tEDM
1.3Temporal Empirical Dynamic Modeling
Overview
Inferring causation from time series data through empirical dynamic modeling (EDM), with methods such as convergent cross mapping from Sugihara et al. (2012) doi:10.1126/science.1227079, partial cross mapping introduced by Leng et al. (2020) doi:10.1038/s41467-020-16238-0, and cross mapping cardinality described in Tao et al. (2023) doi:10.1016/j.fmre.2023.01.007, following a systematic description proposed in Lyu et al. (2026) doi:10.1016/j.compenvurbsys.2026.102435.
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Health
- OK2026-08-0413 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- NOTE2026-08-0112 OK · 1 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- OK2026-06-0813 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- ERROR2026-06-0712 OK · 0 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE
- OK2026-04-2512 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
Show 3 earlier snapshots
- NOTE2026-04-2212 OK · 2 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- ERROR2026-04-1811 OK · 2 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
- 100%
- Return-value docs
- 100%
- References docs
- 80%
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Package metadata
- First published
- 2025-07-15
- Total releases
- 4 / 1 yrs
- License
- GPL-3 OSI
- Minimum R
- ≥ 4.1.0
- Download size
- 2.5 MB
- Installed size
- not tracked yet
- With dependencies
- not tracked yet