Function reference • hydromad

Modelling framework
`hydromad()`	Specify rainfall - runoff (hydrology) models.
`fitted(<hydromad>)` `residuals(<hydromad>)` `observed(<hydromad>)` `vcov(<hydromad>)` `logLik(<hydromad>)` `deviance(<hydromad>)` `print(<hydromad>)` `str(<hydromad.runlist>)` `isValidModel()`	Standard methods for Hydromad model objects
`predict(<hydromad>)`	Generate simulated time series from Hydromad model objects
`simulate(<hydromad>)`	Simulate hydromad models by parameter sampling.
`runlist()` `as.runlist()` `c(<hydromad>)` `c(<runlist>)` `coef(<runlist>)` `summary(<runlist>)` `print(<runlist>)` `residuals(<runlist>)` `fitted(<runlist>)` `update(<runlist>)` `isValidModel(<runlist>)`	Work with a set of model runs
`hydromad.options()` `hydromad.getOption()`	User default settings for hydromad
Assessment
`summary(<hydromad>)`	Assess and summarise performance of Hydromad models
`hydromad.stats()` `buildCachedObjectiveFun()` `hmadstat()`	Statistics for use in hydromad
`nseStat()`	Generalisation of Nash-Sutcliffe Efficiency (R Squared)
`nseVarTd()`	Time-delay corrected performance measure
`plot(<hydromad>)` `xyplot(<hydromad>)` `xyplot(<hydromad.runlist>)` `qqmath(<hydromad>)` `tsdiag(<hydromad>)`	Plot methods for Hydromad model objects
`xyplot(<runlist>)` `qqmath(<runlist>)`	Plot results from a set of model runs
Calibration
`buildTsObjective()` `buildTsLikelihood()`	Generate objective functions with aggregation, transformation and a reference model
`objFunVal()`	Calculate objective function value for a fitted model.
`fitBySampling()`	Fit a hydromad model by sampling the parameter space.
`fitByOptim()`	Fit a hydromad model using general optimisation algorithms.
`fitBySCE()`	Fit a hydromad model using the SCE (Shuffled Complex Evolution) algorithm.
`fitByDE()`	Fit a hydromad model using the DE (Differential Evolution) algorithm.
`fitByDream()`	Fit a hydromad model using the DREAM (DiffeRential Evolution Adaptive Metropolis) algorithm.
`fitByCMAES()`	Fit a hydromad model using CMA-ES (Covariance matrix adapting evolutionary strategy) from cmaes package
`fitByDDS()`	Fit a hydromad model using DDS (Dynamically Dimensioned Search) algorithm.
`fitByNsga2()`	Fit a hydromad model using the NSGA2 genetic algorithm from the mco package
`optimtrace()`	Extract objective function value series from optimisation results
`defineFeasibleSet()`	Extract the feasible parameter sets meeting some criteria.
Discrete events
`findThresh()` `eventseq()` `eventapply()` `eventinfo()`	Identify discrete events from time series and apply functions to them.
`event.xyplot()`	Scatterplots with variables aggregated in event windows
`event.xyplot(<hydromad>)` `event.xyplot(<hydromad.runlist>)`	Visualise systematic model errors against covariates
Soil moisture accounting
`gr4j.sim()` `gr4jrouting.sim()`	GR4J rainfall runoff model
`simhyd.sim()` `simhydrouting.sim()`	SimHyd model
`hbv.sim()` `hbvrouting.sim()` `hbv.ranges()` `hbvrouting.ranges()`	HBV rainfall-runoff model
`cmd.sim()`	IHACRES Catchment Moisture Deficit (CMD) model
`cwi.sim()`	IHACRES Catchment Wetness Index (CWI) model
`awbm.sim()`	Australian Water Balance Model (AWBM)
`bucket.sim()`	Single-bucket Soil Moisture Accounting models
`sacramento.sim()`	Sacramento Soil Moisture Accounting model
`snow.sim()`	Simple degree day factor snow model
`scalar.sim()`	Simple constant runoff proportion
`intensity.sim()`	Runoff as rainfall to a power
`runoffratio.sim()`	Simple time-varying runoff proportion
Routing
`gr4j.sim()` `gr4jrouting.sim()`	GR4J rainfall runoff model
`simhyd.sim()` `simhydrouting.sim()`	SimHyd model
`hbv.sim()` `hbvrouting.sim()` `hbv.ranges()` `hbvrouting.ranges()`	HBV rainfall-runoff model
`armax.sim()` `ssg.armax()` `normalise.armax()`	ARMAX Transfer Function models
`expuh.ls.fit()`	Exponential components transfer function models
`lambda.sim()`	Transfer function with two exponential components and variable partitioning
