All functions

CoV()

Coefficient of variation

Ej()

E_j general efficiency statistic from Criss and Winston (2008)

MRR()

Mean relativ residual

NRMSE()

Normalized root-mean-square error

NSE()

Nash-Sutcliffe efficiency

RRMSE()

Relative root-mean-square error

SDRR()

Standard deviation of relative residual

Sacramento

Remote-sensing data from the Sacramento River

Sacramento_sm

A small version of the Sacramento dataset

bam_check_args()

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

bam_check_nas()

Add missing-data inputs to data list

bam_data()

Preprocess data for BAM estimation

bam_estimate()

Estimate BAM

bam_hydrograph()

Plot flow time series from BAM inference

bam_plot()

Plot a bamr-created object

bam_plot(<bamdata>)

Plot a bamdata object

bam_plot(<bamval>)

Plot a bamval object to show predictive performance

bam_priors()

Establish prior hyperparameters for BAM estimation

bam_qpred()

Flow posterior mean and Bayesian credible interval.

bam_settings()

Options manager for BAM defaults

bam_valdata()

Create a data.frame for BAM validation

bam_validate()

Calculate validation metrics and plots

bamr-package

The 'bamr' package.

cv2sigma()

Convert coefficient of variation to sigma parameter of lognormal diistribution

estimate_b()

Estimate AHG b exponent using bam data

estimate_logA0()

Estimate base cross-sectional area using bam data

ln_moms()

Calculate lognormal moments based on truncated normal parameters

ln_sigsq()

Calculate lognormal sigma parameter based on truncated normal parameters

logNSE()

NSE, computed on log-transformed residuals

maxmin()

Maximum across xs of min across time of width

minmax()

Minimum across xs of max across time of width

rBIAS()

Relative bias

sample_xs()

Take a random sample of a bamdata object's cross-sections.