Jerrod Anderson points us to Table 1 of this paper:

We at STATWORX work a lot with R and we often use the same little helper function within our projects. These functions ease our daily work life by reducing repetitive code parts or by creating overviews of our projects. At first, there was no plan to make a package, but soon I realised, that it will be much easier to share and improve those functions, if they are within a package. Up till the 24th December I will present one function each day from helfRlein. So, on the 2nd day of Christmas my true love gave to me…

I used the Thanksgiving break to push a new update of the TSstudio package to CRAN (version 0.1.3). The new version includes an update for the ts_backtesting function along with two new function – ts_to_prophet for converting time series objects to a prophet input format (i.e., ds and y columns), and ccf_plot for lags plot between two time series. The package can be installed from either CRAN or Github:

After an immense amount of procrastination, I’ve finally bitten the bullet and am redesigning my website. It should be no surprise to anyone, but the World is moving mobile. My old site was yesterday’s future; lovingly hand crafted in HTML using table layout. At long last I’m ditching fixed width table formatting.

Canonical Correspondence Analysis (CCA) In applied statistics, canonical correspondence analysis (CCA) is a multivariate constrained ordination technique that extracts major gradients among combinations of explanatory variables in a dataset. The requirements of a CCA are that the samples are random and independent and that the independent variables are consistent within the sample site and error-free. …

In recent years, there have been tremendous advancements in the field of machine learning. These advancements have been made through both academic as well as industrial research. Lately, a fair amount of research has been dedicated to the usage of generative models in the field of computer vision and image classification. These generative models have been popularized through a new framework called Generative Adversarial Networks. Moreover, many modified versions of this framework have been proposed in the last two years. We study the original model proposed by Goodfellow et al. as well as modifications over the original model and provide a comparative analysis of these models. Comparative Study on Generative Adversarial Networks

This is 3rd part of series about working with Magento 2 REST API in R. If you haven’t read previous posts in this series, I would recommend to do it. This article sample use the functions defined in previous posts. You may find them at R plus Magento 2 REST API revisited: part 2 – filtered search and R plus Magento 2 REST API revisited: part 1- authentication and universal search

NYC buses: C5.0 classification with R; more than 20 minute delay?

© 2018, David E. Giles

There are literally hundreds of programming languages out there, e.g. the whole alphabet of one letter programming languages is taken. In the area of data science there are two big contenders: R and Python. Now why is this blog about R and not Python?