Implements several nonparametric regression approaches for the inclusion of covariate information on the receiver operating characteristic (ROC) framework.

Fits sphere-sphere regression models by estimating locally weighted
    rotations. Simulation of sphere-sphere data according to non-rigid rotation
    models. Provides methods for bias reduction applying iterative procedures
    within a Newton-Raphson learning scheme. Cross-validation is exploited to select
    smoothing parameters. See Marco Di Marzio, Agnese Panzera & Charles C. Taylor
    (2018) <doi:10.1080/01621459.2017.1421542>.

Nonparametric efficiency measurement and statistical inference via DEA type estimators (see Färe, Grosskopf, and Lovell (1994) <doi:10.1017/CBO9780511551710>, Kneip, Simar, and Wilson (2008) <doi:10.1017/S0266466608080651> and Badunenko and Mozharovskyi (2020) <doi:10.1080/01605682.2019.1599778>) as well as Stochastic Frontier estimators for both cross-sectional data and 1st, 2nd, and 4th generation models for panel data (see Kumbhakar and Lovell (2003) <doi:10.1017/CBO9781139174411>, Badunenko and Kumbhakar (2016) <doi:10.1016/j.ejor.2016.04.049>). The stochastic frontier estimators can handle both half-normal and truncated normal models with conditional mean and heteroskedasticity. The marginal effects of determinants can be obtained.

Accompanies the book "Nonparametric Statistical Methods Using R" by Kloke and McKean (2014, ISBN:9781439873434).  Includes methods, datasets, and random number generation useful for the study of robust and/or nonparametric statistics.  Emphasizes classical nonparametric methods for a variety of designs --- especially one-sample and two-sample problems.  Includes methods for general scores, including estimation and testing for the two-sample location problem as well as Hogg's adaptive method.

Multidimensional nonparametric spatial (spatio-temporal) geostatistics.
    S3 classes and methods for multidimensional: linear binning,
    local polynomial kernel regression (spatial trend estimation), density and variogram estimation.
    Nonparametric methods for simultaneous inference on both spatial trend
    and variogram functions (for spatial processes).
    Nonparametric residual kriging (spatial prediction).
    For details on these methods see, for example, Fernandez-Casal and Francisco-Fernandez (2014) 
    <doi:10.1007/s00477-013-0817-8> or Castillo-Paez et al. (2019) <doi:10.1016/j.csda.2019.01.017>.