We define *fDistances*, which generalize Euclidean distances, squared distances, and log distances. The least squares loss function to fit fDistances to dissimilarity data is *fStress*. We give formulas and R/C code to compute partial derivatives of orders one to four of fStress, relying heavily on the use of Fa\`a di Bruno's chain rule formula for higher derivatives.

翻译：我们定义了*fDistances*，它推广了欧氏距离、平方距离和对数距离。将fDistances拟合到相异性数据的最小二乘损失函数称为*fStress*。我们给出了计算fStress一阶至四阶偏导数的公式与R/C代码，其中大量依赖于使用Faà di Bruno链式法则公式来计算高阶导数。