chore: Add package descriptions and links in README.md

README.md

@@ -62,19 +62,9 @@ All the packages are compiled against Python 3.11.
 
 ### `.#bayes3d`
 
-<https://github.com/probcomp/bayes3d>.
+Bayes3D is a 3D scene perception system based on probabilistic inverse graphics.
 
-### `.#distinctipy`
-
-### `.#distributions`
-
-Native library for probability distributions in python used by Loom. NOTE: this ONLY builds for `x86_64` architectures and only runs on linux.
-
-### `.#dm-tree`
-
-### `.#genjax`
-
-### `.#goftests`
+* [GitHub](https://github.com/probcomp/bayes3d)
 
 ### `.#loom`
 
@@ -90,25 +80,94 @@ $ nix build './envs-flake#packages.x86_64-darwin.ociImgLoom'
 
 If you are running on Mac silicon (`aarch64-darwin`), that OCI image will run but behavior is not defined or supported.
 
+### `.#distinctipy`
+
+distinctipy is a lightweight python package providing functions to generate colours that are visually distinct from one another.
+
+* [GitHub](https://github.com/alan-turing-institute/distinctipy)
+* [PyPi](https://pypi.org/project/distinctipy)
+
+### `.#distributions`
+
+Native library for probability distributions in python used by Loom. NOTE: this ONLY builds for `x86_64` architectures and only runs on linux.
+
+* [GitHub](https://github.com/posterior/distributions)
+
+### `.#dm-tree`
+
+Tree is a library for working with nested data structures. In a way, tree generalizes the builtin map function which only supports flat sequences, and allows to apply a function to each "leaf" preserving the overall structure.
+
+* [GitHub](https://github.com/deepmind/tree)
+* [PyPi](https://pypi.org/project/dm-tree)
+
+### `.#genjax`
+
+GenJAX is an implementation of Gen on top of JAX - exposing the ability to programmatically construct and manipulate generative functions, as well as JIT compile + auto-batch inference computations using generative functions onto GPU devices.
+
+* [GitHub](https://github.com/probcomp/genjax)
+
+### `.#goftests`
+
+Goftests is intended for unit testing random samplers that generate arbitrary plain-old-data, and focuses on robustness rather than statistical efficiency. In contrast to scipy.stats and statsmodels, goftests does not make assumptions on the distribution being tested, and requires only a simple (sample, prob) interface provided by MCMC samplers.
+
+* [GitHub](https://github.com/posterior/goftests)
+
 ### `.#open3d`
 
+Open3D is an open-source library that supports rapid development of software that deals with 3D data.
+
+* [GitHub](https://github.com/isl-org/Open3D)
+* [PyPi](https://pypi.org/project/open3d)
+
 ### `.#opencv-python`
 
-### `.#orxy`
+Wrapper package for OpenCV python bindings.
+
+* [GitHub](https://github.com/opencv/opencv-python)
+* [PyPi](https://pypi.org/project/opencv-python)
+
+### `.#oryx`
+
+Oryx is a library for probabilistic programming and deep learning built on top of Jax.
+
+* [GitHub](https://github.com/jax-ml/oryx)
+* [PyPi](https://pypi.org/project/oryx)
 
 ### `.#parsable`
 
+Parsable is a lightweight decorator-based command line parser library. Parsable was written to be simpler than argparse, optparse, and argh.
+
 ### `.#plum-dispatch`
 
+Multiple dispatch in Python.
+
+* [GitHub](https://github.com/beartype/plum)
+* [PyPi](https://pypi.org/project/plum-dispatch)
+
 ### `.#pymetis`
 
+PyMetis is a Python wrapper for the Metis graph partititioning software.
+
+* [GitHub](https://github.com/inducer/pymetis)
+* [PyPi](https://pypi.org/project/PyMetis)
+
 ### `.#pyransac3d`
 
+pyRANSAC-3D is an open source implementation of Random sample consensus (RANSAC) method. It fits primitive shapes such as planes, cuboids and cylinder in a point cloud to many aplications: 3D slam, 3D reconstruction, object tracking and many others.
+
+* [GitHub](https://github.com/leomariga/pyRANSAC-3D)
+* [PyPi](https://pypi.org/project/pyransac3d)
+
 ### `.#sppl`
 
-Python [library by ProbSys](https://github.com/probsys/sppl) packaged for python3.9 .
+Probabilistic programming system for fast and exact symbolic inference.
+
+* [GitHub](https://github.com/probsys/sppl)
+* [PyPi](https://pypi.org/project/sppl)
 
 ### `.#tensorflow-probability`
 
-### `.#loom`
+TensorFlow Probability is a library for probabilistic reasoning and statistical analysis in TensorFlow.
 
+* [GitHub](https://github.com/tensorflow/probability)
+* [PyPi](https://pypi.org/project/tensorflow-probability)