Sdnist

This release uses same resource files as release [v1.4.0-b.1](https://github.com/usnistgov/SDNist/releases/tag/v1.4.0-b.1)
[Code and Readme](https://github.com/usnistgov/SDNist/tree/main/sdnist/report)

changes:
* Fixed synthetic data validation.
* Fixed apparent match metric when no matches occur.
* Fixed sampling error computation to make sub-samples with same size as the target data.
* Update propensity metric to use tree depth 6.
* Update pearson and kendall correlation to have minimum upper range of 0.15 on colorbars.
* Update k-marginal to perform 100 permutations of 3-marginal selection.

**Full Changelog**: https://github.com/usnistgov/SDNist/compare/v0.1.2...v1.3.0

This release is the first stable release and contains all data assets.

Data files in parquet/json and csv are included. Use `pip` install from the unzipped package to install `sdnist`. Download `SDNist-data-1.2.01` and run `sdnist` from the unzipped directory to prevent attempts to download the data from NIST servers.


`SDNist-data-1.2.0.zip` contains all data tables in parquet/json and csv.

**Full Changelog**: https://github.com/usnistgov/SDNist/compare/v0.1.2...v1.2.0

The code in this release is undergoing active testing and is in pre-public status.

Note the data assets are still in v0.1.1

This release:
- updates submission.py to import all necessary packages
- adds `loggeru` as a module requirement during setup

**Full Changelog**: https://github.com/usnistgov/SDNist/compare/v0.1.1...v0.1.2

This is the initial release of SDNist.

This is a fully operational Python implementation of benchmarks for data synthesizers derived from the 2020 NIST PSCR Differential Privacy Temporal Map Challenge.

This release includes:
- SDNist: Benchmarks for data synthesizers (zip and tarball)
- Public and test datasets
- OpenPGP signature

Requirements to run:
Python >= 3.6

Please contact gary.howarthnist.gov with questions.