The Guide to Available Mathematical Software (GAMS) is a cross-index and virtual repository of mathematical and statistical software components of use in computational science and engineering. To see what software that Netlib has available in a certain GAMS category, click the corresponding entry in the list below. Also see the GAMS server at NIST.
A. Arithmetic, error analysis A1. Integer A2. Rational A3. Real A3a. Standard precision A3c. Extended precision A3d. Extended range A4. Complex A4a. Standard precision A4c. Extended precision A4d. Extended range A5. Interval A6. Change of representation A6a. Type conversion A6b. Base conversion A6c. Decomposition, construction A7. Sequences (e.g., convergence acceleration) B. Number theory C. Elementary and special functions (search also class L5) C1. Integer-valued functions (e.g., factorial, binomial coefficient, permutations, combinations, floor, ceiling) C2. Powers, roots, reciprocals C3. Polynomials C3a. Orthogonal C3a1. Trigonometric C3a2. Chebyshev, Legendre C3a3. Laguerre C3a4. Hermite C3b. Non-orthogonal C4. Elementary transcendental functions C4a. Trigonometric, inverse trigonometric C4b. Exponential, logarithmic C4c. Hyperbolic, inverse hyperbolic C4d. Integrals of elementary transcendental functions C5. Exponential and logarithmic integrals C6. Cosine and sine integrals C7. Gamma C7a. Gamma, log gamma, reciprocal gamma C7b. Beta, log beta C7c. Psi function C7d. Polygamma function C7e. Incomplete gamma C7f. Incomplete beta C7g. Riemann zeta C8. Error functions C8a. Error functions, their inverses, integrals, including the normal distribution function C8b. Fresnel integrals C8c. Dawson's integral C9. Legendre functions C10. Bessel functions C10a. J, Y, H_1, H_2 C10a1. Real argument, integer order C10a2. Complex argument, integer order C10a3. Real argument, real order C10a4. Complex argument, real order C10a5. Complex argument, complex order C10b. I, K C10b1. Real argument, integer order C10b2. Complex argument, integer order C10b3. Real argument, real order C10b4. Complex argument, real order C10b5. Complex argument, complex order C10c. Kelvin functions C10d. Airy and Scorer functions C10e. Struve, Anger, and Weber functions C10f. Integrals of Bessel functions C11. Confluent hypergeometric functions C12. Coulomb wave functions C13. Jacobian elliptic functions, theta functions C14. Elliptic integrals C15. Weierstrass elliptic functions C16. Parabolic cylinder functions C17. Mathieu functions C18. Spheroidal wave functions C19. Other special functions D. Linear Algebra D1. Elementary vector and matrix operations D1a. Elementary vector operations D1a1. Set to constant D1a2. Minimum and maximum components D1a3. Norm D1a3a. L_1 (sum of magnitudes) D1a3b. L_2 (Euclidean norm) D1a3c. L_infinity (maximum magnitude) D1a4. Dot product (inner product) D1a5. Copy or exchange (swap) D1a6. Multiplication by scalar D1a7. Triad (alpha*x+y for vectors x, y and scalar alpha) D1a8. Elementary rotation (Givens transformation) D1a9. Elementary reflection (Householder transformation) D1a10. Convolutions D1a11. Other vector operations D1b. Elementary matrix operations D1b1. Initialize (e.g., to zero or identity) D1b2. Norm D1b3. Transpose D1b4. Multiplication by vector D1b5. Addition, subtraction D1b6. Multiplication D1b7. Matrix polynomial D1b8. Copy D1b9. Storage mode conversion D1b10. Elementary rotation (Givens transformation) D1b11. Elementary reflection (Householder transformation) D2. Solution of systems of linear equations (including inversion, LU and related decompositions) D2a. Real nonsymmetric matrices D2a1. General D2a2. Banded D2a2a. Tridiagonal D2a3. Triangular D2a4. Sparse D2b. Real symmetric