Chapter Introduction |
Module 1.1:
nag_lib_support - Library Support Facilities |
nag_lib_ident | Prints details of the Library implementation |
nag_deallocate | Deallocates storage from structures with types defined by the Library |
Module 1.2:
nag_error_handling - Error Handling |
nag_set_error | Controls how errors are to be handled by the Library |
nag_error | Communicates information about error-handling between a user's program and the Library (type) |
Module 1.3:
nag_write_mat - Matrix Printing |
nag_write_gen_mat | Writes a real, complex or integer general matrix |
nag_write_tri_mat | Writes a real or complex triangular matrix |
nag_write_bnd_mat | Writes a real or complex band matrix |
Module 1.4:
nag_sort - Sorting |
nag_sort_vec | Sorts a vector of numeric or character data into ascending or descending order |
nag_rank_vec | Ranks a vector of numeric or character data in ascending or descending order |
nag_reorder_vec | Reorders a vector of numeric or character data into the order specified by a vector of ranks |
nag_rank_mat | Ranks the rows or columns of a matrix of integer or real numbers in ascending or descending order |
nag_rank_arb_data | Ranks arbitrary data according to a user-supplied comparison procedure |
nag_invert_perm | Inverts a permutation, thus converts a rank vector to an index vector, or vice versa |
nag_check_perm | Checks the validity of a permutation |
nag_decomp_perm | Decomposes a permutation into cycles, as an aid to reordering ranked data |
Module 1.5:
nag_math_constants - Mathematical Constants |
nag_pi | Returns an approximation to π |
nag_euler_constant | Returns an approximation to γ (Euler's constant) |
Chapter Introduction |
Module 3.1:
nag_inv_hyp_fun - Inverse Hyperbolic Functions |
nag_arctanh | Inverse hyperbolic tangent, arctanh x |
nag_arcsinh | Inverse hyperbolic sine, arcsinh x |
nag_arccosh | Inverse hyperbolic cosine, arccosh x |
Module 3.2:
nag_gamma_fun - Gamma Functions |
nag_gamma | Gamma function |
nag_log_gamma | Log gamma function |
nag_polygamma | Polygamma functions |
nag_incompl_gamma | Incomplete gamma functions |
Module 3.3:
nag_err_fun - Error Functions |
nag_erf | Error function erf x |
nag_erfc | Complementary error function erfc x |
nag_dawson | Dawson's integral F(x) |
Module 3.4:
nag_bessel_fun - Bessel Functions |
nag_bessel_j0 | Bessel function J0(x) |
nag_bessel_j1 | Bessel function J1(x) |
nag_bessel_j | Bessel function Jν(z) |
nag_bessel_y0 | Bessel function Y0(x) |
nag_bessel_y1 | Bessel function Y1(x) |
nag_bessel_y | Bessel function Yν(z) |
nag_bessel_i0 | Modified Bessel function I0(x) |
nag_bessel_i1 | Modified Bessel function I1(x) |
nag_bessel_i | Modified Bessel function Iν(z) |
nag_bessel_k0 | Modified Bessel function K0(x) |
nag_bessel_k1 | Modified Bessel function K1(x) |
nag_bessel_k | Modified Bessel function Kν(z) |
Module 3.5:
nag_fresnel_intg - Fresnel Integrals |
nag_fresnel_s | Fresnel integral S(x) |
nag_fresnel_c | Fresnel integral C(x) |
Module 3.6:
nag_ell_intg - Elliptic Integrals |
nag_ell_rf | Symmetrised elliptic integral of the first kind |
nag_ell_rc | Degenerate form of elliptic integral of the first kind |
nag_ell_rd | Symmetrised elliptic integral of the second kind |
nag_ell_rj | Symmetrised elliptic integral of the third kind |
Module 3.7:
nag_ell_fun - Elliptic Functions |
nag_ell_jac | Jacobian elliptic functions sn, cn and dn |
Module 3.8:
nag_airy_fun - Airy Functions |
nag_airy_ai | Airy function Ai(z) |
nag_airy_bi | Airy function Bi(z) |
Module 3.9:
nag_kelvin_fun - Kelvin Functions |
