Build and manage real-life data science projects with ease.
-
Updated
Nov 19, 2020 - Python
{{ message }}
Build and manage real-life data science projects with ease.
An interactive graphing library for R
Machine Learning in R
An R-focused pipeline toolkit for reproducibility and high-performance computing
A frictionless, pipeable approach to dealing with summary statistics
RStan, the R interface to Stan
Use RMarkdown to generate PDF Conference Posters via HTML
Paginate the HTML Output of R Markdown with CSS for Print
#108 could have been prevented if knit_print tests captured this breaking change.
Adding test examples checking for knit_print output should provide reasonably detailed tests for brief_entries(), detailed_entries() and bibliography_entries() outputs.
Seamless R and C++ Integration
Tidy data structures, summaries, and visualisations for missing data
Bayesian analysis + tidy data + geoms (R package)
The Dockerfile should default to options(Ncpus = 4) so that install is done faster.
R Interface to the jQuery Plug-in DataTables
Bindings for Tabula PDF Table Extractor Library
Preliminary Exploratory Visualisation of Data
Magic, madness, heaven, sin
Presentation-Ready Data Summary and Analytic Result Tables
Assertive programming for R analysis pipelines
Sustainable transport planning with R
Automate Data Exploration and Treatment
Add a description, image, and links to the r-package topic page so that developers can more easily learn about it.
To associate your repository with the r-package topic, visit your repo's landing page and select "manage topics."
To be more in line with the rest of the regression families I think it would be a good idea to support lognormal distribution reparameterized with mean and standard deviation on the natural scale.
Here is the reparametrization (from ProbOnto, https://sites.google.com/site/probonto/download):
$P\left(x ; \boldsymbol{\mu}{N}, \boldsymbol{\sigma}{N}\right)=\frac{1}{x \sqrt{2 \pi \log \left