Interpretability methods to analyze the behavior and
    predictions of any machine learning model.  Implemented methods are:
    Feature importance described by Fisher et al. (2018)
    <doi:10.48550/arxiv.1801.01489>, accumulated local effects plots described by Apley
    (2018) <doi:10.48550/arxiv.1612.08468>, partial dependence plots described by
    Friedman (2001) <www.jstor.org/stable/2699986>, individual conditional
    expectation ('ice') plots described by Goldstein et al.  (2013)
    <doi:10.1080/10618600.2014.907095>, local models (variant of 'lime')
    described by Ribeiro et. al (2016) <doi:10.48550/arXiv.1602.04938>, the Shapley
    Value described by Strumbelj et. al (2014)
    <doi:10.1007/s10115-013-0679-x>, feature interactions described by
    Friedman et. al <doi:10.1214/07-AOAS148> and tree surrogate models.

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Implements some item response models for multiple
    ratings, including the hierarchical rater model, 
    conditional maximum likelihood estimation of linear 
    logistic partial credit model and a wrapper function
    to the commercial FACETS program. See Robitzsch and
    Steinfeld (2018) for a description of the functionality
    of the package. 
    See Wang, Su and Qiu (2014; <doi:10.1111/jedm.12045>)
    for an overview of modeling alternatives.

Based on large margin principle, this package performs feature selection methods: "IM4E"(Iterative Margin-Maximization under Max-Min Entropy Algorithm); "Immigrate"(Iterative Max-Min Entropy Margin-Maximization with Interaction Terms Algorithm); "BIM"(Boosted version of IMMIGRATE algorithm); "Simba"(Iterative Search Margin Based Algorithm); "LFE"(Local Feature Extraction Algorithm). This package also performs prediction for the above feature selection methods.

Fitting and validation of machine learning algorithms
             for volume prediction of trees, currently for conifer trees based on
			 diameter at breast height and height as explanatory variables.