GLmom 2.0.1

Two corrections to the GLME objective and penalty layer, following the revised methodology paper (Shin et al., 2026).

Bug Fixes

• The constant term of the GLME objective now uses log(det(V))/2 (the correct multivariate-normal constant) instead of log(det(V)), in both glme.gev() and glme.gev11(). This term does not depend on the parameters, so all parameter estimates are unchanged; only the reported nllh.glme values shift by a constant.
• The fixed literature penalties (pen = "ms", "park", "cannon") are now evaluated on the shape-parameter interval (-0.5, 0.5), matching their definitions in the literature (e.g., the Martins-Stedinger geophysical prior), instead of (-1, 0.5). Estimates obtained with these three penalties change; the adaptive "beta"/"norm" penalties, "cd", and "no" are unaffected.

GLmom 2.0.0

This release integrates the revised GLME methodology of Shin et al. (2026 revised version of arXiv:2512.20385) for both the stationary GEV and the non-stationary GEV11 models. All functions and datasets of v1.3.1 remain available and callable, but several defaults, hyperparameter presets, and output fields changed, so numerical results differ from v1.3.1.

New Features

• New exported functions for the non-stationary GEV11 model:
  • lme.gev11() - pure L-moment estimation (renames gado.prop_11(); Shin et al. 2025b, JKSS).
  • strup.gev11() - weighted least squares estimation (Strupczewski & Kaczmarek 2001) with the modified final specification.
  • GN16.gev11() - quantile-based GN16 estimation.
  • ran.gev_all() - random number generation from stationary and non-stationary GEV models (gev, gev01, gev10, gev11, gev20).
• New exported penalty function pk.beta(), the unified data-adaptive beta penalty now shared by the stationary and non-stationary methods. pk.beta.stnary() is kept as an alias of the new implementation.
• The PhliuAgromet documentation now notes the significant increasing trend in the annual maxima (Mann-Kendall tau = 0.235, p = 0.033), which makes it the recommended example data for the non-stationary methods.
• Fitted objects are now classed ("glme", "glme11", "lme11", "magev") with print(), summary(), and plot() methods; plot() draws quantile-quantile diagnostics (on the standard Gumbel scale for the GEV11 fits). The objects remain plain lists, so all documented fields stay accessible; a data field storing the input series was added to support the diagnostics.
• glme.gev() and glme.gev11() gained arguments c0 (beta penalty support half-width), q (fixed beta shape), and show (verbose); glme.gev() also gained method, maxit, abstol for optim().

Breaking Changes

• The streamflow, Trehafod, and glanteifi datasets were removed. All three originate from the UK National River Flow Archive (NRFA), whose data terms and conditions do not permit making the data available for download or redistributing them to third parties, so they cannot be shipped in a CRAN package. Users can obtain the underlying series directly from the NRFA Peak Flow Dataset (<https://nrfa.ceh.ac.uk/peak-flow-dataset>). The remaining example datasets are PhliuAgromet, bangkok, and haenam; all examples, tests, and documentation now use these.
• Penalty hyperparameter presets were re-tuned to match Table 1 and Eq. (15) of the revised paper:
  • beta pen.choice 1-6: (p, c1, c2) = (6,3,1), (6,5,2), (6,7,3), (2,3,0.5), (2,5,1), (2,7,1.5) [was (6,10,5), (6,20,7), (6,30,9), (2,10,5), (2,20,7), (2,30,9)].
  • norm pen.choice 1-4: (mu, std) = (-0.5,0.25), (-0.5,0.15), (-0.6,0.25), (-0.6,0.15) [std was 0.2/0.1].
• Default arguments changed: glme.gev(ntry=5, pen.choice=1, c1=3, c2=1) (was ntry=10, pen.choice=NULL, c1=10, c2=5); glme.gev11(ntry=5, opt.choose="nllh", pen.choice=1, std=0.2, c1=5, c2=2) (was ntry=10, opt.choose="gof", pen.choice=NULL, std=0.3, c1=10, c2=5).
• glme.gev11() was restructured following the revised methodology: the penalized estimation now starts from the WLS pre-estimate of strup.gev11() and uses the fixed asymptotic covariance matrix of the sample L-moments (Eq. 26 of the paper, rescaled by 300/n) instead of the bootstrap covariance. This is substantially faster.
  • Output field para.lme is now returned only when pen="no" (use lme.gev11() to obtain the pure L-moment estimates otherwise).
  • Output fields strup.org, para.wls, para.gado, lme.sta are still returned for compatibility.
  • New output fields: convergence, pen_pen.choice, c0_c1_c2 (in addition to pen, p_q, c1_c2, mu_std).
  • Argument glme.pre is deprecated and ignored (warning); init.rob is still honored (passed to strup.gev11()).
• pk.beta.stnary() behavior changed (now an alias of pk.beta()): the adaptive support is (max(-1, xi-c0), min(0.3, xi+c0)) with default c0=0.35 (was 0.3), the q-adaptation triggers at xi <= -0.05 (was 0), values outside the support return 1e-100 (was 1), and for fixed q the default support is (-1, 0.5) (was (-0.5, 0.5)). Consequently the fixed literature penalties (“ms”, “park”, “cannon”) inside glme.gev() / glme.gev11() are now evaluated on (-1, 0.5), following the revised reference code.
• init.glme() is now the multi-model initializer (first argument data, new arguments model, pretheta); calls using the old named argument xdat= still work. The old behavior is available as init.gevmax().
• glme.gev() output: parameter vectors are now named (mu, sig, xi) and convergence, pen_pen.choice, c0_c1_c2 were added. The v1.x fields pen, p_q, c1_c2, covinv.lmom, lcovdet are still returned.

