R 패키지 메타데이터와 수집 신호를 모아 봅니다.
첫 화면에서 판단해야 할 수집 신호를 먼저 배치합니다.
DESCRIPTION에서 감지한 backend 관련 package입니다.
기본 메타데이터를 작은 카드와 토큰으로 압축합니다.
RcppRcppArmadillo| Package | Type | Spec |
|---|---|---|
| abind CRAN · 1.1.3 · 2026-05-30 | Imports | abind |
| doParallel CRAN · 1.1.3 · 2026-05-30 | Imports | doParallel |
| EnvStats CRAN · 1.1.3 · 2026-05-30 | Imports | EnvStats |
| fGarch CRAN · 1.1.3 · 2026-05-30 | Imports | fGarch |
| foreach CRAN · 1.1.3 · 2026-05-30 | Imports | foreach |
| lmomco CRAN · 1.1.3 · 2026-05-30 | Imports | lmomco |
| MASS CRAN · 1.1.3 · 2026-05-30 | Imports | MASS |
| methods CRAN · 1.1.3 · 2026-05-30 | Imports | methods |
| mvtnorm CRAN · 1.1.3 · 2026-05-30 | Imports | mvtnorm |
| nleqslv CRAN · 1.1.3 · 2026-05-30 | Imports | nleqslv |
| parallel CRAN · 1.1.3 · 2026-05-30 | Imports | parallel |
| Rcpp CRAN · 1.1.3 · 2026-05-30 | Imports | Rcpp (>= 1.0.11) |
| Renext CRAN · 1.1.3 · 2026-05-30 | Imports | Renext |
| stats CRAN · 1.1.3 · 2026-05-30 | Imports | stats |
| Rcpp CRAN · 1.1.3 · 2026-05-30 | LinkingTo | Rcpp |
| RcppArmadillo CRAN · 1.1.3 · 2026-05-30 | LinkingTo | RcppArmadillo |
| 검색 결과가 없습니다. | ||
| Package | Type | Spec |
|---|---|---|
| 표시할 dependency edge가 없습니다. | ||
| 검색 결과가 없습니다. | ||
README code{white-space: pre-wrap;} span.smallcaps{font-variant: small-caps;} span.underline{text-decoration: underline;} div.column{display: inline-block; vertical-align: top; width: 50%;} div.hanging-indent{margin-left: 1.5em; text-indent: -1.5em;} ul.task-list{list-style: none;} GWEX Multi-Site Stochastic Models for Daily Precipitation and TemperatureHelp for package GWEX const macros = { "\\R": "\\textsf{R}", "\\mbox": "\\text", "\\code": "\\texttt"}; function processMathHTML() { var l = document.getElementsByClassName('reqn'); for (let e of l) { katex.render(e.textContent, e, { throwOnError: false, macros }); } return; } Package {GWEX} Contents EGPD.GI.fPWM EGPD.GI.fit.PWM Gwex-class GwexFit-class GwexObs GwexObs-class GwexSim-class PWM.EGPD.GI QtransMat2Array agg.matrix autocor.emp.int cor.emp.int cor.emp.occ cor.obs.occ dailyPrecipGWEX dailyTemperGWEX disag.3D.to.1D dist.functions.EGPD.GI dry.day.frequency find.autocor find.omega find.zeta fit.GWex.prec fit.MAR1.amount fit.copula.amount fit.margin.cdf fitGwexModel functions.EGPD.GI get.M0 get.df.Student get.emp.cdf.matrix get.list.month get.list.season get.listOption get.mat.omega get.period.fitting.month get.vec.autocor getGwexFitPrec infer.autocor.amount infer.dep.amount infer.mat.omega joint.proba.occ lagTransProbaMatrix lagTransProbaVector mask.GWex.Yt modify.cor.matrix month2season print,Gwex-method show,Gwex-method sim.GWex.Yt sim.GWex.Yt.Pr sim.GWex.Yt.Pr.get.param sim.GWex.occ sim.GWex.prec.1it sim.Zt.MAR sim.Zt.Spatial simGwexModel simPrecipOcc unif.to.prec wet.day.frequency Type: Package Date: 2024-02-02 License: GPL-3 Title: Multi-Site Stochastic Models for Daily Precipitation and Temperature Version: 1.1.3 Author: Guillaume Evin [aut, cre] Maintainer: Guillaume Evin <guillaume.evin@inrae.fr> Imports: Rcpp (≥ 1.0.11), EnvStats, MASS, mvtnorm, nleqslv, fGarch, parallel, abind, foreach, doParallel, Renext, lmomco, methods, stats LinkingTo: Rcpp, RcppArmadillo Description: Application of multi-site models for daily precipitation and temperature data. This package is designed for an application to 105 precipitation and 26 temperature gauges located in Switzerland. It applies fitting procedures and provides weather generators described in the following references: - Evin, G., A.-C. Favre, and B. Hingray. (2018) < doi:10.5194/hess-22-655-2018 >. - Evin, G., A.