R is a programming language and software environment for statistical computing and graphics supported by the R Foundation for Statistical Computing. The R language is widely used among statisticians and data miners for developing statistical software and data analysis. Polls, surveys of data miners, and studies of scholarly literature databases show that R’s popularity has increased substantially in recent years.

R is a GNU package. The source code for the R software environment is written primarily in C, Fortran, and R. R is freely available under the GNU General Public License, and pre-compiled binary versions are provided for various operating systems. While R has a command line interface, there are several graphical front-ends available.


Timeseries forecasting with H2O

By expanding a time series horizontally you can use H2O to forecast it.

Predictive Maintenance: pure code

Instead of the easy Dataiku solution, here is a classic code-only approach.

Imbalanced data

Imbalanced data with sample code in R.
Propensity Curve.

Image recognition using MXNet

Image recognition using MXNet. Fast and easy.

Portfolio optimization applied to marketing

Using modern portfolio theory in digital marketing optimization.

Filtering out noise with chi2

Using chi2 to find signals in noisy data.

Stochastic integrals via R

The rules for random things are somewhat different than the ones you know for 'smooth' things. In a university course you get proofs based on Martingales, Wiener measures and whatnot but it all can feel very abstract. Even the basic examples can be confusing. So, here I want to show you that without knowing any high-tech maths you can see from basic examples in R how and that it works.
Category Theory on board.

Monads (with snippets in R and Swift)

The literature and information around monads and categories is sometimes confusing because it has many aspects and depending on the background many overlapping or equivalent terms are used.

Time series in R

A tutorial on forecasting time series with R.