Add a model-independent parameter_plan() registration/restoration contract.
Keep the existing hspec parameter map, sharing rules and saved-fit format.
Add exp_kernel_plan() to validate fixed numeric kernel parameters and
baseline once for repeated deterministic step/wait calculations. Changing
components and arguments are still checked; plans retain no event state or RNG.
Add deterministic exp_kernel_step() and exp_kernel_wait() interfaces for
event-wise state updates and inversion of supplied residual targets. They
support external FRPP simulation without replaying growing path prefixes;
existing Hawkes and legacy FRPP event loops remain unchanged.
Move detailed FRPP examples to the frpp package documentation. Retain a short compatibility example and describe the planned functionality migration; existing residual-law functions and Hawkes residual diagnostics are unchanged.
Add experimental numeric exp_kernel_spec(), as_exp_kernel() and
exp_kernel_replay() interfaces for shared state calculations. Replay returns
kernel-only interval integrals and pre-event sums, with optional component
histories. Numeric marks, mapped kernels and signed states are supported;
row-wise equal decay rates are required. Existing event loops are unchanged.
... consistent count paths and intensity histories with
and without inference output, correcting likelihoods that depended on infer.stats::logLik() on hspec objects. Lazily load maxLik's methods when
saved maxLik fits are first printed, summarized or queried; keep their classes
and preserve the stats optimizer's independence from maxLik.hvol() explicitly.a, ell = 1 limit._R_CHECK_SUGGESTS_ONLY_=false and maxLik
exposed through a site library, alongside the existing --as-cran jobs.
Retain a separate hard-dependencies-only CI job that requires maxLik's absence.lambda_component0 = "auto"
(NULL is an alias). Report whether mean or zero intensity components were
selected. Mean initialization applies to supported stable constant linear
models, including mapped kernels; other models use zero and recommend an
explicit initial state. Components exclude the baseline intensity.hfit(optimizer = "stats"), using R's optim() or constrOptim() without
maxLik. Support fixed/shared parameters, derivatives, linear inequalities and
box bounds, with separate result methods and fitted-model reconstruction.
Make maxLik optional while retaining it as the default provider; install it
explicitly when using the default. Keep numerical score steps within linear
constraints and reject unsupported controls and equality constraints.constraint and constraints, preserving positional arguments.
Reject simultaneous non-NULL values and normalize either spelling for maxLik.fitted_hspec(), fit_initial_state() and fit_diagnostics() to restore
fitted specifications and inspect optimizer, engine and curvature choices.final_hessian = "stable", with scaled differences, analytic-score
differentiation where available and step-stability checks. Withhold Wald
covariance when curvature or constraint checks fail. Stable curvature is
optional for maxLik and the default for stats fits; it does not establish
finite-sample confidence-interval coverage.emhawkes.engine selects automatic, reference R or required C++ likelihoods.
Source installation now requires Rcpp and a C++ compiler.gradient = "analytic" and jacobian. Preserve shared/fixed parameters
and initial-state derivatives; reduce observed-baseline Jacobian storage.
Numerical optimizer differentiation remains the default.expected_tau() for widely separated time scales and a positive
probability of no further event. Correct mark handling in hvol() and reject
models outside the bivariate moment formula's scope.Fixed some bugs
Fixed some bugs, improved efficiency in this version and several features have been added.
The lambda0 argument name used in previous versions has been changed to lambda_component0 in this version. This is to clearly indicate the meaning of the argument and to avoid confusion.
The name of method vol is changed to hvol and this feature is currently experimental.
The Vignette file contains more examples and explanations.
Fixed some bugs, improved efficiency in this version and several features have been added.
Slot eta is introduced which represents the constant part of impact.
The concepts of rambda and rambda_component are introduced. They are closely related to the right-continuous version of the intensity process.
For inference of intensity and goodness of fit, infer_lambda and residual_process functions are implemented.
The method vol to measure the volatility is introduced and this feature is currently experimental.