Enhanced Implementation of MissForest Algorithm
-
Updated
May 15, 2020 - R
Enhanced Implementation of MissForest Algorithm
Raw files for a document covering techniques for speeding up R, especially before parallelization.
Set of functions to semi-automatically build and test Ordinary Least Squares (OLS) models in R in parallel.
Multicore and utility functions for Seurat 2 & 3, using doMC / foreach packages.
A set of R scripts to process the BFAST algorithm in various points and with parallelization
🚀 R package future.callr: A Future API for Parallel Processing using 'callr'
Small model to simulate biomass production on a global scale. Products are Gross Primary Production (GPP), Net Primary Production (NPP). It is based on a modified version of the Farquhar approach (Haxeltine and Prentice 1996, Farquhar et al. 1980). Where possible, it uses vectorization and parallelization and dynamically downloads latest av
Tracking the progress of mc*apply with progress bar.
R functions for project setup, data cleaning, machine learning, SuperLearner, parallelization, and targeted learning.
🚀 R package: future.apply - Apply Function to Elements in Parallel using Futures
🚀 R package: future: Unified Parallel and Distributed Processing in R for Everyone
Add a description, image, and links to the parallelization topic page so that developers can more easily learn about it.
To associate your repository with the parallelization topic, visit your repo's landing page and select "manage topics."