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DIYABC v2.0.4 DOWNLOADS :
DIYABC v184.108.40.206 beta DOWNLOADS :
|Download the windows
|Download the notice document:
|| DIYABC v220.127.116.11beta Documentation
Recommendations for beta
version v18.104.22.168 :
- We do not have the capacity to ensure any technical support (i.e. bug
corrections or developments) of the presently available version of DIYABC-v22.214.171.124beta.
We apologize for this inconvenience. You nevertheless can still use the forum to exchange
ideas and advises,
- We found that the cluster version of DIYABC-v126.96.36.199beta to be very unstable,
- We found that the Microsoft Windows version of DIYABC-v188.8.131.52beta with graphical
interface to be unstable in a Microsoft Windows 7 environment (cf. occurrence of
unexplained bugs in this environment). DIYABC-v184.108.40.206beta should be hence preferentially
used in a Microsoft Windows XP environment,
- The reference table file format has
been modified so that DIYABC v1.x.x is incompatible with reference
tables obtained with the old v.0.xx versions.
The software DIYABC, as well as the companion notice, papers and full examples of DIYABC v2 projects are freely available on this site.
Registering (below) is optional and free
. It gives you access to :
- DIYABC v2 and v1 forums where you can post questions, signal caveats, ...
- Some full examples of DIYABC v2 projects
- the program sources and binaries
- older versions
DIYABC allows considering complex population histories involving any combination of
population divergences, admixtures and population size changes, with population samples
potentially collected at different times. DIYABC can be used to compare competing
evolutionary scenarios and quantify their relative support, and estimate parameters for
one or more scenarios. Eventually, it provides a way to evaluate the amount of confidence
that can be put into the various estimations and to achieve model checking computation
within an approximate Bayesian computation (ABC) framework. The version 2.0 of the program
implements a number of new features and analytical methods allowing extensive analyses of
large molecular datasets, including single nucleotide polymorphism (SNP) data.
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