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library(tidyverse)
library(bayestestR)
library(bayesAB)

# See Manifesto Functions at www.themetricsmanifesto.com
# Put in your same working director
source("manifesto_functions.R")


bayesPhishArrive <- function(prior_vals = c(5,1), total_vals = c(87,12)){

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# Package names
packages <- c("readr", "tidyverse", "lubridate", "survival", "survminer", "shiny", "shinydashboard","reactable","shinyalert")

# Install packages not yet installed
installed_packages <- packages %in% rownames(installed.packages())
if (any(installed_packages == FALSE)) {
  install.packages(packages[!installed_packages])
}

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source("helper_functions.R")

#30MB File Upload
options(shiny.maxRequestSize = 30*1024^2)

ui <- dashboardPage(
  title="Vuln Burndown",
  
  dashboardHeader(title = "Vulnerability Burndown"),

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source("manifesto_functions.R")

################
### Introduction

patches <- 0:20
average <- 23/4
plot(p, dpois(patches, average), type="h", lwd=3, 
     ylab="Probability Of Emergency Patch",
     xlab="Count Of Emergency Patches")

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source("manifesto_functions.R")

### OPTIONAL A/B Case Study
### Code Slide #1

# Declare list to hold configurations
base_configs <- list()

# Amount of simulation
base_configs["n_draws"] <- 100000

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source("manifesto_functions.R")

#########################
### Beta-Binomial Example

# Make grid of 10K sequential possibilities between 0 to 1
plausible_rates  <- data.frame(
  possible_grid = seq(from = 0, to = 1, length = 10000))

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source("manifesto_functions.R")


######################
### Naive Escape Rates

MakeBetaGraph(10, 100, xlab = "Rate", ylab = "Strength", 
              tlab = "January Escape Rates", 
              slab = "Mean and Highest Density Interval", xadj = .001)

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source("manifesto_functions.R")

###########################
### Non-Bayesian Wait Times

# Number of product teams
group_count = 10

# Empty Data Frame to hold results
dt <- tibble()

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source("manifesto_functions.R")

##############################################
### Introduction: Random Bombs and Horse Kicks

# Bomb Narrative 
bombs <- tibble(hits = c(0,1,2,3,4,5), 
                frequency = c(229, 211, 93, 35, 7, 1),
                ratio = frequency/sum(frequency))
bombs

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source("manifesto_functions.R")


##################
### Get Small Data

#Engineer One and Two Remediation Rate Beliefs
eng_one <- c(4,7) #Median and 90% Stretch
eng_two <- c(2,4)