The Rho project (formerly known as CXXR) is working on transforming the current R interpreter into a high performance virtual machine for R. Using modern software engineering techniques and the…

With the help of Shiny, advanced analytics practitioners have been liberated from professional application development constraints: long-turn development cycles, difficult interactions with IT groups…

RosettaHUB-Sheets combine the flexibility of the bi-dimensional data representation model of classic spreadsheets with the power of R, Python, Spark and SQL. RosettaHUB-Sheets are web based, they can…

R is very powerful and flexible, but certain tasks are best solved by using R in combination with other programming languages. GNU R includes APIs to talk to some languages, e.g., Fortran and…

In this talk, I will concentrate on a class of multivariate statistical methods called Partial Least Squares (PLS). They are used for analysing the association between two blocks of 'omics' data,…

Learning R is dangerous. It entices us in by presenting an incredibly powerful tool to solve our particular problem; for free! And as we learn how to do that, we uncover more things that make our…

The Cancer Genomics Cloud (CGC), built by Seven Bridges and funded by the National Cancer Institute hosts The Cancer Genome Atlas (TCGA), that is one of the world's largest cancer genomics data…

I'll discuss R at Google - the explosive growth of R,interfaces between R and other parts of the Google computational infrastructure,documentation and support,Google contributions to the external…

The focus of this talk is scalable machine learning using the H2O R packages. H2O is an open source, distributed machine learning platform designed for big data, with the added benefit that it's easy…

R continues to take the world by storm. At Oracle, we see more and more enterprises using R for mission critical applications, where scalability, performance, and deployment concerns have been…

Running a complex model once is easy, just pull up your statistical program of choice, plug in the data, the model and off you go. The problem comes when you then find yourself trying to scale to…

Global Climate Models (GCMs) can be used to assess the impacts of future climate change on particular regions of interest, municipalities or pieces of infrastructure. However, the coarse spatial scale…

We introduce a new language for deploying analytic models into products, services and operational systems called the Portable Format for Analytics (PFA). PFA is an example of what is sometimes called…

A 'permutation' is a bijection from a finite set to itself. Permutations are important and interesting objects in a range of mathematical contexts including group theory, recreational mathematics, and…

As programming languages go, R has a bit of a reputation for being slow. This reputation is mostly undeserved, and it hinges on the fact that R's copy-on-modify semantics make its performance…

The R package archivist allows you to share and reproduce R objects - artifacts with other researchers, either through a knitr script, embedded hooks in figure/table captions, shared folder or…

Data analyses often produce many different data visualizations. Keeping track of these plots is crucial for both correctness and reproducibility of analytic results. Analysts typically resort to…

Analyzing Big Data in real life poses challenges with respect to performance, methodology and reusability. R is well known for its succinct syntax for analytic tasks as well as plethora of tools for…

Whether it's called data science, machine learning, or analytics, the combination of new data sources and statistical modeling has produced some truly revolutionary applications. Many of these…

R Tools for Visual Studio is a new, Open Source and free tool for R Users built on top of the powerful Visual Studio IDE. In this talk, we will take you on a tour of its features and show you how they…

I announced ggvis in 2014, but there has been little progress on it since. In this talk, I'll tell you a little bit about what I've been working on instead (data ingest, purrr, multiple models, ...)…

Day 3 Siepr 130 Ligtning Talks 1:00 PM - 1:40 PM Host: Julie Josse

Day 3 Econ 140 Lightning Talks 10:30 AM - 12:00 PM Host: Max Kuhn

Day2 Siepr 120 Lightning Talks 10:30 AM 12:00 PM Host Joe Rickert

Recent advances in DNA sequencing have led to the generation of massive amounts of biological sequence data. As a result, there is an urgent need for packages that assist in organizing and evaluating…

In 2013 the City of Chicago was the recipient of a Bloomberg Philanthropies grant to develop a smart data platform. The aim of the platform is to develop tools to help city government increase…

