Package: ssr 0.1.1

Enrique Garcia-Ceja

ssr: Semi-Supervised Regression Methods

An implementation of semi-supervised regression methods including self-learning and co-training by committee based on Hady, M. F. A., Schwenker, F., & Palm, G. (2009) <doi:10.1007/978-3-642-04274-4_13>. Users can define which set of regressors to use as base models from the 'caret' package, other packages, or custom functions.

Authors:Enrique Garcia-Ceja [aut, cre]

ssr_0.1.1.tar.gz
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ssr.pdf |ssr.html✨
ssr/json (API)
NEWS

Peer review:

Bug tracker:https://github.com/enriquegit/ssr/issues

On CRAN:

data-sciencemachine-learningregressionsemi-supervised-learning

2 exports 2 stars 1.10 score 75 dependencies 1 mentions 27 scripts 170 downloads

Last updated 5 years agofrom:bbf935ecac. Checks:OK: 1 NOTE: 6. Indexed: yes.

Exports:split_train_testssr

Dependencies:caretclasscliclockcodetoolscolorspacecpp11data.tablediagramdigestdplyre1071fansifarverforeachfuturefuture.applygenericsggplot2globalsgluegowergtablehardhatipredisobanditeratorsKernSmoothlabelinglatticelavalifecyclelistenvlubridatemagrittrMASSMatrixmgcvModelMetricsmunsellnlmennetnumDerivparallellypillarpkgconfigplyrpROCprodlimprogressrproxypurrrR6RColorBrewerRcpprecipesreshape2rlangrpartscalesshapeSQUAREMstringistringrsurvivaltibbletidyrtidyselecttimechangetimeDatetzdbutf8vctrsviridisLitewithr