Note: This is a working paper which will be expanded/updated frequently. All suggestions for improvement are welcome. The directory gifi.stat.ucla.edu/speed has a pdf version, the complete Rmd file with all code chunks, the bib file, and the R source code.

# 1 Introduction

The multidimensional scaling (MDS) loss function sstress is defined on the set $$\mathbb{R}^{n\times p}$$ of $$n\times p$$ configuration matrices as $$$\label{E:sstress} \sigma(X):=\frac12\sum_{j=1}^n\sum_{j=1}^n w_{ij}(\delta_{ij}^2-d_{ij}^2(X))^2,$$$ where $$\Delta=\{\delta_{ij}\}$$ and $$W=\{w_{ij}\}$$ are symmetric, non-negative, and hollow matrices of dissimilarities and weights (a matrix is hollow if its diagonal is zero). The $$d_{ij}^2(X)$$ in $$\eqref{E:sstress}$$ are squared Euclidean distances, defined as $$$\label{E:dist} d_{ij}^2(X):=(x_i-x_j)'(x_i-x_j)=\mathbf{tr}\ X'A_{ij}X=\mathbf{tr}\ A_{ij}C=c_{ii}+c_{jj}-2c_{ij},$$$

where $$C=XX'$$ and $$A_{ij}:=(u_i-u_j)(u_i-u_j)'$$, with the $$u_i$$ unit vectors (element $$i$$ is $$+1$$, the other elements are zero).

The majorization method to minimize sstress $$\eqref{E:sstress}$$ computes the update of the configuration matrix in iteration $$k$$ using the iteration function $$$\label{E:elegant} \Phi(X):=\Gamma_p(B_\beta(X)).$$$

Here $$\Gamma_p$$ computes the $$n\times p$$ matrix whose row-wise cross-product provides a best least squares rank $$p$$ approximation of its argument. Thus, with $$\mathbf{SSQ}$$ for the (unweighted) sum of squares, $\Gamma_p(B):=\mathop{\mathbf{argmin}}_{X\in\mathbb{R}^{n\times p}}\mathbf{SSQ}\ (B-XX').$ The stationary equation for this minimization problem are $$BX=X(X'X)$$. Suppose $$B=K\Lambda K'$$ is a complete eigen decomposition of $$B$$, and suppose the $$p$$ largest eigenvalues are non-negative. Collect them in the diagonal matrix $$\Lambda_p$$, with the corresponding $$p$$ eigenvectors in $$K_p$$. Then $$K_p\Lambda_p^\frac12$$ is a minimizer of $$\mathbf{SSQ}(B-XX')$$. It is unique, up to rotation, if $$\lambda_p>\lambda_{p+1}$$.

The function $$B_\beta$$ is defined, using a step-size or relaxation type parameter $$\beta$$, by \begin{align} B_\beta(X)&:=XX'+\frac{1}{\beta}R(X),\label{E:B}\\ R(X)&:=\sum_{i=1}^n\sum_{j=1}^n w_{ij}(\delta^2_{ij}-d_{ij}^2(X))A_{ij}.\label{E:R} \end{align}

Note that the rank of $$\Phi(X)$$ is equal to the minimum of $$p$$ and the number of positive eigenvalues of $$B(X)$$. If $$X$$ has rank $$p$$ and $$\beta$$ is large enough, then $$B(X)$$ will also be of rank $$p$$. Matrix $$B_\beta(X)$$ is invariant under rotation of $$X$$. We have $$\mathcal{D}\sigma(X)=-2R(X)X$$ and consequently $$X$$ is a stationary point of $$\sigma$$ if and only if $$B(X)X=X(X'X)$$, i.e. if and only if $$X$$ is a fixed point of $$\Phi$$. Also note that if $$X$$ is centered (columnwise) then $$B(X)$$ is doubly-centered.

We call $$\eqref{E:elegant}$$ the ELEGANT transform, because it derives from the ELEGANT algorithm to minimize sstress (De Leeuw (1975)). The ELEGANT iteration is $$$\label{E:iterate} X^{(k+1)}=\Phi(X^{(k)})=\Gamma_p(B(X^{(k)})).$$$

For a modernized and improved presentation of ELEGANT we refer to De Leeuw, Groenen, and Pietersz (2016). In the original (unweighted) ELEGANT algorithm $$\beta=4n^2$$, in the improved majorization algorithm of De Leeuw, Groenen, and Pietersz (2016) it is $$\beta=4n$$.

