fdaACF
1.0.0Autocorrelation Function for Functional Time Series
Overview
Quantify the serial correlation across lags of a given functional time series using the autocorrelation function and a partial autocorrelation function for functional time series proposed in Mestre et al. (2021) doi:10.1016/j.csda.2020.107108. The autocorrelation functions are based on the L2 norm of the lagged covariance operators of the series. Functions are available for estimating the distribution of the autocorrelation functions under the assumption of strong functional white noise.
Install
Health
- NOTE r-devel-linux-x86_64-debian-clang
- NOTE r-devel-linux-x86_64-debian-gcc
- NOTE2026-06-0911 OK · 2 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- ERROR2026-06-0811 OK · 1 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE
- NOTE2026-03-1012 OK · 2 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
Documentation
- Examples that run
- 100%
- Documented parameters
- 88%
- Return-value docs
- 82%
- References docs
- 21%
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Dependencies
Nothing depends on this yet.
Code & Tests
Datasets
People & History
3 releases. Pick two to compare their code metrics. R releases are shown for context.
- RR 4.6.0 released · 2026-04-24
- RR 4.5.0 released · 2025-04-11
- RR 4.4.0 released · 2024-04-24
- RR 4.3.0 released · 2023-04-21
- RR 4.2.0 released · 2022-04-22
- RR 4.1.0 released · 2021-05-18
- 1.0.0Latest
- 0.2.02020-08-11 · diff ↗
- RR 4.0.0 released · 2020-04-24
- 0.1.02020-01-24
- RR 3.6.0 released · 2019-04-26
Package metadata
- First published
- 2020-01-24
- Total releases
- 3 / 6 yrs
- License
- GPL (>= 2) OSI
- Minimum R
- ≥ 3.5.0
- Bundled data
- 20 KB / 1 file
- Download size
- 43 KB
- Installed size
- not tracked yet
- With dependencies
- not tracked yet
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