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* fix some broken header links
* consistent capitalization
* rename Packages to Code
* remove unused shortcodes
* change landing page Our API to Our Tools and
link to tools page
* update the footer to contain a link to Code
description = "Developing the Theory and Practice of Epidemiological Forecasting"
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mission = "Develop the theory and practice of epidemiological tracking and forecasting, with a long-term vision of making this technology as universally accepted and useful as weather forecasting is today."
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---
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title: Packages
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linkTitle: packages
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title: Code Overview
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linkTitle: Code
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layout: single
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---
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In addition to our API server code, we have a number of user oriented packages, such as API clients and time-series modeling for epidemiology. All source code is freely available on [GitHub](https://github.com/cmu-delphi/).
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## API and its clients
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In addition to the Delphi Epidata API server code, we have a number of user-oriented code packages, such as API clients and time-series modeling for epidemiology.
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All source code is freely available on [GitHub](https://github.com/cmu-delphi/).
R package that implements several methods for epidemiological forecasting empirical bayes (EB), basis regression (BR), and time-weighted kernel density estimation (twkde).
API for getting up-to-date epidemiological data (also available via a web interface through [EpiVis](https://delphi.cmu.edu/epivis/epivis.html) and a [Dashboard](covidcast/dashboard)).
The primary dashboard to interact with delphi data. If you wish to access the results programmatically, see the [API](#Delphi_Epidata_API), or one of it's clients.
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### [Delphi Epidata Dashboard](covidcast/)
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The primary dashboard to interact with Delphi Epidata. If you wish to access the results programmatically, see the API above or one of its clients.
We host a forecast evaluation dashboard to compare the historical performance of the forecasts submitted to the [COVID-19 Forecast Hub](https://covid19forecasthub.org/).
We participate in the weekly Flu forecasting hub run by the CDC during the flu season.
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Our current system for generating forecasts will be available soon [here](https://github.com/cmu-delphi/covid-hosp-forecast).
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In addition to publishing the individual forecasts from the participating groups, the Reich lab makes an ensemble prediction, and hosts a [visualization of both](https://viz.covid19forecasthub.org).
R package that implements several methods for epidemiological forecasting empirical bayes (EB), basis regression (BR), and time-weighted kernel density estimation (twkde).
A collection of data structures and methods for handling epidemiological data.
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The major features are:
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- The epi_df structure, which allows you track epidemiological data measured over location and time (and other potential keys).
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- The epi_archive structure, which allows you to store epi_df objects in a versioned archive and to query the archive for the state of the data at any point in time.
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- Support for applying generic functions across a sliding time window, building on tools like [slider](https://slider.r-lib.org/). Comes with support for
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- handling gaps in time
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- automatically handling grouping of keys
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- Support for version aware data transformation and forecasting, which allows for simple and accurate backtesting of forecasting models (using only the actual data that was available at the time of the forecast and not the revised versions).
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- Growth rate estimation, as estimated using relative rates of change, linear regression, smooth splines, or polynomial trend filtering.
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- Outlier detection and correction, using rolling median or LOESS trend decomposition.
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- Signal correlation over space, time and other keys. It also supports lagged correlations, automatically handles grouping by the specified keys, and handles time gaps.
A framework for building epidata pipelines involving transformation and forecasting. It is built on top of epiprocess and provides a simple interface for defining and running pipelines. It supports:
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- A simple interface for defining a sequence of data pre-processing, forecasting, and post-processing steps.
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- A collection of data processing steps common to epidata.
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- A collection of pre-packaged forecasting models commonly used by Delphi's forecasting team.
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