UpDown
1.2.3Detecting Group Disturbances from Longitudinal Observations
Overview
Provides an algorithm to detect and characterize disturbances (start, end dates, intensity) that can occur at different hierarchical levels by studying the dynamics of longitudinal observations at the unit level and group level based on Nadaraya-Watson's smoothing curves, but also a shiny app which allows to visualize the observations and the detected disturbances. Finally the package provides a dataframe mimicking a pig farming system subsected to disturbances simulated according to Le et al.(2022) doi:10.1016/j.animal.2022.100496.
Install
Health
- OK2026-08-0513 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- NOTE2026-08-0112 OK · 1 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- OK2026-06-0913 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- ERROR2026-06-0812 OK · 0 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE
- OK2026-03-1014 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
Documentation
- Examples that run
- 75%
- Documented parameters
- 75%
- Return-value docs
- 100%
- References docs
- 75%
Downloads
Dependencies
Nothing depends on this yet.
Code & Tests
Datasets
People & History
2 releases. Pick two to compare their code metrics. R releases are shown for context.
- RR 4.6.0 released · 2026-04-24
- 1.2.3Latest
- RR 4.5.0 released · 2025-04-11
- RR 4.4.0 released · 2024-04-24
- 1.2.12023-07-20
- RR 4.3.0 released · 2023-04-21
Package metadata
- First published
- 2023-07-20
- Total releases
- 2 / 3 yrs
- License
- GPL (>= 3) OSI
- Minimum R
- ≥ 4.0.0
- Bundled data
- 1.8 MB / 2 files
- Download size
- 1.8 MB
- Installed size
- not tracked yet
- With dependencies
- not tracked yet
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