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library('shiny')
# the code (with a little cleaning up) for the visualisations is from
# http://alexanderetz.com/2015/07/25/understanding-bayes-updating-priors-via-the-likelihood/
# Alex Etz runs a really nice blog -- go check it out!

shinyApp(
  ui = shinyUI(fluidPage(
    sidebarLayout(
      sidebarPanel(
        div(style = 'display: inline-block;', numericInput('a', label = h4('a'), value = 1)),

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# c('practicality', 'difficulty', 'some color, dunno')

papers <- rbind(
# paper we discuss
c(5, 3, 1), # Lindley (1993)
c(8, 0, 1), # Kruschke (2010)
c(4, 3, 1), # Wagenmakers (2007)
c(5, 2, 1), # Dienes (2011)
c(3, 7, 1), # Vandekerckhove, Matzke, Wagenmakers (2015)
c(4, 8, 1), # Rouder & Morey (2012)

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library('runjags')


lasso.bayes <- function(y, X, n.iter = 10000) {
    ms <- '
    model {
        sigma ~ dunif(0, 100)
        tau <- pow(sigma, -2)

        lambda ~ dunif(0, 10)

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shinyApp(
  options = list(width = '25%', height = '25%'),
  ui = shinyUI(fluidPage(
    sidebarLayout(
      sidebarPanel(
        sliderInput('e', label = 'e', min = 0, max = 100, value = 10, step = 1),
        sliderInput('N', label = 'N', min = 10, max = 500, value = 100, step = 1),
        numericInput('seed', label = 'random seed', value = 1774),
        numericInput('lambda', label = 'penalty (lambda)', value = 0),
        textInput('true', label = 'true function f(x)', value = 'x^2'),

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library('rvest')
library('stringr')


fachschaft = 'http://wiki.psyfako.org/index.php?title=Liste_der_Psychologie_Fachschaften'

emails = read_html(fachschaft) %>%
  html_nodes('p, a, span') %>% html_text(.) %>% 
  str_replace('\\[at\\]', '@') %>% str_replace('\\(at\\)', '@') %>%
  str_replace('\\[ät\\]', '@') %>% str_replace('\\[dot\\]', '.') %>% 

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function PCA(X)
    ## Y = PX

    # required:
    #- P such that
    #- we get rid of 2nd order correlation, Cov(Y) = D [some diagonal matrix]

    # method:
    #- project onto an orthogonal space spanned by the
    #- eigenvectors with variances equal to the eigenvalues

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---
title: "Bayesian effect size for ANOVA designs"
author: "Fabian Dablander, Maarten Marsman"
date: "May 22, 2016"
output: html_document
---

## 1) Example: One-way design
Download the data from [https://osf.io/zcipy/](https://osf.io/zcipy/), save it under
"example1.sav" in the current directory.

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# for https://www.facebook.com/efpsa.jeps/posts/1278843852167100
library('ggplot2')
library('gridExtra')
set.seed(1774)

# generate data
A = c(rnorm(20, 20, 4), rnorm(20, 40, 4)) # mixture distribution
B = rt(30, 1) + 30 # from a heavy-tailed distribution (Cauchy)
C = c(25, 35, 33) # just three data points

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bfx <- function(x, tstat, n, mu, gamma, k, v = n - 2) {
  
  num <- integrate(function(delta) {
    dt(tstat, df = v, ncp = sqrt(n)*delta)/gamma * dnorm((delta - mu)/gamma)
  }, -Inf, Inf)$value
  
  dt(tstat, df = v, ncp = sqrt(n)*x) / num
}

lo <- uniroot(function(x) bfx(x, .5, 10, 0, .3) - 1, interval = c(-1, 0))$root

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# devtools::install_github("dkahle/dirichlet")
library('TeX')
library('ggplot2')
library('gridExtra')
library('dirichlet')


plot_dirichlet <- function(alphas = c(.5, .5, .5), add_points = FALSE) {
  f <- function(v) ddirichlet(v, alphas)
  mesh <- simplex_mesh(.0025) %>% as.data.frame %>% tbl_df