README.md

In tstev/tstevR: Someone's Collection of R functions

tstevR

The goal of tstevR was to learn how to write packages. Best start somewhere. At the same time it served as a collection of functions I have found useful over my short experience with R.

Installation

This package will likely not be on CRAN ever and will likely die slow and painful here on GitHub. Nonetheless if for some reason a wanderer wishes to install this package this can be done as follows:

# install.packages("devtools")
devtools::install_github("tstev/tstevR")

Example

Perhaps the only thing I am proud of in this package is the sparse iterative solver. It uses the (diagonal, only implemented at the moment) preconditioned conjugate gradient solver from the Eigen C++ library using RcppEigen package.

# Same example from RcppEigen::fastLM
library(tstevR)
data(trees, package="datasets")
mm <- cbind(1, log(trees$Girth))   # model matrix
y  <- log(trees$Volume)            # response

coef(sparse_lm(mm, y))
coef(RcppEigen::fastLmPure(mm, y))
unname(coef(lm.fit(mm, y)))

References

Head over to Rcpp Gallery for some awesome examples using the amazing Rcpp package that also served as a source for the sparse_lm() function in this package. In addition to the help on StackOverflow from Dirk Eddelbuettel (author of many packages including Rcpp) and The Coatless Professor (aka James Balamuta).

Code of Conduct

Please note that the tstevR project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

tstev/tstevR documentation built on May 18, 2020, 10:57 p.m.