Estimation of party/candidate ideological positions that
correspond to a Nash equilibrium along a one-dimensional space.

Estimation of party/candidate ideological positions
that correspond to a Nash equilibrium along a
one-dimensional space.

Normal (1-d) Mixture Models {Classes and Methods}
Onedimensional Normal Mixture Models Classes, for, e.g.,
density estimation or clustering algorithms research and teaching;
providing the widely used Marron-Wand densities, see ?MarronWand.
Use the package `mclust' for estimation!

Use the 'mclust' package for estimation!

Normal (1-d) Mixture Models (S3 Classes and Methods)

Onedimensional Normal Mixture Models Classes, for, e.g.,
density estimation or clustering algorithms research and
teaching; providing the widely used Marron-Wand densities, see
?MarronWand.

Onedimensional Normal Mixture Models Classes, for, e.g.,
density estimation or clustering algorithms research and teaching;
providing the widely used Marron-Wand densities.  Now fitting to data
by ML (Maximum Likelihood) or EM estimation.

Onedimensional Normal Mixture Models Classes, for, e.g.,
density estimation or clustering algorithms research and teaching;
providing the widely used Marron-Wand densities.  Efficient random
number generation and graphics; now fitting to data by ML (Maximum
Likelihood) or EM estimation.

Normal aka Gaussian (1-d) Mixture Models (S3 Classes and
Methods)
c(person("Martin", "Maechler", role = c("aut","cre"),
email = "maechler@stat.math.ethz.ch",
comment = c(ORCID = "0000-0002-8685-9910"))
, person("Friedrich", "Leisch", role = "ctb",
comment = c("norMixEM()", ORCID = "0000-0001-7278-1983"))
, person("Erik", "Jørgensen", role = "ctb", comment = "pnorMix(), qnorMix()")
)
Onedimensional Normal (i.e. Gaussian) Mixture Models Classes,
for, e.g., density estimation or clustering algorithms research and teaching;
providing the widely used Marron-Wand densities.  Efficient random
number generation and graphics. Fitting to data by efficient ML (Maximum
Likelihood) or traditional EM estimation.
Martin Maechler [aut, cre] (<https://orcid.org/0000-0002-8685-9910>),
Friedrich Leisch [ctb] (norMixEM(),
<https://orcid.org/0000-0001-7278-1983>),
Erik Jørgensen [ctb] (pnorMix(), qnorMix())