# 2 Derivatives of the ELEGANT transform

## 2.1 Derivatives of Gamma

We start with the definition $$\Gamma_p(B)=K_p\Lambda_p^\frac12$$, where that $$B=K\Lambda K'$$ is the complete eigen decomposition. Assume all $$\lambda_t$$ are different, and define $$\Xi:=K'EK$$. Using results from, for example, De Leeuw (2008), we see that \begin{align*} \lambda_s^\frac12(B+E)&=\lambda_s^\frac12+\frac12\lambda_s^{\frac12}\xi_{ss}+o(\|E\|),\\ k_s(B+E)&=k_s-(B-\lambda_sI)^+Ek_s+o(\|E\|). \end{align*} Thus column $$s$$ of $$Y:=\mathcal{D}\Gamma(X)(E)$$ is $$$\label{E:gamder} y_s=\frac12\lambda_s^{-\frac12}\xi_{ss}k_s-\lambda_s^\frac12\sum_{\substack{t=1\\ t\not=s}}^n\frac{1}{\lambda_t-\lambda_s}\xi_{st}k_t$$$

The formula is implemented in the R function map_dgamma. We can check equation $$\eqref{E:gamder}$$ by computing numerical derivatives, using the funcion jacobian from the numDeriv package (Gilbert and Varadhan (2016)).

set.seed(12345)
b <- crossprod (matrix (rnorm(400), 100, 4)) / 100
b <- doubleCenter (b)
e <- aij (2, 3, 4)
func <- function (z, b, e)
return (map_gamma (b + z * e, p = 2))
mprint (matrix (jacobian (func, 0, b = b, e = e), 4, 2))
##      [,1]            [,2]
## [1,]   -1.6998187769   -0.1816313139
## [2,]    3.0764177033    0.9917597934
## [3,]   -0.7816264815    1.8727796964
## [4,]   -0.5949724449   -2.6829081760
mprint (map_dgamma (b, e, p = 2))
##      [,1]            [,2]
## [1,]   -1.6998187769    0.1816313139
## [2,]    3.0764177033   -0.9917597934
## [3,]   -0.7816264815   -1.8727796964
## [4,]   -0.5949724449    2.6829081759

## 2.2 Derivatives of B

The ELEGANT transform is a composition of the map $$\Gamma_p$$ and the map $$B_\beta$$. Because

$B_\beta(X+E)=B_\beta(X)+XE'+EX'-\frac{2}{\beta}\sum_{i=1}^n\sum_{j=1}^n w_{ij}(\mathbf{tr}\ X'A_{ij}E)A_{ij}+o(\|E\|),$ we find $$$\label{E:bder} \mathcal{D}B_\beta(X)(E)=XE'+EX'-\frac{2}{\beta}\sum_{i=1}^n\sum_{j=1}^n w_{ij}(x_i-x_j)'(e_i-e_j)A_{ij}$$$

Note that this derivative does not depend on the $$\delta_{ij}$$. Also note that, because $$B_\beta$$ is invariant under rotation, if $$E=XS$$ with $$S$$ anti-symmetric, then $$\mathcal{D}B_\beta(X)(E)=0$$. If $$E$$ is a translation, in which all rows are the same, then $$\mathcal{D}B_\beta(X)(E)=XE'+EX'$$.

Again, we check the formula implemented in the function map_db numerically using jacobian.

set.seed(12345)
delta <- 1 - diag (4)
a <- center (matrix (rnorm (8), 4, 2))
b <- center (matrix (rnorm (8), 4, 2))
func <- function (z, a, b) {
return (map_b (delta, x = a + z * b, w = 1 - diag (4), beta = 16))
}
mprint (matrix (jacobian (func, 0, a = a, b = b), 4, 4))
##      [,1]            [,2]            [,3]            [,4]
## [1,]    0.6771742494   -0.7255711172   -0.7145306231    0.7629274909
## [2,]   -0.7255711172   -0.2659852113    1.0607440880   -0.0691877594
## [3,]   -0.7145306231    1.0607440880   -0.0318313857   -0.3143820791
## [4,]    0.7629274909   -0.0691877594   -0.3143820791   -0.3793576523
mprint (map_db (a, b, w = delta, beta = 16))
##      [,1]            [,2]            [,3]            [,4]
## [1,]    0.6771742494   -0.7255711172   -0.7145306231    0.7629274909
## [2,]   -0.7255711172   -0.2659852113    1.0607440880   -0.0691877594
## [3,]   -0.7145306231    1.0607440880   -0.0318313857   -0.3143820791
## [4,]    0.7629274909   -0.0691877594   -0.3143820791   -0.3793576523

