FDApy: a Python package for functional data

• URL: http://arxiv.org/abs/2101.11003v2
• Date: Mon, 12 Aug 2024 08:43:35 GMT
• Title: FDApy: a Python package for functional data
• Authors: Steven Golovkine,
• Abstract summary: FDApy is an open-source Python package for the analysis of functional data. FDApy provides tools for the representation of functional data defined on different dimensional domains and for functional data that is irregularly sampled. The documentation includes installation and usage instructions, examples on simulated and real datasets and a complete description of the API.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce FDApy, an open-source Python package for the analysis of functional data. The package provides tools for the representation of (multivariate) functional data defined on different dimensional domains and for functional data that is irregularly sampled. Additionally, dimension reduction techniques are implemented for multivariate and/or multidimensional functional data that are regularly or irregularly sampled. A toolbox for generating functional datasets is also provided. The documentation includes installation and usage instructions, examples on simulated and real datasets and a complete description of the API. FDApy is released under the MIT license. The code and documentation are available at https://github.com/StevenGolovkine/FDApy.

Related papers

• RobPy: a Python Package for Robust Statistical Methods [1.2233362977312945]
  RobPy offers a wide range of robust methods in Python, built upon established libraries including NumPy, SciPy, and scikit-learn. This paper presents the structure of the RobPy package, demonstrates its functionality through examples, and compares its features to existing implementations in other statistical software.
  arXiv  Detail & Related papers  (2024-11-04T10:27:30Z)
• scikit-fda: A Python Package for Functional Data Analysis [0.0]
  scikit-fda is a Python package for Functional Data Analysis (FDA) It provides a comprehensive set of tools for representation, preprocessing, and exploratory analysis of functional data.
  arXiv  Detail & Related papers  (2022-11-04T16:34:03Z)
• DADApy: Distance-based Analysis of DAta-manifolds in Python [51.37841707191944]
  DADApy is a python software package for analysing and characterising high-dimensional data. It provides methods for estimating the intrinsic dimension and the probability density, for performing density-based clustering and for comparing different distance metrics.
  arXiv  Detail & Related papers  (2022-05-04T08:41:59Z)
• PyGOD: A Python Library for Graph Outlier Detection [56.33769221859135]
  PyGOD is an open-source library for detecting outliers in graph data. It supports a wide array of leading graph-based methods for outlier detection. PyGOD is released under a BSD 2-Clause license at https://pygod.org and at the Python Package Index (PyPI)
  arXiv  Detail & Related papers  (2022-04-26T06:15:21Z)
• DataLab: A Platform for Data Analysis and Intervention [96.75253335629534]
  DataLab is a unified data-oriented platform that allows users to interactively analyze the characteristics of data. toolname has features for dataset recommendation and global vision analysis. So far, DataLab covers 1,715 datasets and 3,583 of its transformed version.
  arXiv  Detail & Related papers  (2022-02-25T18:32:19Z)
• PyHHMM: A Python Library for Heterogeneous Hidden Markov Models [63.01207205641885]
  PyHHMM is an object-oriented Python implementation of Heterogeneous-Hidden Markov Models (HHMMs) PyHHMM emphasizes features not supported in similar available frameworks: a heterogeneous observation model, missing data inference, different model order selection criterias, and semi-supervised training. PyHHMM relies on the numpy, scipy, scikit-learn, and seaborn Python packages, and is distributed under the Apache-2.0 License.
  arXiv  Detail & Related papers  (2022-01-12T07:32:36Z)
• Scikit-dimension: a Python package for intrinsic dimension estimation [58.8599521537]
  This technical note introduces textttscikit-dimension, an open-source Python package for intrinsic dimension estimation. textttscikit-dimension package provides a uniform implementation of most of the known ID estimators based on scikit-learn application programming interface. We briefly describe the package and demonstrate its use in a large-scale (more than 500 datasets) benchmarking of methods for ID estimation in real-life and synthetic data.
  arXiv  Detail & Related papers  (2021-09-06T16:46:38Z)
• giotto-tda: A Topological Data Analysis Toolkit for Machine Learning and Data Exploration [4.8353738137338755]
  giotto-tda is a Python library that integrates high-performance topological data analysis with machine learning. The library's ability to handle various types of data is rooted in a wide range of preprocessing techniques.
  arXiv  Detail & Related papers  (2020-04-06T10:53:57Z)
• MOGPTK: The Multi-Output Gaussian Process Toolkit [71.08576457371433]
  We present MOGPTK, a Python package for multi-channel data modelling using Gaussian processes (GP) The aim of this toolkit is to make multi-output GP (MOGP) models accessible to researchers, data scientists, and practitioners alike.
  arXiv  Detail & Related papers  (2020-02-09T23:34:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.