density estimation from interval-censored data
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/ -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the "body", not the subject !) To: r-help-request at stat.math.ethz.ch _._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._