Showing posts from May, 2018

New R package xplain: Providing interactive interpretations and explanations of statistical results

The package xplain is designed to help users intepret the results of their statistical analyses. It does so not in an abstract way as textbooks do. Textbooks do not help the user of a statistical method understand his findings directly . What does a result of 3.14 actually mean? This is often hard to answer with a textbook alone because the book may provide its own examples but cannot refer to the specifics of the user's case. However, as we all know, we understand things best when they are explained to us with reference to the actual problem we are working on. xplain  is made to fill this gap that textbooks (and other learning materials) leave. The basic idea behind xplain is simple: Package authors or other people intested in explaining statistics provide interpretation information for a statistical method (i.e. an R function) in the format of an XML file. With a simple syntax this interpretation information can reference the results of the user's call of the explained

New introduction article to the R language (in German)

About two months ago, the German online magazine ' Informatik Aktuell ' asked me to write an introductory article on R. And so I did. It's now only a few days ago that the article was published. It focuses on key concepts of the R language and provides an overview of R's eco system. Given the space constraints it does not discuss more advanced topics like environments or package developments. Read the full article here (in German): . Everyone who wants to read a more comprehensive introduction to R in German should try my book " Statistik mit R " (O'Reilly).

Package 'quantification' now on GitHub

My package 'quantification' is now on GitHub: . 'quantification' is a package that provides functions for quantifying qualitative survey data. It supports the Carlson-Parkin method, the regression approach, the balance approach and a new method (conditional expectations approach; click here to learn more about the conditional expectations approach).