`powuh.sim()`	Power law transfer function models
`leakyExpStore.sim()`	Exponential store with zero flows and thresholded loss
`expuh3s.sim()`	Exponential components transfer function models with layered slowflow stores
Routing fitting
`armax.ls.fit()`	Estimate transfer function models by Least Squares.
`armax.sriv.fit()`	Estimate transfer function models by Simple Refined Instrumental Variables method.
`armax.inverse.fit()`	Estimate transfer function models by Inverse Filtering.
`armax.inverse.sim()`	Invert transfer function models to estimate input series.
`tryModelOrders()`	Compare calibrations with different transfer function (ARMA) orders for routing.
`estimateDelay()`	Estimate the dead time between input and output
`estimateDelayFrac()`	Estimate the dead time between input and output, with a fractional component (redistribution of the input)
`deconvolution.uh()`	Unit Hydrograph using deconvolution
Utilities
`convertFlow()`	Convert between units of flow volume
`rollccf()` `xyplot(<rollccf>)` `ccfForLags()`	Rolling cross-correlation at given lags.
`parameterSets()`	Generate parameter sets
`evalPars()`	Evaluate a model for a matrix of parameters
`rotatedSampling()`	Sample within rotated feasible parameter space
`gr4j.transformpar()`	Transform GR4J parameters
`observed()` `numericSummary()`	Observed data values
`SCEoptim()`	Shuffled Complex Evolution (SCE) optimisation.
Datasets
`BinghamTrib`	Rainfall and streamflow for Bingham River Trib at Ernies Catchment.
`Canning`	Rainfall, streamflow and potential evaporation data for Canning River at Scenic Drive.
`Cotter`	Rainfall and streamflow for Cotter River at Gingera.
`Corin`	Daily dataset for the Corin catchment.
`Murrindindi`	Rainfall and streamflow for Murrindindi River at Murrindindi above Colwells.
`Queanbeyan`	Rainfall and streamflow for Queanbeyan River at Tinderry.
`SalmonBrook`	Rainfall and streamflow for Salmon Brook at Salmon Catchment.
`Wye`	Rainfall and streamflow for Wye at Cefn Brwyn.
`HydroTestData`	A simple simulated dataset for use in testing hydrological models.
`Molonglo`	Molonglo
`Orroral`	Orroral
`YeAl97`	Model performance statistics from Ye et al. 1997
`absorbScale()`	Placeholder title for absorbScale
`cmd_unstable.sim()`	Unstable/unoptimised version of IHACRES Catchment Moisture Deficit (CMD) model
`crossValidate()`	Cross-validation of hydromad model specification
`dbm.sim()`	Typical initial model used in Data-Based Mechanistic modelling.
`eigen.plot()`	Eigenplot
`evalParsRollapply()` `evalParsTS()`	Calculate an objective function on a rolling time series for a matrix of parameters
`fdc.sample()`	Title placeholder
`filter_loss()`	Recursive filter with a constant loss
`filter_tv()`	Recursive filter with time-varying coefficient
`findUnivariateBounds()`	Find univariate feasible bounds
`fitDbmToPeaks()`	Title
`hydromad.parallel()`	Support for parallelisation in hydromad
`hydromad_sensitivity`	Support for sensitivity analysis in hydromad
`lagFrac()`	Non-integer time delay
`maexpuh.sim()`	Smoothed exponential stores
`panel.ribbon()`	Plot the area between two series as a filled polygon.
`paretoFilter()`	Pareto filter
`paretoObjectivesNsga2()`	Multi-objective optimisation using NSGAII
`paretoObjectivesVaryWeights()`	Multi-objective optimisation by varying weights
`paretoTimeAnalysis()`	Analysis using Pareto-filtering of model performance across simulation periods
`plotPCNSE()`	Annotated parallel coordinates plot of model performance across periods
`poweroid()`	Poweroid geometry (cones, paraboloids, etc).
`defaultPrefilters()` `makePrefilter()`	Prefilter
`runRSM()` `evalRSM()` `print(<summary.rsm.hydromad>)`	Response Surface Method
`splitData()`	Split data of a model instance
`swimp()`	Simple Wetland Inundation Model using Poweroids
`tellTS()`	Time series sensitivity analysis
`tfParsCheck()`	tfUtils

Reference

Modelling framework

Assessment

Calibration

Discrete events

Soil moisture accounting

Routing

Routing fitting

Utilities

Datasets