nag_kelvin_ber | Kelvin function ber x |
nag_kelvin_bei | Kelvin function bei x |
nag_kelvin_ker | Kelvin function ker x |
nag_kelvin_kei | Kelvin function kei x |
Chapter Introduction |
Module 4.1: nag_mat_norm - Norms of a Matrix |
nag_gen_mat_norm | Computes a norm, or the element of largest absolute value, of a general real or complex matrix |
nag_gen_bnd_mat_norm | Computes a norm, or the element of largest absolute value, of a real or complex square banded matrix |
nag_sym_mat_norm | Computes a norm, or the element of largest absolute value, of a real or complex, symmetric or Hermitian matrix, stored in conventional or packed storage |
nag_sym_bnd_mat_norm | Computes a norm, or the element of largest absolute value, of a real or complex, symmetric or Hermitian band matrix |
nag_trap_mat_norm | Computes a norm, or the element of largest absolute value, of a real or complex trapezoidal matrix |
nag_tri_mat_norm | Computes a norm, or the element of largest absolute value, of a real or complex triangular matrix, stored in conventional or packed storage |
nag_tri_bnd_mat_norm | Computes a norm, or the element of largest absolute value, of a real or complex triangular band matrix |
nag_hessen_mat_norm | Computes a norm, or the element of largest absolute value, of a real or complex upper Hessenberg matrix |
Module 4.2: nag_mat_inv - Matrix Inversion |
nag_gen_mat_inv |
Computes the inverse of a general real or complex matrix |
nag_gen_mat_inv_fac |
Computes the inverse of a general real or complex matrix, with the matrix previously factorized using nag_gen_lin_fac |
nag_sym_mat_inv |
Computes the inverse of a real or complex, symmetric or Hermitian matrix |
nag_sym_mat_inv_fac |
Computes the inverse of a real or complex, symmetric or Hermitian matrix, with the matrix previously factorized using nag_sym_lin_fac |
nag_tri_mat_inv |
Computes the inverse of a real or complex triangular matrix |
Module 4.3: nag_sparse_mat - Sparse Matrix Utilities |
nag_sparse_mat_init_coo |
Initializes a sparse matrix data structure from COO format |
nag_sparse_mat_init_csc |
Initializes a sparse matrix data structure from CSC format |
nag_sparse_mat_init_csr |
Initializes a sparse matrix data structure from CSR format |
nag_sparse_mat_init_dia |
Initializes a sparse matrix data structure from DIA format |
nag_sparse_mat_extract | Extracts details of a sparse matrix from a structure of type nag_sparse_mat_real_wp or nag_sparse_mat_cmplx_wp |
nag_sparse_mat_real_wp | Represents a real sparse matrix |
nag_sparse_mat_cmplx_wp | Represents a complex sparse matrix |
Chapter Introduction |
Module 5.1: nag_gen_lin_sys - General Systems of Linear Equations |
nag_gen_lin_sol | Solves a general real or complex system of linear equations with one or many right-hand sides |
nag_gen_lin_fac | Performs an LU factorization of a general real or complex matrix |
nag_gen_lin_sol_fac | Solves a general real or complex system of linear equations, with coefficient matrix previously factorized by nag_gen_lin_fac |
Module 5.2: nag_sym_lin_sys - Symmetric Systems of Linear Equations |
nag_sym_lin_sol | Solves a real or complex, symmetric or Hermitian system of linear equations with one or many right-hand sides |
nag_sym_lin_fac | Performs a Cholesky or Bunch-Kaufman factorization of a real or complex, symmetric or Hermitian matrix |
nag_sym_lin_sol_fac | Solves a real or complex, symmetric or Hermitian system of linear equations, with coefficient matrix previously factorized by nag_sym_lin_fac |
Module 5.3: nag_tri_lin_sys - Triangular Systems of Linear Equations |
