Parameter estimation and prediction for hidden Markov and
semi-Markov models for data with multiple observation
sequences.
Jared O'Connell <jared@graduate.uwa.edu.au>, Soren Hojsgaard
<sorenh@agrsci.dk>
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.

Jared O'Connell <jared@well.ox.ac.uk>, Soren Hojsgaard
<sorenh@agrsci.dk>

Jared O'Connell <jared@well.ox.ac.uk>, Søren Højsgaard
<sorenh@agrsci.dk>

Jared O'Connell <jared@well.ox.ac.uk>, Søren Højsgaard
<sorenh@math.aau.dk>

Jared O'Connell <jaredoconnell@gmail.com>, Søren Højsgaard
<sorenh@math.aau.dk>

Inference for Hidden Markov and Semi-Markov Models
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.

Multiple Hypotheses Testing For Variable Selection

Multiple Hypothesis Testing for Variable Selection in
High-Dimensional Linear Models
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.

Multiple Hypothesis Test Based on Distribution of p Values
person("Abhirup", "Mallik", email = "malli066@umn.edu", role = c("aut", "cre"))
A framework for multiple hypothesis testing based on distribution
of p values. It is well known that the p values come from different
distribution for null and alternatives, in this package we provide
functions to detect that change. We provide a method for using the change
in distribution of p values as a way to detect the true signals in the
data.