
dplyr basics
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My first R package on CRAN
A couple of weeks ago I have released my first R package on CRAN. For me it turned out to be a far less painful process than many people on the internet portray it to be (even though the package uses quite a lot of C++ code via Rcpp and RcppEigen, and even though R CMD check returns two NOTEs). Some of the most helpful resources for publishing the package were:
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"Testing Statistical Hypotheses" and "Theory of Point Estimation" impressions
I spent much of the last two months reading Lehmann & Romano “Testing Statistical Hypotheses” (3rd ed.) and Lehmann & Casella “Theory of Point Estimation” (2nd ed.), abbr. TSH and TPE. The following is a collection of
READ MORErandom factsobservations I made while reading TSH and TPE. The choice of topics is biased towards application in regression models. 
NMatrix with Intel MKL on my university's HPC
In order to use NMatrix for the statistical analysis of big genomic data, I decided to install it on my university’s high performance computing system (HPC). It is called Cypress (like the typical New Orleans tree), and it’s currently the 10th best among all American universities.
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Statistical linear mixed models in Ruby with mixed_models (GSoC2015)
Google Summer of Code 2015 is coming to an end. During this summer, I have learned too many things to list here about statistical modeling, Ruby and software development in general, and I had a lot of fun in the process!
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Bootstrapping and bootstrap confidence intervals for linear mixed models
(EDIT: I have also written a more theoretical blog post on the topic.)
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A (naive) application of linear mixed models to genetics
The following shows an application of class
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from the Ruby gemmixed_models
to SNP data (singlenucleotide polymorphism) with known pedigree structures. The family information is prior knowledge that we can model in the random effects of a linear mixed effects model. 
Pvalues and confidence intervals
A few days ago I started working on hypotheses tests and confidence intervals for my project
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, and I got pretty surprised by certain things. 
MixedModels Formula Interface and Categorical Variables
I made some more progress on my Google Summer of Code project MixedModels. The linear mixed models fitting method is now capable of handling nonnumeric (i.e., categorical) predictor variables, as well as interaction effects. Moreover, I gave the method a user friendly Rformulalike interface. I will present these new capabilities of the Ruby gem with an example. Then I will briefly describe their implementation.
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Model specification for linear mixed model
Last week I wrote about my implementation of an algorithm that fits a linear mixed model in Ruby using the gem MixedModels, that I am working on right now. See, first rudimentary LMM fit.
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