nag_tri_lin_sol | Solves a real or complex triangular system of linear equations |
nag_tri_lin_cond | Estimates the condition number of a real or complex triangular matrix |
nag_tri_mat_det | Evaluates the determinant of a real or complex triangular matrix |
Module 5.4: nag_gen_bnd_lin_sys - General Banded Systems of Linear Equations |
nag_gen_bnd_lin_sol | Solves a general real or complex banded system of linear equations, with one or many right-hand sides |
nag_gen_bnd_lin_fac | Performs an LU factorization of a general real or complex band matrix |
nag_gen_bnd_lin_sol_fac | Solves a general real or complex banded system of linear equations, with coefficient matrix previously factorized by nag_gen_bnd_lin_fac |
Module 5.5: nag_sym_bnd_lin_sys - Symmetric Banded Systems of Linear Equations |
nag_sym_bnd_lin_sol | Solves a real symmetric or complex Hermitian positive definite banded system of linear equations, with one or many right-hand sides |
nag_sym_bnd_lin_fac | Performs a Cholesky factorization of a real symmetric or complex Hermitian positive definite band matrix |
nag_sym_bnd_lin_sol_fac | Solves a real symmetric or complex Hermitian positive definite banded system of linear equations, with coefficient matrix previously factorized by nag_sym_bnd_lin_fac |
Module 5.6: nag_sparse_prec - Sparse Matrix Preconditioner Set-up and Solve |
nag_sparse_prec_init_jac | Initializes sparse Jacobi preconditioner |
nag_sparse_prec_init_ssor | Initializes sparse SSOR preconditioner |
nag_sparse_prec_init_ilu | Initializes sparse ILU preconditioner for real non-symmetric or complex non-Hermitian matrices |
nag_sparse_prec_sol | Sparse matrix preconditioned system solver |
Module 5.7: nag_sparse_lin_sys - Sparse Linear System Iterative Solvers |
nag_sparse_gen_lin_sol | General sparse linear system solver |
Chapter Introduction |
Module 6.1: nag_sym_eig - Standard Symmetric Eigenvalue Problems |
nag_sym_eig_all |
All eigenvalues, and optionally eigenvectors, of a real symmetric or complex Hermitian matrix |
nag_sym_eig_sel |
Selected eigenvalues, and optionally the corresponding eigenvectors, of a real symmetric or complex Hermitian matrix |
nag_sym_tridiag_reduc |
Reduction of a real symmetric or complex Hermitian matrix to real symmetric tridiagonal form |
nag_sym_tridiag_orth |
Form or apply the transformation matrix determined by nag_sym_tridiag_reduc |
nag_sym_tridiag_eig_all |
All eigenvalues, and optionally eigenvectors, of a real symmetric tridiagonal matrix |
nag_sym_tridiag_eig_val |
Selected eigenvalues of a real
symmetric tridiagonal matrix |
nag_sym_tridiag_eig_vec |
Selected eigenvectors of a real
symmetric tridiagonal matrix |
Module 6.2: nag_nsym_eig - Standard Nonsymmetric Eigenvalue Problems |
nag_nsym_eig_all |
All eigenvalues, and optionally
eigenvectors, of a general real or complex matrix |
nag_schur_fac |
Schur factorization of a general real or
complex matrix |
Module 6.3: nag_svd - Singular Value Decomposition (SVD) |
nag_gen_svd |
Singular value decomposition of a general real
or complex matrix |
nag_gen_bidiag_reduc |
Reduction of a general real or complex matrix to real bidiagonal form |
nag_bidiag_svd |
Singular value decomposition of a real
bidiagonal matrix |
Module 6.4: nag_lin_lsq - Linear Least-squares problems |
nag_lin_lsq_sol |
Solves a real or complex linear
least-squares problem |
nag_lin_lsq_sol_svd |
Solves a real or complex linear least-squares problem, assuming that a singular value decomposition of the coefficient matrix has already been
computed |
nag_qr_fac |
QR factorization of a general real or complex matrix |
