The self-similar discrete power law distribution — R distribution, density, quantile and random deviate functions, and the maximum likelihood estimator. This repository also includes OCaml code for generating self-similar random trees.
dplfit was written by Mitchell Newberry mitchell@localpost.io and is (c) Mitchell Newberry 2019. Bug reports, comments, and feature requests are welcome.
Data to reproduce the results of Newberry and Savage (2019) are in data/.
figures.R reproduces figures and numerical results from these files.
yeh1976tracheobronchial.D6.tsv was hand-transcribed from a US Government
report as cited in Newberry and Savage (2019).
mouse-vasculature-tekin2016.tsv is derived from Angicart scans of MicroCT
data from Tekin et al. (2016) as used in Newberry and Savage (2019).
earthcat/mags.all is assembled by make_earthcat.sh from mirrors of the
Southern California Seismographic Network data catalog on internet archive to
reproduce the data source, time interval and results of Bak et al. (2002).
Bak, P., Christensen, K., Danon, L., & Scanlon, T. (2002). Unified scaling law for earthquakes. Physical Review Letters, 88(17), 178501.
Newberry, M. G., & Savage, V. M. (2019). Self-similar processes follow a power law in discrete logarithmic space. Physical review letters, 122(15), 158303.
Tekin, E., Hunt, D., Newberry, M. G., & Savage, V. M. (2016). Do vascular networks branch optimally or randomly across spatial scales? PLoS computational biology, 12(11), e1005223.