`hdbm`

is a Bayesian inference method that uses continuous shrinkage priors for high-dimensional mediation analysis, developed by Song et al (2018). `hdbm`

provides estimates for the regression coefficients as well as the posterior inclusion probability for ranking mediators.

You can install `hdbm`

via CRAN

`install.packages("hdbm")`

Or devtools

`devtools::install_github("umich-cphds/hdbm", build_opts = c())`

If you wish to install the package via devtools, you will need a C++ compiler installed. This can be accomplished by installing Rtools on Windows and Xcode on MacOS.

Taken from the `hdbm`

help file

```
library(hdbm)
Y <- hdbm.data$y
A <- hdbm.data$a
# grab the mediators from the example data.frame
M <- as.matrix(hdbm.data[, paste0("m", 1:100)], nrow(hdbm.data))
# We just include the intercept term in this example.
C <- matrix(1, nrow(M), 1)
beta.m <- rep(0, 100)
alpha.a <- rep(0, 100)
set.seed(1245)
output <- hdbm(Y, A, M, C, C, beta.m, alpha.a, burnin = 3000, ndraws = 100)
# Which mediators are active?
active <- which(colSums(output$r1 * output$r3) > 50)
colnames(M)[active]
```

Yanyi Song, Xiang Zhou et al. Bayesian Shrinkage Estimation of High Dimensional Causal Mediation Effects in Omics Studies. bioRxiv 467399