nag_qr_orth |
Form or apply the matrix determined by nag_qr_fac |
nag_lin_lsq_sol_qr |
Solves a real or complex linear
least-squares problem, assuming that the factorization of the coefficient
matrix has already been computed |
nag_lin_lsq_sol_qr_svd |
Solves a real or complex linear
least-squares problem using the SVD, assuming that the QR factorization of the coefficient matrix has already been computed |
Module 6.5: nag_sym_gen_eig - Symmetric-definite Generalized Eigenvalue Problems |
nag_sym_gen_eig_all |
All eigenvalues, and optionally eigenvectors, of a real symmetric-definite or complex Hermitian-definite generalized eigenvalue problem |
nag_sym_gen_eig_sel |
Selected eigenvalues, and optionally
the corresponding eigenvectors, of a real symmetric-definite or complex Hermitian-definite generalized eigenvalue problem |
Module 6.6: nag_nsym_gen_eig - Nonsymmetric Generalized Eigenvalue Problems |
nag_nsym_gen_eig_all |
All eigenvalues, and optionally eigenvectors, of a real or complex nonsymmetric generalized eigenvalue problem |
nag_gen_schur_fac |
Generalized Schur factorization of a real
or complex matrix pencil |
Chapter Introduction |
Module 7.1: nag_fft - Discrete Fourier Transforms |
nag_fft_1d |
Single or multiple 1-d complex discrete Fourier transform, or its inverse |
nag_fft_1d_real |
Single or multiple 1-d real or Hermitian discrete Fourier transform, or its inverse |
nag_fft_1d_basic |
Single or multiple 1-d real, Hermitian or complex discrete Fourier transform, which is
overwritten on the input data |
nag_fft_2d |
2-d complex discrete Fourier transform, or its inverse |
nag_fft_2d_basic |
2-d complex discrete Fourier transform, which is overwritten on the input data |
nag_fft_3d |
3-d complex discrete Fourier transform, or its inverse |
nag_fft_3d_basic |
3-d complex discrete Fourier transform, which is overwritten on the input data |
nag_fft_trig |
Trigonometric coefficients for computing
discrete Fourier transforms |
nag_herm_to_cmplx |
Convert Hermitian sequences to general
complex sequences |
nag_cmplx_to_herm |
Convert Hermitian complex sequences to
their compact real form |
nag_conj_herm |
Complex conjugates of Hermitian sequences |
Module 7.2: nag_sym_fft - Symmetric Discrete Fourier Transforms |
nag_fft_sin |
Single or multiple 1-d discrete Fourier sine transform |
nag_fft_cos |
Single or multiple 1-d discrete Fourier cosine transform |
nag_fft_qtr_sin |
Single or multiple 1-d discrete
quarter-wave Fourier sine transform, or its inverse |
nag_fft_qtr_cos |
Single or multiple 1-d discrete
quarter-wave Fourier cosine transform, or its inverse |
Module 7.3: nag_conv - Convolution and Correlation |
nag_fft_conv |
Computes the convolution or correlation of two
real or complex vectors |
Chapter Introduction |
Module 8.1: nag_pch_interp - Piecewise Cubic Hermite
Interpolation |
nag_pch_monot_interp |
Generates a monotonicity-preserving piecewise cubic Hermite interpolant |
nag_pch_eval |
Computes values and optionally derivatives of
a piecewise cubic Hermite interpolant |
nag_pch_intg |
Computes the definite integral of a piecewise
cubic Hermite interpolant |
nag_pch_extract |
Extracts details of a piecewise cubic
Hermite interpolant from a structure of type nag_pch_comm_wp |
nag_pch_comm_wp |
Represents a piecewise cubic Hermite
interpolant (type) |
Module 8.2: nag_spline_1d - One-dimensional Spline Fitting |
nag_spline_1d_auto_fit |
Generates a cubic spline approximation to
an arbitrary 1-d data set, with automatic knot selection |
nag_spline_1d_lsq_fit |
