We have released a new version of anvl, which brings fast array computing to R and aims to serve as a basis for implementing costly numerical algorithms natively in R.
It runs on GPU and CPU and has support for autodiff as well!
It has gotten a lot more features, so you can check it out: https://github.com/r-xla/anvl We have also started the process of submitting it (and it's dependencies) to CRAN!
Let’s say that I’m working in RStudio on a geospatial data-science project that involves some pretty gnarly, extensive code across multiple R scripts. Let’s also say that I do not have access to any AI code assistants or chatbots due to workplace restrictions.
What packages and functions would you use to comprehensively check your code for errors, inefficiencies, conceptual problems (that may be a stretch), etc.?
I’m using lintr::lint() and rstyler::style_file(), but that’s the extent of my knowledge in this area.
Many thanks in advance, friends.
Hi r/rstats,
My colleague and I just published FitVerse on CRAN. It fits parametric probability distributions to continuous data, covering 52 distribution families with three estimation methods: MLE, Method of Moments, and L-Moments.
The basic usage is just:
r
install.packages("FitVerse")
library(FitVerse)
x <- as.numeric(precip)
fit <- fitverse(x, xlab = "Annual precipitation (inches)")
It automatically ranks all distributions by AIC/BIC, runs four goodness-of-fit tests, and produces a diagnostic plot. It also includes bootstrap confidence intervals, return levels, batch fitting, report generation, and a built-in Shiny app.
Full write-up here: https://rpubs.com/KarunaGReddy/FitVerse
CRAN: https://cran.r-project.org/package=FitVerse
Happy to answer any questions!