Simulates data sets in order to explore modeling techniques or better understand data generating processes. The user specifies a set of relationships between covariates, and generates data based on these specifications. The final data sets can represent data from randomized control trials, repeated measure (longitudinal) designs, and cluster randomized trials. Missingness can be generated using various mechanisms (MCAR, MAR, NMAR).

The simstudy package is collection of functions that allow users to generate simulated data sets in order to explore modeling techniques or better understand data generating processes. The user specifies a set of relationships between covariates, and generates data based on these specifications. The final data sets can represent data from randomized control trials, repeated measure (longitudinal) designs, and cluster randomized trials. Missingness can be generated using various mechanisms (MCAR, MAR, NMAR).

Here is some simple sample code, much more in the vignette:

library(simstudy)```
``` rdef <- defData(varname="x", formula = 10, variance = 2)def <- defData(def, varname="y", formula = "3 + 0.5 * x", variance = 1)dt <- genData(250, def) dt <- trtAssign(dt, nTrt = 4, grpName = "grp", balanced = TRUE) dt

```
## id grp x y
## 1: 1 3 10.393817 7.805703
## 2: 2 1 10.235161 5.705590
## 3: 3 1 11.517813 8.210183
## 4: 4 1 12.068125 8.618601
## 5: 5 1 10.078817 5.780655
## ---
## 246: 246 4 11.419577 8.442363
## 247: 247 3 10.567231 9.808930
## 248: 248 1 10.451896 7.720858
## 249: 249 3 7.633381 6.861638
## 250: 250 2 9.347781 6.094965
```

- This is the first submission of simstudy, so there is no news yet!

- Fixed index variable issue related to generating categorical data
- Fixed index variable issue related to generating longitudinal data
- Fixed issue that arised When creating categorical variable in first field
- Increased speed required to generate categorical data with large sample sizes
- Categorical data can now accomodate probabilities condition on covariates
- Fix: package data.table 1.10.0 broke genMissDataMat. genMissDataMat has been updated.