## 2.3 Chain Rule

We can combine $$\eqref{E:gamder}$$ and $$\eqref{E:bder}$$ using the chain rule $$$\label{E:chain} \Phi(X+E)=\Phi(X)+\mathcal{D}\Gamma_p(B_\beta(X))(\mathcal{D}B_\beta(X)(E))+o(\|E\|),$$$

where for computational purposes we substitute $$\eqref{E:gamder}$$ and $$\eqref{E:bder}$$ in $$\eqref{E:chain}$$.

Formula $$\eqref{E:chain}$$ is implemented in map_dphi and can be tested with jacobian.

##      [,1]            [,2]
## [1,]    0.2360189701   -0.0949984809
## [2,]   -0.0241459646   -0.0452932309
## [3,]   -0.2355205803    0.2879923870
## [4,]    0.0236475748   -0.1477006752
##      [,1]            [,2]
## [1,]   -0.2360189702    0.0949984810
## [2,]    0.0241459646    0.0452932309
## [3,]    0.2355205805   -0.2879923873
## [4,]   -0.0236475748    0.1477006754

# 3 Computing Convergence Rates

The obvious way to measure the rate of convergence of iterative methods with convergence order one (i.e. linear convergence, or convergence at the rate of a geometric progression) is to compute the sequence $\eta^{(k)}_Q=\frac{\|X^{(k)}-X^{(k-1)}\|}{\|X^{(k-1)}-X^{(k-2)}\|},$ and then estimate $$\lim_{k\rightarrow\infty}\eta^{(k)}_Q$$. Unfortunately in practice this may not be as easy as it looks. The sequence $$\{\eta^{(k)}_Q\}$$ depends on the norm chosen, but also on the sequence $$\{X^{(k)}\}$$. Sequence $$\{\eta^{(k)}_Q\}$$ may not converge at all, or have more than one converging subsequence. Different sequences converging to the same limit point $$X_\infty$$, for instance from different random starts, will lead to different sequences $$\{\eta^{(k)}_Q\}$$, possibly with different limits. Computation of $$\eta^{(k)}_Q$$ in later iterations is numerically problematic, because it requires us to compute the ratio of two very small quantities. The version of ELEGANT used in this paper, implemented in the R function beyond, computes this estimate of the convergence rate, but if we require to much precision in our MDS solution we do see a lot numerical instability.

The theoretical convergence rate is the spectral radius of the derivative at the solution. i.e. the eigenvalue of maximum modulus of the Jacobian. Thus this theoretical rate is defined only if $$\Gamma_p$$ is differentiable at $$B(X)$$, which requires that the first $$p$$ eigenvalues of $$B(X)$$ are different, and $$\lambda_p>\lambda_{p+1}$$.

## 3.1 Small Example

Our first example has four objects, with squared dissimilarities $\begin{bmatrix} 0&1&2&3\\ 1&0&1&2\\ 2&1&0&1\\ 3&2&1&0 \end{bmatrix}.$

delta <- abs (outer (1:4, 1:4, "-"))
hs <- beyond(delta, bound = "eval", verbose = FALSE, eps = 1e-6, itmax = 5000)
hg <- beyond(delta, bound = "trace", verbose = FALSE, eps = 1e-6, itmax = 5000)

We iterate ELEGANT transforms until the norm of the difference between successive solutions is less than 10^{-6}. The theoretical convergence rate is computed by the function power, an ad-hoc version of the power method that iterates eigenvectors as matrices. For the original ELEGANT bound $$\beta=4n^2=64$$ the observed convergence rate is 0.9204716502, the theoretical convergence rate is 0.9407957901, and we need 87 iterations. For $$\beta=4n=16$$ the observed convergence rate is 0.7598695801, the theoretical convergence rate is 0.7599226041.

