I am aware of the nice R package "logspline", which does smooth
density estimation from interval-censored data (that is, values that
are known to lie in a specified interval rather than known exactly).
Function logspline.fit uses a maximum penalized likelihood method,
with the penalty related to the number of knots used in a cubic
regression-spline fit.
I need to be able to do some things that don't seem straightforward
with the logspline package:
(a) penalize the likelihood also for roughness, e.g. using the
integrated squared second derivative
(b) obtain approximate confidence limits for the density at specified points
My question: is there another R package that can help me with these
things? If so it would be good to know before I embark on
programming them myself.
Thanks -- David
--
David Firth Phone +44 1865 278544
Nuffield College Fax +44 1865 278621
Oxford OX1 1NF Secretary +44 1865 278553
United Kingdom Email david.firth at nuffield.ox.ac.uk
http://www.stats.ox.ac.uk/~firth/
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