Bayesian Treed Distributed Lag Models

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**adj_coexposure:**Adjusting for expected changes in co-exposure (TDLMM)**coExp:**Randomly sampled exposure from Colorado counties**combine.models:**Combines information from DLMs of single exposure**combine.models.tdlmm:**Combines information from DLMs of mixture exposures.**cppIntersection:**fast set intersection tool assumes sorted vectors A and B**dlmEst:**Calculates the distributed lag effect with DLM matrix for...**dlmtree:**Fit tree structured distributed lag models**dlmtreeGPFixedGaussian:**dlmtree model with fixed Gaussian process approach**dlmtreeGPGaussian:**dlmtree model with Gaussian process approach**dlmtreeHDLMGaussian:**dlmtree model with shared HDLM approach**dlmtreeHDLMMGaussian:**dlmtree model with HDLMM approach**dlmtree-package:**dlmtree: Bayesian Treed Distributed Lag Models**dlmtreeTDLM_cpp:**dlmtree model with nested HDLM approach**dlmtreeTDLMFixedGaussian:**dlmtree model with fixed Gaussian approach**dlmtreeTDLMNestedGaussian:**dlmtree model with nested Gaussian approach**dlnmEst:**Calculates the distributed lag effect with DLM matrix for...**dlnmPLEst:**Calculates the distributed lag effect with DLM matrix for...**drawTree:**Draws a new tree structure**estDLM:**Calculates subgroup-specific lag effects for heterogeneous...**exposureCov:**Exposure covariance structure**get_sbd_dlmtree:**Download simulated data for dlmtree articles**mixEst:**Calculates the lagged interaction effects with MIX matrix for...**monotdlnm_Cpp:**dlmtree model with monotone tdlnm approach**pip:**Calculates posterior inclusion probabilities (PIPs) for...**plot.summary.monotone:**Returns variety of plots for model summary of class...**plot.summary.tdlm:**Plots a distributed lag function for model summary of 'tdlm'**plot.summary.tdlmm:**Plots DLMMs for model summary of class 'tdlmm'**plot.summary.tdlnm:**Returns variety of plots for model summary of class 'tdlnm'**pm25Exposures:**PM2.5 Exposure data**ppRange:**Makes a 'pretty' output of a group of numbers**predict.hdlm:**Calculates predicted response for HDLM**predict.hdlmm:**Calculates predicted response for HDLMM**print.hdlm:**Print a hdlm Object**print.hdlmm:**Print a hdlmm Object**print.monotone:**Print a monotone Object**print.summary.hdlm:**Prints an overview with summary of model class 'hdlm'**print.summary.hdlmm:**Prints an overview with summary of model class 'hdlmm'**print.summary.monotone:**Prints an overview with summary of model class 'monotone'**print.summary.tdlm:**Prints an overview with summary of model class 'tdlm'**print.summary.tdlmm:**Prints an overview with summary of model class 'tdlmm'**print.summary.tdlnm:**Prints an overview with summary of model class 'tdlnm'**print.tdlm:**Print a tdlm Object**print.tdlmm:**Print a tdlmm Object**print.tdlnm:**Print a tdlnm Object**rcpp_pgdraw:**Multiple draw polya gamma latent variable for var c[i] with...**rtmvnorm:**Truncated multivariate normal sampler, mean mu, cov sigma,...**ruleIdx:**Calculates the weights for each modifier rule**scaleModelMatrix:**Centers and scales a matrix**shiny:**shiny**shiny.hdlm:**Executes a 'shiny' app for HDLM.**Browse all...**

View source: R/print.dlmtree.R

print.monotone | R Documentation |

Print a monotone Object

```
## S3 method for class 'monotone'
print(x, ...)
```

`x` |
An object of class monotone |

`...` |
Not used. |

Assorted model output.

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