Uses of Interface
pal.math.UnivariateFunction

Packages that use UnivariateFunction
pal.distance Classes for reading and generating distance matrices, including computation of pairwise distances for sequence data (maximum-likelihood and observed distances). 
pal.math Classes for math stuff such as optimisation, numerical derivatives, matrix exponentials, random numbers, special function etc. 
 

Uses of UnivariateFunction in pal.distance
 

Classes in pal.distance that implement UnivariateFunction
 class SequencePairLikelihood
          computation of the (negative) log-likelihood for a pair of sequences
 

Uses of UnivariateFunction in pal.math
 

Classes in pal.math that implement UnivariateFunction
 class LineFunction
          converts a multivariate function into a univariate function
 class OrthogonalLineFunction
          converts a multivariate function into a univariate function by keeping all but one argument constant
 

Methods in pal.math with parameters of type UnivariateFunction
 double UnivariateMinimum.findMinimum(double x, UnivariateFunction f)
          Find minimum (first estimate given)
 double UnivariateMinimum.findMinimum(double x, UnivariateFunction f, int fracDigits)
          Find minimum (first estimate given, desired number of fractional digits specified)
 double UnivariateMinimum.findMinimum(UnivariateFunction f)
          Find minimum (no first estimate given)
 double UnivariateMinimum.findMinimum(UnivariateFunction f, int fracDigits)
          Find minimum (no first estimate given, desired number of fractional digits specified)
static double NumericalDerivative.firstDerivative(UnivariateFunction f, double x)
          determine first derivative
 double UnivariateMinimum.optimize(double x, UnivariateFunction f, double tol)
          The actual optimization routine (Brent's golden section method)
 double UnivariateMinimum.optimize(double x, UnivariateFunction f, double tol, double lowerBound, double upperBound)
          The actual optimization routine (Brent's golden section method)
 double UnivariateMinimum.optimize(UnivariateFunction f, double tol)
          The actual optimization routine (Brent's golden section method)
 double UnivariateMinimum.optimize(UnivariateFunction f, double tol, double lowerBound, double upperBound)
          The actual optimization routine (Brent's golden section method)
static double NumericalDerivative.secondDerivative(UnivariateFunction f, double x)
          determine second derivative