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git subtree split --squash --prefix docs -b gh-pages
git push -f origin gh-pages:gh-pages
git branch -D gh-pages

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library(EpiNow)
library(EpiSoon)
library(data.table)

#1. Input a vector of Rt estimates, and vector to initialise cases, and a generation time
initial_cases <- 100
initial_date <- as.Date("2020-03-01")
rts <- c(rep(2, 20), (2 - 1:15 * 0.1), rep(0.5, 10))

## Simulating cases

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# Packages -----------------------------------------------------------------
require(data.table, quietly = TRUE) 
require(future, quietly = TRUE)
## Require for data and nowcasting
# require(EpiNow, quietly = TRUE)
# require(NCoVUtils, quietly = TRUE)

## Required for forecasting
# require(future.apply, quietly = TRUE)

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#!bin/bash

## This requires java and bfg to be installed
## bfg: https://rtyley.github.io/bfg-repo-cleaner/
## Assumes that the repo is in the current directory

## Remove all .rds and .png from the Git history
java -jar bfg.jar --delete-files "*.rds" $1
java -jar bfg.jar --delete-files "*.png" $1

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sudo docker pull jcrodriguez1989/rco

mkdir rco_results

sudo docker run -v /rco_results:/rco_results -e RCO_PKG=getTBinR jcrodriguez1989/rco

cd rco_results

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#' Disease Model, with Vaccination, Data Based Population Demographics and Ageing as a Pomp Object
#'
#' @param df A data frame of data to be passed to the pomp model.
#' @param t0 An integer value denoting the starting time for the model, this may be negative.
#' @param nstageA The number of age compartments to model.
#' @param timestep Value indicating the timestep over which to solve the model.
#' @param time The variable to use the time parameter from the data.
#' @param covars Additional data to use within the model.
#' @param tcovar A character string indicating the time variable in covar.
#' @param obsnames A character string of the observable variables.

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#  Get required packages - managed using pacman ---------------------------
# This installs packages if they are missing and otherwise loads them.
if (!require(pacman)) install.packages("pacman"); library(pacman)
p_load("tidyverse")
p_load("fs")
p_load("data.table")
p_load("lubridate")
p_load("purrr")
p_load("furrr")

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# Install and load the package --------------------------------------------
if(!require(pacman))install.packages("pacman")

pacman::p_load('dplyr', 'ggplot2', 'getTBinR')
pacman::p_load_gh('thomasp85/patchwork') 
pacman::p_load_gh('bbc/bbplot')

# Look at available datasets\ ---------------------------------------------

available_datasets

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# Install and load the package --------------------------------------------
#Use install.packages(c("getTBinR", "dplyr", "getTBinR")) to get required packages
library(getTBinR) #TB data + visualisations
library(dplyr) # For data manipulation
library(ggplot2) # For additional visualisations etc.
#For storyboards - install with commented out code
# install.packages("devtools")
# devtools::install_github("thomasp85/patchwork")

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## Load latest CRAN version of benchmarkme
library(benchmarkme)


## Run a benchmark for a single core
single_perf <- benchmark_std()


## Run a benchmark on multiple cores (here 4)
mutli_perf <- benchmark_std(cores = 4)