NA NB NC ND NE NF NG NH NI NJ NK NL NM NN NO NP NQ NR NS NT NU NV NW NX NY NZ

- names
- gives the names of all components of a list object.
- ndayofmonth
- returns the day of the month as a number (1-31)
- ndayofweek
- returns the day of the week as a number (1-7, sunday-saturday)
- ndw
- auxiliary quantlet for adedis. It defines the Nadaraya-Watson estimate of the link as a function of the (estimated) index of continuous explanatory variables. In adedis, the quantlet simpsonint is used to integrate over the function.
- nelmin
- nelmin searchs for a minimum of a function. In each iteration step the function is evaluated at a simplex consisting of p+1 points. The simplex contracts until the variance of the evaluated function values is less than eps (or the maximal number of iterations is reached).
- neuronal
- This quantlet computes different networks of the form single layer feedforward perceptron. The quantlet can be used alone or in connection with the library ISTA. The standalone version also needs the parameter data. Just choose 0 for the input. It is possible to split the data in a training and a t
- neuronal2
- This macro computes different networks of the form single layer feedforward perceptron. The macro can be used alone or in connection with the library ISTA. The standalone version also needs the parameter data. Just choose 0 for the input. It is possible to split the data in a training and a test se
- newadeslp
- slope estimation of average derivatives
- newest
- auxiliary quantlet for panunit
- neweywest
- Calculation of the Newey and West Heteroskedastic and Autocorrelation Consistent estimator of the variance. The first argument of the quantlet represents the series and the second optional argument the vector of truncation lags of the autocorrelation consistent variance estimator. If the second opt
- ngau
- Computes the rescaled (multivariate) Gaussian kernel ngau(u) = 5.*gau(5.*u).
- nhour
- returns the hour as a number (0-23)
- nmBFGS
- Broyden-Fletcher-Goldfarb-Shanno method to find a minimum of a given function.
- nmBHHH
- Berndt-Hall-Hall-Hausman method to find a minimum of a given negative log-likelihood function (and maximum of the corresponding likelihood function).
- nmbisect
- bisection method for finding a root of a given function in a given interval
- nmbracket
- This quantlet brackets a minimum of a given scalar function
- nmbrackin
- searches for zero crossings of a given scalar function in n equally spaced subintervals of a given interval
- nmbrackout
- brackets a root of a given scalar function by expanding the range
- nmbrent
- Brent's method for the minimization of a given scalar function
- nmbrentder
- Brent's method for the minimization of a given scalar function using derivatives
- nmbrentroot
- Brent's method for finding a root of a given function in a given interval
- nmcongrad
- conjugate gradient method for finding the minimum of a given function
- nmexpandmat
- expands the input matrix by zeros in places corresponding to fixed parameters
- nmfder1d
- Computes the derivative of a function restricted to a line: (f(t))' = d(func(x0 + t*direc)) / dt
- nmfunc1d
- restricts func to a line: f(t) = func(x0 + t*direc)
- nmGJelim
- Gauss-Jordan elimination with full pivoting
- nmgolden
- Golden section search for the minimum of a given scalar function
- nmgraddiff
- Computes the gradient of a function func at a point x0 using the symmetric difference with a step h: graddiff(f,x,h) = [f(x+h) - f(x-h) / (2*h)]
- nmgraditer
- Computes the gradient of a function func at a point x0 using Ridders' method of polynomial extrapolation
- nmhessian
- computes the hessian matrix of a function func at a point x0 using the difference with a step h: d_(xy) f(x,y) = [f(x+h,y+h) - f(x+h,y-h) - f(x-h,y+h) + f(x-h,y-h)] / (4*h^2)
- nminute
- returns the minute as a number (0-59)
- nmjacobian
- Computes the jacobian of function(s) func (or more generally, the matrix of gradients) at a point x0
- nmlinmin
- Finds a minimum of func along the direction "direc" from x0 (does not use derivatives of func)
- nmlinminappr
- finds a minimum of a function along the direction "direc" from x0 (does not use derivatives of func)
- nmlinminder
- Finds a minimum of func along the direction "direc" from x0 (using derivatives of func)
- nmlinprog
- simplex method for linear programming problem in normal form
- nmlinprogexchange
- auxiliary quantlet for nmlinprog; exchange of a left-hand and a right-hand variable
- nmlinprogmaxel
- auxiliary quantlet for nmlinprog; determines maximum of coeffiecients in a given row and listed columns
- nmlinprogpivot
- auxiliary quantlet for nmlinprog; finds a pivot element in a given column
- nmmin
- Nelder-Mead simplex method to find minimum of a given function.
- nmnewton
- Newton-Raphson method for solving system func(x)=0
- nmnewtonmod
- modified Newton-Raphson method for solving system func(x)=0 with backtracking (guarantees to decrease value of func in every iteration); compared with original Newton-Raphson method, it is less problematic to deal with highly oscillating functions
- nmomnorm
- Auxiliary routine for rICfil which calculates the n-th moment of a standard normal variate truncated at t, i.e. E [X^n (X
- nmonth
- returns the month as a number
- nmparabint
- Inverse parabolic interpolation: finds the point x that is minimum/maximum of a parabola through three points (a,fa), (b,fb), (c,fc). INF is returned, if the three points are linear dependent (i.e. lying on the same line).