Generates a weighted least-squares
cubic spline fit to an arbitrary 1-d data set, with given interior
knots |
nag_spline_1d_interp |
Generates a cubic spline interpolant to
an arbitrary 1-d data set |
nag_spline_1d_eval |
Computes values of a cubic spline and optionally its first three derivatives |
nag_spline_1d_intg |
Computes the definite integral of a
cubic spline |
nag_spline_1d_set |
Initializes a cubic spline with given
interior knots and B-spline coefficients |
nag_spline_1d_extract |
Extracts details of a cubic spline from
a structure of type nag_spline_1d_comm_wp |
nag_spline_1d_comm_wp |
Represents a 1-d cubic spline in
B-spline series form (type) |
Module 8.3: nag_spline_2d - Two-dimensional Spline Fitting |
nag_spline_2d_auto_fit |
Generates a bicubic spline approximation to
a 2-d data set, with automatic knot selection |
nag_spline_2d_lsq_fit |
Generates a minimal, weighted least-squares bicubic spline surface fit to a given set of
data points, with given interior knots |
nag_spline_2d_interp |
Generates a bicubic spline
interpolating surface through a set of data values, given on a rectangular
grid of the xy plane |
nag_spline_2d_eval |
Computes values of a bicubic spline |
nag_spline_2d_intg |
Computes the definite integral of a
bicubic spline |
nag_spline_2d_set |
Initializes a bicubic spline with given interior knots and B-spline coefficients |
nag_spline_2d_extract |
Extracts details of a bicubic spline
from a structure of type nag_spline_2d_comm_wp |
nag_spline_2d_comm_wp |
Represents a 2-d bicubic spline in B-spline series form (type) |
Module 8.4: nag_scat_interp - Interpolation of Scattered Data |
nag_scat_2d_interp |
Generates a 2-d interpolating function
using a modified Shepard method |
nag_scat_2d_eval |
Computes values of the interpolant generated
by nag_scat_2d_interp and its partial derivatives |
nag_scat_3d_interp |
Generates a 3-d interpolating function
using a modified Shepard method |
nag_scat_3d_eval |
Computes values of the interpolant generated
by nag_scat_3d_interp and its partial derivatives |
nag_scat_2d_set |
Initializes a structure of type
nag_scat_comm_wp to represent a 2-d scattered data interpolant |
nag_scat_3d_set |
Initializes a structure of type
nag_scat_comm_wp to represent a 3-d scattered data interpolant |
nag_scat_extract |
Extracts details of a scattered data interpolant from a structure of derived type nag_scat_comm_wp |
nag_scat_comm_wp |
Represents a scattered data interpolant generated either by nag_scat_2d_interp or nag_scat_3d_interp
(type) |
Module 8.5: nag_cheb_1d - Chebyshev Series |
nag_cheb_1d_fit |
Finds the least-squares fit using arbitrary data points |
nag_cheb_1d_interp |
Generates the coefficients of the Chebyshev polynomial
which interpolates (passes exactly through) data
at a special set of points |
nag_cheb_1d_fit_con |
Finds the least-squares fit using arbitrary data points
with constraints on some data points |
nag_cheb_1d_eval |
Evaluation of fitted polynomial in one variable, from Chebyshev series form |
nag_cheb_1d_deriv |
Derivatives of fitted polynomial in Chebyshev series form |
nag_cheb_1d_intg |
Integral of fitted polynomial in Chebyshev series form |
Chapter Introduction |
Module 9.1: nag_qp - Linear and Quadratic Programming |
nag_qp_sol |
Solves a linear or quadratic programming problem |
nag_qp_cntrl_init |
Initialization procedure for
nag_qp_cntrl_wp |
nag_qp_cntrl_wp |
Control parameters for nag_qp_sol (type) |
Module 9.2: nag_nlin_lsq - Unconstrained Nonlinear Least-squares |
nag_nlin_lsq_sol |
Finds an unconstrained minimum of a sum of squares |