-C. Favre, and B. Hingray. (2018) < doi:10.1007/s00704-018-2404-x >. Depends: R (≥ 2.10) Encoding: UTF-8 LazyData: true RoxygenNote: 7.2.3 NeedsCompilation: yes Packaged: 2024-02-02 08:47:58 UTC; eving Repository: CRAN Date/Publication: 2024-02-02 09:00:02 UTC EGPD.GI.fPWM Description Parameter estimation of the unified EGPD distribution with the PWM method. Set of equations which have to be equal to zero Usage EGPD.GI.fPWM(par, pwm, xi) Arguments par vector of parameters kappa,sig,xi pwm set of probability weighted moments of order 0, 1 and 2 xi shape parameter Value differences between expected and target weighted moments Author(s) Guillaume Evin EGPD.GI.fit.PWM Description Parameter estimation of the unified EGPD distribution with the PWM method. Numerical solver of the system of nonlinear equations Usage EGPD.GI.fit.PWM(x, xi = 0.05) Arguments x vector of parameters kappa,sig xi shape parameter Value estimated parameters kappa, sig, xi Author(s) Guillaume Evin Class Gwex Description Defines a generic Gwex object. GWex objects contain two slots: - the version ('vX.X.X') - the type of variable ('Prec' or 'Temp') Author(s) Guillaume Evin Class GwexFit Description Defines a GwexFit object which is a Gwex object containing 'fit', a list containing the fitted parameters, and 'p', the number of stations. See fitGwexModel for some examples. Author(s) Guillaume Evin Constructor Description Constructor of class [ GwexObs ] Usage GwexObs(variable, date, obs) Arguments variable 'Prec' or 'Temp' date vector of class 'Date' obs matrix nTime x nStations of observations Value An object of class [ GwexObs ] Examples # Format dates corresponding to daily observations of precipitation and temperature vecDates = seq(from=as.Date("01/01/2005",format="%d/%m/%Y"), to=as.Date("31/12/2014",format="%d/%m/%Y"),by='day') # build GwexObs object with precipitation data myObsPrec = GwexObs(variable='Prec',date=vecDates,obs=dailyPrecipGWEX) # print GwexObs object myObsPrec # build GwexObs object with temperature data myObsTemp = GwexObs(variable='Temp',date=vecDates,obs=dailyTemperGWEX) # print GwexObs object myObsTemp Class GwexObs Description Defines a GwexObs object which is a Gwex object containing dates and a matrix of observations. Author(s) Guillaume Evin Examples # Format dates corresponding to daily observations of precipitation and temperature vecDates = seq(from=as.Date("01/01/2005",format="%d/%m/%Y"), to=as.Date("31/12/2014",format="%d/%m/%Y"),by='day') # build GwexObs object with precipitation data myObsPrec = GwexObs(variable='Prec',date=vecDates,obs=dailyPrecipGWEX) # print GwexObs object myObsPrec # build GwexObs object with temperature data myObsTemp = GwexObs(variable='Temp',date=vecDates,obs=dailyTemperGWEX) # print GwexObs object myObsTemp Defines a GwexSim object which is a Gwex object containing 'sim', an array containing the simulations, and 'dates', a vector of dates. See simGwexModel for some examples. Description Defines a GwexSim object which is a Gwex object containing 'sim', an array containing the simulations, and 'dates', a vector of dates. See simGwexModel for some examples. Author(s) Guillaume Evin EGPD.GI.mu0, EGPD.GI.mu1, EGPD.GI.mu2 Description Probability Weighted Moments of order 0, 1 and 2 of the unified EGPD distribution Usage EGPD.GI.mu0(kappa, sig, xi) EGPD.GI.mu1(kappa, sig, xi) EGPD.GI.mu2(kappa, sig, xi) Arguments kappa transformation parameter greater than 0 sig Scale parameter xi Shape parameter Value Probability Weighted Moments Author(s) Guillaume Evin QtransMat2Array Description reshape Qtrans.mat to an array Usage QtransMat2Array(n, p, Qtrans.mat) Arguments n matrix of