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