Fitting multivariate data patterns with local principal curves; including simple tools for data compression (projection), bandwidth selection, and measuring goodness-of-fit.
|Author||Jochen Einbeck and Ludger Evers|
|Date of publication||2015-09-22 13:00:32|
|Maintainer||Jochen Einbeck <firstname.lastname@example.org>|
|License||GPL (>= 2)|
calspeedflow: Speed-flow data from Calfornia.
coverage: Coverage and self-coverage plots.
followx: Fit an individual branch of a local principal curve.
gaia: Gaia data
gvessel: North Atlantic Water Temperature Data.
kernels.and.distances: Auxilary kernel and distance functions.
lpc: Local principal curves
lpc.control: Auxiliary parameters for controlling local principal curves.
LPCM-package: Local principal curve methods
lpc.project: Projection onto LPC
lpc.spline: Representing local principal curves through a cubic spline.
lpc.splinefun: Auxiliary functions for spline fitting and projection.
ms: Mean shift clustering.
plot.lpc: Plotting local principal curves
print.lpc: Printing output for lpc and lpc.spline objects
Rc: Measuring goodness-of-fit for principal objects.
unscale: Unscaling local principal objects.