nag_nlin_lsq_cov |
Computes the variance-covariance matrix for
a nonlinear least-squares problem |
nag_nlin_lsq_cntrl_init |
Initialization procedure for nag_nlin_lsq_cntrl_wp |
nag_nlin_lsq_cntrl_wp |
Control parameters for
nag_nlin_lsq_sol (type) |
Module 9.3: nag_nlp - Nonlinear Programming |
nag_nlp_sol |
Solves a dense nonlinear programming problem |
nag_nlp_cntrl_init |
Initialization procedure for
nag_nlp_cntrl_wp |
nag_nlp_cntrl_wp |
Control parameters for nag_nlp_sol (type) |
Module 9.4: nag_con_nlin_lsq - Constrained Nonlinear Least-squares |
nag_con_nlin_lsq_sol |
Please note that this procedure is scheduled for withdrawal from the Library at a future release. Finds a constrained minimum of a sum
of squares |
nag_con_nlin_lsq_sol_1 |
Finds a constrained minimum of a sum of squares |
nag_con_nlin_lsq_cntrl_init |
Initialization procedure for nag_con_nlin_lsq_cntrl_wp |
nag_con_nlin_lsq_cntrl_wp |
Control parameters for
nag_con_nlin_lsq_sol and nag_con_nlin_lsq_sol_1(type) |
Module 9.5: nag_uv_min - Univariate Minimization |
nag_uv_min_sol |
Finds the minimum of a continuous function of a single variable in a given finite interval |
Module 9.6: nag_nlp_sparse - Sparse Nonlinear Programming |
nag_nlp_sparse_sol |
Solves a sparse nonlinear programming problem |
nag_nlp_sparse_cntrl_init |
Initialization procedure for nag_nlp_sparse_cntrl_wp |
nag_nlp_sparse_cntrl_wp | Control parameters for nag_nlp_sparse_sol |
Chapter Introduction |
Module 11.1: nag_quad_1d - Numerical Integration over a Finite Interval |
nag_quad_1d_gen |
1-d quadrature, adaptive, finite interval, allowing for badly behaved integrand, allowing for
singularities at user-specified break-points, suitable for oscillatory integrands |
nag_quad_1d_wt_trig |
1-d quadrature, adaptive, finite
interval, weight function cos(ω x) or sin(ω x) |
nag_quad_1d_wt_end_sing |
1-d quadrature, adaptive, finite
interval, weight function with end-point singularities of algebraico-logarithmic type |
nag_quad_1d_wt_hilb |
1-d quadrature, adaptive, finite
interval, weight function 1/(x−c), Cauchy principal value (Hilbert
transform) |
nag_quad_1d_data |
1-d quadrature, integration of function
defined by data values, Gill-Miller method |
Module 11.2: nag_quad_1d_inf - Numerical Integration over an Infinite Interval |
nag_quad_1d_inf_gen |
1-d quadrature, adaptive, semi-infinite
or infinite interval |
nag_quad_1d_inf_wt_trig |
1-d quadrature, adaptive, semi-infinite interval, weight function cos(ω x) or sin(ω x) |
Module 11.3: nag_quad_md - Multi-dimensional Integrals |
nag_quad_md_rect |
Multi-dimensional adaptive quadrature over
a hyper-rectangle |
nag_quad_md_rect_mintg |
Multi-dimensional adaptive quadrature over
a hyper-rectangle, multiple integrands |
nag_quad_2d |
2-d quadrature, finite region |
nag_quad_monte_carlo |
Multi-dimensional quadrature over hyper-rectangle, Monte-Carlo method |
Module 11.4: nag_quad_util - Numerical Integration Utilities |
nag_quad_gs_wt_absc |
Calculation of weights and abscissae for Gaussian quadrature rules, general choice of rule |
Chapter Introduction |
Module 13.1: nag_pde_helm - Helmholtz Equations |
nag_pde_helm_3d |
Solves the 3-d Helmholtz equation using a standard seven-point
finite difference scheme and a fast Fourier transform method |
Module 13.2: nag_pde_ell_mg - Multigrid Solution of Elliptic PDE's |
nag_pde_ell_rect |
Generates a seven-diagonal system of linear equations which
arises from the discretization of a two-dimensional elliptic PDE's on a rectangle |
nag_pde_ell_mg_sol |
