Package: mhsmm
Type: Package
Title: Parameter estimation and prediction for hidden Markov and
semi-Markov models for data with multiple observation sequences
Version: 0.2.3
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: 2009-04-05 10:16:51 UTC; jared
Repository: CRAN
Date/Publication: 2009-04-14 17:32:17

Author: Jared O'Connell <jared@graduate.uwa.edu.au>, Soren Hojsgaard
sequences.  Suitable for equidistant time series data, with

Author: Jared O'Connell <jared@well.ox.ac.uk>, Soren Hojsgaard

Author: Jared O'Connell <jared@well.ox.ac.uk>, S�ren H�jsgaard

Author: Jared O'Connell <jaredoconnell@gmail.com>, S�ren H�jsgaard

Title: Inference for Hidden Markov and Semi-Markov Models
Description: Parameter estimation and prediction for hidden Markov and semi-Markov models for data with multiple observation sequences.  Suitable for equidistant time series data, with multivariate and/or missing data. Allows user defined emission distributions.

Author: Jared O'Connell <jaredoconnell@gmail.com>, Søren Højsgaard
Packaged: 2017-01-15 11:09:36 UTC; joconnell

Title: Multiple Hypotheses Testing For Variable Selection
Description: Multiple Hypotheses Testing For Variable Selection

Title: Multiple Hypothesis Testing for Variable Selection in
Description: Multiple Hypothesis Testing For Variable Selection in high dimensional linear models. This package performs variable selection with multiple hypothesis testing, either for ordered variable selection or non-ordered variable selection. In both cases, a sequential procedure is performed. It starts to test the null hypothesis "no variable is relevant"; if this hypothesis is rejected, it then tests "only the first variable is relevant", and so on until the null hypothesis is accepted.