mlmc 2.1.1

• Bug fix in parallel processing for main driver and mlmc.test (thanks to Qian Xin, University of Bristol, for bug report).
• At the same time, improve the method of splitting simulations in parallel for the main mlmc driver, so that work is more evenly distributed to keep all cores busy.

mlmc 2.1.0

• Add parameter value checks in mlmc.test.
• Allow user to specify alpha, beta, and gamma to mlmc.test, rather than forcing estimation by linear regression.
  Note this is a departure from the original Matlab code, but if they are left unspecified then the same results as under Matlab are reproduced.
• Improve specificity of some argument documentation in mlmc.test.

mlmc 2.0.2

• Package was removed from CRAN because I didn't notice my old Oxford email address wasn't forwarding any longer.
  In order to comply with CRAN changes, the C++ routines are now registered and maintainer info updated to my Durham email.
• The Matlab driver code by Mike Giles has been quite substantially updated, so this major version bump in the R package addresses updating this code to match the new driver API.
  None of these sub-bullets are bug fixes, merely changing to match the new best-practice for the MLMC driver designed by Mike Giles.
  In particular:
  • User level sampling functions must now also return the total cost of all samples simulated at that level.
    Therefore user level sampler functions must return a list with a sums and cost element.
  • The gamma argument is no longer required, since it is not used in automatic cost computation, and can be estimated as for alpha and beta.
  • mlmc.test() no longer takes M, a level refinement factor, since this was only used to calculate the cost as N*M^l.
    Per above comment, the user now defines cost completely via the return from the level sampler function.
  • Along these lines, mlmc.test() now uses the user returned cost in all places: previously CPU time was measured as cost in the convergence tests section, whilst the MLMC complexity tests previously forced costs to be N*M^l.
• Some (very) old bugs were squashed in the Euler-Maruyama discretisation level sampler, opre_l() which affected lookback call and Heston model options.
• I managed to get hold of a Matlab license, so have now confirmed that the examples in the docs return (within sampling variability) the same results for both Euler-Maruyama and Milstein discretisation example level sampler functions.
• There is now a hex sticker!
  It is hopefully fairly self explanatory: many fast simulations are done at low levels (lots of dice, with the hare running at the bottom of the stairs); fewer simulations are done at higher levels (fewer dice as you go up each step, with a tortoise and fewest dice on top step)!
• There is now a documentation website at https://mlmc.louisaslett.com/

CRAN version 1.0.0 release snapshot

README update.