We use the function janJacobian to compute the Jacobian and its eigenvalues for $$\beta=4n=16$$ and $$\beta=4n^2=64$$.

h1 <- janJacobian (delta, hs$a, beta = 16) h2 <- janJacobian (delta, hg$a, beta = 64)
ea <- eigen (h1)
eb <- eigen (h2)
mprint (Mod(ea$values)) ## [1] 0.7599223785 0.6225704947 0.6144170594 0.4999996330 ## [5] 0.2118440380 0.0000000000 mprint (Mod(eb$values))
## [1]    0.9407953252    0.9177247789    0.9089519333    0.8749994492
## [5]    0.8031848002    0.0000000000

We can also use the jacobian function from the numDeriv package and find the eigenvalues of the numerical Jacobian.

func <- function (alpha, beta) {
return (map_phi (delta, alpha, w = 1 - diag (4), p = 2, beta = beta, basis = matrixBasis (4, 2)))
}
h1 <- jacobian (func, hs$a, beta = 16) h2 <- jacobian (func, hg$a, beta = 64)
e1 <- eigen (h1)
e2 <- eigen (h2)
mprint(Mod(e1$values)) ## [1] 0.7599223785 0.6225704947 0.6144170594 0.4999996329 ## [5] 0.2118440380 0.0000000000 mprint(Mod(e2$values))
## [1]    0.9407953252    0.9177247789    0.9089519333    0.8749994492
## [5]    0.8031848002    0.0000000000

## 3.2 Ekman Example

The second example uses the familiar color data from Ekman (1954). We use four different values of $$\beta$$, compute the solution minimizing sstress, and compute the empirical and theoretical convergence rates at the solution.

## bound   728  ||  itel  1172  sstress  3.3187855776  erate  0.9960504502  frate  0.9963645200
## bound    56  ||  itel   136  sstress  3.3187849642  erate  0.9502152597  frate  0.9516375828
## bound    25  ||  itel    64  sstress  3.3187849612  erate  0.8858979435  frate  0.8881851079
## bound    10  ||  itel    24  sstress  3.3187849607  erate  0.6913989974  frate  0.6938911909

All four values of $$\beta$$ give the same solution. The first two are the original ELEGANT value and the maximum eigenvalue bound of the majorization method of De Leeuw, Groenen, and Pietersz (2016). For these two values the convergence is guaranteed by our theoretical results. The iterations with smaller beta still converge monotonically (i.e. the iterations consistently decrease sstress values). For values of $$\beta$$ less than ten convergence becomes unstable. An interesting problem for further study is how low we can generally go.

# 4 Appendix: Code

## 4.1 auxilary.R

ei <- function (i, n) {
return (ifelse(i == (1:n), 1, 0))
}

aij <- function (i, j, n) {
u <- ei(i, n) - ei (j, n)
return (outer (u, u))
}

kdelta <- function (i, j) {
ifelse (i == j, 1 , 0)
}

mprint <- function (x, d = 10, w = 15) {
print (noquote (formatC (
x, di = d, wi = w, fo = "f"
)))
}

mnorm <- function (x) {
return (sqrt (sum (x ^ 2)))
}

anorm <- function (x) {
return (max (abs (x)))
}

basis <- function (i, j, n) {
s <- sqrt (2) / 2
a <- matrix (0, n, n)
if (i == j)
a[i, i] <- 1
else {
a[i, j] <- a[j, i] <- s
}
return (a)
}

center <- function (x) {
return (apply (x, 2, function (z) z - mean (z)))
}

doubleCenter <- function (x) {
n <- nrow (x)
j <- diag(n) - (1 / n)
return (j %*% x %*% j)
}

squareDist <- function (x) {
d <- diag (x)
return (outer (d, d, "+") - 2 * x)
}

lowerTrapezoidal <- function (x) {
n <- nrow (x)
p <- ncol (x)
if (p == 1) return (x)
for (i in 1:(p - 1))
for (j in (i + 1):p) {
a <- diag (p)
y <- x[i, c(i, j)]
y <- y / sqrt (sum (y ^ 2))
a[i, c (i, j)] <- c(1, -1) * y
a[j, c (j, i)] <- y
x <- x %*% a
}
return (x)
}

