# 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

# 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.

### Development status

Beta

# Acknowledgements

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