matrices D2b1. General D2b1a. Indefinite D2b1b. Positive definite D2b2. Positive definite banded D2b2a. Tridiagonal D2b4. Sparse D2c. Complex non-Hermitian matrices D2c1. General D2c2. Banded D2c2a. Tridiagonal D2c3. Triangular D2c4. Sparse D2d. Complex Hermitian matrices D2d1. General D2d1a. Indefinite D2d1b. Positive definite D2d2. Positive definite banded D2d2a. Tridiagonal D2d4. Sparse D2e. Associated operations (e.g., matrix reorderings) D3. Determinants D3a. Real nonsymmetric matrices D3a1. General D3a2. Banded D3a2a. Tridiagonal D3a3. Triangular D3a4. Sparse D3b. Real symmetric matrices D3b1. General D3b1a. Indefinite D3b1b. Positive definite D3b2. Positive definite banded D3b2a. Tridiagonal D3b4. Sparse D3c. Complex non-Hermitian matrices D3c1. General D3c2. Banded D3c2a. Tridiagonal D3c3. Triangular D3c4. Sparse D3d. Complex Hermitian matrices D3d1. General D3d1a. Indefinite D3d1b. Positive definite D3d2. Positive definite banded D3d2a. Tridiagonal D3d4. Sparse D4. Eigenvalues, eigenvectors D4a. Ordinary eigenvalue problems (Ax = lambda x) D4a1. Real symmetric D4a2. Real nonsymmetric D4a3. Complex Hermitian D4a4. Complex non-Hermitian D4a5. Tridiagonal D4a6. Banded D4a7. Sparse D4b. Generalized eigenvalue problems (e.g., Ax = lambda Bx) D4b1. Real symmetric D4b2. Real general D4b3. Complex Hermitian D4b4. Complex general D4b5. Banded D4c. Associated operations D4c1. Transform problem D4c1a. Balance matrix D4c1b. Reduce to compact form D4c1b1. Tridiagonal D4c1b2. Hessenberg D4c1b3. Other D4c1c. Standardize problem D4c2. Compute eigenvalues of matrix in compact form D4c2a. Tridiagonal D4c2b. Hessenberg D4c2c. Other D4c3. Form eigenvectors from eigenvalues D4c4. Back transform eigenvectors D4c5. Determine Jordan normal form D5. QR decomposition, Gram-Schmidt orthogonalization D6. Singular value decomposition D7. Update matrix decompositions D7a. LU D7b. Cholesky D7c. QR D7d. Singular value D8. Other matrix equations (e.g., AX+XB=C) D9. Singular, overdetermined or underdetermined systems of linear equations, generalized inverses D9a. Unconstrained D9a1. Least squares (L_2) solution D9a2. Chebyshev (L_infinity) solution D9a3. Least absolute value (L_1) solution D9a4. Other D9b. Constrained D9b1. Least squares (L_2) solution D9b2. Chebyshev (L_infinity) solution D9b3. Least absolute value (L_1) D9b4. Other D9c. Generalized inverses E. Interpolation E1. Univariate data (curve fitting) E1a. Polynomial splines (piecewise polynomials) E1b. Polynomials E1c. Other functions (e.g., rational, trigonometric) E2. Multivariate data (surface fitting) E2a. Gridded E2b. Scattered E3. Service routines for interpolation E3a. Evaluation of fitted functions, including quadrature E3a1. Function evaluation E3a2. Derivative evaluation E3a3. Quadrature E3b. Grid or knot generation E3c. Manipulation of basis functions (e.g., evaluation, change of basis) E3d. Other F. Solution of nonlinear equations F1. Single equation F1a. Polynomial F1a1. Real coefficients F1a2. Complex coefficients F1b. Nonpolynomial F2. System of equations F3. Service routines (e.g., check user-supplied derivatives) G. Optimization (search also classes K, L8) G1. Unconstrained G1a. Univariate G1a1. Smooth function G1a1a. User provides no derivatives G1a1b. User provides first derivatives G1a1c. User provides first and second derivatives G1a2. General function (no smoothness assumed) G1b. Multivariate G1b1. Smooth function G1b1a. User provides no derivatives G1b1b. User provides first derivatives G1b1c. User provides first and second derivatives G1b2. General function (no smoothness assumed) G2. Constrained G2a. Linear programming G2a1. Dense matrix of constraints G2a2. Sparse matrix