semvec/pypi-packages huggingface dataset:
pybool 1.3.3
pybool: A package to infer Boolean networks.
License: BSD

pybool: A python package that infers Boolean networks given a set of constraints.

Consider the common scenario of a biologist who is studying a particular
regulatory network. They study a set of genes that are known to play a role in the
network. They have some background knowledge of particular regulatory connections
from his own studies or from the literature. In addition to this they have
perturbation data that reveals the temporal order of expression of the genes under
some conditions. For example, these could be derived from loss-of-function
or over-expression experiments. The biologist would like to elucidate the entire
network and to this end can perform various experiments to test particular regulatory
connections. These experiments are costly and time-consuming. Which
connections should they focus on? This is where the pybool package can help.
By modelling candidate regulatory networks using Boolean logic, pybool evaluates
which networks are consistent with the perturbation data and the known
regulatory connections.
infpy 0.4.13
A python inference library
License: BSD
Keywords: Inference probabilistic models Gaussian processes Dirichlet processes

*Infpy* is a python package I have put together that implements some of the algorithms I (John Reid) 
have used in my research. In particular it has a Gaussian process package that is largely based on 
the excellent book, `Gaussian Processes for Machine Learning`__ by Rasmussen and Williams. 

__ http://www.amazon.co.uk/Gaussian-Processes-Learning-Adaptive-Computation/dp/026218253X/

The Gaussian process package is the only infpy package that is extensively documented so far but you are 
welcome to try out the others. The Gaussian process package has the following attributes:

- *noisy* data is easily modelled
- many different *kernels* are supported out of the box allowing many models to be tested
- kernel *composition* (point-wise sum and product) is intuitive permitting rapid model evaluation 
- *maximum likelihood* estimation of hyper-parameters facilitates model comparison
- *numpy* integration allows easy interoperability with other python scientific toolkits
- high quality *matplotlib* plots are easy to create
- best of both worlds : ease of using an interpreted language but all performance critical linear algebra performed in *compiled code*