coxdev 0.4.0

• CoxDeviance$information_derivative(linear_predictor, direction, sample_weight): the derivative of the information in a direction of the linear predictor, d/dt I(eta + t direction), as a symmetric matrix-free operator (CoxInformationDerivative, with %*%, t(), dim() and as.matrix()), O(n) per product: for the derivatives of log determinants, as in REML.
• The C++ library is header-only: inst/include has coxdev.h and its implementation coxdev_impl.h, all inline, so another package can compile against it with LinkingTo: coxdev and #include <coxdev.h>.
• glmnet is no longer in Suggests: the comparisons with it (and the vignettes’ use of it) are removed, as glmnet will depend on coxdev (LinkingTo: coxdev). The comparisons with survival remain.
• More tests, mirroring the Python package’s: the residuals, the baseline hazard, the expected events and the risk-set variances against direct computations, with zero weights; the C-index against the count of its definition and glmnet::Cindex; strata (a model per stratum, the block diagonal information, labels, strata of unequal sizes against coxph); zero weights as dropped observations, for every kind of ties; the caching of results; the problematic case of tests/test_bad.py; the moments of the "fisher" draws; edge cases (no failures, one failure, everyone failing at once, entries at failure times, strata without failures, extreme linear predictors) and finite differences of the derivatives under every option; and the values of the Python package, draws of sample_score() included, for a fixed data set.

coxdev 0.3.0

The first release of the R package. It runs the C++ library of the Python package coxdev 0.3.0 and has the same interface: CoxDeviance$new(...) in place of CoxDeviance(...).

• CoxDeviance$new() takes the response as vectors or as a Surv object, with start times, strata (any labels), sample weights, and Breslow’s or Efron’s ties.
• compute(): the deviance, its gradient and the diagonal of its Hessian in the linear predictor; information(): the information as a matrix-free operator.
• sample_score() and wild_score(): draws of the score (multinomial, Bernoulli, Fisher’s noncentral hypergeometric, wild).
• score_residuals(), schoenfeld_residuals() and risk_set_variance(), a column at a time for a dense or sparse design.
• baseline_hazard(), expected_events() and survival_at_exit().
• c_index(): Harrell’s concordance index, in O(n log n).

All agree with the survival package, and the vignettes match the Python notebooks.