jrnold

data {
  int jj_n;
  int jj_idx[jj_n];
  int jj_groups[jj_n];
  ...
}
parameters {
  vector[3] beta;
  ...
}

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foo <- structure(rnorm(100), class = "test")
t(foo)

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install.packages(c("ggplot2", "devtools", "shiny"))
library(devtools)
install_github("jrnold/pols_math_camp_2014")
library(mathcamp)
run_shiny_example("functions")

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library("shiny")

x <- seq(-4, 4, by=0.01)
norm_dens <- dnorm(x)

shinyServer(function(input, output) {

    output$plot <- renderPlot({
        t_dens <- dt(x, df = input$df)
        print(ggplot()

jrnold

library("shiny")
library("ggplot2")
library("plyr")

norm_mean <- 0
norm_sd <- 1

##' Take sample from normal dist and calculate confidence interval for normal
smpl_mean_sample_ci <- function(n, p=0.95, pop_mean=0, pop_sd=1) {
    smpl <- rnorm(n, norm_mean, norm_sd)

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## Copyright: Martin Berlin, Jeffrey Arnold <jeffrey.arnold@gmail.com> (2013)
##
## Adapted from original code by Martin Berlin <mno.berlin@gmail.com> http://spark.rstudio.com/berlin/stat/

library(shiny)
library(plyr)
library(ggplot2)

## Generate data

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library("ggplot2")

#' Draw Normal Distribution Density with an area shaded in.
#'
#' @param lb Lower bound of the shaded area. Use \code{-Inf} for a left tail.
#' @param ub Upper bound of the shaded area. Use \code{Inf} for a right tail.
#' @param mean Mean of the normal distribution
#' @param sd Standard deviation of the normal distribution
#' @param limits Lower and upper bounds on the x-axis of the area displayed.
#' @return ggplot object.

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quantile.ordered <- function(x, probs = seq(0, 1, 0.25)) {
  tab <- table(x)
  cdf <- cumsum(tab / sum(tab))
  idx <- sapply(probs, function(p) min(which(cdf >= p)))
  levels(x)[idx] 
}

quantile.factor <- quantile.ordinal
median.ordered <- function(x) quantile(x, 0.5)
median.factor <- median.ordered

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library("stringr")
library("ggplot2")

shinyServer(function(input, output) {

    output$main_plot <- 
        renderPlot({
                    x <- as.numeric(str_split(input$data, ",")[[1]])
                    xseq <- seq(min(x), max(x), length.out=100)
                    xvar <- var(x)

jrnold

data {
  int n;
  int m;
  row_vector[m] alpha;
}
parameters {
  row_vector[m] beta[n-1];
}
transformed parameters {
  matrix[n, m] alpha_beta;