Contains many functions useful for data analysis
    and utility operations.

Interface of 'MIXMOD' software for supervised, unsupervised and
    semi-supervised classification with mixture modelling <doi: 10.18637/jss.v067.i06>.

Provides an interface to many endpoints of Mixpanel's Data Export, Engage and JQL API. The R functions allow for event and profile data export as well as for segmentation, retention, funnel and addiction analysis. Results are always parsed into convenient R objects. Furthermore it is possible to load and update profiles.

Mixture Composer (Biernacki (2015) <https://inria.hal.science/hal-01253393v1>) is a project to perform clustering using mixture models with
    heterogeneous data and partially missing data. Mixture models are fitted using a SEM algorithm.
    It includes 8 models for real, categorical, counting, functional and ranking data.

Mixture Composer <https://github.com/modal-inria/MixtComp> is a project to build mixture models with
    heterogeneous data sets and partially missing data management.
    It includes models for real, categorical, counting, functional and ranking data.
    This package contains the minimal R interface of the C++ 'MixtComp' library.

Mixture Composer <https://github.com/modal-inria/MixtComp> is a project to build mixture models with
    heterogeneous data sets and partially missing data management. This package contains graphical, getter and some utility
    functions to facilitate the analysis of 'MixtComp' output.

Sundry discrete probability distributions and helper functions.

R implementation of Maximum Likelihood Principal Component Analysis
    The main idea of this package is to have an alternative way of PCA for 
    subspace modeling that considers measurement errors.
    More details can be found in Peter D. Wentzell (2009) 
    <doi:10.1016/B978-0-444-64165-6.03029-9>.

The RMM  fits Revenue Management Models using the RDE(Robust Demand Estimation) method introduced in the paper by <doi:10.2139/ssrn.3598259>, one of the customer choice-based Revenue Management Model. Furthermore, it is possible to select a multinomial model as well as a conditional logit model as a model of RDE.