|
|
The Informix-NAG Finance DataBlade module is a carefully chosen selection of routines from the Numerical Algorithms Group (NAG) Fortran Library.
From here you have access to the documentation of the complete NAG Fortran Library but you should note that only those routines listed in the Contents section below are available in this release. However, Informix and NAG would be delighted to hear your views on contents for future releases - please see the Contact Details below.
Contents
- Chapter 05 - Roots of One or More Transcendental Equations
- C05AVF- Binary search for interval containing zero of continuous function (reverse communication)
- C05AXF - Zero of continuous function by continuation method, from given starting value (reverse communication)
- C05AZF - Zero in given interval of continuous function by Bus and Dekker algorithm (reverse communication)
- Chapter E01 - Interpolation
- E01AAF - Interpolated values, Aitken's technique, unequally spaced data, one variable
- E01BAF - Interpolating functions, cubic spline interpolant, one variable
- Chapter E02 - Curve and Surface Fitting
- E02ADF - Least-squares curve fit, by polynomials, arbitrary data points
- E02AEF - Evaluation of fitted polynomial in one variable from Chebyshev series form (simplified parameter list)
- E02AFF - Least-squares polynomial fit, special data points (including interpolation)
- E02BAF - Least-squares curve cubic spline fit (including interpolation)
- Chapter F01 - Matrix Factorizations
- F01CKF - Matrix multiplication
- F01CRF - Matrix transposition
- F01ZAF - Convert real matrix between packed triangular and square storage schemes
- Chapter F02 - Eigenvalues and Eigenvectors
- F02FAF - All eigenvalues and eigenvectors of real symmetric matrix (Black Box)
- Chapter F06 - Linear Algebra Support Routines
- F06EAF - (SDOT/DDOT) Dot product of two real vectors
- F06EDF - Multiply real vector by scalar
- F06FDF - Multiply real vector by scalar, preserving input vector
- F06JLF - (ISAMAX/IDAMAX) Index, real vector element with largest absolute value
- F06QFF - Matrix copy, real rectangular or trapezoidal matrix
- Chapter F07 - Linear Equations (LAPACK)
- F07FDF - (SPOTRF/DPOTRF) Cholesky factorization of real symmetric positive-definite matrix
- F07MDF - (SSYTRF/DSYTRF) Bunch--Kaufman factorization of real symmetric indefinite matrix
- Chapter G01 - Simple Calculations and Statistical Data
- G01AAF - Mean, variance, skewness, kurtosis, etc, one variable, from raw data
- G01ALF - Computes a five-point summary (median, hinges and extremes)
- G01DAF - Normal scores, accurate values
- G01DBF - Normal scores, approximate values
- G01EAF - Computes probabilities for the standard Normal distribution
- G01EBF - Computes probabilities for Student's t-distribution
- G01FAF - Computes deviatesfor the standard Normal distribution
- Chapter G02 - Correlation and Regression Analysis
- G02BAF - Pearson product-moment correlation coefficients, all variables, no missing values
- G02BBF - Pearson product-moment correlation coefficients, all variables, casewise treatment of missing values
- G02BJF - Pearson product-moment correlation coefficients, subset of variables, pairwise treatment of missing values
- G02BUF - Computes a weighted sum of squares matrix
- G02BXF - Computes (optionally weighted) correlation and covariance matrices
- G02DAF - Fits a general (multiple) linear regression model
- G02DKF - Estimates and standard errors of parameters of a general linear regression model for given constraints
- G02GBF - Fits a generalized linear model with binomial errors
- G02HAF - Robust regression, standard M-estimates
- Chapter G03 - Multivariate Methods
- G03AAF - Performs principal component analysis
- G03CAF - Computes maximum likelihood estimates of the parameters of a factor analysis model, factor loadings, communalities and residual correlations
- Chapter G05 - Random Number Generators
- G05CBF - Initialise random number generating routines to give repeatable sequence
- G05CCF - Initialise random number generating routines to give non-repeatable sequence
- G05CFF - Save state of random number generating routines
- G05CGF - Restore state of random number generating routines
- G05DDF - Pseudo-random real numbers, Normal distribution
- G05FDF - Generates a vector of random numbers from a Normal distribution
- Chapter G10 - Smoothing in Statistics
- G10CAF - Compute smoothed data sequence using running median smoothers
- Chapter G13 - Time Series Analysis
- G13AAF - Univariate time series, seasonal and non-seasonal differencing
- G13ABF - Univariate time series, sample autocorrelation function
- G13AUF - Computes quantities needed for range-mean or standard deviation-mean plot
- G13BAF - Multivariate time series, filtering (pre-whitening) by an ARIMA model
- G13CBF - Univariate time series, smoothed sample spectrum using spectral smoothing by the trapezium frequency (Daniell) window
- G13DMF - Multivariate time series, sample cross-correlation or cross-covariance matrices
- G13DNF - Multivariate time series, sample partial lag correlation matrices, chi-square statistics and significance levels
- Chapter M01 - Sorting
- M01CAF - Sort a vector, real numbers
- M01DAF - Rank a vector, real numbers
- ChapterS - Approximations of Special Functions
- Chapter X05 - Date and Time Utilities
- X05AAF - Return date and time as an array of integers
- X05BAF - Return the CPU time
How to Use these Routines
Instructions on how to use the routines and examples can be found at http://www.informix.com/informix/products/options/udo/datablade/dbmodule/nag.htm
Contact Details
For support with this product please contact your local Informix support office.
For more information about Informix see http://www.informix.com/ and for NAG see http://www.nag.co.uk/
|
