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FRB_powerlaw
PublicAn ipython notebook that implements maximum likelihood estimation (MLE) powerlaw fits for fast radio burst (FRB) fluence distributions, compares these to least-squared fits, and finds the MLE methods are significantly more robust, as detailed in Bilous et al. 2024 (arxiv).frbpoppy
PublicFast Radio Burst Population Synthesis in Pythonpsrdada-python
Publicfrbcat
PublicRFIm
PublicALERT_R3
Publicdarc
PublicAMBER_setup
PublicDedispersion
PublicSNR
PublicAMBER
PublicIntegration
PublicpyAccess
Publicfrbcatdb
Publicfrbcat-web
Publicdadafilterbank
Publicdadafits
Publicspack
PublicAMBER_docker
PublicAstroData
Publicringbuffer-sc1
Publicringbuffer-sc4
Publicpsrdada
Publicsingle_pulse_ml
Publictempo
Publicdadatrigger
PublicAA-ALERT-LOG
Publicfrbcatweb
Public