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library(ggplot2)
library(palmerpenguins)

point_plot <- function(d, x, y) {
  ggplot(d) +
    geom_point(aes(x = .data[[x]], y = .data[[y]]))
}

(var_names <- colnames(penguins))

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# 『たのしいベイズモデリング2』第5章のRコード
# CmdStanR版
# R 4.3.1 Mac, Stan 2.23.2, CmdStanR 0.5.3 で動作確認

library(readxl)
library(dplyr)
library(cmdstanr)
options(mc.cores = parallel::detectCores())

# データファイルのURL

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library(sf)
library(tmap)

# 参考にしたところ
# https://qiita.com/ocean_f/items/700aff67f3e35266b0fe

# 国土数値情報からデータをダウンロード
# https://nlftp.mlit.go.jp/ksj/gml/datalist/KsjTmplt-N03-v3_1.html
datafile <- file.path("data", "N03-20230101_01_GML",
                      "N03-23_01_230101.geojson")

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---
title: "Zero-inflated beta regression"
output: html_notebook
---

## Setup

```{r setup}
library(ggplot2)
library(zoib)

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---
title: "Beta regression"
output: html_notebook
---

## Setup

```{r setup}
library(ggplot2)
library(betareg)

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---
title: "R Notebook"
output: html_notebook
---

## An example of likelihood ratio tests in R

```{r setup}
library(lmtest)
library(palmerpenguins)

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---
title: "R Notebook"
output: html_notebook
---

## An example to use glht function in the multcomp package

```{r setup}
library(multcomp)
library(palmerpenguins)

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---
title: "R Notebook"
output: html_notebook
---

```{r setup}
library(ggplot2)
library(cmdstanr)

## Lotka-Volterra competitive equation

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functions {
  vector dz_dt(real t, vector x, array[] real theta) {
    array[2] real r = theta[1:2];
    array[2] real K = theta[3:4];
    array[2] real alpha = theta[5:6];
    real dx1_dt = r[1] * x[1] * (1 - (x[1] + alpha[1] * x[2]) / K[1]);
    real dx2_dt = r[2] * x[2] * (1 - (x[2] + alpha[2] * x[1]) / K[2]);
    return [ dx1_dt, dx2_dt ]';
  }
}

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library(MASS)
library(AICcmodavg)
library(parallel)

# Set number of CPU cores
options(cl.cores = 8)

R <- 100000
N <- 100