Package index • ks

All functions

Hbcv() Hbcv.diag(): Biased cross-validation (BCV) bandwidth matrix selector for bivariate data
Hlscv() Hlscv.diag() hlscv() Hucv() Hucv.diag() hucv(): Least-squares cross-validation (LSCV) bandwidth matrix selector for multivariate data
Hnm() Hnm.diag() hnm(): Normal mixture bandwidth
Hns() Hns.diag() hns() Hns.kcde() hns.kcde(): Normal scale bandwidth
Hpi() Hpi.diag() hpi(): Plug-in bandwidth selector
Hscv() Hscv.diag() hscv(): Smoothed cross-validation (SCV) bandwidth selector
air: Air quality measurements in an underground train station
as.kde(): Quasi kernel density estimate
binning(): Linear binning for multivariate data
cardio: Foetal cardiotocograms
contourLevels() contourSizes() contourProbs() contourBreaks(): Contour functions
grevillea: Geographical locations of grevillea plants
histde() predict(<histde>): Histogram density estimate
hsct: Haematopoietic stem cell transplant
Hamise.mixt() Hmise.mixt() Hamise.mixt.diag() Hmise.mixt.diag() hamise.mixt() hmise.mixt() amise.mixt() ise.mixt() mise.mixt(): Squared error bandwidth matrix selectors for normal mixture densities
kcde() Hpi.kcde() Hpi.diag.kcde() hpi.kcde() predict(<kcde>): Kernel cumulative distribution/survival function estimate
kcopula() kcopula.de(): Kernel copula (density) estimate
kda() Hkda() Hkda.diag() hkda() predict(<kda>) compare() compare.kda.cv() compare.kda.diag.cv(): Kernel discriminant analysis (kernel classification)
kdcde() dckde(): Deconvolution kernel density derivative estimate
kdde() kcurv() predict(<kdde>): Kernel density derivative estimate
kde() predict(<kde>): Kernel density estimate
kde.boundary(): Kernel density estimate for bounded data
kde.local.test(): Kernel density based local two-sample comparison test
kde.test(): Kernel density based global two-sample comparison test
kde.truncate() kdde.truncate(): Truncated kernel density derivative estimate
kdr() kdr.segment() plot(<kdr>): Kernel density ridge estimation
kfe() Hpi.kfe() Hpi.diag.kfe() hpi.kfe(): Kernel functional estimate
kfs(): Kernel feature significance
kms() plot(<kms>) summary(<kms>): Kernel mean shift clustering
kroc() predict(<kroc>) summary(<kroc>): Kernel receiver operating characteristic (ROC) curve
ks ks-package: ks
ksupp() plot(<ksupp>): Kernel support estimate
dnorm.mixt() rnorm.mixt() dmvnorm.mixt() rmvnorm.mixt() rmvt.mixt() dmvt.mixt() mvnorm.mixt.mode(): Normal and t-mixture distributions
plot(<histde>): Plot for histogram density estimate
plot(<kcde>): Plot for kernel cumulative distribution estimate
plot(<kda>): Plot for kernel discriminant analysis
plot(<kdde>): Plot for kernel density derivative estimate
plot(<kde>): Plot for kernel density estimate
plot(<kde.loctest>): Plot for kernel local significant difference regions
mvnorm.mixt.part() kms.part() plot(<kde.part>): Partition plot for kernel density clustering
plot(<kfs>): Plot for kernel feature significance
plot(<kroc>): Plot for kernel receiver operating characteristic curve (ROC) estimate
plotmixt(): Plot for 1- to 3-dimensional normal and t-mixture density functions
pre.sphere() pre.scale(): Pre-sphering and pre-scaling
quake plate quakesf platesf: Geographical locations of earthquakes and tectonic plates
dkde() pkde() qkde() rkde(): Derived quantities from kernel density estimates
tempb: Daily temperature
unicef: Unicef child mortality - life expectancy data
vec() vech() invvec() invvech(): Vector and vector half operators
kde.balloon() kde.sp(): Variable kernel density estimate.
worldbank: Development indicators from the World Bank Group