Solves a seven-diagonal system of linear equations using a multigrid iteration |
Module 13.3: nag_pde_parab_1d - Parabolic PDE's in One Space Variable |
nag_pde_parab_1d_fd |
Integrates a system of parabolic PDE's in one space
variable, coupled with ODE's; using finite differences for the spatial
discretisation with optional automatic adaptive spatial remeshing |
nag_pde_interp_1d_fd |
Interpolates the solution and first derivative of a
system of PDE's solved using finite differences, at a
set of user-specified points |
nag_pde_parab_1d_coll |
Integrates a system of parabolic PDE's in one space
variable, coupled with ODE's; using a Chebyshev C0 collocation method for
the spatial discretisation |
nag_pde_interp_1d_coll |
Interpolates the solution and first derivative of a
system of PDE's solved using a Chebyshev C0
collocation method, at a set of user-specified points |
nag_pde_parab_1d_cntrl_wp |
Control parameters for procedures
nag_pde_parab_1d_fd and nag_pde_parab_1d_coll |
nag_pde_parab_1d_cntrl_init | Initialization procedure for type
nag_pde_parab_1d_cntrl_wp |
nag_pde_parab_1d_comm_wp |
Communicates arrays for the underlying ODE solver between calls to the procedures in nag_pde_parab_1d |
Chapter Introduction |
Module 20.1: nag_normal_dist - Probabilities and Deviate for a Normal Distribution |
nag_normal_prob |
Computes probabilities for various parts of
a univariate Normal distribution |
nag_normal_deviate |
Computes the deviate associated with a
given probability of a standard Normal distribution |
nag_bivar_normal_prob |
Computes the lower tail probability for
a bivariate Normal distribution |
nag_mv_normal_prob |
Computes probabilities for various parts of
a multivariate Normal distribution |
Module 20.2: nag_t_dist - Probabilities and Deviate for a Student's t-distribution |
nag_t_prob |
Computes probabilities for various parts of a Student's t-distribution with ν degrees of freedom |
nag_t_deviate |
Computes the deviate associated with a given probability of a Student's t-distribution |
Module 20.3: nag_chisq_dist - Probabilities and Deviate for a χ2 Distribution |
nag_chisq_prob |
Computes lower or upper tail probability for
a χ2 distribution with ν degrees of freedom |
nag_chisq_deviate |
Computes the deviate associated with a
given lower tail probability of a χ2 distribution with ν degrees of freedom |
Module 20.4: nag_f_dist - Probabilities and Deviate for an F-distribution |
nag_f_prob |
Computes lower or upper tail probability for an F-distribution with ν1 and ν2 degrees of freedom |
nag_f_deviate |
Computes the deviate associated with a given
lower tail probability of an F-distribution with ν1 and ν2
degrees of freedom |
Module 20.5: nag_beta_dist - Probabilities and Deviate for a
Beta Distribution |
nag_beta_prob |
Computes lower or upper tail probability for
a beta distribution with parameters a and b
|
nag_beta_deviate |
Computes the deviate associated with a
given lower tail probability of a beta distribution with parameters
a and b |
Module 20.6: nag_gamma_dist - Probabilities and Deviate for a Gamma Distribution |
nag_gamma_prob |
Computes lower or upper tail probability for
a gamma distribution with shape parameter a and scale parameter
b |
nag_gamma_deviate |
Computes the deviate associated with a
given lower tail probability of a gamma distribution with shape parameter a and scale parameter b |
Module 20.7: nag_discrete_dist - Probabilities for Discrete Distributions |
nag_binom_prob |
Computes lower tail, upper tail or point probability for a binomial distribution with parameters n and p |
nag_poisson_prob |
