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Changelog

Source: NEWS.md

DeCovarT 2.0.0

  • Release aligned with the GitHub v2.0.0 tag and the first CRAN submission, focused on numerical stability and optimiser cost in R/03_03_DeCovarT_estimate_ratios_frequentist.R (documented in vignette("softmax-alr-derivatives"), section Numerical speed-ups and solver safeguards). The CRAN tarball ships the use-cases and softmax/ALR vignettes; remaining articles stay on the pkgdown site.

  • Newton–Raphson evaluation budget. Removed an erroneous eval.max = 1 control passed to stats::nlminb() in deconvolute_ratios_Newton_Raphson(), which capped the entire run at a single objective evaluation and silently returned the equi-balanced start.

  • Cached Cholesky factorisation of \boldsymbol{\Sigma}(\boldsymbol{p}). New internal .sigma_p_factorisation() assembles the mixture covariance once per trial point, shares chol() / chol2inv() across the log-likelihood, gradient and Hessian, and uses a Cholesky-based log-determinant (verified against numDeriv).

  • Analytic gradient for L-BFGS-B. deconvolute_ratios_L_BFGS_B() now passes the unconstrained analytic score instead of finite differences, with finite-penalty guards when box-constrained line searches drive \boldsymbol{\Sigma}(\boldsymbol{p}) near-singular.

  • marqLevAlg Hessian sign under maximisation. When minimize = FALSE, marqLevAlg only negates fn / gr; the analytic Hessian must be negated by hand so the Commenges et al. RDM criterion can fire. Reported upstream as VivianePhilipps/marqLevAlgParallel#3.

  • Documentation. Expanded use-cases and feature-selection vignettes; CRAN-oriented DESCRIPTION (MIT licence, arXiv method reference doi:10.48550/arXiv.2309.09557).

DeCovarT 1.0.0

  • First stabilised release of DeCovarT (semver 1.0.0), marking the end of the exploratory 0.x series. The core ALR-based frequentist deconvolution API, analytic score equations, and simulation tooling are now treated as a stable public surface for downstream use and paper reproduction.

  • Analytic derivatives, numerically verified. The unconstrained and constrained log-likelihood gradients and Hessians (and the additive logistic Jacobian / Hessian) are derived in closed form and unit-tested against numDeriv finite differences. Earlier faulty numerical / finite-difference-only paths that could mislead Marquardt–Levenberg and related optimisers have been replaced by these verified analytic expressions (see vignette("softmax-alr-derivatives") and tests/testthat/test-03_03_DeCovarT.R).

  • Simulation framework beyond the bivariate toy. Synthetic first- and second-order moments now support arbitrary gene panels (G\gg 2) and multi-type mixtures: AutoGeneS-style mean signatures with a cosine dial, graph-constrained precisions (ER, hub/star, scale-free, SBM, small-world), and bulk convolution via simulate_bulk_mixture(). A hybrid multi-topology reference scenario (G=50, J=3) in scripts/generate_random_markov_network.R and vignette("synthetic-scenarios") stresses feature selection under EE / DE / differential-modality-like blocks (scDD / muscat-inspired taxonomy).

  • Feature-selection metrics (compute_average_overlap(), compute_average_jeffreys(), compute_glmnet_gene_scores()) and a shared check_true_theta() validator complete the end-to-end simulation → pre-screen → NSGA-II refinement loop documented in vignette("feature-selection").

DeCovarT 0.1.0

  • First official release of DeCovarT: bulk transcriptomic deconvolution that accounts for gene-gene covariance in purified reference populations.