`simglm`

:
Tidy simulation and power analyses

## Package Installation

This package can be directly installed through CRAN:

`install.packages("simglm")`

The development version of the package can be installed by using the
devtools package.

```
library(devtools)
install_github("lebebr01/simglm")
```

## Introduction to the simglm
package

The best way to become oriented with the `simglm`

package
is through the package vignette. There are two ways to get to the
vignettes (both will open a browser to view the vignette). Below is an
example loading the “Intro” vignette directly:

```
browseVignettes()
vignette("Intro", package = "simglm")
```

Note: If you install the development version of the package, you may
need to tell R to build the vignettes when installing the
`simglm`

package by doing the following:

`install_github("lebebr01/simglm", build_vignettes = TRUE)`

## Features

A flexible suite of functions to simulate nested data.

Currently supports the following features:

- Longitudinal data simulation
- Three levels of nesting
- Specification of distribution of random components (random effects
and random error)
- Specification of serial correlation
- Specification of the number of variables
- Ability to add time-varying covariates
- Specify the mean and variance of fixed covariate variables
- Factor variable simulation
- Ordinal variable simulation

- Generation of mixture normal distributions
- Cross sectional data simulation
- Single level simulation
- Power by simulation
- Vary parameters for a factorial simulation design.
- Can vary model fitted to the data to misspecify directly.

- Simulation of missing data
- Include other distributions for covariate simulation.
- Continuous, Logistic (dichotomous), and Poisson (count) outcome
variables.
- Cross classified simulation and power

## Bugs/Feature Requests

Bugs and feature requests are welcomed. Please track these on GitHub
here: https://github.com/lebebr01/simglm/issues. I’m also open
to pull requests.

Enjoy!