Notes on JuliaConnectoR
Summary Article on Github, July 2023
As the 'JuliaCall' R package is currently broken, I present a short introduction to 'JuliaConnectoR', another package on CRAN that provides a bridge between R and Julia. The connecting technology is TCP which makes it more stable to changes in the versions of R or Julia, but also makes it considerably slower in certain situations. All the examples are taken from the "Notes on JuliaCall".
Differential Evolution for Global Optimization
September 2023
Differential Equations with R
R User Group, Heidelberg, March 2022
Several CRAN packages provide solvers for handling Differential Equations. We will -- through examples -- show how to solve ordinary differential equations (ODEs), systems of DEs, and DEs of second order, mostly with routines from the 'pracma' and 'deSolve' packages. If there is time, it will be demonstrated how the R user can apply Julia's extremely powerful 'DifferentialEquations.jl' through the 'diffeqr' R package.
Voting Procedures (and the 'vote' Package)
R User Group, Heidelberg, October 2021
Die Teilnehmer einer Abstimmung haben bestimmte Präferenzen bzgl. möglicher Alternativen. Darauf aufbauend gibt es verschiedene Abstimmungsverfahren (engl. voting procedures), einige davon sind auch in dem 'vote' Paket implementiert. Aber welche dieser Verfahren sind gerecht, fair oder weniger manipulierbar. Am Ende werden wir einige relativ unbekannte Verfahren und den "Arrowschen Unmöglichkeitssatz" diskutieren.
Abstimmungsverfahren / Voting Procedures
Regular Expressions in R
R User Group, Heidelberg, September 2021
Short reminder of regular expressions, their syntax and basic elements, functions in Base R and/or package 'stringr' for using regular expressions, some examples, and a Shiny application for testing regular expressions.
Key-value Stores
R User Group, Heidelberg, May 2021 (and Wiesbaden, February 2022)
The data structure of key-value stores or associative arrays (computer science) come under different names in several programming languages, such as 'dictionary' (Python, Julia), 'hash' (Perl, Ruby, JavaScript), 'map' (Java, Haskell), or 'table' (Lua), etc. We discuss some R packages that provide this functionality of key-value stores, like 'hash', 'filehash', 'fastmap', 'rredis' or 'RcppRedis', packages that provide key-value stores.
Linear Regression Trees
January 2021
The idea of "Linear (or local) Regression Trees" is to grow a tree, similar to a decision tree, in which every end node is associated with a linear regression for some or all of the variables in the data. The first idea and implementation was done by Ross Quinlan (of C4.5 fame) in his 'M5' program, for R users available in the 'Cubist' package. Other implementations are lmtree() in 'partykit' or M5P() in 'RWeka'; we also compare it with CART and RandomForest trees.
Nichtlineare Regression in R
Wiesbaden R User Group, December 2020
'nls()' in Base R ist eine häufig verwendete Funktion zur "least-squares" Schätzungen der Parameter eines nichtlinearen Modells; ihre Implementierung hat jedoch schwerwiegende Probleme und Einschränkungen. Wir werden Alternativen erörtern, die in R-Paketen verfügbar sind, vor allem nlxb() im 'nlsr' Paket -- und auch, wie Einschränkungen, z.B. Positivitätsbedingungen, für die gesuchten Parameter einbezogen werden können.
R und das S4 Objekt-System
Wiesbaden R User Group, September 2020
Beschreibt das S4 Klassensystem anhand einer Implementierung des Gauss'schen Gesetzes zur Fehlerfortpflanzung; die notwendigen arithmetischen Operationen werden als Operationen auf den S4 Objekten realisiert. Ein analoges Beispiel für "dual numbers" findet man hier:
Implementation of R Dual Numbers
The mlr3 package system
R User Group, Heidelberg, Jul-Aug 2020
Duscussing the 'mlr3' packages with introduction, mlr3 learners (especially Random Forest), prediction and accuracy, resampling and cross-validation, as well as hyperparameter tuning.
mlr3 examples Github repository
Subgroup Discovery mit R
Wiesbaden R User Group, June 2020
Subgroup Discovery (SD) ist eine Technik des beschreibenden und explorativen Data Minings. Es werden 'interessante' Teilmengen der Daten identifiziert, in Hinblick auf ein für die jeweilige Anwendung wichtiges Merkmal. SD ist von praktischer Relevanz für wissenschaftliche wie geschäftliche Datenanalysen und kann für Analysten wie Kunden überraschende Einblicke in die Daten liefern. Ein R Paket wird vorgestellt, das diese Methode implementiert.
Slides - the 'rsubgroup' package
JuliaCall: Calling Julia from R
Github Page, 2017, Updated to Julia 1.0 in 2019
A short description of how to install JuliaCall and make use of Julia functions from R. Includes examples on plotting, for Optimization and Automatic Differentiation (AD), or calculating special functions not available in R.
