Solving Ax = b Nimbly in C++

Routines for solving large systems of linear equations and eigenproblems in R. Direct and iterative solvers from the Eigen C++ library are made available. Solvers include Cholesky, LU, QR, and Krylov subspace methods (Conjugate Gradient, BiCGSTAB). Dense and sparse problems are supported.

Capture Hi-C Analysis Engine

Toolkit for processing and calling interactions in capture Hi-C data. Converts BAM files into counts of reads linking restriction fragments, and identifies pairs of fragments that interact more than expected by chance. Significant interactions are identified by comparing the observed read count to the expected background rate from a count regression model.

Allow Access to the 'Dlib' C++ Library

Interface for 'Rcpp' users to 'dlib' < http://dlib.net> which is a 'C++' toolkit containing machine learning algorithms and computer vision tools. It is used in a wide range of domains including robotics, embedded devices, mobile phones, and large high performance computing environments. This package allows R users to use 'dlib' through 'Rcpp'.

'C++' Implementations of Functional Enrichment Analysis

Fast implementations of functional enrichment analysis methods using 'C++' via 'Rcpp'. Currently provides Over-Representation Analysis (ORA) and Gene Set Enrichment Analysis (GSEA). The multilevel GSEA algorithm is derived from the 'fgsea' package. Methods are described in Subramanian et al. (2005) and Korotkevich et al. (2021) .

C++ Implementations of Phylogenetic Cladogenesis Calculations

Various cladogenesis-related calculations that are slow in pure R are implemented in C++ with Rcpp. These include the calculation of the probability of various scenarios for the inheritance of geographic range at the divergence events on a phylogenetic tree, and other calculations necessary for models which are not continuous-time markov chains (CTMC), but where change instead occurs instantaneously at speciation events. Typically these models must assess the probability of every possible combination of (ancestor state, left descendent state, right descendent state). This means that there are up to (# of states)^3 combinations to investigate, and in biogeographical models, there can easily be hundreds of states, so calculation time becomes an issue. C++ implementation plus clever tricks (many combinations can be eliminated a priori) can greatly speed the computation time over naive R implementations. CITATION INFO: This package is the result of my Ph.D. research, please cite the package if you use it! Type: citation(package="cladoRcpp") to get the citation information.

Fast C++ Primitives for the 'NeuroAnatomy Toolbox'

Fast functions implemented in C++ via 'Rcpp' to support the 'NeuroAnatomy Toolbox' ('nat') ecosystem. These functions provide large speed-ups for basic manipulation of neuronal skeletons over pure R functions found in the 'nat' package. The expectation is that end users will not use this package directly, but instead the 'nat' package will automatically use routines from this package when it is available to enable large performance gains.

C++ ODE Solvers Compiled on-Demand

Wraps the Boost odeint library for integration of differential equations.

Utilities for Scheduling Functions to Execute Later with Event Loops

Executes arbitrary R or C functions some time after the current time, after the R execution stack has emptied. The functions are scheduled in an event loop.

Advanced and Fast Data Transformation

A large C/C++-based package for advanced data transformation and statistical computing in R that is extremely fast, class-agnostic, robust, and programmer friendly. Core functionality includes a rich set of S3 generic grouped and weighted statistical functions for vectors, matrices and data frames, which provide efficient low-level vectorizations, OpenMP multithreading, and skip missing values by default. These are integrated with fast grouping and ordering algorithms (also callable from C), and efficient data manipulation functions. The package also provides a flexible and rigorous approach to time series and panel data in R, fast functions for data transformation and common statistical procedures, detailed (grouped, weighted) summary statistics, powerful tools to work with nested data, fast data object conversions, functions for memory efficient R programming, and helpers to effectively deal with variable labels, attributes, and missing data. It seamlessly supports base R objects/classes as well as 'units', 'integer64', 'xts'/ 'zoo', 'tibble', 'grouped_df', 'data.table', 'sf', and 'pseries'/'pdata.frame'.

Sparse Matrix C++ Classes Including Sparse Cholesky LDL Decomposition of Symmetric Matrices

'C++' classes for sparse matrix methods including implementation of sparse LDL decomposition of symmetric matrices and solvers described by Timothy A. Davis (2016) < https://fossies.org/linux/SuiteSparse/LDL/Doc/ldl_userguide.pdf>. Provides a set of C++ classes for basic sparse matrix specification and linear algebra, and a class to implement sparse LDL decomposition and solvers. See < https://github.com/samuel-watson/SparseChol> for details.