fdabl

library(ggplot2)
library(RColorBrewer)

# Roughly estimates the risk of getting Covid by attending an ADE party(N = 900)
# Main uncertainties:
#   (1) Number of people infectious
#   (2) Number of people a single infectious person infects (R_eff)
#
# For (1), we rely on the number of positive tests from last week. This was 1.5%. Generally, this is an underestimate
# of the true number. Similarly, many people are coming to ADE abroad. We use a distribution over different values below.

fdabl

library(ggplot2)
library(RColorBrewer)

# Roughly estimates the risk of getting Covid by attending an ADE party(N = 900)
# Main uncertainties:
#   (1) Number of people infectious
#   (2) Number of people a single infectious person infects (R_eff)
#
# For (1), we rely on the number of positive tests from last week. This was 1.5%. Generally, this is an underestimate
# of the true number. Similarly, many people are coming to ADE abroad. We use a distribution over different values below.

fdabl

library(ggplot2)
library(RColorBrewer)

# Roughly estimates the number of Covid infectious people at an ADE party(N = 900)
# Main uncertainties:
#   (1) Number of people infectious
#   (2) Number of people a single infectious person infects (R_eff)
#
# For (1), we rely on the number of positive tests from last week. This was 1.5%. Generally, this is an underestimate
# of the true number. Similarly, many people are coming to ADE abroad. We use a distribution over different values below.

fdabl

library(ggplot2)
library(RColorBrewer)

# Roughly estimates the number of Covid infectious people at an ADE party(N = 900)
# Main uncertainties:
#   (1) Number of people infectious
#   (2) Number of people a single infectious person infects (R_eff)
#
# For (1), we rely on the number of positive tests from last week. This was 1.5%. Generally, this is an underestimate
# of the true number. Similarly, many people are coming to ADE abroad. We use a distribution over different values below.

fdabl

library(ggplot2)
library(RColorBrewer)

# Roughly estimates the number of Covid infectious people at an ADE party(N = 900)
# Main uncertainties:
#   (1) Number of people infectious
#   (2) Number of people a single infectious person infects (R_eff)
#
# For (1), we rely on the number of positive tests from last week. This was 1.5%. Generally, this is an underestimate
# of the true number. Similarly, many people are coming to ADE abroad. We use a distribution over different values below.

fdabl

library(ggplot2)
library(RColorBrewer)

# Roughly estimates the number of Covid infectious people at an ADE party(N = 900)
# Main uncertainties:
#   (1) Number of people infectious
#   (2) Number of people a single infectious person infects (R_eff)
#
# For (1), we rely on the number of positive tests from last week. This was 1.5%. Generally, this is an underestimate
# of the true number. Similarly, many people are coming to ADE abroad. We use a distribution over different values below.

fdabl

library('doParallel')
registerDoParallel(cores = 4)


#' Spike-and-Slab Regression using Gibbs Sampling for p > 1 predictors
#'
#' @param y: vector of responses
#' @param X: matrix of predictor values
#' @param nr_samples: indicates number of samples drawn
#' @param a1: parameter a1 of Gamma prior on variance sigma2e

fdabl

# 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

fdabl

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

fdabl

# 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