Changes in version 2.0.0                        

New features

  - estimation() now supports parallel execution across the
    regularisation path via the cores argument. Setting cores > 1
    distributes each lambda value across worker processes using doSNOW,
    yielding 2–3× speed-ups on medium-to-large problems (p ≥ 100 or
    nlambda ≥ 15). Sequential execution (cores = 1) remains the default.

  - data_generator() now accepts asymmetric sample sizes: n_Y can be
    specified independently of n_X, allowing the two samples to have
    different numbers of observations.

  - All differential network matrices returned by estimation() are now
    stored as sparse matrices (dgCMatrix class via the Matrix package),
    reducing memory usage for high-dimensional problems.

New vignettes

  - Parallelisation — covers how to switch between sequential and
    parallel modes, documents benchmark results across five problem
    sizes, and provides guidance on choosing the number of cores.

  - Estimation — step-by-step walkthrough of data generation and the
    estimation workflow.

  - Data Generator — documents the data_generator() function and its
    outputs in detail.

  - Differential Networks — end-to-end tutorial on generating data and
    estimating a differential network.

Bug fixes and improvements

  - Fixed summary.estimation() S3 method signature to match the summary
    generic (object, ...), resolving an R CMD check warning.

  - Fixed partial argument matching ambiguity in data_generator() where
    n matched both n_X and n_Y.

  - Added missing @importFrom foreach foreach %dopar% directive,
    resolving undefined global variable notes in R CMD check.

  - Added Matrix and foreach to Imports and doParallel to Suggests in
    DESCRIPTION.

                 Changes in version 1.0.1 (2021-11-15)                  

  - Initial CRAN release.