The idea of organizing cheap regional conferences, as a link between local R User Groups and international conferences on R, was brought up at the EARL 2015 conference in Boston, which was quickly…

I used R to visualize the best time to post on Reddit using data collected with Google's BigQuery. I queried a publicly accessible dataset containing almost 200 million Reddit posts (all of the posts…

Have you ever considered how to use R when decorating your home? In this presentation I will show how R can be used to generate beautiful mathematical patterns based on complex numbers. In my case, we…

Causal inference aims at the fundamental question of how changing the level of a cause or treatment can affect a subsequent outcome. Whether data analysts want to admit it or not, many analyses in…

The rbokeh package is an R interface to the Bokeh visualization library. The interface is designed to be simple but flexible, allowing the expressiveness required for rapid generation of ad hoc…

The "mlt" package implements maximum likelihood estimation in the class of conditional transformation models. Based on a suitable explicit parameterisation of the unconditional or…

When teaching statistics to non-programmers, the challenges of programming in R often exceed the challenge presented by new statistics concepts. This presentation will discuss a recent paper comparing…

The R package 'GGally' provides several amalgam plots that build on the basic 'ggplot2' plotting system. Functions produce multivariate plots like generalized scatterplot matrices, and parallel…

Irrespective of discipline, data access and analysis barriers result from a range of scenarios: * ethical-legal restrictions surrounding confidentiality and the sharing of, or access to, disclosive…

Rank-based (R) estimation for statistical models is a robust nonparametric alternative to classical estimation procedures such as least squares. R methods have been developed for models ranging from…

R vignettes, blog posts and teaching materials are typically standard web pages generated with R Markdown. DataCamp has developed a framework to make this static content interactive: R code chunks are…

We describe two R packages which facilitate causal inference research in network-dependent data: \pkg{simcausal} package for conducting network-based simulation studies; and \pkg{tmlenet} package for…

Technological advancements of the modern era have enabled the collection of huge amounts of data in science and beyond. Accordingly, computationally intensive statistical and machine learning…

The intuition and experience needed for sound statistics practice can be hard to learn, and a course that combines computing, statistics, and working with data offers an excellent learning environment…

Shiny has become a popular approach for R developers to create interactive dashboards. Given the rich set of features available in Shiny, it has the capability for data entry and collection. This talk…

Interactive statistical graphics toolkits play an important role in the exploratory phase of a data analysis cycle. Web graphics are rarely used during this phase, and are commonly reserved solely for…

Markdown is a simple and popular language for writing. R Markdown (http://rmarkdown.rstudio.com) has made it really easy to author documents that contain R code, and convert these documents to a…

Random forests are widely used in applications, such as gene expression analysis, credit scoring, image processing or genome-wide association studies (GWAS). With currently available software, the…

The call for using {\em real} data in the classroom has long meant using datasets which are culled, cleaned, and wrangled prior to any student working with the observations. However, an important part…

The Vernacular Ecology Index (VEI) is a newly proposed assessment method for sustainable urban development. It is composed of five elements (energy, culture, systems, placeness and vernacular) that…

A Shiny Gadget is an interactive tool that enhances your R programming experience. You make Shiny Gadgets with the same package that you use to make Shiny Apps, but you use Gadgets in a very different…

R is the tool of choice for analyzing data and training sophisticated models, but large scale systems are usually implemented in more traditional languages like Java and C++. I will describe…

The R package 'swirl' allows users to learn R programming by completing interactive lessons within the R console. Lessons (written in the YAML mark-up language) can include educational content such as…

We consider the problem of fitting a regression model that is both flexible and interpretable. We propose two procedures for this task: the Fused Lasso Additive Model (FLAM), which is an additive…

Simple feature access is an open standard for handling feature data (mostly points, lines and polygons) that has seen wide adoption in databases, javascript, and linked data. Currently, R does not…

