# Getting Started with crs

This vignette is meant to be the minimal package-side introduction to `crs`. It focuses on one clean first run and a simple reminder of where spline methods fit relative to the rest of the ecosystem.

Longer conceptual and tuning material is better carried by the gallery site:

- https://jeffreyracine.github.io/gallery/crs.html
- https://jeffreyracine.github.io/gallery/spline_primer.html

## A small spline regression example

    library(crs)
    #> crs 0.15-44
    #> Examples and guides at https://jeffreyracine.github.io/gallery/
    #> See also vignette("crs_getting_started", package = "crs")
    set.seed(42)
    n <- 250
    x1 <- runif(n)
    x2 <- runif(n)
    y <- sin(2 * pi * x1) + x2 + rnorm(n, sd = 0.2)
    dat <- data.frame(y, x1, x2)

    fit <- crs(y ~ x1 + x2, data = dat)
    summary(fit)
    #> Call:
    #> crs.formula(formula = y ~ x1 + x2, data = dat)
    #>
    #> Indicator Bases/B-spline Bases Regression Spline
    #>
    #> There are 2 continuous predictors
    #> Spline degree/number of segments for x1: 6/1
    #> Spline degree/number of segments for x2: 1/1
    #> Model complexity proxy: degree-knots
    #> Knot type: quantiles
    #> Basis type: additive
    #> Pruning of final model: FALSE
    #> Training observations: 250
    #> Rank of model frame: 8
    #> Trace of smoother matrix: 8
    #>
    #> Residual standard error: 0.194 on 242 degrees of freedom
    #> Multiple R-squared: 0.9428,   Adjusted R-squared: 0.9411
    #> F-statistic: 569.6 on 7 and 242 DF, p-value: < 2.2e-16
    #>
    #> Cross-validation score: 0.039118
    #> Number of multistarts: 5
    #> Estimation time: 0.5 seconds

## A simple prediction plot

    plot(x1, y, cex = 0.35, col = "grey")

    grid_x1 <- seq(min(x1), max(x1), length.out = 200)
    newdata <- data.frame(
      x1 = grid_x1,
      x2 = mean(x2)
    )
    pred <- predict(fit, newdata = newdata)

    lines(grid_x1, pred, col = 2, lwd = 2)

## When to use `crs`

Use `crs` when regression splines, derivative structure, or shape restrictions are the natural tool. For kernel-based workflows, see the `np` package instead.

Two common next stops after this first vignette are:

- `?crssigtest` for spline significance testing,
- https://jeffreyracine.github.io/gallery/spline_primer.html for the conceptual background.

## A practical note on search

If cross-validated spline search feels expensive, begin by confirming the modeling problem on a smaller example first. Then narrow the search only if needed by restricting options such as degree or knot complexity.

It is also useful to remember that `crs` contains simple linear-regression-style specifications as special cases when the basis is restricted appropriately.

## Where to go next

- `?crs` and `?crssigtest` for help pages
- https://jeffreyracine.github.io/gallery/spline_primer.html for the conceptual introduction
- https://jeffreyracine.github.io/gallery/spline_search_and_tuning.html for practical search and tuning guidance
- package demos via `demo(package = "crs")`