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Whether you’re a pharmaceutical firm, a biotechnology research group, or an individual scientist or clinician, let us help you turn your data into results.
Our team of biostatisticians, epidemiologists, and software engineers have the training and skills you would see at a large consultancy, but with the agility of a startup.
We work closely with our clients to develop customized, scalable, reproducible solutions.
Recent blog posts
I read data into R from spreadsheets all the time, but today I had a google sheets document from a google survey that I wanted to upload.
One of the greatest thing about R is the thousands of packages available on CRAN and Github. Without being a pro on programming, this lets you to do pretty cool things thanks to all the hard work done by the community.
INLA is a nice (fast) alternative to MCMC for fitting Bayesian models. They each have some pros and cons, but while MCMC is a pretty intuitive method to learn and even implement yourself in simple scenarios, the INLA algorithms were a mathematical stretch for me.
I worked for a few years with academics who insisted on using SAS. It was a new language for me but as I became more experienced, I grew to really enjoy using it.
My work has always spanned several disciplines, but at its core, I spent a lot of time thinking about social issues surrounding health. I was astonished about how many rich sources data were available and (completely free!) from the U.S. Here are some of my favourites.
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