The R package **splines2** is intended to be a
comprehensive, efficient supplement to the base package
**splines**. It provides functions constructing a variety
of regression spline basis functions that are not available from
**splines**. Most functions have a very similar user
interface with the function `splines::bs()`

. To be more
specific, it provides functions to construct basis matrices of

- B-splines
- M-splines
- I-splines
- C-splines
- periodic M-splines
- natural cubic splines
- generalized Bernstein polynomials

along with their integrals (except C-splines) and derivatives of given order by closed-form recursive formulas.

Compared to the package **splines**, the package
**splines2** allows piecewise constant basis functions for
B-splines and provides a more user-friendly interface for their
derivatives with consistent handling on `NA`

’s. Most of the
implementations had been (re)written in C++ with the help of
**Rcpp** and **RcppArmadillo** since v0.3.0,
which boosted the computational performance.

In the remaining of this vignette, we illustrated the basic usage of most functions in the package through examples. See the package manual for the details of function usage.

Function `bSpline()`

provides B-spline basis matrix and
allows `degree = 0`

for piece-wise constant basis function,
which extends the `bs()`

function in package
**splines** with a better computational performance. One
example of linear B-splines with three internal knots is given as
follows:

```
library(splines2)
<- c(0.3, 0.5, 0.6)
knots <- seq(0, 1, 0.01)
x <- bSpline(x, knots = knots, degree = 1, intercept = TRUE)
bsMat matplot(x, bsMat, type = "l", ylab = "y")
abline(v = knots, lty = 2, col = "gray")
```

The closed-form recursive formula of B-spline integrals and
derivatives given by De Boor (1978) is
implemented in function `ibs()`

and `dbs()`

,
respectively. Two toy examples are given as follows:

```
<- ibs(x, knots = knots, degree = 1, intercept = TRUE)
ibsMat par(mfrow = c(1, 2))
matplot(x, bsMat, type = "l", ylab = "y")
abline(v = knots, h = 1, lty = 2, col = "gray")
matplot(x, ibsMat, type = "l", ylab = "y")
abline(v = knots, h = c(0.15, 0.2, 0.25), lty = 2, col = "gray")
```

```
<- bSpline(x, knots = knots, intercept = TRUE)
bsMat <- dbs(x, knots = knots, intercept = TRUE)
dbsMat par(mfrow = c(1, 2))
matplot(x, bsMat, type = "l", ylab = "y")
abline(v = knots, lty = 2, col = "gray")
matplot(x, dbsMat, type = "l", ylab = "y")
abline(v = knots, lty = 2, col = "gray")
```

We may also obtain the derivatives easily by the `deriv()`

method as follows:

```
<- function(a, b) {
is_equivalent all.equal(a, b, check.attributes = FALSE)
}stopifnot(is_equivalent(dbsMat, deriv(bsMat)))
```

`mSpline()`

M-splines (Ramsay 1988) are considered
the normalized version of B-splines with unit integral within boundary
knots. An example given by Ramsay (1988)
was a quadratic M-splines with three internal knots placed at 0.3, 0.5,
and 0.6. The default boundary knots are the range of `x`

, and
thus 0 and 1 in this example.

```
<- mSpline(x, knots = knots, degree = 2, intercept = TRUE)
msMat matplot(x, msMat, type = "l", ylab = "y")
abline(v = knots, lty = 2, col = "gray")
```

The derivative of the given order of M-splines can be obtained by
specifying a positive integer to argument `dervis`

of
`mSpline()`

. Also, for an existing `mSpline`

object generated by `mSpline()`

, the `deriv()`

method can be used conveniently. For example, the first derivative of
the M-splines given in the previous example can be obtained equivalently
as follows:

```
<- mSpline(x, knots = knots, degree = 2, intercept = TRUE, derivs = 1)
dmsMat1 <- deriv(msMat)
dmsMat2 stopifnot(is_equivalent(dmsMat1, dmsMat2))
```

The function `mSpline()`

produces periodic splines based
on M-spline basis functions when `periodic = TRUE`

is
specified. The `Boundary.knots`

defines the cyclic interval.
The construction follows periodic B-splines discussed in Piegl and Tiller (1997, chap. 12).

```
<- seq.int(0, 3, 0.01)
x1 <- mSpline(x1, knots = knots, degree = 3, intercept = TRUE,
pmsMat periodic = TRUE, Boundary.knots = c(0, 1))
matplot(x1, pmsMat, type = "l", xlab = "x", ylab = "Periodic Basis")
abline(v = seq.int(0, 3), lty = 2, col = "gray")
```

We may still specify the argument `derivs`

in
`mSpline()`

or use the corresponding `deriv()`

method to obtain the derivatives when `periodic = TRUE`

.

```
<- deriv(pmsMat)
dpmsMat matplot(x1, dpmsMat, type = "l", xlab = "x", ylab = "The 1st derivatives")
abline(v = seq.int(0, 3), lty = 2, col = "gray")
```

