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Activity Number: 329 - Novel Developments in Functional Data Analysis
Type: Topic Contributed
Date/Time: Tuesday, July 31, 2018 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #329018 Presentation
Title: Tidyfun: a New Framework for Representing and Working with Function-Valued Data
Author(s): Fabian Scheipl* and Jeff Goldsmith
Companies: LMU Munich and Columbia University
Keywords: functional data; sparse functional data; R packages; tidyverse; data processing; exploratory data analysis

We present a new R package tidyfun for representing and working with function-valued data that presents a unified interface for dealing with regularly or irregularly observed function-valued data. The package follows the tidyverse design philosophy of R packages and is aimed at lowering the barrier of entry for analysts in order to quickly and painlessly analyse and interact with functional data and, specifically, datasets that contain both scalar and functional data or multiple types of functional data, potentially measured over different domains. We discuss the available feature set as well as forthcoming extensions and show some application examples.

Authors who are presenting talks have a * after their name.

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