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as_column_array <- function (x) {
  x <- as.array(x)
  if (length(dim(x)) == 1)
    dim(x) <- c(dim(x), 1)
  x
}

add_attribute <- function (object, attribute, name) {
  attr(object, name) <- attribute
  object

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simulate_graf <- function (graf_model, n = 10, newdata = NULL) {
  # simulate n draws from the predictive posterior of a GRaF model, optionally
  # to new data
  
  # get the mean of the latent Gaussian process
  mean_f <- predict(graf_model,
                  newdata = newdata,
                  type = 'latent',
                  CI = NULL)[, 1]
  

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# an 'unpack' operator, to do python/MatLab-like multiple assignment from
# functions

`%=%` <- function (expr, list) {
  
  if (!is.list(list))
    stop ('the right hand side must be a list')
  
  # grab the names to assign to
  expr <- substitute(expr)

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extend <- function (x, factor = 2) {
  # given an evenly-spaced vector `x` of cell centre locations, extend it to the
  # shortest possible vector that is at least `factor` times longer, has a 
  # length that is a power of 2 and nests the vector `x` approximately in the 
  # middle. This is used to define each dimension of a grid mapped on a torus
  # for which the original vector x is approximately a plane.
  
  # get cell number and width
  n <- length(x)
  width <- x[2] - x[1]

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# functions to compile a package/version-sha lookup table
library(git2r)

ModuleVersion <- function (modulePath, version_string = '^Version: ') {
  # given a path to a module, return the module version, or NA if it doesn't
  # have one
  
  # copy the roxygen header to a temporary file, in case the R code is unparseable
  file.create(f <- tempfile())
  lines <- readLines(modulePath, warn = FALSE)

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# devtools::install_github("briatte/ggnet")
library(network)
library(sna)
library(ggplot2)
library(viridis)
library(ggnet)

# random graph
set.seed(1)
n <- 500

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# make a nice viridis GMRF tesselation for Richard
rm(list = ls())
set.seed(1)

library(INLA)
library(raster)
library(viridis)
library(fields)

# grid sizes for sampling the GRF and for the final image

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#!/bin/bash

# Trigger an Rstudio-server instance in docker, linked to the current filesystem
# and open in a browser.

# **WARNING** this will create a .gitconfig file in the working directory, potentially overwriting one that's already there!
# it will also create a .rstudio folder, and possibly other things

# This script should be run from the Docker Quickstart Terminal on OSX.
# I.e. it uses the an up-to-date docker installation which handles the VM side of things, *not boot2docker*

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#!/bin/bash

# modify RStudio's options to send plots to quartz by default,
# and open help and local html in the default browser

echo "
# ~~~~~~~~~~~
# Tweaks to view help and html in the browser, and plots in quartz, not the RStudio viewer pane
browser <- '/usr/bin/open'
device <- 'quartz'

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# set RNG seed
set.seed(1)

# make some fake data
n <- 100
m <- 5
y <- rbinom(n, 1, 0.85)
x <- sample(letters[1:m], n, replace = TRUE)

# fit a logistic regression model