The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.
rem(method = "nn").
nn_control(engine = "torch") trains the same model and
conditional-logistic loss as the built-in pure-R engine using (libtorch
autograd + Adam), markedly faster and, with batch_strata,
scaling to large event logs (optionally on GPU). The fitted object is
interchangeable with the R engine. torch is a
Suggests dependency; the torch engine requires equal-sized
strata.nn_uncertainty() quantifies
uncertainty for a neural fit by a stratum bootstrap, returning
partial-dependence uncertainty bands and a concordance confidence
interval (with print() and plot() methods) —
the inferential counterpart the point-prediction nn backend
otherwise lacks.First CRAN release, under the name amorem. The
package was renamed from the working name amore, which
collided (case-insensitively) with the archived CRAN package
AMORE. This release consolidates the 0.9.0 development line
into the first stable, installable version: the unified
rem() front-end (the clogit, gam,
and nn backends, including the additive-spline
architecture), the Gillespie / tau-leap simulation engine, the
endogenous-statistics catalogue, and the martingale-residual
goodness-of-fit family. Relative to 0.9.0 the package was renamed to
amorem and the exported feature functions dropped their
compute_ prefix —
compute_endogenous_features() and
compute_hyperedge_features() became
endogenous_features() and
hyperedge_features(); the rest of the API is unchanged.
New neural backend and a small number of API refinements; the version intended for the first CRAN release.
nn_control(architecture = "additive_spline", batch_strata = )
fits per-covariate B-spline effects by (mini-batch) stochastic gradient
on the exact case-control partial likelihood — the construction of
Filippi-Mazzola & Wit (2024, JRSS-C, doi:10.1093/jrsssc/qlae023) — giving interpretable
additive smooth curves on the same objective as clogit,
with mini-batching for large event logs.rem(method = "nn") backend: a
multilayer perceptron scores every candidate in a case-control stratum
and is trained on the conditional-logistic partial likelihood (softmax
over each risk set) — a nonlinear, prediction-oriented counterpart of
clogit. Pure-R implementation (no extra dependencies),
configured via nn_control(); summary() reports
in-sample (and, with a validation split, held-out) concordance and
plot(type = "pdp") shows per-feature partial-dependence
curves.method = "gam" (was "degenerate"); the
smooth-term wrappers are tv() / nl() /
tvnl() (was tve() / nle() /
tvnle()); re() is unchanged.rem()’s case argument now defaults to
NULL and is taken from the formula’s left-hand side
(e.g. event ~ x) for the
clogit/nn backends.widen_case_control() auto-detects the 0/1 indicator
column (event or IS_OBSERVED) when
case is not given.widen_case_control() now carries the sender/receiver
identifiers of the case and its matched control into the output
(sender_ev/receiver_ev/
sender_nv/receiver_nv); the new
keep_ids argument controls this (default
TRUE). The dyads behind each pair are no longer lost, and
re() grouping terms can reach the actor levels (#92).rem(method = "gam") now detects long-format
case-control input (a event/IS_OBSERVED
indicator with control rows) and widens it with
widen_case_control() before fitting, emitting a message —
instead of silently misreading raw per-row values as event-minus-control
differences (#93).compute_endogenous_features() gains a
prior_log argument for warm-starting the network state from
events that precede the study window: its rows update the running state
but never appear in the output, separating warm-starting from the
non-event masking role of history_log (#94).cpp_supported_stats() is now exported.First release.
rem() unified fitter for preprocessed case-control
data, with a gam (case-1-control logistic via
mgcv::gam()) and a clogit backend.gam backend
(time-varying, non-linear, time-varying-non-linear) and an
re() grouping random effect; re() reproduces
the Intro-to-REM tutorial parameterization, and rem()
exposes a gam_method argument.simulate_relational_events() (Gillespie
and tau-leap), the endogenous-statistic feature engine, non-event
sampling, and the martingale-residual goodness-of-fit family.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.