- nmpolrootlaguer
- implements Laguerre's method for improving a given complex value until it converges to a root of a given polynomial
- nmqpenalty
- auxiliary quantlet for constrained minimization using nmsimpen. Computes a penalized function value: P(x,delta) = f(x) + delta*sum((constr(x))^2)
- nmregfalsi
- regula falsi (false position) method for finding a root of a given function in a given interval
- nmridders
- Ridders' method (regula falsi modification) for finding a root of a given function in a given interval
- nmsecant
- secant method for finding a root of a given function in a given interval
- nmsimpen
- constrained optimization using simple penalty function
- nnfunc
- nnfunc computes for a given feed forward network the result for a datavector x.
- nnfunc2
- nnfunc2 computes for a given feed forward network the result for a datavector x.
- nninfo
- shows some information about the actual network
- nninit
- nninit checks if a given network is feedforward network and suggest reorderings.
- nninit2
- nninit2 checks if a given network is feedforward network and suggest reorderings.
- nnlayer
- builds a feedforward network
- nnls2
- auxiliary quantlet for spdest2 used in the minimization of nonlinear least squares.
- nnmain
- loads the necessary libraries
- nnrdovm
- nnrdovm optimizes a network for a given dataset.
- nnrdovm2
- nnrdovm2 optimizes a network for a given dataset.
- nnrinfo
- gives information about the net
- nnrload
- loads a network from different files
- nnrnet
- trains a one hidden layer feed forward network. The optional parameter param consists of 8 values. Boolean values for linear output, entropy error function, log probability models and for skip connections. The fifth value is the maximum value for the starting weights, the sixth the weight decay, th
- nnrnet2
- trains a one hidden layer feed forward network. The optional parameter param consists of 8 values. Boolean values for linear output, entropy error function, log probability models and for skip connections. The fifth value is the maximum value for the starting weights, the sixth the weight decay, th
- nnrpredict
- estimates the response for a given net and a dataset
- nnrpredict2
- estimates the response for a given net and dataset
- nnrsave
- saves a network into a file with a given name
- nnrsetnet
- nnrsetnet sets the internal variables to construct a specific network.
- nnrsetnet2
- nnrsetnet2 sets the internal variables to construct a specific network.
- nnrsettrain
- nnrsettrain sets the internal variables to fill a specific network with data and weights.
- nnrsettrain2
- nnrsettrain2 sets the internal variables to fill a specific network with data and weights.
- nnrtest
- nnrtest computes for a given network and dataset the y-values.
- nnrtest2
- nnrtest2 computes for a given network and dataset the y-values and the Hessian.
- nnvisu
- nnvisu computes the visualization for a given feed forward network by non-metric multidimensional scaling.
- nnvisu2
- nnvisu2 computes the visualization for a given feed forward network by non-metric multidimensional scaling.
- normal
- normal generates arrays up to eight dimensions of pseudo random variables with a standard normal distribution. the algorithm by box-muller is used.
- normal2
- Normal2 generates arrays up to eight dimensions of pseudo random variables with the standard normal distribution. The algorithm by Box-Muller is used.
- normalcorr
- generates correlated pseudo random normal variates using the Cholesky factorization.
- normalmix
- generates normal mixture pseudo-variates
- normalmixdens
- evaluating a normal mixture density function
- normalmixselect
- chooses among a set of normal mixture example densities
- normalt
- multivariate normality tests
- nparmaest
- fits a nonparametric ARMA(1,1) process X[t+1] = f(X[t],e[t]) + e[t+1] by inverting deconvolution kernel estimators.
- npgarchest
- fits a nonparametric GARCH(1,1) process e[t+1] = s[t+1]*Z[t+1], s[t+1]^2 = g(e[t]^2,s[t]^2), where Z[t] are iid Gaussian, by inverting deconvolution kernel estimators.
- nsecond
- returns the second as a number (0-59)
- numint2
- Auxiliary routine for rICfil: calculates for dimension p=2 diag(E[ YY' u min(b/|aIhY|,u) ]) and diag(E[ YY' min(b/|aIhY|,u)^2 ]) for u square root of a Chi^2_2-variable, and Y~ufo(S_2) indep of u by using a polar representation of Lambda:= I^{1/2} Y u, u = | I^{-1/2} Lambda |, Y=I^{-1/
- numint2m
- Auxiliary routine for rICfil: calculates for dimension p=2 (E[ YY' u min(b/|aIhY|,u) ]) and (E[ YY' min(b/|aIhY|,u)^2 ]) for u square root of a Chi^2_2-variable, and Y~ufo(S_2) indep of u by using a polar representation of Lambda:= I^{1/2} Y u, u = | I^{-1/2} Lambda |, Y=I^{-1/2} Lambd
- nummathmain
- main routine of nummath library
- nyear
- returns the year as a four digit number (YYYY)

(C) MD*TECH Method and Data Technologies, 05.02.2006 | Impressum |