pred() using point estimate instead of random
draw for first Monte Carlo sample, breaking the parameter-uncertainty
integrationconfint() ignoring the parm
argument. Now honors parameter subsetting by name or index, matching the
stats::confint contract.confint() producing NaN without informative error
when variance-covariance has negative diagonal (bad Hessian). Now gives
actionable error message.combine() giving cryptic “non-conformable arrays”
error when MLEs have different parameter dimensions. Now validates
upfront.sampler.mle_fit_boot() unreachable code branch
(length(x$t) == 1 is never true for boot objects; corrected
to ncol(x$t) == 1L)stud case in
confint.mle_fit_boot() switchpoint() references in roxygen and vignette
prose (now params())bias.mle_normal vignette example missing
... in method signatureS3 class renamed from "mle" to
"mle_fit" to resolve name collision with
stats4::mle (S4 class). Subclasses follow:
"mle_fit_numerical", "mle_fit_boot",
"mle_fit_rmap". Constructor function names
(mle(), mle_numerical(),
mle_boot()) are unchanged.
Removed aic(), bic(), and
loglik_val() generics. Use standard R generics
AIC(), BIC(), and logLik()
instead. logLik() returns a proper "logLik"
object with df and nobs attributes, so
AIC() and BIC() work automatically via
stats::AIC.default.
Added coef.mle_fit() method delegating to
params() for standard R compatibility.
Removed mle_weighted() constructor and
"mle_weighted" class. Use combine() instead —
same inverse-variance weighting with better error handling and a
variadic API.
1:n patterns with seq_len()
throughout to avoid edge-case bugspred()"_PACKAGE"
sentinel)fixing/ directory (removed dead experimental
code)n parameter in
rmap()joint() composes independent MLEs with disjoint
parameter sets into a joint MLE with block-diagonal covariance
structurecombine() optimally weights independent MLEs for the
same parameter via inverse-variance (Fisher information) weightingas_dist() converts MLE objects to their asymptotic
normal distributions, bridging to the algebraic.dist
distribution algebradensity(),
cdf(), inv_cdf(), sup(),
dim(), mean(), conditional()rmap() where
c() merged parameter names with transformation output
namescoef() S3 method for base R compatibility
(delegates to params())logLik() S3 method returning proper
logLik object with df and nobs
attributes, enabling automatic AIC() and BIC()
support from base Rrmap() to accept numeric n parameter
(previously required integer)mle) with methods for:
params,
nparams)vcov, se)confint)AIC, logLik)bias, mse)observed_fim)sampler)pred)expectation)marginal)mle_numerical) for
optim() resultsmle_boot) for small samplesrmap)mle_weighted)