WardBrian

# code adapted from https://github.com/stan-dev/example-models/blob/master/knitr/lotka-volterra/lotka-volterra-predator-prey.Rmd

library(posterior)
library(reshape2)
library(ggplot2)

lynx_hare_df <-
  read.csv("https://raw.githubusercontent.com/stan-dev/example-models/refs/heads/master/knitr/lotka-volterra/hudson-bay-lynx-hare.csv",
           comment.char="#")

WardBrian

# posterior predictive check using the pred_cases generated quantity

install.packages("bayesplot")
library(posterior)
library(ggplot2)

# load from data
d <- jsonlite::read_json('./data.json')
cases <- unlist(d$cases)
n_days <- d$n_days

WardBrian

install.packages("ggplot2")
library(posterior)
library(ggplot2)

# load data from data.json
data <- jsonlite::read_json("./data.json")
x <- unlist(data$x)
y <- unlist(data$y)

# posterior predictive check 

WardBrian

#include <stdexcept>
#include <iostream>

void maybe_throw(int should_throw) {
  if (should_throw)
    throw std::domain_error("exception in C++: dummy");
}

extern "C" int some_fn(int should_throw) {
  try {

WardBrian

/**
 * Demonstration of `scalar_type_t`, `base_type_t`, and `value_type_t`, for
 * examples where they do the same thing and examples where they differ.
 *
 * Place in math repo directory and compile with:
make -f make/standalone test_types
*/

#include <stan/math/prim.hpp>
#include <stan/math/prim/fun/typedefs.hpp>

WardBrian

<#
.Synopsis
Opens explorer in the current working directory
#>
Function Start-FileExplorer {
    Start-Process explorer.exe .
}

<#
.Synopsis