csgillespie/githubactions

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Created over 4 years ago
Updated about 4 years ago

csgillespie/githubactions

csgillespie

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My github actions playground. Will be deleted

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

rtypeform

An R interface to the 'typeform' API.

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.

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

benchmarkme-data

R package containing data from past benchmarks

talks

A list of recent talks

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

riemann-manifold

expt_design

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

dotfiles

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

SparseEm