Furthermore, we can obtain the integrals of the periodic M-splines by
specifying `integral = TRUE`

. The integral is integrated from
the left boundary knot.

```
<- mSpline(x1, knots = knots, degree = 3, intercept = TRUE,
ipmsMat periodic = TRUE, Boundary.knots = c(0, 1), integral = TRUE)
matplot(x1, ipmsMat, type = "l", xlab = "x", ylab = "Integrals")
abline(v = seq.int(0, 3), h = seq.int(0, 3), lty = 2, col = "gray")
```

`iSpline()`

I-splines (Ramsay 1988) are simply the
integral of M-splines and thus monotonically nondecreasing with unit
maximum value. A monotonically nondecreasing (nonincreasing) function
can be fitted by a linear combination of I-spline basis functions with
nonnegative (nonpositive) coefficients *plus a constant*, where
the coefficient of the constant is unconstrained.

The example given by Ramsay (1988) was the I-splines corresponding to that quadratic M-splines with three internal knots placed at 0.3, 0.5, and 0.6. Notice that the degree of I-splines is defined from the associated M-splines instead of their polynomial degree.

```
<- iSpline(x, knots = knots, degree = 2, intercept = TRUE)
isMat matplot(x, isMat, type = "l", ylab = "y")
abline(h = 1, v = knots, lty = 2, col = "gray")
```

The corresponding M-spline basis matrix can be obtained easily as the
first derivatives of the I-splines by the `deriv()`

method.

`stopifnot(is_equivalent(msMat, deriv(isMat)))`

We may specify the `derivs = 2`

in the
`deriv()`

method for the second derivatives of the I-splines,
which are equivalent to the first derivatives of the corresponding
M-splines.

```
<- deriv(isMat, 2)
dmsMat3 stopifnot(is_equivalent(dmsMat1, dmsMat3))
```

`cSpline`

Convex splines (Meyer 2008) called C-splines are scaled integrals of I-splines with unit maximum value at the right boundary knot. Meyer (2008) applied C-splines to shape-restricted regression analysis. The monotone (nondecreasing) property of I-spines ensures the convexity of C-splines. A convex regression function can be estimated using linear combinations of the C-spline basis functions with nonnegative coefficients, plus an unconstrained linear combination of a constant and an identity function \(g(x)=x\). If the underlying regression function is both increasing and convex, the coefficient on the identity function is restricted to be nonnegative as well.

We may specify the argument `scale = FALSE`

in function
`cSpline()`

to disable the scaling of the integrals of
I-splines. Then the actual integrals of the corresponding I-splines will
be returned. If `scale = TRUE`

(by default), each C-spline
basis is scaled to have unit height at the right boundary knot.

```
<- cSpline(x, knots = knots, degree = 2, intercept = TRUE)
csMat1 matplot(x, csMat1, type = "l", ylab = "y")
abline(h = 1, v = knots, lty = 2, col = "gray")
```

Similarly, the `deriv()`

method can be used to obtain the
derivatives. A nested call of `deriv()`

is supported for
derivatives of order greater than one. However, the argument
`derivs`

of the `deriv()`

method can be specified
directly for better computational performance. For example, the first
and second derivatives can be obtained by the following equivalent
approaches, respectively.

```
<- cSpline(x, knots = knots, degree = 2, intercept = TRUE, scale = FALSE)
csMat2 stopifnot(is_equivalent(isMat, deriv(csMat2)))
stopifnot(is_equivalent(msMat, deriv(csMat2, 2)))
stopifnot(is_equivalent(msMat, deriv(deriv(csMat2))))
```

The Bernstein polynomials have also been applied to shape-constrained regression analysis (Wang and Ghosh 2012). The \(i\)-th basis of the generalized Bernstein polynomials of degree \(n\) over \([a, b]\) is defined as follows: \[ B_i^n(x)=\frac{1}{(b-a)^n}{n\choose i}(x-a)^i (b-x)^{n-i},~i\in\{0,\ldots,n\}, \] where \(a\le x\le b\). Obviously, it reduces to regular Bernstein polynomials defined over \([0, 1]\) when \(a = 0\) and \(b = 1\).

