The sparse principal component regression is computed. The regularization parameters are optimized by cross-validation.

The Semi Parametric Piecewise Distribution blends the Generalized Pareto Distribution for the tails with a kernel based interior.

Diverse spatial datasets for demonstrating, benchmarking and teaching spatial data analysis. 
    It includes R data of class sf (defined by the package 'sf'), Spatial ('sp'), and nb ('spdep').
    Unlike other spatial data packages such as 'rnaturalearth' and 'maps', 
    it also contains data stored in a range of file formats including GeoJSON and GeoPackage, but from version 2.3.4, no longer ESRI Shapefile - use GeoPackage instead. 
    Some of the datasets are designed to illustrate specific analysis techniques.
    cycle_hire() and cycle_hire_osm(), for example, is designed to illustrate point pattern analysis techniques.

Inspired by space-time regressions often performed to assess
    the expansion of the Neolithic from the Near East to Europe (Pinhasi et
    al. 2005 <doi:10.1371/journal.pbio.0030410>). Test for significant
    correlations between the (earliest) radiocarbon dates of archaeological
    sites and their respective distances from a hypothetical center of origin.
    Both ordinary least squares (OLS) and reduced major axis (RMA) methods
    are supported (Russell et al. 2014 <doi:10.1371/journal.pone.0087854>).
    It is also possible to iterate over many sites to identify the most likely
    origin.

Contemporary software commonly used to design stated preference experiments are expensive and the code is closed source. This is a free software package with an easy to use interface to make flexible stated preference experimental designs using state-of-the-art methods. For an overview of stated choice experimental design theory, see e.g., Rose, J. M. & Bliemer, M. C. J. (2014) in Hess S. & Daly. A. <doi:10.4337/9781781003152>. The package website can be accessed at <https://spdesign.edsandorf.me>. We acknowledge funding from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant INSPiRE (Grant agreement ID: 793163).