mbjoseph

library(RCurl)

# Fetching burn rasters ---------------------------------------------------
# get and read the file listing from the usgs server
prefix  <- 'https://rmgsc.cr.usgs.gov/outgoing/baecv/BAECV_CONUS_v1_2017/'
download.file(prefix, destfile = 'listing.txt')
out <- readLines('listing.txt')

# now create paths to each tar.gz file by processing the html

mbjoseph

library(rgdal)
library(rgeos)
library(raster)
library(gdalUtils)


# gdalinfo("GFED40_MQ_199506_BA.hdf")
# sds <- get_subdatasets("GFED40_MQ_199506_BA.hdf")
# sds

mbjoseph

mbjoseph

mbjoseph

library(parallel)

# Create matrix to store result -------------------------------------------
M <- 4
N <- 6
A <- matrix(0, nrow = M, ncol = N)

list <- list(3, 4, 6)

# Serial version ----------------------------------------------------------

mbjoseph

# Script to evaluate small sample behavior of Weibull parameter MLEs --------
library(fitdistrplus)
library(dplyr)
library(tidyr)
library(gridExtra)
library(parallel)
library(ggplot2)
library(viridis)

mbjoseph

mbjoseph

# Simulation script for factor analysis ala Leung & Drton (2016) ----------
library(rstan)
library(bayesplot)

m <- 5 # dimension of observed data (e.g., # traits)
k <- 2 # number of latent factors
n <- 100 # number of sample units (e.g., # species)

# residual variance matrix (is diagonal)
Omega <- diag(.3 + abs(rnorm(m, sd = .3)))

mbjoseph

data {
  int<lower=1> n;      
  vector[n] x;
  vector[n] y;
}

parameters {
  real alpha;
  vector[2] beta;
  real<lower=0> sigma;

mbjoseph

# Importing data from USDA into large data file ---------------------------
library(stringr)
library(dplyr)

path_to_data <- "~/Desktop/ams_cattle_data/"
data_files <- list.files(path = path_to_data, pattern = "cattle", 
                         full.names = TRUE)

# Define helper functions -------------------------------------------------