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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.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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