precipitation p number of stations Qtrans.mat transition probabilities, 2 x ncomb matrix Value array array of transition probabilities with dimension n x p x n.comb Author(s) Guillaume Evin agg.matrix Description Simple accumulation of a matrix of precipitation Usage agg.matrix(mat, k, average = F) Arguments mat matrix nDates x nStations to be aggregated k number of days for the accumulation average logical: should we average over the different periods (default=F) Value aggregated matrix Author(s) Guillaume Evin autocor.emp.int Description Finds empirical autocorrelations (lag-1) between intensities corresponding to a degree of autocorrelation of an AR(1) process Usage autocor.emp.int(rho, nChainFit, Xt, parMargin, typeMargin) Arguments rho autocorrelation of the AR(1) process nChainFit number of simulated variates Xt simulated occurrences, nChainFit x 2 matrix parMargin parameters of the margins 2 x 3 typeMargin type of marginal distribution: 'EGPD' or 'mixExp' Value scalar correlation between simulated intensities Author(s) Guillaume Evin cor.emp.int Description Finds observed correlations between intensities corresponding to a degree of correlation of Gaussian multivariate random numbers Usage cor.emp.int(zeta, nChainFit, Xt, parMargin, typeMargin) Arguments zeta correlation of Gaussian multivariates nChainFit number of simulated variates Xt simulated occurrences, n x 2 matrix parMargin parameters of the margins 2 x 3 typeMargin type of marginal distribution: 'EGPD' or 'mixExp' Value scalar correlation between simulated intensities Author(s) Guillaume Evin cor.emp.occ Description Finds observed correlations between occurrences corresponding to a degree of correlation of Gaussian multivariate random numbers Usage cor.emp.occ(w, Qtrans.mat, mat.comb, nLag, nChainFit, myseed = 1) Arguments w correlation of Gaussian multivariates Qtrans.mat transition probabilities, 2 x ncomb matrix mat.comb matrix of logical: ncomb x nlag nLag order of the Markov chain nChainFit number of simulated variates myseed seed of random variates Value scalar correlation between occurrences Author(s) Guillaume Evin cor.obs.occ Description provide observed correlations between occurrences for all pairs of stations see Mhanna et al. (2012) Usage cor.obs.occ(pi00, pi0, pi1) Arguments pi00 joint probability of having dParameter estimation of the unified EGPD distribution with the PWM method. Set of equations which have to be equal to zero
EGPD.GI.fPWM(par, pwm, xi)Parameter estimation of the unified EGPD distribution with the PWM method. Numerical solver of the system of nonlinear equations
EGPD.GI.fit.PWM(x, xi = 0.05)Defines a generic 4classGwex object. GWex objects contain two slots: - the version ('vX.X.X') - the type of variable ('Prec' or 'Temp')
Defines a 4classGwexFit object which is a 4classGwex object containing 'fit', a list containing the fitted parameters, and 'p', the number of stations. See [GWEX]fitGwexModel for some examples.
Constructor of class [4classGwexObs]
GwexObs(variable, date, obs)# Format dates corresponding to daily observations of precipitation and temperature vecDates = seq(from=as.Date("01/01/2005",format="%d/%m/%Y"), to=as.Date("31/12/2014",format="%d/%m/%Y"),by='day') # build GwexObs object with precipitation data myObsPrec = GwexObs(variable='Prec',date=vecDates,obs=dailyPrecipGWEX) # print GwexObs object myObsPrec # build GwexObs object with temperature data myObsTemp = GwexObs(variable='Temp',date=vecDates,obs=dailyTemperGWEX) # print GwexObs object myObsTempDefines a 4classGwexObs object which is a 4classGwex object containing dates and a matrix of observations.