The Food and Drug Administration (FDA) regulates products which account for approximately one fourth of consumer spending in the United States of America, and has global impact, particularly for…

This presentation focuses on the brglm R package, which provides methods for reduced-bias inference in univariate generalised linear models and multinomial regression models with either ordinal or…

Reproducibility is important throughout the entire data science process. As recent studies have shown, subconscious biases in the exploratory analysis phase of a project can have vast repercussions…

In the extended topical sphere of Regional Science, more scholars are addressing empirical questions using spatial and spatio-temporal data. An emerging challenge is to alert "new arrivals"…

In this talk it is shown how to generate a return of tax (German VAT) with R and send it over the internet to the tax administration. As this is certainly not a standard application for R (special…

The R community has historically done a pretty bad job at welcoming newcomers, and has been pretty "closed" ironically. Documentation written by experts for experts, email lists being a…

Organizations have an increasing amount of data that can be converted into information that offers the ability to make better decisions, find new opportunities, and improve efficiency. R is a critical…

The use of realistic statistical models for complex data is often hindered by the high cost of conducting inference about the model parameters. Because of this, it is sometimes necessary to use…

Methodological statisticians spend an appreciable amount of their time writing code for simulation studies. Every paper introducing a new method has a simulation section in which the new method is…

The x13binary package provides pre-built binaries of X-13ARIMA-SEATS, the seasonal adjustment software by the U.S. Census Bureau. X-13 is anwell-established tool for de-seasonalization of timeseries,…

Utility cycling is an increasingly common objective worldwide. The Propensity to Cycle Tool (PCT) www.pct.bike is a planning support system created using open source software; including R (Shiny) for…

High Energy Physics (HEP) has a decades long tradition of statistical data analysis and of using large computing infrastructures. CERN's current flagship project LHC has collected over 100 PB of data,…

As computers have improved, so has the prevalence of simulation studies to explore implications for assumption violations and explore statistical power. The simglm package allows for flexible…

As a statistical consultant, I often find myself repeating similar steps for data analysis projects. These steps follow a pattern of reading, cleaning, summarising, plotting and analysing data then…

Building an R package is a great way of encapsulating code, documentation and data, in a single testable and easily distributable unit. At Mango we are building R packages regularly, and have been…

This package meets the emerging needs of powerful and reliable models for the analysis of spatial discrete choice data. Since the explosion of available and voluminous geospatial and location data,…

One of the key advantages of using R for data mining and machine learning is that one may use the same environmentÊfor both data munging and algorithm execution. The problem with R, however, is that…

In Brockhoff et al (2016), the close link between Cohen's d, the effect size in an ANOVA framework, and the so-called Thurstonian (Signal detection) d-prime was used to suggest better visualizations…

assertive is a group of R packages that lets you check that your code is running as you want it to. assert_* functions test a condition and throw an error if it fails, letting you write robust code…

The popularity of R markdown is unquestionable, but will it prove as useful to the blind community as it is for our sighted peers? The short answer is "yes" but the more realistic answer is…

Non-linear mixed models of various kinds are fundamental extensions of the linear mixed models commonly used in a wide range of applications. An important example of a non-linear mixed model is the…

Databases have a long history of delivering highly scalable solutions for storing, manipulating, and analyzing data, transaction processing and data warehousing, while R is the most widely used…

In order to allocate resources more effectively with the goal of providing safer communities, R's randomForest algorithm was used to identify candidates who may commit or attempt murder. And while…

Analysis of functional magnetic resonance imaging (fMRI) data has traditionally been carried out by analyzing each voxel's time-series independently with a linear model. While this approach has been…

When analyzing microbiome data, biologists often use exploratory methods that take into account the relatedness of the bacterial species present in the data. This helps in the interpretability and…

A join operation combines two (or more) tables on some shared columns based on a condition. An equi-join is a case where this combination condition is defined by the binary operator $==$. It is a…

