Among most popular off-the-shelf machine learning packages available to R, caret ought to stand out for its consistency. It reaches out to a wide range of dependencies that deploy and support model building using a uniform, simple syntax. I have been using caret extensively for the past three years, with a precious partial least squares (PLS) tutorial in … Continue reading The tidy caret interface in R

# Convolutional Neural Networks in R

Last time I promised to cover the graph-guided fused LASSO (GFLASSO) in a subsequent post. In the meantime, I wrote a GFLASSO R tutorial for DataCamp that you can freely access here, so give it a try! The plan here is to experiment with convolutional neural networks (CNNs), a form of deep learning. CNNs underlie … Continue reading Convolutional Neural Networks in R

# Linear mixed-effect models in R

Statistical models generally assume that All observations are independent from each other The distribution of the residuals follows $latex \mathcal{N}(0, \sigma^2)&s=1$, irrespective of the values taken by the dependent variable y When any of the two is not observed, more sophisticated modelling approaches are necessary. Let's consider two hypothetical problems that violate the two respective assumptions, … Continue reading Linear mixed-effect models in R

# Genome-wide association studies in R

This time I elaborate on a much more specific subject that will mostly concern biologists and geneticists. I will try my best to outline the approach as to ensure non-experts will still have a basic understanding. This tutorial illustrates the power of genome-wide association (GWA) studies by mapping the genetic determinants of cholesterol levels using … Continue reading Genome-wide association studies in R