\dontrun{} with unwrapped examples or
\donttest{} per CRAN policy.Trehafod and glme.gev11() examples
in \donttest{} (> 5 sec on Debian).glme.gev() output names
changed for consistency:
lme → para.lmeglme → para.glmenllh.pref → nllh.glmecovinv → covinv.lmomma.gev() output names
changed for consistency:
zp.ma → qua.mazp.bma → qua.bmafin.se.ma → fixw.se.maadj.se.ma → ranw.se.manumk_ma and numk_bma →
run.numkpick_xi_ma and pick_xi_bma →
pick_xiremle1 → para.remle1 (in return and
internal use)remle2 → para.remle2 (in return and
internal use)::: 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()set.prior() now use
pk.beta.stnary() from glme.gev.Rglme.gev11() output
para.jkss renamed to para.lme for consistency.
Users accessing result$para.jkss should update their code
to use result$para.lme.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.glme.gev11() no longer
returns strup.sta in its output.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 6quagev.NS() function for calculating quantiles from
non-stationary GEV models
ma.gev() with new estimation options:
CD = TRUE: Coles-Dixon penalized MLE for shape
parameter regularizationremle = TRUE: Restricted MLE with mean/median
constraintsmle.CD, qua.CD,
remle1, remle2, qua.remle1,
qua.remle2quant in output for conveniencebma.se.between and
bma.se.withinmagev.ksensplot(): K sensitivity analysis to select
optimal number of submodelsmagev.qqplot(): 2x2 Q-Q diagnostic plot comparing MLE,
LME, surrogate, and REMLEmagev.rlplot(): Return level plot with 95% confidence
intervalsbangkok dataset: Annual maximum daily rainfall
from Bangkok, Thailandhaenam dataset: Annual maximum daily rainfall
from Haenam, South Koreanumq = 1) in
ma.gev()ma.gev()) for
high quantile estimation.
like, gLd,
med, cvt and variantsbma=TRUE) with
normal/beta priorsqua.ma) with standard
errorsismev, Rsolnp,
zoo.glme.gev()).glme.gev11())
where location (mu) and scale (sigma) parameters vary linearly with
time.
para.glme: Proposed GLME estimatespara.lme: L-moment based estimates for non-stationary
modelnsgev(): Simple interface for L-moment based
non-stationary estimationgado.prop_11(): Comprehensive estimation with multiple
methods"beta" (default),
"norm", "ms" (Martins-Stedinger),
"park", "cannon", "cd"
(Coles-Dixon), and "no" (no penalty).pen.choice or
direct parameters (p, c1, c2 for
beta; mu, std for normal penalty).streamflow,
PhliuAgromet, and Trehafod.
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