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README.Rmd
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README.Rmd
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---
output: github_document
---
```{r, echo = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "README-"
)
```
# R To Armadillo (`r2arma`)
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[](https://github.com/coatless-rpkg/r-to-armadillo/actions/workflows/R-CMD-check.yaml)
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R is an amazing language to explore and communicate statistics with. However, when we care about attain results quickly, R's primary weakness is shown: speed. The objective of this repository is to strengthen this weakness by providing high performing R base functions within [Armadillo](http://arma.sourceforge.net/docs.html) (and potentially eigen). Futhermore, as the code is written within C++, the code is able to be ported to different platforms at ease. Lastly, an additional benefit of this project is a repository containing programming examples by field.
Feel free to use the functions available within the repository per the MIT License.
# Project Details
The functions written here act either as a means to replicate existing R functionality within [Armadillo](http://arma.sourceforge.net/docs.html) or as a helpful interface to [Armadillo](http://arma.sourceforge.net/docs.html)'s objects to perform crazier operations. These functions are meant to be easily included within existing code bases with minimal dependencies. In its packaged form, the project uses the R to C++ interface afforded by [`RcppArmadillo`](https://github.com/rcppcore/rcpparmadillo)
**Note:** At some point in the future, these functions may end up being merged into the [`RcppArmadillo`](https://github.com/rcppcore/rcpparmadillo) project.
The current implementation has:
* [Distributions](https://github.com/coatless-rpkg/r-to-armadillo/blob/master/src/distributions.cpp)
* Random Sampling
* Wishart
* Inverse Wishart
* [Time Series](https://github.com/coatless-rpkg/r-to-armadillo/blob/master/src/ts.cpp)
* Difference by lag and number of differences
* Discrete Integration by lag, number of differences, and - optionally - initial values.
* Convert ARMA process to an infinite MA process
* Compute the theoretical autocorrelation function (ACF) for an ARMA process.
* Convolution (Moving Average) or Recursive (Autoregression) filters.
* Calculate Discrete Fourier Transformation (DFT) for Autocovariance Function (ACF)
* [Sequences](https://github.com/coatless-rpkg/r-to-armadillo/blob/master/src/seq.cpp)
* Generate Integer Sequence from `a` to `b`
* Generate Double Sequence form `a` to `b` or `-a` to `a` with `n` points.
* Obtain a sequence along a vector.
* Obtain a sequence given a vector length.
* [Manipulations](https://github.com/coatless-rpkg/r-to-armadillo/blob/master/src/manipulations.cpp)
* Vector
* Reverse vector (e.g. `1:3` => `3:1`)
* Matrix
* Reverse subset for row/column (e.g. `0:3` vs. `3:0`)
* Subset Non-connected Regions (e.g. `c(0,1)` and `c(2,1)`)
* Field
* Convert `field<vec>` to `mat`
* Total sum of elements in `field`
# Contributing
Contributions such as translations of R functions to C++ are welcome via a [pull request](https://github.com/coatless-rpkg/r-to-armadillo/pulls). If you have a new feature request, please feel free to write an [issue](https://github.com/coatless-rpkg/r-to-armadillo/issues).
Please note that this project is released with a [Contributor Code of Conduct](CONDUCT.md). By participating in this project you agree to abide by its terms.