Deprecated

• gado.prop_11() is deprecated in favor of lme.gev11(); it still returns the v1.x output fields (para.prop, para.gado, para.wls, strup.org, lme.sta), now reassembled from lme.gev11(), GN16.gev11(), and strup.gev11().
• nsgev() is kept as a convenience wrapper around lme.gev11().

Internal Changes

• New internal functions: check.penalty(), penalty.fun() (unified penalty dispatcher), boot.cov(), fun.lme.gev11(), find_max_beta.pk(), trans.gum01() (vectorized).
• Removed internal functions (affects ::: users only): pk.beta.ns(), obj.lme.gev11(), gev11.GLD(), init.glme.gev11(), strup.glme.gev11(), and the old optim.glme.gev11() (replaced by opt.glme.gev11()).
• MAGEV is untouched in this release: set.prior() (BMA prior) now uses a frozen internal copy of the v1.x beta preference function, so ma.gev() results are identical to v1.3.1.
• Robustness fixes in the multi-start loops: a failed optim()/nleqslv() try no longer aborts the remaining tries.
• time.m.gev11(): fixed the checkmom= typo (now checklmom=FALSE).
• Console output now uses message() or is gated behind show=TRUE.

GLmom 1.3.1

Bug Fixes

• Replaced all \dontrun{} with unwrapped examples or \donttest{} per CRAN policy.
• Wrapped Trehafod and glme.gev11() examples in \donttest{} (> 5 sec on Debian).
• Fixed invalid URL for UK National River Flow Archive Peak Flow Dataset.
• Single-quoted acronyms and proper names in DESCRIPTION to avoid spell-check notes.

GLmom 1.3.0

Breaking Changes

• BREAKING: glme.gev() output names changed for consistency:
  • lme → para.lme
  • glme → para.glme
  • nllh.pref → nllh.glme
  • covinv → covinv.lmom
• BREAKING: ma.gev() output names changed for consistency:
  • zp.ma → qua.ma
  • zp.bma → qua.bma
  • fin.se.ma → fixw.se.ma
  • adj.se.ma → ranw.se.ma
  • numk_ma and numk_bma → run.numk
  • pick_xi_ma and pick_xi_bma → pick_xi
  • remle1 → para.remle1 (in return and internal use)
  • remle2 → para.remle2 (in return and internal use)

