# Getting Started with np

This vignette is meant to be the smallest useful package-side introduction to `np`. The emphasis is on one clean workflow that users can run after installation: choose a bandwidth, fit a model, inspect the result, and plot it.

Broader worked examples, package comparisons, and method-specific articles are better carried by the gallery site:

- https://jeffreyracine.github.io/gallery/np_npRmpi.html
- https://jeffreyracine.github.io/gallery/quickstarts.html

## The basic workflow

In `np`, the bandwidth object is often the key object in the analysis.

1. compute or inspect a bandwidth object,
2. fit the model,
3. summarize or plot the result.

## A simple regression example

    library(np)
    data(cps71, package = "np")

    bw <- npregbw(logwage ~ age, data = cps71)
    summary(bw)
    #>
    #> Regression Data (205 observations, 1 variable(s)):
    #>
    #> Regression Type: Local-Constant
    #> Bandwidth Selection Method: Least Squares Cross-Validation
    #> Formula: logwage ~ age
    #> Bandwidth Type: Fixed
    #> Objective Function Value: 0.316055 (achieved on multistart 1)
    #> Number of Function Evaluations: 47 (fast = 0)
    #>
    #> Exp. Var. Name: age Bandwidth: 1.892158 Scale Factor: 0.4487743
    #>
    #> Continuous Kernel Type: Second-Order Gaussian
    #> No. Continuous Explanatory Vars.: 1
    #> Estimation Time: 0.074 seconds

    fit <- npreg(bws = bw)
    summary(fit)
    #>
    #> Regression Data: 205 training points, in 1 variable(s)
    #>                    age
    #> Bandwidth(s): 1.892158
    #>
    #> Kernel Regression Estimator: Local-Constant
    #> Bandwidth Type: Fixed
    #> Residual standard error: 0.5307943
    #> R-squared: 0.3108675
    #>
    #> Continuous Kernel Type: Second-Order Gaussian
    #> No. Continuous Explanatory Vars.: 1
    #> Estimation Time: 0.074 seconds (optim 0.074s, fit 0s)

## Plotting the fitted relationship

    plot(cps71$age, cps71$logwage, cex = 0.25, col = "grey")
    lines(cps71$age, fitted(fit), col = 2, lwd = 2)

## Mixed data

One important feature of `np` is that it handles mixed data directly. Variable class matters: unordered categorical variables should be factors, and ordered categorical variables should be ordered factors when appropriate.

    set.seed(42)
    mydat <- data.frame(
      y = rnorm(200),
      x_cont = runif(200),
      x_unordered = factor(sample(c("a", "b", "c"), 200, replace = TRUE)),
      x_ordered = ordered(sample(1:4, 200, replace = TRUE))
    )

    bw_mixed <- npregbw(y ~ x_cont + x_unordered + x_ordered, data = mydat)
    fit_mixed <- npreg(bws = bw_mixed)
    summary(fit_mixed)
    #>
    #> Regression Data: 200 training points, in 3 variable(s)
    #>                x_cont x_unordered x_ordered
    #> Bandwidth(s): 1808084   0.6610069 0.9981613
    #>
    #> Kernel Regression Estimator: Local-Constant
    #> Bandwidth Type: Fixed
    #> Residual standard error: 0.9721457
    #> R-squared: 0
    #>
    #> Continuous Kernel Type: Second-Order Gaussian
    #> No. Continuous Explanatory Vars.: 1
    #>
    #> Unordered Categorical Kernel Type: Aitchison and Aitken
    #> No. Unordered Categorical Explanatory Vars.: 1
    #>
    #> Ordered Categorical Kernel Type: Li and Racine
    #> No. Ordered Categorical Explanatory Vars.: 1
    #> Estimation Time: 0.166 seconds (optim 0.166s, fit 0s)

## A note on modern local-polynomial search

For local-polynomial-capable methods, `np` now supports joint selection of polynomial order and bandwidth. The modern route is to use `search.engine = "nomad+powell"` when you want the search to choose both together.

If you want the recommended route without spelling out all of the LP tuning arguments, use `nomad = TRUE`. This is a documented convenience preset, not a generic optimizer alias: it fills only missing values among the LP degree-search controls and leaves compatible explicit overrides in place. This route uses the optional NOMAD backend provided by the suggested package `crs`, so install `crs` first if you want to use `nomad = TRUE` or `search.engine = "nomad"`/`"nomad+powell"`.

    if (requireNamespace("crs", quietly = TRUE) &&
        utils::packageVersion("crs") >= package_version("0.15-41")) {
      set.seed(7)
      n <- 120
      x <- runif(n, -1, 1)
      y <- x + 0.4 * x^2 + rnorm(n, sd = 0.18)

      fit_nomad <- npreg(y ~ x, nomad = TRUE, degree.max = 1L, nmulti = 1L)
      fit_nomad$bws$nomad.shortcut

      # Tune one component explicitly while leaving the rest of the preset in place.
      fit_nomad_direct <- npreg(
        y ~ x,
        nomad = TRUE,
        search.engine = "nomad",
        degree.max = 1L,
        nmulti = 1L
      )
    }

The same convenience entry point is available for the other LP-capable families: `npcdens`, `npcdist`, `npplreg`, `npscoef`, and `npindex`, together with their corresponding `*bw` constructors.

Keep the first run modest and runnable. Fuller worked examples belong on the gallery rather than in this package vignette.

## Data preparation matters

In `np`, the formula interface tells the function which variables are the response and regressors. It is not imposing an ordinary linear-additive model.

It is also important not to pass blocks of 0/1 dummies as if this were a standard linear-model workflow. If the underlying variable is categorical, it is usually better to keep it as one `factor` or `ordered` variable.

## Other common starting points

This vignette keeps the package-side introduction intentionally narrow. Other common first routes are:

- `?npudens` and `?npudist` for unconditional density and distribution work,
- `?npcdens`, `?npcdist`, and `?npqreg` for conditional density, distribution, and quantiles,
- `?npconmode` for classification and conditional mode estimation,
- `?npplreg`, `?npindex`, and `?npscoef` for semiparametric models.

Those broader branches are better carried by help pages and website articles than by a single shipped vignette.

## Where to go next

- `vignette("np_entropy_tests", package = "np")` for a compact package-side testing overview
- `?npreg`, `?npregbw`, `?npudens`, and `?npcdens` for core help pages
- https://jeffreyracine.github.io/gallery/kernel_primer.html for the conceptual kernel overview
- https://jeffreyracine.github.io/gallery/density_distribution_quantiles.html for density, distribution, and quantile workflows
- https://jeffreyracine.github.io/gallery/semiparametric_models.html for partially linear, single-index, and varying-coefficient routes