symmetricFromTriangle <- function (x, lower = TRUE, diagonal = TRUE) {
k <- length (x)
if (diagonal)
n <- (sqrt (1 + 8 * k) - 1) / 2
else
n <- (sqrt (1 + 8 * k) + 1) / 2
if (n != as.integer (n))
stop ("input error")
nn <- 1:n
if (diagonal && lower)
m <- outer (nn, nn, ">=")
if (diagonal && (!lower))
m <- outer (nn, nn, "<=")
if ((!diagonal) && lower)
m <- outer (nn, nn, ">")
if ((!diagonal) && (!lower))
m <- outer (nn, nn, "<")
b <- matrix (0, n, n)
b[m] <- x
b <- b + t(b)
if (diagonal)
diag (b) <- diag(b) / 2
return (b)
}

triangleFromSymmetric <- function (x, lower = TRUE, diagonal = TRUE) {
n <- ncol (x)
nn <- 1:n
if (diagonal && lower)
m <- outer (nn, nn, ">=")
if (diagonal && (!lower))
m <- outer (nn, nn, "<=")
if ((!diagonal) && lower)
m <- outer (nn, nn, ">")
if ((!diagonal) && (!lower))
m <- outer (nn, nn, "<")
return (x[m])
}

columnBasis <- function (n) {
x <- matrix (0, n, n - 1)
x[outer (1:n, 1:(n - 1), "<=")] <- -1
x[outer (1:n, 1:(n - 1), function (i, j)
i - j == 1)] <- 1:(n - 1)
return (apply (x, 2, function (y)
y / sqrt (sum (y ^ 2))))
}

matrixBasis <- function (n, p) {
x <- matrix (0, n * p, p * (n - 1))
for (j in 1:p) {
x [((j - 1) * n) + (1:n), ((j - 1) * (n - 1)) + (1:(n - 1))] <-
columnBasis (n)
}
return (x)
}

## 4.2 elegant.R

suppressPackageStartupMessages(library(mgcv, quietly = TRUE))

torgerson <- function (delta, p = 2) {
z <- slanczos(-doubleCenter(delta / 2), p)
v <- pmax(z$values, 0) return(z$vectors %*% diag(sqrt(v)))
}

beyond <-
function (delta,
w = 1 - diag (nrow (delta)),
p = 2,
xold = torgerson (delta, p),
bound = "eval",
basis = matrixBasis (nrow(delta), p),
itmax = 1000,
eps = 1e-8,
verbose = TRUE) {
n <- nrow (delta)
itel <- 1
vv <- matrix (0, n ^ 2, n ^ 2)
if (is.numeric (bound))
lbd <- bound
if (bound == "eval") {
for (i in 1:n)
for (j in 1:n)
vv <-
vv + w[i, j] * kronecker (aij (i, j, n), aij (i, j, n))
lbd <- slanczos(vv, 1)$values } if (bound == "trace") lbd <- 4 * sum (w) eold <- Inf cold <- tcrossprod (xold) dold <- squareDist (cold) sold <- sum (w * (delta - dold) ^ 2) aold <- drop (as.vector (xold) %*% basis) repeat { anew <- map_phi (delta, aold, w, p, lbd, basis) xnew <- matrix (basis %*% anew, n, p) enew <- mnorm (anew - aold) rnew <- enew / eold cnew <- tcrossprod (xnew) dnew <- squareDist (cnew) snew <- sum (w * (delta - dnew) ^ 2) if (verbose) { cat ( formatC (itel, width = 4, format = "d"), formatC ( sold, digits = 10, width = 15, format = "f" ), formatC ( snew, digits = 10, width = 15, format = "f" ), formatC ( enew, digits = 10, width = 15, format = "f" ), formatC ( rnew, digits = 10, width = 15, format = "f" ), "\n" ) } if ((itel == itmax) || (enew < eps)) break itel <- itel + 1 xold <- xnew aold <- anew dold <- dnew cold <- cnew eold <- enew sold <- snew } return (list ( x = xnew, a = anew, c = cnew, d = dnew, bound = lbd, itel = itel, e = enew, r = rnew, s = snew )) } ## 4.3 partials.R map_b <- function (delta, x, w, beta) { s <- tcrossprod (x) d <- squareDist (s) r <- -2 * w * (delta - d) diag (r) <- -rowSums (r) return (s + r / beta) } map_gamma <- function (b, p) { e <- slanczos (b, p) ea <- e$values
ev <- e$vectors return (ev[, 1:p] %*% diag (sqrt (pmax (0, ea[1:p])))) } map_phi <- function (delta, alpha, w = 1 - diag (nrow (delta)), p = 2, beta = 4 * nrow (delta), basis = matrixBasis (nrow(delta), p)) { n <- nrow (delta) x <- matrix (basis %*% alpha, n, p) b <- map_b (delta, x, w, beta) x <- map_gamma (b, p) return (drop (as.vector(x) %*% basis)) } map_db <- function (x, y, w, beta) { n <- nrow (x) da <- tcrossprod (x, y) + tcrossprod (y, x) dd <- matrix (0, n, n) for (i in 1:n) for (j in 1:n) dd <- dd + w[i, j] * sum ((x[i,] - x[j,]) * (y[i,] - y[j,])) * aij (i, j, n) return (da - 2 * dd / beta) } map_dgamma <- function (b, db, p) { n <- nrow (b) e <- eigen (b) l <- e$values
k <- e\$vectors
xi <- crossprod (k, db %*% k)
dg <- matrix (0, n, p)
for (s in 1:p)
{
dgs <- (1 / (2 * sqrt (l[s]))) * xi[s, s] * k[, s]
for (t in 1:n) {
if (t == s)
next
dgs <- dgs - (sqrt (l[s]) / (l[t] - l[s])) * xi[s, t] * k[, t]
}
dg[, s] <- dgs
}
return (dg)
}

