NEWS

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vennLasso 0.2.0

This release fixes the problems that led to the removal of the package from CRAN and a number of defects in oglasso() and vennLasso(). Several of them change the fitted values that existing code obtains; those are marked (changes results).

CRAN issues

• The vignette is now an HTML vignette (rmarkdown::html_vignette) with pandoc citations, so building it needs no LaTeX installation. Its TikZ figures are included as images; their sources are in vignettes/figures-src in the package repository.
• CVXR is no longer a suggested package: the tests compare with objective values computed once with CVXR, stored in the tests; the script that computed them is inst/reference/make_refs.R.
• The vignette no longer loads natbib. Citations are processed by pandoc, and natbib's author-year check stopped the vignette from building on CRAN (archived 2023-09-21).
• Removed LazyData from DESCRIPTION (the package has no data directory).
• Native routines are registered and symbol search is disabled.
• Corrected the citation (Biometrics 74(2), 557-565, 2018) in inst/CITATION and the vignette, and replaced citEntry() by bibentry().

oglasso()

• Fits with an intercept (the default) for family = "gaussian" no longer crash R. This also fixes vennLasso(one.intercept = TRUE) and the adaptive lasso for nearly separable binomial data.
• The wide solvers (nvars >= 2500 and nobs <= 2 * nvars) now solve the correct linear system when a variable belongs to more than one group, for both the gaussian and the binomial family. Before, the gaussian solver could diverge and the binomial solver could return a non-optimal fit. (changes results)
• standardize = TRUE (the default) now standardizes x for the gaussian and binomial families, as documented; it was silently ignored. Coefficients are returned on the original scale. (changes results)
• Cox models: the survival times are no longer centered and scaled, no intercept is fit, the data no longer need to be sorted by time, and tied times are handled as in the Breslow partial likelihood. Before, the default settings returned coefficients that were wrong by a large factor. (changes results)
• The lambda sequence starts at the smallest value at which the penalized coefficients are guaranteed to be zero for the problem in which the intercept and unpenalized variables are fit. Before, it started too high when an intercept was fit, so many leading values gave the null model. (changes results)
• The path no longer stops silently once more than nobs coefficients are nonzero. A new argument dfmax limits the number of nonzero coefficients, with a warning when the path is stopped. (changes results)
• penalty.factor.fused no longer causes valid calls to fail (it is unused).
• group.weights may be given with one value per group also when an intercept or ungrouped variables are present.
• A group matrix is checked to have one row per variable, as documented (the check compared the number of columns).
• Designs with fewer than five columns no longer fail in the eigenvalue computation.
• The gaussian solvers no longer stop early when started from the previous solution on the lambda path: the restart state of the accelerated iterations is reset for each lambda (and for each Newton step of the binomial and Cox solvers). Before, a fit along a path could stop after a few iterations at a non-optimal point (objective up to 2.5% too high), whatever the tolerance. (changes results)
• Compatibility with Eigen 5: Eigen::Map<Eigen::SparseMatrix> replaces the removed Eigen::MappedSparseMatrix.
• The adaptive lasso no longer crashes for wide designs, and the Cox model no longer crashes for nvars >= 2500 (it uses the tall solver).
• A sparse x is converted to a dense matrix (the sparse solver did not exist and the call failed). The fused penalty (fused) is not implemented and now gives an informative error.

vennLasso() and related functions

• vennLasso() inherits the changes to oglasso() above. In particular (changes results): the lambda sequence now starts at the lambda.max of the problem with unpenalized subpopulation intercepts (for the help example, 0.1454 instead of 0.1465; with adaptive.lasso = TRUE, 0.0693 instead of 0.1465, where before the first 9 fits were the null model), so a fit selected by its position in the path (for example fit$beta[, , 21]) refers to a different lambda; paths are no longer cut short once more than nobs coefficients are nonzero; and fits along the path are now converged (see the warm-start fix above). The help examples now select fits by BIC or AIC instead of by position.

• The effective number of parameters, and so df, aic, bic, aicc and mbic, follow Section 3.3 of Huling et al. (2018): tr{(H + N lambda A)^(-1) H} over the active set, with A_j = sum over the groups g containing j of w_g / ||beta_g||. Before, the group weight was in the denominator, the unpenalized subpopulation intercepts counted (about) zero, and a diagonal shortcut that assumes x_j'x_j = N was used. The standard errors of the gaussian model use the same A. (changes results)

• Binomial standard errors and confidence intervals (compute.se, conf.int) treat the subpopulation intercepts as columns of the design: the refit no longer adds a global intercept, which had left one intercept with an NA standard error and the others with standard errors of contrasts. (changes results)

• Gaussian refitted coefficients (beta.refit, intercept.refit, lower.ci.refit, upper.ci.refit) are now returned; they were all zero. The refits are carried along the path when the selected set does not change. (changes results)

• Variables that are missing for every observation with a condition are removed only from the combinations that include that condition, and variables observed only with a condition only from the combinations that exclude it. Before, the combinations were matched in reversed order. (changes results when x has such missing values)

• A single stratifying condition (a one-column groups) no longer fails.

• The intercepts of the subpopulations are left unpenalized when some variable is constant within a subpopulation. Before, the penalty factors were indexed in the wrong order and some intercepts could be shrunk to zero. (changes results)

• cv.vennLasso() fits every fold on the lambda sequence of the full-data fit. Before, each fold used its own sequence, so the cross-validation error averaged fits at different values of lambda. (changes results)

• predict.vennLasso() uses the correct coefficients when newx does not contain every subpopulation of the fit, works when a subpopulation has a single new observation, and interpolates correctly when s has several values. (changes results)

• Degrees of freedom are now also computed for binomial models with compute.se = TRUE (they were NA).

• Documentation: standardize in vennLasso() scales each subpopulation-specific column over all observations; the length and order of penalty.factor; the returned df and criteria; the memory needed by the Cox solver.

• The plot() method for cv.vennLasso objects counts nonzero coefficients without the intercepts.

• Examples that were wrapped in \dontrun{} now use \donttest{}.

• Added a testthat test suite and an R-hub workflow (.github/workflows/rhub.yaml).