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(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

# 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

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)

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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

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

library(tidyverse)

# Parameters
rates <- seq(0.05, 0.20, length = 10)
time <- seq(1, 50, length = 50)
P <- 100

# Continuous compounding formula: A(t) = P * exp(r * t)
df <- expand_grid(time = time, rate = rates) |>
  mutate(value = P * exp(rate * time),

ericnovik

# x direction
x_t <- function(R, omega, t) {  R * cos(omega * t) }
v_x <- function(R, omega, t) { -R * omega * sin(omega * t) }
a_x <- function(R, omega, t) { -R * omega^2 * cos(omega * t) }

# y direction
y_t <- function(R, omega, t) {  R * sin(omega * t) }
v_y <- function(R, omega, t) {  R * omega * cos(omega * t) }
a_y <- function(R, omega, t) { -R * omega^2 * sin(omega * t) }

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# Normal Distribution Explorer — exposes all parameters & base R functions

library(shiny)
library(ggplot2)

ui <- fluidPage(
  titlePanel("Normal Distribution Explorer"),
  sidebarLayout(
    sidebarPanel(
      h4("Parameters"),