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 J_{0}(x) |
nag_bessel_j1 | Bessel function J_{1}(x) |
nag_bessel_j | Bessel function J_{ν}(z) |
nag_bessel_y0 | Bessel function Y_{0}(x) |
nag_bessel_y1 | Bessel function Y_{1}(x) |
nag_bessel_y | Bessel function Y_{ν}(z) |
nag_bessel_i0 | Modified Bessel function I_{0}(x) |
nag_bessel_i1 | Modified Bessel function I_{1}(x) |
nag_bessel_i | Modified Bessel function I_{ν}(z) |
nag_bessel_k0 | Modified Bessel function K_{0}(x) |
nag_bessel_k1 | Modified Bessel function K_{1}(x) |
nag_bessel_k | Modified Bessel function K_{ν}(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 10.1: nag_polynom_eqn - Roots of Polynomials | |
nag_polynom_roots | Calculates the roots of a polynomial |
Module 10.2: nag_nlin_eqn - Roots of a Single Nonlinear equation | |
nag_nlin_eqn_sol | Finds a solution of a single nonlinear equation |
Module 10.3: nag_nlin_sys - Roots of a System of Nonlinear equations | |
nag_nlin_sys_sol | Finds a solution of a system of nonlinear equations |
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 12.1: nag_ivp_ode_rk - Solution of Initial Value Problems for ODE's by Runge-Kutta Methods | |
nag_rk_setup | Sets up the integration |
nag_rk_interval | Integrates across an interval and provides the solution at user-specified points |
nag_rk_info | Provides statistics about the integration |
nag_rk_global_err | Provides information about global error assessment |
nag_rk_step | Integrates one step at a time |
nag_rk_interp | Interpolates the solution |
nag_rk_reset_end | Resets the end point of integration |
nag_rk_comm_wp | Communicating structure for nag_ivp_ode_rk (type) |
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 C^{0} 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 C^{0} 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 19.1: nag_ip - Integer Programming | |
nag_ip_sol | Solves 'zero-one', 'general', 'mixed' or 'all' integer linear programming problems |
nag_ip_cntrl_wp | Control parameters for nag_ip_sol |
nag_ip_cntrl_init | Initialization procedure for nag_ip_cntrl_wp |
Module 19.2: nag_short_path - Shortest Path Problems | |
nag_short_path_find | Finds the shortest path through a directed or undirected acyclic network |
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 χ^{2} Distribution | |
nag_chisq_prob | Computes lower or upper tail probability for a χ^{2} distribution with ν degrees of freedom |
nag_chisq_deviate | Computes the deviate associated with a given lower tail probability of a χ^{2} 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 ν_{1} and ν_{2} degrees of freedom |
nag_f_deviate | Computes the deviate associated with a given lower tail probability of an F-distribution with ν_{1} and ν_{2} 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 22.1: nag_basic_stats - Basic Descriptive Statistics for Univariate Data | |
nag_summary_stats_1v | Computes basic descriptive statistics for univariate data |
Chapter Introduction | |
Module 25.1: nag_lin_reg - Regression Analysis | |
nag_simple_lin_reg | Performs a simple linear regression analysis for a pair of related variables |
nag_mult_lin_reg | Performs a general multiple linear regression analysis for any given predictor variables and a response variable |
Module 25.2: nag_correl - Correlation Analysis | |
nag_prod_mom_correl | Calculates the variance-covariance matrix and the Pearson product-moment correlation coefficients for a set of data |
nag_part_correl | Calculates the partial variance-covariance matrix and the partial correlation matrix from a correlation or variance covariance matrix |
Chapter Introduction | |
Module 28.1: nag_fac_analysis - Factor Analysis and Principal Component | |
nag_prin_comp | Performs principal component analysis |
Module 28.2: nag_canon_analysis - Canonical Analysis | |
nag_canon_var | Performs canonical variate analysis |
Module 28.3: nag_mv_rotation - Rotations | |
nag_orthomax | Computes orthogonal rotation, using a generalized orthomax rotations |
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 |