Package: dlmtree 1.0.0

dlmtree: Bayesian Treed Distributed Lag Models

Estimation of distributed lag models (DLMs) based on a Bayesian additive regression trees framework. Includes several extensions of DLMs: treed DLMs and distributed lag mixture models (Mork and Wilson, 2023) <doi:10.1111/biom.13568>; treed distributed lag nonlinear models (Mork and Wilson, 2022) <doi:10.1093/biostatistics/kxaa051>; heterogeneous DLMs (Mork, et. al., 2024) <doi:10.1080/01621459.2023.2258595>; monotone DLMs (Mork and Wilson, 2024) <doi:10.1214/23-BA1412>. The package also includes visualization tools and a 'shiny' interface to help interpret results.

Authors:Daniel Mork [aut, cre, cph], Seongwon Im [aut], Ander Wilson [aut]

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dlmtree.pdf |dlmtree.html✨
dlmtree/json (API)
NEWS

# Install 'dlmtree' in R:
install.packages('dlmtree', repos = c('https://danielmork.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/danielmork/dlmtree/issues

Uses libs:
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library
Datasets:
  • coExp - Randomly sampled exposure from Colorado counties
  • exposureCov - Exposure covariance structure
  • pm25Exposures - PM2.5 Exposure data
  • zinbCo - Time-series exposure data for ZINB simulated data

On CRAN:

5.28 score 21 stars 15 scripts 529 downloads 63 exports 62 dependencies

Last updated 5 months agofrom:78105c0426. Checks:OK: 9. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 30 2024
R-4.5-win-x86_64OKOct 30 2024
R-4.5-linux-x86_64OKOct 30 2024
R-4.4-win-x86_64OKOct 30 2024
R-4.4-mac-x86_64OKOct 30 2024
R-4.4-mac-aarch64OKOct 30 2024
R-4.3-win-x86_64OKOct 30 2024
R-4.3-mac-x86_64OKOct 30 2024
R-4.3-mac-aarch64OKOct 30 2024

Exports:adj_coexposurecombine.modelscombine.models.tdlmmcppIntersectiondlmEstdlmtreedlmtreeGPFixedGaussiandlmtreeGPGaussiandlmtreeHDLMGaussiandlmtreeHDLMMGaussiandlmtreeTDLM_cppdlmtreeTDLMFixedGaussiandlmtreeTDLMNestedGaussiandlnmEstdlnmPLEstdrawTreeestDLMget_sbd_dlmtreemixEstmonotdlnm_Cpppipplot.summary.monotoneplot.summary.tdlmplot.summary.tdlmmplot.summary.tdlnmppRangepredict.hdlmpredict.hdlmmprint.hdlmprint.hdlmmprint.monotoneprint.summary.hdlmprint.summary.hdlmmprint.summary.monotoneprint.summary.tdlmprint.summary.tdlmmprint.summary.tdlnmprint.tdlmprint.tdlmmprint.tdlnmrcpp_pgdrawrtmvnormruleIdxscaleModelMatrixshinyshiny.hdlmshiny.hdlmmsim.hdlmmsim.tdlmmsim.tdlnmsplitPIPsplitpointssummary.hdlmsummary.hdlmmsummary.monotonesummary.tdlmsummary.tdlmmsummary.tdlnmtdlmmtdlmm_Cpptdlnmtdlnm_CppzeroToInfNormCDF

Dependencies:base64encbslibcachemclicolorspacecommonmarkcpp11crayondigestdplyrfansifarverfastmapfontawesomefsgenericsggplot2gluegtablehtmltoolshttpuvisobandjquerylibjsonlitelabelinglaterlatticelifecyclemagrittrMASSMatrixmemoisemgcvmimemunsellnlmepillarpkgconfigpromisespurrrR6rappdirsRColorBrewerRcppRcppArmadilloRcppEigenrlangsassscalesshinyshinythemessourcetoolsstringistringrtibbletidyrtidyselectutf8vctrsviridisLitewithrxtable

