ericnovik

library(cmdstanr)

m1 <- cmdstan_model("bern-with-target.stan") # compile the model
data <- list(N = 5, y = c(0, 1, 1, 0, 1), a = 2, b = 7)

f1 <- m1$sample(       # for other options to sample, help(sample)
  data = data,         # pass data as a list, match the vars name to Stan
  seed = 123,          # to reproduce results, Stan does not rely on R's seed
  chains = 4,          # total chains, the more, the better
  parallel_chains = 4, # for multi-processor CPUs

ericnovik

data {
  int<lower=0> N;
  array[N] int<lower=0, upper=1> y;
  real<lower=0> a;
  real<lower=0> b;
}
parameters {
  real<lower=0, upper=1> theta;
}
model {

ericnovik

draw_vector <- function(x, ...) {
  df <- data.frame(x1 = x[1], x2 = x[2])
  a <- arrow(length = unit(0.03, "npc"))
  dims <- ceiling(abs(max(x)))
  p <- ggplot() + xlim(-dims, dims) + ylim(-dims, dims)
  p <- p + geom_segment(aes(x = 0, y = 0, xend = x1, yend = x2), 
                        arrow = a, data = df, ...) +
    geom_hline(yintercept = 0, size = 0.1) +
    geom_vline(xintercept = 0, size = 0.1)
  return(p)

ericnovik

# Avoids the following inconsitent behaviour from R's sample() function

# From the doc: If x has length 1, is numeric (in the sense of is.numeric) and x >= 1, sampling via 
# sample takes place from 1:x. Note that this convenience feature may lead to 
# undesired behaviour when x is of varying length in calls such as sample(x). See the examples.
safe_sample <- function(x, ...) {
  lx <- length(x)
  if (lx == 1 & is.numeric(x)) {
    as.numeric(sample(as.character(x), ...))
  } else {

ericnovik

library(ggplot2)
library(deSolve)
library(tidyr)
theme_set(theme_minimal())

# I: infected, S: susceptible, R: recovered
sir <- function(t, state, pars) {
  with(as.list(c(state, pars)), {
    N <- S + I + R
    dSdt <- -b * I/N * S

ericnovik

library(dplyr)
library(tidyr)
library(magrittr)
library(bayesplot)

plot_areas <- function(d, n, mu, s, cutoff, left = TRUE, h, v) {
  
  if (left) {
    Pr <- apply(d, 2, function (x) mean(x < cutoff))
  } else {

ericnovik

library(manipulate)
manipulate({
  lambda <- seq(0, 5, length = 100)
  prior <- dgamma(lambda, a, b)
  post <- dgamma(lambda, a + sum(y), b + length(y))
  tibble(lambda, prior, post) %>%
    ggplot(aes(lambda, prior)) + geom_line() +
    geom_line(aes(y = post), color = "red") +
    ylab("density") +
    xlab(expression(lambda))

ericnovik

data {
  int<lower=2> K; // number of categories
  int<lower=0> N; // number of observations
  int<lower=1> D; // number of predictors
  int<lower=1, upper=K> y[N];
  matrix[N, D] x;
}
parameters {
  vector[D] beta;
  ordered[K-1] c; // cutpoints

ericnovik

library(cranlogs)
library(ggplot2)
d <- cran_downloads(
  package = c("rstan", "brms", "rstanarm"),
  from = "2015-01-01",
  to = Sys.Date()
)

ggplot(d, aes(x = date, y = count, color = package)) +
  geom_smooth() +

ericnovik

library(cowplot)
f <- function(x) {
  exp(-x^2) + sin(10*x)
}
n <- 3e3
bounds <- c(0, 1)
set.seed(123)
x <- runif(n, bounds[1], bounds[2])
y <- f(x)
p1 <- qplot(x, y, geom = "line")