# Format dates corresponding to daily observations of precipitation and temperature vecDates = seq(from=as.Date("01/01/2005",format="%d/%m/%Y"), to=as.Date("31/12/2014",format="%d/%m/%Y"),by='day') # build GwexObs object with precipitation data myObsPrec = GwexObs(variable='Prec',date=vecDates,obs=dailyPrecipGWEX) # print GwexObs object myObsPrec # build GwexObs object with temperature data myObsTemp = GwexObs(variable='Temp',date=vecDates,obs=dailyTemperGWEX) # print GwexObs object myObsTempDefines a 4classGwexSim object which is a 4classGwex object containing 'sim', an array containing the simulations, and 'dates', a vector of dates. See [GWEX]simGwexModel for some examples.
Probability Weighted Moments of order 0, 1 and 2 of the unified EGPD distribution
EGPD.GI.mu0(kappa, sig, xi) EGPD.GI.mu1(kappa, sig, xi) EGPD.GI.mu2(kappa, sig, xi)reshape Qtrans.mat to an array
QtransMat2Array(n, p, Qtrans.mat)Simple accumulation of a matrix of precipitation
agg.matrix(mat, k, average = F)Finds empirical autocorrelations (lag-1) between intensities corresponding to a degree of autocorrelation of an AR(1) process
autocor.emp.int(rho, nChainFit, Xt, parMargin, typeMargin)Finds observed correlations between intensities corresponding to a degree of correlation of Gaussian multivariate random numbers
cor.emp.int(zeta, nChainFit, Xt, parMargin, typeMargin)Finds observed correlations between occurrences corresponding to a degree of correlation of Gaussian multivariate random numbers
cor.emp.occ(w, Qtrans.mat, mat.comb, nLag, nChainFit, myseed = 1)provide observed correlations between occurrences for all pairs of stations see Mhanna et al. (2012)
cor.obs.occ(pi00, pi0, pi1)Example of daily observations of precipitation (mm) for three fictive stations, for a period of ten years.
data(dailyPrecipGWEX)Example of daily observations of temperature (mm) for three fictive stations, for a period of ten years.
data(dailyTemperGWEX)disag.3D.to.1D
disag.3D.to.1D(Yobs, YObsAgg, mObsAgg, YSimAgg, mSimAgg, prob.class)Density function, distribution function, quantile function, random generation for the unified EGPD distribution
dEGPD.GI(x, kappa, sig, xi) pEGPD.GI(x, kappa, sig, xi) qEGPD.GI(p, kappa, sig, xi) rEGPD.GI(n, kappa, sig, xi)Estimate the dry day frequency (proportion of dry days) for all stations
dry.day.frequency(mat.prec, th)finds the autocorrelation leading to observed autocorrelation
find.autocor(autocor.emp, nChainFit, Xt, parMargin, typeMargin)finds the correlation between normal variates leading to correlation between occurrences
find.omega(rho.emp, Qtrans.mat, mat.comb, nLag, nChainFit)finds the correlation between normal variates leading to correlation between intensities
find.zeta(eta.emp, nChainFit, Xt, parMargin, typeMargin)estimate all the parameters for the G-Wex model of precipitation
fit.GWex.prec(objGwexObs, parMargin, listOption = NULL)estimate parameters which control the dependence between intensities with a MAR(1) process
fit.MAR1.amount(P.mat, isPeriod, th, copulaInt, M0, A)estimate parameters which control the spatial dependence between intensities using a copula
fit.copula.amount(P.mat, isPeriod, th, copulaInt, M0)estimate parameters which control the marginal distribution of precipitation amounts
fit.margin.cdf(P.mat, isPeriod, th, type = c("EGPD", "mixExp"))fitGwexModel: fit a GWex model to observations.
