stablelearner: Stability Assessment of Statistical Learning Methods

Graphical and computational methods that can be used to assess the stability of results from supervised statistical learning.

AuthorMichel Philipp [aut, cre], Carolin Strobl [aut], Achim Zeileis [aut], Thomas Rusch [aut], Kurt Hornik [aut]
Date of publication2017-01-13 15:20:10
MaintainerMichel Philipp <M.Philipp@psychologie.uzh.ch>
LicenseGPL-2 | GPL-3
Version0.1-1

View on R-Forge

Functions

addLearner Man page
barplot.stabletree Man page
bdist Man page
bootstrap Man page
boxplot.stablelearner Man page
boxplot.stablelearnerList Man page
ccc Man page
ckappa Man page
clagree Man page
cosine Man page
cprob Man page
dgp_twoclass Man page
edist Man page
hdist Man page
image.stabletree Man page
jackknife Man page
jsdiv Man page
LearnerList Man page
madist Man page
msdist Man page
pcc Man page
plot.stabletree Man page
print.stablelearner Man page
print.stablelearnerList Man page
print.stabletree Man page
print.summary.stablelearnerList Man page
print.summary.stabletree Man page
qadist Man page
rbfkernel Man page
rmsdist Man page
samplesplitting Man page
similarity_measures Man page
similarity_measures_classification Man page
similarity_measures_regression Man page
similarity_values Man page
splithalf Man page
stab_control Man page
stability Man page
stabletree Man page
subsampling Man page
summary.stablelearner Man page
summary.stablelearnerList Man page
summary.stabletree Man page
tanimoto Man page
titanic Man page
tvdist Man page

Questions? Problems? Suggestions? or email at ian@mutexlabs.com.

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