A collection of pre-optimized space-filling designs, for up to ten 
    parameters, is contained here. Functions are provided to access designs 
    described by Husslage et al (2011) <doi:10.1007/s11081-010-9129-8> and
    Wang and Fang (2005) <doi:10.1142/9789812701190_0040>. The design types 
    included are Audze-Eglais, MaxiMin, and uniform.

Build a constrained high quality Delaunay triangulation from simple 
   features objects, applying constraints based on input line segments, and 
   triangle properties including maximum area, minimum internal angle. The 
   triangulation code in 'RTriangle' uses the method of Cheng, Dey and Shewchuk 
   (2012, ISBN:9781584887300). For a low-dependency alternative with low-quality 
   path-based constrained  triangulation see <https://CRAN.R-project.org/package=decido> and for high-quality configurable
    triangulation see <https://github.com/hypertidy/anglr>. Also consider comparison
    with the 'GEOS' lib which since version 3.10.0 includes a low quality 
    polygon triangulation method that starts with ear clipping and refines to Delaunay.

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>.