Explore our Services

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Statistical consulting

  • Data analysis and statistics
  • Experiment design
  • Method development
  • Machine learning
  • Data and text mining
  • Artificial intelligence
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Scientific programming

  • R, Python, Julia package development
  • Code review and optimization
  • Porting code to low-level languages
  • Parallel and distributed computing
  • In-database data science
  • GPU / FPGA programming
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Application Development and Integration

  • Desktop and web applications
  • Automation of analyses or predictive modeling
  • Data science APIs
  • Scientific data stores
  • Big data architecture
  • Data science tooling
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Data Analysis Hardware and Hosting

  • Custom data analysis hardware
  • Data science compute infrastructure
  • Data analysis platforms hosting
  • Data science APIs as a service
  • Managed services for scientific data stores

Discover our Products

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Architect

  • IDE for data science, state of the art
  • Comfort and productivity for the R, Python and Julia developer
  • Support of low-level languages (C, C++, FORTRAN)
  • Server version for teams and HPC environments
  • Fully open source, including all enterprise features
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ShinyProxy

  • Shiny app deployment for companies and large organizations
  • Highly scalable design using Docker infrastructure
  • Authentication and authorization, single-sign on deployments
  • Usage statistics and administrator views
  • Fully open source, including all enterprise features
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R Service Bus

  • Middleware for automation of R-based jobs
  • Rich set of supported protocols (REST, SOAP, e-mail protocols, etc.)
  • Integrated management of multiple R pools for distributed computing
  • Synchronous and asynchronous APIs, admin API
  • Supports plain R scripts and packages out of the box
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RDepot

  • Corporate management of R package repositories
  • RESTful APIs for package submission and repository generation
  • Authentication and authorization for actions on multiple repositories
  • Support of continuous integration infrastructure
  • Highly available repository set-up and full audit trails

Case studies

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From our blog

Using an R ‘template’ package to enhance reproducible research or the 'R package syndrome'

on November 21, 2017

Motivation Have you ever had the feeling that the creation of your data analysis report(s) resulted in looking up, copy-pasting and reuse of code from previous analyses? This approach is time consuming and prone to errors. If you frequently analyze similar data(-types), e.g. from a standardized analysis workflow or different experiments on the same platform, the automation of your report creation via an R ‘template’ package might be a very useful and time-saving step.

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ShinyProxy 1.0.2

on November 12, 2017

ShinyProxy is a novel, open source platform to deploy Shiny apps for the enterprise or larger organizations. Since our last blog post ten new releases of ShinyProxy have seen the light, but with the 1.0.2 release it is time to provide an overview of the lines of development and advances made. Scalability ShinyProxy now allows to run 1000s of Shiny apps concurrently on a Docker Swarm cluster. Moreover, ShinyProxy will automatically detect whether the Docker API URL is a Docker Engine API or a Swarm cluster API.

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useR!2017 Roundup

on August 23, 2017

Organising useR!2017 was a challenge but a very rewarding experience. With about 1200 attendees of over 55 nationalities exploring an interesting program, we believe it is appropriate to call it a success - something the aftermovie only seems to confirm. Behind the Scenes To give you a glimpse behind the scenes of the conference organization, Maxim Nazarov held a lightning talk on ‘redmineR and the story of automating useR!

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ShinyProxy 0.8.0

on December 19, 2016

In our last blog post we mentioned the need people felt to integrate Shiny apps in content management systems like Drupal. Release 0.7.8 added one extra feature in this respect so people could also include ShinyProxy hosted Shiny apps in external iframes. It also kicked-off our effort to support service scalability and ever more complex apps. In the scalability area, we introduced facilities to manage and set memory limits on individual Shiny applications.

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Technologies