Tired of doing real math 1 - some visualizations of Hillary Clinton and Donald Trump tweets

Generalized inverse of a symmetric matrix

I have always found the common definition of the generalized inverse of a matrix quite unsatisfactory, because it is usually defined by a mere property, \(A A^{-} A = A\), which does not really give intuition on when such a matrix exists or on how it can be constructed, etc… But recently, I came across a much more satisfactory definition for the case of symmetric (or more general, normal) matrices. :smiley:

Logistic regression with categorical data in Ruby

I had some fun analysing the shelter animal data from kaggle using the Ruby gems daru for data wrangling and statsample-glm for model fitting. In this blog post, I want to demonstrate that data wrangling and statistical modeling is not an area of absolute predominance of Python and R, but that it is possible in Ruby too (though, currently to a much lesser extent).

dplyr basics

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:

"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 random facts observations 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.

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!

Bootstrapping and bootstrap confidence intervals for linear mixed models

(EDIT: I have also written a more theoretical blog post on the topic.)

A (naive) application of linear mixed models to genetics

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.