of constraints G2b. Transportation and assignments problem G2c. Integer programming G2c1. Zero/one G2c2. Covering and packing problems G2c3. Knapsack problems G2c4. Matching problems G2c5. Routing, scheduling, location problems G2c6. Pure integer programming G2c7. Mixed integer programming G2d. Network (for network reliability search class M) G2d1. Shortest path G2d2. Minimum spanning tree G2d3. Maximum flow G2d3a. Generalized networks G2d3b. Networks with side constraints G2d4. Test problem generation G2e. Quadratic programming G2e1. Positive definite Hessian (i.e., convex problem) G2e2. Indefinite Hessian G2f. Geometric programming G2g. Dynamic programming G2h. General nonlinear programming G2h1. Simple bounds G2h1a. Smooth function G2h1a1. User provides no derivatives G2h1a2. User provides first derivatives G2h1a3. User provides first and second derivatives G2h1b. General function (no smoothness assumed) G2h2. Linear equality or inequality constraints G2h2a. Smooth function G2h2a1. User provides no derivatives G2h2a2. User provides first derivatives G2h2a3. User provides first and second derivatives G2h2b. General function (no smoothness assumed) G2h3. Nonlinear constraints G2h3a. Equality constraints only G2h3a1. Smooth function and constraints G2h3a1a. User provides no derivatives G2h3a1b. User provides first derivatives of function and constraints G2h3a1c. User provides first and second derivatives of function and constraints G2h3a2. General function and constraints (no smoothness assumed) G2h3b. Equality and inequality constraints G2h3b1. Smooth function and constraints G2h3b1a. User provides no derivatives G2h3b1b. User provides first derivatives of function and constraints G2h3b1c. User provides first and second derivatives of function and constraints G2h3b2. General function and constraints (no smoothness assumed) G2i. Global solution to nonconvex problems G3. Optimal control G4. Service routines G4a. Problem input (e.g., matrix generation) G4b. Problem scaling G4c. Check user-supplied derivatives G4d. Find feasible point G4e. Check for redundancy G4f. Other H. Differentiation, integration H1. Numerical differentiation H2. Quadrature (numerical evaluation of definite integrals) H2a. One-dimensional integrals H2a1. Finite interval (general integrand) H2a1a. Integrand available via user-defined procedure H2a1a1. Automatic (user need only specify required accuracy) H2a1a2. Nonautomatic H2a1b. Integrand available only on grid H2a1b1. Automatic (user need only specify required accuracy) H2a1b2. Nonautomatic H2a2. Finite interval (specific or special type integrand including weight functions, oscillating and singular integrands, principal value integrals, splines, etc.) H2a2a. Integrand available via user-defined procedure H2a2a1. Automatic (user need only specify required accuracy) H2a2a2. Nonautomatic H2a2b. Integrand available only on grid H2a2b1. Automatic (user need only specify required accuracy) H2a2b2. Nonautomatic H2a3. Semi-infinite interval (including exp(-x) weight function) H2a3a. Integrand available via user-defined procedure H2a3a1. Automatic (user need only specify required accuracy) H2a3a2. Nonautomatic H2a4. Infinite interval (including exp(-x^2) weight function) H2a4a. Integrand available via user-defined procedure H2a4a1. Automatic (user need only specify required accuracy) H2a4a2. Nonautomatic H2b. Multidimensional integrals H2b1. One or more hyper-rectangular regions (includes iterated integrals) H2b1a. Integrand available via user-defined procedure H2b1a1. Automatic (user need only specify required accuracy) H2b1a2. Nonautomatic H2b1b. Integrand available only on grid H2b1b1. Automatic (user need only specify