ocrRBBR is an R package to infer Boolean rules linking chromatin accessibility (ATAC-seq peaks) to gene expression (RNA-seq) in both bulk and single-cell multiomic datasets. The package identifies combinations of OCRs (Open Chromatin Regions) that best predict the expression state of a target gene. Features Supports bulk and single-cell multiome data with paired RNA-seq and ATAC-seq per cell or sample. Outputs interpretable Boolean rules with associated performance metrics. Enables parallel computing for faster processing of large datasets. Data Requirements and Normalization RNA-seq (bulk): Quantile-normalized or TPM-normalized to adjust for sequencing depth and gene length. ATAC-seq (bulk): Signal intensities are quantile-normalized to adjust for sequencing depth differences across samples. Single-cell RNA-seq: LogNormalized with a scale factor of 10,000 (using Seurat). Single-cell ATAC-seq: Normalized using the ReadsInTSS method to adjust for cell-specific variations in sequencing depth. ocrRBBR is data-efficient and works well even with small sample sizes. Unlike neural networks, which require many parameters, ocrRBBR uses ridge regression with fewer parameters, making it suitable for datasets with limited samples. It has been tested on both bulk (85 cell types) and single-cell (9,834 cells) datasets and performs similarly on smaller single-cell datasets. In ocrRBBR, samples do not need to originate from the same tissue or cell type. When samples are from the same cell type or tissue, ocrRBBR partitions them into more homogeneous groups, each associated with a distinct Boolean rule within the inferred rule set.