Display processing results using the GWR (Geographically Weighted Regression) method, display maps, and show the results of the Mixed GWR (Mixed Geographically Weighted Regression) model which automatically selects global variables based on variability between regions. This function refers to 
    Yasin, & Purhadi. (2012). "Mixed Geographically Weighted Regression Model (Case Study the Percentage of Poor Households in Mojokerto 2008)". European Journal of Scientific Research, 188-196.
    <https://www.researchgate.net/profile/Hasbi-Yasin-2/publication/289689583_Mixed_geographically_weighted_regression_model_case_study_The_percentage_of_poor_households_in_Mojokerto_2008/links/58e46aa40f7e9bbe9c94d641/Mixed-geographically-weighted-regression-model-case-study-The-percentage-of-poor-households-in-Mojokerto-2008.pdf>.

Functions for computing (Mixed and Multiscale) Geographically Weighted Regression with spatial autocorrelation, Geniaux and Martinetti (2017) <doi:10.1016/j.regsciurbeco.2017.04.001>.

Nonparametric survival function estimates and semiparametric
    regression for the multivariate failure time data with right-censoring.
    For nonparametric survival function estimates, the Volterra, Dabrowska,
    and Prentice-Cai estimates for bivariate failure time data may be
    computed as well as the Dabrowska estimate for the trivariate failure
    time data. Bivariate marginal hazard rate regression can be fitted for
    the bivariate failure time data. Functions are also provided to compute
    (bootstrap) confidence intervals and plot the estimates of the bivariate
    survival function. For details, see "The Statistical Analysis of
    Multivariate Failure Time Data: A Marginal Modeling Approach", Prentice,
    R., Zhao, S. (2019, ISBN: 978-1-4822-5657-4), CRC Press.

MHCnuggets (<https://github.com/KarchinLab/mhcnuggets>) is a Python
    tool to predict MHC class I and MHC class II epitopes.
    This package allows one to call MHCnuggets from R.

Metric halfspace depth for object data, generalizing Tukey's depth for Euclidean data. Implementing the method described in Dai and Lopez-Pintado (2022) <doi:10.1080/01621459.2021.2011298>.