required accuracy) H2b1b2. Nonautomatic H2b2. n-dimensional quadrature on a nonrectangular region H2b2a. Integrand available via user-defined procedure H2b2a1. Automatic (user need only specify required accuracy) H2b2a2. Nonautomatic H2b2b. Integrand available only on grid H2b2b1. Automatic (user need only specify required accuracy) H2b2b2. Nonautomatic H2c. Service routines (e.g., compute weights and nodes for quadrature formulas) I. Differential and integral equations I1. Ordinary differential equations (ODE's) I1a. Initial value problems I1a1. General, nonstiff or mildly stiff I1a1a. One-step methods (e.g., Runge-Kutta) I1a1b. Multistep methods (e.g., Adams predictor-corrector) I1a1c. Extrapolation methods (e.g., Bulirsch-Stoer) I1a2. Stiff and mixed algebraic- differential equations I1b. Multipoint boundary value problems I1b1. Linear I1b2. Nonlinear I1b3. Eigenvalue (e.g., Sturm-Liouville) I1c. Service routines (e.g., interpolation of solutions, error handling, test programs) I2. Partial differential equations I2a. Initial boundary value problems I2a1. Parabolic I2a1a. One spatial dimension I2a1b. Two or more spatial dimensions I2a2. Hyperbolic I2b. Elliptic boundary value problems I2b1. Linear I2b1a. Second order I2b1a1. Poisson (Laplace) or Helmholtz equation I2b1a1a. Rectangular domain (or topologically rectangular in the coordinate system) I2b1a1b. Nonrectangular domain I2b1a2. Other separable problems I2b1a3. Nonseparable problems I2b1c. Higher order equations (e.g., biharmonic) I2b2. Nonlinear I2b3. Eigenvalue I2b4. Service routines I2b4a. Domain triangulation (search also class P) I2b4b. Solution of discretized elliptic equations I3. Integral equations J. Integral transforms J1. Trigonometric transforms including fast Fourier transforms J1a. One-dimensional J1a1. Real J1a2. Complex J1a3. Sine and cosine transforms J1b. Multidimensional J2. Convolutions J3. Laplace transforms J4. Hilbert transforms K. Approximation (search also class L8) K1. Least squares (L_2) approximation K1a. Linear least squares (search also classes D5, D6, D9) K1a1. Unconstrained K1a1a. Univariate data (curve fitting) K1a1a1. Polynomial splines (piecewise polynomials) K1a1a2. Polynomials K1a1a3. Other functions (e.g., trigonometric, user-specified) K1a1b. Multivariate data (surface fitting) K1a2. Constrained K1a2a. Linear constraints K1a2b. Nonlinear constraints K1b. Nonlinear least squares K1b1. Unconstrained K1b1a. Smooth functions K1b1a1. User provides no derivatives K1b1a2. User provides first derivatives K1b1a3. User provides first and second derivatives K1b1b. General functions K1b2. Constrained K1b2a. Linear constraints K1b2b. Nonlinear constraints K2. Minimax (L_infinity) approximation K3. Least absolute value (L_1) approximation K4. Other analytic approximations (e.g., Taylor polynomial, Pade) K5. Smoothing K6. Service routines for approximation K6a. Evaluation of fitted functions, including quadrature K6a1. Function evaluation K6a2. Derivative evaluation K6a3. Quadrature K6b. Grid or knot generation K6c. Manipulation of basis functions (e.g., evaluation, change of basis) K6d. Other L. Statistics, probability L1. Data summarization L1a. One-dimensional data L1a1. Raw data L1a1a. Location L1a1b. Dispersion L1a1c. Shape L1a1d. Frequency, cumulative frequency L1a1e. Ties L1a3. Grouped data L1b. Two dimensional data (search also class L1c) L1c. Multi-dimensional data L1c1. Raw data L1c1b. Covariance, correlation L1c1d. Frequency, cumulative frequency L1c2. Raw data containing missing values (search also class L1c1) L2. Data manipulation L2a. Transform (search also classes L10a1, N6, and N8) L2b. Tally L2c. Subset L2d. Merge (search also class