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VCBART

Fit Varying Coefficient Models with Bayesian Additive Regression Trees

v1.2.4 · Dec 9, 2025 · GPL (>= 3)

Description

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. (2024) <doi:10.1214/24-BA1470>.

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installed package size

  installed size is  6.4Mb
  sub-directories of 1Mb or more:
    libs   6.3Mb
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installed package size

  installed size is  6.5Mb
  sub-directories of 1Mb or more:
    libs   6.4Mb
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NOTE 12 OK · 2 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE Mar 9, 2026
NOTE r-oldrel-macos-arm64

installed package size

  installed size is  6.4Mb
  sub-directories of 1Mb or more:
    libs   6.3Mb
NOTE r-oldrel-macos-x86_64

installed package size

  installed size is  6.5Mb
  sub-directories of 1Mb or more:
    libs   6.4Mb

Dependency Network

Dependencies Reverse dependencies Rcpp MASS VCBART

Version History

new 1.2.4 Mar 9, 2026