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
