Package: dlmtree 1.1.1

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 check model convergence and to help interpret results.

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

dlmtree_1.1.1.tar.gz
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dlmtree_1.1.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
dlmtree/json (API)

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

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

Pkgdown/docs site:https://danielmork.github.io

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:

Conda:

cppopenmp

5.53 score 27 stars 25 scripts 389 downloads 79 exports 61 dependencies

Last updated from:ea95f047e8. Checks:13 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK349
linux-devel-x86_64OK369
source / vignettesOK414
linux-release-arm64OK346
linux-release-x86_64OK364
macos-release-arm64OK307
macos-release-x86_64OK871
macos-oldrel-arm64OK241
macos-oldrel-x86_64OK524
windows-develOK384
windows-releaseOK385
windows-oldrelOK386
wasm-releaseOK220

Exports:adj_coexposurecombine.modelscombine.models.tdlmmcppIntersectiondiagnosediagnose.summary.hdlmdiagnose.summary.hdlmmdiagnose.summary.monotonediagnose.summary.tdlmdiagnose.summary.tdlmmdiagnose.summary.tdlnmdlmEstdlmtreedlmtree.control.diagnosedlmtree.control.familydlmtree.control.hetdlmtree.control.hyperdlmtree.control.mcmcdlmtree.control.mixdlmtree.control.monotonedlmtree.control.tdlnmdlmtreeGPFixedGaussiandlmtreeGPGaussiandlmtreeHDLMGaussiandlmtreeHDLMMGaussiandlmtreeTDLM_cppdlmtreeTDLMFixedGaussiandlmtreeTDLMNestedGaussiandlnmEstdlnmPLEstdrawTreeestDLMget_sbd_dlmtreemixEstmonotdlnm_Cpppipplot.summary.monotoneplot.summary.tdlmplot.summary.tdlmmplot.summary.tdlnmppRangepredictpredict.hdlmpredict.hdlmmprintprint.hdlmprint.hdlmmprint.monotoneprint.summary.hdlmprint.summary.hdlmmprint.summary.monotoneprint.summary.tdlmprint.summary.tdlmmprint.summary.tdlnmprint.tdlmprint.tdlmmprint.tdlnmrcpp_pgdrawrtmvnormruleIdxscaleModelMatrixshinyshiny.hdlmshiny.hdlmmsim.hdlmmsim.tdlmmsim.tdlnmsplitPIPsplitpointssummarysummary.hdlmsummary.hdlmmsummary.monotonesummary.tdlmsummary.tdlmmsummary.tdlnmtdlmm_Cpptdlnm_CppzeroToInfNormCDF

Dependencies:base64encbslibcachemclicodacommonmarkcpp11digestdplyrfarverfastmapfontawesomefsgenericsggplot2ggridgesgluegtablehtmltoolshttpuvisobandjquerylibjsonlitelabelinglaterlatticelifecyclemagrittrMatrixmemoisemgcvmimenlmeotelpillarpkgconfigpromisespurrrR6rappdirsRColorBrewerRcppRcppArmadilloRcppEigenrlangS7sassscalesshinyshinythemessourcetoolsstringistringrtibbletidyrtidyselectutf8vctrsviridisLitewithrxtable

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
diagnosediagnose diagnose.summary.hdlm diagnose.summary.hdlmm diagnose.summary.monotone diagnose.summary.tdlm diagnose.summary.tdlmm diagnose.summary.tdlnm
Calculates the distributed lag effect with DLM matrix for linear models.dlmEst
Fit tree structured distributed lag modelsdlmtree
Diagnostic control settings for dlmtree model fittingdlmtree.control.diagnose
Family control settings for dlmtree model fittingdlmtree.control.family
Control settings for dlmtree model fitting, when used for heterogeneous modelsdlmtree.control.het
Hyperparameter control settings for dlmtree model fittingdlmtree.control.hyper
MCMC control settings for dlmtree model fittingdlmtree.control.mcmc
Control settings for dlmtree model fitting, when used for mixture modelsdlmtree.control.mix
Control settings for dlmtree model fitting, when used for monotone modeldlmtree.control.monotone
Control settings for dlmtree model fitting, when used for TDLNMdlmtree.control.tdlnm
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
predictpredict predict.hdlm predict.hdlmm
printprint print.hdlm print.hdlmm print.monotone print.summary.hdlm print.summary.hdlmm print.summary.monotone print.summary.tdlm print.summary.tdlmm print.summary.tdlnm print.tdlm print.tdlmm print.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 shiny.hdlm 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
summarysummary summary.hdlm summary.hdlmm summary.monotone summary.tdlm summary.tdlmm summary.tdlnm
dlmtree model with tdlmm approachtdlmm_Cpp
dlmtree model with tdlnm approachtdlnm_Cpp
Integrates (0,inf) over multivariate normalzeroToInfNormCDF
Time-series exposure data for ZINB simulated datazinbCo