| Title: | Bayesian Hierarchical Models for Single-Cell Protein Data |
|---|---|
| Description: | Bayesian Hierarchical beta-binomial models for modeling cell population to predictors/exposures. This package utilizes 'runjags' to run Gibbs sampling, parallelizing the chains. Options for different covariances/relationship structures between parameters of interest. |
| Authors: | Chase Sakitis [aut, cre], Brooke Fridley [aut] |
| Maintainer: | Chase Sakitis <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 1.0.1 |
| Built: | 2026-05-26 10:34:09 UTC |
| Source: | https://github.com/cran/BTIME |
Bayesian Immune Cell Abundance Model (BICAM)
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 )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 )
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 |
A list of inputs and results