56 z1=(
x(j)-mu(i))/sig(i);
57 z2=(
x(j+1)-mu(i))/sig(i);
94 z1=(
x(j)-mu(i))/sig(i);
95 z2=(
x(j+1)-mu(i))/sig(i);
115 double xs=0.5*(x[sj+1]-x[sj]);
120 z1=((
x(j)-xs)-mu(i))/sig(i);
121 z2=((
x(j)+xs)-mu(i))/sig(i);
void RETURN_ARRAYS_DECREMENT(void)
Decrements gradient_structure::RETURN_ARRAYS_PTR.
Vector of double precision numbers.
int indexmin() const
Get minimum valid index.
void initialize(void)
Zero initialize allocated dvar_matrix, then saves adjoint function and position data.
double sum(const d3_array &darray)
Author: David Fournier Copyright (c) 2008-2012 Regents of the University of California.
dvar_matrix ageLengthKey(const dvar_vector &mu, const dvar_vector &sig, const dvector &x)
Age Length Key.
int indexmax() const
Get maximum valid index.
Description not yet available.
Library of statistic functions.
Class definition of matrix with derivitive information .
void RETURN_ARRAYS_INCREMENT(void)
Increments gradient_structure::RETURN_ARRAYS_PTR.
double cumd_norm(const double &x)
Culative normal distribution; constant objects.
void initialize(void)
Author: David Fournier Copyright (c) 2008-2012 Regents of the University of California.
Fundamental data type for reverse mode automatic differentiation.
dvar_matrix ALK(dvar_vector mu, dvar_vector sig, dvector x)