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splines2

CRAN_Status_Badge Downloads from the RStudio CRAN mirror Build Status codecov

The R package splines2 (version 0.4.3) provides functions to construct basis matrix of

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

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

Installation of CRAN Version

You can install the released version from CRAN.

install.packages("splines2")

Development

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

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

Getting Started

Online document provides reference for all functions and contains the following vignettes:

Performance

Since v0.3.0, the implementation of the main functions has been rewritten in C++ with the help of the Rcpp and RcppArmadillo package. The computational performance has thus been boosted and comparable with the function splines::splineDesign().

Some quick microbenchmarks are provided for reference as follows:

library(microbenchmark)
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]]), 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.

## 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,
    times = 1e3
)
Unit: microseconds
                  expr     min      lq   mean median     uq    max neval cld
           splines::bs 336.731 349.625 375.79 357.79 372.94 2459.4  1000   c
 splines::splineDesign 207.444 211.532 251.32 213.66 223.17 2452.3  1000  b 
     splines2::bSpline  92.542  99.558 110.78 104.36 107.63 2152.3  1000 a  

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

## 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,
    times = 1e3
)
Unit: microseconds
                  expr    min     lq   mean median     uq    max neval cld
 splines::splineDesign 276.58 281.51 308.49 284.38 300.67 2783.0  1000   b
         splines2::dbs 108.04 115.40 149.81 120.57 124.98 2380.4  1000  a 

The splines package does not provide function producing integrals of B-splines. So we instead performed a comparison with package ibs (version 1.4), where the function ibs::ibs() was also implemented in Rcpp.

## 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,
    times = 1e3
)
Unit: microseconds
          expr     min      lq    mean  median      uq      max neval cld
      ibs::ibs 2403.63 2766.26 3363.20 3277.27 3456.95 158210.4  1000   b
 splines2::ibs  293.95  340.09  370.32  377.29  387.72   1231.8  1000  a 

The function ibs::ibs() returns the integrated B-splines instead of the integrals of spline basis functions. So 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 QR decomposition to find the null space of the second derivatives of B-spline basis functions at boundary knots, while splines2::naturalSpline() utilizes the close-form null space derived from the second derivatives of cubic B-splines, which produces nonnegative basis functions (within boundary) and is more computationally efficient.

microbenchmark(
    "splines::ns" = ns(x, knots = internal_knots, intercept = TRUE,
                       Boundary.knots = boundary_knots),
    "splines2::naturalSpline" = naturalSpline(
        x, knots = internal_knots, intercept = TRUE,
        Boundary.knots = boundary_knots
    ),
    times = 1e3
)
Unit: microseconds
                    expr    min     lq   mean median     uq    max neval cld
             splines::ns 628.24 649.58 742.05 663.07 681.58 3486.3  1000   b
 splines2::naturalSpline 126.33 133.88 154.71 143.34 147.69 2677.3  1000  a 

The function mSpline() produces periodic spline basis functions (based on M-splines) when periodic = TRUE is specified. The splines::periodicSpline() returns a periodic interpolation spline (based on B-splines) instead of basis matrix. So 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() (a wrapper function of splines::splineDesign()).

microbenchmark(
    "pbs::pbs" = pbs::pbs(x, knots = internal_knots, degree = degree,
                          intercept = TRUE, periodic = TRUE,
                          Boundary.knots = boundary_knots),
    "splines2::mSpline" = mSpline(
        x, knots = internal_knots, degree = degree, intercept = TRUE,
        Boundary.knots = boundary_knots, periodic = TRUE
    ),
    times = 1e3
)
Unit: microseconds
              expr    min     lq   mean median     uq     max neval cld
          pbs::pbs 428.75 440.79 523.18 449.95 468.11 10239.2  1000   b
 splines2::mSpline 123.40 133.94 150.27 142.59 147.99  2754.9  1000  a 
Session Information for Benchmarks
sessionInfo()
R version 4.0.5 (2021-03-31)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Arch Linux

Matrix products: default
BLAS:   /usr/lib/libopenblasp-r0.3.13.so
LAPACK: /usr/lib/liblapack.so.3.9.1

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               LC_TIME=en_US.UTF-8       
 [4] LC_COLLATE=en_US.UTF-8     LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                  LC_ADDRESS=C              
[10] LC_TELEPHONE=C             LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] splines   stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] splines2_0.4.3       microbenchmark_1.4-7

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.6        mvtnorm_1.1-1     lattice_0.20-41   codetools_0.2-18  ibs_1.4          
 [6] zoo_1.8-9         digest_0.6.27     MASS_7.3-53.1     grid_4.0.5        magrittr_2.0.1   
[11] evaluate_0.14     rlang_0.4.10      stringi_1.5.3     multcomp_1.4-16   Matrix_1.3-2     
[16] sandwich_3.0-0    rmarkdown_2.7     TH.data_1.0-10    tools_4.0.5       stringr_1.4.0    
[21] survival_3.2-10   xfun_0.22         yaml_2.2.1        compiler_4.0.5    pbs_1.1          
[26] htmltools_0.5.1.1 knitr_1.32       

License

GNU General Public License (≥ 3)