milkyWay

Heliocentric velocity for the Milky Way

datasets

Implements techniques to estimate the unknown quantities
related to two-component admixture models, where the two components
can belong to any distribution (note that in the case of multinomial
mixtures, the two components must belong to the same family).
Estimation methods depend on the assumptions made on the unknown
component density; see Bordes and Vandekerkhove (2010)
<doi:10.3103/S1066530710010023>, Patra and Sen (2016)
<doi:10.1111/rssb.12148>, and Milhaud, Pommeret, Salhi, Vandekerkhove
(2024) <doi:10.3150/23-BEJ1593>. In practice, one can estimate both
the mixture weight and the unknown component density in a wide variety
of frameworks. On top of that, hypothesis tests can be performed in
one and two-sample contexts to test the unknown component density (see
Milhaud, Pommeret, Salhi and Vandekerkhove (2022)
<doi:10.1016/j.jspi.2021.05.010>, and Milhaud, Pommeret, Salhi,
Vandekerkhove (2024) <doi:10.3150/23-BEJ1593>). Finally, clustering of
unknown mixture components is also feasible in a K-sample setting (see
Milhaud, Pommeret, Salhi, Vandekerkhove (2024)
<https://jmlr.org/papers/v25/23-0914.html>).

Xavier Milhaud

admix

Package Admix for Admixture (aka Contamination) Models

Pierre Vandekerkhove

Denys Pommeret

Yahia Salhi

milkyWay function

A data frame with 170,601 rows and 1 column:<dl>
<dt>V1</dt>
<dd>Heliocentric velocity measurements of the Milky way</dd>
</dl>

Format

Heliocentric velocity for the Milky Way — milkyWay

A data frame with 170,601 rows and 1 column:<dl>
<dt>V1</dt>
<dd>Heliocentric velocity measurements of the Milky way</dd>


</dl>

milkyWay: Heliocentric velocity for the Milky Way

Description

Usage

Arguments

Format

References