Our toolbox currently contains implementations of robust methods for location and scale estimation, covariance estimation (FAST-MCD), regression (FAST- LTS, MCD-regression), principal component analysis (RAPCA, ROBPCA), princi- pal component regression (RPCR), partial least squares (RSIMPLS) and classi¯ cation (RDA). Only a few of these methods will be highlighted in this paper. The toolbox also provides many graphical tools to detect and classify the outliers. The use of these features will be explained and demonstrated through the analysis of some real data sets.

Academic License LIBRA.pdf
adjustedboxplot.m
adjustedoutlyingness.m
adm.m
agnes.m
agricul.mat
animal.mat
bagplot.m
bannerplot.m
cda.m
cdq.m
chiqqplot.m
clara.m
classSVD.m
clusplot.m
country.mat
cpca.m
cpcr.m
csimca.m
csimpls.m
cvMcd.m
cvRobpca.m
cvRpcr.m
cvRsimpls.m
daisy.m
daisyc.mexw32
daplot.m
ddplot.m
diana.m
distplot.m
ellipsplot.m
extractmcdregres.m
fanny.m
fannyc.mexw32
flower.mat
greatsort.m
halfspacedepth.m
hl.m
kernelEVD.m
l1median.m
lmc.m
lsscatter.m
ltsregres.m
madc.m
mahalanobis.m
makeplot.m
mc.m
mcdcov.m
mcdregres.m
mcenter.m
medc.mexw32
mlochuber.m
mloclogist.m
mlr.m
mona.m
monac.mexw32
mscalelogist.m
normqqplot.m
obj200.mat
ols.m
pam.m
pamc.mexw32
plotnumbers.m
predict.m
putlabel.m
qn.m
qnm.m
qnmscale.mexw32
randomset.m
rapca.m
rda.m
regresdiagplot.m
regresdiagplot3d.m
removal.m
removeObsMcd.m
removeObsRobpca.m
residualplot.m
rmc.m
robpca.m
robpcaregres.m
robstd.m
rpcr.m
rrmse.m
rsimca.m
rsimpls.m
rsquared.m
rstep.m
ruspini.mat
sclprc.mexw32
scorediagplot.m
screeplot.m
silhouetteplot.m
simcaplot.m
spannc.mexw32
tree.m
twinsc.mexw32
twopoints.m
unimcd.m
uniran.m
updatecov.m

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