rem(method = "nn"). nn_control(engine = "torch") trains the same model and conditional-logistic loss as the built-in
pure-R engine using \pkg{torch} (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.