v1.1.9

In PyBNF version 1.1.9, am was added as a new option for fit_type. When am is selected (fit_type=am in a CONF file), PyBNF executes an adaptive Markov chain Monte Carlo (MCMC) sampling algorithm. The am method has several method-specific options: stablizingCov, adaptive, output_noise_trajectory, output_trajectory, continue_run, calculate_covari, and starting_params. We recommend that am should be used instead of mh. Version 1.1.9 also adds support for new settings for objfunc. When objfunc=neg_bin in a CONF file, PyBNF uses a negative binomial likelihood function in optimization or MCMC sampling. When objfunc=neg_bin_dynamic, PyBNF uses a negative binomial likelihood function in optimization or MCMC sampling and infers the hyperparameter r (dispersion) jointly with model parameters. When objfunc=kl, PyBNF uses the Kullback-Leibler divergence as an objective function in optimization. When objfunc=chi_sq_dynamic, PyBNF uses a Gaussian likelihood function in optimization or MCMC sampling and jointly infers the hyperparameter sigma (standard deviation) jointly with model parameters. The online manual has been updated to explain new features. Please note that versions 1.1.3 through 1.1.8 are prereleases of 1.1.9 and should NOT be used.

v1.1.2

Fixes a version conflict between msgpack and distributed that was breaking new installs

v1.1.1

Adds support for the once between enforcement condition in BPSL

v1.1.0

Adds support for using a likelihood-based objective function for qualitative data, using the new BPSL keywords confidence, tolerance, pmin, pmax

v1.0.1

Added some support for Windows operating systems.

v1.0.0

First stable release of PyBNF.

v0.3.3 Beta

Performs some formatting changes in preparation for the first stable release. Most notably, adds HTML documentation, and changes file extension convention from ".con" to ".prop"

v0.3.2 Beta

Fixed bugs. Added model checking and multimodel parallelism.

v0.3.1 Beta

First public beta release.

v0.3.0 Beta

Updates how we use dask-ssh to run on a cluster. Previously, we used N threads in 1 process, now we use N processes (ie N dask workers) each with 1 thread.
This change improves speed and stability for SBML models, and does not have a large effect on BNGL models.