fitGwexModel(objGwexObs, parMargin = NULL, listOption = NULL)# Format dates corresponding to daily observations of precipitation and temperature vecDates = seq(from=as.Date("01/01/2005",format="%d/%m/%Y"), to=as.Date("31/12/2014",format="%d/%m/%Y"),by='day') ############################################################### # FIT THE PRECIPITATION MODEL ############################################################### # Format observations: create a Gwex object for one station only to show a quick # example. The syntax is similar for multi-site applications. myObsPrec = GwexObs(variable='Prec',date=vecDates,obs=dailyPrecipGWEX[,1,drop=FALSE]) # Fit precipitation model with a threshold of 0.5 mm to distinguish wet and dry # states (th) and keep default options otherwise, e.g. a Gaussian # copula for the spatial dependence (copulaInt) and a mixExp distribution for # marginal intensities ('typeMargin') myParPrec = fitGwexModel(myObsPrec,listOption=list(th=0.5)) myParPrec # print object ############################################################### # FIT THE TEMPERATURE MODEL, COND. TO PRECIPITATION ############################################################### # Format observations: create a G-Wex object myObsTemp = GwexObs(variable='Temp',date=vecDates,obs=dailyTemperGWEX) # Fit temperature model with a long-term linear trend ('hasTrend'), Gaussian margins # ('typeMargin') and Gaussian spatial dependence ('depStation') myParTemp = fitGwexModel(myObsTemp,listOption=list(hasTrend=TRUE,typeMargin='Gaussian', depStation='Gaussian')) myParTemp # print objectFirst parametric family for G(v) = v^kappa: distribution, density and quantile function
EGPD.pGI(v, kappa) EGPD.dGI(v, kappa) EGPD.qGI(p, kappa)find matrix of correlations leading to estimates cor between intensities
get.M0( cor.obs, infer.mat.omega.out, nLag, parMargin, typeMargin, nChainFit, isParallel )Estimates the nu parameter (degrees of freedom) of the multivariate Student distribution when the correlation matrix Sig is given
get.df.Student(P, Sig, max.df = 20)get the cdf values (empirical distribution) of positive precipitation
get.emp.cdf.matrix(X)return a vector of 3-char tags of the 12 months
get.list.month()get the vector of the four seasons c('DJF','MAM','JJA','SON')
get.list.season()get default options and check values proposed by the user
get.listOption(listOption)find omega correlation leading to estimates cor between occurrences
get.mat.omega(cor.obs, Qtrans.mat, mat.comb, nLag, nChainFit, isParallel)get.period.fitting.month
get.period.fitting.month(m.char)find rho autocorrelation leading to empirical estimates
get.vec.autocor(vec.ar1.obs, Xt, parMargin, typeMargin, nChainFit, isParallel)get object GwexFit derived from the parameters replicated for each month
getGwexFitPrec( listOption = NULL, p, condProbaWDstates, parMargin, vec.ar1 = NULL, M0 = NULL, mat.omega = NULL )exFitGwexPrec = getGwexFitPrec(p=2,condProbaWDstates=c(0.7,0.3,0.2,0.1), parMargin=c(0.5,0.1,0.4),vec.ar1=rep(0.7,2),M0=rbind(c(1,0.6),c(0.6,1)), mat.omega=rbind(c(1,0.8),c(0.8,1)))special case of infer.dep.amount where there is only one station
infer.autocor.amount( P.mat, pr.state, isPeriod, nLag, th, parMargin, typeMargin, nChainFit, isMAR, isParallel )estimate parameters which control the spatial dependence between intensities using a copula
infer.dep.amount( P.mat, isPeriod, infer.mat.omega.out, nLag, th, parMargin, typeMargin, nChainFit, isMAR, copulaInt, isParallel )find omega correlation leading to estimates cor between occurrences
infer.mat.omega(P.mat, isPeriod, th, nLag, pr.state, nChainFit, isParallel)joint probabilities of occurrences for all pairs of stations
joint.proba.occ(P, th)Estimate the transition probabilities between wet and dry states, for nlag previous days, for all stations
lagTransProbaMatrix(mat.prec, isPeriod, th, nlag)Estimate the transition probabilities between wet and dry states, for nlag previous days, for one station
lagTransProbaVector(vec.prec, isPeriod, th, nlag)Mask intensities where there is no occurrence
mask.GWex.Yt(Xt, Yt)Modify a non-positive definite correlation matrix in order to have a positive definite matrix
modify.cor.matrix(cor.matrix)transform vector of months to seasons
month2season(vecMonth)print-methods: Create a method to print Gwex objects.
