High-Performance Open-Source Archive
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.
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.
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.
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