# SLATEC Common Mathematical Library -- Table of Contents

## SECTION I. User-callable Routines

Category L. Statistics, probability

L5. Function evaluation
L6. Pseudo-random number generation
L7. Experimental design, including analysis of variance
L8. Regression
L5. Function evaluation (search also class C)
L5A. Univariate
L5A1. Cumulative distribution functions, probability density functions
L5A1E. Error function, exponential, extreme value
ERF-S Compute the error function.
DERF-D
ERFC-S Compute the complementary error function.
DERFC-D
L6. Pseudo-random number generation
L6A. Univariate
L6A14. Negative binomial, normal
RGAUSS-S Generate a normally distributed (Gaussian) random number.
L6A21. Uniform
RAND-S Generate a uniformly distributed random number.
RUNIF-S Generate a uniformly distributed random number.
L7. Experimental design, including analysis of variance
L7A. Univariate
L7A3. Analysis of covariance
CV-S Evaluate the variance function of the curve obtained
DCV-D by the constrained B-spline fitting subprogram FC.
L8. Regression (search also classes G, K)
L8A. Linear least squares (L-2) (search also classes D5, D6, D9)
L8A3. Piecewise polynomial (i.e. multiphase or spline)
EFC-S Fit a piecewise polynomial curve to discrete data.
DEFC-D The piecewise polynomials are represented as B-splines.
The fitting is done in a weighted least squares sense.
FC-S Fit a piecewise polynomial curve to discrete data.
DFC-D The piecewise polynomials are represented as B-splines.
The fitting is done in a weighted least squares sense.
Equality and inequality constraints can be imposed on the
fitted curve.