Pakillo

library(sf)
library(terra)

## Define area
coords <- data.frame(x = c(-5.5, -5.5, -5.3, -5.3),
                     y = c(36.7, 36.8, 36.7, 36.8))
coords.sf <- st_as_sf(coords, coords = c("x", "y"), crs = 4326)

## Download elevation data

scbrown86

## GISFrag metric
## https://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/19950017676.pdf
## 1) Produce a proximity (distance) map between 'patches'
## 2) GISFrag == mean of all values on the proximity map
## 3) Large GISFrag values reflect low forest fragmentation, low values == high fragmentation

gisFrag <- function(x, ...) {
  ## x needs to be a raster where cells with suitable habitat are coded as 1
  ## unsuitable cells coded with 0
  ## extract cell numbers for suitable cells

famuvie

# Comparison estimations of multi-level models between lme4 and INLA
# inspired by question in the r-inla group:
# https://groups.google.com/forum/#!topic/r-inla-discussion-group/tmNdcYQ6KHk

pacman::p_load(INLA, lme4, tidyverse, broom, GGally)

sleepstudy %>% 
  ggplot(aes(Days, Reaction, color = Subject)) +
  geom_point()

florianhartig

library(mvtnorm)   # to draw multivariate normal outcomes
library(R2jags)    # JAGS-R interface

# function that makes distance matrix for a side*side 2D array  
dist.matrix <- function(side)
{
  row.coords <- rep(1:side, times=side)
  col.coords <- rep(1:side, each=side)
  row.col <- data.frame(row.coords, col.coords)
  D <- dist(row.col, method="euclidean", diag=TRUE, upper=TRUE)