The Diffusion of Innovations theory, while one of the oldest social science theories, has embedded and flowed in its popularity over its 100 year or so history. In contrast to contagion models,…

Parallel computing is useful for speeding up computing tasks and many R packages exist to aid in using parallel computing. Unfortunately it is not always trivial to parallelize jobs and can take a…

A heatmap is a popular graphical method for visualizing high-dimensional data, in which a table of numbers are encoded as a grid of colored cells. The rows and columns of the matrix are ordered to…

We present "MonetDBLite", a new R package containing an embedded version of MonetDB. MonetDB is a free and open source relational database focused on analytical applications. MonetDBLite…

The recent Ebola virus disease outbreak in West Africa has been a terrible reminder of the necessities of rapid evaluation and response to emerging infectious disease threats. For such response to be…

We present resource-aware scheduling strategies for parallel R programs leading to efficient utilization of parallel computer architectures by estimating resource demands. We concentrate on…

Packages provide users with software that extends the core functionality of R, as well as data that illustrates the use of that functionality. However, by design the type of data that can be contained…

Quantitative exposures (e.g. smoking, alcohol consumption) in predicting binary health outcomes (e.g. mortality, incidence of a disease) are frequently categorized and modeled with indicator…

With so much hype about "big data" and the industry pushing for distributed computing vs traditional single-machine tools, one wonders about the future of R. In this talk I will argue that…

Software packages for randomization inference are few and far between. This forces researchers either to rely on specialized stand-alone programs or to use classical statistical tests that may require…

We present an R package for prediction of time series based on online robust aggregation of a finite set of forecasts (machine learning method, statistical model, physical model, human expertise,…

Treatments for complicated diseases often help some patients but not all and predicting the treatment effect of new patients is important in order to make sure every patient gets the best possible…

Notebook interfaces for data analysis have compelling advantages including the close association of code and output and the ability to intersperse narrative with computation. Notebooks are also an…

This talk explores modern trends in data storage formats and the tools, packages and best practices to import this data into R. We will start with a quick recap of the existing tools and packages for…

Bell Labs in the 1970s was a hotbed of research in computing, statistics and many other fields. The conditions there encouraged the growth of the S language and influenced its content. The 40th…

During the last decades, the logging of events in a business context has increased massively. Information concerning activities within a broad range of business processes is recorded in so-called…

Kaggle is a community of almost 450K data scientists who have built almost 2MM machine learning models to participate in our competitions. Data scientists come to Kaggle to learn, collaborate and…

R is well-suited to handle data that can fit in memory but additional tools are needed when the amount of data you want to analyze in R grows beyond the limits of your machine's RAM. There have been a…

The NIMBLE R package provides a flexible system for programming statistical algorithms for hierarchical models specified using the BUGS language. As part of the system, we compile R code for…

In old economy companies, the introduction of R is typically a button-up process that follows a pattern of three major stages of maturity: At the first stage, guerrilla projects use R parallel to the…

Estimation for high-dimensional models has been widely studied. However, uncertainty quantification remains challenging. We put forward novel methodology for two-sample testing in high dimensions…

OpenML is an online machine learning platform where researchers can automatically log and share data, code, and experiments, and organize them online to work and collaborate more effectively. We…

BUGS is a language for describing hierarchical Bayesian models which syntactically resembles R. BUGS allows large complex models to be built from smaller components. JAGS is a BUGS interpreter written…

R/qtl is an R package for mapping quantitative trait loci (genetic loci that contribute to variation in quantitative traits, such as blood pressure) in experimental crosses (such as in mice). I began…

The use of GPS-enabled tracking devices and heart rate monitors is becoming increasingly common in sports and fitness activities. The trackeR package aims to fill the gap between the routine…

The Integrated Nested Laplace Approximation (INLA) provides a computationally efficient approach to obtaining an approximation to the posterior marginals for a large number of Bayesian models. In…

