These will include talks that I've given at conferences or other gatherings.

Simulation and Power Analysis of Generalized Linear Mixed Models

Simulation and Power Analysis of Generalized Linear Mixed Models Brandon LeBeau University of Iowa Overview Power simglm package Shiny Demo Power Power is the ability to statistically detect a true effect (i.e. non-zero population effect). For simple models (e.g. t-tests, regression) there are closed form equations for generating power. R has routines for these: power.t.test, power.anova.test Gpower3 Power Example n <- seq(20, 1000, 5) power <- sapply(seq_along(n), function(i) power.

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Make Power Fun (Again?)

Make Power Fun (Again?) Brandon LeBeau University of Iowa Overview (G)LMMs Power simglm package Shiny Demo - Broken! Linear Mixed Model (LMM) Power Power is the ability to statistically detect a true effect (i.e. non-zero population effect). For simple models (e.g. t-tests, regression) there are closed form equations for generating power. R has routines for these: power.t.test, power.anova.test Gpower3 Power Example n <- seq(4, 1000, 2) power <- sapply(seq_along(n), function(i) power.

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Extending accessibility of open-source statistical software to the masses A shiny case study

Extending accessibility of open-source statistical software to the masses: A shiny case study Brandon LeBeau University of Iowa R R is an open source statistical programming language. Pros: Common statistical procedures are found in R Can extend functionality with packages/functions Cons: Need to be comfortable with code Flexibility of R R is powerful and flexible due to the many user written packages.

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