Discover @csgillespie Open Source projects

Colin Gillespie (@csgillespie)

csgillespie

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Registered over 12 years ago
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R
64.3 %

TeX
21.4 %

Shell
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3.6 %

JavaScript
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Location 🇬🇧 United Kingdom
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TeX
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R
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Shell
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efficientR

Efficient R programming: a book

poweRlaw

This package implements both the discrete and continuous maximum likelihood estimators for fitting the power-law distribution to data. Additionally, a goodness-of-fit based approach is used to estimate the lower cutoff for the scaling region.

rprofile

My .Rprofile set-up

efficient

The associated R package for Efficient R programming

benchmarkme

Crowd sourced benchmarking

roxygen2Comment

An Rstudio addin for adding and remove roygen2 comment

travis-examples

addinmanager

RStudio addin management made easy

bmc-microarray

This repo contains the knitr code for the paper: Gillespie, C. S., et al, 2010. Analysing yeast time course microarray data using BioConductor: a case study using yeast2 Affymetrix arrays BMC Research Notes, 3:81.

rtypeform

An R interface to the 'typeform' API.

statslang

Slides and code for http://www.statslife.org.uk/events/events-calendar/eventdetail/284/-/statistical-computing-languages

raddins

A collection of RStudio addins

renamer

In-silico-Systems-Biology

The latex and R code used the in stochastic simulation book chapter in "In silico Systems Biology: A systems-based approach to understanding biological processes".

dratTravis

talks

A list of recent talks

benchmarkme-data

R package containing data from past benchmarks

plmcmc

minifyHTML

A binding the Javascript-based HTML compressor/minifier

illumina-analysis

This repository contains the knitr/latex source, graphics and R code used in book chapter in Cockell, S, Bashton, M, Gillespie, CS. Bioconductor tools for microarray data analysis. In: Microarray Image and Data Analysis: Theory and Practice. CRC Press.

hybrid-pmcmc

dotfiles

riemann-manifold

expt_design

Code for Efficient construction of Bayes optimal designs for stochastic process models.

plbayes

Code and data from the paper Estimating the number of casualties in the American Indian war: a Bayesian analysis using the power law distribution

abcpmcmc

githubactions

My github actions playground. Will be deleted

SparseEm