roualdes

library(boot)
library(dplyr)

## assumed symmetric bootstrapped confidence interval
# function: bootstrap estimated standard error
boot_sd <- function(x, fun=mean, R=1001) {
    fun <- match.fun(fun)
    bfoo <- function(data, idx) {
        fun(data[idx])
    }

roualdes

# to run
# install Julia: https://julialang.org/
# create gist.jl in dir
# $ cd dir
# $ julia gist.jl
# tested on macOS 12.2 and Julia 1.7.2

struct OnlineCov{T <: AbstractFloat}
    mx::Vector{T}
    my::Vector{T}

roualdes

import bridgestan as bs
import optax
import numpy as np
import jax.numpy as jnp
import matplotlib.pyplot as plt
from matplotlib import cm

sm = bs.StanModel("rosenbrock_model.so")

q = np.asarray([0.0, 2.5])

roualdes

library(cmdstanr)
library(bridgestan)
library(mvtnorm)

project_xy <- function(z, sigma = diag(dim(z)[2])) {
  theta <- atan2(z[,2], z[,1]) # atan(z[,2] / z[,1]) + (z[,1] < 0) * pi
  r <- apply(z, 1, function(x) sqrt(t(x) %*% solve(sigma, x)))
  list(x = r * cos(theta), y = r * sin(theta))
}

roualdes

roualdes

functions {
  int fft_nextgoodsize(int N) {
    if (N <= 2) {
      return 2;
    }

    int m = N;
    int n = N;
    while (1) {
      while (m % 2 == 0) {