Cyclops

Cyclops is part of the HADES.

Introduction

Cyclops (Cyclic coordinate descent for logistic, Poisson and survival analysis) is an R package for performing large scale regularized regressions.

Features

• Regression of very large problems: up to millions of observations, millions of variables
• Supports (conditional) logistic regression, (conditional) Poisson regression, as well as (conditional) Cox regression
• Uses a sparse representation of the independent variables when appropriate
• Supports using no prior, a normal prior or a Laplace prior
• Supports automatic selection of hyperparameter through cross-validation
• Efficient estimation of confidence intervals for a single variable using a profile-likelihood for that variable

Examples

  library(Cyclops)
  cyclopsData <- createCyclopsDataFrame(formula)
  cyclopsFit <- fitCyclopsModel(cyclopsData)

Technology

Cyclops in an R package, with most functionality implemented in C++. Cyclops uses cyclic coordinate descent to optimize the likelihood function, which makes use of the sparse nature of the data.

System Requirements

Requires R (version 3.1.0 or higher). Compilation on Windows requires RTools >= 3.4.

Installation

In R, to install the latest stable version, install from CRAN:

install.packages("Cyclops")

To install the latest development version, install from GitHub. Note that this will require RTools to be installed.

install.packages("devtools")
devtools::install_github("OHDSI/Cyclops")

User Documentation

Documentation can be found on the package website.

PDF versions of the documentation are also available: * Package manual: Cyclops manual

Support

• Developer questions/comments/feedback: OHDSI Forum
• We use the GitHub issue tracker for all bugs/issues/enhancements

Contributing

Read here how you can contribute to this package.

License

Cyclops is licensed under Apache License 2.0. Cyclops contains the TinyThread libray.

The TinyThread library is licensed under the zlib/libpng license as described here.

Development

Cyclops is being developed in R Studio.

Acknowledgements

• This project is supported in part through the National Science Foundation grants IIS 1251151 and DMS 1264153.