N7) L2e. Construct new variables (e.g., indicator variables) L3. Elementary statistical graphics (search also class Q) L3a. One-dimensional data L3a1. Histograms L3a2. Frequency, cumulative frequency, percentile plots L3a3. EDA (e.g., box-plots) L3a4. Bar charts L3a5. Pie charts L3a6. X_i vs. i (including symbol plots) L3a7. Lag plots (e.g., plots of X_i vs. X_i-1) L3b. Two-dimensional data (search also class L3e) L3b1. Histograms (superimposed and bivariate) L3b2. Frequency, cumulative frequency L3b3. Scatter diagrams L3b3a. Y vs. X L3b3b. Symbol plots L3b3c. Lag plots (i.e., plots of X_i vs. Y_i-j) L3b4. EDA L3c. Three-dimensional data (search also class L3e) L3e. Multi-dimensional data L3e1. Histograms L3e2. Frequency, cumulative frequency, percentile plots L3e3. Scatter diagrams L3e3a. Superimposed Y vs. X L3e3c. Superimposed X_i vs. i L3e3d. Matrices of bivariate scatter diagrams L3e4. EDA L4. Elementary data analysis L4a. One-dimensional data L4a1. Raw data L4a1a. Parametric analysis L4a1a1. Plots of empirical and theoretical density and distribution functions L4a1a2. Probability plots L4a1a2b. Beta, binomial L4a1a2c. Cauchy, chi-squared L4a1a2d. Double exponential L4a1a2e. Exponential, extreme value L4a1a2f. F distribution L4a1a2g. Gamma, geometric L4a1a2h. Halfnormal L4a1a2l. Lambda, logistic, lognormal L4a1a2n. Negative binomial, normal L4a1a2p. Pareto, Poisson L4a1a2s. Semicircular L4a1a2t. t distribution, triangular L4a1a2u. Uniform L4a1a2w. Weibull L4a1a3. Probability plot correlation coefficient plots L4a1a3c. Chi-squared L4a1a3e. Extreme value L4a1a3g. Gamma, geometric L4a1a3l. Lambda L4a1a3n. Normal L4a1a3p. Pareto, Poisson L4a1a3t. t distribution L4a1a3w. Weibull L4a1a4. Parameter estimates and tests L4a1a4b. Binomial L4a1a4e. Extreme value L4a1a4n. Normal L4a1a4p. Poisson L4a1a4u. Uniform L4a1a4w. Weibull L4a1a5. Transformation selection (e.g., for normality) L4a1a6. Tail and outlier analysis L4a1a7. Tolerance limits L4a1b. Nonparametric analysis L4a1b1. Estimates and tests regarding location (e.g., median), dispersion, and shape L4a1b2. Density function estimation L4a1c. Goodness-of-fit tests L4a1d. Analysis of a sequence of numbers (search also class L10a) L4a3. Grouped and/or censored data L4a4. Data sampled from a finite population L4a5. Categorical data L4b. Two dimensional data (search also class L4c) L4b1. Pairwise independent data L4b1a. Parametric analysis L4b1a1. Plots of empirical and theoretical density and distribution functions L4b1a4. Parameter estimates and hypothesis tests L4b1b. Nonparametric analysis (e.g., rank tests) L4b1c. Goodness-of-fit tests L4b3. Pairwise dependent data L4b4. Pairwise dependent grouped data L4b5. Data sampled from a finite population L4c. Multi-dimensional data (search also classes L4b and L7a1) L4c1. Independent data L4c1a. Parametric analysis L4c1b. Nonparametric analysis L4e. Multiple multi-dimensional data sets L5. Function evaluation (search also class C) L5a. Univariate L5a1. Cumulative distribution functions, probability density functions L5a1b. Beta, binomial L5a1c. Cauchy, chi-squared L5a1d. Double exponential L5a1e. Error function, exponential, extreme value L5a1f. F distribution L5a1g. Gamma, general, geometric L5a1h. Halfnormal, hypergeometric L5a1k. Kendall F statistic, Kolmogorov-Smirnov L5a1l. Lambda, logistic, lognormal L5a1n. Negative binomial, normal L5a1p. Pareto, Poisson L5a1t. t distribution L5a1u. Uniform L5a1v. Von Mises L5a1w. Weibull L5a2. Inverse distribution functions, sparsity functions L5a2b. Beta, binomial L5a2c. Cauchy, chi-squared L5a2d. Double exponential L5a2e. Error function, exponential, extreme value L5a2f. F distribution L5a2g. Gamma, general, geometric