Cyclops

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.

Dependencies

• There are no dependencies.

Getting Started

1. On Windows, make sure RTools is installed.
2. In R, use the following commands to download and install Cyclops:

install.packages("devtools")
library(devtools)
install_github("ohdsi/Cyclops") 

3. To perform a Cyclops model fit, use the following commands in R:

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

Getting Involved

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

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.

Development status

Beta

Acknowledgements

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