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.

Construct an explainable nomogram for a machine learning (ML) model to improve availability of an ML prediction model in addition to a computer application, particularly in a situation where a computer, a mobile phone, an internet connection, or the application accessibility are unreliable. This package enables a nomogram creation for any ML prediction models, which is conventionally limited to only a linear/logistic regression model. This nomogram may indicate the explainability value per feature, e.g., the Shapley additive explanation value, for each individual. However, this package only allows a nomogram creation for a model using categorical without or with single numerical predictors. Detailed methodologies and examples are documented in our vignette, available at <https://htmlpreview.github.io/?https://github.com/herdiantrisufriyana/rmlnomogram/blob/master/doc/ml_nomogram_exemplar.html>.