person("Richèl J.C.", "Bilderbeek", email = "richel@richelbilderbeek.nl", role = c("aut", "cre"), comment = c(ORCID = "0000-0003-1107-7049")))
MHCnuggets (<https://github.com/KarchinLab/mhcnuggets>) is a Python
tool to predict MHC class I and MHC class II epitopes.
This package allows one to call MHCnuggets from R.
Imports: rappdirs, reticulate, stringr, tibble

Title: Minimum Hellinger Distance Test for Normality
Authors@R: c(person(c("Paul", "W."), "Eslinger", role = c("aut", "cre"), email="paul.w.eslinger@pnnl.gov"),
person("Heather", "Orr", role=c("aut", "ctb"), email="heather.orr@pnnl.gov"))
Maintainer: Paul W. Eslinger <paul.w.eslinger@pnnl.gov>
Description: Implementation of a goodness-of-fit test for normality using the Minimum Hellinger Distance.

Title: Parameter estimation and prediction for multiple hidden Markov and semi-Markov models
Author: Jared O'Connell <jared@graduate.uwa.edu.au> with minor contributions from Soren Hojsgaard <sorenh@agrsci.dk>
Description: Parameter estimation and prediction for multiple hidden Markov and semi-Markov models. The times must be equidistant but missing values of the observables are allowed. The observables are allowed to be multivariate. It is possible to have multiple sequences of data. Estimation of the parameters of the models are made using EM--algorithms. Crucial parts of the code is written in C which makes estimation fast. Finally, the package is made so that the user is allow to specify own components of the model.

Package: mhsmm
Type: Package
Title: Parameter estimation and prediction for hidden Markov and
semi-Markov models for data with multiple observation sequences
Version: 0.2.1
Date: 2009-01-21
Author: Jared O'Connell <jared.oconnell@ferring.com>, Soren Hojsgaard
<sorenh@agrsci.dk>
Maintainer: Jared O'Connell <jared.oconnell@ferring.com>
Description: Parameter estimation and prediction for hidden Markov and
semi-Markov models for data with multiple observation
sequences. The times must be equidistant but missing values of
the observables are allowed. The observables are allowed to be
multivariate. It is possible to have multiple sequences of
data. Estimation of the parameters of the models are made using
EM--algorithms. Computationally demanding parts of the
algorithm are implemented in C for performance. Finally, the
package is designed to allow users to specify their own
emission distributions, giving flexibility in the type of data
that may be analysed.
License: GPL (>= 2)
LazyLoad: yes
Depends: mvtnorm
Packaged: 2009-04-04 06:43:38 UTC; jared
Repository: CRAN
Date/Publication: 2009-04-04 20:41:15