Understating statistical models is difficult. Experimentation on **models** should be a part of the learning process. This package provides functions that generate ideal data for generalized linear models. Model parameters, link functions, sample size, and more are adjustable. With data controlled, models can be experimented on.

```
library(GlmSimulatoR)
set.seed(1)
simdata <- simulate_gaussian(N = 200, weights = c(1, 2, 3))
model <- lm(Y ~ X1 + X2 + X3, data = simdata)
summary(model)$coefficients
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 2.9138043 0.7011699 4.155633 4.843103e-05
#> X1 0.9833586 0.2868396 3.428253 7.403616e-04
#> X2 1.7882468 0.2701817 6.618683 3.386406e-10
#> X3 3.2822020 0.2640478 12.430334 1.550439e-26
```

The estimates are close to the weights argument. The mathematics behind the linear model worked well.

- Count data and over dispersion
- Dealing with right skewed data
- Exploring links for the Gaussian distribution
- Stepwise Search
- Tweedie distribution.