NEWS.md
In SUMO: Generating Multi-Omics Datasets for Testing and Benchmarking

✅ Support for simulating >2 omics datasets: A core feature upgrade enabling generation of multi-omics datasets with three or more layers, such as transcriptomics, proteomics, and metabolomics, in a single simulation run.
🔄 Function renamed for clarity:
Previous: OmixCraftHD → Now: simulate_twoOmicsData (for 2-omics simulation).
New: OmixCraftMultiHD (alias simulateMultOmics) introduced to simulate an arbitrary number of omics using the same generative framework.
💡 Non-overlapping latent factors with flexible per-omic and per-factor signal regions are now seamlessly handled for multiple datasets.

Fully modular generative factor model allows:
Signal assignment via latent factors per omic.
num.factor = "single" or "multiple" control.
factor_structure for each factor: shared, unique, mixed, or partial.
Sample blocks and feature blocks are now simulated with sequential, non-overlapping indices, respecting biological plausibility and avoiding signal bleed.

Robust input checks ensure valid parameterization for complex simulation designs.
All alpha (sample score) vectors and beta (feature weight) vectors now support sparse signal encoding and customized signal-to-noise ratios (snr).
Each omics layer can be assigned its own mean and standard deviation for signal blocks, increasing realism.

Improved documentation and usage examples for:
simulateMultiOmics()
Factor assignment logic and feature-sample block allocation
Example usage includes:

r sim_object <- simulateMultiOmics( vector_features = c(3000, 2500, 2000), n_samples = 100, n_factors = 3, snr = 3, signal.samples = c(5, 1), signal.features = list(c(3, 0.3), c(2.5, 0.25), c(2, 0.2)), factor_structure = "mixed", num.factor = "multiple", seed = 123 )