mlf: Machine Learning Foundations

Offers a gentle introduction to machine learning concepts for practitioners with a statistical pedigree: decomposition of model error (bias-variance trade-off), nonlinear correlations, information theory and functional permutation/bootstrap simulations. Székely GJ, Rizzo ML, Bakirov NK. (2007). <doi:10.1214/009053607000000505>. Reshef DN, Reshef YA, Finucane HK, Grossman SR, McVean G, Turnbaugh PJ, Lander ES, Mitzenmacher M, Sabeti PC. (2011). <doi:10.1126/science.1205438>.

Version:	1.2.1
Imports:	stats, utils
Published:	2018-06-25
Author:	Kyle Peterson [aut, cre]
Maintainer:	Kyle Peterson <petersonkdon at gmail.com>
License:	GPL-2
URL:	http://mlf-project.us/
NeedsCompilation:	no
CRAN checks:	mlf results

Documentation:

Reference manual:

mlf.pdf

Downloads:

Package source:	mlf_1.2.1.tar.gz
Windows binaries:	r-devel: mlf_1.2.1.zip, r-release: mlf_1.2.1.zip, r-oldrel: mlf_1.2.1.zip
macOS binaries:	r-release (arm64): mlf_1.2.1.tgz, r-oldrel (arm64): mlf_1.2.1.tgz, r-release (x86_64): mlf_1.2.1.tgz, r-oldrel (x86_64): mlf_1.2.1.tgz
Old sources:	mlf archive

Linking:

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