4methodprintGwex(x) 4methodprintGwexObs(x) 4methodprintGwexFit(x) 4methodprintGwexSim(x)# Format dates corresponding to daily observations of precipitation and temperature vecDates = seq(from=as.Date("01/01/2005",format="%d/%m/%Y"), to=as.Date("31/12/2014",format="%d/%m/%Y"),by='day') # build GwexObs object with temperature data myObsTemp = GwexObs(variable='Temp',date=vecDates,obs=dailyTemperGWEX) # print GwexObs object myObsTempshow-methods: Create a method to show Gwex objects.
4methodshowGwex(object) 4methodshowGwexObs(object) 4methodshowGwexFit(object) 4methodshowGwexSim(object)# Format dates corresponding to daily observations of precipitation and temperature vecDates = seq(from=as.Date("01/01/2005",format="%d/%m/%Y"), to=as.Date("31/12/2014",format="%d/%m/%Y"),by='day') # build GwexObs object with temperature data myObsTemp = GwexObs(variable='Temp',date=vecDates,obs=dailyTemperGWEX) # show GwexObs object myObsTempInverse PIT: from the probability space to the precipitation space
sim.GWex.Yt(objGwexFit, vecMonth, Yt.Pr)generate uniform variates which describe the dependence between intersite amount correlations
sim.GWex.Yt.Pr(objGwexFit, vecMonth)get relevant parameters
sim.GWex.Yt.Pr.get.param(objGwexFit, iM)generate boolean variates which describe the dependence between intersite occurrence correlations and wet/dry persistence
sim.GWex.occ(objGwexFit, vecMonth)Simulate one scenario of precipitation from the GWex model
sim.GWex.prec.1it(objGwexFit, vecDates, myseed, objGwexObs, prob.class)generate gaussian variates which describe the spatial and temporal dependence between the sites (MAR(1) process)
sim.Zt.MAR(PAR, copulaInt, Zprev, p)generate gaussian variates which describe the spatial dependence between the sites
sim.Zt.Spatial(PAR, copulaInt, p)Simulate from a GWex model
simGwexModel( objGwexFit, nb.rep = 10, d.start = as.Date("01011900", "%d%m%Y"), d.end = as.Date("31121999", "%d%m%Y"), objGwexObs = NULL, prob.class = c(0.5, 0.75, 0.9, 0.99), objGwexSim = NULL, nCluster = 1 )# vector of dates vecDates = seq(from=as.Date("01/01/2005",format="%d/%m/%Y"), to=as.Date("31/12/2014",format="%d/%m/%Y"),by='day') ############################################################### # FIT AND SIMULATE FROM THE PRECIPITATION MODEL ############################################################### # Format observations: create a G-Wex object myObsPrec = GwexObs(variable='Prec',date=vecDates,obs=dailyPrecipGWEX[,1,drop=FALSE]) # Fit GWEX precipitation model, default options except for the threshold th myParPrec = fitGwexModel(myObsPrec,listOption=list(th=0.5)) # fit model # Generate 2 scenarios for one year, using the 'GwexFit' object mySimPrec = simGwexModel(objGwexFit=myParPrec, nb.rep=2, d.start=vecDates[1], d.end=vecDates[10]) mySimPrec # print object ############################################################### # FIT AND SIMULATE FROM THE TEMPERATURE MODEL ############################################################### # Format observations: create a G-Wex object myObsTemp = GwexObs(variable='Temp',date=vecDates,obs=dailyTemperGWEX) # Fit GWEX temperature model myParTemp = fitGwexModel(myObsTemp,listOption=list(hasTrend=TRUE,typeMargin='Gaussian', depStation='Gaussian')) # Generate 2 scenarios for one year, using an existing 'GwexFit' object mySimTemp = simGwexModel(objGwexFit=myParTemp, nb.rep=2, d.start=vecDates[1], d.end=vecDates[365],objGwexObs=myObsPrec) mySimTemp # print objectfind matrix of correlations leading to estimates cor between intensities
simPrecipOcc(nLag, n, pr)from uniform variates to precipitation variates
unif.to.prec(pI, typeMargin, U)Estimate the wet day frequency (proportion of wet days) for all stations
wet.day.frequency(mat.prec, th)| Repository | Version | Published | First seen | Last seen | Docs |
|---|---|---|---|---|---|
| CRAN | 1.1.3 | 2026-05-29 | 2026-05-30 |
표시할 OSV 데이터가 없습니다.
표시할 OpenAlex 데이터가 없습니다.