c(
person("Matthias", "Speidel", , "matthias.speidel@googlemail.com", role = c("aut", "cre"),
comment = "Munich, Germany"),
person("Joerg", "Drechsler", role = "aut",
comment = "Institute for Employment Research, Nuremberg, Germany"),
person("Shahab", "Jolani", role = "aut",
comment = "Maastricht University, Maastricht, The Netherlands"))
Matthias Speidel [aut, cre] (Munich, Germany),
Joerg Drechsler [aut] (Institute for Employment Research, Nuremberg,
Germany),
Shahab Jolani [aut] (Maastricht University, Maastricht, The
Netherlands)

Runs single level and multilevel imputation models as described in Speidel, Drechsler and Jolani (2020) <doi:10.18637/jss.v095.i09>. The user just has to pass the data to the main function and, optionally, his analysis model. Basically the package then translates this analysis model into commands to impute the data according to it with functions from 'mice', 'MCMCglmm' or routines build for this package.

A simple implementation of HMLasso (Lasso with High Missing rate).
Takada, M., Fujisawa, H., & Nishikawa, T. (2019) <arXiv:1811.00255>.
c(
person("Masaaki", "Takada", role = c("aut", "cre"), email = "masaaki1.takada@toshiba.co.jp"),
person("Toshiba", role = c("aut", "cph"))
)
Masaaki Takada [aut, cre],
Toshiba [aut, cph]

Hidden Markov models with discrete non-parametric
observation distributions.
Rolf Turner <rolf@math.unb.ca> and Limin Liu.
Fits hidden Markov models with discrete non-parametric
observation distributions to data sets.  Simulates data
from such models.

Rolf Turner <r.turner@auckland.ac.nz> and Limin Liu.

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 and
most probable sequences of such states.

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.