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. |