CoxDeviance$information_derivative(linear_predictor, direction, sample_weight): the derivative of the information in a direction of the
linear predictor, d/dt I(eta + t direction), as a symmetric matrix-free
operator (CoxInformationDerivative, with %*%, t(), dim() and
as.matrix()), O(n) per product: for the derivatives of log
determinants, as in REML.inst/include has coxdev.h and its
implementation coxdev_impl.h, all inline, so another package can compile
against it with LinkingTo: coxdev and #include <coxdev.h>.LinkingTo: coxdev). The comparisons with survival remain.glmnet::Cindex; strata (a model per stratum, the block
diagonal information, labels, strata of unequal sizes against coxph);
zero weights as dropped observations, for every kind of ties; the
caching of results; the problematic case of tests/test_bad.py; the
moments of the "fisher" draws; edge cases (no failures, one failure,
everyone failing at once, entries at failure times, strata without
failures, extreme linear predictors) and finite differences of the
derivatives under every option; and the values of the Python package,
draws of sample_score() included, for a fixed data set.The first release of the R package. It runs the C++ library of the Python
package coxdev 0.3.0 and has the same interface: CoxDeviance$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.