hmm.discnp: Hidden Markov Models with Discrete Non-Parametric Observation Distributions

Fits hidden Markov models with discrete non-parametric observation distributions to data sets. Simulates data from such models. Finds most probable underlying hidden states, the most probable sequences of such states, and the log likelihood of a collection of observations given the parameters of the model.

Install the latest version of this package by entering the following in R:
install.packages("hmm.discnp")
AuthorRolf Turner
Date of publication2016-04-08 11:33:08
MaintainerRolf Turner <r.turner@auckland.ac.nz>
LicenseGPL (>= 2)
Version0.2-4

View on CRAN

Files

inst
inst/Ratfor
inst/Ratfor/gfun.r inst/Ratfor/recurse.r
inst/Ratfor/makefor
inst/Ratfor/bfun.r
inst/Ratfor/RCS
inst/Ratfor/RCS/gfun.r,v
inst/Ratfor/RCS/makefor,v
inst/Ratfor/RCS/recurse.r,v
inst/Ratfor/RCS/afun.r,v
inst/Ratfor/RCS/xfun.r,v
inst/Ratfor/RCS/bfun.r,v
inst/Ratfor/xfun.r inst/Ratfor/afun.r
inst/READ_ME
src
src/afun.f
src/xfun.f
src/recurse.f
src/bfun.f
src/gfun.f
NAMESPACE
data
data/lesionCount.rda
data/colifCount.rda
R
R/ffun.R R/sp.R R/fitted.hmm.discnp.R R/recurse.R R/hmm.R R/charList.R R/revise.tpm.R R/pr.R R/mps.R R/First.R R/revise.rho.R R/init.all.R R/viterbi.R
R/RCS
R/RCS/mat2list.R,v
R/RCS/tidyup.R,v
R/RCS/logLikHmm.R,v
R/RCS/recurse.R,v
R/RCS/mps.R,v
R/RCS/First.R,v
R/RCS/check.yval.R,v
R/RCS/revise.tpm.R,v
R/RCS/sp.R,v
R/RCS/revise.rho.R,v
R/RCS/viterbi.R,v
R/RCS/pr.R,v
R/RCS/hmm.R,v
R/RCS/revise.ispd.R,v
R/RCS/sim.hmm.R,v
R/RCS/ffun.R,v
R/RCS/init.all.R,v
R/RCS/fitted.hmm.discnp.R,v
R/check.yval.R R/sim.hmm.R R/logLikHmm.R R/revise.ispd.R
MD5
DESCRIPTION
ChangeLog
man
man/hmm.discnp-internal.Rd man/logLikHmm.Rd man/sim.hmm.Rd man/lesionCount.Rd man/hmm.Rd man/mps.Rd man/viterbi.Rd man/fitted.hmm.discnp.Rd man/pr.Rd man/sp.Rd man/colifCount.Rd

Questions? Problems? Suggestions? or email at ian@mutexlabs.com.

Please suggest features or report bugs with the GitHub issue tracker.

All documentation is copyright its authors; we didn't write any of that.