### SMEFTsim: codes for the Standard Model Effective Field Theory (SMEFT) in FeynRules

The codes enable theoretical predictions for dimension six operator corrections to the Standard Model
using numerical tools, where predictions can be made based on either the electroweak input parameter
set {a^ew,m^Z,G^F} or {m^W,m^Z,G^F}. All of the baryon and lepton number conserving operators present
in the SMEFT dimension six Lagrangian, defined in the Warsaw basis, are included. A flavour symmetric
U(3)5 version with possible non-SM CP violating phases, a (linear) minimal flavour violating version
neglecting such phases, and the fully general flavour case are each implemented. The SMEFTsim package
allows global constraints to be determined on the full Wilson coefficient space of the SMEFT. As the
number of parameters present is large, it is important to develop global analyses on reduced sets of
parameters minimizing any UV assumptions and relying on IR kinematics of scattering events and
symmetries.

Feynrules and UFO models

### TikZ-network: visualizing graphs and networks in Latex

aims to provide a simple and easy tool to create, visualize and modify complex networks. The packaged
is based on the PGF/TikZ languages for producing vector graphics from a geometric/algebraic description.
Particular focus is made on the software usability and interoperability with other tools. Simple
networks can be directly created within LaTeX, while more complex networks can be imported from
external sources (e.g. igraph, networkx, QGIS, ...). Additionally, tikz-network supports visualization
of multilayer networks in two and three dimensions

code source

### ZhuSuan: A Library for Bayesian Deep Learning

a python probabilistic programming library for Bayesian deep learning, which conjoins the complimentary
advantages of Bayesian methods and deep learning. ZhuSuan is built upon Tensorflow. Unlike existing deep
learning libraries, which are mainly designed for deterministic neural networks and supervised tasks,
ZhuSuan is featured for its deep root into Bayesian inference, thus supporting various kinds of
probabilistic models, including both the traditional hierarchical Bayesian models and recent deep
generative models. We use running examples to illustrate the probabilistic programming on ZhuSuan,
including Bayesian logistic regression, variational auto-encoders, deep sigmoid belief networks and
Bayesian recurrent neural networks

code source