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  • automatic standard deviation estimation to improve error estimate (model and observation errors joined into one error term)
  • automatic lag-1 autocorrelation coefficient estimation (turned off / set to zero if not desired)
  • Observation error can be used to perform mcmc as normal (no sd)
  • Performs DE best fit optimization for comparison and/or mcmc starting point
  • A variable model time-step is allowed, with as many different time-steps as desired
  • MCMC assimilation can be completed with multiple observation sets, with sd, and ac estimation taking place for each observation set independently.
  • Allows for model constraints to easily be added.
  • Multiple confidence interval cut-off calculations are possible, specified by the user.
  • Simulations can be completed completely in R (slow) or in FORTRAN (very fast) to allow for flexibility, testing needs, etc.
  • Batch simulations are possible, easily sent to a queue
  • Allows for easy input of various data sources (file formats, etc.) into R, which can then be preprocessed and analyzed before sending to FORTRAN.
  • Formatting for FORTRAN input happens automatically – no user modifications should be needed.
  • Functions for plotting, visualization of output are included.
  • Code is generated for specific runs into a model directory. This directory can operate on it’s own, producing data, chains, etc. – useful for allowing others to reproduce code without needing entire toolbox…


  • For autocorrelation estimation to work properly, an obs. set must be sequential (no gaps, evenly-spaced) in the x-variable.
  • Standard deviation estimation does not take heteroscedasticity into account – for each obs. set, the sd parameter value will be set to the error at each x: error(:) = p.value
  • Linear interpolation of model to fit the data, handling of end-points is crude at best.


  • Make model output over observation interval finer or equal to the resolution of the obs. set.
  • Make model output equal to or longer (past and future) than the x-range of all obs. sets.
  • Alternatively, trim the beginning and end of the obs. set to x-range of model output, but this is less ideal.


  • Make modifications to the MCMC routine for improved performance (edge effect, optimized step-size determination, variable step-size, etc.)
  • Call FORTRAN model and constraints subroutines from R, to run R version faster (currently R compiles subroutines as FORTRAN 77 though, and FORTRAN90 is preferred – due to array allocation, etc. – workarounds may be possible).
  • Parallelize it!
  • Take heteroscedasticity of error into account (add/multiply factor onto obs. error, etc.).
  • Make autocorrelation estimation work with variable dt observations.

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