LPPL model for bubble burst forcasting

Prof. Sornette has spent years forcasting bubble burst with
"log-periodic power law".    The latest paper  gives "a
self-consistent model for explosive financial bubbles, which combines
a mean-reverting volatility process and a stochastic conditional
return which reflects nonlinear positive feedbacks and continuous
updates of the investors' beliefs and sentiments."

And his  latest  predicting is the burst of Chinese equity bubble at
the end of July.     http://arxiv.org/abs/0907.1827

While waiting to see the result, I wonder whether it is possible to
replicate the forcast with R.  The model is in the page 10 of the "A
Consistent Model of `Explosive' Financial Bubbles With Mean-Reversing
Residuals",  http://arxiv.org/abs/0905.0128  .   The output chart is
in the page 3 of "The Chinese Equity Bubble: Ready to Burst",
http://arxiv.org/abs/0907.1827 .   I guess the authors of the latter
paper use the same model as described in the first paper.

Because statistics is still challenging for me though I could use R
for  basic data manipulations,  I wonder which package or function
would be necessary to implement the model in the paper.  The model
seems more complicated than the models in the R tutorials for me.
By the way, the author of the paper used Python and the codes are
private.

Any suggestion would be highly appreciated.

Wind