goldingn

# load devtools and install the preferred version of circleplot
library(devtools)
install_github('mjwestgate/circleplot')
#install_github('goldingn/circleplot')
library(circleplot)

# set RNG seed
set.seed(1)

# create a fake symmetric matrix

goldingn

fuzzyMatch <- function (a, b) {
  
  # no-frills fuzzy matching of strings between character vectors
  # `a` and `b` (essentially a wrapper around a stringdist function)
  # The function returns a two column matrix giving the matching index
  # (as `match` would return) and a matrix giving the distances, so you
  # can check how well it did on the hardest words.
  # Warning - this uses all of your cores.
  
  # load the stringdist package

goldingn

geti <- function (x, i) {
  
  # return the elements of an array `x` of unknown dimension
  # at the ith index of its first dimension
  
  # throw an error if x isn't an array
  stopifnot(is.array(x))
 
  # get the dimension of x
  k <- length(dim(x))

goldingn

# _____________     ____________       ____________         ___________________
#  \  ______   \__ _\   ______  \     _\   ______  \       _\      ___  \______
#   \       /   \  \ \  \     \  \__ _\ \  \     \  \     _\ \  \/    \  \_____
#    \     /     \  \ \  \     \  \  \ \ \  \     \  \__ _\ \ \  \     \  \____
#     \   /______ \  \ \  \_____\  \  \ \ \  \_____\  \  \ \ \ \  \     \  \___
#      \___________\__\_\___________\__\_\_\___________\__\_\_\_\___________\__
#                      \____________\___\_\____________\___\_\_\____________\__
#                                        \____________\_____\_\____________\___
# SPECIES DISTRIBUTION MODELLING                             \____________\____
#

goldingn

# converting maxent/Poisson model output to true probability of presence

# clear workspace
rm(list = ls())

# set rng seed
set.seed(1)

# number of datapoints
n <- 100000

goldingn

# clear the workspace
rm(list = ls())

# load the relevant libraries

# install.packages(rCUR)
library(rCUR)  # for CUR decomposition
# install.packages(irlba)
library(irlba)  # for fast svd