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)