the dimension barrier, where estimating the covariance is hardest. The max-type high-dimensional test of Cai, Liu and Xia (2013) <doi:10.1080/01621459.2012.758041> is provided for comparison. A pure-C back-end does the numerics and also backs the Python package regstat'.

This package contains functions to interface with variables and variable details sheets, including recoding variables and converting them to PMML.

Run local Large Language Models (LLMs) over many texts from R without sending data to a third party. A community-maintained wrapper for the LM Studio command line interface and API that provides functions to manage the local daemon and server, download and load models, and score, label, or generate text at scale.