A suite of machine learning algorithms written in C++ with the R 
 interface contains several learning techniques for classification and regression.
 Predictive models include e.g., classification and regression trees with
 optional constructive induction and models in the leaves, random forests, kNN, 
 naive Bayes, and locally weighted regression. All predictions obtained with these
 models can be explained and visualized with the 'ExplainPrediction' package.  
 This package is especially strong in feature evaluation where it contains several variants of
 Relief algorithm and many impurity based attribute evaluation functions, e.g., Gini, 
 information gain, MDL, and DKM. These methods can be used for feature selection 
 or discretization of numeric attributes.
 The OrdEval algorithm and its visualization is used for evaluation
 of data sets with ordinal features and class, enabling analysis according to the 
 Kano model of customer satisfaction. 
 Several algorithms support parallel multithreaded execution via OpenMP.  
 The top-level documentation is reachable through ?CORElearn.

Helps users standardise data to the Darwin Core Standard, a global 
             data standard to store, document, and share biodiversity data like 
             species occurrence records. The package provides tools to 
             manipulate data to conform with, and check validity against, the 
             Darwin Core Standard. Using 'corella' allows users to verify that 
             their data can be used to build 'Darwin Core Archives' using the 
             'galaxias' package.

The 'Certifiably Optimal RulE ListS (Corels)' learner by
 Angelino et al described in <doi:10.48550/arXiv.1704.01701> provides interpretable decision
 rules with an optimality guarantee, and is made available to R with this package.
 See the file 'AUTHORS' for a list of copyright holders and contributors.

The Core Microbiome refers to the group of microorganisms that are consistently present in a particular environment, habitat, or host species. These microorganisms play a crucial role in the functioning and stability of that ecosystem. Identifying these microorganisms can contribute to the emerging field of personalized medicine. The 'CoreMicrobiomeR' is designed to facilitate the identification, statistical testing, and visualization of this group of microorganisms.This package offers three key functions to analyze and visualize microbial community data. This package has been developed based on the research papers published by Pereira et al.(2018) <doi:10.1186/s12864-018-4637-6> and Beule L, Karlovsky P. (2020) <doi:10.7717/peerj.9593>.