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Hi all,
Can

Following the tutorial here and just need to add installation of the libraries at the top of the notebook

https://colab.research.google.com/github/googlecolab/colabtools/blob/master/notebooks/colab-github-demo.ipynb#scrollTo=8QAWNjizy_3O

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Summary

I've been taking a course called 'Fairness and Ethics in Digital Health' at columbia and it has made me more conscious of this subject.

I reached out to the ReCODE.Health at UCSD to help me in my cour

This is a great tool!! Kudos!!
This more like a question not an issue.
I was looking for the build_admissions_df sub function and have a question.
So, it looks like, e.g. hospitalization number, was based on "disposition".
And the "disposition" was calculated from "i_hospitalized_v + r_hospitalized_v" in parameter.py.
May I know why the number of hospitalization = (ihosp_ratemarket_share

Right now plot gives you the age (so exhibits boundary conditions at the start of the simulation) while print givens you final duration (so exhibits boundary conditions at the end of the simulation). This is more confusing than it needs to be.

@smjenness has proposed simply removing the plot. @martinamorris and I favor finding a way to preserve them but make them a bit more useful and clear.

Describe the bug
All "Add a Volunteer" data fields are mandatory. All fields should not be a requirement (in current design, particularly geographic information).

Some data fields (right now), such as administrative boundaries are very difficult to remember as text fields (e.g. regions, districts, village). Implies that when trying to rapidly add a volunteer, I am adding bogus informati

On the website, create quick tutorials demonstrating each of the implemented estimators, descriptions of how they work, and why you might want to use them. Might be more digestible than the current docs (also better justify why to choose one over the other)

Reference to base on
https://lifelines.readthedocs.io/en/latest/jupyter_notebooks/Proportional%20hazard%20assumption.html
https://github.

Often super-learner implementations include an arithmetic mean estimator. However, AFAIK sklearn doesn't have this implementation (no substantive reason they would include this).

I have an implementation I wrote that adheres to the sklearn format and is compatible with supylearner. So I will add this in the library for ease

We probably should issue a warning about excessive memory usage when using Cholmod with large grids. Perhaps over 5M cells?

Circuitscape/Circuitscape.jl#232 (comment)

A warning that may be clear is something like "Cholmod is a spare direct solver that consumes a lot of memory. A landscaspe with 5M cells will require roughly XGB of RAM. Please use cg+amg

Add at least:

• source_url: source url(s)
• download_url: download url(s)
• description: prose description from website/data provider
• license: license, if available

Stochastic examples or tests use random values. They should be reproducible in a non-random way in order to ease their debugging. One way to do that is to fix the random seed with always the same value.

Is there a natural interpretation for the value (Arvo) of the events in the simulator (e.g. cases, people)? If so, it would be helpful to let user know up front what it is.

so theres a lot of different variables that need to be called from different datasets for these functions.

And if you get one wrong the warning just gives the generic replacement zero but should be x long.

Maybe we should add in a check to the beginning of the function that makes sure those variable names exist in the appropriate datasets and if not chuck a warning saying this variable doe