Here is something I needed recently that other people have been tweeting about needing, too: http://nbviewer.ipython.org/5649571
This could also a place to collect other ways to do it.
An ipython notebook to diff ipython notebooks
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Filed under software engineering
Journal Club: Improving the Measurement of Maternal Mortality
This week in journal club we will read Improving the Measurement of Maternal Mortality: The Sisterhood Method Revisited by Merdad et al. A method I am quite fond of.
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Filed under global health
Replication, replication
A colleague forwarded me this RFP specifically for replication of controversial impact evaluations. It includes a recent journal club article (by other IHME colleagues) on the candidate study list. Cool!
I’ve also made it really easy for someone to replicate the results in one recent paper I was involved in, on hepatitis C virus seroprevalence. Well, easy if you manage to get dismod installed… making that really easy is still on my to-do list.
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Contest results
I mentioned in February a couple of contests that caught my eye, and the Whale Detection one turned out to be quite interesting. Now it has completed, and the prediction quality (AUC, if I recall correctly) has been raised from .72 to .98. Very cool.
Some links:
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Filed under machine learning
Journal Club: Incorporating Loss to Follow-up in Estimates of Survival
The week journal club will read Incorporating Loss to Follow-up in Estimates of Survival Among HIV-Infected Individuals in Sub-Saharan Africa Enrolled in Antiretroviral Therapy Programs by Vergut et al.
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Journal club: Effects of Newhints home-visits intervention
This week’s paper Effect of the Newhints home-visits intervention on neonatal mortality rate and care practices in Ghana: a cluster randomised controlled trial, by Kirkwood et al. Cluster randomized trials and meta-analysis, a good combination to put new results in the context of the old.
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hello, world of statistical graphics in IPython notebook
A few months ago, I had great success invoking the internet to come up with the “hello, world” of statistical graphics.
There are some exciting new developments in javascript-based plotting, and this graphic is just the thing to compare them. D3js has conquered the world in recent years, and is something that my colleagues are starting to think they need to know. Meanwhile, one of the d3js instigators has unveiled the next in his series of revolutions in data visualization, Vega. This is still in development, but may be more appropriate than d3js for routine plots. And it was very soon after the Vega specification and runtime appeared that a python package for it was also released.
Here is an IPython notebook comparing all of these options. The notebook doesn’t save javascript in a way that redisplays, but if you put it in your own notebook server and execute all the cells you should see something like this:
p.s. google vincent vega to learn the pop culture joke behind this strangely named python package.
Filed under software engineering
Journal Club: Secret Paper
This week in journal club we are reading something that I’m not going to name, because it says “do not cite or distribute without permission” on the top of the paper. This secret paper sounds interesting, maybe I can tell you about it some day.
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Methods debates
An interesting exchange has been going on regarding the GBD 2010 estimates of deaths due to HIV, published as part of our GBD work last December, along with mortality estimates for 234 other causes of death. The UNAIDS reference group comment, and my colleagues respond.
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ML in Python: Naive Bayes the hard way
A recent question on the PyMC mailing list inspired me to make a really inefficient version of the Naive Bayes classifier. Enjoy.
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Filed under machine learning
Healthy Algorithms 