**ddtlcm**: Dirichlet diffusion tree-latent class model
(DDT-LCM)

An R package for Tree-regularized latent class mModels with a DDT process prior on class profiles

**Maintainer**: Mengbing Li (mengbing@umich.edu)

**Contributors**: Briana Stephenson
(bstephenson@hsph.harvard.edu); Zhenke Wu (zhenkewu@umich.edu)

Citation | Paper Link | |
---|---|---|

Bayesian tree-regularized LCM | Li M, Stephenson B, Wu Z (2023). Tree-Regularized Bayesian Latent
Class Analysis for Improving Weakly Separated Dietary Pattern Subtyping
in Small-Sized Subpopulations. ArXiv:2306.04700. |
Link |

```
# install bioconductor package `ggtree` for visualizing results:
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
::install("ggtree")
BiocManager
install.packages("devtools",repos="https://cloud.r-project.org")
::install_github("limengbinggz/ddtlcm") devtools
```

`ddtlcm`

is designed for analyzing multivariate binary
observations over grouped items in a tree-regularized Bayesian LCM
framework. Between-class similarities are guided by an unknown tree,
where classes positioned closer on the tree are more similar *a
priori*. This framework facilitates the sharing of information
between classes to make better estimates of parameters using less data.
The model is built upon equipping LCMs with a DDT process prior on the
class profiles, with varying degrees of shrinkage across major item
groups. The model is particularly promising for addressing weak
separation of latent classes when sample sizes are small. The posterior
inferential algorithm is based on a hybrid
Metropolis-Hastings-within-Gibbs algorithm and can provide posterior
uncertainty quantifications.

**ddtlcm** works for

multivariate binary responses over pre-specified grouping of items

The functionsâ€™ relations in the package

`ddtlcm`

can be visualized by

```
library(DependenciesGraphs) # if not installed, try this-- devtools::install_github("datastorm-open/DependenciesGraphs")
library(QualtricsTools) # devtools::install_github("emmamorgan-tufts/QualtricsTools")
<- funDependencies('package:ddtlcm','ddtlcm_fit')
dep plot(dep)
```

A simple workflow using semi-synthetic data is provided in

*ddtlcm*estimates the tree over classes and class profiles simultaneously