Computes lower tail, upper tail or point probability for a Poisson distribution with parameter λ |
nag_hypergeo_prob |
Computes lower tail, upper tail or point probability for a hypergeometric distribution with parameters
n, l and m |
Chapter Introduction |
Module 21.1: nag_rand_util - Utilities for Random Number Generation |
nag_rand_seed_set |
Sets the seed used by random number
generating procedures to give a repeatable or non-repeatable sequence of random numbers |
nag_seed_wp |
Stores data required to generate successive
random numbers from a given stream (type) |
Module 21.2: nag_rand_contin - Random Numbers from Continuous Distributions |
nag_rand_uniform |
Generates random numbers from a uniform distribution over (a,b) |
nag_rand_normal |
Generates random numbers from a Normal distribution with mean a and standard deviation b |
nag_rand_mv_normal |
Generates a vector of random numbers from a multivariate Normal distribution with mean vector a and covariance matrix C |
nag_rand_beta |
Generates random numbers from a beta distribution with parameters a and b |
nag_rand_neg_exp |
Generates random numbers from a (negative) exponential distribution with mean a |
nag_rand_gamma |
Generates random numbers from a gamma distribution with parameters a and b |
Module 21.3: nag_rand_discrete - Random Numbers from Discrete Distributions |
nag_rand_binom |
Generates random integers from a binomial distribution and/or returns a reference vector for the distribution |
nag_rand_neg_binom |
Generates random integers from a negative binomial distribution and/or returns a reference vector for the distribution |
nag_rand_hypergeo |
Generates random integers from an hypergeometric distribution and/or returns a reference vector for the distribution |
nag_rand_user_dist |
Generates random integers and/or returns a reference vector from a discrete distribution defined in terms of its PDF or CDF |
nag_rand_ref_vec |
Generates random integers from a discrete distribution, using a reference vector |
nag_ref_vec_wp |
Stores a reference vector which is used to generate random integers from a discrete distribution (type) |
Chapter Introduction |
Module 29.1: nag_tsa_identify - Time Series Analysis - Identification |
nag_tsa_acf |
Calculates the sample autocorrelation function of
a univariate time series |
nag_tsa_pacf |
Calculates the sample partial autocorrelation function of a univariate time series |
Module 29.2: nag_tsa_kalman - Kalman Filtering |
nag_kalman_init |
Provides an initial estimate of the Kalman filter state covariance matrix |
nag_kalman_predict |
Calculates a one step prediction for the square root covariance Kalman filter |
nag_kalman_sqrt_cov_var |
Calculates a time-varying square root covariance Kalman filter |
nag_kalman_sqrt_cov_invar |
Calculates a time-invariant square root covariance Kalman filter |
Module 29.3: nag_tsa_spectral - Time Series Spectral Analysis |
nag_spectral_data |
Calculates the smoothed sample spectrum of a univariate time series |
nag_spectral_cov |
Calculates the smoothed sample spectrum of a univariate time series using autocovariances data |
nag_bivar_spectral_data |
Calculates the smoothed sample cross spectrum of a bivariate time series |
nag_bivar_spectral_cov |
Calculates the smoothed sample cross spectrum of a bivariate time series using autocovariances data |
nag_bivar_spectral_coh |
Calculates the squared coherency, the cross amplitude, the gain and the phase spectra |
nag_bivar_spectral_lin_sys |
Calculates the noise spectrum and the impulse response function from a linear system |