All functions

assim.batch()

Run Batch PDA

autoburnin()

Automatically calculate and apply burnin value

correlationPlot()

Flexible function to create correlation density plots

gelman_diag_gelmanPlot()

Calculate Gelman Diagnostic using coda::gelman.plot

gelman_diag_mw()

Calculate Gelman diagnostic on moving window

getBurnin()

Calculate burnin value

load.L2Ameriflux.cf() load.pda.data()

Load Ameriflux L2 Data From NetCDF

makeMCMCList()

Make MCMC list from samples list

pda.adjust.jumps()

Adjust PDA MCMC jump size

pda.adjust.jumps.bs()

Adjust PDA block MCMC jump size

pda.autocorr.calc()

autocorrelation correction

pda.bayesian.tools()

Paramater Data Assimilation using BayesianTools

pda.calc.error()

Calculate sufficient statistics

pda.calc.llik()

Calculate Likelihoods for PDA

pda.calc.llik.par()

pda.calc.llik.par

pda.create.btprior()

Create priors for BayesianTools

pda.create.ensemble()

Create ensemble record for PDA ensemble

pda.define.llik.fn()

Define PDA Likelihood Functions

pda.define.prior.fn()

Define PDA Prior Functions

pda.emulator()

Paramater Data Assimilation using emulator

pda.generate.knots()

Generate Parameter Knots for PDA Emulator

pda.generate.sf()

Generate scaling factor knots for PDA Emulator

pda.get.model.output()

Get Model Output for PDA

pda.init.params()

Initialise Parameter Matrix for PDA

pda.init.run()

Initialise Model Runs for PDA

pda.load.priors()

Load Priors for Paramater Data Assimilation

pda.mcmc()

Paramater Data Assimilation using MCMC

pda.mcmc.bs()

Paramater Data Assimilation using MCMC

pda.mcmc.recover()

Clean up a failed PDA run

pda.neff.calc()

Calculate N_eff

pda.plot.params()

Plot PDA Parameter Diagnostics

pda.postprocess()

Postprocessing for PDA Results

pda.settings()

Set PDA Settings

pda.settings.bt()

Apply settings for BayesianTools

return.bias()

return.bias

return_hyperpars()

return_hyperpars

sample_MCMC()

Helper function to sample from previous MCMC chain while proposing new knots

write_sf_posterior()

Function to write posterior distributions of the scaling factors