As consultants, many of the projects that we work on are similar, with many steps repeated verbatim across projects. Previously, our workflow was based largely in Microsoft Office, with our analysis…

Gene set tests are often used in differential expression analyses to explore the behavior of a group of related genes. This is useful for identifying large-scale co-regulation of genes belonging to…

Recently, the United Nations adopted a probabilistic approach to projecting fertility, mortality and population for all countries. In this approach, the total fertility and female and male life…

Data volume is ever increasing, while single node performance is stagnate. To scale, analysts need to distribute computations. R has built-in support for parallel computing, and third-party…

Introduced by Rubin in 1981, the Bayesian bootstrap is the Bayesian analogue to the classical non-parametric bootstrap and it shares the classical bootstrap's advantages: It is a non-parametric method…

One out of every ten people on the planet uses a spreadsheet and about half of those use formulas: "Let's not kid ourselves: the most widely used piece of software for statistics is Excel."…

In the era of big data, R is rapidly becoming one of the most popular tools forndata analysis. But the R framework is relatively slow and unablento scale to large datasets. The general approach of…

The htmlwidgets package makes it easy to create interactive JavaScript widgets from R, and display them from the R console or insert them into R Markdown documents and Shiny apps. These widgets…

In this talk we will discuss two statistical computing courses taught as part of the undergraduate and masters curriculum in the Department of Statistical Science at Duke University. The primary goal…

Quantile regression represents a flexible approach for modelling the impact of several covariates on the conditional distribution of the dependent variable, which does not require making any…

htmlwidgets is an R package that provides a comprehensive framework to create interactive javascript based widgets, for use from R. Once created, these widgets can be used at the R console, embedded…

With the exponential growth of data, more and more businesses are demanding to become data-driven. Seeking value from their data, big data and data science initiatives; jobs and skill sets have risen…

In this study we present some real cases where R has been a key element on building decision support systems related to the water industry. We have used R in the context of automatic water demand…

The Mobilize project developed a year-long high school level Introduction to Data Science course, which has been piloted in 27 public schools in the Los Angeles Unified School District. The curriculum…

Data scientists are increasingly interested in modeling techniques involving relatively few parametric assumptions, particularly when analyzing large or complex datasets. Though many approaches have…

Julia, like R, is a dynamic language for scientific computing but, unlike R, it was explicitly designed to deliver performance competitive to traditional batch-compiled languages. To achieve this…

CVXR is an R package that provides an object-oriented modeling language for convex optimization. It allows the user to formulate convex optimization problems in a natural mathematical syntax rather…

The goal of reproducible model building is to tie processing instructions to data analysis so that the model can be recreated, better understood, and easily modified to incorporate new field…

intRo is a modern web-based application for performing basic data analysis and statistical routines as well as an accompanying R package. Leveraging the power of R and Shiny, intRo implements common…

mhurdle is a package for R enabling the estimation of a wide set of regression models where the dependent variable is left censored at zero, which is typically the case in household expenditure…

In this work we present the R package bamdit, its name stands for "Bayesian meta-analysis of diagnostic test-data". bamdit was developed with the aim of simplifying the use of models in…

Genetic evidence is often used as evidence in disputes. Mostly, the genetic evidence is DNA profiles and the disputes are often familial or crime cases. In this talk, we go through the statistical…

The efficacy of policy interventions for socioeconomic challenges, like food insecurity, is difficult to measure due to a limited understanding of the complex web of causes and consequences. As an…

In this talk we will discuss an R based first-year undergraduate data science course taught at Duke University for an audience of students with little to no computing or statistical background. The…

In this work we present a new method for detection of Differential Item Functioning (DIF) based on Non-Linear Regression. Detection of DIF has been considered one of the most important topics in…

FiveThirtyEight is a data journalism site that uses R extensively for charts, stories, and interactives. We've used R for stories covering: p-hacking in nutrition science; how Uber is affecting New…