Random Numbers and Walks
R User Groups, February 2019
Presents and discusses several CRAN packages dealing with Random Number Generators (RNGs). Applies random numbers to the simulation of Random Walks and for calculating some of their properties. Use of JavaScript and/or Julia in simulation studies for faster run-times is demonstrated.
Disciplined Convex Programming with CVXR
R User Group, Heidelberg, April 2018
CVXR is a new R package that provides a modeling language for Convex Programming, similar to CVX for MATLAB or CVXPY for Python. The user can formulate optimization problems following the mathematical equations describing it. The talk will give a short introduction to convex optimization, followed by several examples in regression with constraints and quadratic optimization, for instance the "smallest enclosing ball" problem.
Optimization With R — Tips and Tricks
Köln R User Group, September 2017
There are almost 100 packages listed on the CRAN optimization task view. We will discuss some of the more important packages in areas such as nonlinear optimization with and w/o constraints, least-squares problems, non-smooth and global optimization, and mixed integer programming. Tips and tricks will be provided for high precision or high-dimensional problems, for equality constraints, for minimax problems, etc.
Can R Be Used for Numerical Mathematics?
Private Communication
R is a "software environment for statistical computing and graphics." But can R be used in courses on Numerical Mathematics, and is it possible to apply R to real-world numerical problems? Many R packages will be discussed that provide functionality in numerical analysis and linear algebra, and ways to integrate with other scientific environments such as Python, Julia, MATLAB/Octave, or even Mathematica.
Symbolic and Numeric Differentiation With R
Private Communication
Function differentiation is an important technique for optimization problems and differential equations applications, in engineering and many other areas. The talk will discuss packages for symbolic differentiation, automatic forward differentiation (implemented as S4 class), the complex step approach, central difference formulas, Richardson extrapolation for computing numerical Hessians, and higher order derivatives.
Infos on new packages presented at UseR 2016
Meetup Session, August 2016
Some interesting packages presented at the UseR! 2016 at Stanford University will be described in one slide each, including profvis, covr, broom, future, feather. For data mining, packages like xgboost, ranger, or mxnet are useful new contributions. Links to Microsoft Channel 9 are included.
Package V8: Computing With JavaScript in R
Meetup Session, April 2016
Package V8 provides an interface to Google's open source JavaScript engine and thus implements ECMAScript with compiled libraries for high-performance computing. We will present some examples of how to utilize JavaScript for quite efficient numerical computations in R.
R Training - Basic Introduction
ABB Forschungszentrum, Ladenburg, January 2016
Eine kurze Einführung in R Syntax, Einlesen von Daten, Dataframes, statistische Graphen, Programmierung mit R, Beispiele der Regression, etc.
Nonlinear Optimization with R - An engineering example
Wiesbaden R Users Group, October 2015
A prediction task for gas distribution networks with storage tanks will be solved as a nonlinear optimization problem with constraints, by applying oprimization solvers available in R packages. Several different objective functions will be tested. Also shown is how this problem can be formulated in the AMPL modeling language and be sent to NEOS solvers of the COIN-OR project.
Jupyter notebook An Example in Constraint Optimization
Numerik mit MATLAB
DHBW Kurs, 20 Stunden, Mannheim, Herbst 2015
Siehe Unterlagen MatlabKurs
Introduction to Julia for R Users
Köln R User Group, December 2014
Julia is a high-performance dynamic programming language for scientific computing, with a syntax that is familiar to users of other technical computing environments (Matlab, Python, R, etc.). It provides a sophisticated compiler, high performance with numerical accuracy, and extensive mathematical function libraries. User-contributed packages are available for time series, statistics and machine learning, or operations research.
Notes from the Meetup in Cologne
Handout of the Talk
An introduction to the Julia language for scientific computing and its
connection to Python with IJulia
Heidelberg Python Meetup Group, September 2014
Julia is a high-level, high-performance dynamic programming language for technical computing, with syntax that is familiar to users of other technical computing environments. IJulia, a collaboration between the IPython and Julia communities, provides a powerful browser-based graphical notebook interface to Julia and enables easy use of Python within Julia.
Presentation: Julia for Python Users
Die Statistik-Umgebung R: Einführung, Übersicht, Anwendungen
VDI Arbeitskreis “Mess- und Automatisierungstechnik”“, Kassel, Februar
2014
Der Vortrag beinhaltet eine Einführung zu R, seiner Programmiersprache und seinen Entwicklungswerkzeugen. Beispiele vorhandener Methoden zu (nichtlinearen) Regression, zur Optimierung und zum Maschinellen Lernen (Data Mining). Der Vortrag richtet sich an alle, die an statistischer Datenverarbeitung und Visualisierung interessiert sind, bzw. sich über Fähigkeiten einer Open Source Software (OSS) in diesem Bereich informieren möchten.
Folien: Statistics Environment R (in English).