All functions
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CoV()
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Coefficient of variation |
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Ej()
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E_j general efficiency statistic from Criss and Winston (2008) |
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MRR()
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Mean relativ residual |
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NRMSE()
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Normalized root-mean-square error |
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NSE()
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Nash-Sutcliffe efficiency |
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RRMSE()
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Relative root-mean-square error |
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SDRR()
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Standard deviation of relative residual |
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Sacramento
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Remote-sensing data from the Sacramento River |
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Sacramento_sm
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A small version of the Sacramento dataset |
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bam_check_args()
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Performs the following checks:
- types:
- logQ_hat is numeric vector
- everything else matrix
- dimensions:
- all matrices have same dims
- logQ_hat has length equal to ncol of matrices |
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bam_check_nas()
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Add missing-data inputs to data list |
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bam_data()
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Preprocess data for BAM estimation |
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bam_estimate()
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Estimate BAM |
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bam_hydrograph()
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Plot flow time series from BAM inference |
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bam_plot()
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Plot a bamr-created object |
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bam_plot(<bamdata>)
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Plot a bamdata object |
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bam_plot(<bamval>)
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Plot a bamval object to show predictive performance |
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bam_priors()
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Establish prior hyperparameters for BAM estimation |
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bam_qpred()
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Flow posterior mean and Bayesian credible interval. |
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bam_settings()
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Options manager for BAM defaults |
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bam_valdata()
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Create a data.frame for BAM validation |
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bam_validate()
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Calculate validation metrics and plots |
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bamr-package
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The 'bamr' package. |
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cv2sigma()
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Convert coefficient of variation to sigma parameter of lognormal diistribution |
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estimate_b()
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Estimate AHG b exponent using bam data |
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estimate_logA0()
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Estimate base cross-sectional area using bam data |
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ln_moms()
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Calculate lognormal moments based on truncated normal parameters |
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ln_sigsq()
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Calculate lognormal sigma parameter based on truncated normal parameters |
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logNSE()
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NSE, computed on log-transformed residuals |
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maxmin()
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Maximum across xs of min across time of width |
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minmax()
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Minimum across xs of max across time of width |
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rBIAS()
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Relative bias |
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sample_xs()
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Take a random sample of a bamdata object's cross-sections. |