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Fixing typos for publishing
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kgoldfeld committed Oct 26, 2020
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# Summary

The `simstudy` package is a collection of functions for R [@rcoreteam2020], that
The `simstudy` package is a collection of functions for R [@rcoreteam2020] that
allow users to generate simulated data sets in order to explore modeling
techniques or better understand data generating processes. The user defines the
distributions of individual variables, specifies relationships between
covariates and outcomes, and generates data based on these specifications. The
final data sets can represent randomized control trials, repeated measure
designs, cluster-randomized trials, or naturally observed data processes. Other
complexities that can be added include survival data, correlated data, factorial
designs, cluster-randomized trials, or naturally observed data processes. Many other
complexities can be added, including survival data, correlated data, factorial
study designs, step wedge designs, and missing data processes.

Simulation using `simstudy` has two fundamental steps. The user (1) **defines**
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but they can lead us astray if used incorrectly. Choosing the right methodology for the
hypothesis and expected data is crucial for useful, valid results. Data
simulated with `simstudy` under the assumptions derived from a hypothesis
enables researchers to test and refine their analysis methodology without the
enables researchers to test and refine their analysis methodologies without the
need for time-intensive, expensive pre-tests or collection of actual data. Additionally data generated with `simstudy` can be used in generalized, theoretical simulation studies to further the field of methodology.

There are several `R`-packages that allow for data generation under different
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