The groundwater drawdown model WTAQ-2, provided by the United States Geological Survey for free, has been "wrapped" into an R package, which contains functions for writing input files,…

Across disciplines, scholars are waking up to the potential benefits of computational competence. This has created a surge in demand for computational education which has gone widely underserved.…

The speaker will discuss what he considers to be the most important outcome of his work developing TeX in the 1980s, namely the accidental discovery of a new approach to programming --- which caused a…

Like many statistical analytic tools, R can be incredibly memory intensive. A simple GAM (generalized additive model) or K-nearest neighbor routine can devour many multiples of memory size compared to…

Code coverage records whether or not each line of code in a package is executed by the package's tests. While it does not check whether a given program or test executes properly it does reveal areas…

Recently, dashboards have become a common means of communicating the results of data analysis, especially of real-time data, and with good reason: dashboards present information attractively, use…

Since the acquisition of Revolution Analytics in April 2015, Microsoft has embarked upon a project to build R technology into many Microsoft products, so that developers and data scientists can use…

The production of synthetic datasets has been proposed as a statistical disclosure control solution to generate public use files out of protected data, and as a tool to create "augmented…

Many practical optimization tasks, such as finding best parameters for simulators in engineering or hyperparameter optimization in machine learning, are of a black-box nature, i.e., neither formulas…

One of the simplest ways to speed up your code is to buy a faster computer. While this advice is certainly trite, it is something that should still be considered. However it is often unclear to…

The attributable fraction (or attributable risk) is a widely used measure that quantifies the public health impact of an exposure on an outcome. Even though the theory for AF estimation is well…

In this talk, we demonstrate how to develop and deploy end-to-end machine learning solutions for predictive maintenance in manufacturing industry with R. For predictive maintenance, the following…

MXNet is a multi-language machine learning library to ease the development of ML algorithms, especially for deep neural networks. Embedded in the host language, it blends declarative symbolic…

The concept of "tidy data" offers a powerful and intuitive framework for structuring data to ease manipulation, modeling and visualization, and has guided the development of R tools such as…

The release of Shiny 0.13 includes support for modules, allowing you to build Shiny apps more quickly and more reliably. Furthermore, using Shiny modules makes it easier for you to build more-complex…

Despite the hype around "big data", a more immediate problem facing many scientific analyses is that large-scale databases must be assembled from a collection of small independent and…

Interactive Machine Learning (IML) is a relatively new area of ML where focused interactions between algorithms and humans allow for faster and more accurate model updates with respect to classical ML…

The motivation for the RServer project is the ability for data scientists at Electronic Arts to offload R computations from their personal machines to the cloud and to enable modeling at scale. The…

A future is an abstraction for a value that may be available at some point in the future and which state is either unresolved or resolved. When a future is resolved the value is readily available. How…

R AnalyticFlow 3 is an open-source GUI for data analysis on top of R. It is designed to simplify the process of data analysis for both R experts and beginners. It is written in Java and runs on…

The world is a complex dynamical system, a system evolving in time and space in which numerous interactions and feedback loops produce phenomena that defy simple explanations. Differential-equation…

XGBoost is a multi-language library designed and optimized for boosting trees algorithms. The underlying algorithm of xgboost is an extension of the classic gradient boosting machine algorithm. By…

R provides tremendous value to statisticians and data scientists. However, they are often challenged to integrate their work and extend that value to the rest of their organization. This presentation…

In this presentation I will present an extension to the lint package to assist with documentation of R objects. R is the de facto standard for literate programming thanks to packages such as. However,…

Observational studies are a widely used and challenging class of studies. A key challenge is selecting a study cohort from the available data, or "pruning" the data, in a way that produces…

The technique of mapping continuous values to a sequence of colours, is often used to visualise quantitative data. The ability of different colour schemes to facilitate data interpretation has not…

Recommendation engines have a number of different applications. From books to movies, they enable the analysis and prediction of consumer preferences. The prevalence of recommender systems in both the…