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LPCM

This package can be used for fitting multivariate data patterns with local principal curves, including tools for data compression (projection) and measuring goodness-of-fit; with additional functionalities for mean shift clustering.

library(LPCM)
data(calspeedflow)
lpc1 <- lpc(calspeedflow[,3:4])
plot(lpc1, lwd=2, curvecol="red")


ms1 <- ms(calspeedflow[,3:4], plot=FALSE)
plot(ms1)

Try ?LPCM, ?lpc and ?ms.

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Version

Install

install.packages('LPCM')

Monthly Downloads

2,267

Version

0.47-4

License

GPL (>= 2)

Maintainer

Jochen Einbeck

Last Published

March 7th, 2024

Functions in LPCM (0.47-4)

lpc.spline.auxiliary.functions

Auxiliary functions for spline fitting and projection.

Mean shift clustering.

ms.rep

Mean shift procedures.

plot.lpc

Plotting local principal curves and mean shift trajectories

print.lpc

Printing output for lpc, lpc.spline, and ms objects

lpc.project

Projection onto LPC

unscale

Unscaling local principal objects.

lpc.spline

Representing local principal curves through a cubic spline.

followx

Fit an individual branch of a local principal curve.

kernels.and.distances

Auxiliary kernel and distance functions.

gaia

Gaia data

calspeedflow

Speed-flow data from California.

gvessel

North Atlantic Water Temperature Data.

lpc.control

Auxiliary parameters for controlling local principal curves.

lpc

Local principal curves

Measuring goodness-of-fit for principal objects.

coverage

Coverage and self-coverage plots.

LPCM-package

Local principal curve methods