Data Representation: Bayesian Approach That's Sparse
Overview
Feed longitudinal data into a Bayesian Latent Factor Model to obtain a low-rank representation. Parameters are estimated using a Hamiltonian Monte Carlo algorithm with STAN. See G. Weinrott, B. Fontez, N. Hilgert and S. Holmes, "Bayesian Latent Factor Model for Functional Data Analysis", Actes des JdS 2016.
Install
Health
- OK13 OK2026-08-05
- NOTE12 OK, 1 NOTE2026-08-01
- OK13 OK2026-06-09
- ERROR12 OK, 1 ERROR2026-06-08
- OK14 OK2026-03-10
Documentation
- Help pages
- 15 (1 internal)
- Examples that run
- 100%11 run in a plain check
- Exported functions with examples
- 92%
- Documented parameters
- 100%
- Return-value docs
- 100%
- Help pages citing references
- 13%
Downloads
Dependencies
Code & Tests
Datasets
People & History
Previously maintained by Anne Bisson (0.1.5) and Gabrielle Weinrott (0.1.3 to 0.1.4).
Pick two releases to compare their code metrics.
- 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
- 0.1.6Latest
- RR 4.1.0 released · 2021-05-18
- RR 4.0.0 released · 2020-04-24
- 0.1.52019-11-27 · diff ↗
- RR 3.6.0 released · 2019-04-26
- RR 3.5.0 released · 2018-04-23
- RR 3.4.0 released · 2017-04-21
- 0.1.4
- 0.1.32016-05-18
- RR 3.3.0 released · 2016-05-03
Package metadata
- First published
- 2016-05-18
- Total releases
- 4 / 10 yrs
- License
- GPL-3 OSI
- Minimum R
- ≥ 3.1.0
- Bundled data
- 440 KB / 2 files
- Download size
- 554 KB
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