map_dphi <-
function (delta,
alpha,
epsilon,
w = 1 - diag (nrow (delta)),
p = 2,
beta = 4 * nrow (delta),
basis = matrixBasis (nrow(delta), p)) {
n <- nrow (delta)
x <- matrix (basis %*% alpha, n, p)
y <- matrix (basis %*% epsilon, n, p)
b <- map_b (delta, x, w, beta)
db <- map_db (x, y, w, beta)
dg <- map_dgamma (b, db, p)
return (drop (as.vector(dg) %*% basis))
}

janJacobian <- function (delta,
alpha,
w = 1 - diag (nrow (delta)),
p = 2,
beta = 4 * nrow (delta),
basis = matrixBasis (nrow(delta), p)) {
n <- nrow (delta)
m <- p * (n - 1)
j <- matrix (0, m, m)
for (i in 1:m) {
j[i, ] <- map_dphi (delta, alpha, ei (i, m), w, p, beta, basis)
}
return (j)
}

power <-
function (delta,
x,
w = 1 - diag (nrow (x)),
beta = 4 * nrow (x),
itmax = 1000,
eps = 1e-10,
verbose = FALSE,
basis = matrixBasis (nrow(x), ncol(x))) {
n <- nrow (x)
p <- ncol (x)
set.seed (12345)
y <- drop (as.vector (x) %*% basis)
eold <- ei (1, ncol (basis))
lold <- -Inf
itel <- 1
repeat {
enew <- map_dphi (delta, y, eold, w, p, beta, basis)
lnew <- sum (eold * enew)
enew <- enew / mnorm (enew)
edif <- 1 - abs (sum (eold * enew))
if (verbose) {
cat (
formatC (itel, width = 4, format = "d"),
formatC (
lold,
digits = 10,
width = 15,
format = "f"
),
formatC (
lnew,
digits = 10,
width = 15,
format = "f"
),
formatC (
edif,
digits = 10,
width = 15,
format = "f"
),
"\n"
)
}
if ((itel == itmax) || (edif < eps))
break
eold <- enew
lold <- lnew
itel <- itel + 1
}
return (list (e = enew, l = lnew, itel = itel))
}

# References

De Leeuw, J. 1975. “An Alternating Least Squares Approach to Squared Distance Scaling.” Department of Data Theory FSW/RUL.

———. 2008. “Derivatives of Fixed-Rank Approximations.” Preprint Series 547. Los Angeles, CA: UCLA Department of Statistics. http://www.stat.ucla.edu/~deleeuw/janspubs/2008/reports/deleeuw_R_08b.pdf.

De Leeuw, J., P. Groenen, and R. Pietersz. 2016. “An Alternating Least Squares Approach to Squared Distance Scaling.” doi:10.13140/RG.2.2.15357.97766.

Ekman, G. 1954. “Dimensions of Color Vision.” Journal of Psychology 38: 467–74.

Gilbert, P., and R. Varadhan. 2016. numDeriv: Accurate Numerical Derivatives. https://R-Forge.R-project.org/projects/optimizer/.