dlmtree is an R package that provides constrained distributed lag
models (DLMs) using a regression tree approach within the Bayesian
additive regression trees (BART) framework, referred to as treed DLMs.
The package includes various extensions of treed DLMs, allowing for the
incorporation of different scenarios like linear, non-linear
associations, mixture exposures, and heterogeneous exposure effects. The
package is built user-friendly with a single function with three
arguments to specify treed DLMs. Functions for summarizing the model fit
and visualization are also provided.
| Model | Type | Family | Mixture | Heterogeneity |
|---|---|---|---|---|
| Treed distributed lag model (TDLM)$^2$ | Linear | Gaussian | X | X |
| Binary | X | X | ||
| Count | X | X | ||
| Treed distributed lag mixture model (TDLMM)$^2$ | Linear | Gaussian | O | X |
| Binary | O | X | ||
| Count | O | X | ||
| Treed distributed non-linear lag model (TDLNM)$^{1,4}$ | Non-linear | Gaussian | X | X |
| Binary | X | X | ||
| Monotone | Gaussian | X | X | |
| Binary | X | X | ||
| Heterogeneous distributed lag model (HDLM)$^3$ | Linear | Gaussian | X | O |
| Heterogeneous distributed lag mixture model (HDLMM) | Linear | Gaussian | O | O |
Installing package from GitHub:
# install.packages("devtools")
devtools::install_github("danielmork/dlmtree")
library(dlmtree)Installing package from CRAN:
install.packages("dlmtree")
library(dlmtree)The majority of methods implemented in this package are described in the following papers as well as some on going work.
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Mork, D. and Wilson, A. (2022). “Treed distributed lag nonlinear models.” Biostatistics, 23(3), 754–771 (DOI: 10.1093/biostatistics/kxaa051, arXiv preprint)
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Mork, D. and Wilson, A. (2023). “Estimating perinatal critical windows of susceptibility to environmental mixtures via structured Bayesian regression tree pairs.” Biometrics, 79(1), 449-461 (DOI: 10.1111/biom.13568, arXiv preprint)
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Mork, D., Kioumourtzoglou, M. A., Weisskopf, M., Coull, B. A., and Wilson, A. (2024). “Heterogeneous Distributed Lag Models to Estimate Personalized Effects of Maternal Exposures to Air Pollution.” Journal of the American Statistical Association, 119(545), 14-26 (DOI: 10.1080/01621459.2023.2258595, arXiv preprint)
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Mork, D. and Wilson, A. (In press). “Incorporating prior information into distributed lag nonlinear models with zero-inflated monotone regression trees.” Bayesian Analysis. (DOI: 10.1214/23-BA1412, arXiv preprint)