Jorn Lotsch [aut,cre] (<https://orcid.org/0000-0002-5818-6958>),
Sebastian Malkusch [aut] (<https://orcid.org/0000-0001-6766-140X>),
Martin Maechler [ctb],
Peter Rousseeuw [ctb],
Anja Struyf [ctb],
Mia Hubert [ctb],
Kurt Hornik [ctb]

Necessary functions for optimized automated evaluation of the number and parameters of Gaussian mixtures in one-dimensional data. Various methods are available for parameter estimation and for determining the number of modes in the mixture. A detailed description of the methods ca ben found in Lotsch, J., Malkusch, S. and A. Ultsch. (2022) <doi:10.1016/j.imu.2022.101113>.

An Implementation of Ordinal Pattern Analysis
person("Timothy", "Beechey", email = "tim.beechey@protonmail.com",
role = c("aut", "cre"), comment = c(ORCID = "0000-0001-8858-946X"))
Quantifyies hypothesis to data fit for repeated measures
observations and time series data, as described by Thorngate (1987)
<doi:10.1016/S0166-4115(08)60083-7> and Grice et al., (2015)
<doi:10.1177/2158244015604192>. Hypothesis and data are encoded as
pairwise relative orderings which are then compared to determine the
percentage of orderings in the data that are matched by the hypothesis.
Timothy Beechey [aut, cre] (<https://orcid.org/0000-0001-8858-946X>)

Quantifies hypothesis to data fit for repeated measures
and time series data, as described by Thorngate (1987)
<doi:10.1016/S0166-4115(08)60083-7> and Grice et al., (2015)
<doi:10.1177/2158244015604192>. Hypothesis and data are encoded as
pairwise relative orderings which are then compared to determine the
percentage of orderings in the data that are matched by the hypothesis.

Quantifies hypothesis to data fit for repeated measures
and longitudinal data, as described by Thorngate (1987)
<doi:10.1016/S0166-4115(08)60083-7> and Grice et al., (2015)
<doi:10.1177/2158244015604192>. Hypothesis and data are encoded as
pairwise relative orderings which are then compared to determine the
percentage of orderings in the data that are matched by the hypothesis.