L5a2h. Halfnormal L5a2l. Lambda, logistic, lognormal L5a2n. Negative binomial, normal, normal order statistics L5a2p. Pareto, Poisson L5a2t. t distribution L5a2u. Uniform L5a2w. Weibull L5b. Multivariate L5b1. Cumulative multivariate distribution functions, probability density functions L5b1n. Normal L5b2. Inverse cumulative distribution functions L5b2n. Normal L6. Random number generation L6a. Univariate L6a2. Beta, binomial, Boolean L6a3. Cauchy, chi-squared L6a4. Double exponential L6a5. Exponential, extreme value L6a6. F distribution L6a7. Gamma, general (continuous, discrete), geometric L6a8. Halfnormal, hypergeometric L6a12. Lambda, logistic, lognormal L6a14. Negative binomial, normal, normal order statistics L6a16. Pareto, Pascal, permutations, Poisson L6a19. Samples, stable distribution L6a20. t distribution, time series, triangular L6a21. Uniform (continuous, discrete), uniform order statistics L6a22. Von Mises L6a23. Weibull L6b. Multivariate L6b3. Contingency table, correlation matrix L6b5. Experimental designs L6b12. Linear L_1 (least absolute value) approximation L6b13. Multinomial L6b14. Normal L6b15. Orthogonal matrix L6b21. Uniform L6c. Service routines (e.g., seed) L7. Analysis of variance (including analysis of covariance) L7a. One-way L7a1. Parametric L7a2. Nonparametric L7b. Two-way (search also class L7d) L7c. Three-way (e.g., Latin squares) (search also class L7d) L7d. Multi-way L7d1. Balanced complete data (e.g., factorial designs) L7d2. Balanced incomplete data L7d3. General linear models (unbalanced data) L7e. Multivariate L7f. Generate experimental designs L7g. Service routines L8. Regression (search also classes D5, D6, D9, G, K) L8a. Simple linear (i.e., y = b_0 + b_1x) (search also class L8h) L8a1. Ordinary least squares L8a1a. Parameter estimation L8a1a1. Unweighted data L8a1a2. Weighted data L8a1d. Inference (e.g., calibration) (search also class L8a1a) L8a2. L_p for p different from 2 (e.g., least absolute value, minimax) L8a3. Robust L8a4. Errors in variables L8b. Polynomial (e.g., y = b_0 + b_1x + b_2 x^2) (search also class L8c) L8b1. Ordinary least squares L8b1a. Degree determination L8b1b. Parameter estimation L8b1b1. Not using orthogonal polynomials L8b1b2. Using orthogonal polynomials L8b1c. Analysis (search also class L8b1b) L8b1d. Inference (search also class L8b1b) L8c. Multiple linear (i.e., y = b_0 + b_1 x_1 + ... + b_p x_p) L8c1. Ordinary least squares L8c1a. Variable selection L8c1a1. Using raw data L8c1a2. Using correlation or covariance data L8c1a3. Using other data L8c1b. Parameter estimation (search also class L8c1a) L8c1b1. Using raw data L8c1b2. Using correlation data L8c1c. Analysis (search also classes L8c1a and L8c1b) L8c1d. Inference (search also classes L8c1a and L8c1b) L8c2. Several regressions L8c3. L_p for p different from 2 L8c4. Robust L8c5. Measurement error models L8c6. Models based on ranks L8d. Polynomial in several variables L8e. Nonlinear (i.e., y = F(X,b)) (search also class L8h) L8e1. Ordinary least squares L8e1a. Variable selection L8e1b. Parameter estimation (search also class L8e1a) L8e1b1. Unweighted data, user provides no derivatives L8e1b2. Unweighted data, user provides derivatives L8e1b3. Weighted data, user provides no derivatives L8e1b4. Weighted data, user provides derivatives L8e2. Ridge L8e5. Measurement error models L8f. Simultaneous (i.e., Y = Xb) L8g. Spline (i.e., piecewise polynomial) L8h. EDA (e.g., smoothing) L8i. Service routines (e.g., matrix manipulation for variable selection) L9. Categorical data analysis L9a. 2-by-2 tables L9b. Two-way tables (search also class L9d) L9c. Log-linear model L9d. EDA (e.g., median polish) L10. Time