Changes in version 1.0.0 (2026-06-29)                  

  - torch training engine for 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.

                        Changes in version 0.9.0                        

New neural backend and a small number of API refinements; the version
intended for the first CRAN release.

  - STREAM-style additive splines: 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.
  - New 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.
  - API: the degenerate-logistic backend is now 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.

                        Changes in version 0.1.0                        

First release.

  - rem() unified fitter for preprocessed case-control data, with a gam
    (case-1-control logistic via mgcv::gam()) and a clogit backend.
  - Smooth-term formula wrappers for the 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.
  - Simulation via simulate_relational_events() (Gillespie and
    tau-leap), the endogenous-statistic feature engine, non-event
    sampling, and the martingale-residual goodness-of-fit family.