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Changes since the CRAN release 1.0.0 (versions 1.0.1 to 1.0.3 were not released). Fitted coefficients and standard errors are unchanged except in the cases listed under “Behaviour changes”; the fitting engine is about 2.5 times faster (N = 10,000, compact storage, one thread: 46 s in 1.0.0, 18 s now, identical estimates to about 1e-16, same memory).
btd_calibration(by = "level") columns are
renamed. obs_win_i and pred_win_i are
now obs_win_higher and pred_win_higher; code
that refers to the old names must be updated. See the calibration item
below for why the quantity changed.lvl_tte(same_time = "uninformative")
restores the 1.0.0 behaviour.horizon are
now events (see below). Results with horizon = Inf (the
default) are unchanged.fit$n_grad_evals is now always 0 and
fit$n_full_evals equals one initial pass plus one pass per
trial (see “Faster fitting”).btd_control(threads = 1e5) or
options(hpsreg.threads = 1e5)) crashed R with a C stack
overflow. Requests are now capped at 32 (the number of chunks the pairs
are split into) and at the number of processors; a non-numeric request
is an error.btd_calibration(by = "level") compared the
share of pairs won by the first-listed subject with its
prediction. Row order is arbitrary, so that share depended on how the
data were sorted and not on the model (a pure-noise model gave the same
“observed” column). Each pair is now oriented so that the higher-scored
subject comes first; the columns are obs_win_higher and
pred_win_higher (renamed from obs_win_i and
pred_win_i), and the plot marks 0.5 (no predictive
value).data. score_pairs() stores a
fingerprint of the outcome data, and btd(pairs = ) stops if
data has different outcome values or a different row order.
Previously a reordered data frame was accepted silently and gave wrong
estimates.btd() warns when the fitted latent scores imply win
probabilities indistinguishable from 1 (log-odds range above 60 for a
converged fit, or above 40 alongside a convergence failure), and returns
the range as latent_range.status column, a pair in which one subject has the event
and the other is censored at the same time is now scored with the usual
convention: the censored subject outlived the event. Previously such
pairs were treated as uninformative and passed to the next level.
lvl_tte(same_time = "uninformative") restores the old
behaviour. This changes results for data with coincident event and
censoring times.horizon, an event exactly at the horizon was tied with
being event-free. It is now an event, consistent with
hps_cox_grfs() (which counts events with time <=
horizon). Subjects followed beyond the horizon, or censored exactly at
it, are event-free through the horizon and beat every event at or before
it, whatever the tie margin. Results with horizon = Inf
(the default) are unchanged.lvl_binary() warns when the favourable value never
occurs, and lvl_ordinal() warns that order is
ignored for a numeric column.summary() notes that the z test on the
delta row is of delta = 1, not of whether ties occur.sum_{i<j} f_ij (x_i - x_j)(x_i - x_j)' = sum_i D_i x_i x_i' - sum_i x_i m_i',
where D_i and m_i are per-subject scalar and
vector sums. This reduces the per-pair cost from O(p^2) to O(p), with
one O(N p^2) reassembly at the end. Covariate columns are centered
before accumulation and the Hessian block is symmetrised, so the
identity does not lose precision when covariates have a large mean.fit$n_grad_evals is
therefore 0 and fit$n_full_evals is
1 + sum(fit$trials_per_iter).AIC() and
BIC() from the pairwise likelihood are not valid
model-selection criteria.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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