series analysis (search also class J) L10a. Univariate (search also classes L3a6 and L3a7) L10a1. Transformations L10a1a. Elementary (search also class L2a) L10a1b. Stationarity (search also class L8a1) L10a1c. Filters (search also class K5) L10a1c1. Difference L10a1c2. Symmetric linear (e.g., moving averages) L10a1c3. Autoregressive linear L10a1c4. Other L10a1d. Taper L10a2. Time domain analysis L10a2a. Summary statistics L10a2a1. Autocorrelations and autocovariances L10a2a2. Partial autocorrelations L10a2b. Stationarity analysis (search also class L10a2a) L10a2c. Autoregressive models L10a2c1. Model identification L10a2c2. Parameter estimation L10a2d. ARMA and ARIMA models (including Box-Jenkins methods) L10a2d1. Model identification L10a2d2. Parameter estimation L10a2d3. Forecasting L10a2e. State-space analysis (e.g., Kalman filtering) L10a2f. Analysis of a locally stationary series L10a3. Frequency domain analysis (search also class J1) L10a3a. Spectral analysis L10a3a1. Pilot analysis L10a3a2. Periodogram analysis L10a3a3. Spectrum estimation using the periodogram L10a3a4. Spectrum estimation using the Fourier transform of the autocorrelation function L10a3a5. Spectrum estimation using autoregressive models L10a3a6. Spectral windows L10a3b. Complex demodulation L10b. Two time series (search also classes L3b3c, L10c, and L10d) L10b2. Time domain analysis L10b2a. Summary statistics (e.g., cross-correlations) L10b2b. Transfer function models L10b3. Frequency domain analysis (search also class J1) L10b3a. Cross-spectral analysis L10b3a2. Cross-periodogram analysis L10b3a3. Cross-spectrum estimation using the cross-periodogram L10b3a4. Cross-spectrum estimation using the Fourier transform of the cross-correlation or cross-covariance function L10b3a6. Spectral functions L10c. Multivariate time series (search also classes J1, L3e3 and L10b) L10d. Two multi-channel time series L11. Correlation analysis (search also classes L4 and L13c) L12. Discriminant analysis L13. Covariance structure models L13a. Factor analysis L13b. Principal components analysis L13c. Canonical correlation L14. Cluster analysis L14a. One-way L14a1. Unconstrained L14a1a. Nested L14a1a1. Joining (e.g., single link) L14a1a2. Divisive L14a1a3. Switching L14a1a4. Predict missing values L14a1b. Non-nested (e.g., K means) L14a2. Constrained L14b. Two-way L14c. Display L14d. Service routines (e.g., compute distance matrix) L15. Life testing, survival analysis L16. Multidimensional scaling L17. Statistical data sets M. Simulation, stochastic modeling (search also classes L6 and L10) M1. Simulation M1a. Discrete M1b. Continuous (Markov models) M2. Queueing M3. Reliability M3a. Quality control M3b. Electrical network M4. Project optimization (e.g., PERT) N. Data handling (search also class L2) N1. Input, output N2. Bit manipulation N3. Character manipulation N4. Storage management (e.g., stacks, heaps, trees) N5. Searching N5a. Extreme value N5b. Insertion position N5c. On a key N6. Sorting N6a. Internal N6a1. Passive (i.e. construct pointer array, rank) N6a1a. Integer N6a1b. Real N6a1c. Character N6a2. Active N6a2a. Integer N6a2b. Real N6a2c. Character N6b. External N7. Merging N8. Permuting O. Symbolic computation P. Computational geometry (search also classes G and Q) Q. Graphics (search also class L3) R. Service routines R1. Machine-dependent constants R2. Error checking (e.g., check monotonicity) R3. Error handling R3a. Set criteria for fatal errors R3b. Set unit number for error messages R3c. Other utilities R4. Documentation retrieval S. Software development tools S1. Program transformation tools S2. Static program analysis tools S3. Dynamic program analysis tools Z. Other