Package index • PEcAn.assim.batch

All functions
`assim.batch()`	Run Batch PDA
`autoburnin()`	Automatically calculate and apply burnin value
`bounded()`	bounded
`calculate.prior()`	calculate.prior
`correlationPlot()`	Flexible function to create correlation density plots
`ddist()`	ddist
`gelman_diag_gelmanPlot()`	Calculate Gelman Diagnostic using coda::gelman.plot
`gelman_diag_mw()`	Calculate Gelman diagnostic on moving window
`generate_hierpost()`	Helper function that generates the hierarchical posteriors
`getBurnin()`	Calculate burnin value
`get_ss()`	get_ss
`get_y()`	get_y
`gpeval()`	gpeval
`hier.mcmc()`	Hierarchical MCMC using emulator
`is.accepted()`	is.accepted
`load.L2Ameriflux.cf()` `load.pda.data()`	Load Ameriflux L2 Data From NetCDF
`load_pda_history()`	Helper function that loads history from previous PDA run, but returns only requested objects
`makeMCMCList()`	Make MCMC list from samples list
`mcmc.GP()`	mcmc.GP
`minimize.GP()`	minimize.GP
`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.emulator.ms()`	Paramater Data Assimilation using emulator on multiple sites in three modes: local, global, hierarchical First draft, not complete yet
`pda.generate.externals()`	This is a helper function for preparing PDA external objects, but it doesn't cover all the cases yet, use it with care You can use this function just to generate either one of the external.* PDA objects, but note that some args cannot be blank depending on what you aim to generate
`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
`pda.sort.params()`	Function to sort Hierarchical MCMC samples
`prepare_pda_remote()`	helper function for submitting remote pda runs
`return.bias()`	return.bias
`return_hyperpars()`	return_hyperpars
`return_multi_site_objects()`	This is a helper function partly uses pda.emulator code
`runModule.assim.batch()`	Run Batch module
`sample_MCMC()`	Helper function to sample from previous MCMC chain while proposing new knots
`sync_pda_remote()`	helper function for syncing remote pda runs this function resembles remote.copy.from but we don't want to sync everything back
`write_sf_posterior()`	Function to write posterior distributions of the scaling factors

Reference

All functions