An implementation of methods related to sparse clustering and variable importance 
    in clustering. The package currently allows to perform sparse k-means clustering with a group 
    penalty, so that it automatically selects groups of numerical features. It also allows to 
    perform sparse clustering and variable selection on mixed data (categorical and numerical 
    features), by preprocessing each categorical feature as a group of numerical features.
    Several methods for visualizing and exploring the results are also provided. 
    M. Chavent, J. Lacaille, A. Mourer and M. Olteanu (2020)<https://www.esann.org/sites/default/files/proceedings/2020/ES2020-103.pdf>.

The variable importance is calculated using knock off variables. Then output can be provided in numerical and graphical form. Meredith L Wallace (2023) <doi:10.1186/s12874-023-01965-x>.

Provides a programmatic interface in R for the US Department of 
    Transportation (DOT) National Highway Transportation Safety Administration 
    (NHTSA) vehicle identification number (VIN) API, located at 
    <https://vpic.nhtsa.dot.gov/api/>. 
    The API can decode up to 50 vehicle identification numbers in one call, and 
    provides manufacturer information about the vehicles, including make, model, 
    model year, and gross vehicle weight rating (GVWR).

Provides tools for the statistical analysis of regular vine copula 
    models, see Aas et al. (2009) <doi:10.1016/j.insmatheco.2007.02.001> and 
    Dissman et al. (2013) <doi:10.1016/j.csda.2012.08.010>.
    The package includes tools for parameter estimation, model selection,
    simulation, goodness-of-fit tests, and visualization. Tools for estimation,
    selection and exploratory data analysis of bivariate copula models are also
    provided.

Implements D-vine quantile regression models with
  parametric or nonparametric pair-copulas. See 
  Kraus and Czado (2017) <doi:10.1016/j.csda.2016.12.009> and
  Schallhorn et al. (2017) <doi:10.48550/arXiv.1705.08310>.