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This gist compares the performance of Julia, Nim, C++ and R - the latter using either POMP, or LibBi in a simple simulation of an SIR epidemiological model. In addition to keeping track of susceptibles, infecteds and recovereds, I also store the cumulative number of infections. Time moves in discrete steps, and the algorithm avoids language-specific syntax features to make the comparison as fair as possible, including using the same algorithm for generating binomial random numbers and the same random number generator; the exception are the R versions, POMP uses the standard R Mersenne Twister for the random number generator; I'm not sure what LibBi uses. The algorithm for generating random binomial numbers is only really suitable for small np.

Benchmarks were run on a Mac Pro (Late 2013), with 3 Ghz 8-core Intel Xeon E3, 64GB 1866 Mhz RAM, running OSX v 10.11.3 (El Capitan

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Probability Distribution Viewer Shiny App

Base R code created by Irvin Alcaraz
Shiny app files created by Irvin Alcaraz

Cal Poly Statistics Dept Shiny Series 
http://statistics.calpoly.edu/shiny

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@import url(https://fonts.googleapis.com/css?family=Droid+Serif);
@import url(https://fonts.googleapis.com/css?family=Yanone+Kaffeesatz);
@import url(https://fonts.googleapis.com/css?family=Ubuntu+Mono:400,700,400italic);

body {
    font-family: 'Droid Serif';
}
h1, h2, h3 {
    font-family: 'Yanone Kaffeesatz';
    font-weight: 400;

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# context: http://stackoverflow.com/questions/39448808/julia-tcp-server-and-connection

# Use fn to process messages from sock.
# Loop till sock is open and fn returns true.
function processor(fn, sock)
    proc = true
    try
        while proc && ((nb_available(sock) > 0) || isopen(sock))
            proc = fn(sock)
        end

floswald

Plotting

Last Update: March 7, 2016
Offline Version

Contents

• Translating
• Basic Plot
Plot Annotation

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#!/bin/bash

echo "starting qsub script file"
source ~/.bash_profile
date

module load sge/2011.11

# here's the SGE directives
# ------------------------------------------

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Creating interactive web graphs with R: Overview and googleVis tutorial
========================================
```{r results='asis', echo=FALSE, message=FALSE, tidy=FALSE}
library(googleVis)
df <- data.frame(Postcode=c("TF3 4JH", "EC1Y 8LX"),
                 Tip=c("Telford", "RSS"))

## Tree map
T <- gvisTreeMap(Regions,  "Region", "Parent", "Val", "Fac",
                    options=list(width=250, height=150,