An interface to 'spdep' to integrate with 'sf' objects and the 'tidyverse'.

Construct various types of space-filling designs, including Latin hypercube designs, clustering-based designs, maximin designs, maximum projection designs, and uniform designs (Joseph 2016 <doi:10.1080/08982112.2015.1100447>). It also offers the option to optimize designs based on user-defined criteria. This work is supported by U.S. National Science Foundation grant DMS-2310637.

Pleiotropy-informed significance analysis of genome-wide association studies (GWAS) with surrogate functional false discovery rates (sfFDR). The sfFDR framework adapts the fFDR to leverage informative data from multiple sets of GWAS summary statistics to increase power in study while accommodating for linkage disequilibrium. sfFDR provides estimates of key FDR quantities in a significance analysis such as the functional local FDR and q-value, and uses these estimates to derive a functional p-value for type I error rate control and a functional local Bayes' factor for post-GWAS analyses (e.g., fine mapping and colocalization). The sfFDR framework is described in Bass and Wallace (2024) <doi:10.1101/2024.09.24.24314276>.

Gives design points from a sequential full factorial-based
 Latin hypercube design, as described in Duan, Ankenman, Sanchez,
 and Sanchez (2015, Technometrics,
 <doi:10.1080/00401706.2015.1108233>).

Converts between R and Simple Feature 'sf' objects, without depending
  on the Simple Feature library. Conversion functions are available at both the R level, 
  and through 'Rcpp'.

A group of functions that support the 'sf' package, focused primarily on repairing polygons that break when re-projected.

Implements the structural forest methodology for the heterogeneous
    newsvendor model. The package provides tools to prepare data, fit honest
    newsvendor trees and forests, and obtain point and distributional predictions
    for demand decisions under uncertainty.

Identify and understand clusters of points (typically representing 
    the locations of places or events) stored in simple-features (SF) objects.
    This is useful for analysing, for example, hot-spots of crime events. The 
    package emphasises producing results from point SF data in a single step 
    using reasonable default values for all other arguments, to aid rapid data 
    analysis by users who are starting out. Functions available include kernel
    density estimation (for details, see Yip (2020) 
    <doi:10.22224/gistbok/2020.1.12>), analysis of spatial association (Getis
    and Ord (1992) <doi:10.1111/j.1538-4632.1992.tb00261.x>) and hot-spot
    classification (Chainey (2020) ISBN:158948584X).