install.packages("fca")

remotes::install_github("egrueebler/fca")

set.seed(123)# Population df with column for sizepop<-data.frame(  orig_id=letters[1:10],  size=c(100,200,50,100,500,50,100,100,50,500))# Supply df with column for capacitysup<-data.frame(  dest_id=as.character(1:3),  capacity=c(1000,200,500))# Distance matrix with travel times from 0 to 30D<-matrix(runif(30, min=0, max=30),  ncol=10, nrow=3, byrow=TRUE,  dimnames=list(c(1:3),c(letters[1:10])))D#>           a        b        c        d        e         f         g         h#> 1  8.627326 23.64915 12.26931 26.49052 28.21402  1.366695 15.843165 26.772571#> 2 28.705000 13.60002 20.32712 17.17900  3.08774 26.994749  7.382632  1.261786#> 3 26.686179 20.78410 19.21520 29.82809 19.67117 21.255914 16.321981 17.824261#>           i         j#> 1 16.543050 13.698442#> 2  9.837622 28.635109#> 3  8.674792  4.413409

library(fca)# Normalize distancesW<-dist_normalize(D,  d_max=20,  imp_function="gaussian", function_d_max=0.01)# Ensure order of idspop<-pop[order(pop$orig_id),]sup<-sup[order(sup$dest_id),]# Named vectors(p<-setNames(pop$size,as.character(pop$orig_id)))#>   a   b   c   d   e   f   g   h   i   j#> 100 200  50 100 500  50 100 100  50 500(s<-setNames(sup$capacity,as.character(sup$dest_id)))#>    1    2    3#> 1000  200  500

(spai<-spai_3sfca(p,s,W))#>        step3#> a 3.97260949#> b 0.03678999#> c 1.46747735#> d 0.01079342#> e 0.28782748#> f 9.11410451#> g 0.19596873#> h 0.31194607#> i 0.37539987#> j 1.10349481