Tools and features for "Exploratory Landscape Analysis (ELA)" of
	single-objective continuous optimization problems.
    Those features are able to quantify rather complex properties, such as the
    global structure, separability, etc., of the optimization problems.

In order to achieve accurate estimation without sparsity assumption on the precision matrix, element-wise inference on the precision matrix, and joint estimation of multiple Gaussian graphical models, a novel method is proposed and efficient algorithm is implemented.
    FLAG() is the main function given a data matrix, and FlagOneEdge() will be used when one pair of random variables are interested where their indices should be given.
    Flexible and Accurate Methods for Estimation and Inference of Gaussian Graphical Models with Applications, see Qian Y (2023) <doi:10.14711/thesis-991013223054603412>, Qian Y, Hu X, Yang C (2023) <doi:10.48550/arXiv.2306.17584>.

Three methods are implemented in R to facilitate the aggregations of flags in official statistics.  
            From the underlying flags the highest in the hierarchy, the most frequent, or with the highest total weight
            is propagated to the flag(s) for EU or other aggregates. Below there are some reference documents for the topic: 
            <https://sdmx.org/wp-content/uploads/CL_OBS_STATUS_v2_1.docx>,
            <https://sdmx.org/wp-content/uploads/CL_CONF_STATUS_1_2_2018.docx>,
            <http://ec.europa.eu/eurostat/data/database/information>,
            <http://www.oecd.org/sdd/33869551.pdf>,
            <https://sdmx.org/wp-content/uploads/CL_OBS_STATUS_implementation_20-10-2014.pdf>.

Implements the fused lasso additive model as proposed in Petersen, A., Witten, D., and Simon, N. (2016). Fused Lasso Additive Model. Journal of Computational and Graphical Statistics, 25(4): 1005-1025.

Efficient implementations of the algorithms in the 
    Almost-Matching-Exactly framework for interpretable matching in causal
    inference. These algorithms match units via a learned, weighted Hamming
    distance that determines which covariates are more important to match on.
    For more information and examples, see the Almost-Matching-Exactly website.