Readme and manuals

Help Manual

Help pageTopics
Adjusting for expected changes in co-exposure (TDLMM)adj_coexposure
Randomly sampled exposure from Colorado countiescoExp
Combines information from DLMs of single exposurecombine.models
Combines information from DLMs of mixture exposures.combine.models.tdlmm
fast set intersection tool assumes sorted vectors A and BcppIntersection
Calculates the distributed lag effect with DLM matrix for linear models.dlmEst
Fit tree structured distributed lag modelsdlmtree
dlmtree model with fixed Gaussian process approachdlmtreeGPFixedGaussian
dlmtree model with Gaussian process approachdlmtreeGPGaussian
dlmtree model with shared HDLM approachdlmtreeHDLMGaussian
dlmtree model with HDLMM approachdlmtreeHDLMMGaussian
dlmtree model with nested HDLM approachdlmtreeTDLM_cpp
dlmtree model with fixed Gaussian approachdlmtreeTDLMFixedGaussian
dlmtree model with nested Gaussian approachdlmtreeTDLMNestedGaussian
Calculates the distributed lag effect with DLM matrix for non-linear models.dlnmEst
Calculates the distributed lag effect with DLM matrix for non-linear models.dlnmPLEst
Draws a new tree structuredrawTree
Calculates subgroup-specific lag effects for heterogeneous modelsestDLM
Exposure covariance structureexposureCov
Download simulated data for dlmtree articlesget_sbd_dlmtree
Calculates the lagged interaction effects with MIX matrix for linear models.mixEst
dlmtree model with monotone tdlnm approachmonotdlnm_Cpp
Calculates posterior inclusion probabilities (PIPs) for modifiers in HDLM & HDLMMpip
Returns variety of plots for model summary of class 'monotone'plot.summary.monotone
Plots a distributed lag function for model summary of 'tdlm'plot.summary.tdlm
Plots DLMMs for model summary of class 'tdlmm'plot.summary.tdlmm
Returns variety of plots for model summary of class 'tdlnm'plot.summary.tdlnm
PM2.5 Exposure datapm25Exposures
Makes a 'pretty' output of a group of numbersppRange
Calculates predicted response for HDLMpredict.hdlm
Calculates predicted response for HDLMMpredict.hdlmm
Print a hdlm Objectprint.hdlm
Print a hdlmm Objectprint.hdlmm
Print a monotone Objectprint.monotone
Prints an overview with summary of model class 'hdlm'print.summary.hdlm
Prints an overview with summary of model class 'hdlmm'print.summary.hdlmm
Prints an overview with summary of model class 'monotone'print.summary.monotone
Prints an overview with summary of model class 'tdlm'print.summary.tdlm
Prints an overview with summary of model class 'tdlmm'print.summary.tdlmm
Prints an overview with summary of model class 'tdlnm'print.summary.tdlnm
Print a tdlm Objectprint.tdlm
Print a tdlmm Objectprint.tdlmm
Print a tdlnm Objectprint.tdlnm
Multiple draw polya gamma latent variable for var c[i] with size b[i]rcpp_pgdraw
Truncated multivariate normal sampler, mean mu, cov sigma, truncated (0, Inf)rtmvnorm
Calculates the weights for each modifier ruleruleIdx
Centers and scales a matrixscaleModelMatrix
shinyshiny
Executes a 'shiny' app for HDLM.shiny.hdlm
Executes a 'shiny' app for HDLMM.shiny.hdlmm
Creates simulated data for HDLM & HDLMMsim.hdlmm
Creates simulated data for TDLM & TDLMMsim.tdlmm
Creates simulated data for TDLNMsim.tdlnm
Calculates the posterior inclusion probability (PIP).splitPIP
Determines split points for continuous modifierssplitpoints
Creates a summary object of class 'hdlm'summary.hdlm
Creates a summary object of class 'hdlmm'summary.hdlmm
Creates a summary object of class 'monotone'summary.monotone
Creates a summary object of class 'tdlm'summary.tdlm
Creates a summary object of class 'tdlmm'summary.tdlmm
Creates a summary object of class 'tdlnm'summary.tdlnm
Treed Distributed Lag Mixture Models (Deprecated)tdlmm
dlmtree model with tdlmm approachtdlmm_Cpp
Treed Distributed Lag Non-Linear Models (Deprecated)tdlnm
dlmtree model with tdlnm approachtdlnm_Cpp
Integrates (0,inf) over multivariate normalzeroToInfNormCDF
Time-series exposure data for ZINB simulated datazinbCo