splines2

Regression Spline Functions and Classes

https://github.com/wenjie2wang/splines2

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Regression Spline Functions and Classes

README.Rmd

---
title: splines2
output: github_document
---

[![CRAN_Status_Badge][r-pkg-badge]][cran-url]
[![Total_Downloads][cranlog-total]][cran-url]
[![Downloads from the RStudio CRAN mirror][cranlog-badge]][cran-url]
[![Build Status][gha-icon]][gha-url]
[![codecov][codecov-main]][codecov]
[![JDS][jds-badge]](https://doi.org/10.6339/21-JDS1020)


```{r setup, echo = FALSE}
knitr::opts_chunk$set(comment = "", cache = TRUE)
options(width = 100, digits = 5)
```

**Package website**: [release][pkgdown-url] | [development][pkgdown-dev]

The R package **splines2** is intended to be a user-friendly *supplementary*
package to the base package **splines**.

## Features

The package **splines2** provides functions to construct basis matrices of

- B-splines
- M-splines
- I-splines
- convex splines (C-splines)
- periodic splines
- natural cubic splines
- generalized Bernstein polynomials
- their integrals (except C-splines) and derivatives of given order by
  closed-form recursive formulas

In addition to the R interface, **splines2** provides a C++ header-only library
integrated with **Rcpp**, which allows the construction of spline basis
functions directly in C++ with the help of **Rcpp** and **RcppArmadillo**.
Thus, it can also be treated as one of the **Rcpp\*** packages.
A toy example package that uses the C++ interface is available
[here][example-pkg].


## Installation of CRAN Version
You can install the released version from [CRAN][cran-url].

```R
install.packages("splines2")
```

## Development

The latest version of the package is under development at [GitHub][github-url].
If it is able to pass the automated package checks, one may install it by

```R
if (! require(remotes)) install.packages("remotes")
remotes::install_github("wenjie2wang/splines2", upgrade = "never")
```

## Getting Started

The [Online document][pkgdown-url] provides a reference for all functions and
contains the following vignettes:

- [Demonstration of the common usages in R through examples][pkg-demo].
- [Introduction to the usage with Rcpp][pkg-wi-rcpp]

## Performance

Since version 0.3.0, the implementation of the main functions has been rewritten
in C++ with the help of the **Rcpp** and **RcppArmadillo** packages.
The computational performance has thus been boosted and comparable with the
function `splines::splineDesign()`.

Some quick micro-benchmarks are provided below for reference.

```{r bench-setup}
library(microbenchmark)
options(microbenchmark.unit="relative")
library(splines)
library(splines2)

set.seed(123)
x <- runif(1e3)
degree <- 3
ord <- degree + 1
internal_knots <- seq.int(0.1, 0.9, 0.1)
boundary_knots <- c(0, 1)
all_knots <- sort(c(internal_knots, rep(boundary_knots, ord)))

## check equivalency of outputs
my_check <- function(values) {
    all(sapply(values[- 1], function(x) {
        all.equal(unclass(values[[1]]), unclass(x), check.attributes = FALSE)
    }))
}
```

For B-splines, function `splines2::bSpline()` provides equivalent results with
`splines::bs()` and `splines::splineDesign()`, and is about 3x faster than
`bs()` and 2x faster than `splineDesign()` for this example.

```{r bench-bSpline}
## B-splines
microbenchmark(
    "splines::bs" = bs(x, knots = internal_knots, degree = degree,
                       intercept = TRUE, Boundary.knots = boundary_knots),
    "splines::splineDesign" = splineDesign(x, knots = all_knots, ord = ord),
    "splines2::bSpline" = bSpline(
        x, knots = internal_knots, degree = degree,
        intercept = TRUE, Boundary.knots = boundary_knots
    ),
    check = my_check
)
```

Similarly, for derivatives of B-splines, `splines2::dbs()` provides equivalent
results with `splines::splineDesign()`, and is about 2x faster.

```{r bench-dbs}
## Derivatives of B-splines
derivs <- 2
microbenchmark(
    "splines::splineDesign" = splineDesign(x, knots = all_knots,
                                           ord = ord, derivs = derivs),
    "splines2::dbs" = dbs(x, derivs = derivs, knots = internal_knots,
                          degree = degree, intercept = TRUE,
                          Boundary.knots = boundary_knots),
    check = my_check
)
```

