VCBART
1.2.5Fit Varying Coefficient Models with Bayesian Additive Regression Trees
Overview
Fits linear varying coefficient (VC) models, which assert a linear relationship between an outcome and several covariates but allow that relationship (i.e., the coefficients or slopes in the linear regression) to change as functions of additional variables known as effect modifiers, by approximating the coefficient functions with Bayesian Additive Regression Trees. Implements a Metropolis-within-Gibbs sampler to simulate draws from the posterior over coefficient function evaluations. VC models with independent observations or repeated observations can be fit. For more details see Deshpande et al. (2026) doi:10.1214/24-BA1470.
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- OK2026-08-0513 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- NOTE2026-08-0112 OK · 1 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- OK2026-04-2512 OK · 0 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
- 84%
- Return-value docs
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- References docs
- 25%
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2 releases. Pick two to compare their code metrics. R releases are shown for context.
- RR 4.6.0 released · 2026-04-24
- 1.2.5Latest
- 1.2.42026-03-10
- RR 4.5.0 released · 2025-04-11
Package metadata
- First published
- 2025-12-09
- Total releases
- 2 / 1 yrs
- License
- GPL (>= 3) OSI
- Download size
- 49 KB
- Installed size
- not tracked yet
- With dependencies
- not tracked yet
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