fim.likelihood_model no longer forwards the inner
... to the hess_loglik evaluator. DGP-only
kwargs (right-censoring time tau, masking probability
p, observation functor observe, etc.) flow to
rdata only, where they belong. The likelihood layer is
computed on the data alone.
Two motivations for the fix:
Theory: under standard masking condition C3, the masking probability is independent of the system parameters by definition, so the likelihood does not depend on it. The previous behavior leaked a parameter the likelihood is not supposed to see.
R partial-matching: a kwarg named p
would silently partial- match the formal par in any
hess_loglik method, corrupting the parameter vector at the
call site. This made the fim() default fail
deterministically for any model whose hess_loglik had
par as a formal and any DGP kwarg starting with
p.
Behavioral note: the docstring already documented this contract (“Additional arguments passed to rdata”); the implementation now matches the documentation.
likelihood_contr_model (R6 contribution-based
model builder). This functionality has been extracted to the new
likelihood.contr package.likelihood_name() (named distribution wrapper).
Use likelihood.contr::contr_name() instead.weibull_uncensored /
likelihood_exact_weibull (Weibull example model). Use
likelihood.contr for Weibull likelihood contributions.observed_info() generic (was deprecated in
v0.10.0). Use -hess_loglik(model)(df, par) or
observed_fim() on MLE results.R6 from Imports (no longer needed).mvtnorm moved to Suggests (only needed for multivariate
sampler()).dplyr and tibble from
Suggests.loglik, score,
hess_loglik, fim, fit, Fisherian
inference, lrt, fisher_mle,
fisher_boot) is unchanged.exponential_lifetime remains as the reference
implementation.likelihood.contr package.observed_info() is deprecated; use
-hess_loglik(model)(df, par) or observed_fim()
on MLE resultslikelihood_exact_weibull() is deprecated in favor of
weibull_uncensored()loglik.likelihood_name_model() now error instead of warning
(silently returning 0 produced incorrect MLEs during optimization)$loglik(),
$score(), $hess_loglik() from
likelihood_contr_model; use the S3 generics
loglik(model), score(model),
hess_loglik(model) insteadmvtnorm moved from Imports to Suggests (only needed for
multivariate sampler())fim.likelihood_model() Monte Carlo estimator:
replaced biased outer-product-of-scores with negative expected Hessian
approach, which is unbiased at any parameter valuefim.likelihood_model() not forwarding
... to hess_loglik, and added NaN/Inf guard to
prevent silent corruption of FIM estimates... not forwarded in
likelihood_contr_model numerical fallback for
score and hess_loglikparent.frame() in
likelihood_contr_model$new() capturing R6 internals instead
of the caller’s environmentsampler.likelihood_model() ...
scoping bug in bootstrap statistic functionweibull_uncensored: loglik returns penalty,
score/hess_loglik return zeros for invalid
parameters (k <= 0 or l <= 0)loglik.likelihood_name_model(): errors with a clear message
when ob_col is missing from the data framerdata.weibull_uncensored() method for generating
Weibull data from the model’s DGPbias.fisher_mle() now supports optional Monte Carlo
bias estimation when theta and model are
provided; returns zeros (asymptotic result) otherwisemse.fisher_mle() now forwards model and
n_sim to bias() for MC-based MSElikelihood_contr_model$new() gains envir
parameter controlling where loglik.<type>,
score.<type>, and
hess_loglik.<type> are looked upfim.likelihood_model() now adds parameter names to the
output matrix when theta is named, and includes
tryCatch with informative warnings on partial failuresevidence() is now an S3 generic, extensible by model
classeslrt() returns a proper lrt_result class
with a print methodloglik.likelihood_model and
rdata.likelihood_model default methods provide clear error
messages when a model class doesn’t implement the required concept
methodslikelihood_name()
via ob_col_upper parameterlikelihood_name() constructor:
checks that distribution functions existprepare_args_list() with
clear error messagessupport(),
relative_likelihood(), likelihood_interval(),
profile_loglik(), evidence()exponential_lifetime model with closed-form MLE and
right-censoring supportfisher_mle and fisher_boot result
objects with full S3 method suiterowSums error in
likelihood_contr_model score for single-parameter
modelsprint.likelihood_model crash when
ob_col is NULL (e.g., for contr models)algebraic.mle:
params(), nparams(),
observed_fim(), obs(), mse()fisher_mle objects provide a unified
interface across the likelihood ecosystemalgebraic-mle-integration vignette demonstrating
the three-package ecosystem (likelihood.model +
algebraic.mle + algebraic.dist)likelihood-name-model vignette:
left-censoring, interval censoring, mixed types, Fisherian inference,
multiple distributionslikelihood-contributions vignette: Case 2
inference, single-parameter example, best practices, interpretive prose
for computed resultsgetting-started vignette with comprehensive
introductionexponential-lifetime vignettelikelihood_name(),
likelihood_contr_model, and
weibull_uncensored
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