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

Downloads:

Reference manual: mlf.pdf
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
OS X binaries: r-release: mlf_1.2.1.tgz, r-oldrel: mlf_1.2.1.tgz
Old sources: mlf archive

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