mbjoseph

data {
  int<lower=1> n_site;
  int<lower=1> n_distance_bins;
  vector[n_distance_bins + 1] bin_breakpoints;
  array[n_site, n_distance_bins] int y;
  array[n_site] int n_obs;
}

transformed data {
  real max_distance = max(bin_breakpoints);

mbjoseph

library(forcats)
library(ggplot2)
library(testthat)
path <- testthat_example("success")

l <- test_file(path, reporter = "list")

l %>%
  as.data.frame() %>%
  ggplot(aes(y = fct_reorder(test, real), x = real)) + 

mbjoseph

library(tidyverse)
library(sf)
library(here)
library(brms)
library(lubridate)
library(reshape2)

# Get ecoregion data ------------------------------------------------------
download.file("ftp://newftp.epa.gov/EPADataCommons/ORD/Ecoregions/us/us_eco_l4.zip", 
              destfile = "ecoregions.zip")

mbjoseph

# Distance sampling model with data augmentation. 
# Based on section 8.3.1 of the Applied Hierarchical Modeling book by Royle and Kery

library(rstan)

B <- 50

# note that I'm dividing by 10 and adding 1e-6 to put this on a manageable
# scale and prevent values == 0
d_obs <- c(71.93, 26.05, 58.47, 92.35, 163.83, 84.52, 163.83, 157.33,

mbjoseph

mbjoseph

library(tidyverse)
library(reshape2)
library(pbapply)

n_samples <- 100
n_wavelength <- 400

wavelengths <- matrix(nrow = n_samples, ncol = n_wavelength)
reflectances <- matrix(nrow = n_samples, ncol = n_wavelength)
for (i in 1:n_samples) {

mbjoseph

library(lme4)

# adapted from ?predict.merMod
gm1 <- glmer(cbind(incidence, size - incidence) ~ period + (1 |herd), 
             cbpp, 
             binomial)

# add some new levels to herd, which is a random effect
newdata <- with(cbpp, 
                expand.grid(period=unique(period), 

mbjoseph

library(rstan)
options(mc.cores = parallel::detectCores())
rstan_options(auto_write = TRUE)

psi <- .2
theta <- .3
p <- .5

nsite <- 100
ntime <- 4

mbjoseph

mbjoseph