Michael Leung, "Dependence-Robust Inference Using Resampled Statistics", 
Journal of Applied Econometrics, Vol. 37, No. 2, 2022, pp. 270-285.

The zip file ml-files.zip contains a number of Python (.py) program
files and also a number of data (.csv) files. All files are ASCII
files in DOS format. Unix/Linux users should use "unzip -a".

The files were coded for Python 3 and require the following
dependencies: numpy, networkx, pandas, powerlaw, scipy, and snap
(snap.stanford.edu).

RS_module.py:
Functions implementing our methods.

gen_net_module.py:
Functions for simulating data.

jackson_rogers_application.py:
Empirical application.
Prints output as latex table directly to console.

jackson_rogers_data:
Data obtained from http://www.stanford.edu/~jacksonm/JacksonRogers-Data.zip. 

clustering.py:
Clustered data monte carlo (Tables 2 and 3).
Prints output as latex table and also saves as CSV in this directory.

clustering_strongdep.py:
Clustered data with strong dependence monte carlo (results verbally
summarized in section 6). Prints output as latex table directly to
console and also saves as CSV in this directory.

node_stats.py:
Network statistics monte carlo (Tables B.1 and B.2).
First run with simulate_only=True. The rerun with simulate_only=False
to get the results. Prints output as latex table directly to console
and also saves as CSV in this directory.

power_law.py:
Power law monte carlo (Table B.4).
Prints output as latex table directly to console and also saves as CSV
in this directory.

tspill.py:
Treatment spillovers monte carlo (Table B.3).
First run with simulate_only=True. The rerun with simulate_only=False
to get the results. Prints output as latex table directly to console
and also saves as CSV in this directory.