Man pages for markwh/bamr
Bayesian AMHG and Manning discharge estimation using SWOT-like river observations

bam_check_argsPerforms the following checks: - types: - logQ_hat is numeric...
bam_check_nasAdd missing-data inputs to data list
bam_dataPreprocess data for BAM estimation
bam_estimateEstimate BAM
bam_hydrographPlot flow time series from BAM inference
bam_plotPlot a bamr-created object
bam_plot.bamdataPlot a bamdata object
bam_plot.bamvalPlot a bamval object to show predictive performance
bam_priorsEstablish prior hyperparameters for BAM estimation
bam_qpredFlow posterior mean and Bayesian credible interval.
bamr-packageThe 'bamr' package.
bam_settingsOptions manager for BAM defaults
bam_valdataCreate a data.frame for BAM validation
bam_validateCalculate validation metrics and plots
CoVCoefficient of variation
cv2sigmaConvert coefficient of variation to sigma parameter of...
EjE_j general efficiency statistic from Criss and Winston...
estimate_bEstimate AHG b exponent using bam data
estimate_logA0Estimate base cross-sectional area using bam data
ln_momsCalculate lognormal moments based on truncated normal...
ln_sigsqCalculate lognormal sigma parameter based on truncated normal...
logNSENSE, computed on log-transformed residuals
maxminMaximum across xs of min across time of width
minmaxMinimum across xs of max across time of width
MRRMean relativ residual
NRMSENormalized root-mean-square error
NSENash-Sutcliffe efficiency
rBIASRelative bias
RRMSERelative root-mean-square error
SacramentoRemote-sensing data from the Sacramento River
Sacramento_smA small version of the Sacramento dataset
sample_xsTake a random sample of a bamdata object's cross-sections.
SDRRStandard deviation of relative residual
markwh/bamr documentation built on May 8, 2019, 3:48 p.m.