# Software used in the Lessons

These Lessons use about a dozen new R functions. Some of these are used frequently in examples and exercises and are worth mastering. Others appear only in **demonstrations**.

- Training models with data
arguments: i. tilde expression, ii.`lm()`

`data=`

data frame.- Occasionally, you will be directed to use
`glm()`

or`model_train()`

, which work similarly to`lm()`

but are specialized for models whose output is a*probability*. converts a two-level categorical variable to a 0/1 encoding.`zero_one()`

- Summarizing models. These invariably take as input a model produced by
`lm()`

(or`glm()`

) and generate a summary report about that model.: displays model coefficients. Each coefficient is a single number.`conf_interval()`

: displays model coefficients as an`conf_interval()`

*interval*with a lower and upper value.calculates the R`rsquared()`

^{2}of a model, and some related measures., like`regression_summary()`

`conf_interval()`

, but with more detail.

- Evaluating a model on inputs
takes a trained model (as produced by`model_eval()`

`lm()`

) and calculates the model output in both a point form and an interval form.`model_eval()`

can also display the residuals from training or evaluation data.

- Graphics
`model_plot()`

draws a graphic of a model’s function optionally with prediction or confidence intervals.`geom_violin()`

is a modern alternative to`geom_boxplot()`

.

- DAGs (directed, acyclic graphs)
collects simulated data from a DAG`sample()`

`dag_draw()`

draws a picture of a DAG showing how the variables are connected.

- Used within the
`summarize()`

data wrangling function:computes the variance of a single variable.`var()`