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:
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
random facts observations I made while reading TSH and TPE. The choice of topics is biased towards application in regression models.
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.
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!
(EDIT: I have also written a more theoretical blog post on the topic.)
The following shows an application of class
LMM from the Ruby gem
mixed_models to SNP data (single-nucleotide 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.
A few days ago I started working on hypotheses tests and confidence intervals for my project
mixed_models, and I got pretty surprised by certain things.
I made some more progress on my Google Summer of Code project MixedModels. The linear mixed models fitting method is now capable of handling non-numeric (i.e., categorical) predictor variables, as well as interaction effects. Moreover, I gave the method a user friendly R-formula-like interface. I will present these new capabilities of the Ruby gem with an example. Then I will briefly describe their implementation.
During the last two weeks I made some progress on my Google Summer of Code project.
The Ruby gem is now capable of fitting linear mixed models. In this short blog post I want to give an example, and compare the results I get in Ruby to those obtained by
lme4 in R.