Internal Function Renames

• Renamed internal functions for consistency (these are not exported but may affect code using ::: accessor):
  • init.glme() → init.gevmax()
  • new_pf_norm() → pk.norm.stnary() (with backward-compatible alias)
  • gev.rl.delta_new() → gev.rl.delta()
  • lme.boots.new() → lme.boots()
  • cand.xi.new.paper() → cand.xi()
  • weight.com.new() → weight.com()
  • cov.interp.new() → cov.interp()
  • gev.profxi.mdfy.paper() → gev.profxi.mdfy()
  • comp.prof.ci.new() → comp.prof.ci()
  • gev1.CD() → mle.gev.CD()
  • gev.remle() → remle.gev()
  • ginit.max() → init.gevmax()
  • pargev.xifix.ma() → pargev.xifix()
• Penalty functions in set.prior() now use pk.beta.stnary() from glme.gev.R

GLmom 1.2.0

Breaking Changes

• BREAKING: glme.gev11() output para.jkss renamed to para.lme for consistency. Users accessing result$para.jkss should update their code to use result$para.lme.
• BREAKING: glme.gev11() and gado.prop_11() output strup.final renamed to para.wls. Users accessing result$strup.final should update their code to use result$para.wls.
• BREAKING: glme.gev11() no longer returns strup.sta in its output.

New Features

• Enhanced glme.gev11() with new parameters:
  • glme.pre = "wls": Pre-estimation method selection (“wls” or “gado”)
  • choose = "gof": Model selection criterion (“gof” for goodness-of-fit, “nllh” for negative log-likelihood)
  • pen.choice = 6: Default penalty hyperparameter choice changed from NULL to 6
• New quagev.NS() function for calculating quantiles from non-stationary GEV models
  • Supports GEV11, GEV10, GEV20, and stationary GEV00 models
  • Returns time-varying quantiles as vector or matrix
• Enhanced ma.gev() with new estimation options:
  • CD = TRUE: Coles-Dixon penalized MLE for shape parameter regularization
  • remle = TRUE: Restricted MLE with mean/median constraints
  • Returns mle.CD, qua.CD, remle1, remle2, qua.remle1, qua.remle2
  • Returns quant in output for convenience
  • BMA outputs now include bma.se.between and bma.se.within
• New diagnostic plotting functions for MAGEV:
  • magev.ksensplot(): K sensitivity analysis to select optimal number of submodels
  • magev.qqplot(): 2x2 Q-Q diagnostic plot comparing MLE, LME, surrogate, and REMLE
  • magev.rlplot(): Return level plot with 95% confidence intervals
• Added bangkok dataset: Annual maximum daily rainfall from Bangkok, Thailand
• Added haenam dataset: Annual maximum daily rainfall from Haenam, South Korea

Bug Fixes

• Improved handling of single quantile (numq = 1) in ma.gev()

GLmom 1.1.0

• Added Model Averaging GEV estimation (ma.gev()) for high quantile estimation.
  • Combines MLE and L-moment estimates through weighted model averaging
  • Multiple weighting schemes: like, gLd, med, cvt and variants
  • Optional Bayesian model averaging (bma=TRUE) with normal/beta priors
  • Returns model-averaged quantiles (qua.ma) with standard errors
• New dependencies: ismev, Rsolnp, zoo.
• Reference: Shin et al. (2026) SERA, 40(2), 47 (MAGEV method)

GLmom 1.0.0

• Initial CRAN release.
• Stationary GEV parameter estimation using generalized L-moments (glme.gev()).
• Non-stationary GEV11 model estimation (glme.gev11()) where location (mu) and scale (sigma) parameters vary linearly with time.
  • para.glme: Proposed GLME estimates
  • para.lme: L-moment based estimates for non-stationary model
• Compatibility functions for Shin et al. (2025, J. Korean Stat. Soc.):
  • nsgev(): Simple interface for L-moment based non-stationary estimation
  • gado.prop_11(): Comprehensive estimation with multiple methods
• Multiple penalty function options: "beta" (default), "norm", "ms" (Martins-Stedinger), "park", "cannon", "cd" (Coles-Dixon), and "no" (no penalty).
• Flexible hyperparameter specification via pen.choice or direct parameters (p, c1, c2 for beta; mu, std for normal penalty).
• Included datasets: streamflow, PhliuAgromet, and Trehafod.
• References:
  • Shin et al. (2025a) arXiv:2512.20385 (GLME method)
  • Shin et al. (2025b) J. Korean Stat. Soc. 54:947-970 (Non-stationary L-moment)