Changes in version 2025-09-01                      

Major release. Architecture rebuilt for speed, stability, and cleaner
workflows. Recommended upgrade.

Highlights

  - 3–5× faster on typical problems via rewriting core kernels in
    RcppArmadillo.
  - Adaptive lambda search that scales to large problems with fewer
    evaluations.
  - Element-wise penalty control for both coefficients and network
    edges.

Algorithm & Features

  - Consistent search strategy in missoNet() and cv.missoNet().
  - Fine-grained penalty customization through beta.pen.factor and
    theta.pen.factor, enabling exclusion of predictors or structured
    sparsity patterns.
  - relax.net option for de-biased network estimation: refits the active
    edge set without L1 penalties to reduce bias in the precision
    matrix.
  - Support multiple graphical lasso backends (e.g., glassoFast
    (default) / glasso) with automatic selection and reliable fallbacks.
  - Smarter defaults and data-aware initialization tuned to
    dimensionality, missingness, and conditioning.
  - Full parallelization for cross-validation and grid evaluation.

Performance & Robustness

  - Vectorized algebra and memory reuse; internal benchmarks show 3–5×
    speedups
  - The warm-start strategy has been rewritten to enhance stability.
  - Tighter line search and stopping in FISTA; fewer oscillations.
  - Epsilon‑safe numerics; strict finite‑value checks.

Visualization & UX

  - Upgraded S3 plot() for publication-quality output.
  - Clearer progress and diagnostics (stages, backend, convergence).

Documentation

  - New vignettes: introduction, advanced features, case studies.
  - Cross-references and examples aligned across missoNet() and
    cv.missoNet(); arguments and return values documented consistently.

Bug Fixes

  - Fixed Inf propagation in preprocessing.
  - Fixed cross-package reference linking.
  - Removed unused external data to reduce package size.
  - Miscellaneous bugs.

Compatibility: Minor API changes; refined defaults. See ?missoNet,
?cv.missoNet.

                     Changes in version 2023-07-18                      

  - More reliable convergence in difficult edge cases; fewer premature
    stops.
  - Streamlined initialization with better defaults.
  - Removed glmnet dependency.
  - Better diagnostics and messages.

                     Changes in version 2023-05-10                      

  - Final evaluations are now based on warm starts.
  - Tuned default parameters based on empirical testing across
    representative datasets.
  - Dropped C++11 requirement.

                     Changes in version 2022-10-06                      

  - Initial CRAN release.