[R] density estimation from interval-censored data

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From: David Firth (david.firth@nuffield.oxford.ac.uk)
Date: Fri 13 Jul 2001 - 02:33:11 EST 

Message-id: <a0501040eb7737d4ba894@[163.1.41.88]>

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@nuffield.ox.ac.uk

http://www.stats.ox.ac.uk/~firth/
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