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Mikkel Meyer Andersen

Department of Mathematical Sciences
Assistant Professor
Denmark

people.math.aau.dk/~mikl/

I'm an applied statistician working with statistics for forensic genetics. R (as well as other programming languages and technologies) is a core part of my teaching and research. I was part of the local organising committee for useR! 2015 in Aalborg. useR! 2016 is my 3rd useR! conference and I look forward to it!

9:00am PDT

Machine Learning Algorithmic Deep Dive (Part 1) Campbell Rehearsal Hall Never Tell Me the Odds! Machine Learning with Class Imbalances (Part 1) Econ 140

10:30am PDT

Machine Learning Algorithmic Deep Dive (Part 2) Campbell Rehearsal Hall Never Tell Me the Odds! Machine Learning with Class Imbalances (Part 2) Econ 140

1:00pm PDT

An Introduction to Bayesian Inference using R Interfaces to Stan (Part 1) SIEPR 130 Introduction to SparkR (Part 1) Econ 140 Missing Value Imputation with R (Part 1) Barnes Ninja moves with data.table - learn by doing in a cookbook style workshop (Part 1) Lane Regression Modeling Strategies and the rms package (Part 1) Lyons & Lodato

2:30pm PDT

An Introduction to Bayesian Inference using R Interfaces to Stan (Part 2) SIEPR 130 Introduction to SparkR (Part 2) Econ 140 Missing Value Imputation with R (Part 2) Barnes Ninja moves with data.table - learn by doing in a cookbook style workshop (Part 2) Lane Regression Modeling Strategies and the rms package (Part 2) Lyons & Lodato

8:30am PDT

Opening Session McCaw Hall

9:00am PDT

Forty years of S McCaw Hall

10:30am PDT

R in machine learning competitions McCaw Hall

10:48am PDT

Compiling parts of R using the NIMBLE system for programming algorithms Barnes & McDowell & Cranston Implementing R in old economy companies: From proof-of-concept to production Econ 140 RcppParallel: A Toolkit for Portable, High-Performance Algorithms SIEPR 130 Two-sample testing in high dimensions Lane & Lyons & Lodato

11:06am PDT

Bayesian analysis of generalized linear mixed models with JAGS Barnes & McDowell & Cranston R: The last line of defense against bad debt Econ 140

11:24am PDT

Automating our work away: One consulting firm's experience with KnitR Econ 140 Fitting complex Bayesian models with R-INLA and MCMC Barnes & McDowell & Cranston

11:42am PDT

bayesboot: An R package for easy Bayesian bootstrapping Barnes & McDowell & Cranston

1:00pm PDT

Linking htmlwidgets with crosstalk and mobservable McCaw Hall

1:18pm PDT

Fast additive quantile regression in R Barnes & McDowell & Cranston Transforming a museum to be data-driven using R McCaw Hall

1:36pm PDT

CVXR: An R Package for Modeling Convex Optimization Problems McCaw Hall What can R learn from Julia Lane & Lyons & Lodato

1:54pm PDT

Statistics and R in Forensic Genetics McCaw Hall

2:12pm PDT

FiveThirtyEight's data journalism workflow with R McCaw Hall

4:45pm PDT

How to keep your R code simple while tackling big datasets Barnes & McDowell & Cranston R at Microsoft McCaw Hall

5:21pm PDT

broom: Converting statistical models to tidy data frames McCaw Hall Data validation infrastructure: the validate package Barnes & McDowell & Cranston

5:39pm PDT

Rho: High Performance R McCaw Hall

5:57pm PDT

Colour schemes in data visualisation: Bias and Precision McCaw Hall Rethinking R Documentation: an extension of the lint package Econ 140

9:00am PDT

Towards a grammar of interactive graphics McCaw Hall

10:30am PDT

Notebooks with R Markdown McCaw Hall Profvis: Profiling tools for faster R code Econ 140

10:40am PDT

Tie-ins between R and Openstreetmap data SIEPR 120

10:48am PDT

permuter: An R package for randomization inference Barnes & McDowell & Cranston Visualizing Simultaneous Linear Equations, Geometric Vectors, and Least-Squares Regression with the matlib Package for R McCaw Hall

10:50am PDT

madness: multivariate automatic differentiation in R SIEPR 120

11:24am PDT

Heatmaps in R: Overview and best practices Barnes & McDowell & Cranston

1:00pm PDT

R at Google Lane & Lyons & Lodato

1:18pm PDT

A Lap Around R Tools for Visual Studio McCaw Hall

1:30pm PDT

Statistical Computing with R Lane & Lyons & Lodato

1:36pm PDT

Adding R, Jupyter and Spark to the toolset for understanding the complex computing systems at CERN's Large Hadron Collider McCaw Hall GNU make for reproducible data analysis using R and other statistical software Barnes & McDowell & Cranston Simulation and power analysis of generalized linear mixed models Econ 140

1:54pm PDT

How to do one's taxes with R McCaw Hall

10:50am PDT

ranger: A fast implementation of random forests for high dimensional data Lane & Lyons & Lodato

1:15pm PDT

Getting R into your bathroom McCaw Hall

1:25pm PDT

Two Cultures: From Stata to R McCaw Hall