configurePython

Sets up a python environment to use for PLP (can be conda or venv)

A user friendly way to create patient level prediction models using
the Observational Medical Outcomes Partnership Common Data Model. Given a cohort
of interest and an outcome of interest, the package can use data in the Common
Data Model to build a large set of features. These features can then be used to
fit a predictive model with a number of machine learning algorithms. This is
further described in Reps (2017) <doi:10.1093/jamia/ocy032>.

Egill Fridgeirsson

PatientLevelPrediction

Develop Clinical Prediction Models Using the Common Data Model

Jenna Reps

Martijn Schuemie

Marc Suchard

Patrick Ryan

Peter Rijnbeek

Observational Health Data Science and Informatics 

configurePython function

<dl><dt>envname</dt>
<dd>A string for the name of the virtual environment (default is 'PLP')</dd>
<dt>envtype</dt>
<dd>An option for specifying the environment as'conda' or 'python'. If NULL then the default is 'conda' for windows users and 'python' for non-windows users</dd>
<dt>condaPythonVersion</dt>
<dd>String, Python version to use when creating a conda environment</dd></dl>

Arguments

Sets up a python environment to use for PLP (can be conda or venv) — configurePython

<dl>

<dt>envname</dt>
<dd>A string for the name of the virtual environment (default is 'PLP')</dd>


<dt>envtype</dt>
<dd>An option for specifying the environment as'conda' or 'python'. If NULL then the default is 'conda' for windows users and 'python' for non-windows users</dd>


<dt>condaPythonVersion</dt>
<dd>String, Python version to use when creating a conda environment</dd>

</dl>

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configurePython: Sets up a python environment to use for PLP (can be conda or venv)

Description

Usage

Value

Arguments

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Examples