| Title: | Bayesian Hierarchical Models for Single-Cell Protein Data | 
| Version: | 1.0.0 | 
| Description: | Bayesian Hierarchical beta-binomial models for modeling cell population to predictors/exposures. This package utilizes 'runjags' to run Gibbs sampling with parallel chains. Options for different covariances/relationship structures between parameters of interest. | 
| License: | MIT + file LICENSE | 
| Encoding: | UTF-8 | 
| RoxygenNote: | 7.3.2 | 
| Imports: | coda, runjags, VGAM, matlib | 
| Depends: | R (≥ 3.5), rjags | 
| Suggests: | knitr, rmarkdown | 
| VignetteBuilder: | knitr | 
| LazyData: | true | 
| NeedsCompilation: | no | 
| Packaged: | 2025-09-30 21:02:27 UTC; cjsakitis | 
| Author: | Chase Sakitis [aut, cre], Brooke Fridley [aut] | 
| Maintainer: | Chase Sakitis <cjsakitis@cmh.edu> | 
| Repository: | CRAN | 
| Date/Publication: | 2025-10-07 18:00:12 UTC | 
Bayesian Immune Cell Abundance Model (BICAM)
Description
Bayesian Immune Cell Abundance Model (BICAM)
Usage
BICAM(
  dat,
  M,
  adapt,
  burn,
  it,
  thin = 1,
  ran_eff = 1,
  chains = 4,
  cores = 4,
  v0_mu_logit = 0.01,
  ncov = 1,
  model = "Unstr",
  dis = NULL,
  tree = NULL,
  treelevels = NULL
)
Arguments
| dat | data frame with dataset (proper setup displayed in tutorial) | 
| M | number of cell types/parameters of interest | 
| adapt | number of adaptation iterations (for compiling model) | 
| burn | number of burn-in iterations | 
| it | number of sampling iterations (after burn-in) | 
| thin | number of thinning samples | 
| ran_eff | indicate whether to use random subject effect (repeated measurements) | 
| chains | number of chains to run | 
| cores | number of cores | 
| v0_mu_logit | anticipated proportion of cell types/parameters | 
| ncov | number of covariates input into the model | 
| model | covariance model selection | 
| dis | distance matrix for Exp. Decay model | 
| tree | tree-structured covariance matrix for Tree and Scaled Tree models | 
| treelevels | list of matrices for multilevel, tree-structured covariance matrix for TreeLevels model | 
Value
A list of inputs and results
Examples
data(dat)
BICAM(dat,2,1500,250,250)
Example dataset: dat
Description
A sample dataset used for demonstrating the function.
Usage
dat
Format
A data frame with 10 rows and 5 columns:
- suid
- Subject ID's 
- total
- Total number of trials 
- stage
- Binary predictor variable (0/1) 
- M1
- Count data for Marker 1 
- M2
- Count data for Marker 2 
Source
Imported from CSV and saved as RData
Examples
data(dat)
head(dat)