System for Building and Debugging C/C++ Dynamic Libraries

A build system based on 'GNU make' that creates and maintains (simply) make files in an R session and provides GUI debugging support through 'Microsoft Visual Code'.

Solvers for Initial Value Problems of Differential Equations ('ODE', 'DAE', 'DDE')

Functions that solve initial value problems of a system of first-order ordinary differential equations ('ODE'), of partial differential equations ('PDE'), of differential algebraic equations ('DAE'), and of delay differential equations. The functions provide an interface to the FORTRAN functions 'lsoda', 'lsodar', 'lsode', 'lsodes' of the 'ODEPACK' collection, to the FORTRAN functions 'dvode', 'zvode' and 'daspk' and a C-implementation of solvers of the 'Runge-Kutta' family with fixed or variable time steps. The package contains routines designed for solving 'ODEs' resulting from 1-D, 2-D and 3-D partial differential equations ('PDE') that have been converted to 'ODEs' by numerical differencing.

'2bit' 'C' Library

A trimmed down copy of the "kent-core source tree" turned into a 'C' library for manipulation of '.2bit' files. See < https://genome.ucsc.edu/FAQ/FAQformat.html#format7> for a quick overview of the '2bit' format. The "kent-core source tree" can be found here: < https://github.com/ucscGenomeBrowser/kent-core/>. Only the '.c' and '.h' files from the source tree that are related to manipulation of '.2bit' files were kept. Note that the package is primarily useful to developers of other R packages who wish to use the '2bit' 'C' library in their own 'C'/'C++' code.

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.

R and C++11

Rcpp11 includes a header only C++11 library that facilitates integration between R and modern C++.

RUV-III-C

Variations of Remove Unwanted Variation-III (RUV-III) known as RUV-III-C (RUV-III Complete). RUV-III performs normalisation using negative control variables and replication. RUV-III-C extends this method to cases where the data contains missing values, by applying RUV-III to complete subsets of the data. Originally designed for SWATH-MS proteomics datasets. Poulos et al. (2020) .

'Rcpp'-Based Helper Functions to Pass 'Int64' and 'nanotime' Values Between 'R' and 'C++'

'Int64' values can be created and accessed via the 'bit64' package and its 'integer64' class which package the 'int64' representation cleverly into a 'double'. The 'nanotime' packages builds on this to support nanosecond-resolution timestamps. This packages helps conversions between 'R' and 'C++' via several helper functions provided via a single header file. A complete example client package is included as an illustration.

Interpolation From C

Simple interpolation methods designed to be used from C code. Supports constant, linear and spline interpolation. An R wrapper is included but this package is primarily designed to be used from C code using 'LinkingTo'. The spline calculations are classical cubic interpolation, e.g., Forsythe, Malcolm and Moler (1977) .

A Collection of Efficient and Extremely Fast R Functions

A collection of fast (utility) functions for data analysis. Column and row wise means, medians, variances, minimums, maximums, many t, F and G-square tests, many regressions (normal, logistic, Poisson), are some of the many fast functions. References: a) Tsagris M., Papadakis M. (2018). Taking R to its limits: 70+ tips. PeerJ Preprints 6:e26605v1 . b) Tsagris M. and Papadakis M. (2018). Forward regression in R: from the extreme slow to the extreme fast. Journal of Data Science, 16(4): 771--780. . c) Chatzipantsiou C., Dimitriadis M., Papadakis M. and Tsagris M. (2020). Extremely Efficient Permutation and Bootstrap Hypothesis Tests Using Hypothesis Tests Using R. Journal of Modern Applied Statistical Methods, 18(2), eP2898. . d) Tsagris M., Papadakis M., Alenazi A. and Alzeley O. (2024). Computationally Efficient Outlier Detection for High-Dimensional Data Using the MDP Algorithm. Computation, 12(9): 185. . e) Tsagris M. and Papadakis M. (2025). Fast and light-weight energy statistics using the R package Rfast. .

Multiple Precision Arithmetic

Multiple Precision Arithmetic (big integers and rationals, prime number tests, matrix computation), "arithmetic without limitations" using the C library GMP (GNU Multiple Precision Arithmetic).