Statistical Methods for Graphs (statGraph)
Contains statistical methods to analyze graphs, such as graph parameter estimation, model selection based on the GIC (Graph Information Criterion), statistical tests to discriminate two or more populations of graphs (ANOGVA -Analysis of Graph Variability), correlation between graphs, and clustering of graphs.

Similarity and Distance Quantification Between Probability Functions (philentropy)
Computes 46 optimized distance and similarity measures for comparing probability functions. These comparisons between probability functions have their foundations in a broad range of scientific disciplines from mathematics to ecology. The aim of this package is to provide a base framework for clustering, classification, statistical inference, goodness-of-fit, non-parametric statistics, information theory, and machine learning tasks that are based on comparing univariate or multivariate probability functions.

A Fast and Scalable Joint Estimator for Learning Multiple Related Sparse Gaussian Graphical Models (fasjem)
The FASJEM (A Fast and Scalable Joint Estimator for Learning Multiple Related Sparse Gaussian Graphical Models) is a joint estimator which is fast and scalable for learning multiple related sparse Gaussian graphical models. For more details, please see <https://…/2017_JEM_combined.pdf>.

Multiple Correlation (mro)
Computes multiple correlation coefficient when the data matrix is given and tests its significance.

Model and Solve Mixed Integer Linear Programs (ompr)
Model mixed integer linear programs in an algebraic way directly in R. The model is solver-independent and thus offers the possibility to solve a model with different solvers. It currently only supports linear constraints and objective functions. See the ‘ompr’ website <https://…/ompr> for more information, documentation and examples.

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