The **splines** package does not contain an implementation for integrals of
B-splines.  Thus, we performed a comparison with package **ibs** (version
`r packageVersion("ibs")`), where the function `ibs::ibs()`
was also implemented in **Rcpp**.

```{r bench-ibs}
## integrals of B-splines
set.seed(123)
coef_sp <- rnorm(length(all_knots) - ord)
microbenchmark(
    "ibs::ibs" = ibs::ibs(x, knots = all_knots, ord = ord, coef = coef_sp),
    "splines2::ibs" = as.numeric(
        splines2::ibs(x, knots = internal_knots, degree = degree,
                      intercept = TRUE, Boundary.knots = boundary_knots) %*%
        coef_sp
    ),
    check = my_check
)
```

The function `ibs::ibs()` returns the integrated B-splines instead of the
integrals of spline basis functions.  Thus, we applied the same coefficients to
the basis functions from `splines2::ibs()` for equivalent results, which was
still much faster than `ibs::ibs()`.


For natural cubic splines (based on B-splines), `splines::ns()` uses the QR
decomposition to find the null space of the second derivatives of B-spline basis
functions at boundary knots, while `splines2::nsp()` utilizes the
closed-form null space derived from the second derivatives of cubic B-splines,
which produces nonnegative basis functions (within boundary) and is more
computationally efficient.

```{r bench-ns}
microbenchmark(
    "splines::ns" = ns(x, knots = internal_knots, intercept = TRUE,
                       Boundary.knots = boundary_knots),
    "splines2::nsp" = nsp(
        x, knots = internal_knots, intercept = TRUE,
        Boundary.knots = boundary_knots
    )
)
```

The functions `bSpline()` and `mSpline()` produce periodic spline basis
functions based on B-splines and M-splines, respectively, when `periodic = TRUE`
is specified.
The `splines::periodicSpline()` returns a periodic interpolation spline (based
on B-splines) instead of basis matrix.
We performed a comparison with package **pbs** (version `r packageVersion("pbs")`),
where the function `pbs::pbs()` produces a basis matrix of periodic B-spline
by using `splines::spline.des()`.

```{r bench-periodic}
microbenchmark(
    "pbs::pbs" = pbs::pbs(x, knots = internal_knots, degree = degree,
                          intercept = TRUE, periodic = TRUE,
                          Boundary.knots = boundary_knots),
    "splines2::bSpline" = bSpline(
        x, knots = internal_knots, degree = degree, intercept = TRUE,
        Boundary.knots = boundary_knots, periodic = TRUE
    ),
    "splines2::mSpline" = mSpline(
        x, knots = internal_knots, degree = degree, intercept = TRUE,
        Boundary.knots = boundary_knots, periodic = TRUE
    )
)
```

Session Information for Benchmarks
```{r session-info}
sessionInfo()
```



## License

[GNU General Public License][gpl] (≥ 3)

[r-pkg-badge]: https://www.r-pkg.org/badges/version/splines2
[cranlog-badge]: https://cranlogs.r-pkg.org/badges/splines2
[cranlog-total]: https://cranlogs.r-pkg.org/badges/grand-total/splines2
[cran-url]: https://CRAN.R-project.org/package=splines2
[gha-icon]: https://github.com/wenjie2wang/splines2/workflows/R-CMD-check/badge.svg
[gha-url]: https://github.com/wenjie2wang/splines2/actions
[github-url]: https://github.com/wenjie2wang/splines2
[pkgdown-url]: https://wwenjie.org/splines2
[pkgdown-dev]: https://wwenjie.org/splines2/dev
[example-pkg]: https://github.com/wenjie2wang/example-pkg-Rcpp-splines2
[pkg-demo]: https://wwenjie.org/splines2/articles/splines2-intro
[pkg-wi-rcpp]: https://wwenjie.org/splines2/articles/splines2-wi-rcpp
[codecov]: https://app.codecov.io/gh/wenjie2wang/splines2
[codecov-main]: https://codecov.io/gh/wenjie2wang/splines2/branch/main/graph/badge.svg
[jds-badge]: https://img.shields.io/badge/JDS-10.6339%2F21--JDS1020-brightgreen
[gpl]: https://www.gnu.org/licenses/

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