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Cyclops

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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). Installation on Windows requires RTools (devtools >= 1.12 required for RTools34, otherwise RTools33 works fine).

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") 
  1. To perform a Cyclops model fit, use the following commands in R:
library(Cyclops)
cyclopsData <- createCyclopsDataFrame(formula)
cyclopsFit <- fitCyclopsModel(cyclopsData)

Getting Involved

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

Build Status codecov.io

Beta

Acknowledgements

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