We may obtain the basis matrix of the generalized using the function
`bernsteinPoly()`

. For example, the Bernstein polynomials of
degree 4 over \([0, 1]\) and is
generated as follows:

```
<- seq.int(0, 1, 0.01)
x1 <- seq.int(- 1, 1, 0.01)
x2 <- bernsteinPoly(x1, degree = 4, intercept = TRUE)
bpMat1 <- bernsteinPoly(x2, degree = 4, intercept = TRUE)
bpMat2 par(mfrow = c(1, 2))
matplot(x1, bpMat1, type = "l", ylab = "y")
matplot(x2, bpMat2, type = "l", ylab = "y")
```

In addition, we may specify `integral = TRUE`

or
`derivs = 1`

in `bernsteinPoly()`

for their
integrals or first derivatives, respectively.

```
<- bernsteinPoly(x1, degree = 4, intercept = TRUE, integral = TRUE)
ibpMat1 <- bernsteinPoly(x2, degree = 4, intercept = TRUE, integral = TRUE)
ibpMat2 <- bernsteinPoly(x1, degree = 4, intercept = TRUE, derivs = 1)
dbpMat1 <- bernsteinPoly(x2, degree = 4, intercept = TRUE, derivs = 1)
dbpMat2 par(mfrow = c(2, 2))
matplot(x1, ibpMat1, type = "l", ylab = "Integrals")
matplot(x2, ibpMat2, type = "l", ylab = "y")
matplot(x1, dbpMat1, type = "l", ylab = "y")
matplot(x2, dbpMat2, type = "l", ylab = "y")
```

Similarly, we may also use the `deriv()`

method to get
derivatives of an existing `bernsteinPoly`

object.

```
stopifnot(is_equivalent(dbpMat1, deriv(bpMat1)))
stopifnot(is_equivalent(dbpMat2, deriv(bpMat2)))
stopifnot(is_equivalent(dbpMat1, deriv(ibpMat1, 2)))
stopifnot(is_equivalent(dbpMat2, deriv(ibpMat2, 2)))
```

The function `naturalSpline()`

returns nonnegative basis
functions (within the boundary) for natural cubic splines by utilizing
the closed-form null space derived from the second derivatives of cubic
B-splines. While `splines::ns()`

uses QR decomposition to
find the null space of the second derivatives of B-spline basis at
boundary knots with no guarantee that the resulting basis functions are
nonnegative within the boundary. When `integral = TRUE`

,
`naturalSpline()`

returns integral of each natural spline
basis.

```
<- naturalSpline(x, knots = knots, intercept = TRUE)
nsMat <- naturalSpline(x, knots = knots, intercept = TRUE, integral = TRUE)
insMat par(mfrow = c(1, 2))
matplot(x, nsMat, type = "l", ylab = "Basis")
matplot(x, insMat, type = "l", ylab = "Integrals")
```

`stopifnot(is_equivalent(nsMat, deriv(insMat)))`

Similar to `bernsteinPoly()`

, one may specify the argument
`derivs`

in `naturalSpline()`

or use the
corresponding `deriv()`

method to obtain the derivatives of
spline basis functions.

```
<- naturalSpline(x, knots = knots, intercept = TRUE, derivs = 1)
d1nsMat <- deriv(nsMat, 2)
d2nsMat par(mfrow = c(1, 2))
matplot(x, d1nsMat, type = "l", ylab = "The 1st derivatives")
matplot(x, d2nsMat, type = "l", ylab = "The 2nd derivatives")
```

`predict`

The methods for **splines2** objects dispatched by
generic function `predict`

will be useful if we want to
evaluate the spline object at possibly new \(x\) values. For instance, we may evaluate
the value of I-splines object in the previous example at 0.275, 0.525,
and 0.8, respectively, as follows:

```
<- c(0.275, 0.525, 0.8)
new_x names(new_x) <- paste0("x=", new_x)
predict(isMat, new_x)
```

```
## 1 2 3 4 5 6
## x=0.275 0.9994213 0.7730556 0.2310764 0.0000000 0.000000 0.000
## x=0.525 1.0000000 1.0000000 0.9765625 0.2696429 0.000625 0.000
## x=0.8 1.0000000 1.0000000 1.0000000 0.9428571 0.580000 0.125
```

Technically speaking, the methods take all information needed, such
as `knots`

, `degree`

, `intercept`

,
etc., from attributes of the original objects and call the corresponding
function automatically for those new \(x\) values. Therefore, the
`predict`

methods will not be applicable if those attributes
are somehow lost after some operations.

De Boor, Carl. 1978. *A Practical Guide to Splines*. Vol. 27. New
York: springer-verlag. https://doi.org/10.1002/zamm.19800600129.

Meyer, Mary C. 2008. “Inference Using Shape-Restricted Regression
Splines.” *The Annals of Applied Statistics* 2 (3):
1013–33. https://doi.org/10.1214/08-aoas167.

Piegl, Les, and Wayne Tiller. 1997. *The NURBS
Book*. 2nd ed. Springer Science & Business Media.

Ramsay, James O. 1988. “Monotone Regression Splines in
Action.” *Statistical Science* 3 (4): 425–41. https://doi.org/10.1214/ss/1177012761.

Wang, Jiangdian, and Sujit K. Ghosh. 2012. “Shape Restricted
Nonparametric Regression with Bernstein
Polynomials.” *Computational Statistics & Data
Analysis* 56 (9): 2729–41.