bbum: BBUM correction for significance testing of p values

#' bbum: A package to account for secondary effects signal in multiple testing
#'
#' Standard multiple testing correction methods cannot directly handle datasets
#'   that contain a weaker background secondary signal confounding the primary
#'   signal of interest. A bi-beta-uniform mixture (BBUM) model allows the
#'   modeling and correction for the false discovery rate (FDR) of both null and
#'   secondary effects.
#'
#' @section Authors:
#' * Author: Peter Y. Wang
#' * Advisor: David P. Bartel
#'
#' @references
#'
#' Wang PY, Bartel DP. 2023. A statistical approach for identifying primary
#'   substrates of ZSWIM8-mediated microRNA degradation in small-RNA sequencing
#'   data. *BMC Bioinformatics* **24**:195. doi:10.1186/s12859-023-05306-z
#'
#' Markitsis A, Lai Y. 2010. A censored beta mixture model for the estimation of
#'   the proportion of non-differentially expressed genes. *Bioinformatics*
#'   **26**:640-646. doi:10.1093/bioinformatics/btq001
#'
#' Pounds S, Morris SW. 2003. Estimating the occurrence of false positives and
#'   false negatives in microarray studies by approximating and partitioning the
#'   empirical distribution of p-values. *Bioinformatics* **19**:1236-1242.
#'   doi:10.1093/bioinformatics/btg148

#'
#' @docType package
#' @name bbum
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