https://github.com/RfastOfficial/Rfast

A collection of Rfast functions for data analysis. Note 1: The vast majority of the functions accept matrices only, not data.frames. Note 2: Do not have matrices or vectors with have missing data (i.e NAs). We do no check about them and C++ internally transforms them into zeros (0), so you may get wrong results. Note 3: In general, make sure you give the correct input, in order to get the correct output. We do no checks and this is one of the many reasons we are fast.
https://github.com/RfastOfficial/Rfast

Last synced: 18 days ago
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A collection of Rfast functions for data analysis. Note 1: The vast majority of the functions accept matrices only, not data.frames. Note 2: Do not have matrices or vectors with have missing data (i.e NAs). We do no check about them and C++ internally transforms them into zeros (0), so you may get wrong results. Note 3: In general, make sure you give the correct input, in order to get the correct output. We do no checks and this is one of the many reasons we are fast.

• Host: GitHub
• URL: https://github.com/RfastOfficial/Rfast
• Owner: RfastOfficial
• Created: 2019-10-06T14:25:53.000Z (about 5 years ago)
• Default Branch: master
• Last Pushed: 2024-10-16T17:56:55.000Z (28 days ago)
• Last Synced: 2024-10-19T00:53:54.899Z (26 days ago)
• Language: C++
• Size: 7.95 MB
• Stars: 143
• Watchers: 10
• Forks: 19
• Open Issues: 20
• Metadata Files:
  • Readme: README.md
  • Changelog: NEWS.md

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• jimsghstars - RfastOfficial/Rfast - A collection of Rfast functions for data analysis. Note 1: The vast majority of the functions accept matrices only, not data.frames. Note 2: Do not have matrices or vectors with have missing data (i (C++)

README

        
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# Rfast

A collection of fast (utility) functions for data analysis. Column- and row- wise means, medians, variances, minimums, maximums, many t, F and G-square tests, many regressions (normal, logistic, Poisson), are some of the many fast functions. References: a) Tsagris M., Papadakis M. (2018). Taking R to its limits: 70+ tips. PeerJ Preprints 6:e26605v1 <\doi:10.7287/peerj.preprints.26605v1>. b) Tsagris M. and Papadakis M. (2018). Forward regression in R: from the extreme slow to the extreme fast. Journal of Data Science, 16(4): 771–780. <\doi:10.6339/JDS.201810_16(4).00006>.

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